Enabling Activities for the Preparation of Jamaica’s Second National
Communication to the UNFCCC: Climate Scenarios for Vulnerability
and Adaptation
Prepared by
A. Anthony Chen and Michael A. Taylor
In Association with the
Climate Studies Group Mona,
University of the West Indies, Mona
August 31, 2008
Table of Contents
Chapters
1
Introduction - Climate of Jamaica
1.1
Climatology
1.2
Variability
1.3
Report Objectives
Page
4
2
7
Caribbean Climate Trends and Projections from the IPCC
2.1
2.2
2.3
2.4
2.5
2.6
2.7
IPCC
Temperature
2.2.1 Trends
2.2.2 IPCC Projections
2.2.3 Other Supporting Studies
Precipitation
2.3.1 Trends
2.3.2 IPCC Projections
2.3.3 Other Supporting Studies
2.3.4 Precipitation Summary
Hurricanes
2.4.1 Trends
2.4.2 Modelling
2.4.3 Summary
Sea Level rise
2.5.1 Trends
2.5.2 IPCC Projections
Evapotranspiration
IPCC 4th Assessment Summary for the Caribbean
2.7.1 Limitations of IPCC projection
3
Obtaining Future Scenarios for Jamaica
3.1
GCMs, RCMs and Statistical Models
3.2
Methodology
3.2.1 GCMs
3.2.2 PRECIS
3.2.3 SDSM
3.3.
Data – Temperature Rainfall and Streamflow
3.4
General Approach and Study Limitations
24
4
Downscaled results
35
4.1
4.2.
4.3
From GCMs
From PRECIS
4.2.1 Temperature
4.2.2 Rainfall
From SDSM
2
4.4
5
4.3.1 Temperature
4.3.2 Rainfall
4.3.3 Wet and Dry Spells
4.3.3 Streamflows
Physical Basis for Results (Predictors)
Discussion and Conclusions
57
5.1
5.2
5.3
5.4
Temperatures
Precipitation
Wet and Dry Spells
Estimates of All Changes
5.4.1 Temperature and Rainfall
5.4.1.1 A Note about Scenarios
5.4.2 Sea Level Rise, Evaporation and Hurricanes
5.5
Research Priorities
References
66
Appendix 1 SDSM results for 2015, 2030 and 2050
70
Appendix 2 - PRECIS Results
79
Appendix 3 - SDSM Results for Manley, Sangster and URCR
82
Appendix 4 Wet and Dry Spell Results
86
Acknowledgement
87
3
Chapter 1
Introduction - Climate of Jamaica
1.1 Climatology
The climate of Jamaica is basically similar to the climate of the Caribbean, which
has been concisely described by Taylor and Alfaro (2005). It can be broadly
characterized as dry winter/wet summer with orography and elevation being significant
modifiers on the sub regional scale. The dominant synoptic influence is the North
Atlantic subtropical high (NAH). During the winter the NAH is southernmost with
strong easterly trades on its equatorial flank. Due to the combination of a strong trade
inversion, a cold sea surface temperature (SST) and reduced atmospheric humidity, the
region generally is at its driest during the winter. Precipitation during this period
(December to March) is due to the passage of mid-latitude cold fronts, and the higher
elevations receive heavier rainfall, with a rain-shadow effect on their southern coasts
which are distinctively arid. With the onset of the spring, the NAH moves northward, the
trade wind intensity decreases, the sea becomes warmer and the southern flank of the
NAH becomes convergent. At the same time easterly waves traverse the Atlantic from
the coast of Africa into the Caribbean, and frequently mature into storms and hurricanes
under warm sea surface temperatures and low vertical wind shear, generally within a
10ºN-20ºN latitudinal band referred to as the main development region. These waves
represent the primary rainfall source and their onset in June and demise in November
roughly coincides with the mean Caribbean rainy season. Around July a temporary retreat
of the NAH equatorward is associated with diminished rainfall known as the mid-summer
drought. Enhanced precipitation follows the return of the NAH and the passage of the
Inter Tropical Convergent Zone (ITCZ) northward. The timing of the processes are
illustrated graphically for Jamaica in Fig. 1.1.
Also shown in Fig.1.1 is the variation in air temperature which tends to follow the
sun, or more precisely the variation in solar insolation. July is the warmest months while
January/February is the coolest period. There is also spatial variation across the island as
coastal areas exhibit warmer temperatures compared to the cooler mountainous interior of
the island. Sea breezes and the warm ocean temperatures of the Gulf and Caribbean Sea
also help modulate temperature year round.
4
On average Jamaica receives 1800 mm of rain each year, but there is significant
year to year variability (see following section). Northeastern Jamaica receives highest
rainfall, while the southern plains are the driest regions (less that 1200 mm annually).
Climatology (Bar graph – precip, line graph –temp)
Air Temp
follows the sun
mm
150
30
29
28
27
26
25
24
100
50
0
Celsius
(3)
Jamaica
n b r r y n l g p t v c
Ja Fe Ma A p Ma Ju Ju A u Se Oc No De
NAH moves
closer to
equator
Stronger trades
Low SST
Mid-lat fronts
NAH starts
Northward
migration
Weaker trades
SST begins to
increase
NAH
temporarily
retreats
Southward
‘MidSummer
Drought’
NAH return
Northward
High SST
Easterly
waves
ITCZ North
Fig. 1 The timing of climatology processes for Jamaica (NAH refers to North
Atlantic High pressure system; SST, Sea Surface Temperature; ITCZ, Intertropical Convergence Zone)
1.2 Variations from climatology
The dominant mode of variability in precipitation in the dry season (December to
March) is associated with the El Niño Southerly Oscillation1 (ENSO) signal (Stephenson
et al, 2007) with precipitation anomalies behaving oppositely in the north and south
Caribbean. The southeastern Caribbean becomes drier than normal in response to a
warming ENSO (or El Niño) signal because of a shift in atmospheric circulations
(Hadley and Walker circulations).
1
The ENSO signal consists of a warm phase (El Niño) and a cold phase (La Niña). The term El Niño was initially
used to describe a warm-water current that periodically flows along the coast of Ecuador and Perú, disrupting the local fishery.
It has since become identified with a basin-wide warming of the tropical Pacific Ocean east of the dateline. This oceanic event
is associated with a fluctuation of a global-scale tropical and subtropical surface pressure pattern called the Southern
Oscillation.
5
The early rainfall season (May to July) is anomalously wet during the year after
an El Nino event, and anomalously drier during a La Niña event (Chen et al., 1997,
Giannini et al., 2000, Chen and Taylor, 2002, Taylor et al, 2002, Spence et al., 2004,
Ashby, 2005) due to warmer and colder than normal sea surface temperatures
respectively. Again the variation in sea surface temperature is due to shifts in
atmospheric circulations during these events (Wang and Enfield, 2001). The warmer
SST is referred to as ‘warm pool’ in the literature.
The late rainfall season (August, September, October, November) tends to be
drier in El Niño years and wetter in La Niña years (Giannini et al., 2000, Martis et al.,
2002, Taylor et al., 2002, Spence et al., 2004, Ashby et al., 2005, Jury et al.,2007) and
tropical cyclone activity diminishes over the Caribbean during El Niño summers due to
the stronger vertical shears it creates in the wind field (Gray et al., 1994).
The phase of the North Atlantic Oscillation (NAO), which consists of opposing
variations of barometric pressure near Iceland and near the Azores, modulates the
behaviour of warm ENSO events mentioned above (Giannini et al., 2001). A positive
NAO phase implies a stronger than normal NAH and amplifies the drying during a warm
ENSO. On the other hand, a negative NAO phase amplifies the precipitation in the early
rainfall season in the year after an El Niño. The Atlantic Multidecadal Oscillation (AMO)
is also associated with greater hurricane activity during its warm phase (See Hurricane
Trends below).
1.3 Report Objectives
How the climate of Jamaica will be altered by the stresses of global warming is
discussed in the following Chapters. In Chapter 2 we discuss the historical trends in
Caribbean climate and the projections of the IPCC. In Chapters 3 and 4 we discuss the
latest projections for Jamaica based on the use of general circulation models, a regional
climate model and on statistical downscaling. A discussion of the results, uncertainties
and priorities for research are discussed in Chapters 5.
6
Chapter 2
Caribbean Climate Trends and Projections from the IPCC
2.1 IPCC
The climate of Jamaica along with that of the rest of the Caribbean will be altered
by global warming. This is the assessment of the Intergovernmental Panel on Climate
Change (IPCC) in its 4th assessment (IPCC, 2007). The IPCC was founded in 1988 by
the World Meteorological Organization (WMO) and the United Nations Environment
Programme (UNEP). The scientific body consists of 3 working groups, Working Group I
which assesses the scientific aspects of climate change, Working Group II which assesses
impacts, vulnerability and adaptation, and Working Group III which assesses options for
mitigating climate change.
IPCC Working Group I Fourth Assessment (AR4) methodology of arriving at a
conclusion depends on
i.
Assessment based on peer reviewed papers and publications up to March 2006,
consisting of observed trends in climate, General Circulation Models of Climate
(GCMs), downscaling of global models by Regional climate models (dynamic)
and Statistical downscaling
ii.
Strong Physical basis or explanation (theory)
iii.
degree of agreement between observation, modeling and theory
iv.
Assessed Likelihood based on expert judgment
The likelihood of an IPCC projection is expressed in probabilities but based on expert
judgement. The categories are
•
Virtually certain > 99% probability
•
Extremely likely > 95% probability
•
Very likely > 90% probability
•
Likely > 66% probability
•
More likely than not > 50%
There are corresponding analogous statements about unlikeliness.
7
2.2 Temperature
2.2.1 Trends
Global temperatures have increased by about 0.74˚C (0.56˚C to 0.92˚C) since the
19th century (IPCC, 2007). There has been a warming trend from 1950-2001 with
minimum temperatures increasing at a higher rate than maximum (Alexander et al., 2006).
An increasing trend in both variables is also observed for the Caribbean region (Peterson
et al., 2002). Peterson et al. (2002) used ten globate climate indices to examine changes
in extremes in Caribbean climate from 1950 to 2000. They found that the difference
between the highest and lowest temperature for the year (i.e. the diurnal range) is
decreasing but is not significant at the 10% significance level. Temperatures falling at or
above the 90th percentile (i.e. really hot days) are increasing while those at or below the
10th percentile (really cool days and nights) are decreasing (both significant at the 1%
significant level). These results indicate that the region has experienced some warming
over the past fifty years.
2.2.2 IPCC Projections
The IPCC projection is for continued warming through the end of the current
century. IPCC scenarios of temperature change for the Caribbean between the present
(1980-1999) and the future (2080-2099) are based on a coordinated set of climate model
simulations (hereafter referred to as the multi-model data set or MMD ) which are
archived at the Program for Climate Model Diagnosis and Intercomparison2 (PCMDI);
(Christensen et al., 2007). The results of the analysis using A1B Special Report Emission
Scenario (SRES)3 (Nakićenović and Swart, 2000) are summarised in Table 2.1
(Christensen et al., 2007).
In the Table, the small value of T (column 8 for temperature) implies a large
signal-to-noise ratio, so that the temperature results are significant. The probability of
extreme warm seasons is 100% (column 15) in all cases and the scenarios of warming are
all very significant by the end of the century. Table 1 also shows that the MMDsimulated annual temperature increases for the Caribbean at the end of the 21st century
range from 1.4°C to 3.2°C with a median of 2.0°C, somewhat below the global average.
2
3
See http://www-pcmdi.llnl.gov/
See Section 2.3. for an expanded description of Climate models and SRES Scenarios
8
Fifty percent of the models give values differing from the median by only ±0.4°C. There
were no noticeable differences in monthly changes.
Table 2.1. Regional average of Caribbean (CAR) temperature and precipitation
projections from a set of 21 global models in the MMD for the A1B scenario. The mean
temperature and precipitation responses are first averaged for each model over all
available realisations of the 1980 to 1999 period from the 20th Century Climate in
Coupled Models (20C3M) simulations and the 2080 to 2099 period of A1B. Computing
the difference between these two periods, the table shows the minimum, maximum,
median (50%), and 25 and 75% quartile values among the 21 models, for temperature
(°C) and precipitation (%) change. Regions in which the middle half (25–75%) of this
distribution is all of the same sign in the precipitation response are coloured light brown
for decreasing precipitation. T years (yrs) are meaures of the signal-to-noise ratios for
these 20-year mean responses. They are estimates of the times for emergence of a clearly
discernible signal. The frequency (%) of extremely warm, wet and dry seasons, averaged
over the models, is also presented. Values are only shown when at least 14 out of the 21
models agree on an increase (bold) or a decrease in the extremes (From Christensen et
al., 2007).
9
A summary of the monthly projections of temperature up to 2090 for MMD using 21
GSMs is given in Fig. 2.1
Figure 2.1 Monthly temperature change (º C) from 1980-1999 to 2080-2099 obtained
from AR4/PCMDI models using the SRES A1B scenario for the Caribbean (CAR) The
distribution gives the median (dark line), half the model values between the 25% and
75% quartile (dark shading) and the remaining up to the maximum and minimum values
(light shading).
2.2.3 Other Supporting Studies
The GCM results were supported by the work of Angeles et al, 2007, at
University of Puerto Rico, Mayaguez. Fig. 2.2 shows the colour coded projected
changes in temperature of about 1ºC across the Caribbean up to the year 2050 under a
business as usual scenario called IS92a.
10
Fig. 2.2 Temperature changes simulated across the Caribbean by Angeles et al., 2006
Fig. 2.3 Observed and baseline (NCEP) temperatures and temperature scenarios at
Worthy Park in Jamaica for present (1961-90), 2020s, 2050s and 2080s time slices,
obtained by SDSM using HadCM3 with A2 emission scenario. Corresponding results for
the Caribbean region given by HadCM3 and ECHAM4 are also shown.
