1
VEGETATION AND CLIMATE OF THE AFRICAN TROPICS FOR THE LAST
500,000 YEARS
by
Sarah Jean Ivory
_____________________
A Dissertation Submitted to the Faculty of the
DEPARTMENT OF GEOSCIENCES
In Partial Fulfillment of the Requirements
For the Degree of
DOCTOR OF PHILOSOPHY
In the Graduate College
THE UNIVERSITY OF ARIZONA
2013
2
THE UNIVERSITY OF ARIZONA
GRADUATE COLLEGE
As members of the Dissertation Committee, we certify that we have read the dissertation
prepared by Sarah Jean Ivory
entitled Vegetation and climate of the African tropics for the last 500,000 years
and recommend that it be accepted as fulfilling the dissertation requirement for the
Degree of Doctor of Philosophy
_______________________________________________________________________
Date: 4/15/13
Andrew Cohen
_______________________________________________________________________
Date: 4/15/13
Joellen Russell
_______________________________________________________________________
Date: 4/15/13
Owen Davis
_______________________________________________________________________
Date: 4/15/13
Stephen Jackson
_______________________________________________________________________
Date: 4/15/13
Anne-Marie Lézine
Final approval and acceptance of this dissertation is contingent upon the candidate’s
submission of the final copies of the dissertation to the Graduate College.
I hereby certify that I have read this dissertation prepared under my direction and
recommend that it be accepted as fulfilling the dissertation requirement.
________________________________________________ Date: 4/15/2013
Dissertation Director: Andrew Cohen
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STATEMENT BY AUTHOR
This dissertation has been submitted in partial fulfillment of the requirements for
an advanced degree at the University of Arizona and is deposited in the University
Library to be made available to borrowers under rules of the Library.
Brief quotations from this dissertation are allowable without special permission,
provided that an accurate acknowledgement of the source is made. Requests for
permission for extended quotation from or reproduction of this manuscript in whole or in
part may be granted by the head of the major department or the Dean of the Graduate
College when in his or her judgment the proposed use of the material is in the interests of
scholarship. In all other instances, however, permission must be obtained from the
author.
SIGNED: Sarah Jean Ivory
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ACKNOWLEDGEMENTS
Thanks to Mom and Dad, who inspired my love of the natural world in the first place,
and all my family: Jim, Hilary, Elliott, Grandma, Grandpa, the Ivorys (Mary, Mike, Tom,
Cheryl, Matt, Katie, Chris), the Sagstetters (Tom, Mary-Jane, Ben, Dan, Stephanie,
David, Mark, Dawn, Curtis, Christopher, Paul, Jamie), and those I love that are no longer
with us (Heidi, Jean, Grandma Ivory).
To my friends – Preston helped me become a person again, Mike is crazy enough to think
I’m special, Meg was the first friendly face I met, and the gang from those long-ago,
sweet days in Eau Claire who are really all more like family: Justin, Katie, Tyler, Allison,
Jade, Charlie, Megan, Arian, and Noah.
Tous mes amis français avec qui j’ai passé des très beaux séjours – Florian était toujours
prêt pour un aventure, Chimène a apprivoisé une jeune américaine super timide, Vincent
et Marie ne se fâchaient pas quand j’ai mangé tous leurs bonbons, et Andrea fait les
meilleurs gâteaux.
My friends/labmates here and there: Yao, Louis, Kowler, Dominique, and Jordon, who
were always good to me, and, in particular, John Logan and Paul Goodman were patient
at tolerating and helping me along. The staff at LacCore, in particular Amy and Anders,
always helped me when I was in need.
Without Anne-Marie, Jean-Pierre, Annie, and Guillaume, I’d be living in a gutter,
because they taught me everything. However, Joe and Kristina are responsible for
stoking my interest in research way long ago.
Money is key, so I thank DOSECC, UA-GPSC, and the National Science Foundation for
funding the Lake Malawi Drilling Project and a Graduate Research Fellowship.
My committee members have given great guidance: Andy, Anne-Marie, Joellen, Owen,
Peck, and Steve.
This work would not have been possible without support from: Nick Cave, Sir Arthur
Conan Doyle, winemakers everywhere, and pizza.
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DEDICATION
This manuscript is dedicated to a great family who are more important to me than
anything else, including cake.
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TABLE OF CONTENTS
ABSTRACT……………………………………………………………………………..8
INTRODUCTION……………………………………………………………………….10
PRESENT STUDY………………………………………………………………………19
REFERENCES…………………………………………………………………………..26
APPENDIX A: IN THE HOT SEAT: INSOLATION, ENSO, AND VEGETATION IN
THE AFRICAN TROPICS………………………………………………………………32
Abstract…………………………………………………………………………..33
Introduction………………………………………………………………………34
Methods…………………………………………………………………………..36
Results and Interpretation……………………………………………………….39
Discussion………………………………………………………………………..42
Conclusions……………………………………………………………………....47
Acknowledgements……………………………………………………………….49
References……………………………………………………………………..…50
Figures…………………………………………………………………………...58
Tables…………………………………………………………………………….70
Supplementary Material………………………………………………………….71
APPENDIX B: EFFECT OF ARIDITY AND RAINFALL SEASONALITY ON
VEGETATION IN THE SOUTHERN TROPICS OF EAST AFRICA DURING THE
PLEISTOCENE/HOLOCENE TRANSITION………………………………………..74
Permission to reprint from the copyright holder………………………………75
Abstract………………………………………………………………………...81
Introduction…………………………………………………………………….82
Modern Setting and Implications for Palynology………………………………84
Methods………………………………………………………………………...86
Results …………………………………………………………………………87
Interpretation and Discussion……………………………………………………90
Conclusions…………………………………………………………………….101
Acknowledgements…………………………………………………………….103
References………………………………………………………………………104
Figures………………………………………………………………………….111
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TABLE OF CONTENTS – Continued
APPENDIX C: LARGE-SCALE VEGETATION CHANGE IN TROPICAL AFRICA
FROM LAKE MALAWI: DESERTS, FORESTS, AND INSOLATION …………..121
Abstract………………………………………………………………………...122
Introduction…………………………………………………………………….124
Modern Setting…………………………………………………………………127
Methods………………………………………………………………………...129
Results………………………………………………………………………….132
Discussion………………………………………………………………………135
Conclusions…………………………………………………………………….151
Acknowledgements…………………………………………………………….153
References………………………………………………………………………154
Figures………………………………………………………………………….160
Tables…………………………………………………………………………174
APPENDIX D: SIMULATED CLIMATE MODEL VARIABILITY OF
AFROTROPICAL VEGETATION: THE MODERN PREINDUSTRIAL PERIOD AND
THE LAST INTERGLACIAL……………………………………………………..176
Abstract………………………………………………………………………...177
Introduction…………………………………………………………………….179
Methods………………………………………………………………………...181
Results………………………………………………………………………….189
Discussion………………………………………………………………………199
Conclusions…………………………………………………………………….204
Acknowledgements…………………………………………………………….206
References………………………………………………………………………207
Figures………………………………………………………………………….211
Tables…………………………………………………………………………..233
Supplementary Material………………………………………………………..235
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ABSTRACT
In the last few decades, we have been witness to unprecedented changes in
precipitation and temperature. Such alterations to our climate system have important
implications for terrestrial ecosystems that billions of people depend on for their
livelihood. The situation is especially tenuous for those living directly off the landscape
via resources from natural ecosystems or subsistence agriculture as in much of tropical
Africa. Studies of past climates provide potential analogues and help validate models
essential for elucidating mechanisms that link changes in climate mean and variability
and how they may affect ecosystem distribution and productivity. However, despite the
importance of the paleo-record for insight into the future, tropical proxy records are rare,
low resolution, and too short to capture important intervals that may act as analogs, such
as the Last Interglacial (MIS 5e; ~130-115ka).
Long, high-resolution drill cores from Lake Malawi, southeast Africa, provide a
record of tropical climate and vegetation that extends back ~1.2mya, comprising many
continuous glacial-interglacial cycles. My primary research involves conducting pollen
analyses on these cores. First, I analyzed a high-resolution interval of the shortest
Malawi core in order to better understand abrupt vegetation transitions during the Last
Deglaciation. Further analysis was conducted on the longest Malawi core, beginning
with an interval covering all of the Penultimate Glacial through the Last Interglacial. The
resultant pollen data has shown that abrupt, large-scale landscape transitions from forest
9
to desert follow local insolation and lake levels at the site, with a strong dependence of
forest/woodland vegetation types on mean rainfall as well as rainfall seasonality.
The interpretation of paleodata requires a good understanding of modern
processes, thus another project has focused on using model simulations of the Last
Interglacial and modern satellite NDVI time series to highlight dynamical and statistical
relationships between vegetation and climate change. This work suggests that despite
suggested links between monsoon intensity and SSTs in the southern African tropics,
insolation controls on atmospheric circulation are the primary drivers of vegetation
reorganization. In addition, this work highlights the importance of rainfall seasonality
and dry season length in addition to precipitation controls on vegetation.
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INTRODUCTION
Anthropogenic greenhouse gas emissions are likely to change the climate system.
Global circulations patterns and hydrology are already changing at rates unprecedented
during the Holocene (IPCC, 2007). In the tropics, poleward shifts of the jet stream and a
wider yearly migration of the Intertropical Convergence Zone (ITCZ) have already been
observed, resulting in an ongoing widening of the Earth’s dry zones (Lu et al., 2007;
Seidel et al., 2007; Archer and Caldiera, 2008). In additional to changes in mean
hydrology, variability within the tropics caused by changes in ENSO and other climatic
modes is likely to fundamentally alter environmental gradients in the tropics (Collins,
2005; Marchant et al., 2006; Pohl et al., 2007; Chang et al., 2008).
Vegetation within the tropics is strongly influenced by regional hydrology
(Polhill, 1966; White, 1983), thus these alterations of mean state and variability are likely
to have a profound effect on ecosystems. In the semi-arid areas, where rainfall is low and
interannual variability is high, vegetation is particularly sensitive to hydrologic
fluctuations (Hély et al., 2006). Although biotic factors, such as exploitation of resources
by man and over-grazing of rangelands, have frequently been implicated as the primary
cause of semi-arid vegetation degradation, recent studies suggest a much stronger role of
climate in controlling productivity than previously imagined (Hermann et al., 2005).
Studies have long shown the importance of mean annual precipitation; however, the
influence of other climatic factors, such as variability of the rainy season, dry season, and
atmospheric CO2 concentration, is still under much debate (Jolly and Haxeltine, 1998;
Hély et al., 2006; Vincens et al., 2007; Wu et al., 2007; Guiot et al., 2008). As a result,
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great risk exists in the near future of the disappearance or degradation of such fragile
ecosystems (IPCC, 2007).
Arid and semi-arid ecosystems such as grasslands, bushlands, and desert steppe
are common throughout the continent (Polhill, 1966; White, 1983). In these areas,
rainfall is a strong driver of woody cover and vegetation productivity, highlighting the
sensitivity of this vegetation (Sankaran et al., 2005). Even outside the dry regions, Africa
has been classified as a hot spot of biodiversity as a result of its extremely diverse
Guineo-Congolian forests (Myers et al., 2000; Diffenbaugh and Giorgi, 2012). The
ecological importance of many of the most characteristic African biomes has been long
remarked (Hamilton, 1982). Biomes, like the Afromontane region, the Somali-Masai
regional center, and Zambezian miombo woodland, display incredible levels of
endemism (White, 1983). Furthermore, the enormous extent of grasslands and semi-arid
ecosystems in Africa plays an important role in the global carbon cycle which is likely to
be crucial in the future for regulating atmospheric CO2 (Cox et al., 2013).
Further exacerbating these problems is that fact that, across the African continent,
millions of people depend on ecosystem services for daily subsistence (WWF, 2000).
Natural ecosystems and rain-fed agriculture both provide essential resources. Therefore,
threats to tropical vegetation make this region extremely vulnerable and have dangerous,
costly implications for daily economy and subsistence (IPCC, 2007; Few et al., 2004).
However, despite concerns for the future of African ecology and resource
management, controversy has developed over how natural vegetation communities and
cropland productivity will respond to altered atmospheric and oceanic circulation in the
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future. Furthermore, little infrastructure exists to mitigate these changes (Boko et al.,
2007; Brown and deBeurs, 2008). In order to develop management and conservation
strategies, it is first imperative that we understand how vegetation will respond to
projected changes in hydrology, temperature, and CO2, particularly in at-risk ecosystems.
Although some work has focused on modeling future vegetation ranges and vegetation
responses to climate, uncertainty and biases in the parameterization of plant
physiological/phenological processes and carbon/water fluxes makes basing these
procedures solely on the current suite of models a challenge (Shevliakova et al., 2009;
Dunne et al., 2012). Thus in order to better understand how biomes and biodiversity will
change in the future, better constraints need to be developed to link vegetation structure
and composition to climate.
Vegetation-Climate Interactions
Two fundamental methods are employed in order to develop quantitative
constraints on the interaction of climatic processes and vegetation dynamics. Studies of
modern ecological processes as well as vegetation change in the past capture variability
on different temporal and spatial scales, and thus only the integration of both can truly
hope to provide useful insights about the future. The appendices of this dissertation
employ both of these methods in the hopes of elucidating vegetation change across the
African continent on time scales of years to millennia.
The first of these methods involves studies of modern ecology utilizing
techniques that directly or indirectly monitor the landscape and its response to climate.
This method is limited in time to the observational period; however, networks of
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vegetation surveys across wide spatial scales make this method ideal for revealing largescale regional coherency of vegetation response to climatic processes (Lovett et al.,
2005). Additionally, the more recent usage of remotely sensed data to create products
such as Leaf Area Index (LAI) or Normal Difference Vegetation Index (NDVI) allows
for spatially continuous analysis at high resolution comparable with climate data and
model output (eg. Brown and deBeurs, 2008; Brown et al., 2010). The use of
observational ecological data has been integral in identifying biotic and abiotic processes
that alter vegetation structure and composition (Hermann et al., 2005; Sankaran et al,
2005). The results of such studies are then integrated into models or used to validate
processes in dynamical models that may then project climate scenarios into the future.
This method is particularly well-adapted to discerning spatial patterns of variability;
however, this method is limited in that it may only capture the response of vegetation to
climate states that exist today.
Climate in the future is likely to change to mean states and variability that don’t
currently exist on Earth. In order to understand vegetation responses to a larger range of
climate states, studies of past vegetation studies can be conducted. Additionally,
paleoecological records are also essential for evaluating the performance of climate
models used in IPCC projections for the future. Within Africa, paleoecological records
of vegetation come primarily from fossil pollen preserved in a variety of archives such as
middens, outcrops, and marine and lake cores (e.g. Bonnefille, 2010; Beuning et al.,
2011; Chase et al., 2012). Paleoecological data in tropical Africa have proved very
useful in elucidating the factors influencing vegetation productivity and the importance of
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climate-vegetation feedbacks to regional hydrology (e.g. Liu et al., 2006; Vincens et al.,
2007). However, terrestrial vegetation data are scarce within the tropics for times prior to
the Last Glacial Maximum (LGM), and most existing data beyond 21ka comes from
either low-resolution marine cores or short, poorly dated records (Bonnefille, 2010;
Dupont et al., 2011).
I have employed both of these methods as part of my dissertation research. In the
first and fourth appendices, remotely sensed NDVI and an Earth System Model are
employed to investigate variability across broad spatial scales during the observational
period and Last Interglacial Period. As a complement to this, the character of vegetation
transitions in response to varied forcing is evaluated using terrestrial sediment core
records across several glacial-interglacial cycles.
Modern Afrotropical Vegetation
Vegetation distribution in Africa today is constrained by both biotic and abiotic
factors (White, 1983). However, the large-scale distribution of biomes across the
continent is controlled by climatic factors such as mean annual rainfall, dry season
length, and temperature, which determine vegetation structure and phenology (Hamilton,
1982; White, 1983). The annual migration of the Intertropical Convergence Zone (ITCZ)
imparts three broad rainfall zones. A single rainy season occurs in northern and southern
Africa with prolonged dry seasons the rest of the year, and a region with two distinct
rainy seasons is located at the equator. Toward the subtropics, regions of absolute desert
occupy the Sahara and Kalahari/Namib, and finally very restricted areas in the northern
and southern parts of the continent are dominated by winter rainfall (Nicholson, 1996).
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These zones translate to similar vegetation regions. Near the equator, in west central
Africa, are dense tropical evergreen rainforests of the Guineo-Congolian region (White,
1983). In equatorial East Africa, a very different mosaic of dense forests and wooded
grasslands prevails, with an opening gradient eastward into the Somali-Masai center of
endemism in Kenya and Ethiopia. To the north and south, gradients of decreasing
rainfall and increasing dry season result in a dominance of deciduous woodlands, such as
the Sudanian woodlands to the north and Zambezian miombo woodlands to the south
(White, 1983). Near 23ºN and 23ºS, degraded bushlands, shrublands, and steppe
occurring in the Sahel and Kalahari Highveld areas quickly grade into the absolute desert
of the Sahara and Karoo-Namib, respectively (White, 1983). On the northwestern and
southwestern extremes, the bush and woodlands of the winter regions of the Cape and
Mediterranean are found (White, 1983). Finally, in the isolated mountainous regions
within tropical Africa, afromontane forests and high-altitude grasslands are common
(Hedburg, 1951).
Although not much work has been conducted to date on ecological dynamics in
Africa in comparison to other regions, some work has been done to separate the effects of
biotic and abiotic processes (e.g. Hermann et al., 2005; Sankaran et al, 2005).
Disturbance processes are clearly critically important in shaping the structure of
vegetation assemblages. These processes are often stochastic events; however, may also
have links to climate. Of these disturbances, fires and grazing are probably the most
notable. In the Zambezian miombo woodlands, which were a focus of the second and
third appendices of this dissertation, grazing populations and frequent burning are
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important for preventing the encroachment of forest in areas with high enough rainfall for
denser climax communities and also have a pivotal role in maintaining the very uniform
umbrella-like canopy structure of the landscape (Malmer, 2007).
However, despite the importance of stochastic events and biotic factors for
determining vegetation structure and composition, many studies have concluded that the
overriding determinant of change across many biome types is a direct climatic effect
(Hermann et al., 2005; Sankaran et al, 2005). Rainfall, in terms of mean and variability,
is by far the most cited reason for environmental change (Davenport and Nicholson,
1993). Remote sensing data shows a strong regional coherence in the correlation of
vegetation variability to rainfall at the continental scale, despite heterogeneity on the
landscape. Additionally, studies have also suggested a strong control of ENSO and
related tropical modes (Brown et al., 2010).
Although the perception that rainfall amount, both yearly as well as rainy season
amount, is the primary influence on vegetation, the climatic effect is in fact more
complex. New results from models and remotely-sensed vegetation indices suggest that
the length of the dry season, although often coupled with changes in rainfall, plays an
independent role in controlling vegetation (Hély et al., 2006). Although this relationship
varies from biome to biome, in all biomes except savanna, the increases in dry season
length tend to exacerbate the effect of changing rainfall, resulting in transitions to drier
biomes with small rainfall changes.
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Paleoecology in Africa
Paleoecological studies of past vegetation in Africa come principally from pollen
analysis on archives such as middens, marine and lacustrine sediment cores, and outcrops
(e.g. Livingstone, 1971; Van Zinderen Bakker and Coetzee, 1972; Bonnefille, 2010;
Beuning et al., 2011; Chase et al., 2012). Terrestrial sedimentary records are limited,
because of the paucity of lakes in semi-arid and arid climatic regions. Most records from
on the continent which preserve a more proximal record of vegetation are short or
punctuated by hiatuses. Furthermore, beyond the LGM, records are very scarce.
Vegetation in Africa was much different during the Tertiary than today due to
warmer temperatures, fewer topographic boundaries, and more humid, homogeneous
climatic zones throughout the equatorial region (Morley, 2007). The trend to cooler
global temperatures, increasing tropical aridity, and the initiation of East African rifting
over the Cenozoic led to the marked N-S and E-W vegetation gradients that we see today
(Bonnefille, 2010). Although little data exists, the disappearance of the widespread
evergreen forest seems to have occurred in a time-transgressive manner, beginning at
around 10mya in East Africa and continuing in West Africa by about 7mya (Bonnefille,
2010). The spread of woodlands and grasslands occurred in several pulses, but it is clear
that grasslands were already largely established by the beginning of the Quaternary,
suggesting that broad characteristics of modern vegetation composition and structure
were to a degree in place at this time (Cane and Molar, 2001; Bonnefille, 2010). Thus,
the last 2.6 million years of the Quaternary could be characterized more as one of long-
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term variability owing to waxing and waning ice sheets and varying orbital forcing on
reshuffling vegetation assemblages.
Records covering the pre-LGM are scarce; however, they have been important for
illustrating large-scale variability of vegetation over the Quaternary to high and low
latitude forcing (Dupont and Hooghiemstra, 1989; Blome et al., 2012). In particular,
these records have documented the large-scale expansion and mixing of afromontane
forests into the tropical lowlands as well as the development of important ecological
regions (Dupont et al., 2011). These records suggest a clear relationship of forest to
precession forcing as well as wet-dry cycles punctuated by intense aridity when the
precessional effect is accentuated due to higher orbital eccentricity (Dupont et al., 2011).
The greater availability of records covering the Holocene and late Pleistocene has
put heavy emphasis on the response of vegetation to glacial-interglacial transitions as
well as the impact of human occupation on the landscape. These records have been
integral, however, in providing insights into the biophysical limits of important taxa as
well as climate-vegetation feedbacks, dry season length, and non-linear abrupt events
(e.g. Liu et al., 2006, Vincens et al., 2007, Kropelin et al., 2008).
The second and third appendices of this dissertation use high-resolution terrestrial
records of vegetation in order to better understand both high and low latitude forcing on
the tropical vegetation at Lake Malawi, southeast Africa. Two deglacial intervals, the last
Deglaciation (20-9ka) and the Penultimate Deglaciation (160-100ka), have been targeted
due to their dramatically different orbital parameters in order to highlight the differences
in vegetation transitions during different glacial terminations.
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PRESENT STUDY
The papers appended in this document (A-D) give thorough details of the locality,
methods, results, and conclusions of each study. These articles are formatted for
publication in scientific journals. Appendix B is published, Appendix A has been
submitted for publication, and the last two (Appendices C and D) are prepared for
submission. These appendices are all multi-authored papers with me as the lead author
and contributor. The following provides a brief overview of each individual project.
Appendix A
In this study we related vegetation variability across sub-Saharan African to a
variety of climatic indices in order to determine the drivers of productivity and change at
high temporal and spatial resolution. The GIMMS AVHRR Normalized Difference
Vegetation Index (NDVI) data set was acquired covering a 21 year period (1980-2000)
(Tucker et al., 2005). African biome NDVI values range from 0.1 (grassland, bushland)
to 0.6 (evergreen rainforest), and this data represents variability of vegetation cover and
productivity on inter-annual time scales due a variety of both local and regional factors
(Davenport and Nicholson, 1993; Hély et al., 2006). Climate data for the identical period
was also acquired in order to determine the relationship between climate and the NDVI
time-series. As many factors have been thought to influence African vegetation, both
indices of oceanic and atmospheric variability were created based on previous studies
(Hoerling et al., 2006; Seidel et al. 2008). Spatio-temporal regression analysis was
conducted by regressing 3 month means of the NDVI time-series on the climatic indices.
20
Finally, in order to better understand how these variables were inter-related, a principal
components analysis was conducted.
Our analysis showed that NDVI variability in Africa is primarily correlated with
the inter-annual extent of atmospheric circulation related to the Intertropical Convergence
Zone (ITCZ). Our results indicated that inter-annual variability of the ITCZ, rather than
sea surface temperatures or teleconnections to mid/high latitudes, drives E-W and N-S
patterns in African vegetation, resulting from the effects of insolation anomalies and
ENSO events on atmospheric circulation. Furthermore, alterations in the locations of
convection and convergence throughout the tropics are mirrored by mid-latitude shifts in
the jet stream which limit the penetration of Atlantic moisture into much of southern
Africa. Global controls on tropical atmospheric circulation allow for spatially coherent
reconstruction of inter-annual vegetation variability throughout Africa on many timescales. Although vegetation in the Afrotropics has long been associated with mean
annual or summer precipitation amount, the influence of the atmosphere on the length of
the dry-season length plays an important, independent role.
Appendix B
At Lake Malawi, southeast Africa, drill cores were taken in 2005 from two sites:
in the northern basin and in the central basin. The mostly laminated sediments of the
northern basin provide a well-dated, high-resolution record of climate and vegetation
over the last ~80ka (Scholz et al., 2007; Brown et al., 2007). This study presents pollen
analysis on the core from northern Lake Malawi over a 3 meter interval covering the
Pleistocene/Holocene transition (~18-9ka), and is comparable with recent studies of local
21
vegetation from lowland sites reporting contrasting rainfall signals during the Younger
Dryas (YD) (Vincens et al., 2007). The Lake Malawi record tracks regional vegetation
changes and allows comparison with other tropical African records observing vegetation
opening and local forest maintenance during the YD.
This study shows a gradual decline of afromontane vegetation at 18ka, following
the Last Glacial Maximum. Around 14.5ka, tropical seasonal forest and Zambezian
miombo woodland became established. At ~13ka, drier, more open formations were
gradually established. Although tropical seasonal forest taxa were still present in the
watershed during the YD, this drought-intolerant forest type was likely restricted to areas
of favorable edaphic conditions along permanent waterways. The establishment of dry
season-tolerant vegetation followed the reinforcement of southeasterly tradewinds
resulting in a more pronounced dry winter season at ~11.8ka. The establishment of the
driest, most open vegetation type was coincident with the lowstand at the beginning of
the Holocene. Contemporaneous maintenance of tropical seasonal forest during the YD
and transition to more open woodland at the early Holocene is observed at other sides
south of 4ºS until ~11.8ka (Vincens, 1989, 1991, 1993, 2005, 2007). This suggests that
despite local decreases in mean annual rainfall indicated by geochemical studies, a longer
dry season resulting from a southward ITCZ is sufficient for sustaining denser vegetation
during the YD (Castañeda et al., 2007). This study demonstrates the importance of
global climate forcing and local geomorphological conditions in controlling vegetation
distribution.
22
Appendix C
The central basin of Lake Malawi preserves a long sedimentary record of both
regional climate and vegetation change. Thus, the cores taken from Lake Malawi in 2005
provide a unique terrestrial record of environmental change to both high and low latitude
boundary conditions in tropical Africa for the last 1.2 Ma (Scholz et al., 2007; Cohen et
al., 2007; Lyons, 2007). Although the age model is still under-going refinement, in this
study, we conducted pollen analysis on a 53 meter core interval that includes the
Penultimate Glacial (PG) and the Last Interglacial (LIG). The focus on this period allows
us to investigate vegetation responses to observed high-amplitude wet-dry transitions in
phase with local insolation modulated by high eccentricity as well as the penultimate
glacial-interglacial transition.
Pollen analysis on the longest Malawi core (MAL05-1B) extends the detailed
record of East African vegetation back to ~160ka. This interval reveals three large-scale
oscillations between forest and degraded, open vegetation. Although forest phases occur
both in the lowlands and highlands of the watershed, succession during forest expansion
as well as species composition differs during each phase. In the earliest phases, there is a
progressive expansion of tropical seasonal forest, miombo woodland, and afromontane
arboreal taxa, indicating very dense but heterogeneous vegetation. A final, relatively
rapid shift to higher arboreal pollen taxa occurs following the first two phases. However,
although afromontane and woodland taxa return to higher values, moist, closed canopy
tropical seasonal forest remains at low values for the rest of the interval. Climate
response surfaces were developed in order to determine environmental conditions
23
required for both lowland forest types (Watrin et al., 2007). This analysis suggests that
the final forest phase could not support expansive tropical seasonal forest due to a much
longer dry season than previous phases.
The intervening open phases are marked by extreme aridity and dramatic lake
level regressions leading to the expansion of grasses, steppic herbs, and semi-desert trees
(Lyons, 2007). This vegetation assemblage, featuring taxa with Somali-Masai affinities
such as Commiphora africana and Tribulus, is much drier than that found in the Malawi
watershed today. Changes in forest composition over this interval reflect some influence
of global boundary conditions with more deciduous woodlands during what is likely the
LIG (Beuning et al., 2011); however, the large-scale vegetation changes around the lake
seem to occur at a higher frequency at what may be a response to insolation controls as a
result of variable monsoon strength and seasonality during this period of high-amplitude
climate variability.
Appendix D
The focus of Appendix D of this dissertation is vegetation and climate of the Last
Interglacial period (LIG; 130-115ka). This data and other studies suggest that African
hydrology was very different than today with vegetation extending far into the Sahara
and large-scale droughts in much of East Africa (Rossignol-Strick, 1983; Scholz et al.,
2007; Cohen et al., 2007; Verschuren et al., 2009). Warmer temperatures during this
time have led many to use this period as an analog for studying the environmental effects
of future anthropogenic warming. Paleodata have long suggested a strong connection
between summer insolation and hydrology (Rossignol-Strick et al., 1983). Thus, as
24
orbital configuration was much different than today, it is therefore unclear whether the
Last Interglacial can serve as an informative analog. However, paleoclimate simulations
are a powerful method of validating models used for future projections of environmental
change, particularly Earth System Models (ESM), which rely not only on accurate
representation of climate but also biogeochemical processes in the model.
Here we use the GFDL ESM2M to simulate LIG climate and vegetation to investigate
the effect of orbital configuration throughout Africa at a continental-scale (Dunne et al.,
2012a,b). We observe that, though annual insolation and temperature anomalies are
small, the increased amplitude of the seasonal insolation cycle in the northern hemisphere
and a reversed seasonal inter-hemispheric thermal gradient result in large precipitation
anomalies with respect to the present in both northern and southern Africa during the
respective summer seasons. Simulated biomes in areas of modern desert have markedly
denser vegetation, consisting of grassland and wooded grassland, in the northern
hemisphere as far as 20ºN. These results are in agreement with paleodata, suggesting that
Last Interglacial hydrology in Africa was primarily from atmospheric and oceanic
circulation associated with the effect of insolation on the W. African monsoon and the
ITCZ. Observed shifts in loci of rising air masses that produce rainfall result not only in
the reduction or increase of rainfall in certain locations, but also dramatically alter rainy
season lengths and a modified seasonal cycle in much of North Africa. This study
suggests that the alteration of orbital configuration alone used as boundary conditions for
the LIG simulations results in climate and vegetation that match the paleodata (Blome et
al., 2012). This means that the use of the LIG as a direct analog in tropical Africa to
25
future warming due to anthropogenic greenhouse gas emissions should be done with
caution. Also, this suggests that the parameterization of important processes within the
model, particularly related to atmospheric circulations and carbon cycle, are well
represented in ESM2M.
26
REFERENCES
Archer, C. L., and Caldeira, K., 2008, Historical trends in the jet streams: Geophysical
Research Letters, v. 35, L08803.
Blome, M.W., Cohen, A.S., Tryon, C.A., Brooks, A.S., Russell, J., 2012, The
environmental context for the origins of modern human diversity: A synthesis of regional
variability in African climate 150,000–30,000 years ago: Journal of Human Evolution, v.
62, 563-92.
Boko, M., Niang, I., Nyong, A., Vogel, C., Githeko, A., Medany, M., Osman-Elasha, B.,
Tabo R., Yanda, P., 2007, Africa. In: Climate Change 2007: Impacts, Adaptation and
Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by M.L. Parry, O.F. Canziani, J.P.
Palutikof, P.J. van der Linden and C.E. Hanson, Cambridge University Press, Cambridge
UK, pp. 433-467.
Bonnefille, R., 2010, Cenozoic vegetation, climate changes and hominid evolution in
tropical Africa: Global and Planetary Change, v. 72, 390-411.
Brown, E.T., Johnson, T.C., Scholz, C.A., Cohen, A.S., King, J.W., 2007, Abrupt change
in tropical African climate linked to the bipolar seesaw over the past 55,000 years:
Geophysical Research Letters, v. 34, L20702.
Brown, M., and deBeurs, K., 2008, Evaluation of multi-sensor semi-arid crop season
parameters based on NDVI and rainfall: Remote Sensing of the Environment: 112, 22612271.
Brown, M., K. deBeurs, Vrieling, A., 2010, The response of African land surface
phenology to large scale climate oscillations: Remote Sensing of the Environment, 114,
2286-2296.
Beuning, K., Zimmerman, K., Ivory, S., Cohen, A., 2011, Vegetation response to glacial–
interglacial climate variability near Lake Malawi in the southern African tropics:
Palaeogeography, Palaeoclimatology, Palaeoecology, v. 303, 81-92.
Cane, M.A. and Molnar, P., 2001, Closing of the Indonesian seaway as a precursor to east
African aridification around 3–4 million years ago: Nature, v. 411, 157–162.
Castañeda, I.S., Werne, J.P., Johnson, T.C., 2007, Wet and arid phases in the southeast
African tropics since the Last Glacial Maximum: Geology, v. 35, 823-826.
27
Chang, P., Zhang, R., Hazeleger, W., Wen, C., Wan, X., Ji, L., Seidel, H., 2008, Oceanic
link between abrupt changes in the North Atlantic Ocean and the African monsoon:
Nature Geoscience, v. 1, 444-448.
Chase, B. M., Scott, L., Meadows, M. E., Gil-Romera, G., Boom, A., Carr, A. S., Quick,
L. J., 2012, Rock hyrax middens: A palaeoenvironmental archive for southern African
drylands: Quaternary Science Reviews, v. 56, 107-125.
