Downscaling: An Introduction (Regionalisation)

1. Downscaling: An Introduction(Regionalisation) Why do we need to downscale?

2. 300km 50km 10km 1m Point Because there is a mismatch of scales between what climate models can supply and what environmental impact models require. Impact models require ... Global Climate Models supply...

3. 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. A variety of methods and techniques have been developed to address this scale problem: • High resolution and variable resolution AGCM time-slice experiments - numerical modelling • Regional Climate Models (RCMs) - dynamic downscaling • Empirical/statistical and statistical/dynamical models - statistical downscaling

5. But the very simplest approach is the interpolation of grid box outputs • Overcomes problems of discontinuities in change between adjacent sites in different grid boxes But • introduces a false geographical precision to the estimates

6. Interpolated to 0.5° lat/long resolution Interpolation CGCM1 GHG only, Winter, Maximum temperature change (°C), 2020s

7. A D D I N G V A L U E • Main downscaling approaches: • higher resolution experiments • or • empirical/statistical or statistical/dynamical downscaling processes

8. 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. Driven by initial conditions, time-dependent lateral meteorological conditions and surface boundary conditions which are derived from GCMs (or analyses of observations) • Account for sub-grid scale forcings (e.g. complex topographical features and land cover inhomogeneity) in a physically-based way • Enhance the simulation of atmospheric circulations and climatic variables at finer spatial scales REGIONAL CLIMATE MODELS

10. Comparison of detail in precipitation patterns over western Canada as simulated by CGCM1 and CRCM. [Source: G. Flato, in Climate Change Digest: Projections for Canada’s Climate Future, H.G. Hengeveld.]

11. The Canadian RCM - CRCM

12. Screen Temperature (ºC) 5-year mean: Winter CRCM/NCEP CRCM-CRU2 CRU2 Validation = work in progressRuns are underway

13. Precipitation rate (mm/day)5-year mean: Winter CRCM/NCEP CRCM-CRU2 CRU2 Validation = work in progressRuns are underway

14. High Resolution Models • ADVANTAGES • are able to account for important local forcing factors, e.g., surface type & elevation 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

15. Spatial Scale of Scenarios Effect of scenario resolution on impact outcome [Source: IPCC, WGI, Chapter 13]

16. 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. 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. Area Transfer Functions Grid Box Extract predictor variables from GCM output Select predictor variables Predictor variables e.g., MSLP, 500, 700 hPa geopotential heights, zonal/meridional components of flow, areal T&P Transfer function e.g., Multiple linear regression, principal components analysis, canonical correlation analysis, artificial neural networks Calibrate and verify model Drive model Observed station data for predictand Site variables for future, e.g., 2050

19. 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. 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. Weather Typing Pressure fields from GCM 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 Calculate weather types Identify weather types Relationships between weather type and local weather variables Drive model Derive Local weather variables for, say, 2050 Observed weather variables

22. Weather Typing Fundamental Assumption the relationships between weather type and local climate variables will continue to be valid under future radiative forcing • ADVANTAGES • founded on sensible physical linkages between climate on the large scale and weather on the local scale

23. 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. Downscaled vs. original GCM Ex. Animas River Basin (US) with Hydrologic Model Delta Change = HadCM2 results (raw data)Grey area = 20 ensembles with downscaled climate scenarioSimulated = with observed data [Source Hay et al. (1999)]

25. Weather Generators Precipitation Process OccurrenceAmount LARS-WG: wet and dry spell length Non-precipitation variables Maximum temperature Minimum temperature Solar radiation Model calibration Synthetic data generation Climate scenarios

26. Grid Box Area Weather Generators Spatial Downscaling Spatial Downscaling Calibrate weather generator using area-average weather 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 Calibrate weather generator for each individual station within area Station parameter set Calculate changes in parameters from grid box data

27. Weather Generators Temporal Downscaling Observed station data WG Parameter file containing statistical characteristics of observed station data Monthly scenario information Generate daily weather data corresponding to scenario

28. Weather Generators Fundamental Assumption The statistical correlations between climatic variables derived from observed data are assumed to be valid under a changed climate. 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

29. 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. 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