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WFM 6311: Climate Change Risk Management

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

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WFM 6311: Climate Change Risk Management

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

  2. Introduction to the Canadian Climate Change Scenarios Network (CCCSN)www.cccsn.ca

  3. Considerations: Which Scenarios? Which Models? ? Uncertainty in results? How do I get information for my location? What about Downscaling? IPCC images Where do I start? CCCSN.CA

  4. What Information does CCCSN Provide? • New Climate Change Science from IPCC • 25 GCMs from the recent 4th (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. 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. The Typical Model Grid • The models provide GRID cell AVERAGED values - not a single point location

  7. Contents Menu Driven • Text

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  11. 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. 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. Some Considerations: • Bioclimate profiles are a ‘hybrid’ of observed and model projection data Baseline = Observed data at a climate station Projected Value for 2020s, 2050s, 2080s Model Anomaly value + = Grid cell value One of 583 stations

  14. Toronto Area Bioclimate Stations

  15. 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. 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. Emission Scenarios (image sources: TGICA GUIDANCE, IPCC, 2007) ‘A2’ – aggressive growth ‘A1B’ – moderate growth ‘B’ – low growth

  18. 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. 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 > 95th percentile (%) • Fraction of Time < 90th 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. Effect of Emission Scenario(holding model constant) A2 A1B B1

  21. Effect of Model (holding emission scenario constant) All models which produce A1B output

  22. Model considerations: • Newer versions of models are better than older • Increase in temporal and spatial resolution is preferable • Uncertainty in: • Emission scenarios • Parameterization of sub-grid scale processes • Climate sensitivity? Will it be constant? Models represent the best method available to project future climate

  23. What are the International Modeling Centres?

  24. Centres… Also: Canadian Regional Climate Model (CRCM3.7.1, 4.1.1 and 4.2.0) from OURANOS Consortium (EC a member) (Montreal, QC) Coming up… INGV-SGX National Institute of Geophysics and Volcanology Italy

  25. More advanced analysis Some comments on Downscaling…

  26. 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. What is Statistical Downscaling?

  28. 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. 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.

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