Collins, M., 2005, El Niño-or La Niña-like climate change?: Climate Dynamics, v.24, 89104.
Cox, P.M., Pearson, D., Booth, B., Friedlingstein, P., Huntingford, C., Jones, C., Luke,
C., 2013, Sensitivity of Tropical Carbon to Climate Change Constrained by Carbon
Dioxide Variability: Nature, in press.
Davenport, M. L., Nicholson, S. E., 1993, On the relation between rainfall and the
Normalized Difference Vegetation Index for diverse vegetation types in East Africa:
International Journal of Remote Sensing, v. 14, 2369-2389.
Diffenbaugh, N.S., Giorgi, F., 2012, Climate change hotspots in the CMIP5 global
climate model ensemble: Climatic Change, v. 114, 813-822.
Dunne, J.P., John, J.G., Adcroft, A.J., Griffies, S.M., Hallberg, R.W., Shevliakova, E.,
2012a, GFDL's ESM2 global coupled climate-carbon Earth System Models Part I:
Physical formulation and baseline simulation characteristics: Journal of Climate, v. 25,
6646-6665.
Dunne, J.P., John, J.G., Shevliakova, E., Stouffer, R.J., Krasting, J.P., Malyshev, S.L.,
2012b, GFDL's ESM2 global coupled climate-carbon Earth System Models Part II:
Carbon system formulation and baseline simulation characteristics: Journal of Climate, in
press.
Dupont, L.M., Hooghiemstra, H., 1989, The Saharan-Sahelian boundary during the
Brunhes chron: Acta Botanica Neerlandica, v. 38, 405-415.
Dupont, L., 2011a, Orbital scale vegetation change in Africa: Quaternary Science
Reviews, v. 30, 3589-3602.
Few R., M. Ahern, F. Matthies, and S. Kovats, 2004, Floods. Health and climate
change: a strategic review, in Working Paper 63, Tyndall Centre for Climate Change
Research, University of East Anglia, Norwich, pp. 138.
28
Guiot, J., Haibin, W., Wenying, J., Yunli, L., 2008, East Asian Monsoon and
paleoclimatic data analysis: a vegetation point of view: Climate of the Past Discussions, v.
4, 213-231.
Hamilton, A.C., 1982, Environmental history of East Africa: a study of the Quaternary.
London: Academic Press, 328 pp.
Hedberg, O., 1951, Vegetation belts of the East African mountains. Results of the
Swedish East African expedition: Svn. Bot. Tidskr., v. 45, 140–202.
Hély, C., Bremond, L., Alleaume, S., Smith, B., Sykes, M. T., Guiot, J., 2006,
Sensitivity of African biomes to changes in the precipitation regime: Global Ecology and.
Biogeography, v. 15, 258–270.
Herrmann, S., Anyamba, A., Tucker, C., 2005, Recent trends in vegetation dynamics in
the African Sahel and their relationship to climate: Global Environmental Change, v. 15,
394-404.
Hoerling, M., J. Hurrell, J. Eischeid, Phillips, A., 2006, Detection and attribution of
twentieth-century northern and southern African rainfall change: Journal of Climate, 19,
3989-4008.
Intergovernmental Panel on Climate Change (IPCC), 2007, Climate Change 2007: The
Physical Science Basis and Impacts, Adaptation, and Vulnerability. Contribution of
Working Groups I and II to the Fourth Assessment Report of the Intergovernmental Panel
on Climate Change, Cambridge Univ. Press, Cambridge, U. K.
Jolly, D., Haxeltine, A., 1997, Effect of low glacial atmospheric CO2 on tropical African
montane vegetation: Science, v. 276, 786-788.
Kröpelin, S., Verschuren, D., Lézine, A. M., Eggermont, H., Cocquyt, C., Francus, P.,
Engstrom, D. R., 2008, Climate-driven ecosystem succession in the Sahara: the past 6000
years: Science, v. 320, 765-768.
Liu, Z., Wang, Y., Gallimore, R., Notaro, M., Prentice, I. C., 2006, On the cause of
abrupt vegetation collapse in North Africa during the Holocene: Climate variability vs.
vegetation feedback: Geophysical Research Letters, 33, L22709.
Livingstone, D. A., 1971, A 22,000 year old pollen record from the Plateau of Zambia:
Limnology and Oceanography, v. 16, 349–356.
Lovett, J. C., Midgley, G. F., Barnard, P., 2005, Climate change and ecology in Africa:
African Journal of Ecology, v. 43: 167–169.
29
Lu, J., Vecchi, G. A., Reichler, T., 2007, Expansion of the Hadley cell under global
warming: Geophysical Research Letters, v.34, L06805.
Malmer, A., 2007, General ecological features of miombo woodlands and considerations
for utilization and management. In MITMIOMBO–Management of Indigenous Tree
Species for Ecosystem Restoration and Wood Production in Semi-Arid Miombo
Woodlands in Eastern Africa. Proceedings of the First MITMIOMBO Project Workshop
held in Morogoro, Tanzania, pp. 6-12.
Marchant, R., Mumbi, C., Behera, S., Yamagata, T., 2006, The Indian Ocean dipole–the
unsung driver of climatic variability in East Africa: African Journal of Ecology, v. 45, 416.
Morley, R.G., 2007, Cretaceous and Tertiary Climate Change and the Past Distribution of
Megathermal Rainforests Tropical Rain Forest Responses to Climatic Change: M.B.J.
Flenley (Ed.), Springer, pp. 1–31.
Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A., Kent, J., 2000,
Biodiversity hotspots for conservation priorities: Nature, v. 403, 853-858.
Nicholson, S., 1996, A Review of Climate Dynamics and Climate Variability in Eastern
Africa, The Limnology, Climatology, and Paleoclimatology of the East African Lakes,
Johnson, T.C., Odada, E.O., Gordon and Breach Publisher, 664pp.
Pohl, B., Richard, Y., Fauchereau, N., 2007, Influence of the Madden-Julian oscillation
on Southern African summer rainfall: Journal of Climate, v. 20, 4227-4242.
Polhill, R.M., 1966. Flora of Tropical East Africa (ed. by C.E. Hubbard and E. MilneRedhead), Crown Agents for Oversea Governments and Administrations, London.
Rossignol-Strick, M., 1983, African monsoons, an immediate climate response to orbital
insolation: Nature, v. 304, 46-49.
Sankaran, M., Hanan, N. P., Scholes, R. J., Ratnam, J., Augustine, D. J., Cade, B. S.,
Zambatis, N., 2005, Determinants of woody cover in African savannas: Nature, v. 438,
846-849.
Seidel, D. J., Fu, Q., RanDel, W. J., ReichleR, T. J., 2007, Widening of the tropical belt
in a changing climate: Nature Geoscience, v.1, 21-24.
30
Shevliakova, E., Pacala, S.W., Malyshev, S., Hurtt, G.C., Milly, P.C.D., Caspersen, J.P.,
2009, Carbon cycling under 300 years of land use change: Importance of the secondary
vegetation sink: Global Biogeochemical Cycles, v. 23:GB2022.
Tucker, C., J. Pinzon, M. Brown, D. Slayback, E. Pak, R. Mahoney, E. Vermote, Saleous,
N., 2005, An extended AVHRR 8km NDVI dataset compatible with MODIS and SPOT
vegetation NDVI data: International Journal of Remote Sensing, 26, 4485-4498.
Van Zinderen Bakker, E. M., Coetzee, J.A., 1972, A re-appraisal of late-Quaternary
climatic evidence from tropical Africa: Palaeoecology of Africa, v. 7, 151-181.
Verschuren, D., Sinninghe-Damsté, J., Moernaut, J., Kristen, I., Blaauw, M., Fagot, M.,
Haug, G., CHALLACEA project members, 2009, Half-precessional dynamics of
monsoon rainfall near the East African Equator: Nature, v. 462, 637-641.
Vincens, A., 1989, Paleoenvironnements du bassin Nord-Tanganyika (Zaire, Burundi,
Tanzanie) au cours des 13 derniers mille ans: Apport de la palynologie : Review of
Palaeobotany and Palynology, v. 61, 69-88.
Vincens, A., 1991, Late quaternary vegetation history of the South-Tanganyika basin.
Climatic implications in South Central Africa: Palaeogeography, Palaeoclimatology,
Palaeoecology, v. 86, 207-226.
Vincens, A., 1993, Nouvelle séquence pollinique du lac Tanganyika: 30,000 ans
d'histoire botanique et climatique du bassin Nord : Review of Palaeobotany and
Palynology, v. 78, 381-394.
Vincens, A., Buchet, G., Williamson, D., Taieb, M., 2005, A 23,000 yr pollen record
from Lake Rukwa (8oS, SW Tanzania): New data on vegetation dynamics and climate in
Central Eastern Africa: Review of Palaeobotany and Palynology, v. 137, 147-162.
Vincens, A., Garcin, Y., Buchet, G., 2007b, Influence of rainfall seasonality on African
lowland vegetation during the Late Quaternary: pollen evidence from Lake Masoko,
Tanzania: Journal of Biogeography, 34, 1274–1288.
Watrin, J., Lézine, A.-M., Gajewski, K. Vincens, A., 2007, Pollen–plant–climate
relationships in sub-Saharan Africa: Journal of Biogeography, v. 34, 489–499.
White, F., 1983. Vegetation of Africa - a descriptive memoir to accompany the
Unesco/AETFAT/UNSO vegetation map of Africa; Natural Resources Research Report
XX; UNESCO, Paris, France; 356pp.
31
World Water Forum (WWF), 2000, The Africa Water Vision for 2025: Equitable and
Sustainable Use of Water for Socioeconomic Development. UN Water/Africa, pp. 34.
Wu, H., Guiot, J., Brewer, S., Guo, Z., 2007, Climatic changes in Eurasia and Africa at
the last glacial maximum and mid-Holocene: reconstruction from pollen data using
inverse vegetation modeling: Climate Dynamics, v. 29, 211-229.
32
APPENDIX A:
IN THE HOT SEAT: INSOLATION, ENSO, AND VEGETATION IN THE AFRICAN
TROPICS
Sarah J. Ivory*, Joellen Russell*, Andrew S. Cohen*,
*Department of Geosciences, University of Arizona, 1040 E 4th St., Tucson, AZ 85721
Submitted to the Journal of Geophysical Research – Biogeosciences
33
Key Points
1. Atmospheric variability has a dramatic effect on tropical African vegetation.
2. ITCZ position and intensity result from variations in insolation and ENSO.
3. Both mean rainfall and dry season length are strong controls on tropical
vegetation.
Abstract
African climate is changing at rates unprecedented in the Late Holocene with
profound implications for tropical ecosystems and the global hydrologic cycle.
Understanding the specific climate drivers behind tropical ecosystem change is critical
for both future and paleo-modeling efforts. However, linkages between climate and
vegetation in the tropics have been extremely controversial. The Normalized Difference
Vegetation Index (NDVI) is a satellite derived index of vegetation with a high spatial and
temporal resolution. Here, we use regression analysis to show that NDVI variability in
Africa is primarily correlated with the inter-annual extent of the Intertropical
Convergence Zone (ITCZ). Our results indicate that inter-annual variability of the ITCZ,
rather than sea surface temperatures or teleconnections to mid/high latitudes, drives
patterns in African vegetation resulting from the effects of insolation anomalies and
ENSO events on atmospheric circulation. Global controls on tropical atmospheric
circulation allow for spatially coherent reconstruction of inter-annual vegetation
variability throughout Africa on many time-scales through regulation of dry-season
length and moisture convergence, rather than precipitation amount.
34
Introduction
Ecosystems and rain-fed agriculture provide essential resources to millions of
Africans. Threats to tropical vegetation related to rapidly changing climate make this
region extremely vulnerable [IPCC, 2007] and have dangerous, costly implications for
daily economy and subsistence [WWF, 2000; Few et al., 2004]. Despite these concerns,
it remains uncertain how natural vegetation communities and cropland productivity will
respond to altered atmospheric and oceanic circulation in the future, and little
infrastructure exists to mitigate these changes [Boko et al., 2007; Brown and deBeurs,
2008].
Modern observational data and paleoclimate records are both powerful tools for
understanding biosphere dynamics [Bradley, 1985]. Interpreting both types of records,
however, is complicated by much uncertainty surrounding the question of what climatic
mechanisms regulate African vegetation change today. Such changes have been
attributed to: insolation, mid-latitude teleconnections to high or mid-latitudes,
vegetation/soil moisture feedbacks, ENSO, upper-level perturbations, sea surface
temperature (SSTs), Hadley Cell intensity, and ITCZ excursions [Rossignol-Strick, 1983;
DeMenocal and Rind, 1993; Nicholson and Selato, 2000; Hulme, 2001; Mercier et al,.
2002; New et al., 2003; Nicholson and Grist, 2003; Anyah and Semazzi, 2004; Hoerling
et al., 2006; Giannini et al., 2008, Chiang, 2009]. Therefore, studies of both past and
future climate change create the need for quantitative constraints on the modern
relationship between climate controls and vegetation.
35
Rainfall seasonality in tropical Africa is determined by the annual migration of largescale circulation systems like the ITCZ and Congo Air Boundary (CAB) [Nicholson,
1996]. Summer rainfall occurs in southern and northern tropical Africa, and an
equatorial bimodal regime lies in-between. This simplistic model notwithstanding, the
spatial heterogeneity of African vegetation indeed mostly results from regional controls
on rainfall and dry-season length, in addition to local factors such as topography, lake
effects, continentality, and SSTs of adjacent oceans [Nicholson, 1996]. Rainfall varies
over kilometers from <200 to >2000 mm/year and dry season length from 0 to >6
months. While biomes range from tropical rainforest to open desert, Africa is dominated
by semi-arid or other seasonal vegetation that is very sensitive to hydrologic variability
[White, 1983]. As a result of the mosaic of influences, it is unclear whether vegetation
variability is influenced more by large-scale forcing or local/regional changes and how
variability of rainfall amount and dry season length may independently affect vegetation
[Hély et al., 2006].
Satellite data provide continuous, high-resolution datasets of vegetation productivity
and can help identify the direct relationship between vegetation and climate on global to
local scales. These data have previously been used to link climate modes and carbon
drawdown to vegetation productivity in the northern hemisphere [Russell and Wallace,
2004; Lotsch et al., 2005]. For this study, a 21-year 8km-resolution NDVI record (19802000) from NOAA’s Advanced Very High Resolution Radiometer (AVHRR) has been
used to reconstruct patterns of inter-annual vegetation variability throughout tropical
Africa [Tucker et al., 2005]. Whereas studies in the tropics have linked NDVI to SST
36
and precipitation variability, the relative effects of atmospheric and oceanic variability at
the continental-scale has not been addressed [Anyamba and Eastman, 1996; Anyamba et
al., 2002; Martiny et al., 2006; Brown et al., 2010]. In this study, we have regressed
NDVI fields on climatic variables previously proposed as possible regulatory
mechanisms for Afrotropical vegetation [Rossignol-Strick, 1983; DeMenocal and Rind,
1993; Nicholson and Selato, 2000; Hulme, 2001; Mercier et al,. 2002; New et al., 2003;
Nicholson and Grist, 2003; Anyah and Semazzi, 2004; Hoerling et al., 2006; Giannini et
al., 2008, Chiang, 2009]. Our results provide a new perspective on tropical climatevegetation interactions and show that variability within the African tropics is regionally
coherent and strongly linked to global-scale forcing.
Methods
Normalized Difference Vegetation Index (NDVI), a satellite-derived index of
absorbed red versus scattered near-infrared light, is highly correlated with vegetation net
primary productivity. African biome NDVI values range from 0.1 (grassland, bushland)
to 0.6 (evergreen rainforest) [Davenport and Nicholson, 1993; Hély et al., 2006]. In this
study we have used the 1980-2000 GIMMS AVHRR dataset of Tucker et al. (2005). The
statistical methods in this paper follow those of Russell and Wallace (2004). The
purpose of the study is to evaluate variability rather than trend in NDVI, so we chose a
dataset and temporal range of data which has been used in similar studies linking
vegetation to climate (Xu et al., 2010; Meng et al., 2011; Hountondji et al., 2009;
Fabricante et al., 2009; Revadekar et al., 2012) and was also evaluated by Beck et al.
(2011) and found to have the least error associated with temporal change. Future work
37
will focus on the addition of the most recent decade of data and trends in NDVI linked to
climate; however, this is outside of the scope of the current study.
The relationship between climate and the NDVI time-series was then investigated.
First, time-series of SSTs and indicators of ITCZ position and intensity were created for
the period 1980-2000 to act as important climatic variables within the tropical
atmosphere-ocean system (Supplementary Fig. 1). To investigate variability in the
summer season of both hemispheres, December-January-February (DJF) and June-JulyAugust (JJA) series were constructed for each variable. SSTs are latitude-weighted mean
values from the 1°x1° HadISST1 dataset [http://climexp.knmi.nl; Rayner et al., 2006]
within regions of known importance for African rainfall (Southern Atlantic, 0-20°S,
10°E-10°W; North Indian Ocean, 15-25°N, 55-75°E; South Indian Ocean, 16-30°S, 3648°E) [Hoerling et al., 2006]. ITCZ indicators were chosen based on Seidel et al. (2008)
and include the first empirical orthogonal function (EOF) of 1) 200mb geopotential
height (200MH), 2) outgoing longwave radiation (OLR), and 3) incoming shortwave
radiation (insolation) constructed using NCEP/NCAR Reanalysis data
[http://www.esrl.noaa.gov/psd/, Kalnay et al., 1996] within tropical Africa (23°N-23°S,
0-40°E; Figure 1). EOFs are a statistical method allowing us to extract the most
important variability over a broad spatial scale. This technique was used for the ITCZ
indicator time-series as these variables were reconstructed over the whole of the tropics,
including areas north and south of the equator. Unlike the SST time-series, a simple
average would not be representative of variability in both hemispheres at once.
38
Following the established methods of Russell and Wallace (2004), 3-month mean
NDVI fields were regressed on all climatic variables. Regression coefficients displayed
on the resultant regression maps are the slope of the regression relationship between
NDVI and each variable, such that each point represents the NDVI anomaly for one
standard-deviation of each time-series. Thus, this study provides a regionally coherent
and direct quantitative relationship of climate and vegetation.
Next, in order to evaluate the main components of variability in the tropical oceanatmosphere system and the relationships between variables, we performed a principal
components analysis (PCA) on all climatic variables discussed above. Loadings for the 3
significant axes are presented in Supplementary Table 1, and plots of all time-series can
be found in Supplementary Figure 1.
To attribute the patterns observed in the regressions, a time-series of Southern
Oscillation Index (SOI) was obtained from NOAA Climate Prediction Center
(http://www.cpc.ncep.noaa.gov/data/indices/). In addition, a new time-series of southern
(10-20°S; 0-40°E) and northern hemisphere (10-20°N; 0-40°E) dry days (days/year with
< 0.5mm rain) was created from ERA-40 precipitation data [http://badc.nerc.ac.uk/view/
badc.nerc.ac. uk__ATOM__ dataent_ECMWF-E40; Uppala et al., 2005] to evaluate the
effect of dry season length.
Canonical correspondence analysis (CCA) was used to test the statistical significance
of similarity between regression patterns. Significance of individual regression patterns
was determined by a Monte Carlo test. First, the latitude-weighted mean covariance was
calculated for each pattern. This was then compared with mean covariance produced by
39
10,000 regressions of random standardized time-series and takes into account time-series
auto-correlation.
Finally, in order to compare the NDVI regression patterns to changes in tropical
circulation, 1980-2000 NCEP/NCAR Reanalysis 500mb vertical velocity
(http://www.esrl.noaa.gov/psd/) was obtained. 500mb vertical velocity shows areas of
rising (negative numbers) and subsiding (positive numbers) air masses at the middle of
the troposphere related to rainy or dry conditions, respectively. All climatic variables
yielding statistically significant covariance in the NDVI regression were then correlated
with the vertical velocity fields and compared with NDVI anomaly patterns. This analysis
allows us to better understand how vegetation patterns may be related to atmospheric
variability.
Results and Interpretation
The vegetation and climate relationship was investigated by regressing 3-month
means of NDVI on all time-series (Table 1).
The ITCZ indicators consistently show
higher values of mean covariance than the SST time-series. Both JJA and DJF insolation
time-series show significant covariance within the same season. The JJA OLR timeseries yields the highest covariance observed (0.0183) within the same season. Although
no other 3-month period produces a mean covariance with greater than 95% significance,
a few nearly significant values are also observed during the short and long rain seasons
(MAM and OND) for both 200MH and OLR. In contrast, covariance produced by SSTs
is low and never reaches significant values (Table 1). This suggests that inter-annual
vegetation variability is strongly controlled by atmospheric circulation.
40
The patterns yielded by regressing NDVI on DJF and JJA insolation within the same
season are shown in Figures 2a and 2b. The DJF map shows a strong positive anomaly in
southeast Africa with a negative anomaly in all other regions, especially marked in North
and South Africa. The JJA map is similar but with signs reversed, showing a negative
anomaly in East Africa and a strong positive anomaly persisting in North Africa.
Additionally, another positive anomaly stretches from the Indian to the Atlantic coast in
southern Africa.
The pattern yielded by regressing NDVI on JJA OLR within the same season is
shown in Figure 3a. The JJA regression map shows a band of strong negative anomaly
running zonally from 10°N to 15°N, a second weaker negative anomaly in South Africa,
and a positive anomaly in the equatorial region.
Principal Components Analysis of Climatic Variables
In order to investigate the main components of variability within the entire system
and interrelation of these climatic variables, a PCA was performed. The PCA resulted in
three significant axes, explaining 60.0% of the variance (Table 1). A biplot with loadings
of the first two axes shows that nearly half of the variance is explained by the first two
PCs which load strongly on the ITCZ indicators (Figure 4; Supplementary Table 1).
PC1 (23.8% variance) loads strongly on JJA insolation (0.46) and contrasts with DJF
insolation (-0.43) (Figure 4). In this 21-year record, insolation changes are the result of
variation in solar irradiance, which show a strong negative correlation between seasons (r
= -0.812; p-value<0.001). This illustrates the anti-phased nature of summer insolation
anomalies. This PC suggests the existence of an inter-hemispheric gradient such that, for
41
a given year, high summer insolation in one hemisphere corresponds to lower summer
insolation in the opposite hemisphere. The role of insolation is likely much greater on
longer time-scales (e.g. Milankovich) when lower frequency, high-amplitude fluctuations
occur.
PC2 (20.4% variance) loads strongly on 200MH in both seasons (DJF = 0.426; JJA =
0.369) and contrasts with both seasons of OLR (DJF = -0.460; JJA = -0.312; Figure 4).
The biplot supports the notion that the variables are positively correlated between season
(200MH: r =0.82, p<0.001; OLR: r =0.43, p=0.05) and negatively correlated with each
other (DJF: r =-0.631, p=0.001; JJA: r =-0.262, p=0.13). OLR is a metric of atmospheric
water vapor content, with high values recorded on cloudless days. In contrast, high
values of 200MH indicate deep tropospheric convection producing tropical storms,
whereas low values indicate a more stable, stratified troposphere [Seidel et al, 2008].
Thus, PC2 suggests that higher 200MH is coincident with lower OLR throughout the
tropics. Furthermore, the EOFs for each variable are similar, showing larger anomalies
toward the subtropics, indicating a more exaggerated effect in these regions (Figure 1).
All three significant PCs show an important contribution of variance from the SST
time-series (Supplementary Table 1). In addition to insolation and OLR, PC1 loads on
DJF and JJA North Indian (-0.3598, -0.3167) and JJA South Indian SST (-0.3371), while
PC2 shows a very high contribution from DJF South Indian SST (0.3413). PC3 (15.8%)
loads most strongly on Atlantic Ocean variability in DJF (0.440) and JJA (0.388)
(Supplementary Table 1). SST variability in these regions is often considered important
for rainfall patterns throughout Africa [Hoerling et al., 2006]. Although the PCA
42
suggests that SST have a strong role in driving the atmosphere-ocean system and the link
between rainfall and SST is well known, no strong relationship exists between SST and
NDVI (Table 1).
Discussion
Dry Season and Drought Stress
The regression maps produced by DJF and JJA insolation illustrate the effect of the
inter-hemispheric insolation gradient on circulation within the tropical atmosphere with
no lag (Figure 2). Both patterns are significantly similar; however, the anomalies are of
opposing sign due to the anti-phased relationship between the insolation time-series.
This means that when JJA insolation is high, DJF insolation is low that same year, and
suggests that a reversal of the insolation gradient produces a vegetation response of
opposing sign.
Tropical insolation may affect vegetation through two mechanisms: enhanced rainfall
or evapotranspiration. As the positive anomalies for both regression maps occur in the
summer hemisphere when insolation is high, it is unlikely that this pattern is created by
greater evaporation (Figure 2). These patterns are instead linked to summer rains, whose
position and intensity are determined by insolation. This could be related to intensified
Hadley circulation and therefore greater summer rainfall. Although this does explain
positive anomalies for both JJA and DJF in much of the summer hemisphere, a marked
DJF negative anomaly in part of southern Africa during it summer season contradicts this
(Figure 2a). Greater summer precipitation alone cannot fully explain this pattern.
43
Instead, we propose that these patterns are related to excursions northward or
southward of ITCZ mean position. With high JJA insolation, positive anomalies would
be expected throughout North Africa in both seasons as a more northward ITCZ causes
the growing season to begin early and rainfall to extend northward [Brown and deBeurs,
2008]. With high DJF insolation, the situation is reversed and the ITCZ is southward. In
order to localize the position of ITCZ-related convection, 500mb vertical velocity fields
were correlated with DJF and JJA insolation (Figure 5). Vertical velocity is a metric of
vertical motion in the atmosphere with negative values indicating convection; therefore, a
negative (positive) correlation indicates enhanced (reduced) convection with increasing
insolation. In DJF, a band of negative correlation extends southeastward from -10°S to 40°S, south of the mean position, suggesting a more southerly position of convection
when DJF insolation is high (Figure 5b). In JJA, negative correlation is observed
beginning near the mean position centered around 7°N, but extends much further
northward (Figure 5d). This suggests that convection is stronger and more northward
when JJA insolation is high.
In DJF, a positive anomaly in southeast Africa agrees with a southerly ITCZ bringing
more rainfall and lengthening the growing season; however, a strong negative anomaly
persists south of 20°S (Figure 2a). In East Africa, a meridional component of the ITCZ
develops in DJF, the CAB, extending from Namibia northeastward. The CAB represents
the convergence of dry air originating from the northern Indian Ocean with moister air
masses from the Congo and southern Atlantic Ocean. Eastward moisture penetration
from the Atlantic is important for driving rainfall in East Africa during the southern
44
hemisphere rainy season [McHugh, 2004]. The correlation of DJF insolation with 500mb
vertical velocity suggests that a southward shift of tropical circulation is mirrored by a
southward shift of the semi-permanent South Atlantic anti-cyclone, an area of positive
correlation west of southern Africa (Figure 5b). This results in reduced convection on the
western side of the CAB leading to greater eastward Atlantic moisture penetration during
DJF, and promoting a wetting in southeast and drying in southern Africa consistent with
the DJF NDVI pattern. When DJF insolation is low, a northward ITCZ and South
Atlantic anti-cyclone block Atlantic moisture from penetrating further eastward (Figure
5b).
ITCZ displacement alters the length of the growing and dry seasons which in turn
affects vegetation [Brown and deBeurs, 2008]. A time-series of southern (10-20°S; 040°E) and northern hemisphere (10-20°N; 0-40°E) dry days (days/year with < 0.5mm
rain) was created as a metric for the effect of dry season length on vegetation. Figures 2c
and 2d show the regression maps yielded by regressing DJF and JJA NDVI on southern
and northern hemisphere dry days, respectively. Both patterns are significantly similar,
but of opposing sign, to their DJF and JJA insolation counterpart (Figure 2). This
suggests that higher insolation displaces the ITCZ toward the summer hemisphere and
produces a shorter dry season and a longer growing season leading to increased
vegetation productivity (Figure 6a).
Studies of hydrology typically focus around mean annual precipitation (MAP);
however, vegetation productivity is a function of effective moisture [Nicholson, 1986].
We propose that insolation controls vegetation productivity through two mechanisms
45
when summer insolation is high: greater rainfall intensity and shorter dry season length
due to a displacement of the ITCZ and subtropical anti-cyclones toward the summer
hemisphere. Although enhanced convective precipitation associated with high summer
insolation may help drive these vegetation anomalies, the relationship connecting rainy
season productivity to the length of the preceding dry season should not be ignored. This
is consistent with modeling results of Hély et al. (2006), demonstrating increased
sensitivity of semi-deciduous and deciduous biome types to changes in dry season length.
Hadley Circulation and ENSO
The JJA OLR regression pattern results from changes in atmospheric circulation of
the same sign throughout the tropics (Figures 1 and 3). Although the 200MH time-series
never yields covariance values with greater than 90% significance, the PCA biplot
suggests an important anti-phase relationship between these two variables (Figure 4).
When 200MH is high, OLR is low. In addition, both EOF plots imply that anomalies are
greater toward the subtropics (Figures 1).
The ITCZ can vary in intensity, mean position, and width [Nicholson, 1996]. The
equator-subtropics OLR gradient as observed in the EOF suggests that the vegetation
response in JJA is related to tropical belt width (Figure 1). Thus the negative anomalies
near the subtropics and positive anomalies near the equator are due to a narrower ITCZ
during summers when OLR is high (Figure 3). This is supported by correlation of JJA
OLR with JJA 500mb vertical velocity. Figure 5f shows a positive correlation, indicating
reduced convection, covering much of northern and central Africa. This would dry the
subtropics and wet the equatorial region as the ITCZ doesn’t reach as far north. This is
46
further supported by positive vegetation anomalies persisting even in the southern
hemisphere during the JJA dry season, suggesting that enhanced precipitation near the
equator influences vegetation into the dry season. This is perhaps the result of enhanced
starch storage, common in southern African woody plants in years with a wetter, more
productive rainy season [Rutherford, 1984]. Although the regression yields a 95 %
significant value only in northern hemisphere summer, numerous 90% significant values
for regression of 200MH and OLR suggest that this relationship maybe also exist for the
short and long rainy season (MAM and OND) as well as the southern hemisphere
summer (DJF; Table 1).
Such changes in atmospheric circulation throughout the tropics may suggest a link to
large-scale climate variability. Regression of SOI on JJA NDVI produces a pattern that is
significantly similar to JJA OLR (Figure 3). This supports many studies linking ENSO to
vegetation and rainfall anomalies throughout Africa [Anyamba and Eastman, 1996;
Nicholson and Selato, 2000; Philipon et al., 2007]. Higher 200MH throughout the tropics
during ENSO events is consistent with results of our PCA, showing an anti-phased
relationship between OLR and 200MH [Camberlin et al., 2001]. Furthermore, ENSO
warm-event years (1988, 1999) on the PCA biplot occur near the positive end of the
second axis near both seasons of 200MH (Figure 4). Cold events, associated with higher
OLR and lower 200MH, would produce a similar pattern to Figure 3a. During warm
events, the relationship is reversed when OLR is lower.
Our study points to a strong, direct link between atmospheric circulation during
ENSO events and African vegetation variability [Schreck and Semazzi, 2004]. Since SOI
47
is calculated based on surface pressure, rather than SST anomalies, we observed that the
immediate effect of ENSO on African vegetation is not related directly to SSTs but rather
atmospheric height anomalies. The north-south symmetry of the OLR and SOI patterns
in NDVI is consistent with studies illustrating the effect of cold events on the ITCZ that
promote reduced convergence at the northern and southern extremes during rainy seasons
(Figure 6b) [Janicot, 1997].
Conclusions
Despite heterogeneous MAP and vegetation, global atmospheric controls on the ITCZ
have a strong influence on inter-annual vegetation variability throughout Africa. Rainy
season moisture convergence and dry season length have a profound effect on vegetation
in both northern and southern tropical Africa. In contrast, commonly cited mechanisms,
like SSTs, do not exhibit a strong connection.
ITCZ variability and the African hydrologic budget are tightly connected to
ENSO and insolation on short and long time-scales (Figure 6). High summer insolation
produces ITCZ excursions resulting in a shorter dry season and positive vegetation
anomalies in the summer hemisphere (Figure 6a). Higher atmospheric heights and a
wider tropical belt during warm ENSO events result in a symmetrical vegetation pattern
centered about the equator (Figure 6b).
Although we support the suggestion that ENSO is the dominant mode of
variability in the African tropics, our study points to a strong, direct link between
atmospheric circulation, rather than SSTs, and vegetation productivity [Schreck and
Semazzi, 2004]. 1/3 of the African population lives in semi-arid regions dependent on
48
natural and rain-fed agricultural resources especially sensitive to variability [IPCC,
2007]. It is yet unknown how ENSO frequency and amplitude will change with future
warming [Collins et al., 2005; Yamaguchi and Noda, 2006]; however, it will almost
certainly have a dramatic effect on African vegetation. Although the latest generation of
climate models accurately simulate many aspects of atmospheric dynamics, inaccuracies
of ENSO behavior and the ITCZ will hinder accurate predictions of future vegetation
change, effects of tropical vegetation on the carbon and hydrologic cycles, and
simulations of past vegetation dynamics.
Despite the small amplitude changes over the time period studied (~0.25W/m2 or
.018%) [Shindell et al., 2006], insolation has a marked effect on tropical atmospheric
circulation and vegetation productivity. The effect of insolation on short time-scales has
been little investigated despite suggestions that very small solar irradiance changes may
influence tropical rainfall patterns [Shindell et al., 2006]. On longer time-scales, highamplitude insolation variability is likely the primary mechanism of Afrotropical
vegetation change. This supports the many studies showing large-scale ecological and
hydrologic changes in Africa that follow the beat of insolation [Rossignol-Stick, 1983;
Partridge et al., 1997; Cohen et al., 2007; Verschuren et al., 2009].
