© crown copyright met office downscaling techniques and regional climate modelling precis workshop,...

© Crown copyright Met Office

Downscaling Techniques and Regional Climate Modelling

PRECIS Workshop, MMD, KL, November 2012


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

Objectives of the session


Outline

1. Downscaling techniques

- Statistical methods

- Dynamical methods

2. Suitability of downscaling techniques

3. Use of regional climate models


What are downscaling techniques?


• Techniques which allow fine scale information to be derived from GCM output.

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

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

• AOGCM projections lack that regional detail due to coarse spatial resolution

• Downscaling for climate change assessment differs from downscaling of seasonal climate prediction

Climate downscaling


Going from global to local climate


Classification

• Statistical

• Transfer functions

• Weather generators

• Weather typing

• Dynamical

• High resolution and variable resolution AGCMs

• Regional Climate Models


1) Find a function , derived from statistical relationships between observations of fine-scale and large scale variables:

fine scale value = (large-scale variables)

2) Use to find future fine-scale values from future large-scale variables:

future fine-scale value = (AOGCM large-scale variables)

Statistical or empirical techniques


Stretched grid AGCMs

The spatial resolution here is equivalent to a grid mesh of approximately 30 km.

The spatial resolution is progressively relaxed towards the antipode (near New-

Zealand).


Regional atmospheric modelling: nesting into a global state

Regional climate models

Courtesy of H. von Storch


Suitability of regionalisation techniques


Suitability of regionalisation techniques

Method Strengths Weaknesses

Statistical High resolution Computationally

cheap

• Dependent on empirical relationship derived from present-day climate• Dependent on long time-series and good quality historical data• Few variable available• Not easily relocatable

Stretched gridAGCMs

• Dependent on surface boundary conditions from coupled model• Computationally expensive• Have to parameterize across scales

Regionalmodels

High (very high)resolution

Can representextremes

Physically based Many variables RCM: easily

relocatable

• Dependent on driving model and surface boundary conditions• Possible lack of two-way nesting• Have to parameterize across scales (scales )


Regional modelling vs. statisticaldownscaling

• The major theoretical weakness of statistical downscaling methods is that these empirically-based techniques cannot account for possible systematic changes in regional forcing conditions or feedback processes.

• The possibility of tailoring the statistical model to the requested regional or local information is a distinct advantage. However, it has the drawback that a systematic assessment of the uncertainty of this type of technique, as well as a comparison with other techniques, is difficult and may need to be carried out on a case-by-case basis.


Boundary conditions


One way nesting methodology

• 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

• 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 observations

• Lateral boundary condition variables

• Wind

• Temperature

• Water

• Pressure

• Aerosols

LBC

variables

LBC variables

LBC variablesLB

C v

aria

bles


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


Added value of RCMs


RCMs simulate current climate more realistically

Patterns of present-day mean winter precipitation over Great Britain


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.


Predict climate change with more detail

Projected changes in winter precipitation between now and 2080s.


Simulate and predict changes in extremes more realistically

Frequency of winter days over the Alps with different daily rainfall thresholds.


Simulate cyclones and hurricanes

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


Summary

• Downscaling techniques are used to add fine scale details to a GCM projection

• Several methods are available with their own strengths and weaknesses

• PRECIS is a physically-based and computationally accessible regional climate model for downscaling GCM projections

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Contents

• What are Climate Scenarios?

• Types of Climate Scenarios

• Examples of Impacts studies that have used PRECIS

• An impacts case study – why the method for constructing a scenario is important.


• Climate scenario

“A scenario is a coherent, internally consistent and plausible description of a possible future state of the world. It is not a forecast; rather, each scenario is one alternative image of how the future can unfold.”

Key point 1: Internal consistency

Socio-Economic scenario → Emissions scenario → Climate scenario

Key point 2: Scenarios are NOT the same as ‘predictions’: we can have many plausible scenarios.

Climate scenarios


Types of climate scenarios

• Incremental scenarios for sensitivity studies

• Analogue scenarios

• Scenarios based on outputs from Climate Models


• Particular climatic elements are changed incrementally by plausible though arbitrary amounts.

• Use for testing system sensitivity

• Use for identifying critical thresholds or discontinuities in climate

• Potentially leads to unrealistic scenarios

• Not related to anthropogenic emissions

1. Incremental scenarios


• Identify recorded climate regimes which may resemble the future climate in a given region.

