OUCEOUCEOxford University Centre for the EnvironmentOxford University Centre for the Environment
““Applying probabilistic Applying probabilistic climate change information climate change information
to strategic resource to strategic resource assessment and planning” assessment and planning”
Funded byFunded by
ENVIRONMENT AGENCY ENVIRONMENT AGENCY
TYNDALL CENTRETYNDALL CENTRE
OUCEOUCEOxford University Centre for the EnvironmentOxford University Centre for the Environment
Overall ObjectiveOverall Objective
To develop a risk-based framework for To develop a risk-based framework for handling probabilistic climate change handling probabilistic climate change information and for estimating information and for estimating uncertainties inherent to impact uncertainties inherent to impact assessments performed by the Agency assessments performed by the Agency for strategic planning (water resources for strategic planning (water resources and biodiversity in the first instance).and biodiversity in the first instance).
OUCEOUCEOxford University Centre for the EnvironmentOxford University Centre for the Environment
Specific ObjectivesSpecific Objectives To develop and compare methods for generating To develop and compare methods for generating
regional/local scale climate change probabilities regional/local scale climate change probabilities from coarse resolution CP.net data.from coarse resolution CP.net data.
To trial the application of probabilistic climate To trial the application of probabilistic climate change information to Agency-relevant case change information to Agency-relevant case studies (initially for water resources and studies (initially for water resources and biodiversity management).biodiversity management).
To explore the added-value of probabilistic To explore the added-value of probabilistic scenarios for strategic planning and practical scenarios for strategic planning and practical lessons learnt from the case studies.lessons learnt from the case studies.
To share the techniques and experience gained To share the techniques and experience gained from the exemplar projects with a wider from the exemplar projects with a wider community of partner organisations and community of partner organisations and stakeholders.stakeholders.
OUCEOUCEOxford University Centre for the EnvironmentOxford University Centre for the Environment
climateclimatepredictionprediction.net aims to….net aims to…
Sample uncertainty in climate Sample uncertainty in climate models acrossmodels across– PhysicsPhysics– Initial conditionsInitial conditions– Climate forcingClimate forcing
Provide better understanding of Provide better understanding of plausibleplausible future climate changes that future climate changes that can be forecast with can be forecast with oneone GCM GCM speciesspecies
OUCEOUCEOxford University Centre for the EnvironmentOxford University Centre for the Environment
Experimental StrategyExperimental Strategy
Distributed public computing – port Distributed public computing – port HadCM3 to windows/linux/macHadCM3 to windows/linux/mac
Each participant runs a specific Each participant runs a specific experimentexperiment
– Different model physics, initial Different model physics, initial conditions, forcingconditions, forcing
– Currently 17 million model yearsCurrently 17 million model years
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Phase 1Phase 1
2 x CO2 x CO22 equilibrium experiments equilibrium experiments
– 15 years calibration at 1 x CO15 years calibration at 1 x CO22
– 15 years control at 1 x CO15 years control at 1 x CO22
– 15 years at 2 x CO15 years at 2 x CO22
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ClimatePrediction.netClimatePrediction.net
OUCEOUCEOxford University Centre for the EnvironmentOxford University Centre for the Environment
Data AvailableData Available
Global mean time seriesGlobal mean time series
Eight year seasonal climatologiesEight year seasonal climatologies
– Surface air temperatureSurface air temperature– PrecipitationPrecipitation– CloudinessCloudiness– Surface heat budgetSurface heat budget
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Phase 2Phase 2
Transient simulations with HadCM3Transient simulations with HadCM3
– 1920-2000 “hindcast”1920-2000 “hindcast”– 2001-2080 forecast2001-2080 forecast
Launched with BBC in FebruaryLaunched with BBC in February
OUCEOUCEOxford University Centre for the EnvironmentOxford University Centre for the Environment
Data Available in Phase 2Data Available in Phase 2
More variablesMore variables
Global mean monthly time seriesGlobal mean monthly time series
Regional monthly time series (Giorgi; Regional monthly time series (Giorgi; NAO; MOC)NAO; MOC)
UK grid-box monthly seriesUK grid-box monthly series
Ten-year seasonal climatologies Ten-year seasonal climatologies (1920-2080)(1920-2080)
OUCEOUCEOxford University Centre for the EnvironmentOxford University Centre for the Environment
First ResultsFirst Results
Use of CP.Net probabilistic climate Use of CP.Net probabilistic climate change data for water resource change data for water resource assessment in the Thames basinassessment in the Thames basin
– CATCHMOD: water balance model of CATCHMOD: water balance model of River Thames basinRiver Thames basin
– CP.net data available from Experiment 1CP.net data available from Experiment 1– Results and discussionResults and discussion
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CATCHMOD: water balance model CATCHMOD: water balance model of River Thames basin.of River Thames basin.
