Guidance for the use of climate
science to support climate change
adaptation in biodiversity
conservation policies
Andrew Hartley
PhD Upgrade Report
Table of Contents
1
Research problem...................................................................................................... 3
2
Literature Review ...................................................................................................... 5
3
2.1
Quantification and Conservation of Biodiversity .............................................. 5
2.2
Biodiversity impacts projections............................................................................. 8
2.2.1
Bioclimatic envelope models ............................................................................................. 9
2.2.2
Mechanistic models ............................................................................................................ 10
2.2.3
Ecosystems models............................................................................................................. 14
2.2.4
Summary of modelling approaches ............................................................................. 16
2.3
Climate science .............................................................................................................17
2.4
Adaptation strategies in biodiversity conservation .......................................21
2.5
West African Climate ..................................................................................................23
2.5.1
West African Monsoon ...................................................................................................... 23
2.5.2
Land-atmosphere interactions ...................................................................................... 25
Proposed Research ................................................................................................. 27
3.1
Aims..................................................................................................................................27
3.2
Key research questions .............................................................................................27
4
Proposed methods of data collection and analysis ..................................... 30
5
Current progress ..................................................................................................... 34
6
Thesis plan ................................................................................................................ 38
7
Timetable ................................................................................................................... 42
8
Bibliography ............................................................................................................. 43
Annex 1 ................................................................................................................................ 50
Annex 2 ................................................................................................................................ 50
1 Research problem
International policy makers, land managers and conservation scientists need
reliable information from climate science to inform biodiversity impacts models
and to devise effective climate change adaptation strategies. The aim of this
thesis is to address the issues surrounding the use of climate science in
developing climate change adaptation strategies for the biodiversity sector. This
aim can be broken down into three elements:
1. Understand user requirements for climate information in the biodiversity
sector,
2. Assess the results from climate models for use in biodiversity impacts
models, and
3. Apply earth system models for advising climate change adaptation
strategies.
Consequently, the aim of this thesis is directly relevant to understanding where
the greatest impacts are expected to occur, what the potential sources of
uncertainty are, and how to best formulate climate change adaptation policies.
Climate change information is frequently used in biodiversity impacts models
without consideration of the reliability of climate model projections in the
locations, and for the meteorological variables, of greatest importance. In
addition, a range of spatial downscaling methods has been applied to climate
data prior to use in biodiversity impacts models, despite there being poor
knowledge of the effect different downscaling methods may have on the
projection of biodiversity impacts. Therefore, in the first part of this thesis, I will
review requirements for the use of climate data in biodiversity impacts
assessments (aim 1). Secondly, I will provide guidance on the use of climate
models in biodiversity impacts studies, by considering aspects of climate model
verification, assessment of model uncertainty and downscaling techniques (aim
2).
Thirdly, once impacts have been identified, action needs to be taken to adapt
existing conservation strategies to projected changes. Therefore, the final aim of
this thesis concerns the application of climate science in advising adaptation
strategies related to biodiversity and land management. Once knowledge is
gained on the types of impacts that are projected to occur, the next issue to
consider is what can be done to reduce these impacts. These decisions frequently
need to be taken at regional or local scales, therefore necessitating the need for a
specific region of interest.
This part of the thesis will focus on West Africa, because it is an important region
for globally threatened species, and it has been shown to have a strong coupling
between the land surface and the atmosphere during the monsoon period.
Recent observational studies on the West African monsoon have shown that the
existence of forest-cropland boundaries at length scales of approximately 10 to
20km can exert an influence on the initiation and distribution of precipitation in
certain parts of West Africa. This dependence between the land surface and the
atmosphere at a relatively high spatial resolution presents a challenge for
climate models to firstly represent these processes, and secondly to advise on
the how future land management might affect these processes. Since habitat
regeneration and creation are key climate change adaptation strategies in West
Africa, it is therefore necessary and timely that the effect of these strategies is
robustly assessed. Therefore, the third aim of this thesis, I will consider how
different land management practices might affect local and regional precipitation
patterns in West Africa.
2 Literature Review
The scope of this literature review is to provide a summary of the academic
literature and policy documents that are relevant to climate change impacts
studies and adaptation decisions in the field of biodiversity conservation. Given
that this covers a range of academic disciplines, such as biodiversity impacts
modelling, weather and climate science, and systematic conservation planning,
this review is not intended to be comprehensive. Relevant concepts will be
introduced, leaving the opportunity for further exploration within each chapter
of this thesis.
2.1 Quantification and Conservation of Biodiversity
The formal definition of “biodiversity” is the degree of variation of life found in a
particular location (Wilson & Peter 1988). This term usually refers to the
diversity of all plant and animal species, however it is also used to refer to
genetic diversity, or diversity at other taxonomic levels such as sub-species,
genus, family, order, class or phylum. The quantification of biodiversity is limited
by observational constraints in space and time, and as such the amount of
biodiversity that has been documented to occur on the earth is only a small
portion of that thought to exist. Mora et al. (2011) estimate that 8.1 million
species (± 1.3 million) occur on the earth, of which only 14% of species have
currently been documented on land and in the ocean. Of the species for which
taxonomic do records exist, even fewer species have documented range maps.
Despite the limitations of current knowledge on the extent of global biodiversity,
reliable measures are needed if we are to be able to predict the likely impacts of
perceived threats to biodiversity, and make effective conservation priorities.
Variables such as species richness, endemism, or richness of threatened species
for well-documented taxa (birds, mammals, amphibians) are frequently used as
surrogates for less well documented taxa. These assumptions appear to hold at
coarse spatial resolutions (~ 600,000km2), but generally, we cannot use
indicators of threatened or endemic species from one taxon as a surrogate for a
different taxon (Grenyer et al. 2006).
Figure 1. Global distribution of total species richness, endemism, and threatened species richness
for birds, mammals and amphibians. Source: Grenyer et al., 2006
Biodiversity is largely protected by funding conservation projects targeted at
protecting either individual species, groups of species, habitats or ecosystems of
particular importance. However, limited resources are available for biodiversity
conservation so consequently priorities for conservation funding need to be set
(Margules & Pressey 2000). Internationally, the main sources of conservation
funding are organisations such as the World Bank, the United Nations, and the
European Union, while non-governmental organisations such as the World
Wildlife Fund, Conservation International, BirdLife International, and The Nature
Conservancy also raise funds from donations to protect biodiversity. Each of
these organisations requires robust science-based advice on how to prioritise
their conservation funding. However, each organisation has a different approach
to priority setting, some focusing on a particular taxon, such as Birds (e.g.
BirdLife International; Fishpool & Evans 2000), whilst others focus on
ecosystems via internationally recognised protected areas (e.g. European
Commission; Hartley et al. 2007). Brooks et al. (2006) have proposed that most
conservation prioritisations fit into a conceptual framework of irreplaceability
compared to vulnerability. For example, the prioritisation template used by
Conservation International (Biodiversity Hotspots; Myers et al. 2000) prioritises
highly irreplaceability and highly vulnerability locations. In contrast, the
prioritisation template used by the World Wildlife Fund prioritises highly
irreplaceable locations only (G200; Olson & Dinerstein 1998, 2002).