• Spatial analogues

• Temporal analogues

• Palaeoclimatic

• Instrumental

• Not related to anthropogenic emissions

• Often physically implausible

2. Analogue scenarios


• Coupled Atmosphere-Ocean Global Climate Models (AOGCMs)

• Coarse resolution, and often have large biases

• Based on physics

• Internally consistent

• Dynamically downscaled AOGCMs

• High resolution GCMS (e.g. PRECIS)

• Require large computer resources

• Can inherit biases from AOGCM

• Statistically downscaled AOGCMs

• Statistical methods are based on current climate and trained on short-term variability

• Difficult to develop internally consistent climate variables

3. Scenarios based on outputs from Climate Models


Stages required to develop climate change scenarios


More adverse than beneficial impacts on ecological and socioeconomic systems

are projected


• Evaluation of the detrimental and beneficial consequences of climate change on natural and human systems.

• Impacts models require climate scenarios as inputs.

• The impact of the climate change is determined by contrasting the effect of the observed/baseline climate with that of the future climate (scenario) on the exposure unit

Impacts Assessment


Using climate model scenarios with impact models


Climate change impact = System under future climate – system under current climate (baseline)

Baseline Future Appropriate?Observations Observations and

climate model change factor

If we have sufficient obs. data to drive our impact model, yes.

If we see only systematic biases in our model simulations, yes.

Climate model baseline Climate model future If our climate model baseline is realistic, yes.

Observations Climate model future Not normally. Only if model baseline is very similar to observed –otherwise the result is a combination of climate model error and climate change impact.


One approach to combining climate observations and simulations

• If, as a result of systematic biases in the GCM/RCM simulations, the impact baseline is unrealistic then a simple approach is to apply the model change factor rather than the model output directly

• Model change factor = Model future – Model baseline: or

• Model change factor =(model future / model baseline) *100

• We can then add the change factor to an observed record to get a future scenario with the bias seen in the baseline removed

• Future climate scenario = Observed + model change factor: or

• Future climate scenario = Observed * model change factor (%)

• This approach may provide impact results which are more reasonable but the simple change factor applied does not account for changes in variability and may result in inconsistent future climates



Example: Modelling Impacts of climate change on agriculture

Use of PRECIS and the crop model CERES to simulate yield changes per hectare of three grain crops (rice, wheat, maize) in China when applying one future climate scenario and a representation of CO2 fertilization

Xiong et al, 2007, Climate change and critical thresholds in China’s food security, Climatic Change, 81:205-221


Example: modelling climate change impacts on Hydrology

• Change in water stress in the Ganges-Brahmaputra-Meghna Basin derived using the Global Water Availability Assessment (GWAVA) model

(CLASIC project – work with CEGIS)

2020s

2050s


Example: Modelling Storm Surge under climate scenarios

Simulated tropical cyclone and resulting storm surge. Produced using PRECIS and POLCOM storm surge model

SLR projections from GCM


The PRECIS modelling system:

An impacts case-study


Climate change scenarios from a recent climate model: estimating change in runoff in southern Africa

• Nigel Arnell

• (Dept. of Geography, University of Southampton, U.K.)

• Debbie Hudson and Richard Jones

• (Hadley Centre for Climate Prediction and Research)


Methods

• Runoff: calculated from water balance

• runoff = precipitation – evaporation – absorption by soil

• Two sets of models - a climate model and a runoff model

• Baseline climate → Run-off model → Baseline Run-off

• Future climate → Run-off model → Future Run-off

• Compares different methods of constructing future climate scenario


Mean temperature and rainfall

Average annual rainfall is systematically over-estimated by the model


Rainfall variability is accurately represented by the model


Constructing the baseline and future timeseries of data required by the runoff model. For example:

…these are just some of the possibilities

Some different methods for CCS construction

BASELINE FUTURE CLIMATE SCENARIO

Mean Variance Mean Variance

Observed Observed Observed + model

difference

Observed

Simulated Simulated Simulated Simulated


The best method CCS construction in this case?

BASELINE FUTURE CLIMATE SCENARIO

Mean Variance Mean Variance

Observed Observed Observed + model

difference

Observed

Simulated Simulated Simulated Simulated


Summary

• There are several techniques for producing future climate information

• Only climate model based climate change predictions can be used for providing climate scenarios which are plausible and self consistent

• Even when using a single climate model (or family of models) there are many different ways to provide climate change information for impacts studies

• The method of climate scenario construction adds a further uncertainty in assessing impacts of climate change


Questions

© crown copyright met office downscaling techniques and regional climate modelling precis workshop,...

Documents

finescale climate information

climate downscalingtechniques

presentday climate dependent

regional climate models

smaller scale climate

fine scale information

fine scale value

climate change assessment