Regional Dynamical Downscaling ofMediterranean Climate –

Climate Change Perspectives

Heiko Paeth, Institute of Geography, University of Würzburg,

I. Introduction

II. Dynamical downscaling

III. Extreme value statistics

IV. Simulated extreme events

V. Simulated changes

VI. Postprocessing of model data

VII. Conclusions

MedCLIVAR Workshop 2007, La Londe les Maures

I. Introduction

industrialemissions

trafficemissions

biomassburning

over-grazing

heat stress

flood

wind extremes

drought

I. Introduction

How can we infer future changes in the frequency and intensity

of extreme events?

dynamical aspect (climate modelling) statististical aspect (assessment of uncertainty)

II. Dynamical downscaling

low latitudes are dominated by convective rain events the spatial heterogeneity of individual rain events is high regional rainfall estimates are subject to large sampling errors

station data global model regional model statist. interpol.

II. Dynamical downscalingd

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ari

ab

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l p

rec

ipit

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station data are often too sparse to represent regional rainfall global models are too coarse-grid for regional details statistically interpolated data sets fail in mountainous areas dynamic nonlinear regional models account for the effect of orography

II. Dynamical downscaling

the rainfall trends predicted by the global model are barely relevant to political plannings and measures the rainfall trends predicted by the regional model are much more detailed and of higher amplitude more detailed fingerprint or spatial noise added value ???

3 x CO2

II. Dynamical downscaling

consideration of various ensemble members enables the statistical quantification of the human impact on climate in the climate model

Temperature

Precipitation

differentinitial

conditions(stochastic)

differencesbetween

ensemblemembersat certain

time scales

measureof internal variability

variance of the

ensemblemean

measureof externalvariability

statisticalcomparison

II. Dynamical downscaling

REMO:1960-2000

observed GHGconstant LC

ECHAM5/MPI-OM:1960-2000

observed GHGconstant LC

REMO:2001-2050

A1B (GHG+LC)

REMO:2001-2050

B1 (GHG+LC)

ECHAM5/MPI-OM:2001-2050A1B (GHG)constant LC

ECHAM5/MPI-OM:2001-2050B1 (GHG)

constant LC

Land degradation:2001-2050

FAO original

Land degradation:2001-2050

FAO reduced

dynamics: hydrostatic physics: ECHAM4 sector: 30°W-60°E ; 15°S-45°N resolution: 0,5° ; 20 hybrid levels validation: good results

II. Dynamical downscaling

The main features of Mediterranean climate are well reproduced by REMO.

III. Extreme value statistics

The processes, which cause climate extremes, are not necessarily the same as for weak climate variations.

Hence, they usually do not obey a normally distributed random process.

f

climate parameter

III. Extreme value statistics

The Generalized Pareto Distribution (GPD) is a useful statistical distribution, since it is a parent distribution for other extreme value distributions (Gumbel, Exponential, Pareto).

The quantile function x(F) is given by:

= location parameter (expectation)

= scale parameter (dispersion)

= shape parameter (skewness)

The parameters of the GPD can be estimated by the method of L-moments.

Estimation of T-year return values (RVs):

k

F)(æx(F)

k−−+=

11α

k

αζ

kTRV

k

T ˆ

))1

1(1(1ˆˆ

ˆ−−−

+= αζ

dispersion parameter: threshold quantile

T = 5a

q = 99%

RV = 43mm

cumulative GPDs

III. Extreme value statistics

uncertainty of the RV estimate is inferred from bootstrap sampling:

1) from fitted GPD b random samples of size N generated

2) from random samples b indi- vidual RVs estimated

3) these b RVs are normal distri-buted such that STD is a mea-sure of the standard error of the RV estimate

4) signal-to-noise ratio is given by MEAN/STD over b RVs

f

cGPD

0

1

mmnew samples of size N

change in RV is significant at the 1% level, if 90% confidence inter-vals of two PDFs of RVs over b bootstrap samples do not overlap:

RV

Nrandom

numbers

STD

90% conf. interv.

f

RV

present-dayclimate

forcedclimate

III. Extreme value statistics

100-year RV in mm

The 100-year RV estimate ranges between 200 mm and 800 mm, depending on the random sample.

III. Extreme value statistics

probabilistic forecast of future rainfall changes provides a reasonable scientific basis for political plannings and measures

one predicted valuewithout uncertainty range:pretended precision

probabilistic forecastwith mean and uncertaintyrange:more objective basis fordecision makers

se

cu

rity

co

sts

single estimate / simulation

Monte Carlo approach

1%

10%

90%

99%

x=50%

s+=84%

s-=16%

2000

2000

2050

2050

RV

RV

IV. Simulated extreme events

The occurrence of extreme rain events is a function of the land-sea contrast, orography, geographical latitude and seasonal cycle.

1-year return values of heavy daily rainfall

IV. Simulated extreme events

1-year return values of high daily temperature

The occurrence of high temperature is also a function of the land-sea contrast, orography, geographical latitude and seasonal cycle.

IV. Simulated extreme events

The estimate of extrem values is more robust in regions and seasons with large-scale rather than convective precipitation. The choice of long return times in the pre-sence of short time series is unappropriate.

S/N ratio for 1-year RVs of heavy daily rainfall

sea

so

na

l me

an

se

xtre

me

s (1

y-R

V)

α = 5%

V. Simulated changes

PRECIPITATION

2025 minuspresent-day

α = 5%

ext

rem

es

(1y-

RV

)s

eas

on

al m

ea

ns

V. Simulated changes

TEMPERATURE

2025 minuspresent-day

VI. Postprocessing of model data

The assessment of changes in weather extremes is very sensitive to inhomogeneities in observational data. No problem with model data.

1840 1860 1880 1900 1920 1940 1960 1980 2000

da

ily

pre

cip

ita

tio

n

discontinuity

assessed variability

VI. Postprocessing of model data

precipitation is the end product of a complex causal chain each step imposes addititional uncertainty, particularly if it is based on a physical parameterization in the model

differentinitial

conditions(stochastic)

radiationbudget and

energyfluxes

atmosphericand oceaniccirculation

instabilityand

convection

cloudmicro-

physicsprecipitation

time

error

nonlinear error growth

VI. Postprocessing of model data

climate models:area-mean

precipitation

observations:local

station datacomparison ?

REMO grid box(50km x 50km)

observed stationtime series

(local information)

model data station data

VI. Postprocessing of model data

virtual station rainfall(result)

simulatedgrid-box

precipitation(dynamical part)

local topography(physical part)

vr

random distributionin space

(stochastical part)

Weather Generator

VI. Postprocessing of model data

REMO rainfall: - wrong seasonal cycle - underestimated extremes - hardly any dry spells

Weather Generator: - statistical distribution as observed - individual events not in phase with observations

model data

station data

model data postprocessed

original REMO rainfall

rainfall from weather generator

station time series

VII. Conclusions

Regional climate models are required in order to account for the spatial heterogeneity of Mediterranean climate.

The estimate of extreme values and their changes requires appropriate statistical distributions and a probabilistic approach.

When estimating EVs from short time series, it is necessary to restrict the analysis to short return periods.

The occurrence of climate extremes is a function of land-sea contrast, orography, geographical latitude and seasonal cycle.

REMO projects no coherent changes in heavy rainfall whereas warm temperature extremes clearly tend to increase.

Systematic model deficiencies and the grid-box problem can be overcome by use of a weather generator.

The model results now need to be corroborated by available homogeneized long-term observational time series.