Figure 2. Maps of nine global biodiversity conservation priority templates: CE,crisis
ecoregions(21); BH, biodiversity hot spots [(11), updated by (39)]; EBA, endemic bird areas (15);
CPD, centers of plant diversity (12); MC, megadiversity countries (13); G200,global200
ecoregions [(16), updated by (54)]; HBWA, high-biodiversity wilderness areas (14); FF, frontier
forests (19); LW,lastofthe wild (20). Source: Brooks et al. (2006)
In the context of climate change, the challenge for conservation policy makers is
to incorporate information from climate science into existing prioritisation
frameworks, so that future conservation challenges can be identified, and
suitable adaptation measures devised and tested. It has been suggested that this
is the most cost-effective approach to setting conservation targets for the future
(Hannah et al. 2007).
The G200 ecoregions – defined as highly irreplaceable ecosystems that represent
a disproportionately large number of the world’s species – have been assessed
for their exposure to mean monthly temperature change (Beaumont et al. 2011).
The aim of such an approach is to advise future conservation funding at a global
scale. In contrast, Hole et al. (2009) took this approach further by quantifying
inward and outward migration of bird species from sites of high conservation
priority (BirdLife International’s Important Bird Areas; Fishpool & Evans 2001).
In doing so, they allow a further step of suggesting site-specific climate change
adaptation strategies based on species turnover at each site (Hole et al. 2011).
Another approach to assessing the impacts of climate change on conservation
goals is to focus on how climate change might impact the ecosystems in which
species live, rather than the species themselves. This can bring benefits of
framing biological impacts in the context of global mean temperature targets or
greenhouse gas emissions scenarios. For example, Mahlstein et al. (2013) use a
simple climate-ecosystem classification to show how the pace of climate change
for global ecosystems increases as global mean temperature increases. An
extension of this approach is to use our understanding of plant physiology to
model how different types of ecosystems may respond to climate and
environmental stresses. The following section will review the relative merits of
all approaches to climate change.
2.2 Biodiversity impacts projections
Given the challenges of quantifying biodiversity in situ, it may be considered an
even greater challenge to estimate how climate change might affect biodiversity
and related conservation goals. Species’ are thought to respond to climate
change by adapting their behaviour (Menzel et al. 2006), developing
evolutionary adaptations (Parmesan 2006) or by shifting their range (Thuiller
2004), in accordance with the rate and magnitude of change (Huntley et al.
2010). Fossil evidence has shown that some species respond to periods of
warming and cooling by shifting their ranges to track a niche climate. For
example, evidence from the Quaternary period has shown that species range
shifts may be the most likely response to future change (Davis & Shaw 2001).
In order to answer the question of how a species will respond to climate change,
ecologists have developed a variety of approaches. These include estimates of
how a species’ climatic niche may shift, how the population dynamics of a species
may change, or the assessment of a species’ traits that might make it vulnerable
to climate change. Other approaches consider how habitats, ecosystems or
biomes may respond to climate change, as a surrogate for biodiversity. In order
to consider the role of climate science in improving biodiversity impacts
assessments, it is first necessary to describe, and understand these approaches.
2.2.1 Bioclimatic envelope models
The concept of a species occurring within defined environmental tolerances was
first described by Grinnell (1917), who suggested in his study of the Californian
Thrasher that “the nature of these critical [environmental] conditions is to be
learned through the examination of the bird’s habitat”. Bioclimatic envelope
models (BEMs) are statistical models that correlate the observed range of
occurrence of a species with climatological and other environmental variables.
These correlations are then used to estimate future shifts in a species niche
under climate change. BEMs have been used extensively in statistical ecology to
predict future conservation priorities (Hannah et al. 2002; Midgley et al. 2002)
and to infer extinction risk to species (see for example Thomas et al. 2004;
Hannah et al. 2005; Maclean & Wilson 2011).
The projection of a species’ future range is a significant advantage of the BEM
approach. This future range can then be used to form conservation management
plans under climate change. For example, Hole et al. (2011) propose site-based
climate change adaptation strategies for African Important Bird Areas (IBAs)
according to BEM projections of inward and outward migration.
A further advantage of the BEM approach is that it allows the possibility for the
assessment of uncertainty deriving from different sources in the modelling
process (Heikkinen et al. 2006; Buisson et al. 2010; Garcia et al. 2011). One such
source of uncertainty is the choice of which model to employ. A large number of
predictive statistical models have been applied in this context (often varying in
complexity), which include linear regression, additive models, machine learning
techniques (Phillips & Dudík 2008) and hierarchical Bayesian techniques
(Gelfand et al. 2006). Other sources of uncertainty in the BEM approach derive
from uncertainty in observations of the current distribution of the species;
uncertainty in future greenhouse gas emissions scenarios; and the uncertainty in
the projections of climate change from General Circulation Models (GCMs).
Despite the popularity of the BEM approach, it is also clearly has significant
limitations (Wiens et al. 2009). One such limitation is related to the
interpretation of the current range of a species. BEMs assume that the observed
occurrence of a species is a representation of the stable fundamental niche of
that species. However, in practice, the observed occurrence of a species (and
therefore the present day realisation of it’s fundamental niche) is also influenced
by factors, such as species adaptation (or acclimation), dispersal ability, biotic
interactions with other species and human disturbance, which result in the
realisation of only a portion of the fundamental niche (Hampe 2004). Therefore,
as the climate changes, the assumptions on which BEMs are based would not
account for a species realising a different portion of the fundamental niche. One
case in which this limitation may become evident is in the emergence of future
climates where no present-day analogue exists. This limitation of BEMs has been
demonstrated, for example, using fossil-pollen records from the late quaternary
period. Veloz et al. (2012) showed that for species that were abundant in areas
with no present day analogue climate, BEMs were poor predictors of the current
species distribution. They therefore imply that the species significantly shifted
their realised niches from the late glacial period to the present day.
2.2.2 Mechanistic models
The identification of the risks of species becoming extinct under future climate
change is often the motivation for many impacts studies. However, the
application of BEMs for the assessment of extinction risks has raised several
methodological concerns (e.g. Thomas et al. 2004; Thuiller et al. 2004).
Extinction risk is inferred when a species’ projected future range is either
completely dislocated or reduced in size relative to the present day range. As the
geographical area of the niche habitat for the species reduces, so too does the
assumed population of that species, resulting in an increase in probability of
extinction. This relationship between a species’ geographical area of extent and
population size is a concept that is central to biogeography (MacArthur & Wilson
1967), and has been rigorously tested in relation to contraction of habitat area
(Diamond 1972). However, in reviewing the applicability of the species-area
relationship to climate change studies, Lewis (2006) suggests that there are
significant limitations. These include the assumption that a species populates
evenly its range, uncertainty as to the influence of climate over a species current
distribution, and uncertainty as to whether the species is currently filling its
fundamental niche (discussed above).
