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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
15 december 2009
Hydrological impact studies
GCM data
hydrological model
modelleddischarges
statistical / dynamicaldownscaling
statistical / dynamicaldownscaling
hydrological model
hydrological model
Bias-correction
15 december 2009
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)
15 december 2009
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
15 december 2009
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
15 december 2009
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
15 december 2009
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
15 december 2009
Discharge data
GRDC - Global Runoff Data Centre:
- Monthly discharges for 19 large rivers
15 december 2009
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
15 december 2009
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
15 december 2009
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
15 december 2009
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
15 december 2009
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
15 december 2009
GCM discharge compared with CRU
Relative 30 year mean discharge = (QGCM – QCRU) / QCRU
15 december 2009
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
15 december 2009
Modelling hydrological effects of climate change and distinguishing signal
from noise
15 december 2009
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)
15 december 2009
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
15 december 2009
Global changes and model consistencyfuture past
past
Q Q
Q
Nr. of models
significant and consistent
change
A1B
A1B
A2
A2
15 december 2009
Changes in river regimes
15 december 2009
Continental change
• Freshwater discharge increases for all continents
• Freshwater inflow to oceans only decreases for Mediteranean see
• Large uncertainty amongst models
15 december 2009
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