Our analyses suggest that today ITCZ width is mostly controlled by ENSO
variability, but future warming may also affect ITCZ width. A warmer atmosphere from
anthropogenic forcing results in higher 200MH and wider migrations, wetting the
outskirts of the tropics and drying the equatorial region. Seidel et al. (2008) have already
documented this widening of the tropics. This has particular implications, such as crop
49
failure and desertification, for semi-arid vegetation that is extremely sensitive to changes
in dry season length [Hely et al., 2006; McClean et al., 2005].
In addition, southern Africa depends on the placement of mid-latitude westerlies and
semi-permanent anti-cyclones determining the eastward penetration of moisture into
sensitive semi-arid areas like East Africa [McHugh, 2004]. Recent studies suggesting a
pole-ward westerlies displacement as the atmosphere warms imply that these regions
could see a marked drying as moisture needed to drive convergence may no longer be
available [McCabe et al., 2005].
In much of Africa, nothing is more important to human lives and livelihoods than the
seasonal rains following the sun each year and the plants sustained by this rainfall.
Although the human relationship with plants has changed over time in Africa, the overarching climate mechanisms controlling landscape distribution and productivity have not.
The quantitative constraints we provide here may give some insight concerning how
climate change will and did affect the distribution of important ecosystems and the
climate regimes under which crops thrive.
Acknowledgments
We thank the National Science Foundation for a Graduate Research Fellowship
(2009078688); Paul Goodman and Michael McGlue at the University of Arizona for
technical help and critical discussions; and Compton Tucker and Jorge Pinzon at NASA
GIMMS for access to the AVHRR NDVI dataset.
50
References
Anyah, R. and F. Semazzi (2004), Simulation of the sensitivity of Lake Victoria basin
climate to lake surface temperatures. Theor. Appl. Climatol. 79, 55–69, doi:
10.1007/s00704-004-0057-4.
Anyamba, A and J. Eastman (1996), Interannual variability of NDVI over Africa and its
relation to El Niño/Southern Oscillation, Int. J. Remote Sens., 17, 2533-2548, doi:
10.1080/01431169608949091.
Anyamba, A., C. Tucker, and R. Mahoney (2002), From El Ninõ to La Ninã: Vegetation
Response Patterns over East and Southern Africa during the 1997–2000 Period, J.
Climate, 15, 3096-3103, doi:0.1175/1520-0442(2002)015<3096:FENOTL>2.0.CO;2.
Beck, H. E., T.R. McVicar, A. van Dijk, J. Schellekens, R. de Jeu, and L. Bruijnzeel
(2011), Global evaluation of four AVHRR–NDVI data sets: Intercomparison and
assessment against Landsat imagery, Remote Sens. Environ., 115, 2547-2563, doi:
10.5194/hess-16-3835-2012.
Boko, M., I. Niang, A. Nyong, C. Vogel, A. Githeko, M. Medany, B. Osman-Elasha, R.
Tabo and P. Yanda (2007), Africa. In: Climate Change 2007: Impacts, Adaptation
and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report
of the Intergovernmental Panel on Climate Change, edited by M.L. Parry, O.F.
Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson, Cambridge University
Press, Cambridge UK, pp. 433-467.
Bradley, R.S. (1985), Quaternary Palaeoclimatology: Methods of Palaeoclimatic
Reconstruction, pp. 472, Unwin Hyman, London.
51
Brown, M., and K. deBeurs (2008), Evaluation of multi-sensor semi-arid crop season
parameters based on NDVI and rainfall, Remote Sens. Environ., 112, 2261-2271,
doi:10.1016/j.rse.2007.10.008.
Brown, M., K. deBeurs, and A. Vrieling (2010), The response of African land surface
phenology to large scale climate oscillations, Remote Sens. Environ., 114, 2286-2296,
doi:10.1016/j.rse.2010.05.005.
Camberlin, P., S. Janicot, and I. Poccard (2001), Seasonality and atmospheric dynamics
of the teleconnection between African rainfall and tropical sea-surface temperature:
Atlantic vs. ENSO. Int. J. Climatol. 21, 973-1005, doi: 10.1002/joc.673.
Chiang, J. (2009), The Tropics in Paleoclimate. Annual Review of Earth and Planetary
Sciences 37, 263-297, doi: 10.1146/annurev.earth.031208.100217.
Cohen, A., J. Stone, K. Beuning, L. Park, P. Reinthal, D. Dettman, C. Scholz, T. Johnson,
J. King, M. Talbot, E. Brown, and S. Ivory (2007), Ecological consequences of early
Late Pleistocene megadroughts in tropical Africa. PNAS 104, 16422 -16427. doi:
10.1073/pnas.0703873104.
Collins, M., and The CMIP Modelling Groups (2005), El Niño- or La Niña-like climate
change Clim. Dyn. 24, 89–104, doi:10.1007/s00382-004-0478-x.
Davenport, M. and S. Nicholson (1993), On the relation between rainfall and the
Normalized Difference Vegetation Index for diverse vegetation types in East Africa.
Int. J. Remote Sens. 14, 2369-2389, doi:10.1080/01431169308954042.
52
DeMenocal, P. and D. Rind (1993), Sensitivity of Asian and African climate to variations
in seasonal insolation, glacial ice cover, sea-surface temperature, and Asian
orography. J. Geophys. Res. 98, 7265-7287, doi:10.1029/92JD02924.
Fabricante, I., M. Oesterheld, and J. M. Paruelo (2009), Annual and seasonal variation of
NDVI explained by current and previous precipitation across Northern Patagonia. J.
Arid Environ. 73, 745-753, doi: 10.1007/s10265-009-0302-0.
Few R., M. Ahern, F. Matthies, and S. Kovats (2004), Floods. Health and climate
change: a strategic review, in Working Paper 63, Tyndall Centre for Climate Change
Research, University of East Anglia, Norwich, pp. 138.
Giannini, A., M. Biasutti, I. Held, and A. Sobel (2008), A global perspective on Africa
climate. Climatic Change 90, 359-383, doi: 10.1007/s10584-008-9396-y.
Hély, C., L. Bremond, S. Alleaume, B. Smith, M. Sykes, and J.Guiot (2006), Sensitivity
of African biomes to changes in the precipitation regime, Global Ecol. Biogeogr. 15,
258-270, doi: 10.1111/j.1466-822x.2006.00235.x.
Hoerling, M., J. Hurrell, J. Eischeid, and A. Phillips (2006), Detection and attribution of
twentieth-century northern and southern African rainfall change. J. Climate 19, 39894008, doi:10.1175/JCLI3842.1.
Hountondji, Y. C., N. Sokpon, J. Nicolas, and P. Ozer. (2009), Ongoing desertification
processes in the sahelian belt of West Africa: an evidence from the rain-use
efficiency, Recent Advances in Remote Sensing and Geoinformation Processing for
Land Degradation Assessment. ISPRS Series, Taylor and Francis, 173-186.
53
Hulme, M., R. Doherty, and T. Ngara (2001), African climate change: 1900-2100. Clim.
Res. 17, 145–168, doi:10.3354/cr017145.
Intergovernmental Panel on Climate Change (IPCC) (2007), Climate Change 2007: The
Physical Science Basis and Impacts, Adaptation, and Vulnerability. Contribution of
Working Groups I and II to the Fourth Assessment Report of the Intergovernmental
Panel on Climate Change, Cambridge Univ. Press, Cambridge, U. K.
Janicot, S. (1997), Impact of warm ENSO events on atmospheric circulation and
convection over the tropical Atlantic and West Africa. Annales Geophysicae 15, 471475, doi:10.1007/s00585-997-0471-x.
. Kalnay, E., M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Gandin, M. Iredell, S.
Saha, G. White, J. Woollen, Y. Zhu, M. Chelliah, W. Ebisuzaki, W. Higgins, J.
Janowiak, K.C. Mo, C. Ropelewski, J. Wang, A. Leetmaa, R. Reynolds, R. Jenne, and
D. Joseph (1996), The NCEP/NCAR 40-year reanalysis project, Bull. Amer. Meteor.
Soc., 77, 437-470, doi: 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.
Lotsch, A., M. A. Friedl, B. T. Anderson, and C. J. Tucker (2005), Response of terrestrial
ecosystems to recent Northern Hemispheric drought, Geophys. Res. Lett., 32, L06705,
doi:10.1029/2004GL022043.
Martiny, N., P. Camberlin, Y. Richard and N. Philippon (2006), Compared regimes of
NDVI and rainfall in semi-arid regions of Africa, Int. J. Remote Sens., 27, 5201-5223,
doi: 10.1080/01431160600567787.
McCabe, G., M. Clark, and M. Serreze (2001), Trends in Northern Hemisphere surface
cyclone frequency and intensity. J. Clim. 14, 2763–2768, doi: 10.1175/1520-0442.
54
McClean, C., J. Lovett, W. Küper, L. Hannah, J. Sommer, W. Barthlott, M. Termansen,
G. Smith, S. Tokumine and J. Taplin (2005), African plant diversity and climate
change. Ann. Mo. Bot. Gard. 92, 139-152.
McHugh, M. (2004), Near-Surface Zonal Flow and East African Precipitation Receipt
during Austral Summer. J. Climate 17, 4070-4079, doi: 10.1175/1520-0442.
Meng, M., J. Ni, and M. Zong (2011), Impacts of changes in climate variability on
regional vegetation in China: NDVI-based analysis from 1982 to 2000, Ecological
Research, 26, 421-428, doi:10.1007/s11284-011-0801-z.
Mercier, F., A. Cazenave, and C. Maheu, C. (2002), Interannual lake level fluctuations
(1993–1999) in Africa from Topex/Poseidon: connections with ocean-atmosphere
interactions over the Indian Ocean. Global Planet. Change 32, 141-163, doi:
10.1016/S0921-8181(01)00139-4.
New, M., B. Hewitson, C. Jack, and R. Washington (2003), Sensitivity of southern
African rainfall to soil moisture. CLIVAR Exchanges 27, 45–47
Nicholson, S. (1986), The Spatial Coherence of African Rainfall Anomalies:
Interhemispheric Teleconnections. J. Clim. Appl. Meteorol. 25, 1365-1381, doi:
10.1175/1520-0450.
Nicholson, S. (1996), A Review of Climate Dynamics and Climate Variability in Eastern
Africa, The Limnology, Climatology, and Paleoclimatology of the East African
Lakes, Johnson, T.C., Odada, E.O., Gordon and Breach Publisher, 664pp.
Nicholson, S. and J. Selato (2000), The influence of La Niña on African rainfall. Int. J.
Climatol. 20, 1761-1776, doi: 10.1002/1097-0088(20001130)20:14.
55
Nicholson, S.E. and J.P. Grist (2003), The seasonal evolution of the atmospheric
circulation over West Africa and Equatorial Africa. J. Climate 16, 1013-1030,
doi:10.1175/1520-0442(2003)016<1013:TSEOTA>2.0.CO;2.
Partridge, T. et al. (1997), Orbital forcing of climate over South Africa: A 200,000-year
rainfall record from the Pretoria Saltpan. Quat. Sci. Rev. 16, 1125-1133,
doi:10.1016/S0277-3791(97)00005-X.
Philippon, N., L. Jarlan, N. Martiny, P. Camberlin, E. Mougin (2007) Characterization of
the Interannual and Intraseasonal Variability of West African Vegetation between
1982 and 2002 by Means of NOAA AVHRR NDVI Data, J. Climate, 20, 1202–1218,
doi: 10.1175/JCLI4067.1.
Rayner, N., D. Parker, E. Horton, C. Folland, L. Alexander, D. Rowell, E. Kent, and A.
Kaplan (2003) Global analyses of sea surface temperature, sea ice, and night marine
air temperature since the late nineteenth century, J. Geophys. Res., 108, doi:
10.1029/2002JD002670.
Revadekar, J. V., Y.K. Tiwari, and K. Ravi Kumar (2012), Impact of climate variability
on NDVI over the Indian region during 1981–2010. Int. J. Remote Sens., 33, 71327150, doi: 10.1080/01431161.2012.697642.
Rossignol-Strick, M. (1983), African monsoons, an immediate climate response to orbital
insolation, Nature, 304, 46-49, doi:10.1038/304046a0.
Russell, J., and J. Wallace (2004), Annual carbon dioxide drawdown and the Northern
Annular Mode, Global Biogeochem. Cycles 18,GB1012,doi:10.1029/2003GB002044.
56
Rutherford, M.(1984), Relative allocation and seasonal phasing of growth of woody plant
components in a South African savanna, Progress in Biometeorology 3, 200–221.
Seidel, D., Q. Fu, W. Randel, and T. Reichler (2008), Widening of the tropical belt in a
changing climate. Nature Geosci. 1, 21-24, doi:10.1038/ngeo.2007.38.
Schreck III, C. and F. Semazzi (2004), Variability of the recent climate of eastern Africa.
Int. J. Climatol. 24, 681-701, doi: 10.1002/joc.1019.
Shindell, D., G. Faluvegi, R. Miller, G. Schmidt, J. Hansen, and S. Sun (2006), Solar and
anthropogenic forcing of tropical hydrology, Geophys. Res. Lett., 33, L24706,
doi:10.1029/2006GL027468.
Tucker, C., J. Pinzon, M. Brown, D. Slayback, E. Pak, R. Mahoney, E. Vermote and N.
Saleous (2005), An extended AVHRR 8km NDVI dataset compatible with MODIS
and SPOT vegetation NDVI data, Int. J. Remote Sens., 26, 4485-4498,
doi:10.1080/01431160500168686.
Uppala, S., P. Kallberg, A. Simmons, U. Andrae, Bechtold, M. Fiorino, J. Gibson, J.
Haseler, A. Hernandez, G. Kelly, X. Li, K. Onogi, et al. (2005), The ERA-40 reanalysis, Q.J.R. Meteorol. Soc., 131, 2961-3012. doi:10.1256/qj.04.176
Vershuren, D., J.S. Damsté, J. Moernaut, I. Kristen, M. Blaauw, M. Fagot, G. Haug and
CHALLACEA project (2009), Half-precessional dynamics of monsoon rainfall near
the East African Equator, Nature, 462, 637-641, doi:10.1038/nature08520.
White, F. (1983), Vegetation of Africa - a descriptive memoir to accompany the
Unesco/AETFAT/UNSO vegetation map of Africa; Natural Resources Research
Report XX; UNESCO, Paris, France; 356pp.
57
World Water Forum (WWF), (2000). The Africa Water Vision for 2025: Equitable and
Sustainable Use of Water for Socioeconomic Development. UN Water/Africa, pp. 34.
Xu, D.Y., X.W. Kang, D.F. Zhuang, and J.J. Pan (2010), Multi-scale quantitative
assessment of the relative roles of climate change and human activities in
desertification – A case study of the Ordos Plateau, China, J. Arid Environ., 74, 498507, doi: 10.1016/j.jaridenv.2009.09.030.
Yamaguchi, K., and A. Noda (2006), Global warming patterns over the North Pacific:
ENSO versus AO. J. Meteorol. Soc. Japan, 84, 221–241.
58
Figures
Figure 1. Leading EOFs used to produce time-series for: a) DJF and JJA Insolation, b)
DJF and JJA OLR, c) DJF and JJA 200mb geopotential height (200MH).
59
60
Figure 2. NDVI regressions on Insolation and Dry Days with mean covariance (COV) as
displayed in Table 1 in bottom right of each panel. a) Regression map of DJF NDVI on
DJF insolation, b) regression map of JJA NDVI on JJA insolation, c) regression map of
DJF NDVI on southern hemisphere dry days, d) regression map of JJA NDVI on
northern hemisphere dry days. ** indicates 99% confidence, and * represents 95%
confidence.
61
62
Figure 3. NDVI regressions on OLR and SOI with mean covariance (COV) as displayed
in Table 1 in bottom right of each panel. a) Regression map of JJA NDVI on JJA OLR,
b) regression map of JJA NDVI on DJF SOI. ** indicates 99% confidence, and *
represents 95% confidence.
63
64
Figure 4. Axes 1 and 2 for PCA of all standardized climate time-series with loadings of
all variables. Red dots represent the loadings of individual years with years of warm
ENSO events (1988, 1999) during marked in italics.
65
66
Figure 5. African mean vertical velocity fields and correlations on climate variables. a)
mean 1980-2000 DJF 500mb vertical velocity field, b) DJF 500mb vertical velocity field
correlated with DJF insolation, c) mean 1980-2000 mean JJA 500mb vertical velocity
field, d) JJA 500mb vertical velocity field correlated with JJA insolation, e) mean 19802000 JJA 500mb vertical velocity field, f) JJA 500mb vertical velocity field correlated
with JJA OLR. Bold contours on correlation plots are significant to 95%.
67
68
Figure 6. Location of ITCZ (cloud position) and generalized patterns of vegetation
anomalies (green = positive, brown = negative). These patterns are created by a)
insolation variability producing vegetation anomalies which are anti-phased between the
north and south hemispheres due to ITCZ excursions, and b) OLR/200MH variability
producing vegetation anomalies which are symmetrical about the equator due to ITCZ
width changes.
69
70
Tables
Table 1. Covariance of climate variable regressions.
Latitude-weighted mean covariance of regression maps for 3-month means of African
NDVI. ** values are significant to 99% and * values are significant to 95%.
significant to 90%.
NDVI
DJF
200MH
DJF
JFM
FMA
MAM
AMJ
MJJ
JJA
JAS
ASO
SON
OND
NDJ
0.0095
0.0093
0.0104
0.0122
0.0120
0.0118
0.0082
0.0076
0.0081
0.0081
0.0085
0.0082
NDVI
DJF
Atlantic
DJF
JFM
FMA
MAM
AMJ
MJJ
JJA
JAS
ASO
SON
OND
NDJ
0.0083
0.0083
0.0083
0.0091
0.0094
0.0108
0.0121
0.0126
0.0130†
0.0118
0.0107
0.0104
JJA
200MH
DJF
Insolation
0.0155*
0.0117
0.0105
0.0104
0.0099
0.0104
0.0127
0.0113
0.0106
0.0121
†
0.0130
0.0131†
DJF OLR
JJA OLR
0.0069
0.0069
0.0068
0.0085
0.0098
0.0113
0.0132†
0.0098
0.0098
0.0103
0.0111
0.0102
0.0101
0.0085
0.0094
0.0097
0.0082
0.0080
0.0183**
0.0118
0.0134†
0.0135†
†
0.0135
0.0116
JJA
Atlantic
DJF S.
Indian
JJA S.
Indian
DJF N.
Indian
0.0108
0.0080
0.0083
0.0096
0.0103
0.0111
0.0095
0.0091
0.0093
0.0100
0.0107
0.0110
0.0083
0.0082
0.0082
0.0086
0.0084
0.0090
0.0092
0.0085
0.0086
0.0092
0.0113
0.0103
0.0083
0.0075
0.0076
0.0079
0.0080
0.0083
0.0105
0.0104
0.0094
0.0085
0.0094
0.0108
0.0107
0.0094
0.0084
0.0091
0.0088
0.0088
0.0106
0.0098
0.0088
0.0100
0.0101
0.0094
0.0128
†
0.0130
0.0131†
0.0121
0.0120
0.0116
0.0111
0.0108
0.0128
0.0129†
†
0.0133
0.0132†
JJA
Insolation
0.0106
0.0079
0.0083
0.0091
0.0098
0.0110
0.0141*
0.0111
0.0084
0.0089
0.0091
0.0123
JJA N.
Indian
0.0089
0.0083
0.0084
0.0091
0.0098
0.0107
0.0139†
0.0120
0.0107
0.0098
0.0097
0.0114
†
values are
71
Supplementary Material
Supplementary Figure 1. Time series and PC plots
a) Standardized time series for atmospheric and SST variables used in principal
components analysis. b) Three significant principal components used for regression
analysis and the variance explained by each. c) Standardized time series of indices and
environmental variables not used in PCA.
72
73
Supplementary Table 1. PC loadings
Loadings of all standardized time series on all three principal components. Bold values
indicate the strongest loadings for each.
Variable
DJF Incoming
Shortwave
Radiation
DJF Outgoing
Longwave Radiation
DJF 200mb
Geopotential Height
DJF Southern
Atlantic SST
DJF Southern
Indian SST
DJF Northern Indian
SST
JJA Incoming
Shortwave
Radiation
JJA Outgoing
Longwave Radiation
JJA 200mb
Geopotential Height
JJA Southern
Atlantic SST
JJA Southern Indian
SST
JJA Northern Indian
SST
PC1
PC2
PC3
-0.4346
-0.1191
-0.3496
-0.2786
-0.4597
0.2718
0.2838
0.4258
0.1278
-0.2265
0.1654
0.4402
-0.0291
0.3413
-0.2830
-0.3598
0.2890
0.0414
0.4555
-0.0975
0.2405
0.1569
-0.3120
0.3791
-0.0500
0.3689
0.2795
-0.1513
0.2989
0.3880
-0.3371
-0.1369
0.2853
-0.3167
0.1214
0.0199
74
APPENDIX B:
EFFECT OF ARIDITY AND RAINFALL SEASONALITY ON VEGETATION IN
THE SOUTHERN TROPICS OF EAST AFRICA DURING THE
PLEISTOCENE/HOLOCENE TRANSITION
Sarah J. Ivory†, Anne-Marie Lézine*, Annie Vincens‡, Andrew S. Cohen†,
†
University of Arizona – Department of Geosciences, Tucson, Arizona
*Laboratoire des Sciences du Climat et de l’Environnement, UMR 1572 CNRS, CEA
UVSQ, Orme des Merisiers, 91191 Gif-sur-Yvette cedex, France
‡
CEREGE-CNRS, UMR 6635, Université Paul Cézanne, B.P. 80, F-13545 Aix-enProvence, France
Published in Quaternary Research
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Quaternary Research
Effect of aridity and rainfall seasonality on vegetation in the
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Sarah J. Ivory,Anne-Marie Lézine,Annie Vincens,Andrew S.
Cohen
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Abstract
Fossil pollen analyses from northern Lake Malawi, southeast Africa, provide a highresolution record of vegetation change during the Pleistocene/Holocene transition (~189ka). Recent studies of local vegetation from lowland sites have reported contrasting
rainfall signals during the Younger Dryas (YD). The Lake Malawi record tracks regional
vegetation changes and allows comparison with other tropical African records identifying
vegetation opening and local forest maintenance during the YD. Our record shows a
gradual decline of afromontane vegetation at 18ka. Around 14.5ka, tropical seasonal
forest and Zambezian miombo woodland became established. At ~13ka, drier, more
open formations gradually became prevalent. Although tropical seasonal forest taxa were
still present in the watershed during the YD, this drought-intolerant forest type was likely
restricted to areas of favorable edaphic conditions along permanent waterways. The
establishment of drought-tolerant vegetation followed the reinforcement of southeasterly
tradewinds resulting in a more pronounced dry winter season after ~11.8ka. The onset of
the driest, most open vegetation type was coincident with a lake lowstand at the
beginning of the Holocene. This study demonstrates the importance of global climate
forcing and local geomorphological conditions in controlling vegetation distribution.
Keywords : Lake Malawi, palynology, Younger Dryas, deglaciation, ITCZ, pollen,
tropical Africa
82
Introduction
The African Tropics, which today play a major role in the global hydrological cycle,
have undergone tremendous changes over the last 20,000 years (Gasse, 2000). As the
distribution of tropical African vegetation is largely constrained by regional hydrology,
past climate changes are often associated with reorganizations of biome distribution
(Gajewski et al., 2002; Gasse et al., 2008). However, vegetation response to climate
change is poorly understood. Southeast African vegetation is particularly sensitive to
changes in precipitation and rainfall seasonality (Hely et al., 2006) resulting from
complex interactions between the African Monsoon, Intertropical Convergence Zone
(ITCZ), and Congo Air Boundary (CAB; Fig. 1; Leroux, 2001). Changes in regional
vegetation recorded in high-resolution lake sediment cores can serve as a useful tool for
deciphering long-term as well as abrupt seasonal changes in these important circulation
patterns.
Though paleoclimate and vegetation records in tropical African during the last glacial
termination are in close agreement until ~13ka, changes during the YD interval are
unclear and vary regionally (Gasse et al., 2008). In much of North Africa, an abrupt
return of aridity is observed resulting from a southward ITCZ displacement, and
vegetation cover was greatly reduced (Hooghiemstra, 1988; Zhao et al., 2000). In
contrast, rainforests remained significant until the early Holocene in coastal southwestern
Africa (Dupont and Behling, 2006).
Multi-proxy analysis of sediments covering the YD chronozone (13-11.8ka) from
Lake Malawi (9-14oS) have suggested a return to drier, cooler conditions and intense
83
northeasterly (NE) tradewinds (Powers, 2005; Barker et al., 2007; Brown et al, 2007;
Castañeda et al., 2007; Filippi and Talbot, 2005; Woltering et al., 2011). Although this
points to climatic conditions in southeast Africa similar to those further north, the
vegetation record of the YD in southeast Africa is not as straightforward. Recent studies
from Lake Masoko (9o20’S; 840m asl; Fig. 1), a small maar lake within the Lake Malawi
watershed located less than 100km from the northern lakeshore, document an expansion
of tropical seasonal forest during the YD corresponding with a shortened dry season
(Garcin et al., 2006, 2007; Vincens et al., 2007a).
Because of the small size (<1km2) and morphometry of Lake Masoko, it is difficult to
differentiate local (<1km2) from regional (> 1km2) signals in the pollen record. In order
to understand the local increase in drought-intolerant forest given regionally drier
conditions, comparison with a regional vegetation record is essential. However, until now
Lake Masoko has provided the only continuous high-resolution record of vegetation
capable of capturing abrupt events in southeast Africa during this climatic transition.
Lake Malawi drill core MAL05-2A sediments provide a continuous, high-resolution
record of changing vegetation in a large watershed (65,000km2) from 18-9ka, which
make this record conducive for capturing both rapid and gradual environmental changes
at a regional scale (Brown et al., 2007). Lake Malawi is located at the modern southern
extent of the ITCZ, rendering it climatically sensitive to shifts in atmospheric circulation
observed by previous studies of the Lake Malawi Scientific Drilling Project (Fig. 1;
Brown et al., 2007; Cohen et al., 2007; Scholz et al., 2007; Scholz et al., 2011).
84
Here pollen analyses are presented from the north basin of Lake Malawi in order to
better understand the relationship between regional vegetation shifts and abrupt climatic
events. The situation of Lake Masoko within the greater Malawi watershed and its
proximity (~100km) to the Malawi lakeshore ensures that both lakes are influenced by
similar regional climatic conditions (Fig. 1). In particular, the ongoing debate (Brown et
al., 2007; 2008; Garcin, 2008) about the effect of the YD on hydrology and vegetation
distribution in southeast Africa can be resolved by the comparison of our record with the
signal observed at Lake Masoko.
Modern Setting and Implications for Palynology
Lake Malawi is the southernmost lake in the East African Rift Valley and lies in a
series of half-grabens. It is permanently stratified, with anoxic conditions below 250m,
and is drained by the Shire River to the south (Eccles, 1974). The watershed rises steeply
from the lake (478m asl) and is immediately bordered by mountains to the north and west
with elevations of 3000m in the Rungwe Highlands and Nyika Plateau (Malawi
Government, 1983).
The distribution of most vegetation in tropical Africa is controlled by rainfall and
rainfall seasonality, although temperature is also a constraint in high altitudes (Polhill,
1966; White, 1983; Hély et al., 2006). Within the Lake Malawi watershed, mean annual
precipitation (MAP) ranges from 800mm/yr in the lowlands to 2400mm/yr on the slopes
of Rungwe Highlands (Debusk, 1994). High rainfall in the highlands feeds rivers that are
an important source of pollen to the northern basin. A single rainy season occurs from
November–April when prevailing surface winds are northeasterly (NE), marking the
85
passage of the ITCZ (Fig. 1). May-October is the dry season, dominated by strong
southeasterly (SE) tradewinds (Malawi Government, 1983).
Lowland vegetation surrounding Lake Malawi (<1500m asl) is comprised of both
wetter and drier Zambezian woodland, growing in areas with more or less than
1000mm/yr, respectively (Fig. 1; White, 1983). These low diversity woodlands include
mainly deciduous species of Uapaca, Brachystegia, Isoberlinia, Julbernardia, and
Combretaceae, all of which tolerate a long dry season (>6 months). Drier locations
display more open canopies and higher proportion of grasses and Combretaceae.
Flooded grasslands with Cyperaceae and Typha are present at the lakeshore and along the
Songwe River. Closed canopy, drought-intolerant tropical seasonal forests with trees like
Myrica, Macaranga, Ulmaceae, and Moraceae, and shade-loving herbs (Urticaceae) are
not widespread but occur in the low to mid-altitudes, in areas with moister edaphic
conditions and poor drainage such as along streams (Polhill, 1966). Many species of fern
are also common along rivers and streams. Above 1500m, afromontane grasslands in
areas north of the lake are interrupted by discontinuous patches of afromontane forests,
whose composition varies with rainfall. Olea capensis is typical of moister forest
assemblages (1500-2000mm/yr; 0-3 dry months) from 1500-2500m, whereas
Podocarpus, Juniperus, Ericaceae and Olea africana are common above 2000m in drier
sites (800-1700mm/yr; ~4 dry months; White, 1983). Grasslands are widespread at all
elevations, but are concentrated at the southern end of the lake where rainfall is lowest.
Although the watershed of the lake includes an area of 65,000 km2, pollen flux is
dominated by input from a smaller source area. Prior taphonomic and pollen transport
86
studies at Lake Malawi have shown that pollen is transported to the north basin through
wind or river input and is representative of the vegetation in the northern third of the
watershed, about 20,000km2 (DeBusk, 1997). Pollen from grassland and montane taxa
are transported preferentially by seasonal winds whose strength varied during the late
Pleistocene (Filippi and Talbot, 2005). Today, most wind-transported pollen comes from
the north during the austral summer when NE tradewinds predominate and the majority
of plants flower. However, increased input from the south is possible during times of
enhanced SE tradewinds. Tropical seasonal forest, aquatics, and Pteridophytes are
preferentially transported by rivers draining the Rungwe Highlands and the Nyika Plateau
(Songwe River and South Rukuru River, respectively; Patterson and Kachinjika, 1995;
Debusk, 1997).
Methods
MAL05-2A was collected in 2005 (Fig. 1; 10o1.1’S, 34o11.2’E; 359m water
depth). Coring site, stratigraphy and age model details are provided elsewhere (Brown et
al., 2007; Scholz et al., 2007). 40 samples were counted from a 3-m section (6-9 mblf)
chosen to bracket the YD interval, consisting mainly of diatomaceous silty clay. The age
model of upper 22-m is based on a best-fit second-order polynomial of 24 calibrated
AMS dates, four of which fall within our section (Fig. 2; calibrated with “Fairbanks
0107” calibration curve [Fairbanks et al., 2005]). We processed 1cm3 samples using
standard methods with the addition of Lycopodium spores and sieved at 10 microns
(Faegri and Iversen, 1989). Average resolution between samples is 208 years but less
than 100 years from 14-11ka. At least 500 grains were counted per sample and no barren
87
samples were observed. Pollen from 215 taxa were indentified using the African Pollen
Database (APD: http://medias.obs-mip.fr/apd; Vincens et al., 2007b) and atlases of pollen
morphology (eg. Maley, 1970; Bonnefille, 1971a,b; Bonnefille and Riollet, 1980).
Pollen percentages were calculated against a sum of all pollen and spores minus
undeterminable grains, aquatics, and Bryophytes, which were calculated separately (Fig.
2). Pollen influx rates (grains/cm2/year) were calculated using the concentration values
and sediment accumulation rates. We drew pollen diagrams using Tilia 1.0.1 (Grimm,
1990) and determined zonation by constrained cluster analysis using CONISS (Fig. 2;
Grimm, 1987). Non-significant variations recorded by the most characteristic taxa were
smoothed by the use of five-sample running mean of pollen influx rates (Fig. 3c). Pollen
taxa assemblages described here are based on biomes outlined by Vincens et al. (2006)
for East African vegetation and Debusk (1994; 1998) for Lake Malawi. Tropical
seasonal forest and Zambezian woodland are based on the grouping used by Garcin et al.
(2007) at Lake Masoko for ease of comparison. A gap in samples exists from 15.514.7ka as no sediment remained in this interval after previous sub-samping and is
excluded from the pollen zones. The pollen data is compared with geochemical analyses
from this core (Zr:Ti; Brown et al., 2007) and the adjacent, previously studied piston
cores from the Malawi north basin (Hydrogen Index [HI], δ13Calk, δ18Odiatom, TEX86;
Filippi and Talbot, 2005; Powers, 2005; Barker et al., 2007; Castañeda et al., 2007).
Results
Pollen preservation is excellent throughout and concentrations are high (mean =
25,333 grains/cm3). Abundance of broken/reworked grains is never more than 4.6% but
88
typically <1%. Our pollen stratigraphy is divided into three zones based on dominant
vegetation assemblages and further subdivided into subzones to illustrate trends in
important taxa (Fig. 2). Both percentages and influx values for prominent pollen taxa and
vegetation assemblages will be described in order to eliminate possible transport artifacts
in the percentage data (Fig. 2, 3c).