An alternative, or potentially complementary, approach to BEMs is rooted in a
more mechanistic understanding of the extent to which weather and climate
affect the population dynamics of a species. This may include interactions
between species, between the species and suitable habitats, and the demography
of the species (Maschinski et al. 2006). Such a model has been applied spatially
for plant species populations in the South African Fynbos (Keith et al. 2008). In
this case study, annual climate was used to drive a habitat suitability model,
which was used to calculate carrying capacity of a habitat patch for a given
species for use in a stochastic population model (Figure 3). The results from
Keith et al. (2008) indicate that complex interactions between life history,
disturbance regime and distribution pattern can affect the assessment of
extinction risk. Furthermore, by addressing population mechanisms directly,
they avoid making over-simplifications of the link between habitat suitability
and species populations.
Figure 3. Schematic of a habitat suitability model coupled with a stochastic population model.
Each simulation starts at step 1, and after the first cycle is complete (step 5), subsequent cycles
include step 6. Source: Keith et al. (2008)
Another mechanistic approach to the relationship between a species and climate
is described in the field of biophysical ecology. Here, the principles of
thermodynamics are applied to organisms in order to develop a mechanistic
understanding of the processes affecting them, and their physiological responses
to change in these processes (Porter & Gates 1969). Specifically, biophysical
models concern the transfer of heat, biomass and momentum from the
environment to the energy budget of the organism (Kearney & Porter 2009).
Figure 4. Energy transfer from the environment to an animal species. Source: Porter & Gates
(1969)
The mechanistic understanding of species’ biophysical interaction with the
environment can inform key traits such as body temperature, energy budget and
water balance. In turn, this can inform the assessment of a species survival and
reproduction rates, and as a consequence becomes a means of quantifying the
fundamental niche of a species (Kearney & Porter 2009). Furthermore, this
fundamental niche can then be mapped, and potentially combined with a more
basic correlative (BEM) approach to form a consensus view of future species
distribution. Buckley et al. (2010) tested how projections based on the
correlative approach differ from those based on mechanism for two species: the
sachem skipper (Atalopedes campestris) and the eastern fence lizard (Sceloporus
undulates). They found generally that correlative models and mechanistic
models performed similarly in estimating the current range of both species,
although correlative models had greater success in identifying the western limit
of S. undulates. Additionally, mechanistic models predicted greater range shifts
under a uniform 3°C warming scenario. Similarly congruent results have also
been found using both mechanistic and correlative models for a species of
Australian possum (Kearney et al. 2010).
The mechanistic approach to species modelling has the advantage of
incorporating physiologically based environmental constraints that influence
both the distribution and abundance of a species (Kearney & Porter 2009). These
physiological processes are strongly related to flows of mass and energy as a
species interacts with its environment. Therefore, by understanding such
processes the impacts of climate change on biodiversity can be assessed without
reliance on observations of uncertain range limits under current climatic
conditions. However, a limitation of the application of this approach to the
regional or global scale is clearly the time and effort involved in understanding a
species’ physiology and environmental constraints.
2.2.3 Ecosystems models
An alternative to modelling species responses to climate change is to model the
response of the habitat or ecosystem instead. The idea of ecosystems being
controlled by large-scale climatic factors was first developed in the late 19th, and
early 20th centuries (Von Humboldt 1867; Koeppen 1900; Geiger 1961;
Holdridge 1967) alongside theories of biogeography. The Holdridge Life Zone
system (Holdridge, 1967) is one such classification. It has the advantage of being
relatively simple to implement whilst allowing the objective relation of
temperature and precipitation variables to potential biomes, altitudinal zones or
potential vegetation types (the combination of which was termed “Life Zones” by
Holdridge). Essentially, these types of climate-vegetation classifications are
similar to the correlative approach used in BEMs. An important caveat in this
approach is acknowledged in the term 'potential'. Climate is only one of many
factors that contribute towards determining the existence of a particular
vegetation type at a given time and location. Other factors that may influence
vegetation type, such as CO2 effects, ozone, nutrient availability and soil
condition are not accounted for by the Holdridge system. Nevertheless, similar
approaches to both the Koeppen-Geiger classification and the Holdridge Life
Zone System are still used in modern climate change impacts studies (Lugo et al.
1999; Velarde et al. 2005; Kottek et al. 2006; Good et al. 2011; Metzger et al.
2013).
More recently, mechanistic models of vegetation physiology have been
developed (Box et al. 1981; Prentice et al. 1992; Sitch et al. 2003; Clark et al.
2011), in much the same way as models of animal physiology. These dynamic
global vegetation models (DGVMs) model not only physiological differences
between functional groups of plants, but also account for differences in
allometry, morphology, phenology, bioclimate and response to disturbances such
as fire. As such, DGVMs characterise how the vegetation (or land surface)
responds to the atmosphere, and how the atmosphere responds to the
vegetation cover, via fluxes of heat, moisture, carbon and momentum. DGVMs
quantify these interactions at daily, monthly and annual time steps, thus allowing
the influence of both large scale limiting factors (such as CO2 concentration,
climate, altitude and soil), and seasonally dependent factors (such as leaf
phenology, water balance, evapotranspiration, snow cover, and soil
temperature). Therefore, DGVMs are valuable tools for modelling the terrestrial
carbon and water cycles as well as the response of large-scale ecosystems to
climate change.
While DGVMs coupled with climate models are powerful tools for making
predictions of the effects of climate change on vegetation and the carbon cycle
(Cramer et al. 2001; Friedlingstein et al. 2006), their limitations include
significant divergence of projections especially under more extreme greenhouse
gas emissions scenarios (Sitch et al. 2008). One possible cause of this divergence,
which is also a significant limitation for the application of DGVMs in conservation
science, is the low thematic detail in their definition of plant functional types
(PFTs). PFTs are defined as groups of species or taxa that exhibit similar
responses to physical or biotic changes in the environment. It has been noted
that the low number of PFTs in DGVMs (typically 5 to 15), the ad hoc definition of
their parameters, and the lack of integration with current research in functional
ecology are some of the limitations of DGVMs (Harrison et al. 2010). Boulangeat
(2012) suggest a hybrid approach to identify a minimum set of plant traits to link
plant functional groups to species diversity, as well as dynamic vegetation
models at the regional scale. The approach has the potential to be applied in
other regions, however it is yet to be used to account also for vegetation
dynamics.
2.2.4 Summary of modelling approaches
The following table summarises the main advantages and disadvantages of the
approaches to modelling the impacts of climate change on biodiversity.
