Climate Scenario and Uncertainties in the Caribbean
Anthony Chen,Cassandra Rhoden,Albert OwinoChen,Cassandra Rhoden,Albert OwinoClimate Studies Group Mona,Department of PhysicsClimate Studies Group Mona,Department of PhysicsUniversity of the West Indies,Mona, JamaicaUniversity of the West Indies,Mona, Jamaica
????SIS 06 The Threat of Dengue Fever - Assessment of Impacts and Adaptation to Climate Change in Human Health in the Caribbean
Outline• Scenario needs• General Problems with GCM in Scenario
Generation, briefly• Downscaling results 1• Problems with downscaling • Local problems• Downscaling results2• Discussion• Conclusion
What is your Scenario need?• How many scenarios do you want? Which
uncertainties are you going to explore?• What non-climate information do you need in your
scenario(s)?• Do you need local data for case studies/sites, or
national/regional coverage?• What spatial resolution do you really need – 300k,
100k, 50k, 10k, 1k? Can you justify this choice?• Do you need changes in average climate, or in
variability?• Do you need changes in daily weather, or just monthly
totals?• What climate variables are essential for your study?
What are SIS06’s needs?• How many scenarios do you want? -
Statistical (A2 & B2) and Dynamics (Will not be available until 6 mths time)
• Which uncertainties are you going to explore- Model uncertainties (Annual & seasonal)
• What non-climate information do you need in your scenario(s) – Use IPCC SRES
• Do you need local data for case studies/sites, or national/regional coverage? - Local
SIS06 needs (cont
)
• What spatial resolution do you really need – point (SDSM), 50 km (PRECIS)
• Do you need changes in average climate, or in variability? – average climate
• Do you need changes in daily weather, or just monthly totals? Daily
• What climate variables are essential for your study? – Temperature, Precip, Relative Humidity
Problems with With GCM in creating Climate Scenarios
Problem 1. Models are not accurate ….
… so we ‘cannot’ use data from climate models directly in environmental or social simulation models
Problems with GCM in creating Climate Scenarios
• Problem 2. Different climate models give different results …
• … so we have difficulty knowing which climate model(s) to use
Model vs Observation Pattern Correlation over the Caribbean by Dr. Ben Santer, Lawrence Livermore NL
Problems with GCM in creating Climate Scenarios
• Problem 3. It is expensive to run many (global/regional) climate model experiments for many future emissions ….
• .… so we often have to make choices about which emissions scenarios from which we build our climate scenarios
Problems with GCM increating Climate Scenarios – many different Storylines
Problems with GCM in creating Climate Scenarios
•Problem 4. Climate models give us results
at the ‘wrong’ spatial scale …
•… so we have to develop and apply one or more downscaling methods.
Problems with GCM in creating Climate Scenarios
• Problem 4. Historical climate data may not be available… necessary as a baseline and also to explore historical/current variability/vulnerability
Downscaling
A technique to take GCM atmospheric fields
and
derive climate information at a spatial / temporal scale
finer than that of the GCM.
MAGICC/SCENGEN
Model for the Assessment of Greenhouse-gas Induced Climate Change MAGICC is the climate model that has been used in all IPCC assessments to produce projections of global-mean temperature. Results courtesy of Tom Wigley, NCAR
Jamaica (Temp.)
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3.5
1 2 3
1: 2000 2:2050 3:2100
Temp.(deg. C)
All
BMRC98
CCC199
CCSR96
CERF98
CSI296
CSM_98
ECH395
ECH498
GFDL90
GISS95
HAD295
HAD300
IAP_97
LMD_98
MRI_96
PCM_00
WM_95
Jamaica (Precip.)
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-50
-40
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-20
-10
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1 2 3
1:2000 2:2050 3:2100
(% Precip)
All
BMRC98
CCC199
CCSR96
CERF98
CSI296
CSM_98
ECH395
ECH498
GFDL90
GISS95
HAD295
HAD300
IAP_97
LMD_98
MRI_96
PCM_00
WM_95
Barbados, St Lucia, Trinidad (Temp)Magicc/Scengen
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1
1.5
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1 2 3
All
BMRC98
CCCI99
CCSR96
CERF98
CSI296
CSM_98
ECH395
ECH498
GFDL90
GISS95
HAD295
HAD300
IAP_97
LMD_98
MRI_96
PCM_00
WM_95
Barbados, St. Lucia, Trinidad (%Precip)Magicc/Scengen
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-40
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-10
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10
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1 2 3
All
BMRC98
CCC199
CCSR96
CERF98
CSI296
CSM_98
ECH395
ECH498
GFDL90
GISS95
HAD295
HAD300
IAP_97
LMD_98
MRI_96
PCM_00
WM_95
Scenarios from Weather Generators (SDSM)
• Downloaded from http://www.sdsm.org.uk/
Multiple, low cost, single-site scenarios of daily surface weather variables under current and future climate forcing
Main Advantages and Disadvantages of the SDSM. Advantages of SDSM
• site or locality specific scenarios, long and multiple daily weather sequences produced• Use of specific Scenarios, depending on how the climate system is changing. (Site or locality
specific)• Cheap, computationally undemanding.