11
Statistical Downscaling of Temperature at Worthy Park in Jamaica shown in Fig. 2.3
also show projected increase from 1960-1990 to 2080’s using A2 to be approximately
3ºC (Chen, Rhoden and Taylor, 2006).
Increases in atmospheric greenhouse gas concentration by man-made activity causing
increased trapping of terrestrial radiation and global warming also produced a strong
physical basis for temperature increases (IPCC, 2007) as depicted in fig. 2.4
Fig. 2.4 Increases in the envelope of greenhouse gases in the atmosphere cause trapping
of terrestrial radiation and global warming
It is very likely then that Jamaica and Caribbean temperatures will increase based on
agreement of observation, global models, statistical downscaling and a good physical
basis. The extent of the warming will depend on the actual amount of green house gas
emissions. Even if emissions ceased today temperatures will rise at the end of the
century due to long lifetimes of CO2 and methane and long ‘memory’ of the oceans.
2.3 Precipitation
2.3.1 Trends
Two of the Caribbean precipitation indices used by Peterson et al. (2002) show
significant changes. The greatest 5 day rainfall total increased over the period under
analysis (10% significance level) while the number of consecutive dry days decreased
(1% significant level). The results, however, may not take into account differences in the
12
precipitation regime between the north and south Caribbean. Using several observed data
sets, Neelin et al., (2006) also noted a modest but statistically significant drying trend for
the Caribbean’s summer period in recent decades.
2.3.2 IPCC Projections
IPCC scenarios of percentage precipitation change for the Caribbean are also
based on the multi-model data set (MMD) and are also summarised in Table 1 for the
A1B scenario. The large value of T for precipitation (column 14) implies a small signalto-noise ratio. In general, then, the signal-to-noise ratio is greater for temperature change
than for precipitation change, implying that the temperature results are more significant.
In other words, it takes a long time for the change in precipitation to become significant.
From Table 2.1, most models project decreases in annual precipitation, with a few
suggesting increases. Generally, the change varies from –39 to +11%, with a median of
-12%. Figure 2.5 (Christensen et al., 2007) shows that the annual mean decrease is
spread across the entire region (left panels). In December, January and February (DJF),
some areas of increases are evident (middle panels), but by June, July and August (JJA)
the decrease is region-wide and of larger magnitude (right panels), especially in the
region of the Greater Antilles, where the model consensus is strong (right bottom panels).
13
Figure 2.5 Precipitation changes over the Caribbean from the MMD-A1B simulations.
Top row: Annual mean, DJF and JJA fractional precipitation change between 1980 to
1999 and 2080 to 2099, averaged over 21 models. Bottom row: number of models out of
21 that project increases in precipitation (From Christensen et al., 2007).
Figure 2.6 Monthly changes in precipitation response (% change) from 1980-1999 to
2080-2099 obtained from AR4/PCMDI models using the SRES A1B scenario for the
Caribbean (CAR) The distribution gives the median (dark line), half the model values
between the 25% and 75% quartile (dark shading) and the remaining up to the maximum
and minimum values (light shading).
14
The monthly precipitation change over the Caribbean from 21 models (MMD) using AIB
from 1980’s to 2080’s are shown in Fig. 2.6. It can be seen that most models project
decreases in JJA
2.3.3 Other Supporting Studies
Angeles et al. (2007) also simulate decreases up to the middle of the century in the
vicinity of the Greater Antilles but not in the other islands in the late rainfall season.
Theoretically, drying is likely in the subtropics, and the Greater Antilles, (Chou and
Neelin, 2004) since water is expected to be transported horizontally by the atmosphere
from regions of moisture divergence (particularly in the subtropics) to regions of
convergence.
2.3.4 Precipitation Summary
There is general agreement among GCMs that in the vicinity of the Greater Antilles there
will be significant drying in JJA. A global model run by Angeles et al, 2007, for the
Caribbean show decrease in JJA in the vicinity of the Greater Antilles. A drying trend has
been observed in at least one data set. Theoretically drying is expected in the subtropics.
Other indirect support for drying comes from projections of increases in the positive
phase of the North Atlantic Oscillation (NAO) and of El Niño like projections in the
Pacific, both of which are associated with drying in the Caribbean.
2.4 Hurricanes
2.4.1 Trends
Analysis of observed tropical cyclones in the Caribbean and wider North Atlantic
Basin shows a dramatic increase since 1995. This increase however has been attributed to
the region being in the positive (warm) phase of a multidecadal signal (Atlantic
Multidecadal Oscillation or AMO) and not necessarily due to global warming
(Goldenburg et al., 2001). Results per year obtained from Goldenburg et al. (2001), show
that during the negative (cold) phase of the oscillation the average number of hurricanes
in the Caribbean Sea is 0.5 per year with a dramatic increase to 1.7 per year during the
positive phase. Attempts to link warmer sea surface temperatures (SSTs) with the
15
increased number of hurricanes have proven to be inconclusive (Peilke et al., 2005).
Webster et al., (2005) found that while SSTs in tropical oceans have increased by
approximately 0.5˚C between 1970 and 2004, only the North Atlantic Ocean (NATL)
shows a statistically significant increase in the total number of hurricanes since 1995.
In an analysis of the frequency and duration of the hurricanes for the same time
period, significant trends were only apparent in the NATL. Both frequency and duration
display increasing trends significant at the 99% confidence level. Webster et al., (2005)
also noted an almost doubling of the category 4 and 5 hurricanes in the same time period
for all ocean basins. While the number of intense hurricanes has been rising the
maximum intensity of hurricanes has remained fairly constant over the 35 year period
examined.
2.4.2 Modelling
Knutson et at (2006) used a regional climate model of the Atlantic basin to
reproduce the observed rise in hurricane counts between 1980 and 2006, along with much
of the interannual variability, when forced with observed sea surface temperatures and
atmospheric conditions. In a later paper (Knutson et al., 2008), they used the same
regional model and methodology above in a downscaling exercise (See Chapter 3) to
investigate the changes in large-scale climate that are projected to occur by the end of the
twenty-first century by an ensemble of global climate models, and found that Atlantic
hurricane and tropical storm frequencies are reduced. At the same time, near-storm
rainfall rates increase substantially. This is in agreement with earlier model simulations
of tropical cyclones in a warmer climate, which showed that there will be an increase in
precipitation associated with these systems (Knutson and Tuleya, 2004). The mechanism
is simply that as the water vapour content of the tropical atmosphere increases, the
moisture convergence for a given amount of dynamical convergence is enhanced. This
should increase rainfall rates in systems like tropical cyclones, where moisture
convergence is an important component of the water vapour budget.
The simulated reduction in frequency of Atlantic hurricanes and storms seemingly
contradicts one of the results obtained by Oouchi et al., (2006), who used a high
resolution global 20-km grid atmospheric model capable of generating tropical cyclones
16
that began to approximate real storms, as opposed to the downscaling technique used by
Knutson et al., (2008). The model was run in time slice experiments for a present-day
10-year period and a 10-year period at the end of the 21st century under the A1B scenario.
In the study, tropical cyclone frequency decreased 30% globally, but increased by about
34% in the North Atlantic. The strongest tropical cyclones with extreme surface winds
increased in number while weaker storms decreased. (It should be noted that the results
of Knutson et al., (2008) do not contradict the possibility of increases of stronger storms,
only with the total increase of Atlantic cyclones.) The tracks were not appreciably
altered, and maximum peak wind speeds in future simulated tropical cyclones increased
by about 14% in that model, although statistically significant increases were not found in
all basins (Meehler et al., 2007). It must be noted, however, that these regional changes
are largely dependent on the spatial pattern of future simulated SST changes (Yoshimura
et al., 2006) which are uncertain.
The results of Oouchi et al., (2006), are shown in Fig. 2.7. The top diagram
shows the actual (observed) tracks 1979-1988; the middle diagram, the simulated present
day tracks. Both compare favourably. The bottom diagram shows the simulation of
future tracks (2080-2099), with a greater density of storm tracks in the Atlantic and less
dense tracks in other oceans compared to the top diagram.
17
Fig. 2.7 20 Km Japanese model result of Hurricane tracks (Oouchie et al, 200)
2.4.3 Summary
There were not enough results from hurricane simulations to make statement
about the Caribbean and other hurricanes. IPCC merely states that it is more likely than
18
not (> 50% probability) that human activity contribution to observed trend and that it is
likely that intense tropical cyclone will increase in some regions (>66%). This cautious
statement was likely the result of the controversy which has involved some proponents of
climate change and some hurricane experts. Both sides have valid arguments and it is
quite likely that future hurricanes will bear out both sides, with the climate change
proponents having the last say toward the end of the century. A statement which was
authored by participants of the WMO International Workshop on Tropical Cyclones,
IWTC-6, San Jose, Costa Rica, November 2006 states that ‘Given the consistency
between high resolution global models, regional hurricane models and Maximum
Potential Intensity theories, it is likely that some increase in tropical cyclone intensity
will occur if the climate continues to warm.’
2.5 Sea Level Rise
2.5.1 Trends
Global sea level rise over the 20th century is estimated to have been 0.17 ± 0.05
m. From estimates of observed sea level rise from 1950 to 2000 by Church et al. (2004),
the rise in the Caribbean appeared to be near the global mean. There have been large
observed variations in sea level rise, especially in the Pacific Ocean mainly due to ocean
circulation changes associated with ENSO events. From 1993 to 2001, all the data show
large rates of sea level rise over the western Pacific and eastern Indian Ocean and sea
level falls in the eastern Pacific and western Indian Ocean (Church et al., 2006).
2.5.2 IPCC Projections
Global sea level is projected to rise between the present (1980–1999) and the end of this
century (2090–2099) by 0.35 m (0.23 to 0.47 m) for the A1B scenario (IPCC, 2007). Due
to ocean density and circulation changes, the distribution will not be uniform. However,
large deviations among models make estimates of distribution across the Caribbean
uncertain. The range of uncertainty cannot be reliably quantified due to the limited set of
models addressing the problem. The changes in the Caribbean are, however, expected to
be near the global mean.
19
Fig. 2.8 shows the projected average global sea level rise (m) due to thermal expansion
during the 21st century relative to 1980 to 1999 under SRES A1B scenario. The
differences are substantial.
Fig. 2. 8 Differing model results of sea level rise in an A1B scenario (Fig. 10.31, Meehl
et al., 2006)
Figure 2.9 gives the local sea level change in meters due to ocean density and
circulation change relative to the global average (2080 to 2099 minus 1980 to 1999) for
the A1B scenario. There is little or no change from the global average for Jamaica.
20
Fig. 2.9 Local sea level change (m) due to ocean density and circulation change relative
to the global average (i.e., positive values indicate greater local sea level change than
global) during the 21st century, calculated as the difference between averages for 2080
to 2099 and 1980 to 1999, as an ensemble mean over 16 AOGCMs forced with the SRES
A1B scenario
2.6 Evapotranspiration
The IPCC report does not address evapotranspiration specifically within the
Caribbean. However, mean annual changes in evaporation for the SRES A1B scenario
are given on a global scale as shown in Figure 2.10. From the figure it appears that by
the end of the century (2080-2099) evaporation in the vicinity of Jamaica will increase by
about 0.3 mm day-1 relative to current (1980-1999) values. It is to be noted that the
evaporation value in the vicinity of Jamaica is given over the ocean, and evaporation over
land may be less.
21
Figure 2.10 Multi-model mean changes in evaporation (mm day–1). To indicate consistency in the sign of change, regions are stippled where at least 80% of models agree on
the sign of the mean change. Changes are annual means for the SRES A1B scenario for the
period 2080 to 2099 relative to 1980 to 1999.
2.7 IPCC 4th Assessment Summary for the Caribbean
Based on the SRES A1B scenario, the following summary can be made about
future climate conditions within the Caribbean (from Christensen et al., 2007).

Sea levels are likely to continue to rise on average during the century around
the small islands of the Caribbean Sea …. Models indicate that the rise will not be
geographically uniform but large deviations among models make regional
estimates across the Caribbean … uncertain.
Note: Based on the personal judgement of the consultants, the increase will
probably follow the global average.

All Caribbean … islands are very likely to warm during this century. The
warming is likely to be somewhat smaller than the global annual mean warming
in all seasons.

Summer rainfall in the Caribbean is likely to decrease in the vicinity of the
Greater Antilles but changes elsewhere and in winter are uncertain.
22
Note: On-going analysis of precipitation changes by the Climate Studies Group
Mona warrants upgrading the ‘likely’ decrease of precipitation in the Greater
Antilles to ‘very likely’ (See Chapters 3 and 4)

It is likely that intense tropical cyclone activity will increase (but
tracks and the global distribution are uncertain).
It is to be noted that the A1B scenario on which these statements are predicated
gives an average global increase in temperature of 2.8º C over the present century. If all
developed countries were to cut greenhouse gas emissions at the rate now proposed by
the United Kingdom and France4, then the global temperature increase would be limited
to just under 2º C.
2.7.1 Limitations of IPCC projection
The IPCC projections rely largely on GCMs whose resolution are too course to
give detailed projections for Jamaica; they give one value for a grid of approximately 2º
latitude x 2º longitude, which would include Jamaica, the surrounding Caribbean Sea,
part of Haiti and Cuba. Little dynamic and statistical downscaling was included in the
report. Insufficient model runs to determine regional distribution of cyclone changes and
large deviations among models make regional distribution of sea level rise uncertain. No
projections were made for storm surges because of the limited number of storm surge
models in use worldwide. Uncertainty about future El Niños was also a problem since
the latter impacts on climate variability in Jamaica. In addition many Caribbean climatic
processes are not well understood. This is a shortcoming for regional and statistical
downscaling as well.
4
The proposed reductions below 1990 emission levels are approximately 50% by 2050 and 80% thereafter.
