© Crown copyright Met Office Regional Climate Modelling and Dynamical Downscaling Climate Data for Agricultural Modelling Workshop, Kasetsart University,

© Crown copyright Met Office

Regional Climate Modelling and Dynamical Downscaling

Climate Data for Agricultural Modelling Workshop,

Kasetsart University, 26th February -1st March 2013


• To introduce the PRECIS regional climate modelling system

• To review the method for obtaining fine-scale climate information from global climate models (GCMs) from regional climate models (RCMs) such as PRECIS.

Objectives of the session


What is PRECIS?

• Providing REgional Climates for Impact Studies

• Regional climate modelling (RCM) system that can be applied to any area of the globe

• Used to generate detailed projections of future climate


Why was PRECIS developed?

• UNFCCC requirement to assess national vulnerability and plans for adaptation

• National Communications

• Both need estimates of impacts

• Impacts need detailed scenarios of future climate

• PRECIS can provide these detailed scenarios of future climate

• UNFCCC requirement on the UK to assist capacity building and technology transfer


Who is PRECIS for?

• Anyone interested in understanding climate change and its potential impacts

• Highly relevant for scientists involved in vulnerability and adaptation studies (particularly for National Communications documents)


Predicting impacts


What is a Regional Climate Model? • Mathematical model of the atmosphere and land surface (and

sometimes the ocean)• ‘High’ resolution: Produces data in

grid cells < 50km in size• Spans a limited area (region) of the globe

• Contains representations of many of the important physical processes within the climate system

• Cloud• Radiation• Rainfall• Atmospheric aerosols• Soil hydrology• etc.


The components of PRECIS

• The RCM

• User interface to design and configure RCM experiments

• Display and data processing software

• Lateral boundary conditions (the input data)

• Training course and materials

• Technical and Scientific Support (by internet forum and email)

• Website (http://www.metoffice.gov.uk/precis)

http://www.metoffice.gov.uk/precis


The PRECIS user interface


• Region specification

• Choice of domain

• Land surface configuration

• RCM and Emissions scenario

• Period of the simulation

• Output data

• Run

PRECIS user interface: Main functionality


Example of graphical runtime monitoring

PRECIS user interface


How fast does it go?

• 1 core: ~ 2.5 months

• 4 cores:~ 2.75 weeks

• 8 cores:~ 12.5 days

30 year integration, 100x100, 50km grid points

Minimum hardware requirements

Computer: PC running under the Linux operating system

Memory : 512MB minimum; 1+ GB recommended

Minimum 250GB disk space + offline storage for archiving data

Simulation speed proportional to CPU speed


Support and follow-up

• Support• E-mail to the Hadley Centre ([email protected])

• Online discussion forum hosted by http://climateprediction.net

• Web site• http://www.metoffice.gov.uk/precis• news

• updates

• resources

• Collaboration/workshops


What PRECIS can deliver

• PRECIS can provide:

• climate scenarios for any region

• an estimate of uncertainty due to different emissions

• an estimate of uncertainty due to climate variability

• Data available from PRECIS

• Comprehensive and consistent meteorological and physical data for the atmosphere and land-surface

• Hourly and daily data as well as longer timescale averages


PRECIS: Summary

• PRECIS: The Hadley Centre’s regional climate modelling system

• Resolution of 50km (25km for small areas)

• Runs on a Linux based PC; supplied on a DVD with sample driving data

• Can be set up by the user over any area of the globe

• Useful for vulnerability and adaptation studies and climate research

• Provides the capacity to locally produce scenarios of climate change


Why downscaling?


Main reason: 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

• 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

Why downscaling?

… from a GCM grid to the point of interest.

From global to local climate …


How to downscale?