Modelling
Approach
Brief description
Advantages
Disadvantages
- Assumes present-day
distribution is a
representation of the
stable fundamental niche
of a species
- Assumes a species
populates evenly its
range,
- Uncertainty as to the
influence of climate over
a species current
distribution
- Incomplete and
inconsistent observations
of species occurrence
- Assumes that speciesarea relationship can be
used to identify
extinction risks
- Does not account for
species adaptation to
climates with no present
day analogy
- Requires a detailed
understanding of a
species, consequently
more labour intensive
than BEMs
- Adequate data only
available for a few
species, therefore not
viable for assessing large
scale impacts on species
- Despite a more holistic
approach, comparisons
Bioclimatic
envelope
models
Statistical models that
correlate the observed
range of occurrence of a
species with
climatological and other
environmental variables
- Projection of future
range useful for
adaptation planning
- Only observations of
species presence in a
location is required
- Models can be tested by
withholding a fraction of
species observations
- Multiple sources of
uncertainty can be
quantified
- Quick and easy to use
- Can be applied to all
species with an observed
geographical range map
Mechanistic
models
Process-based models to
understand the extent to
which weather and
climate affect the
population dynamics of a
species.
- Incorporates
physiologically based
environmental
constraints that influence
both the distribution and
abundance of a species
- Incorporates dispersal
ability and competition
between species
- Can be based on in-situ
and laboratory
observations of a species
Ecosystem
models
Mechanistic models of
vegetation physiology for
understanding the
response of ecosystems
to change.
population response to
change
- Quantifies complex
interactions between life
history, disturbance
regime and distribution
pattern that can affect the
assessment of extinction
risk
- Models changes in
ecosystem or habitat
distribution which may
have a greater influence
on where a species
occurs
- Fully coupled with
climate models
- Changes in biomass and
other vegetation related
indices may affect
herbivores and
consequently other
aspects of the food chain
with BEMs show largely
congruent results for
predicting species’
current ranges
- Direct links between
biomass production and
biodiversity are difficult
to prove at local and
regional scales
- Low thematic detail of
plant functional types
means that it is difficult
to relate results to
habitats
- projections diverge
especially under more
extreme greenhouse gas
emissions scenarios
Table 1 Summary of the advantages and disadvantages of the modelling approaches to the
impacts of climate change on biodiversity.
2.3 Climate science
General Circulation Models (GCMs; also termed Global Climate Models) are the
main tools employed in climate science. GCMs are numerical models of physical
processes that occur in the dynamical earth system. This can involve processes
in the atmosphere, ocean, land surface and cryosphere. GCMs are used to run
experiments on the response of the earth system to different potential drivers of
change. The ultimate aim of a GCM is to provide a realistic simulation of the
major global scale physical processes that occur in the earth system. GCMs
typically are designed to run at horizontal grid resolutions of approximately 100
to 300km2, with 10 to 20 vertical layers in the atmosphere, and up to 30 vertical
layers in the ocean. The can be initiated from observations of historical climate,
and whilst running can be forced with different global concentrations of
greenhouse gases.
A key factor for determining future climate change will be the quantity of
greenhouse gas emissions. These will depend on the global population; it’s
lifestyle, and the way this is supported by the production of energy and the use
of the land. A large population whose lifestyle demands high energy
consumption and the farming of large areas of land, in a world with its main
energy source being fossil fuel consumption, will inevitably produce more
greenhouse gas emissions than a smaller population requiring less land and
energy and deriving the latter from non-fossil sources. These factors could vary
in a multitude of ways; the international community is already examining how
energy demand and production can be modified to cause lower emissions, but
the implementation of this will depend on both the international political
process and the actions of individuals. Even if no specific action is taken to
reduce emissions, the future rates of emissions are uncertain since the future
changes in population, technology and economic state are difficult if not
impossible to forecast. Therefore, rather than make predictions of future
greenhouse gas emissions, climate science examines a range of plausible
scenarios in order to examine the implications of each scenario and inform
decisions on reducing emissions and/or dealing with their consequences.
The climate models that have contributed towards the Intergovernmental Panel
on Climate Change (IPCC) Forth Assessment Report (AR4) have generally used a
set of scenarios known as “SRES” (Special Report on Emission Scenarios;
Nakicenovic et al. 2000). These scenarios were grounded in plausible storylines
of the human socio-economic future, with differences in economy, technology
and population but no explicit inclusion of emissions reductions policies.
Developed in the mid 1990s, these scenarios extended out to 2100 and varied
widely in their projected emissions of greenhouse gases. For the next IPCC
Assessment Report, GCMs have been forced with radiative forcing from 4
different Representative Concentration Pathways (RCPs) that are described in
terms of greenhouse gas concentrations at the end of the 21st Century in
equivalent CO2 concentration values (Vuuren et al. 2011). The RCPs represent
total radiative forcing of greenhouse gases rather than a particular scenario of
emissions.
Figure 5. Radiative forcing from the 4 Representative Concentration Pathways developed for the
IPCC AR5. Source: van Vuuren et al. (2011)
Biodiversity impacts model tend to use a very small subset of the variables that
are produced by GCMs. A frequently used set of bioclimatic variables, based on
temperature and precipitation indices, is described in Hijmans et al. (2005; see
table 2). These variables are derived from time averaged monthly minimum and
maximum temperature and monthly precipitation fields, and have been shown to
be biologically relevant variables in the prediction of a species' climatic niche
(Hijmans & Graham 2006). A large number of papers use these variables for
fitting bioclimatic envelope models (see for example Elith et al. 2006; Hijmans &
Graham 2006; Garcia et al. 2011).
Bioclimatic variable
Mean annual temperature
Mean diurnal range
Isothermality
Temperature seasonality
Max temperature of warmest
month
Min temperature of coldest
month
Temperature annual range
Description
Mean annual temperature (tas)
Annual mean of monthly (tasmax – tasmin)
(Mean diurnal range / Temperature annual
range) * 100
Standard deviation * 100
Maximum monthly temperature
Minimum monthly temperature
Max temp of warmest month – min temp of
coldest month
Mean temperature of wettest
quarter
Mean temperature of driest
quarter
Mean temperature of warmest
quarter
Mean temperature of coldest
quarter
Total annual precipitation
Precipitation of wettest month
Precipitation of driest month
Precipitation Seasonality
Precipitation of Wettest Quarter
Precipitation of Driest Quarter
Precipitation of Warmest
Quarter
Precipitation of Coldest Quarter
Mean temperature of wettest quarter
Mean temperature of driest quarter
Mean temperature of warmest quarter
Mean temperature of coldest quarter
Total annual precipitation
Maximum monthly precipitation rate
Minimum monthly precipitation rate
Coefficient of variation of monthly
precipitation
Maximum quarterly precipitation rate
Minimum quarterly precipitation rate
Precipitation rate in warmest month
Precipitation rate in coldest month
Table 2 Bioclimatic variables frequently used in correlative species models following Hijmans et
al. (2005)
All of the above variables are dependent on observations of precipitation and
minimum, mean and maximum temperature being available. Long time series of
quality controlled meteorological observations are important for use in
understanding historical trends, model biases and making valid associations
between a species’ range and the above climate variables. Table 3 shows the
observational datasets that are available for West Africa, the case study area that
will be described later in this report.