23
Chapter 3
Obtaining Future Scenarios for Jamaica
3.1 GCMs, Special Emission Scenarios, RCMs and Statistical Models
As has been clearly illustrated in the previous section, information on future
climates commonly comes from General Circulation Models (GCMs). GCMs are
mathematical representations of the physical and dynamical processes in the atmosphere,
ocean, cryosphere and land surfaces. They solve for (calculate) and step forward in time
equations of motion, the first law of thermodynamics, the physics of water vapor and
clouds. Physical processes include atmospheric chemistry, land - atmosphere interactions
and atmosphere- ocean interactions. Due to computational and storage burdens, these
processes need to be simulated on a supercomputer, and the results are given on a gross
scale (~ 2º Lat x 2º Long (~222 km x ~214km) or more). Sub-grid processes are assumed
or parameterized, e.g., cloud dynamics, precipitation, radiation and land – surface
processes. Nonetheless their physical consistency and skill at representing current and
past climates make them useful for simulating future climates due to differing scenarios
of increasing greenhouse gas concentrations.
SRES
GCMs are run under different scenarios of greenhouse gas emissions called IPCC
Special Report on Emissions Scenarios (SRES) (Nakicenovic et al., 2000).
The
scenarios are images of how the future might unfold, or alternative futures. Since many
physical and social systems are poorly understood, and information on the relevant
variables is incomplete, prediction is not possible in such cases and thus the scenarios are
neither predictions nor forecasts, but are rather representations of plausible alternative
futures. The emissions scenarios are the basis for the assessment of possible mitigation
strategies and policies to prevent climate change. They are summarized in Figure 3.1.
24




A1 storyline and scenario family: a future
world of very rapid economic growth, global
population that peaks in mid-century and
declines thereafter, and rapid introduction
of new and more efficient technologies.
A2 storyline and scenario family: a very
heterogeneous world with continuously
increasing global population and regionally
oriented economic growth that is more
fragmented and slower than in other
storylines.
B1 storyline and scenario family: a
convergent world with the same global
population as in the A1 storyline but with
rapid changes in economic structures
toward a service and information economy,
with reductions in material intensity, and the
introduction of clean and resource-efficient
technologies.
B2 storyline and scenario family: a world in
which the emphasis is on local solutions to
economic, social, and environmental
sustainability, with continuously increasing
population
(lower
than
A2)
and
intermediate economic development.
Figure 3.1 Special Report on Emission Scenarios (SRES) schematic and storyline summary
(Nakicenovic et al., 2000).
As seen in the box above, the SRES contain four different scenarios (A1, A2, B1, B2).
Two scenarios emphasize material wealth, and the other two scenarios emphasize
sustainability and equity. Additionally, two scenarios emphasize on globalization and two
on regionalization:

A1: globalization (market forces), emphasis on human wealth

A2: regionalization (regional oriented economic growth), emphasis on human
wealth

B1: globalization, emphasis on sustainability and equity

B2: regionalization, emphasis on sustainability and equity
A set of scenarios thus assists in understanding possible future developments of complex
systems. Some emission scenarios and subsets, and corresponding concentrations (CO2,
SO2 and N2O) are illustrated in Figure 3.2. The A1B scenario is a subset of A1. It
represents a "balanced" progress across all resources and technologies from energy
supply to end use. Many IPCC projections are based on this scenario. In this report we
25
also take the average between A2 and B2 to get a ‘balanced’ view between emphasis on
wealth and emphasis on sustainability for scenarios of regional oriented economic growth.
Figure 3.2 Fossil CO2, CH4 and SO2 emissions for six illustrative SRES non-mitigation
emission scenarios, their corresponding CO2, CH4 and N2O concentrations (From Meehl
et al., 2007)
Due to the large resource requirements, GCMs are normally run by large research
centers worldwide and the data made freely available. Table 3.1 (to be discussed in detail
in the Sections 3.2.1 and 3.2.2) lists the properties of three GCMs for which data have
been extracted for the Caribbean region and for Jamaica in particular (Watson et al.,
2008). The table suggests a limitation of GCMs, particularly for a study of this nature.
Their coarse resolution (row 4) relative to the scale of required information (country or
station level) suggests the need for downscaling techniques in order to yield more
detailed information at a finer scale.
26
Table 3.1 Characteristics of Climate models from which Caribbean climate information
has been extracted.
ECHAM5-OM HADCM3
CGCM2.3.2
PRECIS
Acronym
ECH
HAD
MRI
PRECIS
Model
GCM
GCM
GCM
RCM
Data Type
gridded monthly gridded
gridded monthly
gridded
monthly
monthly
Resolution
1.875˚×1.875˚
2.5˚×3.75˚
Scenarios
Baseline, A2
Baseline, A2 Baseline, A2
Baseline, A2
A1B, B1
A1B, B1
B2
Simulation Period 1860-2100, 2001- 1860-1999,
Organization
2.8˚×2.8˚
A1B, B1
50km×50km
1850-2000, 2001- 1961-90, 2001-
2200
2000-2199
2300
-2099
Max-Planck
Hadley
Meteorological
Climate Studies
Institute for
Centre,
Research Institute, Group Mona,
Meteorology,
UK
Japan
Germany
University of
the West
Indies,
Jamaica
Typically, two downscaling methods are applied – dynamical downscaling and
statistical downscaling. With dynamical downscaling a regional climate model (RCM)
uses the outputs of the GCMs as boundary conditions to provide more detailed
information over a smaller geographical area. Like GCMs, the RCMs rely on
mathematical representations of the physical processes. They are especially useful for
spatial representations of future climates.
Statistical downscaling enables the projection into the future of a local variable by
first developing statistical relationships between the local variable and large scale climate
variables for current or baseline periods. The relationships are assumed to hold true for
the future and so the local variable can be predicted utilising GCM simulated future large
27
scale conditions as predictors. Statistical downscaling is especially useful for generating
projections at a particular location.
Data from all three methods (GCMs, RCMs, and statistical downscaling) are
employed to assess both changes in rainfall and streamflow for Jamaica under future
global warming scenarios. The methodology is detailed below.
3.2 Methodology
In climate modelling the conventional wisdom is to run a number of models and use an
ensemble mean of the model runs as a best value. In the case of modeling in Jamaica this
is time consuming and beyond our means. The next best approach is to use the output of
a GCM that reasonably represents the present climate of Jamaica as input to our regional
and statistical downscaling model.
For GCMs we looked at simulations under the A2, B2 and A1B scenarios. A2 and B2
are relatively high and low emission scenarios, while A1B falls somewhere in between,
as explained above. For regional and statistical downscaling we used A2 and B2
scenarios and found the average of these two to get an in-between estimate since A1B
‘post-processed’ outputs were not readily available in a form suitable as inputs for the
regional and statistical models.
For GCMs the periods for which projections were made are
•
2015’s – 2010 to 2019
•
2030’s – 2025 to 2034
•
2050’s – 2045 to 2054
For RCMs and Statistical downscaling the projections were extended to 2080’s (2075 to
2084). In addition in Appendix 1 we give the projections for the years 2015, 2030 and
2050 by statistical downscaling, as these years were specified in the terms of reference.
The projections around a decadal period were done since these periods would be more
meaningful for projections than a single year.
28
3.2.1 GCMs
The GCM chosen was HADCM3 since it reasonably represented the climate of
Jamaica and outputs were readily available. At the same time data were also extracted
from simulations carried out using the other 2 GCMs detailed in Table 3.1 for
comparison. Time series of current and future monthly precipitation are extracted for
each model’s grid box over Jamaica for the baseline climate (1960-1990) and for decadal
periods centered on the 2015s, 2030s and 2050s. The models are run under the A2, B2
and A1B scenarios.. The future change in Jamaica’s rainfall regime under each scenario
and for each model is determined by subtracting the baseline climatology (as simulated
by the models) from the simulated future climate.
3.2.2 PRECIS
The methodology for generating future Jamaican climate using an RCM is
identical to that already described for the GCM. Available RCM data come from the
PRECIS (Providing Regional Climates for Impact Studies) model which was run within
the region at the University of the West Indies (Taylor et al., 2007). Details of the
PRECIS model are also provided in Table 3.1. Noteworthy is its 50 km resolution and its
restriction to a Caribbean domain. Because of its finer resolution, data are extracted for
seven grid boxes (as opposed to the one GCM gird box) which cover Jamaica (see Figure
3.3 and Appendix 2 for detailed coordinates). Data for three grid boxes (boxes 3, 4 and 5)
covering three watershed regions for which station data were available (see section 2.3.3)
are analyzed in conjunction with statistical downscaling. Data are extracted for the
baseline and future time periods noted above and for the A2 and B2 scenarios.
29
4
5
6
7
1
2
3
4
Figure 3.3 PRECIS grid boxes surrounding Jamaica. Grid boxes are labelled between 1 and
7 for ease of reference in the text.
3.2.3 SDSM
Statistical downscaling is facilitated by the use of the Statistical Downscaling
Model (SDSM) (www.sdsm.org.uk). The model allows for the development of
statistical relationships between local variables (in this case rainfall and streamflow) and
large scale weather indices from GCMs. Details about the rainfall and streamflow data
used in this study are given in the following section. In the development of the
relationships, reanalysis data from the National Centers for Environmental Prediction
(NCEP) are used to provide the large scale current climate information. Future data for
the same large scale predictors are then extracted from the HADCM3 GCM run under the
A2 and B2 scenarios and used to generate the future values of the local variables. The
data were extracted from a gridbox centered at Latitude: 17.5°N and Longitude: 75 W
(East of Jamaica) since rainfall in the main wet season is influenced by waves to the east.
The climate change data (future minus baseline) are generated for the same future time
periods noted above. In addition to temperature and rainfall, some streamflows, which
are forced mainly by precipitation, are also projected to support the projections for
precipitation. This is done because of the limited data available for statistical
downscaling (See section 3.3)
30
3.3
Data - Rainfall and Streamflow and Temperature
Streamflow data for three rivers in Jamaica are utilised in this study. The rivers
are the Great River (St. James) at Lethe, the Rio Grande (Portland) at Fellowship and the
Hope River (St.Andrew) at Gordon Town, all of which are primarily fed by rainfall
runoff run-off (Fernandez, 2008). Details of the streamflow data are provided in Table
3.2. The approach taken is to directly downscale the streamflow data using SDSM, and
to compare the results to downscaled rainfall results for three nearby rainfall stations.
The requirement of SDSM for at least 20 years of daily data proved a constraint,
and as a result the rainfall stations used in the study were from both airports Manley
(Kingston) and Sangster (St. James) and an Upper Rio Cobre Rainfall (URCR) index
which combines rainfall data from four stations in the Rio Cobre watershed region:
Swansea, Enfield, New Hall and Worthy Park (Brown, 2008). Characteristics of the
rainfall data are given in Table 3.2. The stations were the nearest to the watershed regions
which had data of sufficient length and quality to inspire confidence when using the
SDSM technique. Although a large amount of data was missing from Sangster, SDSM
was able to simulate the baseline data adequately, especially for Sangster (See Figure The
location of all the stations are given in Figure 3.4.
For downscaling temperature for the specific years 2015, 2030 and 2050, data
from Worthy Park alone (last column of Table 3.2) was used, as it was the only station
with a sufficiently long time series of baseline data. However, in downscaling rainfall for
these years, the Worthy Park data was inadequate since over 75% of the data was missing.
The Sangster rainfall data was used instead. These runs for the specific years 2015, 2030
and 2050 were done before the Brown (2008) dataset became available. The latter
dataset and the streamflows became available as a result of a consultation entitled
Development of a National Water Sector Adaptation Strategy to Address Climate Change
in Jamaica Prepared for: Mainstreaming Adaptation to Climate Change Project c/o
Caribbean Community Climate Change Centre (ESL/MACC, 2008)
31
Table 3.2 Temperature, Rainfall and Streamflow Data Characteristics. Data provided by
Water Resources Authority and the National Meteorological Service of Jamaica. In the
7th column URCR is a combined rainfall index from four stations in the Upper Rio Cobre
Region.
Streamflow
Station
Temp
Sangster URCR
Worthy
Great
Rio
Hope
River
Grande
River
Data
1960-
1960-
1960-
1961-
1961-
1960-
1960-
Length
2000
2000
2000
1990
1990
1990
1990
0%
0%
40%
39%
0%
9%
%Missing 0%
Manley
Rainfall
Park
Data
Sangster (Rfl)
Available Station Data for SDSM
Great River at Lethe (Sf)
Worthy Park (Rfl)
Rio Grande at
Fellowship (Sf)
Upper Rio Cobre Basin (Rfl)
Fig. 3.4 Location of rainfall and streamflow stations
32
Manley (Rfl)
Hope River at Gordon
Town (Sf)
3.4 General Approach and Study Limitations
Generally, the approach taken is a comparative one. That is:

The outputs from the three GCMs are compared to provide a range of possible
future rainfall and temperature regimes for Jamaica. Though each model run
under each scenario is a plausible future, the results are analysed for general
consensus. This is done for setting context.

The output from the RCM is compared to that from the GCMs for consistency of
the projected climate trends. Additionally, the output over the RCM grid boxes
covering the three watershed regions is extracted and region specific scenarios of
future climate generated.

The SDSM generated future rainfall scenarios for the three rainfall stations are
compared with the RCM generated future climates for the grid box in which the
station resides.

The SDSM generated future streamflows are compared with the future rainfall
scenarios (i) for the nearby station as generated by SDSM and (ii) for the RCM
grid box in which the watershed region resides. This is done to determine
consistency (if any) between the trend in future rainfall in the watershed regions
and the future streamflows.
It is based on the above comparisons that the severity of the impact of global warming on
Jamaica’s climate and streamflows are deduced and conclusions drawn.
Each methodology employed possesses its inherent limitations. For example, the
GCM information is coarse and may not in fact account for land interactions, since the
country is smaller than the grid box. The GCMs also do not all possess the same skill at
simulating all variables across all seasons for the region of interest. Both the RCM and
the SDSM, on the other hand, may be biased by the forcing GCM. SDSM technique in
addition assumes stationarity i.e. a constancy into the future of the developed empirical
relationships on which the predictions are based. Data limitation is also a problem since
there are few observations with which to compare the RCM results for present climate.
