mark new ana lopez, fai fung, milena cuellar funded by tyndall and environment agency
DESCRIPTION
From Climate Data to Adaptation Large-ensemble GCM Information and an Operational Policy-Support Model. Mark New Ana Lopez, Fai Fung, Milena Cuellar Funded by Tyndall and Environment Agency. Adaptation Challenges. Uncertainty in climate information Interactions with other uncertain changes - PowerPoint PPT PresentationTRANSCRIPT
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
Wimbleball Water Resource Zone
Route Map
• Large ensemble climate data
• River flow ensemble
• Water resource system modelling
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: Precipitation
Downscaling: PET
• Calculate GCM PET from– Temperature, RH & cloud-cover (radiation)– Adjust for climatological bias– No daily downscaling
Downscaling: PET
River Flows
River Flows
Month
% C
hang
e
Mean Flow Change: 2020-2039 from 1961-1990
Wimbleball Water Resource Model
• Supplies:– Somerset & Devon (Exeter, Tiverton)
• River & reservoir dominated• 50 ML/d Groundwater• Lancmod WR model
Wimbleball Reservoir: Historic
Monthly Storage, 1930-2005
Month
Sto
rage
(M
l x 1
04 )
Wimbleball Reservoir: 2040 Ensemble
Monthly Storage, 2040
Month
Sto
rage
(M
l x 1
04 )
Wimbleball Reservoir: Changing Risk
September Storage
Year
Sto
rage
(M
l x 1
04 )
Failure to Meet Demand
Devon Demand Failure
Year
No.
Sim
ulat
ions
Failure to Meet Demand
Devon Demand Failure
No.
Sim
ulat
ions
Year
Ave
. Sho
rtfa
ll
Outstanding Issues / Future Work
• Biases in runoff simulations• Simplistic downscaling• Higher multiple year failures in simulations• Scenarios / ensembles of changing demand• Incorporating adaptation options• Staged methodology• Relative likelihoods• Comparison with UKCIP08 / ENSEMBLES