The Canadian Climate Impacts Scenarios (CCIS) Project is funded by the Climate Change Action Fund...

Prepared by Elaine Barrow, CCIS Project

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

Downscaling: An Introduction

(Regionalisation)

Why do we need to downscale?




300k

m

50km

10km

1m

Poin

tGlobal 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.




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




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




• 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




Interpolation

CGCM1 GHG only, Winter, Maximum temperature change (°C), 2020s

Interpolated to 0.5° lat/long resolution




Main downscaling approaches:

• higher resolution experiments

or

• empirical/statistical or statistical/dynamical downscaling processes

A

D

D

I

N

G

V

A

L

U

E




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)




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




CRCMCGCM1

[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.




The Canadian RCM - CRCM




Screen Temperature (ºC) 5-year mean: WinterCRCM/NCEP

CRU2

CRCM-CRU2

Validation = work in progressRuns are underway




Precipitation rate (mm/day)5-year mean: WinterCRCM/NCEP

CRU2

CRCM-CRU2

Validation = work in progressRuns are underway




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




Effect of scenario resolution on impact outcome

Spatial Scale of Scenarios

[Source: IPCC, WGI, Chapter 13]




Empirical/Statistical, Statistical/Dynamical

MethodsPREDICTAND PREDICTORSSub-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




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




Transfer FunctionsGrid Box

Transfer functione.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




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




Transfer FunctionsDISADVANTAGES

• 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




Weather TypingStatistically 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




ADVANTAGES• founded on sensible physical linkages between

climate on the large scale and weather on the local scale

Weather TypingFundamental Assumption

the relationships between weather type and local climate variables will continue to be valid under

future radiative forcing




Weather TypingDISADVANTAGES

• 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




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 scenarioSimulated = with observed data




Weather Generators

Precipitation ProcessOccurrence Amount

Non-precipitation variables

Maximum temperatureMinimum temperature

Solar radiation

Model calibration

Synthetic data generation

Climate scenarios

LARS-WG: wet and dry spell length




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 DownscalingSpatial Downscaling




Weather GeneratorsTemporal Downscaling

Parameter file containing statistical characteristics of observed station data

Observed station data

WG

Monthly scenario information

Generate daily weather data corresponding to

scenario




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 AssumptionThe statistical correlations between climatic variables derived from observed data are assumed to be valid

under a changed climate.




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




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

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