33
The data used in the SDSM methodology are sparse5 and do not give a comprehensive
representation of rainfall in Jamaica and the few available station data of sufficient length
include missing data stretches.
Temperature projections are expected to be less uncertain than projections of
precipitation since the signal to noise signal for temperature is much stronger than that for
precipitation in dynamic models, and the regression equations for precipitation in
statistical models are much more complex than that for temperature.
The limitations of the methodologies and the data do not however preclude their
use. Instead, the full range of future climates derived from each method is reported as a
means of expressing the uncertainty in the results. It is then based on the degree of
consensus between all or most of the methods that expressions of confidence are attached
to the conclusions drawn. The results and conclusions drawn are presented in the
following sections.
Confidence in the results is boosted by agreement with observed trends and
theoretical understanding of the results, some of which were discussed in Chapter 2 in the
IPCC projections and some of which, namely, the changes in the SDSM predictors, are
discussed in the next chapter.
Data were destroyed in fire at the Meteorological Office in the 1980’s, and search to find copies has had
little success.
5
34
Chapter 4
Downscaled Results
Some of the results and commentary presented in this chapter naturally coincide
with the report on the ESL/MACC (2008) Water Sector Adaptation project since some of
the PRECIS data and Sangster and Manley data from this project were used in the
ESL//MACC (2008), and conversely some of the streamflow and URCR rainfall data
from the ESL//MACC (2008) project are used in this report to provide as large a dataset
as possible. The results given offer a look at future temperatures and rainfall for Jamaica
as simulated by GCMs and downscaled using PRECIS and SDSM (as explained in
Section 2.3).
4.1 From GCMs
To provide context, the values for precipitation and temperature for Jamaica from the
three GCMs (Table 3.1) are first analyzed to produce scatter plots of projected change
relative to the 1961-90 baseline. The results are shown for the annual change and the
change during June July August (JJA) for the 2015s, 2030s and 2050s using the A2, A1B,
B1 scenarios (Figure 4.1).
All models show increases in annual temperature between 0.4ºC to 0.7ºC for the
2015s, 0.6ºC and 1.0ºC for the 2030s’ and 1.0ºC and 1.5ºC for the 2050s. Changes in JJA
are not much different from the annual changes. Most models show a decrease in
precipitation, which is more pronounced in JJA and become greater with time. Tables
4.1 and 4.2 give the actual values.
35
(a)
Scatter 2015s
20
15
10
5
0
-5
-10
-15
-20
-25
HAD A2
10.00
Had A1B
5.00
HAD B1
MRI A2
0.00
MRI A1B
-5.00
MRI B1
ECH A2
-10.00
0.00
0.20
0.40
0.60
0.80
HAD A2
precipitation (mm)
precipitation (mm)
15.00
Scatter JJA 2015s
ECH A1B
Had A1B
HAD B1
MRI A2
MRI A1B
MRI B1
ECH A2
0
0.2
ECH B1
temperature (oC)
0.4
0.6
temperature (oC)
0.8
ECH A1B
ECH B1
(b)
Scatter 2030s
Scatter JJA 2030s
HAD A2
5.00
Had A1B
0.00
HAD B1
precipitation (mm)
precipitation (mm)
10.00
MRI A2
-5.00
MRI A1B
-10.00
-15.00
0.00
ECH A2
0.40
0.60
0.80
1.00
1.20
HAD A2
30
20
Had A1B
10
MRI A2
0
MRI A1B
HAD B1
-10
-20
MRI B1
0.20
40
MRI B1
ECH A2
-30
ECH A1B
0
0.5
1
temperature (oC)
ECH B1
temperature (oC)
(c)
Had A1B
0.00
HAD B1
MRI A2
-10.00
MRI A1B
-20.00
-30.00
0.00
MRI B1
ECH A2
0.50
1.00
temperature (oC)
1.50
2.00
ECH A1B
ECH B1
10
5
0
-5
-10
-15
-20
-25
-30
ECH B1
HAD A2
precipitation (mm)
precipitation (mm)
HAD A2
10.00
ECH A1B
Scatter JJA 2050s
Scatter 2050s
20.00
1.5
Had A1B
HAD B1
MRI A2
MRI A1B
MRI B1
ECH A2
0
0.5
1
1.5
temperature (oC)
2
ECH A1B
ECH B1
Figure 4.1 Left panels: Scatter graphs of scenarios of annual precipitation changes vs temperature
changes relative to a 1961-90 baseline, simulated by Hadley (HAD, United Kingdom),
Meteorological Research Institute (MRI, Japan) and European Centre for Medium-range Weather
Forecasts High resolution (ECH) General Circulation Models for (a) 2015s, (b) 2030s and (c)
2050s using A2, A1B, B1 Special Emission Report Scenarios. Right panels: Same as for left panels
but for June July August (JJA) changes. Note that the scales are not the same and the position of the
0% change on
the precipitation axis varies.
36
Table showing mean annual temperature change
for Jamaica 2015's, 2030's & 2050's
HAD
A2
A1B
2015s
0.50
0.50
2030's
0.85
2050's
1.46
MRI
B1
A2
ECH
A1B
B1
A2
A1B
B1
0.59 0.48
0.47
0.57
0.68
0.56
0.48
0.97
0.93 0.88
0.82
0.82
0.94
1.03
0.69
1.56
1.00 1.17
1.49
1.07
1.51
1.51
1.35
Numbers in red indicate changes outside of
the standard deviation
Table 4.1. Mean annual temperature change for Jamaica simulated by HADCM3, MRI
and ECH for A2, A1B and B1 scenarios for 2015s, 2030s and 2050s.
Table showing mean annual precipitation change (%) for
Jamaica 2015's, 2030's & 2050's
HAD
MRI
ECH
A2
A1B
B1
A2
A1B
B1
A2
A1B
B1
2015s
-1.60
-5.98
6.93
4.17
12.45
-7.40
-3.71
-2.37
2.64
2030's
-12.61
-5.48
6.74
-4.90
8.58
-13.23
-2.44
0.67
-4.25
2050's
-15.63
-21.87
-28.27
5.51
7.33
9.43
-10.20
2.44
-3.76
Table 4.2 Same as for Table 4.1 but for precipitation change (%)
37
4.2 From PRECIS
4.2.1 Temperature
In table 4.3 we summarize the annual changes in temperature in the 7 PRECIS
grid boxes outlined in Figure 3.3 for the 2015s, 2030s, 2050s and 2080s. The values are
averages of A2 and B2 results to give an intermediary value between low and high
emission scenarios, similar to the A1B scenario used in IPCC 4th Assessment. A
progressive increase in temperature is seen, reaching an average over all boxes of 1.1ºC
by 2050s and a maximum of 2.5ºC by 2080s. Not all boxes change at the same rate as
seen from the table and illustrated in Figure 4.2 for the 2050s. In the 2050s the changes
were greatest over the southwestern Jamaica (Boxes 1 and 2). The changes over the
seasons are also different with greater changes in the latter half of the year, as shown for
Box 1 in 2050s (Figure 4.3).
Table 4.3 Changes in annual temperature (average of A2 and B2) relative to 1961-90
average for 2015s, 2030s, 2050s and 2080s
Changes in annual temperature (average of A2 and
B2) relative to 1961-90 average for 2015s, 2030s,
2050s and 2080s
Box 1
Box 2
Box 3
Box 4
Box 5
Box 6
Box 7
AVG ALL
2015s
0.91
0.68
0.38
0.36
0.54
0.46
0.38
0.53
2030s
0.97
0.81
0.53
0.51
0.66
0.60
0.52
0.66
38
2050s
1.77
1.35
0.76
0.73
1.06
0.90
0.75
1.05
2080s
3.45
3.26
1.93
1.84
2.55
2.27
1.90
2.45
Avg A2&B2 change in annual temperatue PRECIS Boxes by 2050s relative to
1961-90
2.00
1.80
Change in degree C
1.60
1.40
Box1
Box2
Box3
Box4
Box5
Box6
Box7
1.20
1.00
0.80
0.60
0.40
0.20
0.00
Boxes
Fig. 4.2 Average of A2 and B2 changes in annual temperature for PRECIS boxes in
2050s relative to 1961-1990 average
Season Changes in Box 1 in 2050s.
2.50
Degree C
2.00
1.50
DJF
MAM
JJA
SON
1.00
0.50
0.00
1
Seasons
Fig. 4.3 Seasonal changes in Box 1 in 2050s relative to 1961-1990
39
4.2.2 Rainfall
Rainfall change in the 7 PRECIS grid boxes were extracted for the future time
slices previously noted under the A2 and B2 scenarios. The results of the averaging both
scenarios for the 2050s and 2080s are shown in Figure 4.4. Precipitation begins to
decrease in most regions by the 2050s, and by the 2080s the decrease in precipitation
becomes significant in all regions, ranging from 25 to 40%. Detailed results for all time
slices are given in Appendix 2.
40
Avg A2B2 %change for all Precis Boxes by 2050s relative to 1961-90
25.00
20.00
15.00
% change
10.00
Box1
Box2
Box3
Box4
Box5
Box6
Box7
5.00
0.00
-5.00
-10.00
-15.00
-20.00
Boxes
Avg A2B2 %change for all Precis Boxes by 2080s relative to 1961-90
0.00
-5.00
-10.00
% change
-15.00
Box1
Box2
Box3
Box4
Box5
Box6
Box7
-20.00
-25.00
-30.00
-35.00
-40.00
-45.00
Boxes
Figure 4.4 Average of percentage changes for A2 and B2 2050s and 2080s scenarios
downscaled for the 7 regions outlined in Fig. 3.3 using PRECIS.
41
4.3 From SDSM
4.3.1 Temperature
Projected temperature changes at Worthy Park, averaged for A2 and B2 scenarios,
are shown in Table 4.4 for 2015s, 2030s, 2050s and 2080s. The average temperature was
found by averaging the changes in the maximum temperature (TMAX) and the minimum
temperature (TMIN). The changes are found by subtracting the projected temperatures
(in 2015s, 2030s, 2050s and 2080s) from the baseline model values for 1961-1990
(Current Climate Forcing, CCF61-90). The changes are given for annual values and
seasonal values (December to February, DJF; March to May, MAM; June to August, JJA;
September to November, SON). The maximum annual change is about 2.4ºC by the
2080s.
Table 4.4 Temperature changes average for A2 and B2 scenarios. Yellow rows represent
the average to maximum (TMAX) and minimum (TMIN) temperatures
Annual Mean WP Temperature (deg C)
2015s
CCF61-90 2015s change 2030s
TMAX
29.11 29.84
0.73 30.38
TMIN
17.76 18.21
0.45 18.53
Avg
23.43 24.02
0.59 24.45
2030s
change
1.27
0.77
1.02
2050s
30.93
18.93
24.93
2050s
change
1.82
1.17
1.50
2080s
31.96
19.70
25.83
2080s
change
2.85
1.94
2.39
DJF Mean WP Temperature (deg C)
2015s
CCF61-90 2015s change
TMAX
27.52 28.37
0.85
TMIN
15.95 16.37
0.42
Avg
21.74 22.37
0.63
2030s
29.07
16.83
22.95
2030s
change
1.55
0.88
1.21
2050s
29.71
17.29
23.50
2050s
change
2.19
1.34
1.76
2080s
30.87
18.17
24.52
2080s
change
3.35
2.22
2.79
MAM Mean WP Temperature (deg C)
2015s
CCF61-90 2015s change 2030s
TMAX
28.94 29.70
0.77 30.37
TMIN
17.26 17.90
0.63 18.28
Avg
23.10 23.80
0.70 24.33
2030s
change
1.44
1.02
1.23
2050s
31.04
18.68
24.86
2050s
change
2.11
1.42
1.76
2080s
32.41
19.64
26.02
2080s
change
3.48
2.37
2.93
JJA Mean WP Temperature (deg C)
2015s
CCF61-90 2015s change
TMAX
0.39
30.30098 30.69
TMIN
19.09 19.52
0.43
Avg
24.70 25.11
0.41
2030s
change
0.79
0.68
0.73
2050s
31.35
20.21
25.78
2050s
change
1.05
1.11
1.08
2080s
32.06
20.85
26.46
2080s
change
1.76
1.76
1.76
2030s
31.09
19.77
25.43
42
SON Mean WP Temperature (deg C)
2015s
CCF61-90 2015s change
TMAX
29.66 30.59
0.93
TMIN
0.32
18.7303 19.05
Avg
24.20 24.82
0.63
2030s
30.98
19.24
25.11
2030s
change
1.31
0.51
0.91
2050s
31.60
19.56
25.58
2050s
change
1.94
0.83
1.38
2080s
32.49
20.13
26.31
2080s
change
2.83
1.40
2.11
4.3.2 Rainfall
An attempt is made to similarly quantify future changes in rainfall, but at the three
rainfall stations previously identified (Manley, Sangster and URCR), using SDSM.
Detailed results for the A2 and B2 scenarios are given in Appendix 3. Some graphical
representations for the annual and seasonal (DJF, MAM, JJA, SON) rainfall and rainfall
change under the A2 scenario for baseline, 1915s, 1930s, 1950s and 1980s are also given
in Figures 4.5 through 4.7.
A general pattern of decreased rainfall is again seen. The decrease in annual
precipitation is almost linear, except for the 2015s and 2050s at Manley and the 2050s at
URCR. Decreases occur in all time slices except for the 2015s at Manley, where a slight
increase is seen. The decrease is also only 2% at Manley in 2050s. By the 2080’s
decreases are close to 20% at Manley, 60% at Sangster and 14% at URCR. By the 2050s
the seasonal decrease is more pronounced in JJA and SON. Similar trends are noted
under the B2 scenario.
4.3.3 Wet and Dry Spells
The percentage of days (wet day %) that exceed a wet-day threshold limit of 0.3
mm, the average length of continuous wet-days with amounts greater than or equal to the
wet-day threshold (mean wet spell length), and the average length of continuous dry days
with amounts less than the wet-day threshold (mean dry spell length) for Manley are
given in Figure 4.8. The corresponding graphs for Sangster and the Upper Rio Cobre
River are given in Figures 4.9 and 4.10. The values used in the graphs are given in
Appendix 4.