Dataset
Source
GSOD
Meteorological
Stations (via
WMO)
CRU TS3.1
Interpolated
observations
from WMO
Interpolated
observations
from WMO
and other
national
WorldClim
Relevant
Climate
Variables
Temperature,
wind,
precipitation,
snow depth
Temperature,
precipitation,
PET
Min, mean
and max
temperature,
precipitation
Spatial
Resolution
Temporal
Coverage
Temporal
Resolution
Point
locations
with
distribution
varying
through time
~55km
1901 –
present
Daily
1901present
Monthly
Varying from
1km to
~19km
1950 – 2000
Monthly
climatology
sources
Observations
Precipitation 2.5 degree
1979 –
Monthly
and satellite
present
TRMM
Satellite using Precipitation 0.25 degree
1998 –
3-hourly to
observations
present
monthly
for ground
climatology
truthing
FEWSNET
Satellite using
Precipitation 8km
1995 –
10-daily
observations
present
for ground
truthing
Table 3 Observational datasets available for applications in biodiversity impacts models for West
Africa. Acronyms: GSOD = Global Summary of Day; NOAA/NCDC = US National Ocean and
Atmospheric Administration / National Climate Data Center; CRU = Climate Research Unit,
University of East Anglia; PET = Potential Evapo-Transpiration; GPCP = Global Precipitation
Climatology Project; TRMM = Tropical Rainfall Measuring Mission
GPCP
2.4 Adaptation strategies in biodiversity conservation
One of the main science-policy related issues that the new Intergovernmental
Platform on Biodiversity and Ecosystem Services (IPBES) is likely to address is
how to adapt current conservation strategies to the projected impact of climate
change on biodiversity (Perrings et al. 2011). The global network of protected
areas (henceforth PAs) has been shown to be an effective tool for the protection
of biodiversity (Bruner et al. 2001), and is considered to be one of the principal
tools for protecting biodiversity and implementing strategies to adapt to climate
change (Mawdsley et al. 2009). However, despite the majority of global
biodiversity occurring in tropical developing countries, there is limited evidence
in the academic literature of adaptation actions being undertaken in the
developing world (Ford et al. 2011). Ford et al. (2011) found that currently,
adaptation actions are most frequently being implemented in the infrastructure
and utilities sectors as opposed to ecosystem management or forestry.
In their review of climate change adaptation plans for wildlife management in
USA, Canada, England, Mexico, and South Africa Mawdsley et al. (2009) suggest
four main categories into which adaptation strategies can be grouped: land and
water protection and management; direct species management; monitoring and
planning; and law and policy. In particular, they suggest increasing the extent of
protected areas, restoring and creating new habitat in order to maximise future
resilience, and increasing landscape connectivity. Similar suggestions also put
forward by Hannah et al. (2008) for climate change adaptation in Madagascar.
They suggest that restoration and protection of the riverine corridor forests are
important for species migrations, especially between fragmented habitats with
high genetic divergence between populations. However, the costs of this strategy
are high. Restoring forests to maintain connectivity between Madagascar’s
fragmented forests would cost approximately US$0.8 billion, albeit with an
estimated extra income of US$ 72 - 144 million annually from the post-Kyoto
protocols on reducing emissions from deforestation and degradation (REDD).
Another approach to adaptation planning that is relevant for site-based
conservation management involves the integration of projections of species
range shifts from BEMs. As species ranges shift with climate change, species will
no longer occur in some sites, but will colonise new sites. Hole et al. (2011)
suggest different adaptation strategies for Important Bird Area sites
experiencing different types of inward or outward migration (Figure 6).
Figure 6. (a) Proportion of priority bird species projected to emigrate relative to proportion of
priority species projected to colonize (log scale) by 2085 each of the 803 mainland sub-Saharan
Africa Important Bird Areas (IBAs). Climate-change adaptation strategy (CCAS) categories into
which IBAs are classified are purple, high persistence; green, increasing specialization; red, high
turnover; blue, increasing value; yellow, increasing diversification. (b) Spatial distribution of
IBAs in the five CCAS categories. Source: Hole et al. (2011)
Following the identification of CCAS categories, Hole et al. went on to suggest
different adaptation strategies according to each category. This has the
advantage of offering site-specific advice on how to manage habitats for expected
future migrations of globally threatened bird species.
Finally, once impacts have been identified, and adaptation strategies agreed
upon, the final stage in the process is to implement plans. Heller and Zavaletta
(2009) review the biodiversity adaptation literature, and issue several
recommendations for actions. They stress the importance of regional institutions
to coordinate adaptation projects, the incorporation of climate change into all
areas of planning and policy, and an inclusive approach to local communities.
Furthermore, they suggest that many adaptation plans lack detailed information
on who how the plan will be implemented and by whom. They suggest that
regional planning, site scale management and modification of existing
conservation plans is the best way to over come these issues.
2.5 West African Climate
Rainfall is extremely important for the livelihoods and ecosystems of West
Africa. With a rapidly growing population that is dependent on subsistence
agriculture, and intense human pressure on the remaining natural ecosystems, it
is vitally important to be able to understand and predict the dynamics of the
West African monsoon for the present day and into the future.
2.5.1 West African Monsoon
The dominant feature of the West African climate is the monsoon period
between May and October. The main driver of seasonal variations in the West
African climate is the north-south movement of the Inter-Tropical Convergence
Zone (see ITD in Figure 7). During the period of the monsoon, low-level flows of
moist air are pulled inland from the Gulf of Guinea. This is due to a pressure
gradient between the land and sea created by warm sea surface temperature
cooling as the move north to form high pressure, and the Saharan heat low
creating low pressure and anti-cyclonic winds. As the moist air from the sea
converges with hot dry northerly winds from the Sahara, convective cells form
and develop into large-scale convective cells. During the first part of the
monsoon, precipitation is concentrated over the coastal areas, until the
beginning of July when the monsoon jumps northwards by approximately 8°N
latitude (Figure 8). After remaining at a latitude of approximately 12°N for July
and August, the zone of peak rainfall gradually retreats southwards towards the
coast. Coastal areas therefore experience a second rainy season in October.
Figure 7. Three-dimensional schematic view of the West African Monsoon. ITD, inter-tropical
discontinuity; TEJ, tropical easterly jet; STWJ, subtropical westerly jet; AEJ, African easterly jet.
The oscillation of the AEJ yellow tube figures an African easterly wave. Source: Lafore et al.
(2011).
Figure 8. The mean seasonal cycle of rainfall over West Africa through a latitude cross-section.