Disadvantages of SDSM
• Requires high quality daily data for model calibration (30 years of historic data )
• based on empirical relationships which may change.
• SDSM cannot analyze extreme events of weather thus a regional climate model (RCM) has to be developed
GCM Models vs SDSM - Baseline Temp for Trinidad
Trinidad(Temp.baseline) Models vs SDSM
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28.5
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Baseline (61-90)
Temp(deg.C)
Obs
Baseline(Ncep)
ECHAM 4 A21
HadCM 3 A21
HadCM3(SDSM)
GCM Models vs SDSM - Temp Scenarios for Trinidad
Trinidad (Temp)Models vs SDSM
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baseline(61-90) 2020s 2050s 2080s
Time Slices
Temp (deg.C)
Obs
Baseline(Ncep)
ECHAM 4 A21
HadCM 3 A21
HadCM3(SDSM)
GCM Models vs SDSM - Baseline Precip for Trinidad
Trinidad (Precip.baseline) Models vs SDSM
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Baseline(61-90)
Precip (mm/day)
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Baseline(Ncep)
ECHAM 4 A21
HadCM3 A21
HadCM3 (SDSM)
GCM Models vs SDSM - Precip Scenarios for Trinidad
Trinidad Precipitation (Models vs SDSM)
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baseline 2020s 2050s 2080s
Time Slices
Precip (mm/day)
Obs
Baseline(Ncep)
ECHAM 4 A21
HadCM3 A21
HadCM3 (SDSM)
GCM Models vs SDSM - Baseline Temp for Barbados
Barbados (Temp. baseline) Models vs SDSM
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Baseline(61-90)
Temp (deg.C)
Obs
Baseline(Ncep)
ECHAM 4 A21
HadCM 3 A21
HadCM3(SDSM)
GCM Models vs SDSM - Temp Scenarios for Barbados
Barbados (Temp) Models vs SDSM
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baseline(61-90) 2020s 2050s 2080s
Time Slices
Temp (deg.C)
Obs
Baseline(Ncep)
ECHAM 4 A21
HadCM 3 A21
HadCM3(SDSM)
GCM Models vs SDSM - Baseline Precip for Barbados
Barbados (Precip.baseline) Models vs SDSM
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Baseline (61-90)
Precip (mm/day)
Obs
Baseline(Ncep)
ECHAM 4 A21
HadCM3 A21
HadCM3 (SDSM)
GCM Models vs SDSM - Precip Scenarios for Barbados
Barbados Precipitation (Models vs SDSM)
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baseline 2020s 2050s 2080s
Time Slices
Precip (mm/day)
Obs
Baseline(Ncep)
ECHAM 4 A21
HadCM3 A21
HadCM3 (SDSM)
Seasonal Analysis
• Seasonal variations are important for SIS06
• Baseline comparisons are good for annual data but falls down for seasonal data
Barbados T(Max) - baseline
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30.5
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31.5
Jan FebMar Apr MayJun Jul AugSepOct NovDec
Temp. (deg. C)
Observations 1961-90
SDSM baseline (NCEP)
Barbados T(Max) - baseline
2727.5
2828.5
2929.5
3030.5
3131.5
Jan FebMarApr MayJunJulAugSepOct NovDec
Temp. (deg. C)
Observations 1961-90
HadCM3 (SDSM)baseline
Barbados T(Max) - 2080s
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Jan FebMar Apr MayJun Jul AugSepOct NovDec
Temp. (deg. C)
Observations 1961-90
HadCM3 - 2080s
Winter in the summer?
Model does not simulate mid-summer drought properly
Barbados T(Min) - baseline
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Jan Feb Mar Apr MayJunJulAugSepOct NovDec
Temp. (deg. C)
Observations 1961-90
SDSM baseline (NCEP)
Barbados T(Min) - baseline
19.5
2020.5
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21.522
22.5
2323.5
24
Jan Feb Mar Apr MayJun Jul Aug Sep Oct Nov Dec
Temp. (deg. C)
Observations 1961-90
HadCM3 (SDSM) baseline
Barbados T(Min) -2080Barbados T (Min) - 2080s
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Temp.(deg.C)
Observations(1961-90)
HadCM3 2080s
Barbados Pptn. baseline
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Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Precip (mm/day)
Observations (1961-90)
SDSM baseline (NCEP)
Barbados Pptn. baseline
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Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Precip (mm/day)
Observations (1961-90)HadCM3 (SDSM) Baseline
Barbados Pptn -2080s
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Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Precip (mm/day)
Observations (1961-90)HadCM3 2080s
Downscaling UncertainitesAssumption 1
“Local” Climate = f (larger scale atmospheric forcing)
R = f (L)
R: predictand - (a set of) regional scale variables
L: predictors - large scale variables from GCM
f: stochastic or quantitative transfer function conditioned by L, or a dynamical regional climate model.
