Downscaling GCMs to local and regional levels

SECC – CCSP MeetingNovember 7, 2008

Downscaling GCMsDownscaling GCMs to local and regional levels to local and regional levels

Institute of Food and Agricultural Sciences

Guillermo A. BaigorriaGuillermo A. Baigorriae-mail:e-mail: [email protected]@ifas.ufl.edu

http://plaza.ufl.edu/gbaigorr/GB/http://plaza.ufl.edu/gbaigorr/GB/

SECC-WMO joint Meeting on SECC-WMO joint Meeting on Climate Change Impacts and Adaptations to Agriculture, Forestry and FisheriesClimate Change Impacts and Adaptations to Agriculture, Forestry and Fisheries

at the National and Regional Levels at the National and Regional LevelsOrlando, Florida, USA, 18-21 November 2008Orlando, Florida, USA, 18-21 November 2008


James W. JonesJames W. JonesUniversity of FloridaUniversity of Florida

Muthuvel ChelliahMuthuvel ChelliahNOAA – Climate Prediction CenterNOAA – Climate Prediction Center



James J. O’BrienJames J. O’BrienCenter for Ocean-Atmospheric Prediction Center for Ocean-Atmospheric Prediction

Studies – Florida State UniversityStudies – Florida State University

Dong W. ShinDong W. ShinCenter for Ocean-Atmospheric Prediction Center for Ocean-Atmospheric Prediction

Studies – Florida State UniversityStudies – Florida State University

James W. HansenJames W. HansenInternational Research Institute International Research Institute

for Climate and Societyfor Climate and Society


GB

GBGB

1 m 0.5 km 10 km

20 km 100 km 400 km

Regional NumericalClimate Model

Global NumericalClimate Model

Radardata

Spatial resolutionSpatial resolution

Weather stationdata

Low High Low HighLow High

Low High Low High

GB


GB

GB

Sp

ace

Sp

ace

PastPast

Weather stationWeather stationnetworknetwork

RadarRadar

dayyear~

400x

400

km2

1x1

m2

Historical Record

Climate month

Low High

FutureFuture

ClimateSeasonalclimate

RCMRCM

G/RCMG/RCM

GCMGCM

Operational levelOperational levelfor crop andfor crop andenvironmental environmental modelingmodeling

ReanalysisReanalysis

G/RCM’s hindcastsG/RCM’s hindcasts G/RCM’s forecastsG/RCM’s forecasts

GB


To present the different statistical downscaling

methods developed, extended and used by

the Southeast Climate Consortium (SECC)

ObjectiveObjective

GB


Observed latent heat flux anomalies (July)Observed latent heat flux anomalies (July)W mW m-2-2

8

6

4

2

0

-2

-4

-6

-8

-10

-12

Observed mean surfaceObserved mean surface temperature anomalies (July)temperature anomalies (July)

00

-0.2-0.2

-0.4-0.4

-0.6-0.6

-0.8-0.8

-1.0-1.0

°C°C

Weather station network Weather station network


Observed latent heat flux anomalies (July)Observed latent heat flux anomalies (July)W mW m-2-2

8

6

4

2

0

-2

-4

-6

-8

-10

-12

Observed mean surfaceObserved mean surface temperature anomalies (July)temperature anomalies (July)

00

-0.2-0.2

-0.4-0.4

-0.6-0.6

-0.8-0.8

-1.0-1.0

°C°C



0

10

20

30

40

Mar Apr May Jun Jul Aug Sep

0

3

6

9


0.0

0.2

0.4

0.6

0.8

1.0


0

30

60

90

120

150

180


County: DeKalbCounty: DeKalb

pp

qq ss

Gamma distributionGamma distribution Beta distributionBeta distribution Gaussian distributionGaussian distribution

0

10

20

30

40

50


0

4

8

12

16


x(rainfall)(rainfall) (Incoming solar radiation)(Incoming solar radiation) (Max. and Min. Temperatures)(Max. and Min. Temperatures)

