1. The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action Fund and provides climate change scenarios and related information to the VIA community in Canada Prepared by Elaine Barrow, CCIS Project Downscaling: An Introduction (Regionalisation) Why do we need to downscale?

2. The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action Fund and provides climate change scenarios and related information to the VIA community in Canada Prepared by Elaine Barrow, CCIS Project 300km 50km 10km 1m Point Global Climate Models supply... Impact models require... Because there is a mismatch of scales between what climate models can supply and what environmental impact models require.

3. The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action Fund and provides climate change scenarios and related information to the VIA community in Canada Prepared by Elaine Barrow, CCIS Project Downscaling Using GCMs GCM output is generally the starting point of any regionalisation technique, so: GCMs should perform well in simulating circulation and climatic features affecting regional climates, e.g., jet streams, storm tracks it is better to use variables where sub-grid scale variations are weak, e.g., mean sea level pressure Main advantage of using GCMs is that: internal physical consistency is maintained

4. The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action Fund and provides climate change scenarios and related information to the VIA community in Canada Prepared by Elaine Barrow, CCIS Project A variety of methods and techniques have been developed to address this scale problem: 1.High resolution and variable resolution AGCM time-slice experiments - numerical modelling 2.Regional Climate Models (RCMs) - dynamic downscaling 3.Empirical/statistical and statistical/dynamical models - statistical downscaling

5. The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action Fund and provides climate change scenarios and related information to the VIA community in Canada Prepared by Elaine Barrow, CCIS Project Overcomes problems of discontinuities in change between adjacent sites in different grid boxes But introduces a false geographical precision to the estimates But the very simplest approach is the interpolation of grid box outputs

6. The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action Fund and provides climate change scenarios and related information to the VIA community in Canada Prepared by Elaine Barrow, CCIS Project Interpolation CGCM1 GHG only, Winter, Maximum temperature change (°C), 2020s Interpolated to 0.5° lat/long resolution

7. The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action Fund and provides climate change scenarios and related information to the VIA community in Canada Prepared by Elaine Barrow, CCIS Project Main downscaling approaches: higher resolution experiments or empirical/statistical or statistical/dynamical downscaling processes ADDINGVALUEADDINGVALUE

8. The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action Fund and provides climate change scenarios and related information to the VIA community in Canada Prepared by Elaine Barrow, CCIS Project High Resolution Models Numerical models at high resolution over region of interest GCM time-slice experiments variable resolution GCMs high resolution limited area models (regional climate models - RCMs)

9. The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action Fund and provides climate change scenarios and related information to the VIA community in Canada Prepared by Elaine Barrow, CCIS Project REGIONAL CLIMATE MODELS 1.Driven by initial conditions, time-dependent lateral meteorological conditions and surface boundary conditions which are derived from GCMs (or analyses of observations) 2.Account for sub-grid scale forcings (e.g. complex topographical features and land cover inhomogeneity) in a physically-based way 3.Enhance the simulation of atmospheric circulations and climatic variables at finer spatial scales

10. The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action Fund and provides climate change scenarios and related information to the VIA community in Canada Prepared by Elaine Barrow, CCIS Project [Source: G. Flato, in Climate Change Digest: Projections for Canada’s Climate Future, H.G. Hengeveld.] Comparison of detail in precipitation patterns over western Canada as simulated by CGCM1 and CRCM.

11. The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action Fund and provides climate change scenarios and related information to the VIA community in Canada Prepared by Elaine Barrow, CCIS Project The Canadian RCM - CRCM

12. The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action Fund and provides climate change scenarios and related information to the VIA community in Canada Prepared by Elaine Barrow, CCIS Project Screen Temperature (ºC) 5-year mean: Winter CRCM/NCEP CRU2 CRCM-CRU2 Validation = work in progress Runs are underway

13. The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action Fund and provides climate change scenarios and related information to the VIA community in Canada Prepared by Elaine Barrow, CCIS Project Precipitation rate (mm/day) 5-year mean: Winter CRCM/NCEP CRU2 CRCM-CRU2 Validation = work in progress Runs are underway

14. The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action Fund and provides climate change scenarios and related information to the VIA community in Canada Prepared by Elaine Barrow, CCIS Project High Resolution Models DISADVANTAGES dependent on a GCM to drive models computationally demanding few experiments may be ‘locked’ into a single scenario, therefore difficult to explore scenario uncertainty, risk analyses ADVANTAGES are able to account for important local forcing factors, e.g., surface type & elevation

15. The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action Fund and provides climate change scenarios and related information to the VIA community in Canada Prepared by Elaine Barrow, CCIS Project Effect of scenario resolution on impact outcome Spatial Scale of Scenarios [Source: IPCC, WGI, Chapter 13]

16. The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action Fund and provides climate change scenarios and related information to the VIA community in Canada Prepared by Elaine Barrow, CCIS Project Empirical/Statistical, Statistical/Dynamical Methods PREDICTANDPREDICTORS Sub-grid scale climate  = f(larger-scale climate) Transfer functions - calculated between large-area and/or large-scale upper air data and local surface climates Weather typing - relationships calculated between atmospheric circulation types and local weather Weather generator parameters can be conditioned upon the large-scale state

17. The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action Fund and provides climate change scenarios and related information to the VIA community in Canada Prepared by Elaine Barrow, CCIS Project Main Assumptions Predictors are variables of relevance to the local climate variable being derived (the predictand) and are realistically modelled by the GCM The transfer function is valid under altered climatic conditions The predictors fully represent the climate change signal