43
The percentage wet days all decrease below the 1961-90 baseline in JJA except
for Manley in the 2050s. The decrease is also noted in SON, except for the 2015s and
2030s at Manley and the 2015s at URCR. The concomitant decrease in wet spell length
and increases in dry spell length are also noted in JJA and SON.
44
(a)
(b)
(c)
(d)
Figure 4.5 Absolute values of SDSM results using HAD3 A2 scenarios for (a) annual and (c) seasonal (DJF, MAM, JJA, SON)
rainfall for 1960-90, 1915s, 1930s, 1950s and 1980s; percentages changes in (b) annual and (d) seasonal rainfall in 1915s, 1930s,
1950s and 1980s compared to 1961-90, for Manley
45
(b)
(a)
(d)
(c)
Figure 4.6 Same as for Fig. 4.5 but for Sangster.
46
(b)
(a)
(d)
(c)
Figure 4.7 Same as for Fig. 4.5 but for URCR.
47
(a)
(b)
(c)
Figure 4.8 SDSM results (a) % wet days, (b) wet spell length and (c) dry spell length at Manley for 1961-90, 2015s, 2030s, 2050s and
2080s using HAD3 A2 scenarios.
48
(a)
(b)
(c)
Figure 4.9 Same as for Fig.4.8, but for Sangster.
49
(a)
(b)
(c)
Figure 4.10 Same as for Fig. 4.8, but for URCR.
50
51
4.3.4 Streamflow
To supplement the precipitation results, streamflow rates for the three river basins
(Great River, Hope River and Rio Grande) were downscaled by SDSM. The results for
are shown in Figures 4.11 through 4.13 respectively and are compared with the
precipitation results.
Streamflow changes at Great River and Hope River compare favourably with
precipitation at Sangster. Annual values decrease progressively for 1960-90 to 2080s,
except that the decrease at Great River in the 2030s and 2050s is practically the same.
Seasonal decreases are greatest in JJA, followed by decreases in SON. While annual
values of streamflow at Rio Grande also decrease progressively from 1960-90 to the
2080s, the seasonal pattern is different, showing marked decreases in all seasons, but
least decrease in JJA. In addition the decreases are so marked that the streamflow is
reduced to nil by 2080s. There is a greater uncertainty in the projections for Rio Grande
and this will be discussed in Section 5.2.
4.4 Physical Basis for Results (Predictors)
The predictors which correlated significantly with precipitation and streamflows
are shown in Table 4.5. Since precipitation and streamflows decrease by 2080s, these
predictors should be able to explain the decrease. Increasing geopotential heights (p500na
and p850na) are associated with increasing atmospheric high pressure systems and
therefore with less precipitation. Decreasing meridional velocity (8_vna) and vorticity
(8_zna and zna) are linked with less atmospheric convergence and therefore less
precipitation. Decreasing relative humidity (r500na and r850na) means that less moisture
is in the atmosphere and therefore the chances of precipitation are less likely. Note that
near surface relative humidity (rhumna) is increasing but the atmosphere dries out by the
time it reaches 850 and 500 hPa or is transported southward to the ITCZ (Chou and
Neelin,2004). Surface air flow strength (fna) is a predictor only for Rio Grande. The Rio
Grande is in a valley surrounded by high mountains. Orographic precipitation occurs
when winds drive moisture up the mountains. Therefore decreasing air flow strengths
would be associated with decreased precipitation.
52
Table 4.5 List of predictors used in SDSM and their tendenc;y in 2080s. x’s under a
station indicate that the predictor was used.
Tendency
Predictors
in 2080s
Great Hope
Manley Sangster URCR River
River
Rio
Grande
500 hPa Geopotential
Height (p500na)
Increasing
x
x
850 hPa Geopotential
Height (p850na)
Increasing
x
x
850 hPa Meridional
Velocity (8_vna)
Decreasing
850 hPa Vorticity (8-zna)
Decreasing
x
x
Near Surface Relative
Humidity (rhumna)
Increasing
x
Relative Humidity
at 500 hPa (r500na)
Decreasing
X
x
x
Decreasing
X
x
x
x
x
x
Relative Humidity
at 850 hPa (r850na)
Surface Air Flow Strength
(fna)
Decreasing
Surface Vorticity (zna)
Decreasing
x
x
53
x
(b)
(a)
(c)
(d)
Figure 4.11 Absolute values of SDSM results using HAD3 A2 scenarios for (a) annual and (c) seasonal (DJF, MAM, JJA, SON)
streamflow at Great River for 1960-90, 1915s, 1930s, 1950s and 1980s; percentages changes in (b) annual and (d) seasonal rainfall
in 1915s, 1930s, 1950s and 1980s compared to 1961-90.
54
Annual % change in daily streamflow at Hope relative to 1961-90
Had3 A2 Box 77
1
0
0.8
-5
1960-90
0.6
2015s
2030s
2050s
0.4
2080s
streamflow (m3/s)
streamflow (m 3/s)
Annual Average Streamflow at Hope
Had3 A2 Box 77
0.2
-10
2015s
2030s
2050s
2080s
-15
-20
0
-25
Years
(b)
(a)
% Seasonal Changes in Hope River Streamflow Relative to 1961-90
Had3 A2 Box77
Seasonal Streamflow at Hope
(Had3 a2 Box 77)
10
1.5
5
0
1961-90
2015s
2030s
2050s
2080s
0.5
Precip Changes (%)
Streamflow m 3/s
DJF
1
MAM
JJA
-10
2015s
-15
2030s
2050s
-20
2080s
-25
-30
-35
-40
0
DJF
MAM
JJA
SON
SON
-5
-45
(d)
(c)
Figure 4.12 As in Figure 4.11 but for Hope River.
55
(b)
(a)
(c)
(d)
Figure 4.13 As in Figure 4.11 but for Rio Grande.
56
Chapter 5
Discussion and Conclusion
5.1 Temperatures
As previously suggested, the comparison of results from the GCMs, PRECIS and
SDSM provides a means of assessing the confidence in results. The general agreement
among GCMs that temperatures will increase gives a high probability that increases will
occur in the Caribbean, especially since the temperature signal to noise ratio is high
(Section 2.3.2) for the GCMs. The probability is increased because of agreement with
PRECIS and SDSM results. Confidence is further enhanced because of observed trends
in warming and the science of global warming is well understood and almost universally
accepted. The temperature increase will depend on the future emissions. Under the A1B
scenario temperatures in the Caribbean are expected to rise by about 1.5ºC by 2050s and
be just under the global average of 2.8ºC by the end of the 21st century. Temperature
increases over the 7 PRECIS boxes were given in the previous chapter along with
temperature increases at Worthy Park obtained from SDSM results. Worthy Park lies in
Box 3 but close to the border with Box 2, so some comparison is possible. The increases
are based on averages of A2 and B2 scenarios.
Figure 5.1 does show that the changes in Temperature at Worthy Park is close to
an average of changes in Boxes 2 and 3 in 2015s and 2080s, but is higher than either
boxes in 2030s and 2050s. Thus while there is great confidence that temperatures are
very likely to increase over Jamaica, the exact value of the changes are uncertain. For
temperature changes over Jamaica it may be prudent to use the average of all 7 boxes,
which is also given in Figure 5.1.
57
Avg A2&B2 Annual Temperature Change
3.50
3.00
Degree C
2.50
2.00
Box 3
WP
Box2
All Boxes
1.50
1.00
0.50
0.00
2015s
2030s
2050s
2080s
Fig, 5.1 Average of A2 and B2 annual temperatures changes in Box 3, Worthy Park (WP),
Box 2 and in all boxes (averaged).
5.2 Rainfall
Most GCM simulations of future Caribbean precipitation show a decrease in
annual values and in JJA, by the end of the century under the A1B scenario. However,
the signal to noise ratio is low and the precipitation signal does not become significant
until the latter half of the century. The PRECIS and SDSM results, averaged for A2 and
B2 scenarios, given in Chapter 4 support the general trend and the probability is therefore
high that decreases in precipitation will occur, especially by the 2080s. The magnitude of
the decrease however is uncertain.
To help to reduce the uncertainty, we compare the PRECIS and SDSM results,
i.e. we compare the station projections from SDSM with the projections from PRECIS in
the grid boxes in which they fall. In doing so, it is noted that the uncertainty in rainfall
decreases in the 2050s, based on projections by PRECIS ,will also be compounded by the
58
fact that the precipitation signal to noise ratio for GCMs does not become significant until
late in the century and the same may be true for PRECIS.
Figure 5.2 shows the average annual precipitation percentage change for Great
River streamflow, Sangster rainfall and PRECIS box 5 precipitation. There is good
agreement between PRECIS and SDSM with respect to rainfall, with projected decreases
being 40% and 55% respectively by the 2080s. The corresponding decrease in
streamflow is over 10%. The streamflow here is not used as an estimate of precipitation,
but merely to support the projection of a decrease. A conservative estimate of 40%
decrease i.e., the decrease projected by PRECIS in box 5, is therefore recommended for
use in estimating water resources in 2080s. For the 2050s the decreases given by
PRECIS and SDSM are 4% and 36%. Since the change at Sangster is much greater, a
conservative recommendation for decrease in precipitation in region 5 by 2050s is 10%.
Figure 5.3 gives the average of A2 and B2 projections of change in streamflow at
Hope River, rainfall at Manley, rainfall in the Upper Rio Cobre Region, and PRECIS Box
3 precipitation for the 2015s, 2030s, 2050s and 2080s. The results from Manley are
questionable because of the large fraction of missing daily station data that were used in
the SDSM analysis, and because the simulated baseline did not fit as well with the
observed data, compared to the Sangster results. Decreases have set in from 2030s. By
the 2080s PRECIS shows a precipitation deficit of over 30%. Taking into consideration
the smaller deficit projected for the Upper Rio Cobre River basin, a conservative estimate
of reduction in rainfall by 2080s is 20%. The deficit in streamflow at Hope River and
rainfall at Manley support this estimate of reduction. Again based on the precipitation
deficits in Region 3 and in the Upper Rio Cobre River region, the estimate of reduction in
rainfall in the 2050s is 10%.
59
Figure 5.2 Average of A2 and B2 projected changes in streamflow at Great River and
precipitation at Sangster and in Box 5 for 2015s, 2030s, 2050s and 2080s.
Avg A2B2 Annual % Change for Box 3 Area
5
0
2015s
2030s
2050s
2080s
-5
-10
Hope R.
Manley
-15
URCR
Box 3
-20
-25
-30
-35
Figure 5.3 Same as for Figure 4.15 at Hope River, Manley, URCR and in Box 3
The PRECIS results given in Figure 4.4 are reproduced below for ease of reading.
There are no SDSM results for comparison in Regions 1, 2, 6 and 7. For these regions an
60
estimate of reduction in precipitation by the 2080s is 30%, which is close to the PRECIS
results. For the 2050s the estimate of changes in regions 2, 6 and 7 for this time slice is
10%, while for box 1 there is a small increase in precipitation. Owing to the uncertainty
about the precipitation signal for the 2050s, it is best to assume that there is no change in
precipitation in box 1 at this time.
Avg A2B2 %change for all Precis Boxes by 2050s relative to 1961-90
25.00
20.00
15.00
% change
10.00
Box1
Box2
Box3
Box4
Box5
Box6
Box7
5.00
0.00
-5.00
-10.00
-15.00
-20.00
Boxes
Avg A2B2 %change for all Precis Boxes by 2080s relative to 1961-90
0.00
-5.00
-10.00
% change
-15.00
Box1
Box2
Box3
Box4
Box5
Box6
Box7
-20.00
-25.00
-30.00
-35.00
-40.00
-45.00
Boxes
Figure 4.4 Average of percentage changes for A2 and B2 2050s and 2080s scenarios
downscaled for the 7 regions outlined in Fig. 3.3 using PRECIS.
61
The region covered by box 4 clearly poses a problem. Whereas PRECIS
simulated an increase in precipitation in the 2050s, the downscaling of streamflow in the
Rio Grande (which lies in box 4) projected decreases in streamflow of 60% i.e. a severe
reduction of rainfall. By the 2080s streamflow is reduced to nil using SDSM
downscaling, yet reduction in rainfall in box 4 was only simulated to decrease by about
25% by PRECIS.
To make some sense of this the rainfall process in the region should be considered.
The highest peaks in Jamaica, some over 2000 m, are situated in the parishes of Portland
and St. Thomas, which comprise the region occupied by box 4. The rainfall is
orogrophic6, driven by winds pushing moisture up the mountains on the windward side in
Portland. The windward side is consequently wetter (annual rainfall of 367 cm) and the
leeward side in St. Thomas drier (annual rainfall of 229 cm). The major predictor for
streamflow in Portland is the surface airflow strength, which forces the wind up the
mountains. Future global warming will cause the surface airflow strength to decrease,
leading to reduced orographic rainfall, and consequently a decrease of rainfall in Portland,
affecting rainfall more in Portland than in St. Thomas. The decrease simulated in region
4 by PRECIS would likely be comprised of a greater decrease in Portland and a lesser
decrease in St. Thomas.
The decrease in streamflow in the Rio Grande valley downscaled by SDSM is too
severe to be accepted, since it is produced by only one simulation. Another SDSM
simulation which did not use surface airflow strength gives less reduction in streamflow.
As a compromise, then, it is suggested that the rainfall in box 4 be considered in 2 parts that in Portland and that in St. Thomas - and that the estimate of reduction in Portland be
40% and in St. Thomas be 20% by 2080s. Because of the problem of possible low
precipitation signal to noise ratio in the 2050s, no estimate is made for box 4 despite the
projected increase in rainfall.