GB

Observed parameterObserved parameterForecasted parameter Forecasted parameter (5(5thth, 25, 25thth, 75, 75thth, 95, 95thth percentiles percentiles from 20 ensemble members) from 20 ensemble members)

xx


Bias correction based on cumulative probability distributionsBias correction based on cumulative probability distributions

(a) Frequency correction(a) Frequency correction

(b) Amount correction(b) Amount correction

Observed climatologyRaw hindcast

Observed climatologyFreq. corrected

Observed climatologyFreq. corrected

Frequency correctedHindcast value

Frequency & amount correctedHindcast value

x

F(x)

x

F(x)

x

F(x)

x

F(x)

Observed climatologyFreq. & amountcorrected

GB

Baigorria, GA, Jones, JW, Shin, DW, Mishra, A, O’Brien, JJ. 2007. Assessing uncertainties in crop model simulations using daily bias-corrected regional circulation model outputs. Climate Res. 34(3): 211-222


GCM’s Nov-Dec-Jan rainfall dataGCM’s Nov-Dec-Jan rainfall data

GB

mm0 10 20 30 40 50

Baigorria, GA, Hansen, JW, Ward, N, Jones, JW, O’Brien, JJ. 2008. Assessing predictability of cotton yields in the Southeastern USA based on regional atmospheric circulation and surface temperatures. J. Applied Meteorol. Climatol. 47(1): 76-91

To extract the To extract the historical recordhistorical record

Time series NDJ’s Rainfall


GCM’s Nov-Dec-Jan rainfall dataGCM’s Nov-Dec-Jan rainfall data

GeospatialGeospatialaggregationaggregation

GB

mm0 10 20 30 40 50

Baigorria, GA, Hansen, JW, Ward, N, Jones, JW, O’Brien, JJ. 2008. Assessing predictability of cotton yields in the Southeastern USA based on regional atmospheric circulation and surface temperatures. J. Applied Meteorol. Climatol. 47(1): 76-91


Historical recordHistorical recordof daily valuesof daily values


• Rainfall• Max temp• Min temp

Historical recordHistorical recordof Monthly valuesof Monthly values


Te

mp

ora

l a

gg

reg

ati

on

Ge

os

pa

tia

la

gg

reg

ati

on

GB


GCM’s NDJ rainfall dataGCM’s NDJ rainfall data Weather station network Weather station network

Cross-validated monthly forecasts Cross-validated monthly forecasts GB


GB

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

Yea

rsLeave-one-out Cross ValidationLeave-one-out Cross Validation

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

Yea

rs

Retroactive ValidationRetroactive Validation

GCM’s HindcastGCM’s Hindcast

Weather stationWeather station

Forecasted periodForecasted period





Weather GeneratorWeather Generator

WeatherWeatherGeneratorGenerator

parametersparameters

Pa

ram

ete

re

sti

ma

tio

n








Temporal downscaling

Ensemble of daily values based on the climatology for each weather station Ensemble of daily values based on the climatology for each weather station GB

rvv ˆv96.1 96.1

0 1

sofTotal

sfollowingsofP

sofTotal

sfollowingsofP

'1 #

'1 '1 #ˆ

'0 #

'0 '1 #ˆ

11

01

Temperature, Incoming solar radiation,Rainfall amount:

Rainfall events:Two-state First-order Markov Chain





Weather GeneratorWeather Generator

WeatherWeatherGeneratorGenerator

parametersparameters

Pa

ram

ete

re

sti

ma

tio

n

Parameterperturbation








Temporal downscaling

Ensemble of daily downscaled forecast for each weather station Ensemble of daily downscaled forecast for each weather station

Cross-validated monthly forecasts Cross-validated monthly forecasts

Cross-validated Cross-validated monthly forecast monthly forecast

for each weather stationfor each weather station

Ge

os

pa

tia

ld

isa

gg

reg

ati

on

GB


-0.50 – -0.25-0.25 – 0.00 0.00 – 0.25 0.25 – 0.50 0.50 – 0.75 0.75 – 1.00

Weather stationLake

Pearson’s correlation

Geospatial correlations of observed rainfall events and amountsGeospatial correlations of observed rainfall events and amounts

