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Usefulness of GCM data for predicting
global hydrological changes
Frederiek Sperna Weiland
Rens van Beek
Jaap Kwadijk
Marc 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
statistical /
dynamical
downscaling
modelled
discharges
hydrological
model
statistical /
dynamical
downscaling
Biascorrection
15 december 2009
hydrological
model
Background
-
GCM
General Circulation Model (GCM) Global Climate Model:
• Energy balance
• Resolution:
1.875 – 3.75
9 - 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
Multiple AOGCM’s
Datasets 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
15 december 2009
FEWS
• 12x GCM input
• CRU/ERA
FEWS-World:
• Spatial/temporal interpolation
• Unit conversion
• Calculation of evaporation
• PCRGLOB-WB model run
15 december 2009
• 13 x calculated:
- Channel flow
- Soil moisture
- Snow cover
- Actual evaporation
FEWS-World system
15 december 2009
FEWS-World system
15 december 2009
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
80000
GRDC
ERA_CRU
70000
BCM2.0
ECHO-G
60000
CGCM3.1
50000
CGCM2.3.2
GFDL-CM2.1
40000
GISS-ER
30000
CSIRO-Mk3.0
ECHAM5
20000
IPSL-CM4
10000
MIROC3.2(med)
CCSM3
0
0
2
4
6
8
15 december 2009
10
12
14
HADGEM
Hydrological regime - Brahmaputra
Brahmaputra
GRDC
80000
ERA_CRU
70000
BCM2.0
ECHO-G
60000
CGCM3.1
50000
CGCM2.3.2
GFDL-CM2.1
40000
GISS-ER
30000
CSIRO-Mk3.0
ECHAM5
20000
IPSL-CM4
MIROC3.2(med)
10000
CCSM3
0
0
2
4
6
8
15 december 2009
10
12
14
HADGEM
Hydrological regime - MacKenzie
RivDis
MacKenzie
20000
ERA_CRU
BCM2.0
ECHO-G
discharge m3/s
16000
CGCM3.1
CGCM2.3.2
12000
GFDL-CM2.1
GISS-ER
8000
CSIRO-Mk3
ECHAM5
4000
IPSL-CM4
MIROC3.2(med)
0
1
2
3
4
5
6 7 8
month
9 10 11 12
CCSM3
HADGEM
RivDis
ERA_CRU
15 december 2009
Hydrological regime - Rhine
Rhine
7000
6000
5000
4000
3000
2000
1000
0
1
2
3
4
5
6
7
8
15 december 2009
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
GCM discharge compared with CRU
Relative 30 year mean discharge = (QGCM – QCRU) / QCRU
15 december 2009
Top 5 per catchment - mean discharge
Amazone MICRO
Bramaputra
ERA_CRU
CGCM
ECHAM
HADCM
GFDL
Murray
GISS
HADCM
CCCMA
ERA_CRU
NCAR
CCCMA
GFDL
HADCM
CSIRO
Niger
Congo
ECHO
Danube
IPSL
ERA_CRU
CGCM
GFDL
IPSL
Nile
ECHAM
CGCM
ERA_CRU
CSIRO
GFDL
IPSL
HADCM
CSIRO
ECHAM
Orange river CSIRO
CGCM
IPSL
CCCMA
GISS
Ganges
ERA_CRU Indus
ECHAM
GFDL
BCCR
HADCM
GISS
BCCR
ECHAM
CGCM
CSIRO
Parana
CGCM
ECHAM
NCAR
GFDL
CCCMA
Rhine
HADCM
CSIRO
ERA_CRU
IPSL
CGCM
Lena
IPSL
HADCM
BCCR
ECHO
NCAR
IPSL
CCCMA
ECHAM
BCCR
CSIRO
Volga
CGCM
ERA_CRU
GFDL
IPSL
BCCR
Yangtze
GFDL
CCCMA
IPSL
ERA_CRU
HADCM
Mekong
HADCM mississippi
ERA_CRU
GFDL
ECHO
CGCM
ERA_CRU
CSIRO
HADCM
ECHO
CGCM
Zambezi
CCCMA
HADCM
ECHAM
NCAR
IPSL
MacKenzie
BCCR
Yellow river
ERA_CRU
ECHO
IPSL
GFDL
15 december 2009
HADCM
CCCMA
BCCR
ECHAM
CGCM
ERA_CRU
HADCM
IPSL
GFDL
CGCM
CCCMA
ECHAM
BCCR
CSIRO
ECHO
NCAR
GISS
MICRO
37
37
36
31
28
26
26
20
19
14
11
10
5
Modelling hydrological effects of
climate change and distinguishing signal
from noise
15 december 2009
selected IPCC scenarios
20CM3:
• Control experiment
(IPCC, 2007)
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 scenarios
2000-2006: observed emissions larger than estimated (Global Carbon Project,
2008)
15 december 2009
Modeling change
Relative change for ensemble of 12 GCMs:
Q past
Mean discharge control experiment, period
1971-1990
Q future Mean discharges future experiments A1B and
A2, period 2081-2100
Relative change 
Q future  Q past
Q past
15 december 2009
Global changes and model consistency
Q future  Q past
Q past
A2
A1B
A2
A1B
Nr. of models
significant and consistent
change
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
15 december 2009