Downscaling Techniques and Regional Climate Modelling

Downscaling Techniques and Regional Climate Modelling PRECIS workshopTanzania Meteorological Agency, 29th June – 3rd July 2015

Objectives of the session

To review downscaling methods of obtaining fine-scale climate information from global climate models (GCMs), with an emphasis on regional climate models (RCMs).

Outline

1. Why downscaling?

2. How to downscale (downscaling techniques) Statistical methods

Dynamical methods

3. Suitability of downscaling techniques

Why downscaling?

Why downscaling?

GCM lack regional details due to coarse resolution for many climate studies -> needs fine scale information to be derived from GCM output.

• Smaller scale climate results from an interaction between global climate and local physiographic details and from unresolved motions and processes

• There is an increasing need to better understand the processes that determine regional climate

• Impact assessors need regional detail to assess vulnerability and possible adaptation strategies

Coarse spatial resolution is not the only problem

At their typical resolutions, GCMs can skilfully resolve large weather systems (e.g extra-tropical cyclones). These systems have a lifetime of several days. Shorter time scales and details of these systems requires higher horizontal resolutions

How to downscale?

Downscaling techniques

• Statistical: based on statistical relationship between large- and local- scale, derived from present climate fine scale value = F (large-scale variables)

• Dynamical: Numerical models at high resolution over region of interest limited area model (regional climate model) high resolution AGCM

• Statistical/Dynamical method

Coarse atmospheric data (T, Q, winds, pressure etc)

Local surface data (T, rainfall, winds, Q etc)

100-300km

50km-1km

Why do they work?

The largest fraction of the energy of the atmospheric motion is due to very large scale systems. These systems are the main drivers of local scale weather.

from Cullen (2002), ECMWF

Statistical Downscaling

Categories of Statistical Downscaling

• Weather generators• Stochastic methods designed to reproduce statistical

properties of local variables (mean, variance) and their temporal structure. Include Markov chain, spell length approach

• Transfer functions• Methods based on fitting linear or non-linear regression

relationship between local variables and large scale weather variables. Includes simple linear regression, piecewise interpolation, artificial neural networks, Generalised Linear Models etc.

• Weather typing• Methods based on weather classification (cluster analysis, self

organising maps, etc) and analogue method, local variables are related to the most relevant large scale weather patterns

Assumptions made for Statistical Downscaling

Relies on large-scale predictors for which Climate System Models are most skilful:

• GCM skilful scale is assumed to be several grid lengths• Dynamic variables (geopotential, wind, temperature)• Tropospheric variables (away from the surface)

The transfer function must remain valid in the changed climate (stationarity assumption)

• Hard to demonstrate• Can be evaluated by comparison with other approaches

Predictors can be selected by numerical algorithms (e.g stepwise regression)

• Importance of testing several predictors• Uncertainties related to the choice of predictors• Necessity to include predictors describing climate change

Advantages of Statistical Downscaling

• Computationally cheap • The calibration of statistical models is usually done only

once and it can be applied to large sets of GCM climate scenarios

• Produces local scale surface variables • Many impact studies need “point value” surface

variables. These very high resolution are not easily obtainable by climate modelling. Statistical methods applicable to multi-site and multi-variable problems are also available.

• Statistical downscaling packages available• SDSM (weather generator), clim.pact (R package,

linear and analogue methods, not supported)

Regional Climate Modelling

What is a Regional Climate Model?

• Comprehensive physical high resolution climate model that covers a limited area of the globe

• Includes the atmosphere and land surface components of the climate system (at least)

• Contains representations of the important processes within the climate system

• e.g. clouds, radiation, precipitation

One way nesting

• A RCM is a limited area model (LAM), similar to those used in numerical weather prediction (NWP), i.e. short term weather forecasting

• LAMs are driven at the boundaries by GCM or observed data

• Lateral (side) and bottom (sea surface)

• LAMs are highly dependent on their boundary conditions and can not exist without them

Lateral Boundary Conditions (I)

LBC

variables

LBC variables

LBC variables

LBC

var

iabl

es

• LBCs = Meteorological boundary conditions at the lateral (side) boundaries of the RCM domain

• They constrain the RCM throughout its simulation

• Provide the information the RCM needs from outside its domain

• Data come from a GCM or reanalysis (quasi-observed lbcs)

• Lateral boundary condition variables• Wind

• Temperature

• Water

• Pressure

• Aerosols

Lateral Boundary Conditions (II)

State variables

State variables

RCM

interior

• Relaxation method (PRECIS)

• Large scale forcing merged with internal solution over a lateral buffer zone

• Spectral nesting

• Large scale forcing of low wave number components

• Important issues

• Spatial resolution of driving data

• Updating frequency of driving data

Sea Surface Boundary Conditions

• Two methods of supplying SST and sea ice:• Using outputs from a coupled AOGCM

• Need good quality simulation of SST and sea ice in model

• Necessary for future simulations

• Using observed values

• Useful for the present-day simulation.

• For future climate need add changes in SST and ice from a coupled GCM to the observed values – complicated

Simulation length

• Minimum period• 10 years to reasonably study the mean climate

• Longer periods are better• 30 years or more to study higher order statistics,

climate variability, extremes, etc

• Multi-annual mode of variability should also be considered

Optimal choice of domain

• Continental scale (5000km x 5000km)

smaller domain do not allow the development of mesoscale features, larger domain may loose the consistency with large scale atmospheric flow (Jones et al, QJRMS, 1995)

• Region of interest toward the middle of the domain

• Buffer zone preferably placed on regions of smooth orography.

Sources of errors in RCMs

• The RCM adds fine detail to the large-scale and shouldn’t deviate from it, otherwise the one-way nesting approach is not valid. The choice of the domain is important for these reasons.

• Two sources of error:

• Large scale driving fields (external)

• Regional Model formulation (internal).

Added Value of RCMs

RCMs simulate current climate more realistically

Patterns of present-day winter precipitation over Great Britain

Represent smaller islands

Projected changes in summer surface air temperature between present day and the end of the 21st century.

Add physically consistent details to climate change projections

Projected changes in winter precipitation between 1970s and 2080s.

Describe daily extremes more accurately

Frequency of winter days over the Alps with different daily rainfall thresholds.RCM and Obs aggregated at GCM scale

Resolve intense mesoscale systems

A tropical cyclone is evident in the RCM (right) but not in the GCM

Can be used to drive other models

A cyclone-like feature in the Bay of Bengal simulated by an RCM and the resulting high water levels in the Bay simulated by a coastal shelf model.

Suitability of Downscaling Techniques

Suitability of Regionalisation Techniques

Method Strengths WeaknessesStatistical · High resolution

· Computationally cheap

· Dependent on empirical relationshipsderived for present-climate variability

· Few variables available·

Not easily relocatable

· Computationally expensive

Regional climatemodels · High (very high)

resolution· Can represent

extremes· Physically based· Many variables· Easily relocatable

·

Dependent on driving model &surface boundary conditions

·

Possible lack of two-way nesting

·

(

Have to parameterise across scales

)

· Local scale (point value)Dependent on the availability of good quality obs ·

Summary

• Downscaling techniques are used to add fine scale details to GCM projections

• Statistical downscaling and dynamical downscaling are the two most commonly used methods, each with its own strengths and weaknesses.

• Impact studies for which downscaled scenarios are needed and the design of the downscaling experiment will determine whether to use one (or both) methods.

Questions

Criteria for Suitability of Downscaling Techniques

• Consistency at regional level with global projections

• Physical plausibility and realism

• Appropriateness of information for impact assessment

• Representativeness of the potential range of future climate change

• Accessibility for use in impact assessments