Zone 1 – 18.1-15.5ka
Subzone 1a (18.1-16.4ka) shows both percentages and influx values dominated by
afromontane taxa (Podocarpus, Ericaceae undiff., Juniperus-type, Olea africana-type,
Olea capensis-type, Other Montane). These taxa are at their peak values (27%; 450
grains/cm2/yr) and stay relatively stable throughout this subzone. A strong presence of
Poaceae (36%; 520 grains/cm2/yr) indicates that grasses were widespread. In addition,
aquatic taxa and indicators of shoreline proximity, such as Typha, are at their maximum
values (5%; 8.4 grains/cm2/yr) but decline at the top of the subzone.
Subzone 1b (16.4-15.5ka) shows the first major change in the record.
Afromontane taxa begin a steady decline (20%; 250 grains/cm2/yr) coincident with the
expansion of tropical seasonal forest taxa (3-7%; 20-60 grains/cm2/yr; Macaranga-type,
Moraceae, Myrianthus-type holstii, Trema-type orientalis) and Zambezian woodland
pioneers (4-7%; 50-75 grains/cm2/yr; Brachystegia, Combretaceae, Uapaca). The first
large peak of Urticaceae (1.4%; 19 grains/cm2/yr) occurs at the beginning of this
subzone, linked with the expansion of tropical seasonal forest.
Zone 2 – 14.7-11.8ka
89
Subzone 2a (14.7-13ka) is marked by abundant grass (45%; 650 grains/cm2/yr).
Zambezian woodland percentages show a gradual increase starting at ~ 14.5ka.
Afromontane abundances continue to decline steadily (19-7%; 250-98 grains/cm2/yr).
Tropical seasonal forest (10%; 95 grains/cm2/yr) initially expands but remains at
relatively low values.
Subzone 2b (13-11.8ka) brackets the YD interval, and influx values show a twophase change in vegetation (Fig. 3c). The first phase lasts from 13-12.3ka and is
characterized by maximum values of tropical seasonal forest (167 grains/cm2/yr) and
Urticaceae (26 grains/cm2/yr). A slight increase of afromontane taxa (163-250
grains/cm2/yr) and Pteridophytes (19-65 grains/cm2/yr) is also observed. This increase in
both lowland and montane forest taxa is at the expense of Poaceae, which reaches its
lowest flux (232 grains/cm2/yr). The second phase, from 12.3-11.8ka, is marked by the
decline of all taxa that reached peak values during the first phase in favor of Poaceae (454
grains/cm2/yr).
Zone 3 – 11.8-9.5ka
Subzone 3a (11.8-10.7ka) is marked by a relatively rapid increase in Poaceae and
its highest percentages (54%; 691 grains/cm2/yr). Gradual increases of Zambezian
woodland (9-14%; 70-116 grains/cm2/yr) continue during this subzone. Tropical
seasonal (13-6%; 98-24 grains/cm2/yr) and afromontane forest (11-2%; 81-17
grains/cm2/yr) percentages decline gradually, and Pteridophytes (~1%; 10 grains/cm2/yr)
are nearly absent by the end of the record. Subzone 3b (10.7-9.5ka) is distinguished by a
90
small increase of Zambezian woodland (14-19%; 116-221 grains/cm2/yr) associated with
a larger contribution of Combretaceae.
Interpretation and Discussion
Decline of Afromontane Forest (18.1-15.5ka)
Afromontane taxa dominated this first period along with indicators of freshwater
flow (Fig. 3c). Freezing conditions at higher altitudes, cooler lowland temperatures, and
lower pCO2 caused montane taxa to grow at a lower elevation than present allowing for
closer proximity to the lakeshore (Jolly and Haxeltine, 1997; Street-Perrott et al., 1997;
Wu et al., 2007). Previous pollen-derived estimates of Last Glacial Maximum (LGM)
temperature in East Africa suggested that the lower montane forest limit descended to
600-900m asl (relative to ~1500m asl modern; Coetzee, 1967; Livingstone, 1967;
Debusk, 1994). However, low Podocarpus percentages (~5%) at Lake Masoko (840m
asl) suggest that the limit was near but still above this elevation (Vincens et al., 2007a).
As Masoko receives no fluvially transported pollen, this percentage indicates that
montane forests grew within 25km, based on studies of aerial transport of Podocarpus
grains (Vincens, 1982). Thus, montane forest likely grew at the upper end or even above
the altitude suggested by earlier estimates. Our placement of the lower montane forest
limit near 900m asl, rather than as low as 600m asl, is consistent with more recent TEX86
from Lake Malawi yielding a -3.5oC temperature anomaly relative to modern for this
period, much less than the previous pollen-derived estimates.
Close proximity of Masoko and Malawi ensures that the wind-transported
montane fraction in both lakes is largely similar, thus the difference at Malawi represents
91
the fluvial contribution ranging from 15-35%. Due in part to anthropogenic forest
clearance, modern afromontane vegetation occurs in discontinuous patches of forest
surrounded by extensive high-altitude grasslands (White, 1983). As a result, these
afromontane pollen taxa in modern sediments from the north basin never exceed 14%
(Debusk, 1997); however, soil samples from within the forest range from 26-30%
(Vincens et al., 2006). Similar high values in Malawi sediments from 18.1-14.7ka (Fig.
2; 20-40% of which 15-35% is via rivers) imply a compositional change of upper
watershed vegetation such that afromontane forests were more extensive and less
fragmented than present. A reduction of montane grasslands relative to today is also
supported by δ13Calk from Malawi, a record of C3/C4 abundance from plant leaf waxes,
suggesting predominantly C4 inputs at the very beginning of this record (Fig. 3a;
Castañeda et al., 2007). This implies lowland grass input, possibly from the Songwe
River alluvial plain (Livingstone and Clayton, 1980).
During the gradual decline of afromontane taxa at Malawi, lowland forest and
woodland pioneers begin to increase between 17-16ka (Fig. 3c). At the same time, a
dramatic rise of moister montane taxa (mainly Olea capensis-type) is observed at Lake
Masoko but not accompanied by an increase of Zambezian woodland. This suggests that
the establishment of woodland was gradual throughout the Malawi watershed. This
transition reflects rising temperature (~1oC/ka) and episodic wetting (Powers, 2005;
Barker et al., 2007). This is consistent with declining C4 input to the core site (~-5%;
Castañeda et al., 2007) as well as continued retreat of the afromontane belt (Fig. 3).
92
Interestingly, subzone 1b (16.4-15.5ka; Fig. 2) is roughly coincident with the
timing of Heinrich Event 1 (H1; Hemming, 2004). However, rather than the abrupt
cooling and aridity recorded elsewhere in Africa and the tropics (Stager et al., 2011), this
zone is instead defined by gradual increases in both lowland vegetation types (Zambezian
woodland and tropical seasonal forest). Similar trends are also observed at Lakes
Tanganyika and Rukwa, implying that this is a regional vegetation change likely related
to the slow temperature increase seen in the TEX86 record (Fig. 3a; Vincens, 1991, 1993,
2005; Powers, 2005). One exception is Olea africana-type, the only component of the
drier montane forest which increases at the time of H1; however, this is possibly a
pioneer response to the decline in other montane forest taxa.
Expansion of Lowland Forest (14.7-13ka)
The progressive warming following the LGM corresponds with continued retreat
of afromontane taxa to high-altitudes recorded in both records at Malawi and Masoko.
This opening provoked a reorganization of the lowland vegetation that had been
dominated by afromontane forests and grasslands. The increase in both drought-tolerant
woodland (Zambezian woodland) and drought-intolerant forest (tropical seasonal forest)
suggests that vegetation was more heterogeneous and forests more prevalent than present.
However, the stronger woodland presence until 13.8ka suggests that the dry season must
have been severe enough to limit dense forest at least locally. Drought-intolerant forest
assemblages preferentially established along rivers that would have received enhanced
runoff from the northern highlands (Johnson and Ng’ang’a, 1990).
93
Increases of grass during this zone could derive from high- or lowland sources;
however, decreased grass percentages at Lake Masoko suggest that lowland grasses were
actually less prevalent (Vincens, 2007a). Instead, the expansion of high-altitude
grasslands, previously limited by afromontane forest, likely accounts for the regional
increase in grass. This is supported by depleted δ13Calk implying greater C3 plants
contribution (Fig. 3a; Castañeda et al., 2007); however this may also be partially related
to the increases in arboreal taxa described above.
Though there is evidence of early post-glacial warming in southeast Africa
(Powers, 2005; Tierney et al., 2008), significant increases in moisture are not observed
until ~14.5ka associated with warming in the NH high-latitudes (Barker et al., 2007;
Castañeda et al., 2007). Higher effective moisture and enhanced riverine transport at this
time is reflected by the gradual increase of tropical seasonal forest, shade-dwelling herbs
typical of forest undergrowth (Urticaceae), and ferns (Pteridophytes) beginning at 14.7ka
(Fig. 3c). This conclusion is also supported by stable isotopic analyses, which trend
towards more depleted values coeval with the shift to moister lowland forest vegetation
(Fig. 3a; Barker et al., 2007; Castañeda et al., 2007).
At 13.8ka, grass and most herbaceous taxa return to lower abundances until after
11.8ka; however, tropical seasonal forest continues increasing. At Lake Masoko, an
abrupt transition from moister montane forests to tropical seasonal forest also occurs at
this time (Vincens et al., 2007a). Though δ18Odiatom and δ13Calk values from Malawi
suggest higher MAP, for further expansion of drought-intolerant taxa, very low dry
season severity would have also been necessary (Fig. 3; Barker et al., 2007; Tierney et
94
al., 2008). High HI values, indicating algal organic matter inputs consistent with greater
north basin upwelling, suggest that this was a period of weakened SE tradewinds (Fig.
3b; Filippi and Talbot, 2005). This, in addition to a 2oC cooling at Malawi, probably
resulted in higher effective moisture and decreased evaporative stress during the dry
season (Fig. 3a; Powers, 2005).
Records from both equatorial east African (EEA; 4oN-6oS; discussed here: Lakes
Malawi, Tanganyika, Rukwa, and Masoko) and southeast African (SEA; 6oS-14oS;
discussed here: Lakes Emakat and Victoria and Rusaka Swamp) show similar vegetation
trends until ~13ka (Fig. 1). As at Malawi and Masoko, records from Lakes Tanganyika
(8o30’S) and Rukwa (8o25’S) also show a decline of afromontane forest taxa followed by
an increase in tropical seasonal forest around 14.5ka (Fig. 4; Vincens, 1989, 1991, 1993,
2005, 2007a). In EEA, moist Hagenia forest became prevalent in the highlands near Lake
Emakat (2o55’S) and Rusaka Swamp (3o26’S). Lack of moister forest at Lake Victoria
(0o34.5’S) until after 11.5ka suggests that lowland conditions were less favorable to
forest establishment (Bonnefille et al., 1995; Beuning et al., 1997; Ryner et al., 2006).
Two-phase Vegetation Change at the YD (13-11.8ka)
During the YD, two different assemblages occur in the watershed from 13-12.3ka
and 12.3-11.8ka. The first assemblage shows small increases in afromontane taxa that
reflect cooler, drier conditions and steady increases in tropical seasonal forest, implying a
continued lack of a long dry season (Fig. 3c). It is unlikely that the increases in montane
forest resulted from enhanced wind transport from stronger NE tradewinds, which are
relatively weak during this first phase in relation to the second phase (Fig. 3b; Brown et
95
al., 2007). Although this does not seem to indicate a lowering of the afromontane belt to
LGM altitudes, it is most likely that the 2o cooling recorded at Malawi allowed these
assemblages a temporary reestablishment at lower altitudes than today (Powers, 2005).
Increased values of river transported taxa such as Pteridophytes also increase coeval with
tropical seasonal forest; however, Barker et al. (2007) record this as period of low river
input to the basin. High values of this taxon during a time of reduced river input instead
are likely a result of forest expansion due to the strong presence of ferns associated with
dense forest (White, 1983).
The second assemblage shows a recovery of grass at the expense of montane and
lowland forest taxa (Fig. 3c). In addition, taxa associated with dense forest, such as
Urticaceae and Pteridophytes, show a similar decline. Maintenance of Zambezian
woodlands suggests that although high and lowland forests were replaced by grassland,
drier woodlands were unaffected.
Interpreting these finely-resolved vegetation phases requires a small subsampling
interval and good age control. This subzone includes an AMS date at 12,067ka with
small error (10,577 +/-72 14C yr BP), and assemblages are based on 8 samples (Fig. 2;
Brown et al., 2007). Given this control and the internal consistency in results between
adjacent samples, we feel confident in these phases despite their short duration.
Similar to Malawi, regional maintenance of tropical seasonal forest also occurred
at Lake Masoko; however, woodland behaved differently between the two sites (Fig. 4;
Vincens et al., 2007a). Uapaca, a drought-tolerant pioneer species, underwent no abrupt
change in either direction during the YD at Malawi. In contrast, at Masoko, no presence
96
of woodland is recorded until after the YD. Strong woodland abundance regionally at
Malawi, but not locally at Masoko, suggests that from 13-12.3ka, the denser tropical
seasonal forest was growing exclusively in areas of edaphically moister conditions.
Zambezian woodland was extensive in the better drained, less sheltered locations. Stable
Zambezian woodland influx values support that idea that increased tropical seasonal
forest at both Masoko and Malawi, until 12.3ka and 11.7ka, respectively, do not indicate
higher MAP (Fig. 4; Barker et al., 2007; Tierney et al., 2008). This is consistent with
indications of aridity after 13ka at Lake Malawi and throughout the African tropics
(Gasse, 2000; Barker et al., 2007). With modern rainfall seasonality, an expansion of
grasslands or dry woodland would be expected given lower rainfall (Hély et al., 2006). It
is likely that decreased dry season severity is responsible for controlling higher local
effective moisture at more sheltered localities, like Lake Masoko, within the Malawi
catchment.
During cold phases in the NH, the ITCZ is displaced southward and can be
narrower (Haug et al., 2001; Lea et al., 2003). An ITCZ excursion into southeast Africa
has been used to explain higher relative moisture in parts of this region (Finney et al.,
1996; Garcin et al., 2007) as well as summer rainfall as far south as 26-30oS (Van
Zinderen Bakker and Butzer, 1973). However, during the LGM, H1, and YD, no increase
in moisture is recorded at Lake Malawi (Barker et al., 2007; Brown et al., 2007;
Castaneda et al., 2007). Tierney et al. (2007; 2008) have suggested that the effect of
ITCZ position on regional hydrology is secondary to other controls, such as moisture
advection and monsoon strength. Due to decreases in Indian and Atlantic SSTs during
97
the YD, reduced latent heat flux and weak moisture advection to the continent are in
agreement with other indications of reduced MAP (Sonzogni et al., 1998; Schefuß et al.,
2005).
Zr:Ti ratios from the MAL05-2A core, recording volcanoclastic input from the
Rungwe Highlands north of the core site rich in Zr, date the beginning of stronger NE
trades to ~12.7ka (Fig. 3b; Brown et al., 2007). This suggests a southward ITCZ over
Malawi acted to even out rainfall seasonality. In addition, a second 2oC temperature
decrease at the beginning of this interval coupled with weaker SE tradewinds, indicated
by HI, would have reduced evapotranspiration during the dry season (Fig. 3b; Filippi and
Talbot, 2005; Powers, 2005; Schefuß et al., 2005). In summary, despite lower MAP, the
resulting change in rainfall seasonality and weak winds created edaphically favorable
conditions along rivers and in areas receiving runoff from the highlands, thereby allowing
the persistence of drought-intolerant taxa until ~12.3ka.
The impact of effective moisture on vegetation assemblages during the YD
controlled by temperature, tradewind strength, and rainfall seasonality is further
illustrated during the second vegetation phase (Fig. 3; 12.3-11.8ka). After the peak of
tropical seasonal forest and Pteridophytes at the end of the first vegetation phase, both
assemblages began to decline gradually. The opposite is observed in the grasses, which
increased until the end of the record.
The decline of the drought-intolerant forest must have been caused by one of
three controls: an increase in rainfall seasonality, a further decrease in MAP, or higher
evaporative stress. A rainfall seasonality change seems unlikely, as records of aridity in
98
northern Africa and relatively moister condition in South Africa suggest that the ITCZ
remained in a southerly position throughout both vegetation phases (13-11.8ka; Van
Zinderen Bakker and Butzer, 1973; Gasse, 2000). An even further decline in MAP is
also not likely given relatively stable values of δ18Odiatom throughout the second phase
(Fig. 3a; Barker et al., 2007).
The most dramatic climatic change at this time is the maximum NE tradewinds
intensification from ~12.3-11.8ka (Fig. 3; Brown et al., 2007; Talbot et al., 2007). It
seems likely that further evaporative stress caused by stronger austral summer trades
could have initiated the degradation of the drought-intolerant forest. Stronger evaporation
could also explain the slight changes in the isotopic records without any further change in
MAP (Fig. 3a).
We propose that from 13-12.3ka, a more southerly ITCZ coupled with cooler
temperatures reduced dry season severity at Lake Malawi. This change allowed droughtintolerant tropical seasonal forest to flourish despite reduced MAP. From 12.3-11.8ka,
enhanced NE tradewinds promoted slow, progressive drying of the watershed. The
increased evaporative stress, coupled with low MAP, caused a gradual retreat of tropical
seasonal forest as local edaphically moist areas, such as Lake Masoko, passed critical
thresholds and became rarer while runoff and river input simultaneously decreased.
From ~13-11.8ka in East Africa, a complicated pattern developed that illustrates
the effects of a southward ITCZ and reduced MAP throughout the region (Gasse, 2000).
In EEA, moist forest development was interrupted at ~13ka, and grasslands expanded
until 11.5ka when moister vegetation types returned during the Early Holocene
99
(Bonnefille et al., 1995; Beuning, 1997; Ryner et al., 2006). In contrast, in SEA,
woodland and tropical seasonal forest continued to develop until ~11.8ka (Fig. 4;
Vincens et al., 2005, 2007a). Two core records from the extreme north and south ends of
Lake Tanganyika highlight the different regional responses. In Tanganyika's north basin
(4o30’S), grassland expansion during the YD is similar to the pattern in EEA, while the
south basin (8o30’S) experienced a transition to Zambezian woodland after the YD as
seen in SEA (Vincens, 1991, 1993).
Though drought-intolerant species dominated locally at Lake Masoko until
11.7ka, records from southern Lake Tanganyika and at Lakes Rukwa and Malawi
suggests the importance of both drought-tolerant and intolerant vegetation types in SEA.
On a regional scale, Zambezian woodland and grasslands were widespread from ~14.5ka,
long before the abrupt post-YD succession to dry woodland at Masoko. The later
response at Masoko is probably due to hydrological buffering by groundwater (Barker et
al., 2003). The gradual regional decline of forest at Lake Malawi signifies that a shorter
dry season allowed some areas in the watershed to remain sufficiently moist to sustain
forest until the 11.7ka termination at Lake Masoko, when establishment of the driest
conditions occurred.
Two regions of spatially coherent vegetation responses occur north and south of
~6oS despite uniform MAP decreases. A rainfall regime in SEA similar to what is
observed today in EEA, with a bimodal pattern rather than a long, dry season and single
rainy season, likely occurred. (Fig. 5; Garcin et al., 2006, 2007; Vincens et al., 2007a).
The deterioration of moist forest in EEA during the YD implies an opposite change in
100
rainfall repartition resulting in a longer dry season exacerbated by decreased MAP. The
increase of grassland highlights the dependence of EEA moist forest vegetation on the
twice-yearly ITCZ passage.
Rise of Woodland/Grassland (11.8-9.5ka)
A substantial increase of grass occurred at Lake Malawi during the early
Holocene around 11.8ka (Fig. 3c). This important change represents an opening of the
vegetation that followed the progressive decline of tropical seasonal forest, Urticaceae,
and Pteridophytes. At Lake Masoko, the local, lowland vegetation change was more
abrupt and coincident with the reorganization of dominant surface winds over the lake
(Fig. 4; Filippi and Talbot, 2005).
The reinforcement of the dry season SE tradewinds, as indicated by low HI values
(Fig. 3b), marked the beginning of a -100m lowstand at Malawi from 11.8-10.3ka, out of
phase with other East African lakes (Gasse, 2000; Johnson et al., 2002; Barker et al.,
2007). Higher rainfall north of 9oS at the beginning of the humid Early Holocene
(DeMenocal et al., 2000; Tierney et al., 2008), suggests that the ITCZ was displaced
northward in response to insolation and high-latitude forcing. This extended the length
of the dry season over southeast Africa, exacerbated by greater wind stress (Fig. 3b;
Filippi and Talbot, 2005; Tierney and Russell, 2007).
These conditions would have favored more drought-tolerant taxa like C4 grasses,
Uapaca, and Combretaceae, a drier woodland taxon that increases around 10.7ka.
Though C3 plant input began to increase during this period, it is likely that the expanding
lowland grasses were predominantly C4 (Castañeda et al., 2007). The slow decrease in
101
δ13Calk is explained by increased C3 arboreal woodland taxa in the lowland as well as an
expansion of montane grasslands, as afromontane forests reached their lowest abundance
of the record (Fig. 3).
Zambezian woodland diversified during the Early Holocene throughout East
Africa (Bonnefille et al., 1995; Ryner et al., 2006; Vincens et al., 2007a). At Malawi,
δ18Odiatom and δ13Calk values became more depleted indicating higher MAP around
10.3ka; however, the tropical seasonal forest which characterized the deglacial interval
did not recover (Fig. 3; Barker et al., 2007; Castañeda et al., 2007). The success of
Zambezian woodland relative to tropical seasonal forest in the Early Holocene supports
the ideas that dry season length rather than MAP is limiting to the later biome. Collins et
al. (2011) have suggested that changes in ITCZ width rather than mean position may not
have responsible for aridity in the tropics during northern hemisphere cold periods;
however, this cannot explain the change in rainfall seasonality observed over SEA during
the last deglaciation.
Conclusions
Our vegetation record shows a regional signal of high montane taxa abundances
that diminish following the LGM. We suggest a lowering of the tree-line to ~900m asl,
less than previously proposed. In addition, an expanded arboreal component of the
afromontane biome occurred.
This study highlights the importance of local conditions and rainfall seasonality in
maintaining vegetation. Tropical seasonal forest began to increase at 14.5ka and
continued to expand in SEA during the first part of the YD. Despite regional aridity,
102
drought-intolerant assemblages were maintained because of reduced evaporative stress,
which allowed for moister edaphic conditions in parts of the watershed. From 12.311.8ka, a reinforcement of the NE tradewinds caused a slow opening of the lowland
vegetation and replacement of drought-intolerant tropical seasonal forest by grassland
and woodland.
The local signal at Lake Masoko compared with the Malawi regional signal
supports the notion that drought-intolerant vegetation persisted in the midst of aridity as a
result of optimal geographic location. These records suggest decreased rainfall
seasonality and dry season severity from ~14.5-11.8ka. Our record shows that whereas
reductions in rainfall and evaporative stress from intense winds caused a gradual forest
decline beginning at ~12.3ka affecting well-drained areas, the change in rainfall
seasonality at 11.8ka to more modern long dry season resulted in a rapid conversion from
forest to woodland of even relatively buffered moist edaphic locations.
The YD in SEA illustrates that although rainfall is a primary control on vegetation
distribution, other factors, such as wind, temperature, and local setting, all contribute to
overall effective moisture on the landscape and cannot be ignored. Clearly, the
interpretation of palynological records, as well as predictions of future vegetation
changes, should be viewed in terms of a complex system rather than simple physiological
responses to temperature or MAP. In addition, the comparison of local versus regional
records helps us to discern environmental responses to regional and local hydrologic
thresholds.
103
Acknowledgements
We thank the National Science Foundation for fellowship funding (2009078688) and the
Lake Malawi Drilling Project-Earth System History Program (NSF-EAR-0602404).
Thanks to Jean-Pierre Cazet, Guillaume Buchet, Owen Davis, and John Logan for help
with sample processing and pollen identification, Michael McGlue for discussions/maps,
and the National Lacustrine Core Repository (LacCore) at the University of Minnesota
for sub-sampling and core curation.
104
References
P.A. Barker, D. Williamson, F. Gasse, and E. Gibert, Climatic and volcanic forcing
revealed in a 50,000-year diatom record from Lake Massoko, Tanzania, Quaternary
Research. 60 (2003), pp. 368-376.
P.A. Barker, M.J. Leng, F. Gasse, and Y. Huang, Century-to-millennial scale climatic
variability in Lake Malawi revealed by isotope records, Earth and Planetary Science
Letters. 261 (2007), pp. 93-103.
K.R.M. Beuning, Late-glacial and holocene vegetation, climate and hydrology of Lakes
Albert and Victoria, East Africa. PhD Thesis, University of Minnesota (1997).
R. Bonnefille, Atlas des pollens d'Ethiopie, principales espèces des forêts de montagne,
Pollen et Spores. 13 (1971a), pp. 15–72.
R. Bonnefille, Atlas des pollens d'Éthiopie. Pollens actuels de la Basse Vallée de l'Omo,
récoltes botaniques 1968, Adansonia. 2, (1971b), pp. 463–518.
R. Bonnefille and G. Riollet, Pollens des savanes d'Afrique orientale. CNRS, Paris
(1980).
R. Bonnefille, G. Riollet, G. Buchet, M. Icole, R. LaFont, M. Arnold, and D.Jolly,
Glacial-interglacial record from intertropical Africa, high resolution pollen and carbon
data at Rusaka, Burundi, Quaternary Science Reviews. 14 (1995), pp. 917-936.
E.T. Brown, T.C. Johnson, C.A. Scholz, A.S. Cohen, and J.W. King, Abrupt change in
tropical African climate linked to the bipolar seesaw over the past 55,000 years,
Geophysical Research Letters. 34 (2007), L20702.
E.T. Brown, T.C. Johnson, C.A. Scholz, A.S. Cohen, and J.W. King, Reply to comment
by Yannick Garcin on “Abrupt change in tropical African climate linked to the bipolar
seesaw over the past 55,000 years”, Geophysical Research Letters. 35 (2008), L04702.
I.S. Castañeda, J.P. Werne, and T.C. Johnson, Wet and arid phases in the southeast
African tropics since the Last Glacial Maximum, Geology. 35 (2007), pp. 823-826.
A.S. Cohen, J.R. Stone, K.R.M. Beuning, L. Park, P. Reinthal, D. Dettman, C. Scholz, T.
Johnson, J. King, M. Talbot, E.T. Brown, and S.J. Ivory, Ecological consequences of
early Late Pleistocene megadroughts in tropical Africa, Proceedings of the National
Academy of Sciences. 104 (2007), pp. 16422-16427.
105
J. Collins, E. Schefuß, D. Heslop, S. Mulitza, M. Prange, M. Zabel, R. Tjallingii, T.
Dokken, E. Huang, A. Mackensen, M. Schulz, J. Tian, M. Zarriess, and G. Wefer,
Interhemispheric symmetry of the tropical African rainbelt over the past 23,000 years,
Nature Geoscience. 4 (2011), pp. 42–45.
G.H. DeBusk, Transport and stratigraphy of pollen in Lake Malawi, Africa, PhD Thesis.
Duke University (1994).
G.H. DeBusk, The distribution of pollen in the surface sediments of Lake Malawi, Africa,
and the transport of pollen in large lakes, Review of Palaeobotany and Palynology. 97
(1997), pp. 123-153.
G.H. DeBusk, A 37,500-year pollen record from Lake Malawi and implications for the
biogeography of afromontane forests, Journal of Biogeography. 25 (1998), pp. 479-500.
P. DeMenocal, J. Ortiz, T. Guilderson, and M. Sarnthein, Abrupt onset and termination
of the African Humid Period: rapid climate responses to gradual insolation forcing,
Quaternary Science Reviews. 19 (2000), pp. 347-361.
L. Dupont and H. Behling, Land-sea linkages during deglaciation: High-resolution
records from the eastern Atlantic off the coast of Namibia and Angola (ODP site 1078),
Quaternary International. 148 (2006), pp. 19-28.
D.H. Eccles, An Outline of the Physical Limnology of Lake Malawi (Lake Nyasa).
Limnology and Oceanography. 19 (1974), pp. 730-742.
K. Faegri and J. Iversen, Textbook of Pollen Analysis, Wiley, Chichester, UK (1989).
R.G. Fairbanks, R. Mortlock, T. Chiu, L. Cao, A. Kaplan, T. Guilderson, T. Fairbanks,
A. Bloom, P. Grootes, and M. Nadeau, Radiocarbon calibration curve spanning 0 to
50,000 years BP based on paired 230Th/234U/238U and 14C dates on pristine corals,
Quaternary Science Reviews. 25 (2005), pp. 1781-1796.
M.L Filippi and M.R. Talbot, The palaeolimnology of northern Lake Malawi over the last
25ka based upon the elemental and stable isotopic composition of sedimentary organic
matter, Quaternary Science Reviews. 24 (2005), pp. 1303-1328.
B. Finney and T. Johnson, Sedimentation in Lake Malawi (East Africa) during the last
10,000 years: a continuous paleoclimatic record from the southern tropics,
Palaeogeography, Palaeoclimatology, Palaeoecology. 85 (1991), pp. 351-366.
106
K. Gajewski, A.M. Lézine, A. Vincens, A. Delestan, M. Sawada, and the African Pollen
Database, Modern climate–vegetation–pollen relations in Africa and adjacent areas,
Quaternary Science Reviews. 21 (2002), pp. 1611–1631.
Y. Garcin, A. Vincens, D. Williamson, J. Guiot, and G. Buchet, Wet phases in tropical
southern Africa during the last glacial period, Geophysical Research Letters. 33 (2006)
L07703.
Y. Garcin, A. Vincens, D. Williamson, G. Buchet, and J. Guiot, Abrupt resumption of the
African Monsoon at the Younger Dryas--Holocene climatic transition, Quaternary
Science Reviews. 26 (2007), pp. 690-704.
Y. Garcin, Comment on “Abrupt change in tropical African climate linked to the bipolar
seesaw over the past 55,000 years” by E. T. Brown, T. C. Johnson, C. A. Scholz, A. S.
Cohen, and J. W. King. Geophysical Research Letters. 35 (2008), L04701.
F. Gasse, Hydrological changes in the African tropics since the Last Glacial Maximum,
Quaternary Science Reviews. 19 (2000), pp. 189-211.
F. Gasse, F. Chalié, A. Vincens, M.A. Williams, and D. Williamson, Climatic patterns in
equatorial and southern Africa from 30,000 to 10,000 years ago reconstructed from
terrestrial and near-shore proxy data, Quaternary Science Reviews. 27 (2008), pp. 23162340.
E.C. Grimm, CONISS: a FORTRAN 77 program for stratigraphically constrained cluster
analysis by the method of incremental sum of squares, Computers & Geosciences. 13
(1987), pp. 13-35.
E.C. Grimm, TILIA and TILIAGRAPH: PC spreadsheet and graphic software for pollen
data. INQUA Comm. Stud. Holocene Working Groups on Data-handling methods,
Newsletter. 4 (1990), pp. 5–7.
G. Haug, K. Hughen, D. Sigman, L. Peterson, and U. Röhl, Southward Migration of the
Intertropical Convergence Zone Through the Holocene, Science. 293 (2001), pp. 13041308.
C. Hély, L. Bremond, S. Alleaume, B. Smith, M. Sykes, and J. Guiot, Sensitivity of
African biomes to changes in the precipitation regime, Global Ecology and
Biogeography. 15 (2006), pp. 258-270.
S.R. Hemming, Heinrich events: Massive late Pleistocene detritus layers of the North
Atlantic and their global climate imprint, Reviews of Geophysics. 42 (2004), RG1005.
107
H. Hooghiemstra, Changes of major wind belts and vegetation zones in NW Africa
20,000–5000 yr B.P., as deduced from a marine pollen record near cap blanc, Reviews of
Palaeobotany and Palynology. 55 (1988), pp.101-140.
T.C. Johnson and P. Ng’ang’a, Reflections on a Rift Lake. In: Katz, B.J. (Ed.) Lacustrine
Basin Exploration, American Association of Petroleum Geologists Memoir. 50 (1990),
pp. 113-135.
T.C. Johnson, E.T. Brown, J. McManus, S. Barry, P. Barker, and F. Gasse, A highresolution paleoclimate record spanning the past 25,000 years in southern east Africa,
Science. 296 (2002), pp. 113-116.
D. Jolly and A. Haxeltine, Effect of Low Glacial Atmospheric CO2 on Tropical African
Montane Vegetation, Science. 276 (1997), pp. 786-788.
D. Lea, D. Pak, L. Peterson, and K. Hughen, Synchroneity of Tropical and High-Latitude
Atlantic Temperatures over the Last Glacial Termination, Science. 301 (2003), pp. 13611364.
M. Leroux, The meteorology and climate of tropical Africa. London; New York;
Chichester, UK: Springer ; Praxis Pub. (2001).
D.A. Livingstone, Postglacial Vegetation of the Ruwenzori Mountains in Equatorial
Africa, Ecological Monographs. 37 (1967), pp. 25-52.
D.A. Livingstone and W. Clayton, An altitudinal cline in tropical African grass floras and
its paleoecological significance, Quaternary Research. 13 (1980), pp. 392-402.
Malawi Government, The National Atlas of Malawi. National Atlas Committee and
Department of Surveys of Malawi, (1983).
J. Maley, Contributions à l'étude du bassin tchadien : atlas de pollens du Tchad, Bulletin
du Jardin Botanique National de Belgique. 40 (1970), pp. 29–48.