Because of the low signal to noise ratio in the rainfall signal in PRECIS and
limited station data for SDSM analysis, no reliable estimate can be made of precipitation
in the 2015s and 2030s. Instead we simply list the average of all PRECIS boxes as
‘estimates’ for all of Jamaica over these years.
6
No empirical study of orographic clouds has been done for Blue Mountains in the vicinity of Portland and
St. Thomas, but the process is well known (See e.g., Wallace and Hobbs, Atmospheric Science, Academic
Press, 1997), and the rainfall pattern there conforms to this process.
62
5.3 Wet Spells and Dry Spells
Based on SDSM analysis of precipitation, wet-day % and wet spells lengths will
decrease while dry spells lengths will increase. For Sangster (located in box 5), the
decreases in wet-day% and wet spell are 24% and 7% respectively for the 2050s and 44%
and 10% for the 2080s. Dry spells will increase by 32% and 80% by the 2050s and
2080s respectively. For box 3, based on an average of values for Manley and URCR, the
decreases in wet-day% and wet spell lengths are 2% and 3% respectively by the 2050s
and 7% and 6% respectively by the 2080s. Dry spell lengths will increase by 1% and 4%
in the 2050s and 2080s respectively. There are no data with which to make estimates for
the other regions. The major difference between Sangster, on the one hand, and Manley
and URCR on the other seems to be that Sangster rainfall is controlled by a high pressure
system (geopotential height) as well as relative humidity, whereas the others are
controlled primarily by relative humidity.
5.4 Estimates of All Changes
5.4.1 Temperature and Rainfall
Based on the above discussion, the estimates for temperature and rainfall changes
are summarised in the following table (Table 5.1). The values in red are more uncertain
because of the low signal to noise ratios.
Table 5.1 Absolute change in temperature for Jamaica and percentage change in rainfall
for 7 regions. Regions refer to the portion of Jamaica contained in the PRECIS boxes 1
through 7 shown in Figure 3.3. The values in red are more uncertain.
Estimated changes
from 1961-1990
averages
2015s
Temperature change
(ºC)
Precipitation change
(%)
Region 1
Region 2
Region 3
Region 4:
Portland
St. Thomas
2030s
2050s
20280s
0.53
0.66
1.05
2.45
-2.28
-2.28
-2.28
-2.28
-2.28
-2.28
4.54
4.54
4.54
4.54
4.54
4.54
0
-10
-10
-30
-30
-20
No estimate
No estimate
-40
-20
63
Region 5
Region 6
Region 7
-2.28
-2.28
-2.28
4.54
4.54
4.54
-10
-10
-10
-40
-30
-30
Wet-day%
Manley, URCR
Sangster
0
-9
-2
-18
-2
-24
-7
-44
Wet spell length
Manley, URCR
Sangster
-1
-4
-4
-8
-3
-7
-6
-10
Dry spell length
Manley, URCR
Sangster
0
8
3
19
1
32
4
83
5.4.1.1 A Note about Scenarios
All the estimates given above are based on averages of A2 (higher emission) and
B2 (lower emission) scenarios. Many scientists and international organizations are now
advocating significant cutbacks in greenhouse gases in order to limit temperature rises to
less than 2ºC during this century (UNDP, 2007). Several countries of the European Union
have given commitments to these drastic reductions. The Governments of France and the
United Kingdom, for example, have stated their intention to cut emissions by
approximately 80% by 2050. However the chances of limiting temperature rise to less
than 2ºC is slim because of economic and political hurdles. Energy Information
Administration (Washington, DC), in its International Energy Outlook 2008 report
released in June, predicts that world energy demand and carbon dioxide emissions will
grow by about 50 percent over the next two decades.
5.4.2 Sea Level Rise, Evaporation and Hurricanes
By the end of the century sea levels are also expected to rise by 0.21 to 0.48
meters under an A1B scenario using IPCC (2007) projections, but the models exclude
future rapid dynamical changes in ice flow. A recent study of ice flows suggests that the
rate of rise may actually double (Science Daily, Feb. 12, 2008) or be greater
(guardian.co.uk, September 01 2008).
64
Evaporation is also projected to increase by approximately 0.3 mm/day over the
sea. As noted before, the changes over land may be less.
The frequency of hurricanes increasing or decreasing is uncertain but it is likely
that with increased sea surface temperatures, rainfall amounts from storms and hurricanes
will increase. While frequency of occurrence is uncertain, one model (Oochie et al.,
2006) has projected more intense, hurricanes in the Atlantic. Given the consistency
between high resolution global models, regional hurricane models and Maximum
Potential Intensity theories, it is likely that some increase in tropical cyclone intensity
will occur if the climate continues to warm.
5.5 Research Priorities
The limitation of the scenarios and estimates described in this report are outlined in
section 3.4 (General Approach and Study Limitations). In order to overcome some of
these uncertainties the following steps are recommended:
i)
All available climate data from sources, such as sugar plantations, private
weather stations, national weather stations, public works agencies, etc., should
be collected and subject to quality control so that they can be used to validate
regional models and calibrate statistical models.
ii)
Support should be given to research institutions to run as many regional and
statistical downscaling models as possible for calibration and intercomparison purposes in order to reduce uncertainty.
65
References
Alexander, L. V., Zhang, X., Peterson, T. C., Caesar, J., Gleason, B., Klein Tank, A., et al.
(2006). Global observed changes in daily climate extremes of temperature and
precipitation. Journal of Geophysical. Research, 111(D5) 5109 DOI:
10.1029/2005JD006290.
Angeles, M.E., J.E. Gonzalez, D.J. Erickson, and J.L. Hernández, 2007: Predictions of
change in the Caribbean region using global general circulation models Int. J. Climatol.,
27, 555-569, doi:10.1002/joc.1416.
Ashby, S. A., Taylor, M. A., & Chen, A. A. (2005). Statistical models for predicting
Caribbean rainfall. Theor. and Appl. Climatology, 82, 65-80.
Brown (2008): Assessing the Impact of Climate Change on Rio Cobre Streamflow using
Statistical Downscaling, M.Phil. thesis, University of the West Indies, submitted.
Chen,A.A., A.Roy,J.McTavish,M.Taylor and L.Marx, Using SST Anomalies to Predict
Flood and Drought Conditions for the Caribbean, Report No. 49, Center for Ocean-LandAtmosphere Studies, 1997
Chen, A. A, and M. A. Taylor, 2002: Investigating the link between early season
Caribbean rainfall and the El Niño+1 year. International Journal of Climatology , 22, 87106.
Chou, C., & Neelin, J. D. (2004). Mechanisms of global warming impacts on regional
tropical precipitation. J. Climate 17, 2688-2701
Christensen, J.H., B. Hewitson, A. Busuioc, A. Chen, X. Gao, I. Held, R. Jones, R.K.
Kolli, W.-T. Kwon, R. Laprise, V. Magaña Rueda, L. Mearns, C.G. Menéndez, J.
Räisänen, A. Rinke, A. Sarr and P. Whetton, 2007: Regional Climate Projections. In:
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.
Church, J. A., White, N. J., Coleman, R., Lambeck, K., Mitrovica, J. X. (2004). Estimates
of the regional distribution of sea-level rise over the 1950-2000 period. J. Climate 17,
2609-2625
Church, J.A., N.J. White, and J.R. Hunter, 2006: Sea level rise at Topical Pacific and
Indian Ocean Islands. Global Planet. Change, 53(3), 155–168.
ESL/MACC, 2008: Development of a National Water Sector Adaptation Strategy to
Address Climate Change in Jamaica, Prepared for Mainstreaming Adaptation to Climate
66
Change Project c/o Caribbean Community Climate Change Centre (CCCCC) by
Environmental Solutions Limited
Fernandez, Basil, National Water Authority, 2008: Private communication
Giannini, A., Y. Kushnir, and M.A. Cane, 2000: Interannual variability of Caribbean
rainfall, ENSO and the Atlantic Ocean. J. Clim., 13, 297–311.
Giannini, A., M.A. Cane, and Y. Kushnir, 2001: Interdecadal changes in the ENSO
teleconnection to the Caribbean region and North Atlantic Oscillation. J. Clim., 14,
2867–2879.
Goldenburg, S. B., Landsea C., Mestas-Nuñez, A. M. & Gray, W. M. (2001). The recent
increase in Atlantic hurricane activity: Causes and implications. Science, 293, 474-479.
Gray, W. M., C. W. Landsea, P. W. Milke Jr. and K. J. Berry, 1994: Predicting Atlantic
Basin seasonal tropical cyclone activity by 1 June. Weather and Forecasting, 9, 103-115.
IPCC, 2007: Summary for Policymakers. In: Climate Change 2007: The Physical Science
Basis. Contribution of WorkingGroup 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.
Jury, M., Malmgrem, B. A., & Winter A. (2007). Subregional precipitation climate of the
Caribbean and relationships with ENSO and NAO. J. Geophys. Res., 112,
doi:10.1029/2006JD007541, 2007.
Knutson, T.R., and R.E. Tuleya, 2004: Impact of CO2-induced warming on simulated
hurricane intensity and precipitation: Sensitivity to the choice of climate model and
convective parameterization. J. Clim., 17, 3477–3495.
Knutson, T. R., Sirutis, J. J., Garner, S. T., Held, I. M. & Tuleya, R. E. Simulation of the
recent multidecadal increase of Atlantic hurricane activity using an 18-km-grid regional
model. Bull. Am. Meteorol. Soc. 88, 1549–1565 (2007).
Knutson, T.R., Sirutis, J.J., Garner, S.T., Vecchi, G.A. & Held, I.M, 2008. Simulated
reduction in Atlantic hurricane frequency under twenty-first century warming conditions.
Nature Geoscience, 1, 359-364
Martis, A., Van Oldenborgh G. J., & Burgers G. (2002). Predicting rainfall in the Dutch
Caribbean – More than El Niño? Int. J. of Climatology, 22, 1219-1234.
Meehl, G.A., T.F. Stocker, W.D. Collins, P. Friedlingstein, A.T. Gaye, J.M. Gregory, A.
Kitoh, R. Knutti, J.M. Murphy, A. Noda, S.C.B. Raper,I.G. Watterson, A.J. Weaver and
Z.-C. Zhao, 2007: Global Climate Projections. In: Climate Change 2007: The Physical
67
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.
Nakićenović, N., and R. Swart (eds.), 2000: Special Report on Emissions Scenarios. A
Special Report of Working Group III of the Intergovernmental Panel on Climate Change.
Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA,
599 pp.
Neelin, J. D., Munnich, M., Su, H., Meyerson, J. E., & Holloway, C. (2006). Tropical
drying ttrends in global warming models and observations. Proc. Nat. Acad. Sci., 103,
6110-6115.
Oouchi, K., et al., 2006: Tropical cyclone climatology in a global-warming climate as
simulated in a 20km-mesh global atmospheric model: Frequency and wind intensity
analyses. J. Meteorol. Soc. Japan, 84, 259–276.
Peilke Jr, R. A. Landsea, C., Mayfield, M., Laver, J. & Pasch, R. (2005). Hurricanes and
global warming. Bull. Amer. Meteor. Soc., 86, 1571-1575.
Peterson, T. C., Taylor M. A., Demeritte R., Duncombe D., Burton S., Thompson F., et al.
(2002). Recent changes in climate extremes in the Caribbean region, J. Geophys. Res.,
107(D21), 4601, doi:10.1029/2002JD002251
Spence, J. M., M. A. Taylor and A. A. Chen (2004), The effect of concurrent sea
9 surface temperature anomalies in the tropical Pacific and Atlantic on Caribbean
10 Rainfall, Int. J. Climatol., 24, 1531-1541, doi:10.1002/joc.1068.
Taylor, M., and E. Alfero, 2005: Climate of Central America and the Caribbean. In: The
Encyclopedia of World Climatology [Oliver, J. (ed.)]. Encyclopedia of Earth Sciences
Series, Springer Press, 854 pp.
Taylor, M., D. Enfield, and A. Chen, 2002: The influence of the tropical Atlantic vs. the
tropical Pacific on Caribbean rainfall. J. Geophys. Res., 107 (C9), 3127,
doi:10.1029/2001JC001097
Taylor, M. A., Centella, A., Charlery, J., Borrajero, I., Bezanilla, A., Campbell, J., Rivero,
R., Stephenson, T. S., Whyte, F., Watson, R. (2007). Glimpses of the Future: A Briefing
from the PRECIS Caribbean Climate Change Project, Caribbean Community Climate
Change Centre, Belmopan, Belize. 24 pp.
UNDP, 2007: United Nations Development Programme, Human development Report
2007/2008, Palgrave Macmillan (Publishers), New York, 2007.
68
Watson, R. A., M. A. Taylor, T. S. Stephenson and J. D. Campbell, 2008: Jamaica’s
future climate deduced from climate models. Eighth Bi-annual Conference of the Faculty
of Pure and Applied Sciences, University of the West Indies, Mona, February 26-28,
2008.
Wang, C. & Enfield, D. (2001). The tropical Western Hemisphere warm pool. Geophy.
Res. Lett., 28(8), 1635-1638.
Webster, P. J., Holland, G. J., Curry, J. A., Chang, H. R. 2005. Changes in tropical
cyclone number, duration and intensity in a warming environment. Science, 309, 18441846
69
Appendix 1 SDSM results for 2015, 2030 and 2050
SDSM results during the years 2015, 2030 and 2050 are given since these years
were specifically mentioned in the terms of reference of the project. They are not as
meaningful for planning purposes as the projections for the decades centered around 2015,
2030, 2050 and 2080. The simulations are under A2 and B2 emissions. Temperature
changes are given only for Worthy Park, a station in the Upper Rio Cobre basin, since
this was the only station with sufficient data, while precipitation changes are given for
both Worthy Park and Sangster.
Temperature Changes
Figure A1.1 gives the comparison of the monthly mean observed maximum
temperature (blue line) at Worthy Park during the period 1961-1990 (baseline) with
simulations from a weather generator (purple line) used in SDSM, and 2 simulations of
temperature during the baseline period by SDSM (red-green lines). It can be seen that the
temperatures simulated by SDSM agree fairly closely with the observed data, and the
weather generator data, suggesting the SDSM adequately simulated the baseline data.
