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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CSI296

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IAP_97

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PCM_00

WM_95

Jamaica (Precip.)

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ECH395

ECH498

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GISS95

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IAP_97

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WM_95

Barbados, St Lucia, Trinidad (Temp)Magicc/Scengen

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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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BMRC98

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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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ECHAM 4 A21

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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

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Temp (deg.C)

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HadCM 3 A21

HadCM3(SDSM)

GCM Models vs SDSM - Baseline Precip for Trinidad

Trinidad (Precip.baseline) Models vs SDSM

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GCM Models vs SDSM - Precip Scenarios for Trinidad

Trinidad Precipitation (Models vs SDSM)

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GCM Models vs SDSM - Baseline Temp for Barbados

Barbados (Temp. baseline) Models vs SDSM

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GCM Models vs SDSM - Temp Scenarios for Barbados

Barbados (Temp) Models vs SDSM

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GCM Models vs SDSM - Baseline Precip for Barbados

Barbados (Precip.baseline) Models vs SDSM

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GCM Models vs SDSM - Precip Scenarios for Barbados

Barbados Precipitation (Models vs SDSM)

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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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Temp. (deg. C)

Observations 1961-90

SDSM baseline (NCEP)

Barbados T(Max) - baseline

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3131.5

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Temp. (deg. C)

Observations 1961-90

HadCM3 (SDSM)baseline

Barbados T(Max) - 2080s

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HadCM3 - 2080s

Winter in the summer?

Model does not simulate mid-summer drought properly

Barbados T(Min) - baseline

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Barbados T(Min) - baseline

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HadCM3 (SDSM) baseline

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Barbados Pptn. baseline

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Barbados Pptn -2080s

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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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Annual and Seasonal % change in Mean Temperatures for SIA using B2 Scenario with respect to baseline

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% change in Mean Temperatures

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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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Annual and Seasonal % change in Precipitation for SIA using B2 Scenario with respect to baseline

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The problem with bias

Annual and Seasonal Precipitation for SIA using B2 Scenario

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Obs (61-90) baseline 2020s 2050s 2080s

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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