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From Climate Data to Adaptation Large-ensemble GCM Information and an
Operational Policy-Support Model
Mark NewAna Lopez, Fai Fung, Milena CuellarFunded by Tyndall and Environment Agency
Adaptation Challenges
1. Uncertainty in climate information
2. Interactions with other uncertain changes
3. Integrated assessment
Large GCM Ensemble: CPdN• Explore model uncertainty by varying settings of poorly
constrained model parameters• HADCM3L model: standard atmosphere & low resolution
ocean.• 26 perturbed parameters (radiation, large scale cloud
formation, ocean circulation, sulphate cycle, sea ice formation and energy convection)
• Initial condition ensembles.• Transient runs:
– 1920-2000 forced with historical CO2, solar and volcanic forcing.– 2000-2080 forced with different possible scenarios
First 246 Completed Simulations
IPCC 4AR models
CPDN model runs
Global Mean Temperature: SRES A2
An
om
aly
fro
m 1
96
1-1
99
0
Data Available• 10-year seasonal mean fields• Monthly mean (time series):
– Large regions (Giorgi)– Selected grid-boxes (including UK)
• Variables include– Total precipitation rate– Convective cloud amount– Surface air temperature (1.5m)– Relative humidity (1.5m)
Modelling Set-up
• Downscale climate in space and time– SW England -> River Exe– Monthly -> Daily
• Generate ensemble of daily river flows– CATCHMOD rainfall-runoff model
• Run flow-ensemble through water resource model
Downscaling: Precipitation
• Gamma transform method– Remove GCM monthly biases– Select daily values from observations
August 1930-1985
Fre
quen
cy
Monthly Precip (mm/d)
ModelObserved
August 2020-2060
Fre
quen
cy
Monthly Precip (mm/d)
ModelObserved
Downscaling: PET
• Calculate GCM PET from– Temperature, RH & cloud-cover (radiation)– Adjust for climatological bias– No daily downscaling
Wimbleball Water Resource Model
• Supplies:– Somerset & Devon (Exeter, Tiverton)
• River & reservoir dominated• 50 ML/d Groundwater• Lancmod WR model