Fig A1.1 Observed and simulated monthly maximum temperature during the baseline
period 1961-1990
Figures A1.2 through A1.4 give projected changes in monthly maximum
temperatures for 2015, 2030 and 2050. The solid green line gives the simulated baseline
70
period (1961-90) for comparison. The other solid line in each figure gives the A2
simulation, while the dashed line gives the B2 simulation. There does not appear to be an
emerging pattern in the relationship between A2 and B2 scenarios on a monthly basis.
On an annual basis, A2 emissions give greater temperature changes than B2, except for
2050, surprisingly (Fig. A1.5)
Fig. A1.2 Baseline and 2015 monthly maximum temperatures under A2 and B2
emissions.
Fig. A1.3 Baseline and 2030 monthly maximum temperatures under A2 and B2
emissions.
71
Fig. A1.4 Baseline and 2050 monthly maximum temperatures under A2 and B2
emissions.
Fig. A1.5 Annual maximum temperature changes in 2015, 2030 and 2050 under A2 and
B2 emissions
72
Fig A1.6 gives the relative number of maximum temperature peaks over a
temperature threshold of 32ºC (89.6ºF). The observed peaks (purple) and simulated
peaks (green) in the baseline period are given for comparison. The simulation
overestimates the peaks. However it can be seen that the peaks in 2015, 2030 and 2050
gradually increase above the simulated baseline peaks. In other words, the number of
days above 32ºC continually increases as we go from the baseline period to 2050
Fig. A1.6 Relative peaks over a threshold of 32ºC for 2015, 2030 and 2050 as compared
with the simulated baseline period (green). The observed number of beaks is given
(purple).
Except for a difference in colours, Figs. A1.7 to A1.10 are corresponding changes
in minimum temperature in 2015, 2030 and 2050. Fig. A1.10 shows clearly that annual
73
temperatures are increasing and that the changes under B2 emission are greater than
under A2 emissions. The results of all temperature analyses are summarized in Table A1.
Fig. A1.7 Baseline and 2015 monthly minimum temperatures under A2 and B2
emissions.
Fig. A1.8 Baseline and 2030 monthly minimum temperatures under A2 and B2
emissions.
74
Fig. A1.9 Baseline and 2050 monthly minimum temperatures under A2 and B2
emissions.
Fig. A1.10 Annual minimum temperature changes in 2015, 2030 and 2050 under A2 and
B2 emissions
Table A1 The seasonal and annual maximum and minimum temperature results in
Celsius degrees are summarized . The temperatures values given are based on an average
75
of the results using A2 and B2 emissions. For each period, the maximum (TMAX) and
minimum (TMIN) temperatures are given for the baseline period (CCF61-90) 7 , 2015,
2030 and 2050, as well as the changes during these years from the baseline values. The
averages of the A2 and B2 values are highlighted in yellow.
Annual Mean WP Temperature (deg C) based on average of A2 and B2
TMAX
TMIN
Avg
CCF61-90
29.16
17.76
23.46
2015
29.86
18.19
24.02
2015 change
0.70
0.43
0.56
2030
30.35
18.52
24.43
2030 change
1.19
0.76
0.97
2050
30.72
18.69
24.70
2050
change
1.55
0.93
1.24
2050
29.37
17.16
23.27
2050
change
1.74
1.21
1.48
2050
30.61
18.34
24.47
2050
change
1.74
1.08
1.41
2050
19.75
19.77
19.76
2050
change
0.65
0.67
0.66
2050
31.41
19.47
25.44
2050
change
1.52
0.74
1.13
DJF Mean WP Temperature (deg C) based on average of A2 and B2
TMAX
TMIN
Avg
CCF61-90
27.63
15.95
21.79
2015
28.35
15.95
22.15
2015 change
0.72
0.00
0.36
2030
29.12
17.05
23.09
2030 change
1.49
1.10
1.30
MAM Mean WP Temperature (deg C) based on average of A2 and B2
TMAX
TMIN
Avg
CCF61-90
28.87
17.26
23.07
2015
29.93
18.05
23.99
2015 change
1.06
0.79
0.93
2030
30.41
18.31
24.36
2030 change
1.54
1.05
1.29
JJA Mean WP Temperature (deg C) based on average of A2 and B2
TMAX
TMIN
Avg
CCF61-90
19.10
19.09
19.10
2015
19.40
19.66
19.53
2015 change
0.30
0.56
0.43
2030
19.72
19.66
19.69
2030 change
0.62
0.56
0.59
SON Mean WP Temperature (deg C) based on average of A2 and B2
TMAX
TMIN
Avg
CCF61-90
29.89
18.73
24.31
2015
30.70
19.11
24.90
2015 change
0.81
0.38
0.59
Precipitation Changes
7
CCF refers to current climate forcing
76
2030
30.83
19.05
24.94
2030 change
0.95
0.32
0.63
Similar analyses were made for precipitation. Table A2 and A3 give summaries
of the results for precipitation at Worth park and Sangster corresponding to the summary
Table A1 for temperature.
Table A2 As for Table A1, except for precipitation at Worthy Park generated using A2
and B2 scenarios.
Annual Rainfall
(mm/month)
CCF61-90
A2
B2
Avg
172
171
171
2015
157
188
172
2015%
change
-9
10
1
2030
181
145
163
-4
2
-1
2030
146
140
143
-16
34
9
2030
156
170
163
-6
3
-1
2030
189
161
175
-9
2
-3
2030
233
110
171
2030%
change
6
-15
-5
2050
149
148
148
2050 %change
-13
-14
-14
15
12
14
2050
112
136
124
2050 %change
-12
9
-1
3
9
6
2050
144
154
149
2050 %change
-5
-1
-3
3
-12
-4
2050
146
137
142
2050 %change
-20
-25
-23
4
-50
-23
2050
192
164
178
2050 %change
-14
-26
-20
DJF Rainfall (mm/month)
CCF61-90
A2
B2
Avg
127
125
126
2015
122
128
125
2015%
change
2030%
change
MAM Rainfall (mm/month)
CCF61-90
A2
B2
Avg
152
156
154
2015
128
208
168
2015%
change
2030%
change
JJA Rainfall (mm/month)
CCF61-90
A2
B2
Avg
183
182
183
2015
173
189
181
2015%
change
2030%
change
SON Rainfall (mm/month)
CCF61-90
A2
B2
Avg
223
221
222
2015
204
226
215
2015%
change
77
2030%
change
Table A3 As for Table A1, except for precipitation at Sangster generated using A2 and
B2 scenarios.
Annual Rainfall
(mm/month)
CCF61-90
A2
B2
Avg
98
96
97
2015
88
103
95
2015%
change
-11
7
-2
2030
90
80
85
-5
-3
-4
2030
95
84
89
14
68
41
2030
80
93
86
-21
-4
-12
2030
72
82
77
-19
-7
-13
2030
112
59
86
2030%
change
-9
-17
-13
2050
76
69
72
2050 %change
-23
-29
-26
1
-8
-3
2050
82
83
83
2050 %change
-12
-8
-10
21
48
35
2050
73
84
79
2050 %change
12
33
23
-38
-27
-32
2050
47
41
44
2050 %change
-60
-63
-62
-5
-50
-28
2050
100
66
83
2050 %change
-16
-44
-30
DJF Rainfall (mm/month)
CCF61-90
A2
B2
Avg
94
91
92
2015
89
87
88
2015%
change
2030%
change
MAM Rainfall (mm/month)
CCF61-90
A2
B2
Avg
66
63
64
2015
75
106
90
2015%
change
2030%
change
JJA Rainfall (mm/month)
CCF61-90
A2
B2
Avg
116
112
114
2015
92
108
100
2015%
change
2030%
change
SON Rainfall (mm/month)
CCF61-90
A2
B2
Avg
118
119
119
2015
95
111
103
2015%
change
Appendix 2 - PRECIS Results
78
2030%
change
The exact coordinates of the grid boxes in Figure 3.3 are given in Table A2. The
downscaled results for annual temperature were given in Table 4.3. The downscaled
results for precipitation for time slices 2015s, 2030s, 2050s and 2080s for the 7 PRECIS
boxes are give in the following 7 tables (Boxes 1 to 7). Column 1 of each table gives the
periods examined: annual, December to February (DJF), March to May (MAM), June to
August (JJA) and September to November (SON). The second column gives the baseline
precipitation (mm/month) for the periods. The remaining columns give the precipitation
changes for the various time slices under A2 and B2 scenarios, and their average values.
It can be seen that whereas both positive and negative changes are obtained for the earlier
time slices, they begin to converge to negative changes by 2050s and the decreases are
pronounced by 2080s.
Table A2 Coordinates of grid boxes in Figure 3.3
Grid Box 1
Grid Box 2
Grid Box 3
Grid Box 4
Grid Box 5
Grid Box 6
Grid Box 7
Latitude
17.75 - 18.25
17.75 - 18.25
17.75 - 18.25
17.75 - 18.25
18.25 - 18.75
18.25 - 18.75
18.25 - 18.75
Longitude
281.70 - 283.18
283.00 - 284.30
284.30 - 285.60
285.60 - 286.90
281.70 - 283.18
283.00 - 284.30
284.30 - 285.60
79
Box1
Grid Box 1
Baseline
Annual
80.51
DJF
36.79
MAM
54.76
JJA
101.10
SON
129.37
2015s
A2
B2
8.48
-0.19
2.06
23.47
60.80
-4.47
-16.49 -11.13
-12.45
-8.64
Avg
2015s
A2
B2
-7.33 -2.25
-7.62
5.68
0.87 -2.93
-12.49 -6.32
-10.09 -5.43
Avg
2015s
A2
B2
-8.83 -4.09
-7.37 -1.73
-2.38 -3.65
-13.41 -6.07
-12.14 -4.91
Avg
2015s
A2
B2
-2.32
12.67
3.52
51.58
3.22
-2.26
-7.04
-4.64
-8.99
5.99
Avg
4.15
12.77
28.17
-13.81
-10.55
2030s
A2
B2
-1.50
8.27
15.02 27.85
7.32 22.73
-15.79
-9.46
-12.57
-8.04
Avg
2030s
A2
B2
-4.65
0.64
6.22
8.25
-3.11
3.61
-11.68 -4.56
-10.04 -4.75
Avg
2030s
A2
B2
-5.38
0.88
6.25 11.19
-1.80
4.33
-12.77
-5.84
-13.18
-6.15
Avg
2030s
A2
B2
10.20
25.05
40.02
86.99
11.41
15.22
-1.45
1.19
-9.17
-3.19
Avg
3.39
21.44
15.03
-12.62
-10.30
2050s
A2
B2
12.56
-6.21
-4.39 16.51
107.90
-4.09
-27.63 -20.06
-25.64 -17.21
Avg
2050s
A2
B2
-15.38
-7.90
-12.66
1.35
-6.04
-9.57
-22.27 -10.40
-20.53 -12.98
Avg
2050s
A2
B2
-17.49 -10.25
-11.11
-4.66
-11.11 -11.64
-23.87 -11.81
-23.89 -12.89
Avg
2050s
A2
B2
2.78
39.04
27.35 155.84
5.91
-3.36
-5.19
3.41
-16.95
0.25
Avg
3.17
6.06
51.91
-23.84
-21.43
2080s
A2
B2
-39.63 -20.04
-20.32
-7.67
-18.88
12.97
-64.12 -44.38
-55.20 -41.09
Avg
2080s
A2
B2
-42.33 -22.76
-25.72 -10.64
-37.53 -15.44
-53.17 -27.93
-52.88 -37.05
Avg
2080s
A2
B2
-42.75 -23.01
-22.80
0.65
-31.39 -19.86
-58.05 -34.25
-58.74 -38.58
Avg
2080s
A2
B2
-31.69 -14.19
-22.52
24.81
-18.15 -18.39
-37.84 -19.34
-48.27 -43.85
Avg
-29.84
-14.00
-2.95
-54.25
-48.14
Box2
Grid Box 2
Baseline
Annual
140.23
DJF
79.78
MAM
111.69
JJA
180.88
SON
188.58
-4.79
-0.97
-1.03
-9.40
-7.76
-2.01
7.24
0.25
-8.12
-7.40
-11.64
-5.65
-7.81
-16.34
-16.76
-32.55
-18.18
-26.49
-40.55
-44.97
Box 3
Grid Box 3
Baseline
Annual
161.33
DJF
113.78
MAM
138.16
JJA
171.04
SON
222.36
-6.46
-4.55
-3.01
-9.74
-8.53
-2.25
8.72
1.26
-9.31
-9.67
-13.87
-7.88
-11.37
-17.84
-18.39
-32.88
-11.08
-25.63
-46.15
-48.66
Box 4
Grid Box 4
Baseline
Annual
64.05
DJF
28.61
MAM
58.03
JJA
64.47
SON
105.09
5.17
27.55
0.48
-5.84
-1.50
17.63
63.50
13.32
-0.13
-6.18
80
20.91
91.60
1.28
-0.89
-8.35
-22.94
1.14
-18.27
-28.59
-46.06
Box 5
Grid Box 5
Baseline
Annual
29.96
DJF
25.29
MAM
25.22
JJA
21.11
SON
48.23
2015s
A2
B2
-1.37 -3.66
-4.45 -0.80
24.54
2.57
-12.12 -9.72
-13.45 -6.71
Avg
-2.52
-2.63
13.56
-10.92
-10.08
2030s
A2
B2
6.54 18.63
6.08 22.17
40.69 59.11
-8.41
-2.28
-12.22
-4.46
Avg
12.58
14.13
49.90
-5.35
-8.34
2050s
A2
B2
-2.10
-6.76
-6.05 -12.33
40.12
14.02
-18.28 -13.89
-24.18 -14.83
Avg
-4.43
-9.19
27.07
-16.09
-19.50
2080s
A2
B2
-50.82 -29.93
-37.33 -24.12
-31.61
8.28
-63.23 -49.29
-71.11 -54.59
Avg
2080s
A2
-38.81
-22.63
-27.72
-49.19
-55.72
Avg
B2
-16.19
-5.70
-2.91
-24.11
-32.05
2080s
A2
-42.08
-22.37
-26.13
-57.36
-62.47
B2
-27.63
-1.43
-18.97
-42.97
-47.15
-40.38
-30.73
-11.66
-56.26
-62.85
Box 6
Grid Box 6
Baseline
Annual
72.60
DJF
66.26
MAM
66.11
JJA
60.04
SON
97.99
2015s
A2
-6.73
-8.95
2.05
-8.79
-11.22
Avg
B2
-2.48
-3.84
0.10
-0.40
-5.80
2015s
A2
-8.77
-6.31
-1.25
-13.59
-13.91
B2
-4.98
-1.28
-4.64
-7.70
-6.28
-4.61
-6.39
1.08
-4.60
-8.51
2030s
A2
-2.56
-0.55
5.27
-4.71
-10.27
Avg
B2
4.97
3.27
18.80
1.87
-4.06
2030s
A2
-0.89
17.90
4.33
-11.33
-14.46
B2
3.41
21.11
8.98
-7.45
-9.00
1.20
1.36
12.03
-1.42
-7.17
2050s
A2
-14.28
-15.86
-6.54
-13.75
-20.98
Avg
B2
-6.94
-11.74
-1.21
0.15
-14.95
2050s
A2
-14.83
-6.57
-6.59
-20.76
-25.40
B2
-11.81
-7.75
-12.28
-11.88
-15.35
-10.61
-13.80
-3.87
-6.80
-17.97
-27.50
-14.17
-15.32
-36.65
-43.89
Box 7
Grid Box 7
Baseline
Annual
138.95
DJF
94.79
MAM
122.42
JJA
131.60
SON
206.98
Avg
-6.87
-3.80
-2.95
-10.65
-10.10
Avg
1.26
19.50
6.66
-9.39
-11.73
81
Avg
-13.32
-7.16
-9.43
-16.32
-20.38
Avg
-34.86
-11.90
-22.55
-50.16
-54.81
Appendix 3 - SDSM Results for Manley, Sangster and URCR
The summary of SDSM downscaled results for precipitation using outputs from
the HADCM3 GCM for A2, B2 scenarios and the average of A2 and B2 for annual and
seasonal (DJF, MAM, JJA and SON) precipitation at Manley, Sangster and the Upper
Rio Cobre River are given in the tables A3.1 to A3.3 below. Precipitation values
(mm/month) are given for the 1961-90 (CCF61-90) baseline, for the 2015s, 2030s, 2050s
and 2080s, as well as the percentage change for each time slice. As with the PRECIS
results, the changes are all negative by 2080s.