Downscaling
• Assumptions:
• f is valid under altered climatic conditions - stationarity LocalResponse
Since downscaling propagates the GCM error,consider another assumption
• Assumption 2:
• The GCM is skillful (enough) with regard to the predictors used in the downscaling -- Are they “adequately” simulated by the GCM?
• “Adequate” requires evaluating the GCM in terms of the predictor variables at the space and time scales of use!
• e.g: For RCMs this could mean the full 3-dimensional fields of motion, temperature, and humidity, on a 6-12 hour time interval, over the domain of interest.
Problems encountered locally adding to uncertainties
• Absence of quality data• Available predictors may not be the
major drivers of climate• Lack of Resources to do ensembles• Lack of adequate understanding of
regional climate for reliable prognosis
• Seasonal biases in SDSM
Absence of Quality Data in SIS06
• Jamaica’s daily data prior to 1992 were lost due to a fire in the Met Office.
• No daily Relative Humidity available
• Quality control not assured
Attempt to fill in missing daily temperature data using monthly mean data
daily temperature = daily anomalies + long-term monthly temperature average:
)()(,,)(,, AmAymdAymd TTT +Δ=
Algorithm for calculating daily anomaly uses daily data from a station elsewhere in the island
Source of algorithm – Dr. Xianfu Lu
Graph of WP synthetic daily vs WP observed daily (Temp ◦C)
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WP obs mean temp 1961-1990
WP synthetic mean temp 1961-1990
Annual and Seasonal % change in Temperature for SIA with respect to Baseline
Annual and Seasonal % change in Mean Temperatures for SIA using A2 Scenario with respect to baseline
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Winter Spring Summer Autumn Annual
Time Slices
% change in Mean Temperature
2020s
2050s
2080s
Annual and Seasonal % change in Mean Temperatures for SIA using B2 Scenario with respect to baseline
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Winter Spring Summer Autumn Annual
Time Slices
% change in Mean Temperatures
2020s
2050s
2080s
A2 B2
Attempt to fill in missing daily precipitation data using monthly mean data
• Similar to temperature method but used proportionalities
• Source of algorithm – Dr. Xianfu Lu
Graph of WP synthetic daily vs WP observed daily (Precip mm/day)
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WP daily observed precip
WP daily synthetic precip
Series1
Annual and Seasonal % change in Precip for SIA with respect to Baseline
Annual and Seasonal % change in Precipitation for SIA using A2 Scenario with respect to baseline
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Winter Spring Summer Autumn Annual
Time Slices
% change in Precipitation
2020s
2050s
2080s
Annual and Seasonal % change in Precipitation for SIA using B2 Scenario with respect to baseline
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Winter Spring Summer Autumn Annual
Time Slices
% change in Precipitation
2020s
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2080s
A2 B2
The problem with bias
Annual and Seasonal Precipitation for SIA using B2 Scenario
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Obs (61-90) baseline 2020s 2050s 2080s
Time Slices
Precipitation
Winter
Spring
Summer
Autumn
Annual
Predictors not most applicable for SIS06
• Sea Surface Temperatures (SST’s) are significant predictors for climate in the Caribbean, likely also for all SIS’s
• Historical gridded SST’s are available
• GCM SST predictors not readily available for use in SDSM
Lack of Resources to do ensemble in SIS06
• MACC project however will do runs using PRECIS regional model
• Results will not be available until 2005
Inadequate Understanding of Climate in SIS06
• Several papers have been published on interannual and seasonal variability
• However no adequate predictive model for variability has been produced for SIS06 countries due to inadequate understanding
Recall Downscaling UncertainitesAssumptions
“Local” Climate = f (larger scale atmospheric forcing)R = f (L)R: predictand - (a set of) regional scale variableL: predictors - large scale variables from GCMf: stochastic or quantitative transfer function
conditioned by L, or a dynamical regional climate model.
We need to know f more accurately
Problems encountered in running SDSM
• Overflow in Calculation - This is due to the number of missing data
Conclusion• Downscaling can add value to GCM outputs, works better
for Temperature than for Precip• A few cases where GCM agreed with SDSM (Temp for
Trinidad)• For site studies, one must use downscaled results (Those
cases where SDSM does not agree with GCM)• Problems compounded by lack of adequate predictor and
observed data• There were seasonal biases in SDSM calibration
especially for precipitation.• SDSM cannot analyze extreme events of weather thus a
regional climate model (RCM) has to be developed .• Since SDSM and other statistical models assume that
climate transfer function is stationary, we need to know more about current climate, to fully evaluate SDSM and decrease the uncertainties.
• Use of many models, ensembles needed to minimize uncertainty
Thank You for listening