M. McGlue, K. Lezzar, A.S. Cohen, J. Russell, J.-J. Tiercelin, A. Felton, E. Mbede, and
H. Nkotagu, Seismic records of late Pleistocene aridity in Lake Tanganyika, tropical East
Africa, Paleolimnology. 40 (2008), pp. 635-653.
G. Patterson and O. Kachinjika, Effect of wind-induced mixing on the vertical
distribution of nutrients and phytoplankton in Lake Malawi. In: Menz, A.(Ed.) Fishery
Potential and Productivity of the Pelagic Zone of Lake Malawi/Niassa, Chatham, UK,
Natural Resources Institute (1995).
108
R.M. Polhill, Flora of Tropical East Africa (ed. by C.E. Hubbard and E. Milne-Redhead),
Crown Agents for Oversea Governments and Administrations, London (1966).
L.A. Powers, T.C. Johnson, J.P. Werne, I. Castañeda, E. Hopmans, J. Sinninghe-Damste,
and S. Schouten, Large temperature variability in the southern African tropics since the
Last Glacial Maximum, Geophysical Research Letters. 32 (2005), L08706.
M.A. Ryner, R. Bonnefille, K. Holmgren, and A. Muzuka, Vegetation changes in
Empakaai Crater, northern Tanzania, at 14,800-9300 cal yr BP, Review of Palaeobotany
and Palynology. 140 (2006), pp. 163-174.
E. Schefuß, S. Schouten, and R. Schneider, Climatic controls on central African
hydrology during the past 20,000 years, Nature. 437 (2005), 1003-1006.
C.A. Scholz, T.C Johnson, A.S Cohen, J. King, J. Peck, J. Overpeck, M. Talbot, E.
Brown, L. Kalindekafe, P. Amoako, R. Lyons, T. Shanahan, I. Castañeda, C. Heil, S.
Forman, L. McHargue, K. Beuning, J. Gomez, and J. Pierson, East African megadroughts
between 135 and 75 thousand years ago and bearing on early-modern human origins,
Proceedings of the National Academy of Sciences. 104 (2007)16416-16421.
C. Sonzogni, E. Bard, and F. Rostek, Tropical Sea-Surface Temperatures During the Last
Glacial Period: a View Based on Alkenones in Indian Ocean Sediments, Quaternary
Science Reviews. 17 (1998), pp. 1185-1201.
J. Stager, D. Ryves, B. Chase, and F. Pausata, Catastrophic Drought in the Afro-Asian
Monsoon Region During Heinrich Event 1, Science. 311 (2011), pp. 1299-1302.
F.A. Street-Perrott, Y. Huang, R.A. Perrott, G. Eglinton, P. Barker, L.B. Khelifa, D.
Harkness, D. Olago, Impact of Lower Atmospheric CO2 on Tropical Mountain
Ecosystems, Science. 278 (1997), pp. 1422-1426.
M. Talbot, M. Filippi, N. Jensen, and J.-J. Tiercelin, An abrupt change in the African
monsoon at the end of the Younger Dryas, Geochemistry Geophysics Geosystems. 8
(2007), Q3005.
J.E. Tierney and J.M. Russell, Abrupt climate change in southeast tropical Africa
influenced by Indian monsoon variability and ITCZ migration, Geophysical Research
Letters. 34 (2007), L15709.
J.E. Tierney, J.M. Russell, Y. Huang, Y., J. Sinninghe Damsté, E. Hopmans, and A.S.
Cohen,. Northern Hemisphere Controls on Tropical Southeast African Climate During
the Past 60,000 Years, Science. 322 (2008), pp. 252-255.
109
E. Van Zinderen Bakker and K. Butzer, Quaternary Environmental Changes in Southern
Africa, Soil Science. 116 (1973), pp. 236-248.
A. Vincens, Palynologie, environnements actuels et plio-pleistocènes à l’Est du lac
Turkana (Kenya). PhD Thesis, University of Aix-Marseille II (1982).
A. Vincens, Paleoenvironnements du bassin Nord-Tanganyika (Zaire, Burundi, Tanzanie)
au cours des 13 derniers mille ans: Apport de la palynologie, Review of Palaeobotany
and Palynology. 61 (1989), pp. 69-88.
A. Vincens, Late quaternary vegetation history of the South-Tanganyika basin. Climatic
implications in South Central Africa, Palaeogeography, Palaeoclimatology,
Palaeoecology. 86 (1991), pp. 207-226.
A. Vincens, Nouvelle séquence pollinique du lac Tanganyika: 30,000 ans d'histoire
botanique et climatique du bassin Nord, Review of Palaeobotany and Palynology. 78
(1993), pp. 381-394.
A. Vincens, G. Buchet, D. Williamson, and M. Taieb, A 23,000 yr pollen record from
Lake Rukwa (8oS, SW Tanzania): New data on vegetation dynamics and climate in
Central Eastern Africa, Review of Palaeobotany and Palynology. 137 (2005), pp. 147162.
A. Vincens, L. Bremond, S. Brewer, G. Buchet, and P. Dussouillez, Modern pollen-based
biome reconstructions in East Africa expanded to southern Tanzania, Reviews of
Palaeobotany and Palynology. 140 (2006), pp. 187-212.
A. Vincens, Y. Garcin, and G. Buchet, Influence of rainfall seasonality on African
lowland vegetation during the late Quaternary: a pollen evidence from Lake Masoko,
Tanzania, Journal of Biogeography. 34 (2007a), pp. 1274–1288.
A. Vincens, A.-M. Lezine, G. Buchet, D. Lewden, A. Le Thomas, and contributors,
African pollen database inventory of tree and shrub pollen types, Review of Palaeobotany
and Palynology. 145 (2007b), pp. 135-141.
F. White, Vegetation of Africa - a descriptive memoir to accompany the
Unesco/AETFAT/UNSO vegetation map of Africa; Natural Resources Research Report
XX; UNESCO, Paris, France, (1983).
M. Woltering, T.C. Johnson, J. Werne, S. Schouten, and J. Sinninghe Damste, Late
Pleistocene temperature history of Southeast Africa: A TEX86 temperature record from
Lake Malawi, Palaeogeography, Palaeoclimatology, Palaeoecology. 303 (2011), pp. 93102.
110
H. Wu, J. Guiot, S. Brewer, and Z. Guo, Climatic changes in Eurasia and Africa at the
last glacial maximum and mid-Holocene: reconstruction from pollen data using inverse
vegetation modeling, Climate Dynamics. 29 (2007), pp. 211–229.
M. Zhao, G. Eglinton, S. Haslett, R. Jordan, M. Sarnthein, and Z. Zhang, Marine and
terrestrial biomarker records for the last 35,000 years at ODP site 658C off NW Africa,
Organic Geochemistry. 31 (2000), pp. 919-930.
111
Figures
Figure 1. A) Map of Africa showing position of the Intertropical Convergence Zone
(ITCZ) and the Congo Air Boundary (CAB) for January and July (top) and modern
latitudinal extent of summer unimodal and bimodal rainfall regimes (bottom). The
rectangle represents the East African field shown in part B. Upper part of the box shows
equatorial east Africa (EEA) and lower part of the box shows southeast Africa (SEA). B)
Map of east and southeast Africa showing sites of paleo-vegetation studies discussed in
this article: 1) Lake Victoria [Beuning, 1999], 2) Lake Emakat [Ryner et al., 2006], 3)
Rusaka Swamp [Bonnefille et al., 1995], 4-5) Lake Tanganyika [SD14TAN and
MPU12TAN; Vincens et al, 1991, 1993], 6) Lake Rukwa [Vincens et al., 2005], 7) Lake
Masoko [Vincens et al., 2007 ; Garcin et al., 2007], 8) Lake Malawi [this study]. Arrows
are prevailing northeasterly winds in the austral summer (clear heads and tails) and
southeasterly winds in austral winter (solid heads and tails). Inset shows 1979-2010
NCEP/DOE Reanalysis-2 average monthly precipitation at Malawi versus Masoko over
one annual cycle. Rectangle surrounds Lake Malawi as pictured in part C. C)
Vegetation map adapted from White (1983) of Lake Malawi with location of core
MAL05-2A (white dot).
112
113
Figure 2. Pollen diagram of MAL05-2A percentages. Pollen sums are all taxa less
aquatics, Bryophytes and undeterminable grains, which are calculated separately. Grey
curves are exaggerated 5 times. Other Montane grouping includes : Afrocrania volkensii,
Anthospermum, Dodonaea viscosa-type, Hagenia abyssinica, Ilex mitis, Nuxia/Ficalhoa,
Prunus africana-type. Pollen zones are based on constrained cluster analysis
(Grimm,1987). AMS dates that are part of the age model and fall within the section are
presented on the right side of the diagram.
114
115
Figure 3. A) TEX86 temperature (Powers, 2005), δ13Calk (Castaneda et al., 2007), δ
18
Odiatom (Barker et al., 2007) from the north basin of Lake Malawi cores M98-1P and
M98-2PG. B) Hydrogen Index (HI; M98-1P; M98-2PG; Fillipi and Talbot, 2005) and
Zr:Ti (MAL05-2A; Brown et al., 2007) from the north basin of Lake Malawi. C) Influx
values smoothed with five-point running mean of Poaceae, Afromontane [Podocarpus,
Ericaceae undiff., Juniperus-type, Olea africana-type, Olea capensis-type, Other
Montane] , Zambezian Woodland [Brachystegia, Combretaceae, Uapaca], Tropical
Seasonal Forest [Celtis, Macaranga-type , Moraceae, Myrianthus-type holstii, Trematype orientalis], Urticaceae, and Pteridophytes. Solid lines are zone limits and dashed
lines are subzone limits. Dark grey box indicate first YD vegetation phase, light grey box
indicates second YD vegetation phase. Gap between subzones 1b and 2a is due to no
samples available in this interval.
116
117
Figure 4. A) Pollen percentages of drought-intolerant tropical seasonal forest from 16.59ka for sites in southeast Africa (Vincens, 1991, 1993, 2005, 2007). Bottom panel is
Zr:Ti from the north basin of Lake Malawi (Brown et al., 2007). Grey box marked ‘NE’
represents northeast tradewind reinforcement. B) Pollen percentages of drought-tolerant
Zambezian woodland from 16.5-9ka for sites in southeast Africa (Vincens, 1991, 1993,
2005, 2007). Bottom panel is Hydrogen Index (HI) from the north basin of Lake Malawi
(Filippi and Talbot, 2005). Grey boxes marked ‘SE’ represents southeast tradewind
reinforcement.
118
119
Figure 5. A) Modern latitudinal extent of summer unimodal and bimodal rainfall zones
in tropical Africa. B) Pollen-inferred latitudinal extent of summer unimodal and bimodal
rainfall zones in tropical Africa during pollen subzone 2b from ~13-11.8ka.
120
121
APPENDIX C:
LARGE-SCALE VEGETATION CHANGE IN TROPICAL AFRICA FROM LAKE
MALAWI: DESERTS, FORESTS, AND INSOLATION
Sarah J. Ivory†, Anne-Marie Lézine*, Annie Vincens‡, Andrew Cohen†, Michael M.
McGlue**
†
University of Arizona – Department of Geosciences, Tucson, Arizona
*LOCEAN, IPMC CNRS, University Paris 5, Paris, France
‡
CEREGE-CNRS, UMR 6635, Université Paul Cézanne, B.P. 80, F-13545 Aix-enProvence, France
** Central Energy Resources Science Center, U.S. Geological Survey, PO Box 25046,
Denver Federal Center MS 977, Denver, CO 80225, USA
Prepared for submission to Earth and Planetary Science Letters
122
Abstract
The African Tropics, which today play a major role in driving the hydrological cycle,
are a key area for better understanding controls on global climate. As the distribution
of African vegetation is largely constrained by regional hydrology, past climate
changes are often associated with large-scale reorganizations of biome distribution;
however, the response of individual taxa to climate change is poorly understood.
Sediment cores from Lake Malawi provide a unique terrestrial record of changing
climate and vegetation in tropical Africa for the last ~1.2 Ma. Pollen analysis on a
core interval including the Penultimate Glacial (PG) and the Last Interglacial (LIG)
allows us to investigate vegetation responses to observed high-amplitude wet-dry
transitions in phase with local insolation modulated by high eccentricity and changing
boundary conditions.
Pollen analysis on the longest Malawi core (MAL05-1B) reveals three large-scale
oscillations between forest and degraded, open vegetation. Succession during forest
expansion as well as climax species composition differs during each phase. Early
forest phases show a progressive expansion of tropical seasonal forest, miombo
woodland, and afromontane arboreal taxa. A final, relatively rapid shift to higher
proportions of arboreal pollen taxa occurs near the top of the studied interval.
However, while afromontane and woodland taxa return to higher values, moist,
closed canopy tropical seasonal forest remains at low values for the rest of the
interval. Between forests phases are more open phases that are marked by extreme
aridity and dramatic lake level regressions leading to the expansion of grasses, steppic
123
herbs, and semi-desert trees. This vegetation assemblage, with Somali-Masai
phytogeographic affinities, is much drier than that found in the Malawi watershed
today. Changes in forest composition over this interval reflect some influence of
global boundary conditions with more deciduous woodlands during the LIG;
however, the large-scale vegetation changes around the lake seem to occur in
response to insolation controls as a result of variable monsoon strength and
seasonality during this period of high-amplitude climate variability.
Highlights
1. Temporal changes in vegetation in southeast Africa are primarily controlled
by insolation when eccentricity is high
2. Dense humid forest and woodlands dominate the landscape during insolation
maxima during the Penultimate Glacial as a result of enhanced rainfall and
short dry season.
3. Fire-prone woodland and wooded grasslands predominate during the Last
Interglacial period because of a northerly displacement of the ITCZ and a long
dry season.
Keywords: tropical vegetation, ITCZ, Last Interglacial, Africa, Malawi
124
Introduction
The African tropics play an essential role in the dynamics of the global climate
system and are ecologically important because of their tremendous levels of endemism
and biodiversity (Diffenbaugh and Giorgi, 2012). Additionally, tropical ecosystems have
long provided necessary resources to millions of people living directly off the landscape.
However, these areas today are under great threat due to rapidly changing climate (IPCC,
2007). As the distribution of African vegetation is closely related to regional hydrology,
large-scale reorganization of biomes, desertification, and forest collapse linked to
changes in climate may dramatically alter the tropical biodiversity and the availability of
resources. In addition, the fragility of tropical grasslands and forests to climate change is
likely to have an important influence on our ability to control atmospheric CO2 in the
future (Cox et al., 2013).
Semi-arid vegetation, such as woodlands and wooded grasslands, dominate the
African continent and are particularly sensitive to hydrological changes (Fischer and
Turner, 1978). Although we know that changes in atmospheric and oceanic circulation
have a profound effect on the environment, the response of vegetation to climate beyond
an overly simplified interpretation of more or less rainfall is still very unclear (Ivory et
al., submitted). In order to better understand the effect of future environmental change on
biome distribution as well as climate-biosphere feedbacks, it is absolutely essential to put
constraints on the response of vegetation to climate.
Records of past vegetation provide important tools for evaluating future
environmental change under climatic conditions unlike today, as well as for evaluating
125
the performance of climate models used in IPCC projections for the future.
Paleoecological data in tropical Africa have proven very useful in elucidating the factors
influencing vegetation productivity and the importance of climate-vegetation feedbacks
to regional hydrology (Liu et al., 2006). However, terrestrial vegetation data is scarce
within the tropics beyond the Last Glacial Maximum, and most existing data older than
21ka comes from either low-resolution marine cores, short, poorly dated records, or
discontinuous and low-resolution outcrop records (Bonnefille, 2010; Dupont et al.,
2011a).
Drill cores taken from Lake Malawi in March 2005 provide a continuous record of
regional vegetation from the continent for the last 1.2Ma years (Scholz et al., 2011a,
2011b). The Lake Malawi watershed provides an ideal laboratory for examining how
climate change affects tropical ecology (Debusk, 1998; Beuning, et al., 2011). As a
result of its position near the southern boundary of the modern austral summer
Intertropical Convergence Zone (ITCZ), records from the lake are sensitive to changes in
tropical circulation linked to global climate, which greatly alter regional hydrology and
watershed vegetation (Cohen et al., 2007; Beuning et al., 2011; Ivory et al., 2012).
Given the paucity of long records, much paleoclimate research has focused on the
Pleistocene-Holocene transition and the effect of the northern hemisphere high-latitudes
on tropical hydrology (Gasse, 2000; Gasse et al., 2008). For many years, both the LGM
and Heinrich Event 1 have individually been thought to have been the most arid period of
the Late Pleistocene (Gasse, 2000; Stager et al., 2011; Lézine et al., 2013). However,
over longer time-scales, hydrology at Lake Malawi and elsewhere in tropical Africa has
126
been found to have a strong link to precessional cycles and local summer insolation
(Rossignol-Strick, 1983; Scholz et al., 2007; Cohen et al., 2007; Verschuren et al., 2009).
During the Penultimate Glacial (PG) and Last Interglacial Period (LIG), higher orbital
eccentricity resulted in an enhancement of the effect of precession on insolation. This led
to large-scale wet-dry cycles that have been observed to occur asynchronously across the
continent until eccentricity decreased after ~60ka (Scholz et al., 2007; Cohen et al.,
2007). Periods of extreme aridity far greater than the LGM have been identified at
locations across Africa, particularly well illustrated by ~100-500m lake level fluctuations
in Lakes Malawi and Tanganyika, two of the world’s largest and deepest lakes (McGlue
et al., 2008; Scholz et al., 2007; Cohen et al., 2007). In southeast Africa, previous studies
from marine cores have suggested that vegetation as well may respond to precessional
forcing; however, the relative responses of vegetation to high-latitude forcing and
insolation forcing at this period remains unclear (Dupont et al., 2011a).
Pollen analysis on a 53-m long interval of the longest Malawi core (MAL05-1B)
extends the detailed record of African vegetation, allowing us to investigate the
superimposed influence of large-scale high-amplitude environmental gradient in this
region of the tropics that are very unlike today. This period is unique as it occurs when
precession is enhanced by high eccentricity resulting in high-amplitude insolation
variability. However, this period also brackets the Termination II transition between the
PG and the LIG, a period slightly warmer than the current Interglacial, often used as an
analogue for future warming.
127
Modern Setting
Lake Malawi, the southernmost lake in the East African Rift, is composed of a series
of alternating N-S oriented half-graben basins (Fig. 1). It is a permanently stratified lake,
with anoxic conditions below 250m. The lake is hydrologically open, drained by the
Shire River to the south, although most water loss occurs via evaporation (Eccles, 1974).
Mountain ranges in the northern (Livingstone Mountains and Rungwe Highlands) and the
western (Nyika Plateau) parts of the watershed rise steeply from the lake shore (478 m
asl) to nearly 3000m and were formed principally through extension and volcanism
(Malawi Government, 1983).
The lake is today situated at the southernmost limit of the ITCZ and east of the Congo
Air Boundary (CAB; Fig. 2; Nicholson, 1996). The yearly migration of the ITCZ imparts
patterns in MAP and rainy season length across the continent that greatly influences the
environment (Fig. 2). Within the watershed, mean annual precipitation (MAP) is very
heterogeneous and ranges from 800mm/yr in the lowlands to 2400mm/yr on the slopes of
Rungwe Highlands (Debusk, 1994). Rainfall is highly seasonal, with a single rainy
season from November to April, when prevailing surface winds are northeasterly (NE),
and rainfall reaches 250 mm/month (Fig. 1). A long dry season from May to October
lasts ~6 months when virtually no precipitation occurs and strong southeasterly (SE)
tradewinds prevail (Fig. 2; Malawi Government, 1983). Temperature varies little (Fig.
1), however, lower temperatures in the highlands can result in freezing conditions in
alpine regions.
128
Vegetation in the Malawi watershed is largely constrained by rainfall and rainfall
seasonality (Ivory et al., submitted). In the low to mid-altitudes (<1500m asl), lowdiversity deciduous Zambezian miombo woodlands grow in areas of highly seasonal
rainfall and are dominated by deciduous trees such Brachystegia, Berlinia, and
Isoberlinia (Fig. 1). These woodlands can be subdivided into a drier and wetter type
found in areas of more or less than 1000mm/yr, respectively (White, 1983). The wetter
miombo type has a higher, denser canopy cover and is frequently associated with other
trees such as Uapaca (White, 1983). The drier miombo type displays a more open
canopy and higher proportion of grasses and Combretaceae (White, 1983).
Closed-canopy tropical seasonal forests are semi-deciduous assemblages dominated
by evergreen trees like Myrica, Macaranga, Ulmaceae, and Moraceae, as well as shadeloving herbs like Urticaceae and ferns. These forests are not widespread but also occur in
the low to mid-altitudes along streams as gallery forests and in areas with moister edaphic
conditions (Fig. 1; Polhill, 1966). Flooded grasslands with Cyperaceae and Typha are
present at the lakeshore.
Above 1500m asl, afromontane grasslands in the mountainous areas are interrupted
by discontinuous patches of afromontane forests, whose composition varies with rainfall.
Olea capensis is typical of moister areas (1500-2000mm/yr; 0-3 dry months) from 15002500m asl, whereas Podocarpus, Juniperus, Ericaceae and Olea africana are common in
drier areas above 2000m asl (800-1700mm/yr; ~4 dry months; White, 1983). Grasslands
are widespread at all elevations but are concentrated at the southern end of the lake where
rainfall is lowest (White, 1983).
129
Prior taphonomic and pollen transport studies suggest that pollen deposited in the
central basin is transported predominantly by wind (DeBusk, 1994; Debusk, 1997).
Today, most wind transported pollen comes from north of the lake during the austral
summer, when NE tradewinds predominate and when the majority of plants flower.
Although no large rivers drain into the central basin adjacent to the core site, pollen
transport of riparian taxa, such as tropical seasonal forest, aquatics, and pteridophytes,
suggests that some pollen is delivered to the lake along streams as well (Debusk, 1997).
Methods
MAL05-1B was collected from the central basin of Lake Malawi in 2005 (Fig. 1;
11º18’S, 34º26’E; 592m water depth). Further details of the core site and age model are
provided in Scholz et al. (2011a, 2011b), Lyons (2007), and Lyons et al. (2011a). This
study focuses on a 53-meter core section (61-114 mblf). The age model is based on 16
AMS radiocarbon dates, 15 dates on known paleo-magnetic excursion events, 2 OSL
dates on sand grains, and 1 Ar-Ar date on a tephra, identification of Brunhes-Matuyama
at 227.5mblf, and additional magnetic excursion and intensity events (Lyons, 2007; King
et al., unpubl. data; Tables 1 and 2). Three options of the paleomagnetic interpretation
which change the age of the base of this interval (>80mblf) are currently under discussion
and listed in Tables 1 and 2. One OSL date and five magnetic excursions picks fall
directly within our section.
The full stratigraphy and lithology of the interval studied is presented in Figure 3.
The sediments vary greatly throughout the section but are dominated by silty and clayey
diatom oozes and homogenous silty clays. Several intervals show laminae and micro-
130
laminae when lake levels were high, and carbonate muds are present as well during
evaporative periods when the basin is closed (Lyons, 2007).
All 172 samples were processed using standard methods with the addition of
Lycopodium spores (Faegri and Iverson, 1989) and were sieved at 10 microns. Samples
were taken every ~30 cm, with a target temporal resolution of ~300 years. Between 300500 grains were counted per sample with the exception of the six uppermost samples
which range from 17-149 grains/sample due to extremely low pollen concentrations. 227
pollen taxa and 5 freshwater algae were identified using the African Pollen Database and
atlases of pollen morphology (Maley, 1970; Bonnefille, 1971a, 1971b; Bonnefille and
Riollet, 1980). Pollen percentages were calculated against a sum of all pollen and spores
less undeterminable grains, aquatics (Cyperaceae, Typha, Nymphaea, Polygonum
senegalense-type, Ottelia, Laurembergia) and undifferentiated bryophytes, which were
calculated separately (Fig. 4). Pollen diagrams were drawn using Tilia (Grimm, 1990),
and zonation was determined by constrained cluster analysis using CONISS (Grimm,
1987). The percentage values presented in the results section are average percentages for
a subzone unless otherwise specified. Pollen nomenclature follows Vincens et al.
(2007a), and pollen taxa assemblages described here are based on biomes outlined by
Vincens et al. (2006) for East African vegetation and used in previous studies in this
watershed (Vincens et al. 2007b; Ivory et al., 2012). Tropical seasonal forest is the total
of Moraceae, Macaranga-type, Celtis, and Trema-type orientalis, and afromontane is
Podocarpus, Olea, Ericaceae undifferentiated, Myrica, Juniperus-type, Faurea-type, and
Ilex mitis. Miombo woodland is only represented by Uapaca in summary figures as this
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is the highest-dispersing, most indicative taxon in this vegetation type. Margalef’s and
Berger-Parker indices of alpha and beta diversity, respectively, were also calculated for
all pollen samples as a method of determining total biodiversity through time (Margalef,
1958; Berger and Parker, 1970).
Climate response surfaces were constructed for all taxa used in the grouping miombo
woodland and tropical seasonal forest (Macaranga, Myrianthus-type holstii, Celtis,
Trema-type occidentalis, Uapaca-type kirkiana, and Uapaca-type nitida) in order to
constrain their biophysical limits following the method of Watrin et al. (2007). This
technique involves the creation of probability density functions based on known
geographical ranges of indicator taxa. Although a similar technique is used to
quantitatively reconstruct climatic variables through time (Gajewski et al., 2002), a study
by Debusk (1997) on modern pollen deposition within the lake suggests that the immense
size of the basin and transport within the central basin makes the use of this technique
inappropriate for quantitative reconstructions. Furthermore, considering the large-scale
lake level fluctuations occurring during this period, it is likely that the pollen source area
changed dramatically through time (Lyons et al., 2011b). Thus, given the uncertainties,
we have decided to bracket potential climatic limits given these vegetation assemblages
rather than attempt quantitative reconstructions at this time. Geographical extent of the
selected taxa was determined using the Tropicos botantical database of collected
specimens (www.tropicos.com). Latitude and longitude of specimens was then used to
extract climatic data using the Climate Research Unit (CRU) 1961-1990 0.5°x0.5°
datasets of precipitation and wet days (days/year with <0.1 mm rainfall; New et al.,
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2000). Although this technique gives some indication of the climatic ranges of
vegetation assemblages, several uncertainties exist. First, as tropical seasonal forest taxa
grow frequently along rivers, the range of precipitation values in which they may be
found may not accurately reflect their sensitivity to rainfall. Secondly, due to the 10
minute spatial resolution of the climate dataset used for this analysis, very fine scale
microclimatic features due to topography or local conditions are not observed.
Results
Pollen preservation is excellent throughout this record and concentrations are high (
= 23,480 grains/cm3), with the exception of the six uppermost samples ( = 2758
grains/cm3). Abundance of broken or reworked grains is never more than 5.8% but
typically less than 2%. Our pollen stratigraphy is divided into seven zones (Fig. 4). One
zone has been further subdivided to illustrate trends in important taxa. Intervals of core
disturbance greater than 1 meter related to the drilling process will not be discussed and
are represented by grey rectangles in all figures.
Zone 1 (115-110.5 mblf)
The base of this interval contains high proportions of herbaceous taxa, particularly
Poaceae (max = 73%) and Amaranthaceae (max = 1.6%). Arboreal taxa, such as Celtis
(from 0% to a max of 4.8%) and Uapaca (from 0% to a max of 1.8%) typical of tropical
seasonal forest and miombo woodland, respectively, increase toward the top of this zone
at the expense of Poaceae.
Zone 2 (110.5-93.25 mblf)
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Subzone 2a marks the first large-scale forest phase of the record with high
proportions of lowland miombo taxa like Uapaca (max = 1.7%) and a sharp increase of
Afromontane forest, especially Podocarpus (from 1.3% to a max of 59.8%) and Olea
(from 0% to a max of 12.0%). Also, indicators of freshwater input to the lake, such as
Cyperaceae (max = 16.9%), are abundant. Lowland moist tropical seasonal forest taxa,
like Celtis (from a max of 7.3% to 0.86%) and Trema (from a max of 2.2% to 0%),
decrease abruptly, however, while Afromontane forest taxa remain at high proportions
for the rest of this subzone.
Although arboreal pollen remains at high proportions throughout subzone 2b,
Afromontane forest (Podocarpus [24.8%]) shows an immediate abrupt decline, and
lowland dry miombo taxa, such as Combretaceae (from 0% to a max of 2.6%), rebound.
Tropical seasonal forest taxa, such as Moraceae (max = 3.0%) and Macaranga (max =
3.1%), reach an abrupt maximum at the bottom of this subzone only to immediately
decrease to lower proportions (0.51% and 0.34%, respectively). At the top of this
subzone, Poaceae (17.9% to 67.2%) increases in two abrupt steps (95 and 93 mblf) as all
arboreal taxa decline.
Zone 3 (93.25-90 mblf)
This zone shows generally high Poaceae proportions (mean = 78.8%). Semidesert herbaceous and arboreal taxa both become important at the bottom of this zone.
Obvious expansions of Amaranthaceae (mean = 1.9%), Hymenocardia (mean = 0.48%),
Commiphora (mean = 0.34%), Acacia (mean = 0.20%), and Parkinsonia (max = 2.0%)
occur in succession. However, near the top of the zone, lowland forest, including Celtis
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(from 0% to a max of 2.3%), Macaranga (from 0% to a max of 2.3%), Trema (from 0%
to a max of 2.2%), and Uapaca (from 0% to a max of 7.0%), once again begins to
increase at the expense of Poaceae and semi-desert taxa.
Zone 4 (90-80.5 mblf)
Forest expansion is observed by increases in arboreal taxa. Tropical seasonal
forest (Celtis [mean = 0.98%], Macaranga [mean = 1.0%], Trema [mean = 0.66%],
Moracaeae [mean = 1.6%]) and miombo woodland (Uapaca [mean = 3.1%]) taxa are
already relatively common at the bottom of the zone and remain so until the middle of the
zone where a gradual decrease is observed. Afromontane forest taxa (Podocarpus [from
0.2% to a max of 41.2%]) and fresh water indicators, like Cyperaceae (from 0% to a max
of 24.5%), increase abruptly and stay at relatively stable abundances throughout the zone.
Zone 5 (80.5-75.5 mblf)
This zone registers four relatively abrupt changes. Poaceae (mean = 79.3%) and
Amaranthaceae (mean = 0.24%) increase at the base of this zone, while arboreal taxa
decrease. Combretaceae (from 0.7% to a max of 1.9%) and Buxus (from 0.7% to a max
of 3.2%) are the sole trees that initially increase in abundance and remain relatively
constant in abundance throughout the zone. At 78 mblf, an event is recorded, where
arboreal taxa from both afromontane forest (Podocarpus [max = 21.9%]) and miombo
woodland (Uapaca [max = 2.4%]) increase then returns to low proportions at 77.5mblf.
Poaceae (from a max of 94.1% to 21.1%) and Amaranthaceae (from a max of 1.6% to
0%) remain abundant until a very rapid decrease at the very top of the zone.
Zone 6 (75.5-65.5 mblf)
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The final forest phase of the record begins at the base of this zone with afromontane
forest (Podocarpus [mean = 36.6%]) and miombo woodland (Uapaca [mean = 1.4%])
returning to high values. Although afromontane taxa remain abundant, miombo
woodland, particularly Uapaca and Brachystegia, decrease near 70mblf, well below the
decrease of highland trees. Whereas freshwater indicators increase within this zone
(Cyperaceae [mean = 16.4%]), tropical seasonal forest taxa (mean = 1.0%) remain at low
proportions.
Zone 7 (65.5-61.9 mblf)
This zone is marked by the final decline in all forest taxa and high abundances of
Poaceae (mean = 61.2%). The upper 6 samples of this zone from 61.9-63.7 mblf differ
from the rest in this study as they have extremely low pollen concentrations ( = 2758
grains/cm3) but good preservation (~2% broken/reworked).
Discussion
Vegetation within the Malawi watershed underwent large-scale reorganization
throughout the period covered by this record. Although there may be some influence of
differential transport throughout the interval, the influence of rivers at the core site should
be minimal (Lyons, 2010). Three forest phases are readily observable from 110.5-93.25
mblf (Phase 1), 80-80.5 mblf (Phase 2), and 75.5-60.5 mblf (Phase 3), separated by more
open vegetation phases (Fig. 5). Phase 3, the most recent forest phase, is likely
coincident with the LIG given an OSL date of 126,270 +/- 9500 occurring within this
zone at 78.752 mblf (Lyons, 2007). Given the placement of this date and sedimentation
rate constraints on an adjacent core from the same site, it is also likely that the older two
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forest phases occur during the PG. Although all forest phases are similar in that they
involve a relatively abrupt expansion of arboreal taxa in the lowlands and highlands of
the watershed, each phase involves a unique complex of biomes. Following the rapid
decline of forest at the end of each phase, punctuated periods of profound aridity are
marked by higher values of steppic herbs (Amaranthaceae) and semi-desert trees (Fig. 5).
These transitions indicate very strongly contrasting environment gradients through time
and complex vegetation mosaics very like today (White, 1983; Debusk, 1997).
Afromontane Forests
Afromontane arboreal taxa are an important part of the pollen assemblages during all
three forest phases (Fig. 5). Although this primarily results from the presence of
Podocarpus, other forest elements, such as Olea africana-type, Olea capensis-type,
Juniperus, and Ericaceae undifferentiated, follow this trend as well (Fig. 4). Podocarpus
pollen often travels very long distances and is produced in such great quantities that this
taxon may in fact be found with values up to 1% in surface samples collected over 150
km from the nearest individual (Vincens, 1982). Although this likely explains the
dominance of Podocarpus amongst the arboreal taxa in this record, the occurrence of the
other arboreal montane taxa suggests that expansion of montane forest is occurring in the
watershed and is not purely an artefact of transport.
