15 december 2009

Usefulness of GCM data for predicting global hydrological changes

Frederiek Sperna WeilandRens van BeekJaap KwadijkMarc Bierkens

15 december 2009

Overview

• Validating GCM produced climate datasets on their usability for hydrological studies

• Modelling hydrological effects of climate change and distinguishing signal from noise

•Validating bias-corrected GCM datasets on their usability for hydrological studies

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Hydrological impact studies

GCM data

hydrological model

modelleddischarges

statistical / dynamicaldownscaling

statistical / dynamicaldownscaling

hydrological model

hydrological model

Bias-correction

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Background - GCM

General Circulation Model (GCM) Global Climate Model:

• Energy balance

• Resolution:1.875 – 3.759 - 26 layers

• Forcings:- Greenhouse gas- Aerosols

• No predictions on day to day base

Wikipedia, 2009

15 december 2009

What has been said about GCMs….

• GCM data can show large deviations from reality, especially for precipitation (Covey, 2003)

• Differences between GCM results are large and can be larger than differences between emission scenarios (Arnell, 2003)

• The model mean might show the best results (Murphy, 2004; Covey, 2003)

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Datasets - Climate model data

• Intergovernmental Panel for Climate Change (IPCC):

http://www.ipcc-data.org/

Provides data on a monthly timestep

• PCMDI data portal:

Program for Climate Model Diagnosis and Intercomparison

https://esg.llnl.gov:8443/index.jsp

Provides data on a daily timestep

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Datasets - Multiple AOGCM’s

Model Institute Country Acronym

BCM2.0 Bjerknes Centre for Climate Research Norway BCCR

CGCM3.1 Canadian Centre for Climate modelling and Analysis

Canada CCCMA

CGCM2.3.2 Meteorological Research Institute Japan CGCM

CSIRO-Mk3.0 Commonwealth Scientific and Industrial Research Organisation

Australia CSIRO

ECHAM5 Max Planck Institute Germany ECHAM

ECHO-G Freie Universität Berlin Berlin ECHO

GFDLCM 2.0 Geophysical Fluid Dynamics Centre USA GFDL

GISS ER Goddard institute for Space Studies USA GISS

IPSL CM4 Institute Pierre Simon Laplace France IPSL

MIROC3.2 Center of Climate System Research Japan MIROC

NCAR PCMI National Center for Atmospheric Research

USA NCAR

HADGEM1 Met Office’s Hadley Centre for Climate Prediction

UK HADGEM

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Parameters

- Precipitation

- Temperature

Calculation of potential reference evapotranspiration Penman-Monteith:

- Incominging and outgoing shortwave radiation

- Incoming and outgoing longwave radiation

- Airpressure

- Windspeed

- Temperature and minimum temperature

Calculation of potential reference evapotranspiration Blaney-Criddle:

- Temperature

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Reference dataset - CRU / ERA40

CRU:• Climate Reasearch Unit, University of East-Anglia• Timeseries with monthly values• 1901-1995

ERA40:• ECMWF• Daily values• 1957 – 2002

Validation period: 1961 - 1990

- Downscaling CRU data to daily values based on ERA40

- Projection on 0.5 degrees model grid

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Discharge data

GRDC - Global Runoff Data Centre:

- Monthly discharges for 19 large rivers

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PCR-GLOBWB (Beek, 2007)

• Global distributed hydrological model• Daily time-step• 0.5 degrees resolution (360*720)• Sub-grid cell parameterisation• Contains three soil layers, lakes, rivers, snow, vegetation• Solves water balance per cell• Direction of surface runoff calculated with drainage direction map•River discharge calculated with routing scheme based on kinematic wave• Natural water availability – little antropoghenic influences included

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FEWS

• 12x GCM input• CRU/ERA FEWS-World:

• Spatial/temporal interpolation• Unit conversion• Calculation of evaporation• PCRGLOB-WB model run

• 13 x calculated: - Channel flow - Soil moisture - Snow cover - Actual evaporation

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FEWS-World system

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FEWS-World system

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First step: Validate models

•PCR-GLOBWB is run for period 1961-1990 with:

- data from all individual GCMs

- reference meteo dataset (CRU/ERA-40)

•30-year average statistics are derived for the GCM runs and reference run and observations (GRDC)

•GCM statistics are compared with CRU/ERA-40 and observations

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Hydrological regime - Brahmaputra

Brahmaputra

0

10000

20000

30000

40000

50000

60000

70000

80000

0 2 4 6 8 10 12 14

GRDC

ERA_CRU

BCM2.0

ECHO-G

CGCM3.1

CGCM2.3.2

GFDL-CM2.1

GISS-ER

CSIRO-Mk3.0

ECHAM5

IPSL-CM4

MIROC3.2(med)

