statistical challenges in climatology chris ferro climate analysis group department of meteorology...

Statistical Challenges in Climatology

Chris Ferro

Climate Analysis Group

Department of Meteorology

University of Reading

‘Climate is what we expect, weather is what we get.’ Mark Twain (?)

RSS Birmingham Local Group, Coventry, 11 December 2003

Overview

• History and general issues

• Examples of research topics

• Climate change simulations

• Concluding remarks

History

Jule G.Charney

VilhelmBjerknes

The Earth SimulatorLewis FryRichardson

1950

computerforecasts

1922

manualforecast

‘primitive’equations

1904 2002

GilbertWalker

40 Tflops10 Tbytes

southernoscillation

1923

General Issues

Dependent

Nonstationary

Huge datasets

Limited data

space and time: many scales

space and time: periodicities,

shocks, external forcings

station, satellite, simulation

short record, no replication

Examples of Research Topics

• Observations

• Climate modes

• Numerical models

• Data assimilation

• Forecast calibration

• Other topics

Observations

• Buoys• Field Stations• Ships & Aircraft

• Satellites• Radiosondes• Palaeo-records

• homogeneity, missing data, errors and outliers• network design and adaptive observations• statistical models to reconstruct past climates

Climate Modes

• Principal components: multi-site observations

• Identifies patterns of simultaneous variation

• Physical significance• Reduces dimension• Rotated, simplified etc.

North Atlantic Oscillation,courtesy of Abdel Hannachi

General Circulation Models

• Differential equations• Physical schemes• External forcings• Initial conditions• Numerical scheme• Deterministic output:

temp, precip, wind, pressure etc.

Data Assimilation

State

Observation

Solution

• Assumptions, approximations, choice of

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Forecast Calibration climate model

combinedregression model

Caio Coelho & Sergio Pezzulli

Prior: climate-model forecastLikelihood: regression model

Other Topics

• Model validation

• Forecast verification

• Statistical downscaling

• Climate change attribution

• Stochastic models of processes

Climate Change Simulations

• The PRUDENCE project

• Temperature and precipitation

• Distributional changes

• Extreme values

• Model uncertainty

PRUDENCE

• European Climate• 30-year control

simulation, 1961-1990• 30-year A2 scenario

simulation, 2071-2100• 10 high-resolution

regional models• 6 global models

From www.ipcc.ch

Mean Daily Rainfall

mm mm

Control (1961-1990) Scenario – Control

Mean Daily Rainfall

DJF MAM

SONJJA

Control (1961-1990) Scenario – ControlDJF MAM

JJA SON

mm mm

Simultaneous Confidence Intervals

such that ) ,( Find change. where

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Mean Daily Rainfall Response

DJF JJA

Mean Daily Rainfall Response

DJF JJA

Mean Temperature

ºC

Control (1961-1990) Scenario – Control

ºC

Mean TemperatureControl (1961-1990) Scenario – Control

ºC ºC

SONJJAJJA SON

DJF MAMDJF MAM

Distributional Changes

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Daily Rainfall ResponseDJF

Temperature ResponseDJF

Model Uncertainty

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Scenario YearModelAnnual Mean

Global model 1 2Regional model 1 2 3 4 5 … 1 2Control x x x x xA2 Scenario x x x x xB2 Scenario

Temperature: R2

Temperature: Model Effects

°C

Temperature: Model Response

°C

Extreme Values

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satisfies ,,levelreturn period- The

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Rainfall 10-DJF Return LevelsControl A2 Scenario / Control

GEV Parameter Estimates

Scale-change Model

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Concluding Remarks

Need for sophisticated statistical techniques to help to analyse large amount of complex data.

‘There is, to-day, always a risk that specialists in two subjects, using languages full of words that are unintelligible without study, will grow up not only, without knowledge of each other’s work, but also will ignore the problems which require mutual assistance.’ Sir Gilbert Walker, 1927

Further Information

PRUDENCE

Climate Analysis Group

9th International Meeting

on Statistical Climatology,

Cape Town, 24-28 May 2004

prudence.dmi.dk

www.met.rdg.ac.uk/cag

www.csag.uct.ac.za/IMSC

c.a.t.ferro@reading.ac.uk