Although today afromontane forests occur only in the mountains above 1500m asl,
many studies of the Last Glacial Maximum (LGM) observed a descent of these
assemblages to lower altitudes caused in part by lower temperature and lower
atmospheric CO2 (Coetzee, 1964; Livingstone, 1967; Debusk, 1994 ; Jolly and Haxeltine,
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1996; Street-Perrott et al., 1997). Although descent into the lowlands was observed in
the Malawi record for the LGM, Ivory et al. (2012) suggest that afromontane forest
remained well above 900m asl, thus remaining at a minimum a half a kilometer from the
lakeshore, which was near modern (478m asl; Lyons et al., 2007). In the MAL05-1B
record, very high percentages (>40%), particularly during Phase 1, suggest a much
greater lowering than even during the LGM (Fig. 5). In modern Malawi sediments,
despite the proximity of such populations along the escarpment margins in places within
a kilometre from the lake shore, values of afromontane forest do no exceed 1% at the
core site (Debusk, 1997). Values above 40% are characteristic of samples taken within
dense afromontane forest, thus these values in fact suggest that forests may have at least
periodically reached the lakeshore and may have been very widespread in the lowlands
(Vincens, 1982).
Given the OSL date at 78.752 mblf of 126,270 +/- 9500, it is likely that the oldest of
these forest phases occurred during the PG. If this is the case, the increase in
afromontane forest taxa during the first two phases may in part be a response to lower
temperature and atmospheric CO2 (Fig. 5). However, similar high percentages of
afromontane forest during the most recent forest phase, which likely corresponds to the
LIG, cannot be explained by lower temperatures or CO2.
Thus it is reasonable that another driver is primarily responsible for the afromontane
forest expansion during all three of these phases. In fact, many studies have linked preLGM afromontane expansion to enhanced monsoons and greater MAP rather than a
glacial response to temperature and CO2 (Hamilton, 1982). Marine records bracketing
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this time period also record large-scale expansions of afromontane forests into the
lowlands in West Africa as well as subtropical southeast Africa (Dupont, 2011a; Dupont
et al., 2011b). As the relative ages are at this time uncertain, records located off of west
central Africa may not immediately be considered coeval due to their long distance from
Malawi, however, a record just south of Malawi off of the Limpopo River also records
peaks of Podocarpus of similar scale to those observed here (Dupont et al., 2011b). This
suggests that the expansion of afromontane vegetation at Malawi may be associated with
the similar aged peaks elsewhere in tropical and subtropical Africa. Occupation of the
lowland tropics by afromontane species during these forest phases may have led to
mixing and migration of otherwise isolated populations that could in part explain the
structural and compositional similarity of very disparate populations occupying highlands
today through Central and South Africa (Hamilton, 1982; White, 1983).
Additionally, these high Podocarpus percentages suggest that perhaps the
organization of afromontane forests during these phases was much different than today.
Modern montane forests within the Malawi watershed today exist in small and often
degraded patches separated by wide afromontane grasslands because of harvesting of
wood and agriculture (White, 1983). Thus percentages of arboreal pollen taxa within the
afromontane zone today are relatively low (1-15%; Debusk, 1994). Additionally,
percentages throughout the Holocene in the northern basin, the basin nearest to these
vegetation communities, have remained under 5% (Debusk, 1998). The extremely high
percentages suggest that in addition to expansion in range due to climatic factors, the
afromontane zone must have had a larger contribution of forest prior to impact by man.
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Podocarpus percentages throughout the late Quaternary in the East African lowlands
remain very low. Late Pleistocene/Early Holocene records from northern and southern
Lake Tanganyika as well as Lake Bogoria show maximum expansion during climatic
optima that never exceeds 23% (with the exception of a single sample from Bogoria near
28ka of 75% due to exceptional low pollen counts; Vincens, 1987, 1989, 1991, 1993). In
the mid-altitudes of Ethiopia at Lakes Abiyata and Langeno, extremely high percentages
(55-74%), similar to those in the MAL05-1B record, occur during the Early Holocene (76ka) (Lézine, 1981; Mohammed, 1992). These expansions of dry Podocarpus forests to
~1500m asl are interpreted as representing a transition a more seasonal rainfall regime;
however, Podocarpus does not descend into the lowlands at this time. From the midHolocene, no records below 4100 m asl record percentages of Podocarpus higher than
40% (Hamilton, 1962; Livingston, 1967; Bonnefille and Chalié, 2000). This suggests
that the expansions observed in our record which extend well into the lowlands were
likely that final descent of such populations into the lowlands. Additionally, it seems that
the contribution of arboreal taxa was likely similar in magnitude to the pre-Iron Age,
extremely high elevation, remote populations observed at Lake Kimili (Hamilton, 1982).
Finally, rather than systematic and stable high percentage values, each low-frequency
forest phase is punctuated by short-lived higher frequency events (ex. ~110mblf and
70mblf) of rapid expansion and contraction (Fig. 5). Although core disturbance may be
responsible for a few of these events, such as the apparent rapid expansion of forest at
79mblf, much of this variability occurs when sediments are intact and are therefore not
artefacts (Fig. 3). It is possible that the higher frequency events superimposed on the
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longer phases could correspond to abrupt wet events. Given the uncertainty of the ages
of this interval at the moment, however, it is also plausible that these represent autocyclic events such as disturbances within part of the watershed.
Lowland Vegetation
In the lowland, below 1500m asl, three forest phases can also be identified and are
coeval with the expansion of arboreal taxa in the highlands (Fig. 5). However, although
lowland forest expansion occurs with similar timing as in the highlands, the character of
each phase is unique and much less regular. This likely reflects high sensitivity of
lowland vegetation assemblages to fluctuations in effective moisture. Furthermore, even
though taxa specific to tropical seasonal forest and miombo woodland have similar
responses, the succession of taxa suggests an independent response to environmental
change.
Miombo woodland was relatively common during all three forest phases (Fig. 5).
This vegetation type is almost exclusively represented by three pollen taxa in our record
(Combretaceae, Uapaca, and Brachystegia). The low representation of miombo despite
its important presence in the modern watershed is due both to the very homogenous and
low diversity nature of miombo woodland and the predominance of low pollen
producing, zoophilous taxa from the Caesalpinioideae subfamily (White, 1983). Uapaca,
a very common and relatively easily dispersed taxon, is by far the most prevalent
indicator of woodland expansion and occurs at high abundances in all forest phases (Fig.
4). However, despite this, Uapaca only remains consistently high throughout Phase 2. In
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contrast, in Phases 1 and 3, its highest abundances occur only at the beginning of the
phase and decrease long before afromontane forest.
Unlike Uapaca, Brachystegia only reaches high abundances very rarely (Fig. 4). In
modern miombo woodlands within the Malawi watershed, Brachystegia is the most
common arboreal species (White, 1983). This dominant woodland taxon is typical in
denser woodland communities with a taller structure and may also grow in moister,
waterlogged soils (Backeus et al., 2006). However, the relatively large pollen grains and
the zoophilous pollination strategy of this taxon results in a very poor representation in
the pollen record. In lake surface sediment samples at modern Lake Malawi adjacent to
areas where miombo is the predominant vegetation type, this pollen type never exceeds
2.4% and is most commonly ~0.7% (Debusk, 1997). Therefore, an occurrence of
Brachystegia, even when sporadic and short-lived, likely represents phases of woodland
expansion. Higher percentages occurred twice during Phase 2 (82 mblf and 86 mblf) as
well as twice during Phase 1 (68 mblf and 74.5 mblf) (Fig. 4). The relatively low but
consistent values of Brachystegia during Phase 3 signifies a mosaic of woodland types of
varying structure within the watershed at this time and the relative stability of this
generally sensitive biome.
Although the other arboreal assemblages appear during each forest phase, the
expansion of tropical seasonal forest occurs only during the oldest two forest phases (Fig.
5). Furthermore, the highest abundance of this moist, drought-intolerant forest type only
occurs briefly and does not last the entirety of the forest phase. During Phase 1, two
peaks are recorded at the beginning and near the middle of the phase (92 mblf and 112
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mblf). Phase 2, however, only records a single peak in tropical seasonal forest abundance
near 90 mblf.
In contrast, unlike miombo woodland taxa, tropical seasonal forest never completely
disappears from the record, even during more open phases when arboreal taxa are rare
(Fig. 5). Although this seems at odds with the idea that this assemblage cannot tolerate a
long dry season or extreme aridity, it likely suggests the presence of small corridors of
riparian forest along permanent streams and waterways even during hyper-arid
conditions. In modern sediments, tropical season forest only occurs in high values near
river mouths (Debusk, 1997). The lower lake levels during these hyper-arid times
(<180m depth; ~<66m asl) position the core site much nearer river deltas (Lyons, 2007;
Stone et al., 2011). Thus pollen grains from these moister habitat taxa still appear in the
record despite very open vegetation as long as a few areas with enough edaphic moisture
to support them persist. During the forest phases, the highest peaks of tropical seasonal
forest only occur during periods of high lake level with near modern lake surface
elevation (Fig. 5). At these times, this vegetation type must have greatly expanded in
comparison today, as deltas and sources areas would be much more distant from the core
site yet pollen percentages remain high (Lyons, 2007).
The absence of tropical seasonal forest during Phase 3 is curious given the high
contribution of arboreal taxa from other highland and lowland vegetation assemblages
(Fig. 5). The presence of wetter miombo and descent of afromontane forests as well as
high lake levels suggest that the monsoon was intensified at this time (Lyons, 2007).
Climate response surfaces produced for both lowland forest types show that they do
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indeed occur under similar values of MAP (Fig. 6). In fact, the range of tolerance of
tropical season forest has a 95% probability distribution from 687 to 2625 mm/yr, which
is much wider than that of miombo (95% probability of 884-2170 mm/yr). As the range
of probabilities for miombo falls completely within that of tropical seasonal forest, the
presence of miombo and absence of tropical seasonal forest cannot easily be
distinguished based on this climatic parameter for Phase 3. However, figure 6 also shows
the climate response surfaces of both assemblages for the average length of rainy season,
here called wet days. It is clear that while there is some overlap, miombo woodland
tolerates a much shorter rainy season length (95% probability of 86-135 days) than
tropical season forest (95% probability of 102-198 days). This suggests that during
Phases 1 and 2 when both lowland assemblages occur contemporaneously, the rainy
season length must have been greater than 102 days and less than 135 days/year. In
contrast, in Phase 3 there must have been a much shorter rainy season between 86 and
102 days/year. This conclusion is in agreement with a sensitivity analysis performed by
Hély et al. (2006) using the LPJ-GUESS model which showed a very high sensitivity of
this vegetation type to dry season length. This suggests that while vegetation may have
been denser and more heterogeneous during the first two phases, MAP was not
necessarily greater.
Between the forest phases, arboreal taxa drop to very low values (Fig. 5). These two
intervals, from 90-93 mblf and to 75.5-80.5 mblf, are dominated by grass and other
herbaceous taxa (Fig. 5). These taxa are primarily xerophytic and steppic herbs, such as
those of the Amaranthaceae family. These two phases represent a dramatic recession of
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forest and woodland taxa in both the lowland and highland. Woodland virtually
disappears during these times from the watershed, and low values of tropical seasonal
forest suggest perhaps remnant communities restricted to riparian corridors. The few
trees that do occur are semi-desert or wooded grassland taxa such as Acacia, Parkinsonia,
Commiphora, Tribulus, and Hymenocardia (Fig. 5). This implies that the vegetation was
very different than today within the Malawi watershed during these times, with some
open and wooded grasslands or scrubland. Although some Acacia wooded grassland
does occur in the southern region of the watershed, the absence of charcoal during these
phases highlights a more substantial reduction in vegetation cover, such that vegetation is
likely highly degraded and discontinuous (Fig. 5).This is a strong indication that the
landscape was likely dominated by a semi-desert vegetation too discontinuous to sustain
fires (Cohen et al., 2007). This illustrates that both ecosystem structure and function
were much different than today, as regular fires are an important factor in the modern
watershed to sustain woodlands (Malmer, 2007). These two semi-arid phases are much
drier and likely represent a longer dry season than all three forest phases. The near
disappearance of taxa such as Uapaca and Moraceae during these phases suggests that
precipitation at this time was less than 500 mm/year and the dry season extended to more
than 200 days/year (Fig. 6).
Despite the arid conditions during these two phases, the driest period occurs at the top
of the core interval studied here where very low pollen counts and high grass percentages
result from a coupling of poorer preservation due to oxidation of pollen deposited at the
core site. This phase corresponds to a severe megadrought recorded and dated in the
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adjacent core MAL05-1C to 105-95ka (Cohen et al., 2007). During this phase, the lake
was reduced to a shallow (~125m), saline, alkaline, well-mixed lake. The pollen
concentrations during this zone are too low to make accurate interpretations of the
vegetation cover (Fig. 3); however, other paleoecological research suggests near desert
conditions during this time and rainfall <400mm/yr (Cohen et al., 2007).
At the inception and end of each forest phase, the expansion and collapse of arboreal
taxa is abrupt (Fig. 5). However, lake level changes of up to 500m also occur throughout
this interval (Lyons, 2007). Although the forest transitions appear very abrupt in the
pollen record, the effect of differential transport and changing source areas on the
vegetation recorded by pollen should be taken into account throughout this time.
Particularly in the case of afromontane forest, where lowering lake level during arid
phases increases the altitude between the lake shore and these assemblages’ source area
artificially, this factor may have a dramatic influence and must be taken into account.
The lowering percentages of afromontane taxa therefore may partially be due to the
increased in distance that a pollen grain must travel in order to reach the lake surface and
be deposited. Additionally, although sedimentation rates changes cannot be addressed at
this stage, lithostratigraphic evidence of significant rate changes during lake regressions
and transgressions may have played some complementary role. Thus, we argue that the
abruptness of the afromontane forest transitions may be accentuated by the lake level
changes but are not driven by them. Percentages of very well dispersed grains such as
Podocarpus and Olea reach zero values during these arid phases (Fig. 4). In modern
Malawi sediments, even in the southern tip of the lake, which is situated nearly 200km
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from any afromontane forest, these pollen types are always present and are commonly
above 1% (Debusk, 1997). Furthermore, abrupt transitions are observed as well in the
miombo woodland assemblages, particularly Uapaca, whose expansion and collapse
occur relatively gradually during the first phase but mirror the abruptness in the highaltitude forest during the other phases (Fig. 4, 5). This lowland taxon frequently occurs
within proximity to the lake and is well-dispersed, thus should not be taphonomically
affected by lake level change.
This suggests that despite the effect of lake level changes, the response of vegetation
to an altered moisture regime was in reality relatively rapid, occurring at a sub-millennial
time-scale. This suggests very steep environmental gradients through time in the Malawi
watershed, expressed in the core record over a depth of several meters. It seems likely
that vegetation changed from a lush mosaic of afromontane, tropical season forests and
woodlands to semi-desert (Fig. 5).
Vegetation communities on the leeward side of the basin and promontories receive
large amounts of moisture recycled off the lake surface, particularly during austral winter
when dry SE winds are common. Reduction of lake surface area during times of
lowering lake level likely has dramatic effect on vegetation communities. The abruptness
of these shifts could in part have been caused by a reduction in recycled moisture and a
non-linear collapse of woodland and forest communities following the lowering of the
lake below a required threshold.
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LIG/PG Biodiversity
Along with the large-scale changes in the vegetation assemblages of the Malawi
watershed over our study interval, an associated change in biodiversity is also observed.
As the pollen spectra record changes in the aggregate of vegetation communities across a
broad landscape, rather than a single community, these indices represent an intercommunity diversity at the eco-regional scale. Species richness, or the number of species
across the eco-region, was estimated by the Margalef’s Index, with high values indicating
higher species richness (Margalef, 1958). This index shows higher values in general
during the forest phases and lower values during the degraded phases (Fig. 5). Within
each individual forest phase, however, the values are not stable. In particular, during the
Phases 1 and 3, high-amplitude, oscillations occur that likely reflect the variability of the
contribution of tropical seasonal forest in the lowland, suggesting these values are highly
sensitive to minor climatic perturbations. Phase 2 is clearly the most species rich and
also the phase with lowest internal variability, possibly because of the high contributions
of both lowland vegetation assemblages.
Perhaps the most striking observation, however, is the difference in species richness
between degraded phases (Fig. 5). The oldest degraded phase records the minimum
richness of the record; however, although the second degraded phase does show some
diminishment of richness, the decrease is very small. This is in part due to the higher
contribution of wooded grassland trees, such as Hymenocardia and Commiphora, as well
as a short-lived abrupt expansion of forest at 79 mblf.
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Evenness, a metric of the contribution and dominance of each species, is estimated
using the Berger-Parker Index where high values suggest greater evenness. This index
also shows greater values during the forest phases relative to the degraded phases (Fig.
5). This suggests that although the forest phases are more species rich, the lowland and
highland forests are very even. This likely results in part from high concentrations of
Podocarpus pollen during these phases; however, miombo woodlands and afromontane
forests today are in fact frequently dominated by a few taxa (White, 1983). Thus, this
likely also reflects the true character of the arboreal assemblages at the time.
Also, unlike species richness, variability of evenness during the forest phases is
minimal (Fig. 5). This implies that the dominant taxa in the record which create much of
the evenness within a vegetation assemblage are largely unvarying within a phase.
Instead, the rare taxa vary much more within a single phase. This could have been
caused by either competition of species struggling to establish or by greater
environmental sensitivities of certain taxa.
Total biodiversity is the combination of richness and evenness. The first two indices
imply that total diversity contrast for forest and degraded phases. In general, the forest
phases are more species rich but very even, whereas the degraded phases are species poor
and heterogeneous (Fig. 5). There is however a marked decoupling of these indices
during the transitional periods. During the expansion of forest for all three phases,
species richness increases first and is then followed several meters above by an increase
in evenness. This suggests that during the transitions, expanding forests and retreating
grasslands/semi-desert trees occurred contemporaneously for a period of time.
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Implications for paleoclimate
The age model for MAL05-1B will be untuned and is still under discussion;
however, relative placement in time of the events in the record is possible. The
uncertainty in ages for the upper sections of our studied MAL05-1B core interval is much
lower than the older sections. This is a result of correlation of the upper portion of our
studied interval of MAL05-1B with the previously dated adjacent core MAL05-1C until
145ka (85mblf). Additionally, an OSL date on core MAL05-1C occurring within this
interval of 126,270 +/- 9500 at 78.752 mblf provides additional constraint (Lyons, 2007;
Scholz et al., 2007; Table 1). Assuming continuity within this interval, this allows us to
form some broad conclusions concerning the climate controls on vegetation during the
LIG and PG. For further precision, the majority of this interval is constrained by
paleomagnetic excursion events; however, beginning at 80mblf, 3 alternative
interpretations exist (Tables 1, 2). As a result of this uncertainty in the lower half of the
studied interval, only vegetation changes with resptect to the PG and LIG will be
discussed.
Based on our existing age constraints, the largest-scale oscillations between forest
phases and open, degraded vegetation occur during both the PG and LIG at 10ºS (Fig. 7).
Expansion of arboreal taxa in this record occurs coeval to increases in lake level that
suggest overall higher rainfall during these times and positive effective moisture (Lyons,
2007). This result suggests a possible relationship to other studies which observe
enhancement of the tropical circulation during the summer seasonal when insolation is
locally high (Rossignol-Strick, 1983; Cohen et al., 2007). Due to the lack of precise age
150
information in this interval, it is impossible to say if the dominant vegetation signal
which determines the contribution of woody biomass on the landscape is related to the
intensity of summer rains which follows regional southern hemisphere controls, instead
of a global northern hemisphere high-latitude control. However, as the steep
environmental gradients inferred from the pollen record occur during both glacial and
interglacial periods, this highlights the importance of a higher frequency forcing
mechanism not associated with the high latitudes. The strong control of North Atlantic
temperature and ice volume on African hydrology during Termination 1 is likely due
very low eccentricity over the last 60ka (Cohen et al., 2007).
The vegetation assemblages within each forest phase are unique. Phases 1 and 2,
which likely occur during the PG, show pulses of moist, drought-intolerant tropical
seasonal forest. Phase 3, most recent phase, however, was different. This phase, which
likely occurred during the LIG, was characterized by a more open, fire-prone landscape
dominated by miombo woodland. This differential response of vegetation during the LIG
and PG suggests that northern hemisphere teleconnections were important for vegetation
at Malawi, but that this forcing was superimposed on the higher frequency forcing (Fig.
7). During the forest phases of the PG, the dry season was short, thus the ITCZ was
further south. Additionally, as a result of high ice volume and cooler global temperatures
which existed at the time particularly in the northern hemisphere, the ITCZ may have
spent more time in the southern hemisphere, further increasing effective moisture at the
site. During the PG degraded phases, the ITCZ must have been very far north, which
made for a long dry season with higher insolation and low summer rainfall.
151
The case, however, is more complicated when northern hemisphere ice was low,
and temperature was high. The inter-hemispheric temperature gradient of the LIG would
have been more like today. This suggests that during this time, the ITCZ would have
been displaced toward the north. Thus, the forest phase during the LIG occurred during a
time of enhanced monsoon rainfall, but the ITCZ must have spent more time in the
northern hemisphere as a result of high northern hemisphere temperatures. This would
have resulted in a longer dry season and more evaporation during the austral winter.
Conclusions
Pollen from the Lake Malawi drill core MAL05-1B records the vegetation history of
southeast Africa over a time corresponding to the end of the PG and the LIG. During
this interval, vegetation tracked regional hydrology and lake level fluctuations very
closely. This resulted in three large-scale cycles of wet periods marked by forest
expansion alternating with extreme aridity indicated by open, degraded grasslands and
semi-desert vegetation.
Despite the seeming simplicity of the oscillations, each forest phase was marked by a
unique mosaic of vegetation assemblages. During the oldest two phases, watershed
vegetation was extremely heterogeneous, with expansion of afromontane forests into the
lowlands. Additionally, miombo woodland dominated the drier, better drained areas, and
tropical seasonal forest expanded away from the riparian corridors, which are restricted in
the watershed today. These mosaics indicate a very lush, forest dominated landscape
indicative of moderate MAP and very short dry seasons. The final forest phase differed
in the relative absence of tropical seasonal forest. Biophysical limits of forest and
152
woodland assemblages suggest that this phase was therefore a time of high MAP but very
long dry season.
Although the forest phases were very species rich in comparison to the open phases,
the dominance of certain taxa suggest that, despite the mosaic of vegetation assemblages,
composition within each assemblage was relatively even. The composition of the wellstudied vegetation associations with in the watershed today are not greatly altered from
these earlier forests despite the dramatic reshuffling imposed by climate.
The pollen record demonstrates the migration and elevational expansion/contraction
of two important centres of endemism, tracking the profound environmental changes
which were occurring over this interval. During wetter phases, afromontane forests were
displaced into the lowlands. These descents from their prior high-elevation “sky island”
settings were likely contemporaneous with expansions elsewhere, suggesting the
possibility for very recent mixing of high-elevation flora. This could explain the similar
composition of assemblages throughout central and southern Africa. In addition, during
the driest phases, taxa with Somali-Masai biogeographic affinities were present in the
Malawi watershed. It seems likely that the high magnitude environmental variability and
extreme aridity in the region at the time resulted in large range displacements of species
whose ranges are now quite limited.
Finally, we show that preliminary age data on this record suggest that forest
expansion occurred during the LIG and PG. The expansion of forests was controlled by
higher effective moisture and short dry seasons in the watershed linked to a southerly
ITCZ. In contrast, the open phases and extreme aridity observed in the record seem to
153
have been related to long dry seasons and low summer rainfall caused by a northward
displacement of the ITCZ. Further refinements of our age model may allow us to
investigate the possible causing of the higher frequency forcing mechanism resulting in
these high gradient environmental changes in the southern hemisphere.
This study has important implications for projected future hydrologic changes in
Africa and vegetation assemblage migration patterns that created today’s unique diverse
biogeography. Additionally, as this is a key area for hominid evolution, this may provide
some insight on the environmental conditions and migration patterns of early humans.
Acknowledgements
The authors would like to thank Owen Davis, Guillaume Buchet, and John Logan for
useful discussion, and help with pollen extraction and identification, and Dominique
Genty, Meg Blome, Vincent Montade, Frank Bassinot, Louis Fevrier and Valerie Masson
for useful discussions, and the staffs of DOSECC, SeaCore, and the Limnological
Research Center (LRC-University of Minnesota), and the National Lacustrine Core
Repository (LacCore) for assistance with core acquisition and sampling. The cores are
archived and were subsampled at the Limnological Research Center at the University of
Minnesota, and this work was funded by the National Science Foundation (Lake Malawi
Drilling Project, NSF-EAR 0602350) and an NSF Graduate Research Fellowship to the
senior author.
154
References
Backéus, I., Pettersson, B., Strömquist, L., & Ruffo, C. 2006. Tree communities and
structural dynamics in miombo (Brachystegia–Julbernardia) woodland, Tanzania. Forest
ecology and management, 230, 171-178.
Berger, W.H., Parker, F.L., 1970. Diversity of planktonic foraminifera in deep-sea
sediments. Science, 168, 1345–134.
Beuning, K., Zimmerman, K., Ivory, S., Cohen, A., 2011. Vegetation response to
glacial–interglacial climate variability near Lake Malawi in the southern African tropics,
Palaeogeogr., Palaeoclimat., Palaeoeco., 303, 81-92, doi: 10.1016/j.palaeo.2010.01.025.
Bonnefille, R., 1971a. Atlas des pollens d'Ethiopie, principales espèces des forêts
demontagne, Pollen et Spores, 13, 15–72.
Bonnefille, R., 1971b. Atlas des pollens d'Éthiopie. Pollens actuels de la Basse Vallée de
l'Omo, récoltes botaniques 1968, Adansonia, 2, 463–518.
Bonnefille R., Riollet, G., 1980. Pollens des savanes d'Afrique orientale. CNRS, Paris.
Bonnefille, R., Chalié, F., 2000. Pollen-inferred precipitation time-series from equatorial
mountains, Africa, the last 40 kyr BP. Glob. Planet. Change. 26, 25-50.
Brown, E.T., Johnson, T.C., Scholz, C.A., Cohen, A.S., King, J.W., 2007. Abrupt change
in tropical African climate linked to the bipolar seesaw over the past 55,000
years,Geophys. Res. Lett,. 34, L20702.
Coetzee, J. A., 1964. Evidence for a considerable depression of the vegetation belts
during the Upper Pleistocene on the East African mountains. Nature 204, 564 – 566,
doi:10.1038/204564a0.
Cohen, A.S., Stone, J.R., Beuning, K.R., Park, L.E., Reinthal, P.N., Dettman, D., et al.
2007. Ecological consequences of early Late Pleistocene megadroughts in tropical Africa,
PNAS 104, 16422-16427, doi: 10.1073/pnas.0703873104.
Cox, P.M., Pearson, D., Booth, B., Friedlingstein, P., Huntingford, C., Jones, C., Luke,
C., 2013. Sensitivity of Tropical Carbon to Climate Change Constrained by Carbon
Dioxide Variability. Nature, in press. doi:10.1038/nature11882.
DeBusk, G.H., 1994. Transport and stratigraphy of pollen in Lake Malawi, Africa, PhD
Thesis. Duke University.
155
DeBusk, G.H., 1997. The distribution of pollen in the surface sediments of Lake Malawi,
Africa, and the transport of pollen in large lakes, Rev. Palaeobot. Palynol. 97, 123-153.
DeBusk, G.H., 1998. A 37,500-year pollen record from Lake Malawi and implications
for the biogeography of afromontane forests, J. Biogeogr., 25, 479-500.
Diffenbaugh, N.S., Giorgi, F., 2012. Climate change hotspots in the CMIP5 global
climate model ensemble. Climatic Change, 114, 813-822. doi: 10.1007/s10584-0120570-x.
Dupont, L., 2011a. Orbital scale vegetation change in Africa, Quat. Sci. Rev., 30, 35893602, doi: 10.1016/j.quascirev.2011.09.019.
Dupont, L.M., Caley, T., Kim, J.-H., Castañeda, I., Malaizé, B., Giraudeau, J., 2011b.
Glacial-interglacial vegetation dynamics in South Eastern Africa coupled to sea surface
temperature variations in the Western Indian Ocean, Clim. Past, 7, 1209-1224,
doi:10.5194/cp-7-1209-2011, 2011.
Eccles, D.H., 1974. An Outline of the Physical Limnology of Lake Malawi (Lake Nyasa),
Limnol. Oceanogr., 19, 730-742.
Faegri K., Iversen, J., 1989. Textbook of Pollen Analysis, Wiley, Chichester, UK.
Fischer, R. A., Turner, N. C., 1978. Plant productivity in the arid and semiarid zones.
Annual Review of Plant Physiology, 29, 277-317.
Gajewski, K., Lézine, A.M., Vincens, A., Delestan, A., Sawada, M., and the African
Pollen Database, 2002. Modern climate–vegetation–pollen relations in Africa and
adjacent areas, Quat. Sci. Rev. 21, 1611–1631.
Gasse, F., 2000. Hydrological changes in the African tropics since the Last Glacial
Maximum, Quat. Sci. Rev., 19, 189-211.
Gasse, F., Chalié, F., Vincens, A., Williams, M.A., Williamson, D., 2008. Climatic
patterns in equatorial and southern Africa from 30,000 to 10,000 years ago reconstructed
from terrestrial and near-shore proxy data, Quat. Sci. Rev., 27, 2316-2340.
Grimm, E.C., 1987. CONISS: a FORTRAN 77 program for stratigraphically constrained
cluster analysis by the method of incremental sum of squares, Computers & Geosciences,
13, 13-35.
156
Grimm, E.C., 1990. TILIA and TILIAGRAPH: PC spreadsheet and graphic software for
pollen data. INQUA Comm. Stud. Holocene Working Groups on Data-handling methods,
Newsletter, 4, 5–7.
Hamilton, A.C., 1982. Environmental history of East Africa: a study of the Quaternary.
London: Academic Press, 328 pp.
Havinga, A.J., 1967. Palynology and pollen preservation, Rev. Palaeobot. Palynol., 2, 8198, doi: 10.1016/0034-6667(67)90138-8.
Hély, C., Bremond, L., Alleaume, S., Smith, B., Sykes, M. T. and Guiot, J., 2006.
Sensitivity of African biomes to changes in the precipitation regime. Global Ecol.
Biogeogr., 15, 258–270, doi: 10.1111/j.1466-8238.2006.00235.x.
IPCC, 2007: Climate Change 2007: The Physical Science Basis. Contribution of Working
Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate
Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor
and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and
New York, NY, USA.
Ivory, S., Lézine, A.-M., Vincens, A., Cohen, A., 2012. Effect of aridity and rainfall
seasonality on vegetation in the southern tropics of East Africa during the
Pleistocene/Holocene Transition, Quat. Res., 77, 77-76.
Ivory, S., Russell, J., Cohen, A., In the hot seat: Insolation, ENSO, and vegetation in the
African tropics, submitted to JGR-Biogeosciences, Aug. 2012.
Jolly D., Haxeltine, A., 1997. Effect of Low Glacial Atmospheric CO2 on Tropical
African Montane Vegetation, Science. 276, 786-788.
Jouzel, J., Masson-Delmotte, V., Cattani, O., Dreyfus, G., Falourd, S., Hoffmann, G.,
Minster, B., Nouet, J., Barnola, J., et al., 2007. Orbital and Millennial Antarctic Climate
Variability over the Past 800,000 Years, Science, 317, 793-796, doi:
10.1126/science.1141038.
Lézine, A. M., 1981. Le Lac Abiyata (Ethiopie) - Palynologie et paléoclimatologie du
Quaternaire récent. Unpublished Thesis, Université de Bordeaux I, 125p.
Lézine, A. M., Assi-Kaudjhis, C., Roche, E., Vincens, A., Achoundong, G., 2013.
Towards an understanding of West African montane forest response to climate change,
Journal of Biogeography, 40, 183-196.
157
Liu, Z., Wang, Y., Gallimore, R., Notaro, M., & Prentice, I. C., 2006. On the cause of
abrupt vegetation collapse in North Africa during the Holocene: Climate variability vs.
vegetation feedback. Geophys. Res. Lett., 33, L22709.
Livingstone, D.A., 1967. Postglacial Vegetation of the Ruwenzori Mountains in
Equatorial Africa, Ecological Monographs. 37, 25-52.
Lyons, R., 2007. Stratigraphic and hydrologic responses to tropical climate variability:
Scientific drilling in Lake Malawi, East Africa, PhD dissertation, Syracuse University.
Lyons, R., Scholz, C., Buoniconti, M., Martin, M., 2011a. Late Quaternary stratigraphic
analysis of the Lake Malawi Rift, East Africa: An integration of drill-core and seismicreflection data. Palaeogeogr., Palaeoclimat., Palaeoeco., 303, 20-37, doi:
10.1016/j.palaeo.2009.04.014.
Lyons, R., Kroll, C., Scholz, C., 2011b. An energy-balance hydrologic model for the
Lake Malawi Rift Basin, East Africa. Glob. Planet. Change, 75, 83-97, doi:
10.1016/j.gloplacha.2010.10.010.
Malawi Government, 1983. The National Atlas of Malawi. National Atlas Committee
and Department of Surveys of Malawi.
Maley, J., 1970. Contributions à l'étude du bassin tchadien : atlas de pollens du Tchad,
Bulletin du Jardin Botanique National de Belgique, 40: 29–48.