Daily Monthly

January

GB

Baigorria, GA, Jones, JW, O’Brien, JJ. 2007. Understanding rainfall spatial variability in the southeast USA.Int. J. Climatol. 27(6): 749-760


5050thth

5050thth 5050thth

5050thth

2525thth percentile percentile



Statistical distribution of Statistical distribution of downscaled datadownscaled data


RegionRegion

Spatial Aggregation of Downscaled Data Spatial Aggregation of Downscaled Data Using Point Weather GeneratorsUsing Point Weather Generators

Overestimation of worst and best scenariosOverestimation of worst and best scenariosGB


5050thth

5050thth

2525thth5050thth 2525thth

5050thth 2525thth

2525thth






RegionRegion




5050thth 7575thth

5050thth

2525thth

7575thth

5050thth 2525thth

7575thth

5050thth 2525thth 7575thth

2525thth






RegionRegion




-0.2

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6 0.8 1.0

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6 0.8 1.0

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6 0.8 1.0

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6 0.8 1.0

Observed Pearson’s correlations (r)Observed Pearson’s correlations (r)

Gen

erat

ed r

Gen

erat

ed r

Generated rainfall for seven weather stations for a thousand yearsGenerated rainfall for seven weather stations for a thousand years

Rainfall amountsRainfall amountsRainfall eventsRainfall events

WGENWGEN WGENWGEN

GiSTGiST GiSTGiST

1:1

1:1

1:1

1:1

December-January-FebruaryMarch-April-MayJune-July-AugustSeptember-October-November

GB


-0.2

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6 0.8 1.0

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6 0.8 1.0

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6 0.8 1.0

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.2 0.4 0.6 0.8 1.0

Observed Pearson’s correlations (r)Observed Pearson’s correlations (r)

Gen

erat

ed r

Gen

erat

ed r

Generated temperatures for seven weather stations for a thousand yearsGenerated temperatures for seven weather stations for a thousand years

Minimum temperatureMinimum temperatureMaximum temperatureMaximum temperature

WGENWGEN WGENWGEN

GiSTGiST GiSTGiST

1:1

1:1

1:1

1:1

December-January-FebruaryMarch-April-MayJune-July-AugustSeptember-October-November

GB


0

100

200

300

400

500

600

700

800

900

1 8 15 22 29

Point weather generatorPoint weather generator

Geospatial weather generatorGeospatial weather generator

Days

Days

Generated rainfall for seven weather stations for 31 daysGenerated rainfall for seven weather stations for 31 days

Acc

umul

ated

rai

nfal

l (m

m)

Acc

umul

ated

rai

nfal

l (m

m)

Wea

ther

sta

tions

with

rai

nfal

lW

eath

er s

tatio

ns w

ith r

ainf

all

0

1

2

3

4

5

6

7

1 8 15 22 29

0

1

2

3

4

5

6

7

1 8 15 22 29

GB


Spatial Aggregation of Downscaled Data Spatial Aggregation of Downscaled Data Using a Geospatial Weather GeneratorUsing a Geospatial Weather Generator


RegionRegion

InterpolationInterpolation




GB


ConclusionsConclusions

GB

1. There is no best statistical downscaling method.

2. The best method depends on the Geospatio-temporal

resolution of the input data (GCM, RCM or Reanalysis),

and the Geospatio-temporal resolution of the output

data needed.


ConclusionsConclusions

GB

3. But always, it is necessary to perform the downscaling

incorporating the uncertainty produced by the method.

This can be achieved by generating several equally

probable realizations and to assign probability levels

to the results.

4. For regional assessment, it is important to incorporate

the geospatial correlations among places to avoid

overestimating the worst and the best scenarios.


Downscaling GCMsDownscaling GCMs to local and regional levels to local and regional levels

Institute of Food and Agricultural Sciences

Guillermo A. BaigorriaGuillermo A. Baigorriae-mail:e-mail: [email protected]@ifas.ufl.edu

http://plaza.ufl.edu/gbaigorr/GB/http://plaza.ufl.edu/gbaigorr/GB/



Documents

Downscaling GCMs to local and regional levels