18. The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action Fund and provides climate change scenarios and related information to the VIA community in Canada Prepared by Elaine Barrow, CCIS Project Transfer Functions Grid Box Transfer function e.g., Multiple linear regression, principal components analysis, canonical correlation analysis, artificial neural networks Site variables for future, e.g., 2050 Predictor variables e.g., MSLP, 500, 700 hPa geopotential heights, zonal/meridional components of flow, areal T&P Area Select predictor variables Calibrate and verify model Extract predictor variables from GCM output Drive model Observed station data for predictand

19. The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action Fund and provides climate change scenarios and related information to the VIA community in Canada Prepared by Elaine Barrow, CCIS Project Transfer Functions Fundamental Assumption the observed statistical relationships will continue to be valid under future radiative forcing ADVANTAGES much less computationally demanding than physical downscaling using numerical models ensembles of high resolution climate scenarios may be produced relatively easily

20. The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action Fund and provides climate change scenarios and related information to the VIA community in Canada Prepared by Elaine Barrow, CCIS Project Transfer Functions DISADVANTAGES large amounts of observational data may be required to establish statistical relationships for the current climate specialist knowledge required to apply the techniques correctly relationships only valid within the range of the data used for calibration - projections for some variables may lie outside this range may not be possible to derive significant relationships for some variables a predictor which may not appear as the most significant when developing the transfer functions under present climate may be critical for determining climate change

21. The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action Fund and provides climate change scenarios and related information to the VIA community in Canada Prepared by Elaine Barrow, CCIS Project Weather Typing Statistically relate observed station or area-average meteorological data to a weather classification scheme. Weather classes may be defined objectively (e.g. by PCA, neural networks) or subjectively derived (e.g., Lamb weather types [UK], European Grosswetterlagen) Select classification scheme Relationships between weather type and local weather variables Pressure fields from GCM Calculate weather types Identify weather types Derive Drive model Local weather variables for, say, 2050 Observed weather variables

22. The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action Fund and provides climate change scenarios and related information to the VIA community in Canada Prepared by Elaine Barrow, CCIS Project ADVANTAGES founded on sensible physical linkages between climate on the large scale and weather on the local scale Weather Typing Fundamental Assumption the relationships between weather type and local climate variables will continue to be valid under future radiative forcing

23. The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action Fund and provides climate change scenarios and related information to the VIA community in Canada Prepared by Elaine Barrow, CCIS Project Weather Typing DISADVANTAGES the fundamental assumption may not hold - differences in relationships between weather type and local climate have occurred at some sites during the observed record scenarios produced are relatively insensitive to future climate forcing - using GCM pressure fields alone to derive types, and thence local climate, does not account for the GCM projected changes in, e.g., temperature and precipitation, so necessary to include additional variables such as large-scale temperature and atmospheric humidity

24. The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action Fund and provides climate change scenarios and related information to the VIA community in Canada Prepared by Elaine Barrow, CCIS Project Downscaled vs. original GCM [Source Hay et al. (1999)] Ex. Animas River Basin (US) with Hydrologic Model Delta Change = HadCM2 results (raw data) Grey area = 20 ensembles with downscaled climate scenario Simulated = with observed data

25. The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action Fund and provides climate change scenarios and related information to the VIA community in Canada Prepared by Elaine Barrow, CCIS Project Weather Generators Precipitation Process Occurrence Amount Non-precipitation variables Maximum temperature Minimum temperature Solar radiation Model calibration Synthetic data generation Climate scenarios LARS-WG: wet and dry spell length

26. The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action Fund and provides climate change scenarios and related information to the VIA community in Canada Prepared by Elaine Barrow, CCIS Project Weather Generators Area Grid Box Calibrate weather generator using area-average weather Calibrate weather generator for each individual station within area Station parameter set Calculate changes in parameters from grid box data Area parameter set Apply changes in parameters derived from difference between area and grid box parameter sets to individual station parameter files; generate synthetic data for scenario Spatial Downscaling

27. The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action Fund and provides climate change scenarios and related information to the VIA community in Canada Prepared by Elaine Barrow, CCIS Project Weather Generators Temporal Downscaling Parameter file containing statistical characteristics of observed station data Observed station data WG Monthly scenario information Generate daily weather data corresponding to scenario

28. The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action Fund and provides climate change scenarios and related information to the VIA community in Canada Prepared by Elaine Barrow, CCIS Project Weather Generators ADVANTAGES the ability to generate time series of unlimited length opportunity to obtain representative weather time series in regions of data sparsity, by interpolating observed data ability to alter the WG’s parameters in accordance with scenarios of future climate change - changes in variability as well mean changes Fundamental Assumption The statistical correlations between climatic variables derived from observed data are assumed to be valid under a changed climate.

29. The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action Fund and provides climate change scenarios and related information to the VIA community in Canada Prepared by Elaine Barrow, CCIS Project Weather Generators DISADVANTAGES seldom able to describe all aspects of climate accurately, especially persistent events, rare events and decadal- or century- scale variations designed for use, independently, at individual locations and few account for the spatial correlation of climate

30. The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action Fund and provides climate change scenarios and related information to the VIA community in Canada Prepared by Elaine Barrow, CCIS Project Further Reading IPCC TAR(2001) - Chapter 10 & 13 (www.ipcc.ch) Wilby & Wigley (1997): Downscaling general circulation model output: a comparison of methods. Progress in Physical Geography 21, 530-548 Hewitson & Crane (1996): Climate downscaling: techniques and application. Climate Research 7, 85-95 Goodess et al. (2003) : The identification & evaulation of suitable scenario development methods for the estimation of future probabilities of extreme events,Tyndall Centre, Rep. 4. report