Malmer, A., 2007. General ecological features of miombo woodlands and considerations
for utilization and management. In MITMIOMBO–Management of Indigenous Tree
Species for Ecosystem Restoration and Wood Production in Semi-Arid Miombo
Woodlands in Eastern Africa. Proceedings of the First MITMIOMBO Project Workshop
held in Morogoro, Tanzania pp. 6-12.
Margalef, R., 1958. Temporal succession and spatial heterogeneity in phytoplankton. In:
Perspectives in Marine biology, Buzzati-Traverso (ed.), Univ. Calif. Press, Berkeley, pp.
323-347.
McGlue, M., Lezzar, K., Cohen, A.S., Russell, J., Tiercelin, J.-J., Felton, A., Mbede, E.,
Nkotagu, H., 2008. Seismic records of late Pleistocene aridity in Lake Tanganyika,
tropical East Africa, Paleolimnology, 40, 635-653.
Mohammed, M. U., 1992. Paléoenvironnement et paléoclimatologie des derniers
millénaires en Ethiopie. Contribution palynologique. PhD dissertation, Université AixMarseille III, 209p.
158
New, M., Hulme, M. and Jones, P.D., 2000: Representing twentieth century space-time
climate variability. Part 2: development of 1901-96 monthly grids of terrestrial surface
climate. J. Clim. 13, 2217-2238,
DOI:10.1175/15200442(2000)013<2217:RTCSTC>2.0.CO;2
Nicholson, S. 1996. A Review of Climate Dynamics and Climate Variability in Eastern
Africa, The Limnology, Climatology, and Paleoclimatology of the East African Lakes,
Johnson, T.C., Odada, E.O., Gordon and Breach Publisher, 664pp.
Polhill, R.M., 1966. Flora of Tropical East Africa (ed. by C.E. Hubbard and E. MilneRedhead), Crown Agents for Oversea Governments and Administrations, London.
Rossignol-Strick, M., 1983. African Monsoons, an Immediate Climate Response to
Orbital Insolation, Nature, 304, 46–49, doi:10.1038/304046a0.
Scholz, C.A., Johnson, T.C., Cohen, A.S., King, J.W., Peck, J.A., Overpeck ,J.T., et al.,
2007. East African megadroughts between 135 and 75 thousand years ago and bearing on
early-modern human origins, PNAS, 104, 16416-16421, doi: 10.1073/pnas.0703874104.
Scholz, C.A., Cohen, A.S., Johnson, T.C., King, J., Talbot, M.R., Brown, E.T., 2011a.
Scientific drilling in the Great Rift Valley: The 2005 Lake Malawi Scientific Drilling
Project — An overview of the past 145,000 years of climate variability in Southern
Hemisphere East Africa. Palaeogeogr., Palaeoclimat., Palaeoeco., 303, 3-19, doi:
10.1016/j.palaeo.2010.10.030.
Scholz, C.A., Talbot, M.R., Brown, E.T., Lyons, R.P., 2011b. Lithostratigraphy, physical
properties and organic matter variability in Lake Malawi Drillcore sediments over the
past 145,000 years. Palaeogeogr., Palaeoclimat. Palaeoeco., 303, 38-50, doi:
10.1016/j.palaeo.2010.10.028.
Stager, J., Ryves, D., Chase, B., Pausata, F., 2011. Catastrophic Drought in the AfroAsian Monsoon Region During Heinrich Event 1, Science, 311, 1299-1302.
Stone, J. R., Westover, K. S., Cohen, A. S., 2011. Late Pleistocene paleohydrography
and diatom paleoecology of the central basin of Lake Malawi, Africa, Palaeogeography,
Palaeoclimatology, Palaeoecology, 303, 51-70.
Street-Perrott, F.A., Huang, Y., Perrott, R.A., Eglinton, G., Barker, P., Khelifa, L.B.,
Harkness, D., Olago, D., 1997. Impact of Lower Atmospheric CO2 on Tropical Mountain
Ecosystems, Science. 278, 1422-1426.
Verschuren D, Sinninghe-Damsté J, Moernaut J, Kristen I, Blaauw M, Fagot M, Haug G,
CHALLACEA project members, 2009. Half-precessional dynamics of monsoon rainfall
near the East African Equator. Nature 462, 637-641. doi:10.1038/nature08520.
159
Vincens, A., 1982. Palynologie, environnements actuels et plio-pleistocènes à l’Est du lac
Turkana (Kenya). PhD Thesis, University of Aix-Marseille II.
Vincens, A., 1987. La sédimentation pléistocène supérieur-holocène. Pollens et spores :
indices de l'évolution climatique. In : Le demi-graben de Baringo-Bogoria, Rift Gregory,
Kenya. 30000 ans d'histoire hydrologique et sédimentaire. Tiercelin J.J. and Vincens A.
(coords), Bulletin des Centres de Recherches Exploration-Production Elf-Aquitaine,
(Pau, France) 11, 437-451.
Vincens, A., 1989. Paléoenvironnements du bassin North-Tanganyika (Zaire, Burundi,
Tanzanie) au cours des 13 derniers mille ans: apport de la palynologie, Rev. Palaeobot.
Palynol., 61, 69-88.
Vincens, A., 1991. Late quaternary vegetation history of the South-Tanganyika basin.
Climatic implications in South Central Africa, Palaeogeogr., Palaeoclim., Palaeoeco., 86,
207-226.
Vincens, A., 1993. Nouvelle séquence pollinique du lac Tanganyika: 30,000 ans
d'histoire botanique et climatique du Bassin Nord, Rev. Paleobot. Palynol., 78, 381-394.
Vincens, A., Bremond, L., Brewer, S., Buchet, G., Dussouillez, P., 2006. Modern pollenbased biome reconstructions in East Africa expanded to southern Tanzania, Rev.
Palaeobot.and Palynol., 140, 187-212, doi:10.1016/j.revpalbo.2006.04.003.
Vincens, A., Lézine, A.-M., Buchet, G., Lewden, D., Le Thomas, A., 2007a. African
pollen database inventory of tree and shrub pollen types, Rev. Palaeobot. Palynol., 145,
135-141, doi:1016/j.revpalbo.2006.09.004.
Vincens, A., Garcin, Y. and Buchet, G., 2007b. Influence of rainfall seasonality on
African lowland vegetation during the Late Quaternary: pollen evidence from Lake
Masoko, Tanzania, J. Biogeog., 34, 1274–1288, doi: 10.1111/j.1365-2699.2007.01698.x.
Wang, Y., Cheng, H., Edwards, R.L., Kong, X., Shao, X., Chen, S., Wu, J., Jiang, X.,
Wang, X., An, Z., 2008. Millennial- and orbital-scale changes in the East Asian monsoon
over the past 224,000 years, Nature, 451, 1090 – 1093, doi: 10.1038/nature06692.
Watrin, J., Lézine, A.-M., Gajewski, K. Vincens, A., 2007. Pollen–plant–climate
relationships in sub-Saharan Africa, J. Biogeog., 34, 489–499, doi: 10.1111/j.13652699.2006.01626.
White, F., 1983. Vegetation of Africa - a descriptive memoir to accompany the
Unesco/AETFAT/UNSO vegetation map of Africa; Natural Resources Research Report
XX; UNESCO, Paris, France; 356pp.
160
Figures
Figure 1. a) Map of East Africa showing Lake Malawi, b) map of core site and watershed
vegetation (adapted from White, 1983), c) climatology of the modern watershed with
monthly mean rainfall (blue) and temperature (red) based on NCEP/NCAR Reanalysis
data.
161
162
Figure 2. a) Mean annual precipitation in Africa and boundaries of major convergence
zones, b) mean rainy season length based on CRU 1961-1990 0.5°x0.5° datasets (New et
al., 2000).
163
164
Figure 3. Lithology and stratigraphy of MAL05-1B over the interval used for pollen
analysis. Samples indicated by black dots.
165
166
Figure 4. MAL05-1B pollen percentage diagram of important taxa and biome
associations. At the far right of the diagram are pollen zones, forest phases, and the core
stratigraphy within this section.
167
168
Figure 5. MAL05-1B vegetation indicators. Percentages of biome assemblages
(Afromontane forest, miombo woodland, tropical seasonal forest, semi-arid trees,
Poaceae, Amaranthaceae), charcoal concentration, and indices of species richness and
evenness.
169
170
Figure 6. Climate response surfaces of lowland arboreal vegetation associations for wet
days and mean annual precipitation (MAP).
171
172
Figure 7. MAL05-1B vegetation and climate. Percentages of afromontane forest, tropical
seasonal forest, miombo woodland, dry woodland, and charcoal concentration from Lake
Malawi. The OSL date the shows the relative placement of the LIG and PG is marked at
the bottom.
173
174
Tables
Table 1. Ages for the chronology of site 1 from cores MAL05-1C and MAL05-1B.
mblf
0.555
1.7
3.705
5.46
6.705
7.5075
7.9
8.5075
9.4565
9.5565
10.545
11.524
12.458
14.09
15.806
21.053
28.09
55.972
78.752
227.5
243
250.5
271.5
293.5
314.5
347.5
355.5
382.5
age (BP)
816
1772
4273
7141
11009
12628
13329
14565
18451
18799
21528
23973
26590
30890
36139
50457
73000
107420
126270
730000
921000
932000
987000
1068000
1115000
1190000
1215000
1255000
±
67
50
95
90
138
79
145
313
222
180
470
267
427
214
423
3243
7580
9500
method
AMS radiocarbon
AMS radiocarbon
AMS radiocarbon
AMS radiocarbon
AMS radiocarbon
AMS radiocarbon
AMS radiocarbon
AMS radiocarbon
AMS radiocarbon
AMS radiocarbon
AMS radiocarbon
AMS radiocarbon
AMS radiocarbon
AMS radiocarbon
AMS radiocarbon
AMS radiocarbon
Toba ash
OSL
OSL
Brunhes/Matayama
Ar/Ar on tephra
Santa Rosa
Upper Jaramillo
Lower Jaramillo
Punaruu
Cobb Mountain top
Cobb Mountain Bottom
Bjorn
175
Table 2. Paleomagnetic excursion events identified from core MAL05-1B and alternate
chronologies.
excursion event
4a
5a
5b
6a
6b
7a
7b
8a
9a
9b
11a
13a
14a
15a
15b
17a
age (BP)
61000
100000
123000
132000
160000
190000
220000
260000
300000
330000
400000
495000
535000
575000
605000
665000
Alternative 1 (mblf)
26.3
68.2
75.8
80
101
116.5
129.5
137
143
146
150.3
171.5
181.18
192.22
194.9
207.96
Alternative 2 (mblf)
26.3
68.2
75.8
80
83.9
91
108.2
116.5
129.5
143
150.3
171.5
181.18
192.22
194.9
207.96
Alternative 3 (mblf)
26.3
68.2
75.8
80
91
106
116.5
129.5
143
150.3
156
171.5
181.18
192.22
194.9
207.96
176
APPENDIX D:
SIMULATED CLIMATE MODEL VARIABILITY OF AFROTROPICAL
VEGETATION: THE MODERN PREINDUSTRIAL PERIOD AND THE LAST
INTERGLACIAL
Sarah J. Ivory*, Joellen Russell*, Andrew Cohen*
*Department of Geosciences, University of Arizona, 1040 E 4th St., Tucson, AZ
85721
Prepared for submission to Climates of the Past
177
Abstract
Warmer temperatures during the last Interglacial (130-115ka) have led many to use this
period as an analog for studying the environmental effects of future anthropogenic
warming. Paleodata have long suggested a strong connection between summer insolation
and hydrology. In fact, African hydrology then was very different from today with
vegetation extending far into the Sahara and large-scale droughts in much of East Africa.
Since orbital configuration was much different than today, however, it is unclear whether
the Last Interglacial can serve as an informative analog. Nevertheless, paleoclimate
simulations are a powerful method of validating models used for future projections of
environmental change, particularly Earth System Models, which rely not only on accurate
representation of climate but also biogeochemical processes in the model.
In this analysis, we use the GFDL ESM2M to compare and contrast the climate and
vegetation on continental Africa for the modern Preindustrial period with preliminary
results from a simulation of the last Interglacial. Although, annual insolation and
temperature anomalies are small, the increased magnitude of the seasonal insolation cycle
in the northern hemisphere and a reversed seasonal inter-hemispheric thermal gradient
result in large precipitation anomalies with respect to the present in both northern and
southern Africa during the respective summer seasons. Simulated biomes in areas of
modern desert are markedly denser, consisting of grassland and wooded grassland, in the
northern hemisphere as far as 20ºN. These results are in agreement with paleodata,
suggesting that the main differences from today of last Interglacial hydrology in Africa
are primarily from atmospheric and oceanic circulation associated with the effect of
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insolation on the W. African monsoon and the ITCZ. It must be noted, however, that
absolute changes from today are small, even for the seasonal extremes, and that the
overall differences between the last Interglacial and today fall within the simulated range
of variability of the modern period in the model. As the alteration of orbital configuration
alone as boundary conditions for the Last Interglacial simulations result in climate and
vegetation that match the paleodata, using this period for a direct analog in tropical
Africa to future warming should be done with caution. Future work, including more
detailed analysis of the variability within ESM2M, across different Earth System Models,
and other last Interglacial simulations to create an ensemble, will strengthen our ability to
make definitive claims about using the last Interglacial as an analog for our
anthropogenic future.
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Introduction
Anthropogenic climate change is one of the most pressing challenges facing scientists
today. Tropical African ecosystems in particular are very sensitive to thermal and
hydrological fluctuations (IPCC, 2007). In the tropics, ecological shifts are likely to have
devastating effects on millions living directly off the land, and little infrastructure exists
to mitigate altered resource availability. In addition, the fragility of tropical grasslands an
forests to climate change is likely to have an important influence on our ability to control
atmospheric CO2 in the future (Cox et al., 2013). Projections of future climate and
vegetation rely on dynamical models, such as those used for the IPCC; yet, much
uncertainty stills exists due to our incomplete understating of feedbacks between the
biosphere and climate, as well as the presence of known and unknown model biases (Cox
et al., 2013). Simulations of past climates and comparisons with paleoclimate and
paleoenvironmental data are important to both validate models and test the models.
Assessing the modern model simulations is essential for understanding potential
ecological shifts and changes in resource availability in the near and long term in climate
change hotspots like tropical Africa (Diffenbaugh and Giorgi, 2012).
Additionally, studies of past climates can elucidate the mechanisms that control
changes in the Earth system. Recently, many studies have focused on past periods of
warmer than modern temperatures, such as the Last Interglacial (MIS 5e; LIG), as
possible analogs for understanding potential future implications of the present warming
(Otto-Bliesner et al., 2006; Braconnot et al., 2008). The LIG, beginning about 130ka was
a period of warmer than modern temperatures (+ 0-4°C; Otto-Bliesner et al., 2006).
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During the LIG, hydrology in Africa was much different. An enhanced African monsoon
increased Nile discharge into the Mediterranean (Rossignol-Strick, 1983). Lakes existed
in areas of the Sahara that are now dry, and the dunes of the Kalahari were inactive (PetitMaire et al., 1994; Stokes et al., 1997). Although modern preindustrial radiative forcing
was similar to that of the LIG, orbital configuration was much altered, leading to a
significantly different inter-hemispheric thermal gradient (Berger, 1978). Because of the
influence of insolation on monsoon circulation, it is unclear how appropriate the LIG may
be as an analog for the warming as a result of higher GHGs in tropical Africa.
For many years, studies within inter-tropical Africa have observed a strong
correlation between local summer insolation and regional hydrology (Rossignol-Strick,
1983; Scholz et al., 2007; Cohen et al., 2007; Verschuren et al., 2009). Though few
records of past climate and vegetation exist in Africa prior to the Last Glacial Maximum
(LGM; ~21-18ka), existing marine and terrestrial data show multiple relatively rapid and
large-scale transitions of rainfall and land cover throughout the African tropics from 16070ka (Partridge et al., 1997; Beuning et al., 2011; Dupont, 2011). During the LIG,
evidence of unusually humid conditions in north tropical Africa coeval with drought in
southern Africa suggest that these high amplitude changes, observed in many African
records, are indicative of a long-term pattern of rapid wet-dry transitions that occurred
asynchronously across the continent. Cohen et al. (2007) attribute this enhanced
variability and the migration of arid conditions to high-amplitude shifts in insolation due
to the effects of increased eccentricity on precessional cycles from ~135-90ka. These
large fluctuations in insolation and seasonality, which had earlier been observed in high-
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latitude ice cores and oxygen isotope records, are expressed as dramatic changes in
atmospheric circulation and rainfall.
In order to better understand the role of insolation in LIG hydrology in Africa, we
performed simulations using the GFDL Earth System Model ESM2M. This fully
coupled biogeochemical model allows for the reconstruction of African climate and
biomes under LIG boundary conditions. Finally, in order to validate the simulation, these
simulations are then evaluated against existing marine and terrestrial paleo-records from
the literature.
Methods
Insolation and Orbital Parameters
The amount of insolation received at any point on the Earth’s surface varies as a
result of obliquity, precession, and eccentricity, known collectively as the Milankovich
cycles. Whereas precession is largely responsible for changes in the timing and length of
the seasons relative to the solstices and equinoxes, it can also affect the intensity of the
seasons depending on the relative position of the Earth at perihelion. For example, at
present during the northern hemisphere (NH) winter solstice and southern hemisphere
(SH) summer solstice, the Earth is near perihelion (Fig. 1). As the Earth is closer to the
sun during the NH winter season and farther during the NH summer season, this buffers
the NH from extremes in both seasons. In addition, the passage at aphelion is slower than
at perihelion, resulting in a milder and longer summer in the NH (Berger, 1978). The
opposite is true for the SH, which experiences harsher, longer winters.
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The situation was much different at the beginning of the LIG when the seasons
were shifted 180º relative to the vernal equinox. At this time, summer NH insolation was
high and winter insolation was low, causing enhanced seasonality. Although precession
is a key parameter in climate on long time scales, eccentricity modulates the magnitude
of precession. Precessional effects are larger when eccentricity is large, making long
summers longer and long winters longer (Braconnot et al., 2008). In addition, the
increased amplitude of insolation with high eccentricity implies that for each half
precessional cycle (high to low or low to high), a greater net change in insolation occurs
over the same amount of time.
Despite the fact that LIG precession was 180º shifted, annual net insolation was
not much altered, as higher summer insolation and lower winter insolation cancel each
other out. However, peak summer insolation anomalies were quite notable. At 23ºN,
insolation during summer was as much as 11.9% higher than modern, while at 23ºS,
insolation in summer was as much as 7.7% lower (Fig. 1; Berger, 1978).
Experimental Design
We performed a set of equilibrium experiments to investigate the effect of orbital
configuration on LIG climate and vegetation in Africa in the Geophysical Fluid
Dynamics Laboratory (GFDL) Earth System Model (ESM). The experimental period
considered (a preliminary simulation of 125ka) is compared to a simulation from 0ka
(pre-industrial control) that was downloaded from the Climate Model Intercomparison
Project 5 (CMIP5) website (http://www-pcmdi.llnl.gov/). The 125ka period was chosen
in order to the highlight the effect a 180º shift of precession and higher eccentricity on the
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seasonal distribution of insolation, the position of the ITCZ, and the intensity of
monsoons. The LIG simulation was integrated with boundary conditions for greenhouse
gases and ice sheet extent identical to those used in the pre-industrial control (Table 1).
The only alteration was the orbital configuration (eccentricity, precession, and obliquity),
which follow Berger (1978; Table 1). Although there are slight differences in greenhouse
gases and ice sheet extent during the LIG in comparison to pre-industrial, we seek to
evaluate the effect of orbital configuration alone. Regardless, the use of modern ice is
likely a good analog to LIG conditions as recent studies suggest Greenland ice sheets
were only several meters lower than present and of similar spatial extent (Dahl-Jansen et
al., 2013).
The GFDL ESM2M is a global climate model with fully-coupled, dynamic carbon
chemistry and is composed of the same atmosphere and ocean components as CM2.1, a
physical coupled climate model used in IPCC AR4 future climate projections (IPCC,
2007). CM2.1 has been evaluated alongside other models in numerous studies and has
been found to perform better than most others of its generation (IPCC, 2007).
Additionally, ESM2M has been found to simulate the major features of climate and the
carbon cycle, including mean state and variability, with high fidelity (Dunne et al.,
2012a). Full diagnostics of model performance in comparison with observations can be
found in Dunne et al. (2012a) and Dunne et al. (2012b), thus a full comparison of model
output with modern climate is outside of the scope of this article.
ESM2M is composed of atmosphere, ocean, sea ice, iceberg, land, and vegetation
components which interact dynamically. The atmosphere and ocean are virtually
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identical to CM2.1, as described in Delworth et al. (2006), with the additional of
biogeochemistry. The atmosphere component, based on the AM2 model, has a horizontal
resolution of 2º x 2.5º with 24 vertical levels and uses a D grid with finite volume
advection (Anderson et al., 2004). The ocean component is the Modular Ocean Model
version 4p1 (MOM4p1) and uses vertical pressure layers. MOM4p1 has a horizontal
resolution of 1º narrowing to 1/3º at the equator with 50 vertical levels (Griffies, 2009).
The land component has updated hydrology, physics, and terrestrial ecology
(Shevliakova et al., 2009). The atmosphere, ocean, and land components are updated
every 30 minutes, with fluxes between components occurring on a time step of 2 hours.
The LM3 land component of ESM2M is dynamically coupled to the atmosphere and
ocean in order to represent terrestrial water, carbon, and energy fluxes. This land surface
model has representations of the dynamic processes that control ecosystems, including
physiological, biophysical, and biogeochemical processes in addition to population
dynamics, competition between plant functional types (PFT), and disturbances in order to
reconstruct the spatial extent of PFTs and their carbon and water budgets (Shevliakova et
al., 2009). LM3 has 5 carbon pools (leaves, fine roots, sapwood, heartwood, and labile)
which are updated daily and determine vegetation type and structure. The LM3
framework reconstructs five PFTs (only three of which are common in Africa: tropical
evergreen trees, tropical deciduous trees, grasses) based on leaf physiology, leaf
longevity, and carbon biomass between stems, roots, and leaves.
In order to more accurately represent African vegetation, we defined biomes by using
the method developed by Hely et al. (2006). For this, we used model calculated values of
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Leaf Area Index (LAI), woody biomass, and phenology that have been associated with
specific, well-studied African biomes adapted from the potential vegetation of White
(1983): equatorial moist tropical rainforest, tropical seasonal forest, afromontane,
deciduous forest or woodlands, savannah, Sahelian grassland or steppe, and desert. LAI
(m2 of leaves/m2 of ground) is an important indicator of canopy structure of vegetation.
The advantage of this method over modeled PFTs is that reconstructions of biomes from
simulations are directly comparable with reconstructions from modern and fossil pollen
flora assemblages (Vincens et al., 2006). A summary of these definitions can be found in
Table 2. The afromontane biome occurs only in isolated islands in the mountainous areas
of East and West Africa and is not distinguishable in the model reconstructions because
of the resolution of the LM3 grid.
Initialization and integration of the pre-industrial simulations is described in detail
in Dunne et al. (2012a). ESM2M was initialized with present day ocean temperature and
salinity based on the World Ocean Atlas, then integrated for 1000 years with preindustrial boundary conditions for solar and radiative forcing (Table 1). The land
component is initialized off-line in order to establish base-line carbon pools, then coupled
to the ESM for integration. For the LIG simulation, we began from the pre-industrial
spin-up described above. Then the model was integrated for 850 years using the boundary
conditions listed in Table 1. Based on previous studies, vegetation carbon pools should
reach equilibrium after 250 years (Shevliakova et al., 2009), although soil carbon takes
longer to reach equilibrium. The model was run for 850 years until soil carbon pools and
deep ocean temperatures were at equilibrium. For all further investigation of model
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output, 20 years of data was used from years 800-820. For an in depth look at biases in
Africa, ESM2M pre-industrial precipitation and LAI are compared with observational
data from University of East Anglia’s Climate Research Unit, the 1961-1990 CRU 10
minute precipitation and 2000 MODIS LAI. Data was downloaded from
http://www.cru.uea.ac.uk/cru/data/hrg/tmc/ and http://neo.sci.gsfc.nasa.gov/Search.html.
Calculated Indices and Paleodata
In order to represent changes in rainfall seasonality in the model, we investigated
two metrics representing the length of the dry season and the timing of rainfall. For the
former, we calculated an index of dry season length from simulated daily precipitation.
Mean dry season length is the total number of days per year with less than 0.5mm
rainfall. In order to investigate rainfall timing, an empirical orthogonal function (EOF)
was performed on a monthly time series of simulated pre-industrial precipitation. This
EOF yielded two significant principal components, explaining over 55.5% of the variance
and representing two large-scale precipitation regimes in Africa. PC1 (46.5%) loads
strongly with the unimodal regime, and PC2 (9%) loads strongly with the bimodal
regime. In order to evaluate how the spatial extent of these rainfall zones was altered in
the LIG, both principal components were regressed on the fields of simulated monthly
LIG precipitation.
Finally, LIG simulations were evaluated against paleohydrological and
paleoecological data from the literature. Paleohydrological data was taken from a recent
regional synthesis of LIG hydrology by Blome et al. (2012). Classifications of wet/dry
were made based on interpretations in the original literature. Paleo-vegetation data was
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taken from the African Pollen Database of all sites with pollen samples within 1 kyrs of
125ka. From these samples, biomes were reconstructed following the biomization
method of Jolly et al. (1998) and Elenga et al. (2000). Biomization is a technique that
assigns known biomes to pollen samples based on well-studied species assemblages and
affinities. A full list of sites is included in the supplementary information.
Modern Climate and Vegetation
Precipitation in Africa is strongly heterogeneous with large spatial variability
caused by local controls such as topography and SSTs of adjacent ocean basins
(Nicholson, 1996). Despite this heterogeneity, however, intra- and inter-seasonal
variability is spatially coherent across much of the continent (Nicholson, 2000). Rainfall
in the tropics is highly seasonal: bands of clouds and deep atmospheric convection
associated with the Intertropical Convergence Zone (ITCZ) following the sun from north
to south with a short lag. This seasonal migration of the ITCZ results from an interhemispheric thermal gradient favoring rainfall in the summer hemisphere caused by more
incident shortwave radiation and imparts zones of seasonal rainfall across the continent.
One unimodal regime of summer rainfall occurs in JJA in the NH, one unimodal regime
of summer rainfall in occurs DJF in the SH, and a bimodal regime occurs around the
equator. Although ITCZ migration results in the large-scale continental patterns seen in
Africa, other climatic phenomena have a strong influence on hydrology. In West Africa,
the land-sea thermal gradients create a monsoonal circulation that also plays a strong role
in the delivery of rainfall to the continental interior (Nicholson, 1996). Furthermore,
wave instabilities in the middle and upper troposphere, the African Easterly Jet and the
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Tropical Easterly Jet, have important impacts on monsoon penetration and frequency of
rainfall (Nicholson, 2000). Finally, inter-seasonal as well as multi-decadal variability of
rainfall is strongly connected to both atmospheric circulation and SST anomalies
associated with ENSO (Camberlin and Janicot, 2001).
Much like rainfall, vegetation varies dramatically over short distances. Although
biomes range from tropical rainforest to open desert, Africa is dominated by semi-arid
and seasonal vegetation that is very sensitive to hydrologic variability (White, 1983). In
much of the lowland areas, vegetation distribution and structure is tightly constrained by
both precipitation amount and length of the dry season (Ivory et al., submitted); however,
in the montane areas in parts of West and East Africa above 2000 m asl, temperature also
plays an integral role.
ESM2M simulates the large-scale patterns of climate and vegetation in Africa
with great accuracy (Shevliakova et al., 2009; Dunne et al., 2012a; Dunne et al., 2012b).
There are however several known model biases in precipitation and land cover: while the
simulation of precipitation is particularly good over most of Africa, rainfall is
systematically overestimated on the fringes of the ITCZ (Fig. 2). The nodes of highest
mean annual rainfall are well represented in comparison to observations, a coarser grid
resolution causes these regions to have a larger spatial extent resulting in slight
overestimates of rainfall in West Africa. Seasonal rainfall in DJF and JJA is particularly
well simulated (Fig. 2). Overestimates, however, do still occur on the borders of
maximum rainfall zones at the maximum extent of ITCZ migration during the summer
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seasons resulting in higher than observed precipitation in Southern Africa and into the
Sahel.
Overestimates of rainfall lead to slightly greater coverage of savanna. In
particular, the limit of the Sahel is slightly further north than modern, especially in West
Africa (Fig. 3; Dunne et al., 2012b). Although LAI is particularly well simulated, with
only slight overestimations in southeast Africa, reconstructed biomes show some marked
differences. Additionally, the modern Guinean rainforest of West Africa is simulated as
tropical seasonal forest. This underestimation of the persistence of evergreen forest is
likely the result of local topographic constraints that focus rainfall along the coast which
are not represented in the model due to grid resolution. Finally, ESM2M simulates
vegetation both in terms of climate and carbon cycle. However, as CO2 was set to preindustrial levels, its effect on vegetation cannot be inferred from this study.
Results
LIG climate
Large climate features at the continental scale in our preliminary LIG simulation
show great seasonal differences from the pre-industrial (Fig. 4). In contrast, when
precipitation is averaged over the course of a year, the values of anomalies are
comparatively small. This is particularly true of precipitation, which shows only slight
differences in mean annual rainfall (MAP). The only exception on an annual basis is in
the Sahel and Sahara, where large annual anomalies are observed, with a node of
maximum rainfall located at about 15ºN. This wetter area is sandwiched between two
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regions of lower MAP to the southeast and west occupying much of West Africa and East
Africa.
In contrast, the seasonal differences in rainfall are quite marked over a larger
proportion of the continent. In JJA, large increases in rainfall relative to pre-industrial
(on the order of 1mm/day) are observed throughout most of North Africa, extending from
the tropics into the subtropics (Fig. 4). However, along the Atlantic coast of North and
West Africa, two large regions of reduced rainfall occur at ~ 5ºN and 15ºN. South of the
equator, during the austral winter dry season, rainfall is virtually unchanged from the preindustrial simulation. In DJF, once again, large anomalies of rainfall are observed;
however, the situation is reversed (Fig. 4). During the austral summer, large anomalies
can be seen in South and East Africa. Along the southeast Indian Ocean coast, summer
rainfall is over 1mm/day lower than the pre-industrial simulation, while just to the north,
from the equator to ~15ºS, simulated summer rainfall is higher by a similar magnitude.
The large seasonal rainfall anomalies in the LIG simulation only occur in the
corresponding summer hemisphere. This suggests that the seasonal component of
insolation may affect hydrology without greatly affecting the yearly rainfall amount.
Furthermore, in both cases, there is a systematic geography to the sign of rainfall
anomalies. In both seasons, increases in rainfall occur northward and decreases occur at
the southerly end of the summer rainfall zone (Fig. 4); this indicates a change in the
location of convergence over the continent in response to higher insolation in the NH and
lower insolation in the SH. This suggests that the enhanced summer radiation over North
Africa results in a northward displacement of tropical moisture during the LIG resulting
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from strong influence of orbital configuration on hydrology. As may be expected of a
reversed seasonal insolation gradient favoring higher than modern radiation in the NH
during JJA, the African monsoon seems greatly enhanced during this period. The
exception to this is the West and North African Atlantic coast where lower rainfall is
likely the result of the strong influence of Atlantic SSTs in this region (Prommel et al.,
2013). In contrast, unlike the amplified seasonal cycle of insolation in the NH, in the SH,
the change in insolation from summer to winter is damped, and summer rainfall does not
extend as a far south as today.
Temperature during the LIG in tropical Africa is not much different than the preindustrial as a result of changes in orbital parameters. Although temperature differences
in the high-latitudes may have been as much as 8º warmer relative to the late preindustrial (Dahl-Jansen et al., 2013), mean annual temperature, as well as temperature
during both DJF and JJA, show only small anomalies no larger than 2ºC, which are well
within the inter- and intra-model variability we’ve begun to analyze (see the Discussion
Section for further details; Fig. 5). Furthermore, despite the slightly higher global input
of shortwave radiation during the LIG, temperature anomalies observed from the
simulations are either higher or lower than pre-industrial depending on the region.
Although this temperature signal could be in part a result of higher evaporation
associated with an enhanced monsoon in North Africa, the largest temperature anomaly is
a decrease of 1-2ºC.
Seasonality and Circulation
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The northward positive rainfall anomalies in both summer seasons are mirrored
by altered atmospheric circulation over the African continent during the LIG. We use
500mb vertical velocity to show areas of rising (negative numbers) and subsiding
(positive numbers) air masses at the middle of the troposphere related to rainy or dry
conditions, respectively (Fig. 6). In JJA, 500mb vertical velocity shows a pattern similar
to rainfall with more negative LIG values indicating lofting air masses into North Africa
as far as 30ºN and more positive values indicating increased subsidence just south along
the West African coast. This pattern indicates that a northward displacement of tropical
instability and enhancement of the African Monsoon is responsible for the penetration of
JJA rainfall into the Sahara. This is corroborated by increased surface winds over the
Sahara delivering Atlantic moisture from the south and east as far into the interior as
~25ºN. Similarly, the reduced vertical velocity in parts of West Africa is observed in the
same region as reduced rainfall. These decreases of rainfall and convection along the
Atlantic coast are related to anomalous surface winds during the LIG which blow parallel
to the coast rather than inland, reducing monsoon rains in this area (Fig. 6). This result is
similar to RCM results of Prommel et al. (2013). Similarly, in DJF, more negative values
indicating lofting air occur northward of the modern day extent of the summer rainfall
zone at ~10ºS and more positive values indicating increased subsidence just to the south
(Fig. 6).