82
Annual average rainfall at Manley (mm/mth)
CCF61-90 2015s 2015% change
A2
67.59
69.01
2.11
B2
67.61
66.89
-1.07
Avg
67.60
67.95
0.52
2030s
59.57
61.34
60.45
2030% change
-11.87
-9.28
-10.57
2050s
66.12
67.23
66.68
2050 %change
-2.17
-0.57
-1.37
2080s
53.55
56.78
55.17
2080 %change
-20.76
-16.03
-18.40
DJF rainfall (mm/mth)
CCF61-90 2015s
A2
29.46
27.50
B2
29.86
30.02
Avg
29.66
28.76
2015% change
-6.66
0.53
-3.06
2030s
20.99
26.37
23.68
2030% change
-28.75
-11.69
-20.22
2050s
23.85
25.45
24.65
2050 %change
-19.03
-14.78
-16.91
2080s
23.24
22.68
22.96
2080 %change
-21.13
-24.07
-22.60
MAM rainfall (mm/month)
CCF61-90 2015s
A2
44.69
39.46
B2
43.36
45.90
Avg
44.02
42.68
2015% change
-11.71
5.86
-2.92
2030s
33.57
53.13
43.35
2030% change
-24.88
22.54
-1.17
2050s
53.09
52.21
52.65
2050 %change
18.80
20.41
19.60
2080s
39.22
38.54
38.88
2080 %change
-12.25
-11.11
-11.68
JJA rainfall (mm/month)
CCF61-90 2015s
A2
93.67
86.59
B2
92.51
91.65
Avg
93.09
89.12
2015% change
-7.55
-0.93
-4.24
2030s
77.02
73.86
75.44
2030% change
-17.77
-20.16
-18.97
2050s
96.72
97.69
97.21
2050 %change
3.26
5.60
4.43
2080s
67.58
77.37
72.48
2080 %change
-27.85
-16.37
-22.11
SON rainfall (mm/month)
CCF61-90 2015s
A2
101.54 119.76
B2
103.73
97.00
Avg
102.64 108.38
2015% change
17.94
-6.49
5.72
2030s
104.58
89.36
96.97
2030% change
2.99
-13.86
-5.43
2050s
88.42
91.03
89.73
2050 %change
-12.92
-12.25
-12.58
2080s
81.85
86.26
84.06
2080 %change
-19.39
-16.84
-18.12
Table A3.1 Summary of SDSM results for A2, B2 scenarios and their average for annual and seasonal (DJF, MAM, JJA and SON)
precipitation at Manley, comparing 2015s, 2030s, 2050s and 2080s results with 1961-90 (CCF61-90) baseline.
83
Annual average rainfall at Sangster (mm/mth)
CCF61-90 2015s 2015% change
A2
67.22 57.32
-14.72
B2
66.97 54.89
-18.05
Avg
67.09 56.10
-16.38
2030s
48.45
48.27
48.36
2030% change
-27.92
-27.92
-27.92
2050s
42.68
43.58
43.13
2050 %change
-36.50
-34.92
-35.71
2080s
28.30
32.48
30.39
2080 %change
DJF average rainfall at Sangster (mm/mth)
CCF61-90 2015s 2015% change
A2
56.60 46.65
-17.59
B2
56.30 49.49
-12.11
Avg
56.45 48.07
-14.85
2030s
39.50
41.79
40.65
2030% change
-30.21
-25.78
-28.00
2050s
36.88
38.42
37.65
2050 %change
-34.84
-31.75
-33.30
2080s
28.21
32.12
30.17
2080 %change
MAM Rainfall(mm/month)
CCF61-90 2015s
A2
45.58 45.75
B2
44.84 48.76
Avg
45.21 47.26
2015% change
0.38
8.73
4.56
2030s
41.35
46.00
43.68
2030% change
-9.27
2.58
-3.35
2050s
50.70
46.18
48.44
2050 %change
11.23
2.98
7.11
2080s
44.96
44.77
44.87
2080 %change
JJA Rainfall (mm/month)
CCF61-90 2015s
A2
87.04 60.04
B2
85.20 51.46
Avg
86.12 55.75
2015% change
-31.02
-39.60
-35.31
2030s
44.08
41.26
42.67
2030% change
-49.36
-51.58
-50.47
2050s
31.46
33.45
32.45
2050 %change
-63.86
-60.74
-62.30
2080s
7.14
12.54
9.84
2080 %change
SON Rainfall(mm/month)
CCF61-90 2015s
A2
77.76 72.18
B2
79.66 64.89
Avg
78.71 68.54
2015% change
-7.17
-18.54
-12.85
2030s
64.91
59.88
62.39
2030% change
-16.52
-24.83
-20.67
2050s
48.01
52.43
50.22
2050 %change
-38.25
-34.18
-36.22
2080s
30.07
37.27
33.67
2080 %change
Table A3.2 Same as for Table A3.1. but for Sangster
84
-57.89
-51.50
-54.70
-50.16
-42.95
-46.55
-1.36
-0.16
-0.76
-91.79
-85.28
-88.54
-61.33
-53.21
-57.27
Annual average rainfall at URCR (mm/mth)
CCF61-90
2015s
2015% change
A2
175.18
171.51
-2.10
B2
176.02
168.36
-4.36
Avg
175.60
169.93
-3.23
DJF average rainfall at URCR (mm/mth)
CCF61-90
2015s 2015% change
A2
101.61
97.55
-3.99
B2
103.16
99.78
-3.28
Avg
102.39
98.67
-3.64
2030s
161.11
165.39
163.25
2030% change
-8.03
-6.04
-7.04
2050s
163.86
165.30
164.58
2050 %change
-6.46
-6.10
-6.28
2080s
150.34
152.52
151.43
2080 %change
-14.18
-13.35
-13.77
2030s 2030% change
91.16
-10.29
95.60
-7.34
93.38
-8.81
2050s
93.43
91.23
92.33
2050 %change
-8.05
-11.57
-9.81
2080s
97.61
92.38
94.99
2080 %change
-3.94
-10.46
-7.20
MAM Rainfall (mm/month)
CCF61-90
2015s
A2
137.79
125.07
B2
139.22
140.38
Avg
138.50
132.72
2015% change
-9.23
0.84
-4.20
2030s 2030% change
124.71
-9.49
139.72
0.36
132.21
-4.57
2050s
137.10
134.17
135.64
2050 %change
-0.50
-3.62
-2.06
2080s
119.35
125.66
122.50
2080 %change
-13.38
-9.74
-11.56
JJA Rainfall (mm/month)
CCF61-90
2015s
A2
249.61
234.13
B2
247.24
226.88
Avg
248.43
230.50
2015% change
-6.20
-8.24
-7.22
2030s 2030% change
226.15
-9.40
222.09
-10.17
224.12
-9.79
2050.00
233.84
236.01
234.92
2050 %change
-6.32
-4.54
-5.43
2080s
205.18
203.82
204.50
2080 %change
-17.80
-17.56
-17.68
SON Rainfall (mm/month)
CCF61-90
2015s
A2
208.33
219.54
B2
211.04
196.41
Avg
209.68
207.98
2015% change
5.38
-6.93
-0.77
2030s 2030% change
193.30
-7.21
194.59
-7.79
193.95
-7.50
2050.00
181.74
190.65
186.19
2050 %change
-12.76
-9.66
-11.21
2080s
169.47
179.00
174.23
2080 %change
-18.65
-15.18
-16.92
Table A3.3 Same as for Table A3.1. but for URCR
85
86
Appendix 4 Wet and Dry Spell Results
The results for wet and dry spell are given in section 4.3.3 are based on Table A4.
Table A4 The percentage of wet-days (as fractions), Dry-spells and wet-spells (days) for
DJF, MAM, JJA, SON and annually at Manley, Sangster and URCR for 1961-90, 2015s,
2030s, 2050s and 2080s.
1961-90
DJF
MAM
JJA
SON
Annual
Wetdays%
0.13
0.14
0.23
0.22
0.18
Manley
Dryspell
7.33
7.62
4.48
4.79
6.04
Wetspell
1.19
1.27
1.42
1.45
1.36
Wetdays%
0.22
0.20
0.32
0.29
0.26
Sangster
Dry-spell
4.53
5.32
3.34
3.49
4.18
Wetspell
1.33
1.39
1.62
1.49
1.48
Wetdays%
0.38
0.43
0.69
0.59
0.52
URCR
Dryspell
2.68
2.55
1.58
1.83
2.20
Wetspell
1.68
1.94
3.44
2.63
2.43
2015s
DJF
MAM
JJA
SON
Annual
Wetdays%
0.13
0.13
0.22
0.24
0.18
Dryspell
7.63
7.51
4.66
4.53
6.04
Wetspell
1.18
1.24
1.40
1.52
1.36
Wetdays%
0.21
0.20
0.26
0.28
0.24
Dry-spell
4.68
5.28
4.16
3.69
4.53
Wetspell
1.29
1.36
1.50
1.48
1.42
Wetdays%
0.38
0.43
0.67
0.61
0.52
Dryspell
2.68
2.49
1.61
1.81
2.19
Wetspell
1.68
1.87
3.26
2.77
2.39
2030s
DJF
MAM
JJA
SON
Annual
Wetdays%
0.11
0.12
0.22
0.23
0.17
Dryspell
8.18
8.19
4.60
4.79
6.33
Wetspell
1.15
1.20
1.34
1.47
1.32
Wetdays%
0.20
0.18
0.21
0.26
0.21
Dry-spell
5.00
5.81
4.86
3.87
4.97
Wetspell
1.30
1.32
1.34
1.43
1.35
Wetdays%
0.38
0.42
0.67
0.58
0.51
Dryspell
2.72
2.58
1.59
1.86
2.22
Wetspell
1.65
1.85
3.19
2.59
2.33
2050s
DJF
MAM
JJA
SON
Annual
Wetdays%
0.12
0.14
0.25
0.21
0.18
Dryspell
7.86
7.13
4.23
5.20
6.07
Wetspell
1.17
1.28
1.43
1.41
1.35
Wetdays%
0.21
0.20
0.16
0.22
0.20
Dry-spell
4.86
5.52
6.62
4.57
5.52
Wetspell
1.31
1.48
1.32
1.37
1.37
Wetdays%
0.38
0.43
0.66
0.57
0.51
Dryspell
2.72
2.49
1.65
1.90
2.23
Wetspell
1.65
1.93
3.14
2.49
2.31
2080s
DJF
MAM
JJA
SON
Annual
Wetdays%
0.12
0.14
0.21
0.19
0.17
Dryspell
7.70
7.19
4.84
5.64
6.46
Wetspell
1.17
1.22
1.33
1.42
1.30
Wetdays%
0.20
0.17
0.05
0.17
0.15
Dry-spell
4.92
6.36
18.86
6.49
7.64
Wetspell
1.28
1.38
1.12
1.39
1.32
Wetdays%
0.39
0.42
0.63
0.55
0.50
Dryspell
2.58
2.47
1.68
1.97
2.21
Wetspell
1.67
1.83
2.88
2.43
2.21
87
Acknowledgement
We would like to acknowledge the contribution of the following members of the Climate
Studies Group Mona, University of the West Indies:
Lawrence Brown
Jayaka Campbell
Cassandra Rhoden
Rhodene Watson
88