Downscaling techniques

• Statistical: based on statistical relationship between large- and local- scale

fine scale value = F (large-scale variables)

• Dynamical: Numerical models at high resolution over region of interest

• high resolution AGCM, limited area model (regional climate model)

• Statistical/Dynamical

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

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

100-300km

50km-1km: region, city,Fields etc


Statistical Downscaling

• Weather generators

• Markov chain, spell length

• Transfer functions

• linear regression, piecewise interpolation, artificial neural networks

• Weather typing

• Analogue methods, classification and tree analysis

Categories of statistical techniques

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

• Several grid lengths• Tropospheric variables (away from the surface)• Dynamic variables (geopotential, wind, temperature)

The transfer function must remain valid in different climate conditions:

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

The predictors must encompass the entire climate change signal:

• Importance of testing several predictors• Uncertainties related to the choice of predictors

Assumptions made for statistical downscaling


Regional climate models(Dynamical downscaling)

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 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


Boundary conditions

Limited area regional models require meteorological information at their edges (lateral boundaries)

This data provides the interface between the regional model’s domain and the rest of the world.

The climate of a region is always strongly influenced by the global situation

These data are necessarily provided by global general circulation models (GCMs)

or from observed datasets with global coverage (re-analysis experiments)


Lateral boundary conditions (I)

• 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

• 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

State variables

State variables

RCM

interior

Lateral boundary conditions (II)


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

Sources of errors in RCMs

• The RCM adds fine detail to the large-scale and shouldn’t deviate from it.

• Two sources of error:

• Large scale driving fields (external)

• Model physical formulation (internal).

Simulation length

• Minimum period

• 10 years to reasonably study the mean climate

• Preferably

• 30 years to study higher order statistics, climate variability, extremes, etc


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.


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

RCM data can be used to drive other models

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

Crop impacts using high resolution projections from PRECIS

* Clear messages on future temperatures from the application of a regional climate model

* Precipitation projections are less certain, giving climate change information with different levels of confidence

* Application of this information to assess climate change impacts on crops still provides clear messages for the need for adaptation


Temperature changes over Caribbean land areas in two climate projections

• High resolution modelling delivers consistent message on large warming over land even with different sea temperature changes

• Temperature changes >3K by 2080s under the B2 scenario


The message on precipitation change is less clear

• Precipitation changes of up to +/- 20% or greater by 2080s under the B2 scenario


Clear impact on Caribbean crops in 2050s with +2ºC and+/-20% precip.

Crop Temperature Change

(oC)

% Change in Precipitation

Yield (kg/ha)

Change in Yield

Rice

0

+2

+2

0

+20

-20

3356

3014

2888

-10%

-14%

Beans

0

+2

+2

0

+20

-20

1354

1164

1093

-14%

-19%

Maize

0

+2

+2

0

+20

-20

4511

3737

3759

-22%

-17%

Crop Temperature Change

(oC)

% Change in Precipitation

Yield (kg/ha)

Change in Yield

Rice

0

+2

+2

0

+20

-20

3356

3014

2888

-10%

-14%

Beans

0

+2

+2

0

+20

-20

1354

1164

1093

-14%

-19%

Maize

0

+2

+2

0

+20

-20

4511

3737

3759

-22%

-17%

Table: Simulated crop yields under current climate and with a 2 ºC temperature increase accompanied by either a 20% increase or decrease in rainfall.

Suitability of regionalisation techniques

Method Strengths Weaknesses

Statistical · High resolution· Computation

ally cheap

· Dependent on empiricalrelationships derived for present-day climate

· Few variables available· Not easily relocatable

High-resAGCMs

· Dependent on surface boundaryconditions from couple model

· Computationally expensive

Regionalmodels

· High (very high)resolution

· Can representextremes

· Physically based· Many variables· RCM: easily

relocatable

· Dependent on driving model &surface boundary conditions· Possible lack of two-way nesting· Computationally expensive· (Have to parameterise across

scales )


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


Questions

© Crown copyright Met Office Regional Climate Modelling and Dynamical Downscaling Climate Data for Agricultural Modelling Workshop, Kasetsart University,

Documents

climate impacts

future climate change

understanding climate

regional climate models

global climate models

finescale climate information

use of precis

developing countries