1. WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam WFM 6311: Climate Change Risk Management Akm Saiful Islam Lecture-5d: Climate Change Scenarios Network December, 2009 Institute of Water and Flood Management (IWFM) Bangladesh University of Engineering and Technology (BUET)

2. WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Introduction to the Canadian Climate Change Scenarios Network (CCCSN) www.cccsn.ca

3. WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Considerations: Which Models? Which Scenarios? How do I get information for my location? Uncertainty in results? ? What about Downscaling? IPCC images CCCSN.CA Where do I start?

4. WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam What Information does CCCSN Provide? New Climate Change Science from IPCC 25 GCMs from the recent 4 th (AR4) assessment Canadian Regional Model (North America) New ‘Extreme’ Variables New Scatterplots, Downscaling Tools, Bioclimate Profiles for nearly 600 locations in Canada Download GCM/RCM data for custom analysis Download Downscaling software and input data

5. WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam This Training Session: Use of GCM / RCM grid cell output from many models and scenarios Best approach for the uncertainty More detailed investigation (of a single location) would require statistical downscaling techniques Statistical Downscaling (using SDSM, LARS, ASD, etc) is not the focus of this training CCCSN has downscaling tools and input data required by them

6. WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam The Typical Model Grid The models provide GRID cell AVERAGED values - not a single point location

7. WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Contents Text Menu Driven

8. WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Contents Text

9. WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Contents Text

10. WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Contents Text

11. WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam CCCSN Visualization: Maps –see an overview of a single model across Canada (zoomable) Scatterplot – see an overview of one or many models for a single location Bioclimate Profiles – see an overview of a single model at a single location Advanced Spatial Search – see where on a map specific criteria you select are found Don’t like our visualizations? Download the data and generate your own custom maps/charts/tables

12. WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Some Considerations: The models generally use 1961-1990 as their ‘baseline period’ - most recent is 1971-2000 ‘Anomalies’ are the DIFFERENCE between a future period projection and a baseline Maps can output model values OR anomalies Scatterplots output anomalies (the change) from the baseline value Future projections tend to be averaged over standard periods as well (but they don’t have to be): 2020s = 2011-2040 2050s = 2041-2070 2080s = 2071-2100

13. WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Some Considerations: Bioclimate profiles are a ‘hybrid’ of observed and model projection data Baseline = Observed data at a climate station + Model Anomaly value = Projected Value for 2020s, 2050s, 2080s One of 583 stations Grid cell value

14. WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Toronto Area Bioclimate Stations

15. WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Bioclimate profiles Example: Water Balance Profile: Profiles available for these locations: -Temperature-Heating DD and Cooling DD -Daily and Monthly GDD-CHU -Frost Profile-Water Balance -Frequency of Precipitation-Temperature Threshold -Freeze/Thaw Cycles-Accumulated Precipitation

16. WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam So… for any selected location: The model selected affects the result The emission scenario selected affects the result There are about 25 GCMs with 2 or 3 emission scenarios for each (about 50-75 outcomes) Within Canada we also have the CRCM (several versions) using one emission scenario (A2)

17. WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Emission Scenarios ‘A2’ – aggressive growth ‘A1B’ – moderate growth ‘B’ – low growth (image sources: TGICA GUIDANCE, IPCC, 2007)

18. WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam What Variables? Timescale? CCCSN has a reduced number of GCM/RCM variables including: 2 m Air Temperature (mean, max, min) (C) Precipitation (mm/d) Sea Level Pressure (mb) Specific Humidity/Relative Humidity (kg/kg or %) 10 m Windspeed (mean, U and V) (m/s) Incoming Shortwave Radiation (W/m2) TIMESCALE: minimum is MONTHLY on CCCSN

19. WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Extreme Variables include (some models): 2 m Air Temperature Range (C) Consecutive Dry Days (days) Days with Rain > 10 mm/d (days) Fraction of Annual Total Precip > 95 th percentile (%) Fraction of Time < 90 th percentile min temp (%) Number of Frost Days (days) Maximum Heat Wave Duration (days) Maximum 5 Day Precipitation (mm) Simple Daily Intensity Index (mm/day) Growing Season Length (days)

20. WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Effect of Emission Scenario (holding model constant) A2 A1B B1

21. WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Effect of Model (holding emission scenario constant) All models which produce A1B output

22. WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Model considerations: Newer versions of models are better than older Increase in temporal and spatial resolution is preferable Uncertainty in: 1. Emission scenarios 2. Parameterization of sub-grid scale processes 3. Climate sensitivity? Will it be constant? Models represent the best method available to project future climate

23. WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam What are the International Modeling Centres? BCM2.0Bjerknes Centre for ClimateNorway CGCM3T47 CGCM3T63 Canadian Centre for Climate and Modelling Analysis Canada CNRMCM3Centre National de Recherches Meteorologiques France CSIROMK3 CSIROMK3 5 Commonwealth Scientific and Industrial Research Organisation (CSIRO) Australia ECHAM5O M Max Planck Institute für MeteorologieGermany ECHO-GMeteorological Institute, University of Bonn Germany FGOALS- G10 Institute of Atmospheric Physics, Chinese Academy of Sciences China GFDLCM20 GFDLCM21 Geophysical Fluid Dynamics Laboratory USA GISSE-H GISSE-R Goddard Institute for Space StudiesUSA HADCM3 HADGEM1 Hadley Centre – UK Meteorological Office UK

24. WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Centres… CGCM232Meteorological Research InstituteJapan INMCM30Institute for Numerical MathematicsRussia IPSLCM4Institut Pierre Simon LaplaceFrance MIROC32HI MIROC32M ED National Institute for Environmental Studies Japan NCARPCM NCARCCS M3 National Center for Atmospheric Research USA Coming up… INGV-SGX National Institute of Geophysics and Volcanology ItalyNational Institute of Geophysics and Volcanology Also: Canadian Regional Climate Model (CRCM3.7.1, 4.1.1 and 4.2.0) from OURANOS Consortium (EC a member) (Montreal, QC)

25. WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Some comments on Downscaling… More advanced analysis

26. WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Two Main Downscaling Methods: (1) Dynamical Downscaling Regional Climate Models (RCMs) Benefit: physically based – but still use parameterization Limitations: computation time, complexity, dependent on initialization data (GCM) (2) Statistical Downscaling Establish relationships between model scale information and local ‘point’ information Benefit: relatively easy to implement – but not for the untrained Limitations: -stationarity – are the statistical relationships developed valid in the future? -need good observational data and model predictor data

27. WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam What is Statistical Downscaling?

28. WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Statistical Downscaling CCCSN provides tools: 1. Automated Statistical Downscaling (ASD) 2. Statistical Downscaling Model (SDSM) 3. Weather Generator (LARS-WG) CCCSN provides the necessary input data: 1. Access to observed data (weatheroffice / DAI) 2. Access to required projection predictors from HadCM3 and CGCM2/CGCM3 via Data Access Interface (DAI)

29. WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam Conclusions: Many GCMs and more and more Regional Climate Models coming on-line (NARRCAP project) Results can vary widely between models and emission scenario selected Some models do better than others at reproducing the historical climate as we shall see In complex environments (coastal, mountainous, sea ice), extra care is required (grid cell averaging and process parameterization) Downscaling of even RCMs is likely required for some investigations It is critical to not rely on any single model/scenario for decision- making. Due diligence requires the consideration of more than a single possible outcome.