WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam WFM 6311: Climate Change Risk Management Akm Saiful Islam Lecture-6: Approaches to Select GCM data December, 2009 Institute of Water and Flood Management (IWFM) Bangladesh University of Engineering and Technology (BUET)

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Approaches for selecting a Global Climate Model for an Impact Study

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam The IPCC has a guidance document of interest… “General Guidelines on the use of Scenario Data for Climate Impact and Adaptation Assessment” Version 2, June 2007 Prepared by T.R. Carter with contributions from other authors The Task Group on Data and Scenario Support for Impact and Climate Assessment (TGICA) of IPCC This PDF is provided on the CCCSN Training DVD IPCC-TGICA, 2007

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam From the Range of Projections… IPCC recommends * the use of more than simply ONE model or scenario projection (one should use an ‘ensemble’ approach) – we saw why earlier The use of a limited number of models or scenarios provides no information of the uncertainty involved in climate modelling Alternatives to an ‘ensemble approach’ might involve the selection of models/scenario combinations which ‘bound’ the max/min of reasonable model projections (used in IJC Lake Ontario-St. Lawrence Regulatory Study) * (IPCC-TGICA, 2007)

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Two Tests for the selection of a Model: TEST 1: How well does a model reproduce the historical climate? TEST 2: How does the model compare with all other models for future projections? Commonly called ‘Model Validation’

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam First test: Baseline (historical) climate A model should be able to accurately reproduce past climate (baseline) as a criterion for further consideration We can test how well a model has reproduced the historical baseline climate (Model VALIDATION) Require reliable, long-term observed climate data from the location of interest OR we could use GRIDDED global datasets at the same scale as the models IMPORTANT: Remember we are comparing site-specific to a grid cell average, so an exact match is not to be expected.

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Second test: Future Projection We can check how a model performs in comparison with many others in a future projection 5 criteria outlined by IPCC: 1. Consistency with other model projections 2. Physical plausibility (realistic?) 3. Applicability for use (correct variables? timescale?) 4. Representative 5. Accessibility of data A model should not be an outlier in the community of model results

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Check maps - CGCM3 - Temperature? OBS Stations NCEP GRIDDED CGCM3T Mean ANNUAL TEMPERATURE Reasonable pattern, with models slightly cold

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Example: CGCM3 – Timeseries in the Historical Period The model is too cold, but the TREND is good

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Check maps - CGCM3 - Precipitation? OBS Stations NCEP GRIDDED CGCM3T Mean ANNUAL PRECIPITATION Pattern not quite right –units here are mm/day

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Example: CGCM3 – Timeseries in the Historical Period The model is too wet,TREND is reasonable

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Test 1: Baseline Methodology: Comparison of Annual, Seasonal, Monthly means over the same historical period Use the variables of interest – most common – precipitation and temperature from the Archive Keep in mind that we are comparing a single site location (meteorological station) against a gridded value An improved method would be to include other nearby stations in the analysis as well with long records We then obtain from CCCSN the model baseline values for the same location using the SCATTERPLOT

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Test 1: (continued) Compare the annual values and the distribution of temperature over the year Models which best match the annual mean and the monthly distribution pattern can be identified NOTE: it doesn’t matter which emission scenario we select since for the historical period, the models use the same baseline

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Test 1: Baseline Methodology… Annual Temperature Annual Precipitation too wet too dry too cold too warm observed means

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Test 1: Baseline Methodology… Looking at Temp and Precip together Again, SCATTERPLOT on CCCSN – simply select BOTH variables at the same time and all models or combine the 2 initial results in a single spreadsheet ‘Perfect’ model Almost all models are too wet Most models are too cold Outliers can be identified

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Test 1: Baseline Methodology… Rank the models for the baseline period - ANNUAL TemperaturePrecipitation Model A rank Model B rank Model C rank Model D rank Model E rank Model F rank … Total Score + Model A rank Model B rank Model C rank Model D rank Model E rank Model F rank … Sum of Model A ranks Sum of Model B ranks Sum of Model C ranks Sum of Model D ranks Sum of Model E ranks Sum of Model F ranks … Lowest Score Model is Closest to Baseline

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Test 1: Baseline Methodology The same analysis can be done on a month and seasonal basis –this can be very important This method is best used to reject models (models with largest scores) We effectively remove from consideration those models with lowest agreement (largest scores) The moderating effect of lakes, local elevation effects, lake-induced precip are all complicating factors

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Test 2: Future Projections No complications like observed data! We look at the range of model projections for the same location and see how they vary Models with outlier projections (excessive anomalies – which are too large or too small) are best rejected Finding the anomalies is a simple process using SCATTERPLOT on CCCSN

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Test 2: Future Projections Which projection period are we interested in? (2050s is a common period for planning purposes) Is an annual, seasonal or monthly projection needed? - depends on the study The or period as baseline?