March–November daily precipitation values (mm/day) from GPCP satellite-estimated values are
averaged over 5 ◦ W – 5 ◦ E and over the period 1997 – 2006. A 7-day moving average has been
applied to remove high-frequency variability. The black horizontal line at 5◦N represents the
Guinean Coast. Source: Janicot et al. (2011)
2.5.2 Land-atmosphere interactions
There are considerable social, economic and environmental stresses on land use
in West Africa. In situ measurements (Garcia-Carreras et al. 2010) and
observational studies (Taylor et al. 2007) have shown that during certain times
of day, the land surface is strongly coupled to the planetary boundary layer. As a
consequence, the land surface (in particular soil moisture gradients) has been
shown to have an important role in the initiation of convection (Taylor et al.
2011) and in modulating where precipitation falls in the region (Taylor et al.
2012). In addition to soil moisture, there is growing evidence that forestcropland or forest-grassland gradients can create similar gradients of heat,
moisture and momentum fluxes in the atmosphere, thus also having an influence
on the initiation of convective rainfall. Therefore, it would be reasonable to
hypothesize that land use policy has the potential to influence local and regional
scale precipitation patterns in West Africa.
Figure 9. The mechanism by which convection is proposed to initiate over forest boundaries.
Source: Garcia-Carreras et al. (2010)
Figure 9 summarises the mechanism by which convection initiates over forest
boundaries. As cool moist air flows at low levels over the forest, when it reaches
the forest boundary it meets dry northerly winds from the Sahara, and warm
land with a high albedo and subsequently rapidly rising air mass. This
convergence results in a high convective available potential energy (CAPE),
which effectively results in the vertical movement of moist air that condenses to
form cumulus and cumulus congestus clouds on the southern side of the warm
grassland.
3 Proposed Research
3.1 Aims
To provide guidance on the use of climate science for advising climate change
adaptation strategies in biodiversity conservation policy.
3.2 Key research questions
Since the aim of this thesis is potentially very broad, I intend to frame my
research questions in the context of the different stages involved in developing a
conservation adaptation policy (Figure 10). Before addressing the research
questions that this thesis will attempt to answer, it is helpful to understand the
process by which biodiversity conservation adaptation decisions are taken. In
the final thesis, these assumptions will be verified by consulting with
conservation policy makers (see Thesis plan, Chapter 1).
Figure 10. Generalised schematic of the key questions in the process of setting future
conservation priorities and adaptation strategies. Blue boxes denote key policy and practical
conservation questions; green boxes denote key science questions; red boxes denote how this
thesis will contribute to each stage of the above processes.
In general, policy makers and conservation practitioners need to know what the
impact of climate change will be on the species of greatest conservation value,
and what actions to take to reduce these impacts (shown in blue in Figure 10).
The key research questions that this thesis will address are as follows:
1. What are user requirements for climate information in the biodiversity
sector?
2. How suitable are climate model outputs for use in biodiversity impacts
models?
3. How can earth system models advise climate change adaptation strategies in
West Africa?
In relation to question 1, international conservation funding organisations, such
as the United Nations, World Bank or European Commission, might require
climate change information to set funding priorities. The aim of these
organisations is to target resources to the habitats and species that are projected
to be most severely impacted by climate change. National governments may also
require this information in order to advise setting global emissions reductions
targets. For example, quantification of the impacts of different greenhouse gas
emissions scenarios on global biodiversity may provide useful information on
what level of climate change is considered ‘dangerous’.
The question of how biodiversity might be impacted by climate change is also
relevant for policy makers at regional or national scales. Information on
biodiversity impacts of climate change may provide information on the direction
of expected species migrations, or the expected persistence of unique habitats.
Currently, scientists in the fields of conservation biology and climate science are
the main providers of information to inform these decisions (Figure 10, green
boxes). While a large amount of the literature addresses modelling approaches
and uncertainties in biodiversity impacts, little attention has been paid to the
most appropriate use of climate science and the effect of different methods of
downscaling climate information on conservation decisions (question 2).
Once global, regional and national scale priorities for biodiversity conservation
have been set, it is then necessary to develop effective adaptation strategies
(question 3). The aim of adaptation in biodiversity conservation is either to
improve the resilience of species and habitats or to assist their response to
climate change in order to prevent extinctions. Typically, climate science has not
been involved in advising adaptation strategies. However, this is now becoming
feasible with the inclusion of land surface processes in many established climate
models, and advances in the understanding of land-atmosphere interactions. The
case study of West Africa provides particularly interesting challenges and
opportunities for assessing climate change adaptation options.
4 Proposed methods of data collection and analysis
Firstly, I will collect information to guide this study by canvasing expert opinion
on the key challenges for policy makers and conservation scientists with regard
to the impacts of climate change on biodiversity. The main source of data for this
thesis however, comes from climate and meteorological models. As this thesis
progresses through the process of translating climatological information into
adaptation advice, I will employ models ranging from coarse resolution global
models to fine resolution meteorological models. Each data source is discussed
in more detail below.
Expert survey of key issues
For chapter 1, I intend to devise a questionnaire to be distributed amongst
attendees at an international climate change and nature conservation conference
to be held in Bonn on 25th to 27th June 2013. The questionnaire will ask
participants for their experiences in using climate change information in the field
of conservation science. The aim of the questionnaire will be to understand what
are the challenges and gaps in climate data currently available, and to identify
potential applications for climate science in developing climate change
adaptation policies. The methodological issues that are relevant to this approach
are developing a questionnaire that can be both quantitative, and allows for the
collection of expert opinion. This might be overcome by creating preliminary
questions to understand the role of the participant, and their level of
understanding of key issues. The questionnaire is being developed with input
from conservation scientists and policy experts from the Royal Society for the
Protection of Birds (RSPB) and The British Trust for Ornithology (BTO).
General circulation models
In order to assess the reliability of General Circulation Models (GCMs) for
chapter 2, I compared all the models published under the Fifth Coupled Model
Inter-Comparison Project (CMIP5) to the CRU_TS3.1 observations for the
historical period between 1950 and 2000. The methodological steps involved
firstly calculating 19 bioclimatic variables commonly used in biodiversity
impacts projections that are based on monthly minimum and maximum
temperature and monthly total precipitation. I then tested the ability of each
model to simulate the observed inter-annual variability of each variable. As an
additional step, I also tested the ability of each GCM to simulate the observed
mean seasonal cycle of a subset of variables. A full explanation of the
methodology can be found in Annex 1.
In order to show the impacts of climate change mitigation on global ecosystems, I
used a perturbed physics ensemble of projections from the HadCM3 GCM. For
each model ensemble member, I calculated a simple metric (Hdistance) using
bio-temperature and annual precipitation, based on the Holdridge Life Zone
classification system. The Hdistance metric provides a measure of change that is
relevant for global ecosystems, using variables for which GCMs are largely
considered reliable. Then, using the Hdistance, I identified critical thresholds in
the rate and magnitude of change that may be relevant for large scale
ecosystems. These critical thresholds were assessed for a climate change
mitigation scenario (RCP2.6) and a business as usual scenario (A1B) to identify
how climate change mitigation may reduce damaging impacts on the worlds
ecosystems.