CCSM3

HADGEM

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Hydrological regime - Brahmaputra

Brahmaputra

0

10000

20000

30000

40000

50000

60000

70000

80000

0 2 4 6 8 10 12 14

GRDC

ERA_CRU

BCM2.0

ECHO-G

CGCM3.1

CGCM2.3.2

GFDL-CM2.1

GISS-ER

CSIRO-Mk3.0

ECHAM5

IPSL-CM4

MIROC3.2(med)

CCSM3

HADGEM

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Hydrological regime - MacKenzie

MacKenzie

0

4000

8000

12000

16000

20000

1 2 3 4 5 6 7 8 9 10 11 12month

dis

ch

arg

e m

3/s

RivDis

ERA_CRU

BCM2.0

ECHO-G

CGCM3.1

CGCM2.3.2

GFDL-CM2.1

GISS-ER

CSIRO-Mk3

ECHAM5

IPSL-CM4

MIROC3.2(med)

CCSM3

HADGEM

RivDis

ERA_CRU

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Hydrological regime - Rhine

Rhine

0

1000

2000

3000

4000

5000

6000

7000

1 2 3 4 5 6 7 8 9 10 11 12

GRDC

ERA_CRU

BCM2.0

ECHO-G

CGCM3.1

CGCM2.3.2

GFDL-CM2.1

GISS-ER

CSIRO-Mk3.0

ECHAM5

IPSL-CM4

MIROC3.2(mde)

CCSM3

HADGEM

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GCM discharge compared with CRU

Relative 30 year mean discharge = (QGCM – QCRU) / QCRU

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Top 5 per catchment - mean discharge

ERA_CRU 37HADCM 37IPSL 36GFDL 31CGCM 28CCCMA 26ECHAM 26BCCR 20CSIRO 19ECHO 14NCAR 11GISS 10MICRO 5

Amazone MICRO Bramaputra GFDL Murray NCAR Niger HADCMERA_CRU GISS CCCMA CCCMACGCM HADCM GFDL BCCRECHAM CCCMA HADCM ECHAMHADCM ERA_CRU CSIRO CGCM

Congo ECHO Danube IPSL Nile GFDL Orange river CSIROIPSL ECHAM IPSL CGCMERA_CRU CGCM HADCM IPSLCGCM ERA_CRU CSIRO CCCMAGFDL CSIRO ECHAM GISS

Ganges ERA_CRU Indus GISS Parana CGCM Rhine HADCMECHAM BCCR ECHAM CSIROGFDL ECHAM NCAR ERA_CRUBCCR CGCM GFDL IPSLHADCM CSIRO CCCMA CGCM

Lena IPSL MacKenzie IPSL Volga CGCM Yangtze GFDLHADCM CCCMA ERA_CRU CCCMABCCR ECHAM GFDL IPSLECHO BCCR IPSL ERA_CRUNCAR CSIRO BCCR HADCM

Mekong HADCM mississippi BCCR Yellow river ERA_CRU Zambezi CCCMAERA_CRU ERA_CRU CSIRO HADCMGFDL ECHO HADCM ECHAMECHO IPSL ECHO NCARCGCM GFDL CGCM IPSL

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Modelling hydrological effects of climate change and distinguishing signal

from noise

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selected IPCC scenarios

20CM3:• Control experiment

A1B:• Rapid economic growth with a peak in global population in mid 21st century followed by a population decline • Fast introduction of efficient technologies • Decrease of social and regional differences

A2:• Heterogeneous world with fragmented technological developments and large regional differences• Continuous increase of CO2 emission

Relative negative scenarios2000-2006: observed emissions larger than estimated (Global Carbon Project, 2008)

(IPCC, 2007)

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Modeling change

Relative change for ensemble of 12 GCMs:

Mean discharge control experiment, period 1971-1990

Mean discharges future experiments A1B and A2, period 2081-2100

Relative change future past

past

Q Q

Q

futureQ

pastQ

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Global changes and model consistencyfuture past

past

Q Q

Q

Nr. of models

significant and consistent

change

A1B

A1B

A2

A2

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Changes in river regimes

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Continental change

• Freshwater discharge increases for all continents

• Freshwater inflow to oceans only decreases for Mediteranean see

• Large uncertainty amongst models

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Conclusions

•GCM derived discharges show large deviations from observations and each other

•Multi-model ensembles provide a ‘relative good mean’ and give uncertainty information

•By quantifying significance and consistency of change, regions and catchments with high potential of hydrological change can be detected