Similar to rainfall, negative anomalies in 500mb vertical velocity are displaced
northward in both hemispheres. This suggests that in both summer seasons, the band of
summer convection and convergence over the continent was displaced northward. The
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reversed thermal gradient during the LIG favors enhanced summertime convection and
convergence of air masses in the NH, whereas summer circulation is damped as one
moves away from the equator in the SH.
Displacement of the ITCZ northward during the LIG strongly alters the length and
inception of dry seasons. As JJA rainfall reached much further north and DJF rainfall did
not progress as far south, the most marked changes in dry season length is likely for the
ITCZ’s northern and southern extremes. Additionally, in the equatorial region, it is
probable that the timing of the onset of rainy and dry seasons may have been shifted.
Figure 7 shows the difference in LIG dry season length from pre-industrial simulation.
Reduced dry season length is observed into northern Africa, with areas of the Sahel and
Sahara showing a shorter dry season by as much as 40 days. The clear exception to this
is in northern Egypt where dry season increases. The distance of this area from moisture
sources in the Indian and Atlantic Oceans makes it beyond the reach of the monsoonal
circulation. Furthermore, this is likely partially the result of general insensitivity of
westerly winds in the model to changes in temperature (Dunne et al., 2012a). A second
short band of reduced dry season is observed just south of the equator, where DJF rainfall
was increased. Thus, the northward penetration of the ITCZ and associated circulation in
boreal summer favors a shorter dry season in the NH and a fewer rainy days in the SH.
The EOF analysis also supports that rainfall regimes during the LIG were altered
throughout Africa as a result of enhanced insolation seasonality in the NH. Figure 8
shows the major modes of rainfall seasonality in Africa represented by the two significant
principal components (PC) from the pre-industrial simulation. PC1 (46.5% variance)
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represents the unimodal rainfall regimes as found at the northern and southern edges of
the tropics. A positive correlation represents an area where rainfall occurs in JJA, and a
negative correlation represents an area where rainfall occurs in DJF. PC2 (9% variance)
represents the bimodal rainfall regime found primarily along the equator where two ITCZ
passages yearly result in a short and long rainy season. The EOF shows the extent of
these zones and their regional coherence throughout Africa (Fig. 8). When the preindustrial PCs are correlated with the fields of LIG monthly precipitation, we see
systematic differences in the distribution of these primary rainfall zones that mirror the
changes in dry season length. Most notable, the unimodal rainfall regime reaches much
further into North Africa, to nearly 30ºN (Fig. 8). Although there are areas of positive
correlation suggesting enhanced JJA rainfall to the western edge of this sector, significant
values are concentrated over the central and eastern portion of this sector. Furthermore,
along the southern West African coast, a side-by-side negative and positive correlation is
observed just offshore (Fig. 8). This suggests that despite the penetration of monsoon
rains into the continental interior, rainfall decreases along the coast during JJA, falling
preferentially over the southern Atlantic. In the SH, no significant change in seasonality
is observed on land; however, in both the southern Atlantic and Indian Ocean, side-byside anomalies are observed (positive to the north, negative to the south). This shift to
higher JJA rainfall further north in the SH suggests a change in the influence of the
winter rainfall zones resulting from a mirrored northward shift of the westerlies during
austral winter.
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The ESM2M LIG simulations of circulation change and seasonality from vertical
velocity, dry season length, and the EOF analysis all point to shifts in both tropical
summer and mid-latitude winter circulation in comparison to the pre-industrial. The
higher magnitude of seasonal insolation in the NH results in higher rainfall further into
the continental interior, particularly the Sahel and Sahara, caused by an enhanced
monsoon and more northerly center of convergence. Higher available moisture and
heating created instability which drove the ITCZ northward to almost 30ºN and
lengthened the growing season in much of North Africa. In contrast, in the SH, the
damped magnitude of seasonal insolation has a reversed effect, suggesting that although
the ITCZ no longer extends as far southward and rainfall is reduced, dry season length is
only moderately longer. Lastly, although the SH westerlies seem to extend further north
during austral winter, there seem to be no significant evidence to suggest greater JJA
precipitation on land. Clement et al. (2004) suggested that high-eccentricity precession
should result in greater precipitation over the Indian Ocean and a reduction over land.
Although, a clear reduction of the dry season length is observed over the western Indian
Ocean (Fig. 7) and seems to contradict this, increased DJF and stable JJA precipitation
are actually in agreement (Fig. 4).
LIG Vegetation
As demonstrated above, hydrology during the LIG is markedly different than
during the pre-industrial in terms of MAP and seasonal timing. As vegetation
assemblages in the lowland tropics and subtropics are strongly controlled by variability in
both of these factors, observable shifts in biome structure and phenology should be
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apparent in the ESM simulations. In the Sahel and Sahara of North Africa, the sporadic
rains observed today were replaced during the LIG with a less variable, prominent
rainfall peak in JJA caused by inland penetration of the African monsoon (Fig. 9). This
region also displays the most striking changes in vegetation. Particularly noticeable near
20ºN, the modern border between desert and steppe is pushed several degrees northward
(Fig. 9). Within the Sahel, LAI increases by about 1 and woody biomass by as much as
1-2 kg/m2 (Fig. 10). This may indicate a larger contribution of deciduous trees at the
desert fringe. The biome reconstructions of this area reflect this and show the largest
change of vegetation structure in much of tropical Africa. In this region, grasslands and
wooded grasslands expand northward into areas of desert and steppe (Fig. 9).
In West Africa, vegetation proves the most problematic. Although the West
African forests of Sierra Leone, Guinea, and the Ivory Coast are simulated in the preindustrial as being drier than observations (seasonal forest rather than observed
rainforest), the LIG biome reconstructions show a change to tropical rainforest despite
decreases in summer rainfall surrounding this region (Fig. 9). There is a slight decrease
in dry season length in this region at this time, in addition to a slight decrease in dry
season temperature, which may be responsible for higher effective moisture and denser
forest, but otherwise this simulated change to denser vegetation is difficult to explain.
Other areas of west central Africa show small changes in biomes from tropical seasonal
forest to tropical rainforest (Fig. 9). This however is principally the result of higher JJA
rainfall and MAP.
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As a result of the complex circulation as well as topographic complexity which
interact with insolation forcing in East Africa, biome structure changes are not
straightforward. In the northern sector of East Africa, south of the Ethiopian Highlands,
summer rainfall increases observed in other parts of the northern tropics are not recorded,
largely due to blocking of the adjacent high elevation areas. Precipitation in both seasons
changes little; however, large increases in dry season length that stretch from the eastern
Indian Ocean basin on land result in degradation of the current savanna biome to desert
and semi-desert (Fig. 9).
Further south, around the equator, another opening of the vegetation occurs,
particularly around Lake Victoria (Fig. 9). Tropical seasonal forests disappear from this
region during the LIG to be replaced by wooded grassland and savanna. The
disappearance of drought intolerant vegetation at this time to be replaced by open,
deciduous biomes is likely linked to the seasonality change. In the pre-industrial, this
region has highly bimodal rainfall, with a peak occurring in the transitional seasons;
however, during the LIG, PC2, which represents the bimodal regime, correlates less
strongly in this region (Fig. 8).
Although one would expect large-scale shifts toward denser vegetation types in
the area of East Africa with higher DJF rainfall and the opposite to occur south of 10ºS
where DJF rainfall is greatly reduced, this does not seem to be the case. The pattern for
both the biome reconstructions and LAI is nearly the opposite (Figs. 9 and 10). The EOF
analysis and simulated dry season length suggests that the timing of rainfall in the SH
unimodal rainfall zone stays very much the same (Figs. 7 and 8). This is in agreement
198
with studies suggesting that large changes of both rainfall and seasonality are needed to
result in biome reorganization (Hély et al., 2006). It is clear that although slight changes
in effective moisture occur, they do not cross the threshold necessary to greatly alter
vegetation. Additionally, the potential contribution of JJA rainfall due to northward
westerlies may lead to denser vegetation.
South of 15ºS, a marked difference in vegetation is seen from east to west. In the
west, on the Atlantic side of the meridional convergence boundary known as the Congo
Air Boundary (CAB), biomes transition to drier, more open types, particularly from
wooded grassland to savanna (Fig. 9). In the east however to 30ºS, a marked increase
along the coast of tropical rainforest is observed. This increase is mirrored by higher
woody biomass as well as LAI in the LIG simulations (Fig. 10). This seems to contradict
changes in DJF rainfall, which show a gradient of anomalies from higher than modern to
lower than modern from west to east (Fig. 2). Instead, these vegetation changes more
resemble the more subtle JJA rainfall anomalies, suggesting that northward westerlies
alter the position of the CAB and allow for penetration of mid-latitude moisture during
this time. This is also observed in the temperature fields in the LIG simulation, where
cooler temperatures are observed along the southern Indian Ocean Coast and warmer
temperatures into the Kalahari. This suggests that the steep decrease of DJF precipitation
may have been offset to some degree in southeast Africa by lower evaporation and higher
effective moisture.
199
Discussion
The ESM2M LIG simulation demonstrates the influence of orbital configuration,
particularly eccentricity-enhanced precessional forcing, on the climate and vegetation of
Africa. Although many studies have shown higher than modern temperatures for the
LIG, particularly in the polar regions, the ESM2M simulation shows no uniform
continental temperature response in Africa. Instead, temperature response in JJA seems
to show large areas of cooling, probably due to higher evaporation. Warmer
temperatures are almost exclusively in areas where seasonal radiation increases are not
offset by higher moisture, such as southern Africa during austral winter.
Large-scale precipitation changes seem to result from the effect of the reversed
and heighted seasonal insolation gradient between hemispheres on tropical and midlatitude circulation. A heighted summer insolation in the NH by 11.9% with respect to
modern enhances the monsoon and penetration of moist air masses into the continental
interior. Energetic lofting air masses extend far into the modern Sahara. The opposite
change to a lower summer insolation and a damped seasonal cycle in the SH results in the
opposite change during austral summer. Rainfall is markedly less to the south and higher
to the north in areas which are today at the southern extreme of the ITCZ. The northern
displacement of rainfall during both summer seasons suggests a more northerly mean
position of the ITCZ and associated circulation. In the south, this pattern is complicated
by mid-latitude circulation which also takes a more northerly position bringing some
winter moisture to southern Africa during JJA.
200
Much like rainfall, the northward expansion of the ITCZ has a marked effect on
seasonality. More time spent north of the equator results in shorter dry season and less
variable rainfall in the northern tropics and subtropics. In the south, the result is less
straightforward. As the calculation of dry season length used here is a sum of the yearly
days with less than 0.5 mm of rainfall, additions of winter rainfall in the SH caused by
displaced mid-latitude circulation may confound this metric. The EOF analysis,
however, also supports that minimal change in seasonality is observed.
Despite rather marked changes in precipitation, particularly during the summer
season in both the NH and SH, vegetation changes vary greatly by region. The most
dramatic and systematic change is in the NH where denser, more woody, deciduous
biomes establish themselves. The response of the southern sector is more complex, likely
due to controls on vegetation related to multiple factors such as precipitation, dry season
length, and insolation/temperature controls on effective moisture.
A significant caveat of this study should be noted. We have only analyzed one
LIG simulation, and the differences we observe, especially the annual mean differences,
do not fall outside of the envelope of intra- and inter-model variability we have explored
so far. The significance of our findings will be bolstered by our future work on this
subject. To this end, we will quantify the internal variability in our ESM2M simulations
of the PI and the LIG by comparing different 20-year slices to strengthen our case that the
LIG is in fact statistically different from the PI. We will also quantify the variability
across different models for the PI, in order to strengthen our confidence in the
significance of our results. Lastly, we will carry out at least 2 more LIG simulations so
201
that we can analyze the ensemble, giving us perhaps the best indication of the
significance of our findings.
Comparison with other models
Few other paleoclimate modeling studies have been conducted on the LIG. The
majority of these have focused on changes in ice sheet extent and NH temperature (OttoBliesner et al., 2006; Kubatzi, 2006). Very few have evaluated in-depth changes within
the monsoonal systems in the tropics despite the known connection of rainfall to
insolation; even fewer have evaluated the effect of orbital forcing on vegetation.
However, studies by Braconnot et al. (2008) and Prommel et al., (2013) show a similar
increase in precipitation in the northern summer season over North Africa and a decrease
in southern summer precipitation over southern Africa. Additionally, the decrease of
precipitation over coastal West Africa during JJA is also observed in these studies,
suggesting that this is a robust and systematic feature of the surface circulation change.
Some studies have observed increases in summer temperature over the Sahara due to
increases in insolation (Prommel et al., 2013); however, we see stable, if not slightly
cooler, conditions. Our results are similar to those of Shurger et al. (2006). As both
studies have included fully-coupled dynamic vegetation, it is possible that this effect
results from the response of evaporative cooling associated with larger evaporation.
Despite the difference in simulated temperature caused by the effect of land surface in
each study, simulated precipitation across studies is remarkably similar.
Recently, Prommel et al. (2013) used a regional climate model (RCM) to evaluate
the LIG climate of Africa south of 25ºN and east of 15ºW. Although this study did not
202
cover the entire continent, high-resolution results obtained bear a strong resemblance to
the ESM2M output in precipitation. However, when BIOME4 was forced offline with
the RCM output, the LIG biomes simulations look strikingly different than those of
ESM2M. Although the expansion of steppe, grassland, and woodland in the north have
coherent and similar features as well as latitudinal range, the southern African sector in
BIOME4 shows large expansion of hot desert where ESM2M simulates no change from
modern savanna. However, the biggest difference is perhaps the ESM2M simulation of
rainforest along the southern Indian Ocean coast where BIOME4 simulates no change.
Comparisons with Paleodata
Currently, very little data exists to validate LIG simulations. The vast majority of
the existing data comes from marine cores whose records of terrestrial conditions are
often troubled by low-resolution, dating uncertainty, and distance from source regions. A
recent synthesis of paleohydrological data and databases of paleo-ecological data,
however, do give some opportunity for validation of the ESM2M LIG simulations from
both terrestrial and marine sources. Some considerations do need to be made when
comparing these paleo- records with model output. In particular, for these comparisons,
1000 years on each side of the 125ka boundary conditions used for the simulation are
considered in order to take into account dating uncertainty. Additionally, marine records,
particularly paleoecological records, incorporate a signal of vegetation from the mainland
that is averaged over a large region. However, due to the relatively coarse grid of the
model, comparisons between pollen biome reconstructions and ESM2M LIG biome
reconstructions are feasible if only the broadest scale is considered.
203
Paleohydrolgical records show a striking resemblance to the ESM2M LIG
simulations (Fig. 11). However, although there is some agreement between simulated
MAP and the determination of wetter/drier from the paleo-records, it is clear that the
strongest correlation is seen during each summer season. This means that there is more
agreement in the records in the NH during JJA and the records from the SH during DJF.
During JJA, the marked decrease in precipitation along the southern West African coast
is not in agreement with the two samples from off the coast which record wetter
conditions. These samples, taken from studies of marine cores by Lézine and Casanova
(1991) and Pokras and Mix (1985), actually record dust fluxes that have a source region
far into the continental interior, a region which does in fact record wetter conditions in
the ESM2M simulations. In the SH, most terrestrial samples from South Africa record
drier conditions. Several samples located at the border of the simulated wetter and drier
regions do not record wetter conditions; thus, it is likely that this wetter zone may not
reach as far south.
Biomes determined from paleoecological samples within the tropics show a very
strong correspondence to the reconstructed biomes of our LIG simulation (Fig. 11). Most
samples come from offshore and therefore must represent only a regional average of the
mainland pollen. Reconstructions of woodland/tropical seasonal forest and rainforest are
particularly good, with one sample off the Horn of Africa reconstructing a slightly wetter
biome (grassland/steppe) than the LIG simulation (desert). Pollen reconstructions are
poorly representative of their source vegetation in the driest biomes (Vincens et al.,
2006), thus lower fidelity may be expected in this region.
204
Finally, the only consistent mismatch between the pollen reconstructions and
model output are those representing subtropical Mediterranean biomes. Given the model
output, it is currently impossible to distinguish this particular assemblage as it differs
from scrubland and steppe only in species composition rather than phenology or
physiology. Thus, until the taxonomic resolution of reconstruction biomes within the
model is more sophisticated, the model reconstruction of steppe is a good approximation
of the structure and function of the pollen-based biome for the LIG. Another possible
complication in modeling this region, however, is the general insensitivity of westerly
winds in the model to temperature (Dunne et al., 2012a)
Conclusions
The LIG is a period often used as an analog for future warming due to warmer
than modern temperatures despite its dramatically altered orbital configuration with a
reversed inter-hemispheric insolation gradient heighted by higher eccentricity. Insolation
has long been used to explain periods of heightened monsoon activity throughout the
tropics, thus studies in ESMs may help elucidate and better understand the contribution of
different forcing mechanisms on LIG climate. In order to evaluate its potential as an
analog, we conducted a preliminary simulation in the GFDL Earth System Model
ESM2M by simply altering the orbital configuration. The model output shows no
dramatic change in seasonal or mean annual temperature. In fact, lower than preindustrial temperatures are observed in areas with higher evaporation due to greater than
modern rainfall.
205
Hydrology is greatly altered by both changes in seasonal precipitation as well as
the length of dry seasons. Although no large change is seen in MAP, a shift of the mean
position of the dominant circulation systems northward, notably the ITCZ, during each
hemisphere’s summer seasons results in greater rainfall to the north and less to the south.
The most marked change is the penetration of the African monsoon deep into the
continental interior to 30ºN, likely aided by positive feedbacks involving recycled
moisture. This increase in rainfall is centered over the Sahel and Sahara. Additionally, a
more northerly ITCZ results in changes in the seasonal timing of rainfall, such that areas
in the north receive over 40 more rainy days per year than pre-industrial, and the bimodal
rainfall zones around the equator have a less marked two peak rainfall regime. The effect
of changes in seasonality as well as rainfall is not as clear in the SH, perhaps due to
higher winter inputs from mid-latitude circulation.
The result of this dramatically altered hydrology causes changes in biome
distribution throughout Africa. The most systematic changes occur in North Africa with
grassland and wooded grassland expanding into areas which are currently absolute desert
up to 20ºN and large expansions of drought intolerant rainforests and seasonal forests in
west and central Africa due to low seasonality and higher effective moisture. Once again,
the SH vegetation response to climate change in the LIG simulation is complex, with
lower summer rainfall being offset by winter inputs resulting in forest expansion in parts
of South Africa.
Despite differences with other model studies of LIG climate, ESM2M LIG
simulations of hydrology and biomes look strikingly like the fossil data. This suggests not
206
only that ESM2M results are robust, but also that alteration of orbital configuration is
largely responsible for these changes, not other forcing factors such as greenhouse gases.
This suggests that any comparison of LIG warming with the possible environmental
impacts of future warming should be done with extreme caution.
Acknowledgements
We thank the National Science Foundation for a Graduate Research Fellowship
(2009078688); Paul Goodman and the staff of the Geophysical Fluid Dynamics
Laboratory at Princeton University for technical help and critical discussions.
207
References
Anderson JL, Balaji V, Broccoli A (2004) The new GFDL global atmosphere
and land model AM2–LM2: Evaluation with prescribed SST simulations. J. Climate, 17:
4641–4673. doi: 10.1175/JCLI-3223.1
Berger A (1978) Long-term variations of daily insolation and Quaternary climatic
changes. J. Atmos. Sci. 35:2362-2367. doi:
10.1175/15200469(1978)035<2362:LTVODI>2.0.CO;2
Beuning KR, Zimmerman KA, Ivory SJ, Cohen AS (2011) Vegetation response to
glacial–interglacial climate variability near Lake Malawi in the southern African tropics.
Palaeogeogr. Palaeoclimat. Palaeoeco. 303: 81-92. doi: 10.1016/j.palaeo.2010.01.025
Blome MW, Cohen AS, Tryon CA, Brooks AS, Russell J (2012) The environmental
context for the origins of modern human diversity: A synthesis of regional variability in
African climate 150,000–30,000 years ago. J. Human Evolution 62:563-92. doi:
10.1016/j.jhevol.2012.01.011
Braconnot P, Marzin C, Grégoire L, Mosquet E, Marti O (2008) Monsoon response to
changes in Earth's orbital parameters: comparisons between simulations of the Eemian
and of the Holocene. Clim. Past 4:281-294. doi:10.5194/cp-4-281-2008
Camberlin P, Janicot S, Poccard I (2001) Seasonality and atmospheric dynamics of the
teleconnection between African rainfall and tropical sea-surface temperature: Atlantic vs.
ENSO. Int. J. Climatol. 21:973-1005. doi: 10.1002/joc.673
Clement AC, Hall A, Broccoli AJ (2004) The importance of precessional signals in the
tropical climate. Clim. Dyn. 22:327-341.
Cohen AS, Stone JR, Beuning KR, Park LE, Reinthal PN, Dettman D, et al. (2007)
Ecological consequences of early Late Pleistocene megadroughts in tropical Africa.
PNAS 104:16422-16427. doi: 10.1073/pnas.0703873104
Cox PM, Pearson D, Booth B, Friedlingstein P, Huntingford C, Jones C, Luke C (2013)
Sensitivity of Tropical Carbon to Climate Change Constrained by Carbon Dioxide
Variability. Nature, in press. doi:10.1038/nature11882.
Dahl-Jansen D, and NEEM community members (2013) Eemian interglacial
reconstructed from a Greenland folded ice core. Nature 493:489–494.
doi:10.1038/nature11789.
208
Delworth, TL, Broccoli A, Rosati A, Stouffer R, et al. (2006) GFDL's CM2 Global
Coupled Climate Models. Part I: Formulation and Simulation Characteristics. J.Climate
19(5), doi:10.1175/JCLI3629.1.
Diffenbaugh NS, Giorgi F (2012). Climate change hotspots in the CMIP5 global climate
model ensemble. Climatic Change 114:813-822. doi: 10.1007/s10584-012-0570-x
Dunne JP, John JG, Adcroft AJ, Griffies SM, Hallberg RW, Shevliakova E, et al. (2012)
GFDL's ESM2 global coupled climate-carbon Earth System Models Part I: Physical
formulation and baseline simulation characteristics. J Clim 25:6646-6665. doi:
10.1175/JCLI-D-11-00560.1
Dunne JP, John JG, Shevliakova E, Stouffer RJ, Krasting JP, Malyshev SL, et al. (2012).
GFDL's ESM2 global coupled climate-carbon Earth System Models Part II: Carbon
system formulation and baseline simulation characteristics. J Clim in press.
doi:10.1175/JCLID-12-00150.1
Griffies SM (2009) Elements of MOM4p1. GFDL Ocean Group Tech. Rep. 6, 377 pp.
Dupont L (2011) Orbital scale vegetation change in Africa. Quat. Sci. Rev. 30:35893602. doi: 10.1016/j.quascirev.2011.09.019.
Elenga H, Peyron O, Bonnefille R, Jolly D, Cheddadi R, et al. (2000) Pollen based biome
reconstruction for southern Europe and Africa 18,000 yr BP. J. Biogeogr. 27:621-634.
doi: 10.1046/j.1365-2699.2000.00430.x
Hély C, Bremond L, Alleaume S, Smith B, Sykes MT, Guiot J (2006) Sensitivity of
African biomes to changes in the precipitation regime. Global Ecol. .Biogeogr. 15:258270. doi: 10.1111/j.1466-8238.2006.00235.x
Jolly D, Prentice IC, Bonnefille R, Ballouche A, Bengo M, et al. (1998) Biome
reconstructions for southern Europe and Africa. J. Biogeogr. 27:621–634.
Nicholson S (1996) A review of climate dynamics and climate variability in eastern
Africa. The limnology, climatology and paleoclimatology of the East African lakes, 2556.
Nicholson SE (2000) The nature of rainfall variability over Africa on time scales of
decades to millenia. Glob. Planet. Change 26:137-158. doi: 10.1016/S09218181(00)00040-0
209
Otto-Bliesner B, Marshall S, Overpeck J, Miller G, Hu A, CAPE Last Interglacial Project
members (2006) Simulating Arctic Climate Warmth and Icefield Retreat in the Last
Interglaciation. Science 311:1751-1753. doi:10.1126/science.1120808
Partridge TC, Demenocal PB, Lorentz SA, Paiker MJ, Vogel JC (1997) Orbital forcing of
climate over South Africa: a 200,000-year rainfall record from the Pretoria Saltpan. Quat.
Sci. Rev. 16:1125-1133. doi: 10.1016/S0277-3791(97)00005-X
Petit-Maire N, Reyss JL, Fabre J (1994) Un paléolac du dernier interglaciaire dans une
zone hyperaride du Sahara malien (23°N). C. R. Acad Sci, Paris 319:805–809.
Prömmel K, Cubasch U, Kaspar F (2013) A regional climate model study of the impact
of tectonic and orbital forcing on African precipitation and vegetation. Palaeogeogr.
Palaeoclimatol. Palaeoecol. 369:154-162. doi: 10.1016/j.palaeo.2012.10.015.
Rossignol-Strick M (1983) African monsoons, an immediate climate response to orbital
insolation. Nature 304:46-49. doi:10.1038/304046a0
Scholz CA, Johnson TC, Cohen AS, King JW, Peck JA, Overpeck JT, et al. (2007) East
African megadroughts between 135 and 75 thousand years ago and bearing on earlymodern human origins. PNAS, 104:16416-16421. doi: 10.1073/pnas.0703874104
Schurgers G, Mikolajewicz U, Gröger M, Maier-Reimer E, Vizcaíno M, Winguth A
(2006) Dynamics of the terrestrial biosphere, climate and atmospheric CO2 concentration
during interglacials: a comparison between Eemian and Holocene. Clim. Past 2:205-220.
doi:10.5194/cp-2-205-2006
Shevliakova E, Pacala SW, Malyshev S, Hurtt GC, Milly PCD, Caspersen JP, et al.
(2009) Carbon cycling under 300 years of land use change: Importance of the secondary
vegetation sink. Glob. Biogeochem. Cycles, 23:GB2022. doi:10.1029/2007GB003176
Stokes S, Thomas DS, Washington R (1997) Multiple episodes of aridity in southern
Africa since the last interglacial period. Nature 388:154-158.
Verschuren D, Sinninghe-Damsté J, Moernaut J, Kristen I, Blaauw M, Fagot M, Haug G,
CHALLACEA project members (2009) Half-precessional dynamics of monsoon rainfall
near the East African Equator. Nature 462:637-641. doi:10.1038/nature08520
Vincens A, Bremond L, Brewer S, Buchet G, Dussouillez P (2006) Modern pollen-based
biome reconstructions in East Africa expanded to southern Tanzania. Rev. Palaeobot. and
Palyn. 140:187-212. doi: 10.1016/j.revpalbo.2006.04.003
210
White F. (1983) Vegetation of Africa - a descriptive memoir to accompany the
Unesco/AETFAT/UNSO vegetation map of Africa; Natural Resources Research Report
XX; UNESCO, Paris, France; 356pp.
211
Figures
Figure 1. a) Orbital configuration at the vernal equinox for the LIG simulation with high
eccentricity at 125ka, b) same but for the low eccentricity pre-industrial simulation at
0ka, c) seasonal insolation at 23ºN and 23ºS for both LIG and pre-industrial (adapted
from Braconnot et al., 2008).
212
213
Figure 2. a) Mean annual precipitation 1961-1990 10’ x 10’ Climate Research Unit
(CRU) data in mm/day with 1948-2010 NCEP Reanalysis wind direction, b) same but for
DJF, c) same but for JJA (New et al, 2002). d) Mean annual precipitation and wind
direction for GFDL ESM2M pre-industrial control, e) same but for DJF, f) same but for
JJA.
214
215
Figure 3. a) 2000 MODIS 1km leaf area index [LAI], b) ESM2M LAI, c)
phytogeographic regions of Africa based on White (1983), d) ESM2M biomes as defined
in Table 1.
216
217
Figure 4. ESM2M simulated precipitation (mm/day), a) Mean annual precipitation
anomaly from the LIG-preindustrial, b) same but for DJF, c) same but for JJA.
218
219
Figure 5. ESM2M simulated temperature (ºC), a) Mean annual temperature anomaly from
the LIG-preindustrial, b) same but for DJF, c) same but for JJA.
220
221
Figure 6. a) DJF vertical velocity and surface wind anomaly from the LIG-preindustrial,
b) JJA vertical velocity and surface wind anomaly from LIG-preindustrial.
222
223
Figure 7. ESM2M simulated dry season length anomaly (days/yr) from the LIGpreindustrial.
224
225
Figure 8. a) EOF1 of ESM2M pre-industrial control monthly precipitation, b) EOF2 of
ESM2M pre-industrial control monthly precipitation, c) PC1 of ESM2M pre-industrial
control monthly precipitation correlated with ESM2M monthly LIG precipitation, d) PC2
of ESM2M pre-industrial control monthly precipitation correlated with ESM2M monthly
LIG precipitation, e) Difference of EOF1 LIG precipitation and EOF1 pre-industrial
control monthly precipitation, f) Difference of EOF2 LIG precipitation and EOF2 preindustrial control monthly precipitation, g) PC1 time-series of ESM2M pi-control
monthly precipitation, h) PC2 time-series of ESM2M pi-control monthly precipitation.
Black lines represent correlations and differences with 95% confidence.
226
227
Figure 9. ESM2M simulated African vegetation biomes during the a) LIG (125ka) and b)
pre-industrial periods. c) Biome change from LIG-preindustrial.
228
229
Figure 10. ESM2M simulated African vegetation anomalies from LIG to pre-industrial
period. a) LIG-preindustrial LAI, b) LIG-preindustrial woody biomass (kg/m2).
230
231
Figure 11. Paleoclimate proxy hydrologic records and ESM2M simulated effective
moisture anomaly for a) mean annual effective moisture, b) DJF, and c) JJA. Circles
indicate locations and signs of paleoclimate reconstructions of moisture balance at 125ka
with blue meaning wetter conditions, black meaning transitional conditions, and red
meaning drier conditions. d) Fossil pollen reconstructed biomes and ESM2M simulated
biomes for 125ka.
232
233
Tables
Table 1. African biome classifications
Step 1
if 0 ≤ LAI < 0.5
if 0.5 ≤ LAI < 2
if 2 ≤LAI < 4
if LAI ≥ 4
Step 2
Step 3
Woody Biomass = 0
0 < Woody Biomass < 1
Woody Biomass ≥ 1
LAI max - LAI min ≥ 0.5
LAI max - LAI min ≥ 0.5
Biome
Desert
Steppe
Steppe
Savanna
Tropical Dry Forest
Tropical Seasonal Forest
Tropical Rainforest
234
Table 2. Boundary conditions for pre-industrial control (0ka) and LIG (125ka)
simulations
125ka
0ka
CO2
same as pi-control
280 ppm
N2O
same as pi-control
270 ppb
CH4
same as pi-control
760 ppb
Solar constant
same as pi-control 1370 W m-2
Eccentricity (°)
0.0397
0.0167
Obliquity (°)
23.9
23.4
Precession (ω-180°)
201
102
235
Supplementary Material
Supplementary Table 1. Sources of paleovegetation (green) and paleohydrological
records (grey = neutral, orange = drier, purple = wetter).
Source
Dupont and Weinelt, 1996
Dupont et al., 2000
Dupont et al., 2000
Hooghiemstra et al., 1992
Hooghiemstra et al., 1992
Hooghiemstra et al., 1992
Lezzine and Casanova, 1991
Van Campo et al., 1982
Gasse, 2001
Ivory et al., in progress
Dupont et al., 2011
Causse et al., 2003
Crombie et al., 1997
Stone et al., 2011
Bateman et al., 2011
Brook et al., 1996
Burrough et al., 2007
Burrough et al., 2007
Partridge, 1999, tuned
Bar-Matthews et al., 2003
Bar-Matthews et al., 2003
Hooghiemstra et al., 1992
Hooghiemstra et al., 1992
Hooghiemstra et al., 1992
Lezzine and Casanova, 1991
McKenzie, 1993
Moernaut et al., 2010
Osmond and Dabous, 2004
Pokras and Mix, 1985
Pokras and Mix, 1985
Pokras and Mix, 1985
Pokras and Mix, 1985
Rossignol-Strick, 1985
Schwarcz et al., 1993
Szabo et al., 1995
Vaks et al., 2007
Proxy
pollen
pollen
pollen
pollen
pollen
pollen
pollen
pollen
pollen
pollen
pollen
dates on shells
travertine
lake level
dune
speleothem, tufa, sand dune
shorelines
shorelines
rainfall
speleothems
speleothems
18
δ O
18
δ O
18
δ O
dinocysts
U-Th
lake level
U-Th
diatom
diatom
diatom
diatom
sapropels
U-Th
U-Th
speleothems
Latitude Longitude
4.8
3.4
-11.75
11.7
-6.6
10.3
29.05
-12.1
30
-10.65
34.9
-7.8
15
-18
14.4
50.5
-19.5
47
-11.3
34.45
-26.15
34
33
9
24
32.3
-11.3
34.45
-27
22
-19
18
-20
22.5
-20.02
21.35
-25.6
28.08
32.58
35.19
31.5
35
29.05
-12.1
30
-10.65
34.9
-7.8
15
-18
23
29
-3.3
37.7
27
32
-2.28
5.18
-0.2
-23.15
3.05
-11.82
18.26
-21.1
33.42
25
22.9
28.75
22
29
31.6
35.1