Regional climate models
The model simulations were run from December 1949 to December 2100 using
the HadRM3 regional climate model with the MOSES2.2 tiled land-surface
scheme and the A1B SRES scenario, on the 50km resolution Africa CORDEX
domain (Giorgi et al. 2009). They provide a comprehensive dataset of surface
and atmospheric climate variables including minimum and maximum
temperatures and precipitation at the daily and monthly timescale and at a
spatial resolution of 50km.
The lateral boundary conditions for the simulations were taken from a subset of
5 ensemble members sampled from a perturbed physics ensemble based on the
HadCM3 GCM. The model selection was primarily based on regional analysis of
the GCMs for Africa and its sub-regions with a focus on several regions including
West and Central Africa. Members of the ensemble were selected in order to
capture the spread in outcomes produced by the full ensemble, whilst excluding
any members that do not represent the African climate realistically.
The RCM simulation for Africa have already been run (by others within the Met
Office), and pre-processed by myself. In chapter 4, I attend to assess the skill of
these RCM simulations over West Africa, and discuss this suitability for use in
BEMs. In chapter 5, the results of the dynamical downscaling method will be
compared to less computationally expensive and more frequently used
downscaling methods, in order to provide guidance on the advantages and
limitations of each approach to downscaling.
High spatial and temporal resolution limited area models
The advantage of using limited area models (LAMs) at 4km spatial resolution,
with time steps every 10 seconds, is that they are sufficiently high resolution to
model convective rainfall. Coarse resolution models such as GCMs and RCMs are
typically run for grid resolutions of between 200km and 50km at between 3hour and 1-hour time intervals. The resolution of these models is not sufficient
to model the typical scale of this process. Therefore, GCMs and RCMs must
employ a statistical parameterisation of convection that estimates the amount of
convective rainfall based on the values of other more variables in the
atmosphere.
A nested LAM will be used in this thesis to firstly identify interactions between
land cover and the atmosphere during a period of 4 days during the West African
monsoon. This model has already been run, and will contribute towards chapter
6. For chapter 7, the same LAM will be run for one full season of the West African
monsoon period (approximately 3 months). In this second experiment, different
land cover configurations will be tested for their influence on local and regional
scale precipitation patterns. The land cover configurations will represent
different climate change adaptation options for the region as follows:
-
a “great green wall” across the Sahelian belt,
-
a policy of increasing protected area connectivity by forest regrowth and
planting policies
-
an increasing degradation of forest around the boundaries of current
protected areas.
Climate observations
For all chapters, there will be an element of model evaluation and assessment. In
order for this to occur, robust observations datasets are needed, covering both
the global extent at coarse spatial resolutions and the West African domain at
higher spatial resolutions. The challenges of working in this area are limited
availability of meteorological station observations, variable quality of
observations, and inconsistent recording. For this reason, in addition to station
based gridded observations, I will also compare model results to observations
from satellites (such as data from the Tropical Rainfall Measuring Mission, and
FEWSNET), to model based reanalyses of surface pressure observations (such as
the NCEP 20th Century Reanalysis), and to hybrid products combining
observations and reanalysis (such as the CPC Merged Analysis of Precipitation).
5 Current progress
Currently, I have submitted two articles to peer reviewed journals. I have
provided the abstracts below, and the manuscripts are uploaded as separate
documents to this upgrade report. These two articles will be chapter 2 and
chapter 3 of my thesis respectively.
For each chapter, I outline below progress and possible problems that I may
encounter.
Chapter 1
I currently have a basic draft of the questionnaire that will be distributed at a
climate change and nature conservation policy conference in later June. The aim
of the questionnaire is to gain insight into the issues that are currently relevant
to policy makers and conservation scientists relating to climate change, and to
understand where climate science can better advise such decisions. The
questionnaire will be both printed, and distributed via the SurveyMonkey
website. Potential problems may include a low response rate and questions that
do not address the main issues in the field.
Chapter 2: Submitted article
Title: The reliability of the CMIP5 GCM ensemble for assessing the impacts
of climate change on biodiversity
Authors: Andrew Hartley, Jon Olav Skøien, Gregoire Dubois
Abstract: Following the inception of the Intergovernmental Platform on
Biodiversity and Ecosystem Services (IPBES), there is a renewed focus on the
adaptation of existing biodiversity conservation strategies to climate change, and
consequently the validity of General Circulation Models (GCMs) for such
applications. Here, we assess the ability of the Coupled Model Inter-Comparison
Project 5 (CMIP5) GCM ensemble to simulate historical observations of the
bioclimatic variables that are frequently used in biodiversity and ecosystem
impacts studies. We analyse the inter-annual variability of 19 bioclimatic
variables, mean seasonal cycle and ability to reproduce observed seasonal
minima and maxima for 24 GCMs from the CMIP5 ensemble. Our findings show
that for most of the world, temperature variables such as mean annual
temperature have the highest GCM agreement with observations. Lower
agreement is found for temperature and precipitation variables with a seasonal
component, especially in arid and semi-arid locations. The seasonality of
monthly precipitation was found to have low model agreement in Central and
Eastern Europe, East Africa, Southern Australia and parts of Asia. This was found
to be due in part to GCMs not simulating the months of either maximum or
minimum precipitation reliably. These results show that in general the CMIP5
ensemble reliably simulates bioclimatic variables, but care needs to be taken in
its use for certain parts of the world and certain variables. These results will be
made available to conservation practitioners via a protected area information
system. We propose that conservation scientists may reduce uncertainties in
biodiversity projections by selecting a subset of the most reliable models.
Chapter 3: Submitted article
Title: Climate change mitigation policies reduce the rate and magnitude of
ecosystem impacts
Authors: Andrew Hartley, Richard J. J. Gilham, Carlo Buontempo and Richard A.
Betts
Abstract:
Aim
To show the impacts of climate change mitigation on the rate and magnitude of
change in the climate that influences large-scale ecosystems.
Location
Results are calculated for all terrestrial land areas free of ice, and summarized
for 35 places of high conservation priority. We focus on 6 areas of high
conservation priority: Altai-Sayan Montane Forests, Orinoco River and Flooded
Forests, Chihuahuan Deserts, Congo Basin, Southwest Australia, and Coastal
West Africa.
Methods
We use a simple metric of change based on statistical distance within the
Holdridge Life Zone classification space (Hdistance) to quantify ecosystemrelevant change in climate between a baseline average climate (1961-1990) and
each year in a 150 year time series (1950-2099). We apply this metric to a 58
member ensemble of GCM projections, for a business as usual scenario and an
aggressive climate change mitigation scenario. The rate and magnitude of change
in the Hdistance is calculated for each ensemble member.
Results
We find that more than 50% of high conservation priority areas show divergence
in the rate and magnitude of change in the Hdistance metric when comparing a
business as usual emissions scenario (A1B) with an aggressive carbon dioxide
mitigation scenario (RCP2.6). In other high priority areas we find that potentially
important thresholds are exceeded even with small changes in the Hdistance
under scenario A1B.