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Annual Temperature/Precipitation Change Scatterplot for Toronto Grid Cell: 2050s (ONLY SRES) Median T and P for all models/scenarios 1 Std. Dev

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam What do all the models and emission scenarios tell us for this gridcell? Median Annual Temperature Change in 2050s Median Annual Precipitation Change in 2050s % o +3.3 o +1.8 o +9.7%+0.4% o 7.2 C Toronto Pearson A Observed Normal 780.8mm LOWER UPPER

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam TEST 2: Which Models are Closest to the Median Projection? Rank the models for the 2050s Projections - ANNUAL TemperaturePrecipitation Model A rank Model B rank Model C rank Model D rank Model E rank Model F rank … Total Score + Model A rank Model B rank Model C rank Model D rank Model E rank Model F rank … Sum of Model A ranks Sum of Model B ranks Sum of Model C ranks Sum of Model D ranks Sum of Model E ranks Sum of Model F ranks … Lowest Score Model is Closest to ALL MODEL MEDIAN

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam NCARCCSM3 HADCM3 INMCM3.0 GISSAOM CGCM3T47-Mean CGCM3T63 GISSE-R CNRMCM3 HadGEM1 Is there a ‘best’ model for both tests? Resulting Models TEST 1 TEST 2 (baseline) (projections) FGOALS-g1.0.SR-A1B CSIROMk3.0.SR-A2 MRI-CGCM2.3.2a.SR- A1B GISSAOM.SR-A1B CGCM3T63.SR-B1 GFDLCM2.0.SR-B1 GFDLCM2.1.SR-A2 HADCM3.SR-A2 BCM2.0.SR-A1B BCM2.0.SR-B1 MRI-CGCM2.3.2a.SR- A2 HADCM3 GISSAOM CGCM3T63 Resulting Models Best Models from both TESTS

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam The Caveats: We have only considered ANNUAL values, not SEASONAL or MONTHLY baseline (TEST 1) or projections (TEST 2) The seasonal and monthly options are available on the SCATTERPLOT selector) ‘Extreme variables’ have greater uncertainty than normals Models can show good ANNUAL agreement with baseline and good agreement with all model projections, but they can still have incorrect seasonal or monthly distributions

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Will Regional Climate Model (RCM)s help? They offer higher spatial resolution (~50 x 50 km) versus GCM at km The models are driven by an overlying model or gridded data source – so biases in those gridded datasets will also be included in the RCM The time requirements and processing power available means there are fewer emission scenarios available = fewer future pathways for consideration Some investigations will always require further statistical downscaling

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Will RCMs Help in TEST 1? Annual Temperature Annual Precipitation too wet too dry too cold too warm CRCM3.7.1: 6.1 C CRCM4.1.1: 4.9 C CRCM4.2.2: 6.1 C all cold CRCM3.7.1: 758.5mm too dry CRCM4.1.1: 542.8mm too dry CRCM4.2.2: 860.7mm too wet

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Median T and P 1 Std. Dev Will RCMs Help in TEST 2? crcm3.7.1 crcm4.1.1 crcm4.2.0

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Running Scatterplots for all parameters

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam CCCSN.CA website Select Scenarios - Visualization

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Select Scatterplots

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Get data Input lat long Select AR4 Select Variable Tmean Select Model(s) validated to Tmean Click Get Data

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Website Output Plus output table under chart

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Get data for all variables including climate extremes You can select an ensemble of models by using Ctrl- Enter

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Ensemble of CCCSN.CA Results for Ptotal at Windsor

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Climate Extremes available for some models

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Future Consecutive Dry Days at Windsor Using 3 GCM model output Can average all model results for ensemble