Main conclusions
We conclude that potentially dangerous impacts to high priority ecosystems can
be avoided in many parts of the world by a global policy of aggressive climate
change mitigation. Even though in some cases, the long term magnitude of
change threshold is exceeded under RCP2.6, this generally occurs later in the
century, allowing more time for ecosystems to adapt.
Chapter 4
The 5-member RCM has already been run, and some observational datasets
assembled. Problems may be encountered with the observational datasets, due
to the low number of observational stations and issues of data quality.
Chapter 5
I will be second author on a paper addressing this research question. Dr. David
Baker, a post-doctoral researcher at Durham University, has drafted a basic plan
for this paper. My role will be to apply different downscaling methods to the 5member GCM ensemble, and compare each method to the dynamical
downscaling approach discussed in chapter 4. Dr. Baker will use the downscaled
climate data to build Bioclimatic Envelope Models for restricted range bird
species in West Africa. We will both analyse the results.
Chapter 6
As mentioned in section 4, the LAM has already been run, and the majority of the
analysis completed. Results currently show that there is an increased likelihood
of convection to initiate on forest-grass boundaries during the afternoon period.
Analysis is still ongoing to establish a link between forest patches and mesoscale convective systems.
Chapter 7
This chapter has yet to be started, although the same model setup will be used as
in chapter 6.
6 Thesis plan
Each of the following research questions will form a chapter of the thesis. Every
chapter will constitute an individual paper in a peer-reviewed journal, with the
exception of chapter 1.
Introduction and review of relevant literature
The introduction will present issues in global conservation planning, and assess
the approaches for quantifying the impact of climate change on biodiversity. The
second section of this report will form the basis of the introduction to the thesis.
Chapter 1: How are climate change adaptation policies formulated, and what
information is required by policy makers to decide on conservation priorities?
This chapter will identify the main challenges facing policy makers and scientists
in the field of conservation prioritisation with regard to climate change. From
reviewing the academic literature on this subject, it is expected that the
challenges will be related to understanding uncertainty in models, and
implementing and assessing climate change adaptation actions. I will also
directly address the use of climate data in the field, and scope opportunities for
extending the use of climate models.
Chapter 2: How reliable are climate change projections for applications in
biodiversity impacts projections?
This chapter will test the models in the Coupled Model Intercomparison Project
5 (CMIP5) for their ability to simulate observed climatology of variables that are
frequently used in biodiversity impacts studies. The full chapter can be found in
Annex 1.
Chapter 3: What is the impact of climate change mitigation policies on the rate
and magnitude of ecosystem change and how is this affected by climate model
uncertainty?
One of the relevant issues for policy makers is to quantify the benefits of
different climate change mitigation strategies. This paper will compare a
scenario of aggressive climate change mitigation to a business as usual scenario
in the context of impacts on global ecosystems. I will show how the rate of
climate change can be reduced below rates that may be dangerous for the
ecosystems in most locations. The full chapter can be found in Annex 2.
Chapter 4: What is the skill of the HadRM3 regional model for simulating the
climate of West Africa?
Regional climate models provide detailed dynamic projections of how large scale
climatic changes affect local and regional climate. They are an important tool for
use in climate impacts studies, and are an especially important for biodiversity
conservation planning at scales approaching the size of protected areas and
species ranges. Prior to their use in impacts models, the skill of regional models
needs to be assessed in terms of indicators for large scale processes such as
surface pressure seasonality, winds and precipitation. Once we have confidence
that the model can simulate processes such as the monsoon, then we can use
projections with more confidence in impacts studies.
Chapter 5: How does choice of downscaling methodology affect biodiversity
impacts projections in West Africa?
High resolution information on climate is required to make projections of species
future ranges under climate change. Despite several reviews of appropriate
scales in climate impacts studies on biodiversity (Pearson & Dawson 2003;
Wiens & Bachelet 2010), there has been little effort to quantify the effects of
different downscaling techniques on the results of bioclimatic envelope models.
This paper will quantify the effects of three different approaches to downscaling
GCM data for use in a regional climate impacts study: dynamical downscaling
with a regional climate model; empirically based statistical downscaling; and a
change factor approach.
Chapter 6: In a high-resolution limited area model, how does the land surface
interact with the atmosphere during the West African monsoon?
This paper will use a limited area model to explore the relationship between the
land surface and the planetary boundary layer in numerical model with explicit
convection over West Africa. I will examine the model results for evidence of
initiations of convection over forest-grass boundaries, and for evidence of the
influence of forest cover on local and regional rainfall patterns. This will involve
examining local and regional scale precipitation patterns, latent and sensible
heat fluxes before, during and after precipitation, and local winds.
Chapter 7: How do forest related adaptation strategies affect local and regional
precipitation patterns in West Africa?
Following on from chapter 6, I will run the same model for the whole period of
the monsoon (approximately 3 months). Having established a potential
mechanism for interactions between the vegetation cover and the atmosphere, I
will test the affects of different anthropogenic modifications to the land surface
on local and regional scale precipitation. I aim to test the following land cover
scenarios:
-
Great Green Wall across the whole of the Sahara. This is a plan that has
been put into place by governments across the whole of the Sahel with the
intention to stop desertification in these countries. The assumption is that
a forest barrier will retain water in local environments, thus benefitting
local communities. With such a large scale project, the potential for
negative affects on precipitation over dry areas needs to be assessed.
-
Increased protected area size and connectivity. This is a climate change
adaptation that is often suggested will benefit biodiversity, without
consideration of feedbacks to local and regional climate.
-
Increased degradation of forest lands and reduction in forest patches.
While such a scenario may have a detrimental impact on biodiversity, the
increase in number of forest patches may have a positive impact on local
precipitation initiations.
Each of these scenarios will be mapped, and tested in a nested limited area
model. This represents the first study of its kind, and could reveal interesting
insights into climate change adaptation in West Africa.
Chapter 8: Discussion of findings and final conclusions
In this final chapter, I will discuss the implications of this thesis for conservation
and climate change adaptation, with a particular focus on West Africa.
7 Timetable
2013
2014
2015
Act
ion J J A S O N D J F M A M J J A S O N D J F M A M J J A S
5A
5B
5C
6A
6B
2
3
4A
4B
7A
7B
7C
5A
5B
5C
Code
5A
5B
5C
6A
6B
2
3
4A
4B
7A
7B
7C
Action
GCM downscaling methodologies
Data analysis for chapter 5
Co-write chapter 5
Finish analysis
Write up results
Resubmit chapter 2
Resubmit chapter 3
Analysis of RCM skill
Write-up chapter 4
Setup LAM for land cover runs
Analyse model results
Write up chapter 7
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Annex 1
Please see the document entitled “HARTLEY_upgardereport_Annex1.docx”
Annex 2
Please see the document entitled “HARTLEY_upgardereport_Annex2.docx”