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River Thames Basin upstream of River Thames Basin upstream of Kingston gauge and GCM grid-boxesKingston gauge and GCM grid-boxes
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CATCHMOD: parametersCATCHMOD: parameters
Six key parameters controllingSix key parameters controlling
– Direct runoffDirect runoff– Soil WC at which evaporation is reducedSoil WC at which evaporation is reduced– Drying curve gradientDrying curve gradient– Storage constant for unsaturated zoneStorage constant for unsaturated zone– Storage constant for saturated zoneStorage constant for saturated zone
Wilby and Harris (2005)
OUCEOUCEOxford University Centre for the EnvironmentOxford University Centre for the Environment
CATCHMODCATCHMOD
InputsInputs: daily time series of : daily time series of precipitation (PPT) and potential precipitation (PPT) and potential evaporation (PET)evaporation (PET)
OutputOutput: daily time series of river flow: daily time series of river flow
ParametersParameters :chosen as the ones that :chosen as the ones that best reproduce observed flows for best reproduce observed flows for the period 1960-1991the period 1960-1991
OUCEOUCEOxford University Centre for the EnvironmentOxford University Centre for the Environment
CP.net DataCP.net Data
Grand ensemble of 2578 simulations Grand ensemble of 2578 simulations of the HadAM3 GCMof the HadAM3 GCM
Explores 7 parameter perturbations Explores 7 parameter perturbations and perturbed initial conditionsand perturbed initial conditions
450 IC ensembles (model versions)450 IC ensembles (model versions)
OUCEOUCEOxford University Centre for the EnvironmentOxford University Centre for the Environment
CP.net variables and CATCHMOD CP.net variables and CATCHMOD InputsInputs
8-year seasonal means for:8-year seasonal means for:
– total cloud amount in LW radiationtotal cloud amount in LW radiation– surface (1.5m) air temperaturesurface (1.5m) air temperature– total precipitation ratetotal precipitation rate
Use these to calculate Use these to calculate change factorschange factors for PPT and PET over Thamesfor PPT and PET over Thames
Change factors used to perturb Change factors used to perturb CATCHMOD daily time series of PPT & CATCHMOD daily time series of PPT & PETPET
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Results: Change FactorsResults: Change Factors
PPT (%CF) PET (%CF)Temperature at 2xCO2
PPT vs PET
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+ unperturbed HadAM3
* present day
Results: Standard CATCHMODResults: Standard CATCHMOD
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Results: CP.net and CATCHMODResults: CP.net and CATCHMOD
Q50
Q50
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Results: CP.net and CATCHMODResults: CP.net and CATCHMOD
Q95
Q95
OUCEOUCEOxford University Centre for the EnvironmentOxford University Centre for the Environment
Factors not ConsideredFactors not Considered
Full set of CP.net perturbationsFull set of CP.net perturbations
Emissions uncertaintyEmissions uncertainty
Downscaling uncertaintyDownscaling uncertainty
Alternative model structures (GCM Alternative model structures (GCM and Hydrological)and Hydrological)
Coupled transient climate responseCoupled transient climate response
OUCEOUCEOxford University Centre for the EnvironmentOxford University Centre for the Environment
Are Probabilistic Approaches Are Probabilistic Approaches Useful?Useful?
CP.net provides useful climate information CP.net provides useful climate information – particularly joint probabilities of key – particularly joint probabilities of key variablesvariables
Enable more informed decision makingEnable more informed decision making Issues for Water Utility stakeholdersIssues for Water Utility stakeholders
– Understanding the informationUnderstanding the information– Having time and resources to use informationHaving time and resources to use information– Regulatory constraintsRegulatory constraints– In many cases other (non-climate) factors are In many cases other (non-climate) factors are
more uncertain more uncertain
OUCEOUCEOxford University Centre for the EnvironmentOxford University Centre for the Environment
CP.net parametersCP.net parametersParameterParameter DescriptionDescription
VF1(m/s)VF1(m/s) Ice fall speed.Ice fall speed.
CT(1/s)CT(1/s) Cloud droplet to rain conversion rate.Cloud droplet to rain conversion rate.
RHCRITRHCRIT Threshold of relative humidity for Threshold of relative humidity for cloud formation.cloud formation.
CW_sea CW_sea (1/kgm^3)(1/kgm^3)
CW_landCW_land
Cloud droplet to rain conversion Cloud droplet to rain conversion threshold.threshold.
EACFEACF Empirically adjusted cloud fraction.Empirically adjusted cloud fraction.
ENTCOEFENTCOEF Scales rate of mixing between Scales rate of mixing between environmental air and convective environmental air and convective plume.plume.
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Potential EvaporationPotential EvaporationPenman PET is a function of mean air T, mean vapour pressure (vp), sunshine and wind speed
Present : calculate monthly Penman PET using observed climate variables for London (monthly long term means 1961-1990, UK national grid)
2xCO2 : calculate monthly Penman PET assuming:
wind speed = constant
relative humidity = constant thus relative change in vp=relative change in svp
relative change in sunshine = - relative change in cloud amount
T at 2xCO2= observed T + deltaT
vp at 2xCO2= observed vp x (1+CF(svp))
sunshine at 2xCO2 = observed sunshine x (1-CF(cloud))
CF calculated using control and 2xCO2 phases for all the variables.
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Smoothed frequency Smoothed frequency distributions and distributions and
CDFs: Q50CDFs: Q50
Uncertainties:•Climate model parameterization•Hydrological model parameterization•No downscaling•No hydrological model structure
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Smoothed Smoothed frequency frequency
distributions and distributions and CDFs: Q95CDFs: Q95
Uncertainties:•Climate model parameterization•Hydrological model parameterization•No downscaling•No hydrological model structure
OUCEOUCEOxford University Centre for the EnvironmentOxford University Centre for the Environment
Smoothed frequency Smoothed frequency distributions and CDFs: distributions and CDFs:
Q95Q95
Uncertainties:•Climate model parameterization•Hydrological model parameterization•No downscaling•No hydrological model structure
OUCEOUCEOxford University Centre for the EnvironmentOxford University Centre for the Environment
Frequency distribution of Frequency distribution of flows: annual statisticsflows: annual statistics
Uncertainties:•CP.net parameter dependence •No hydrological model•No downscaling•No hydrological model structure
OUCEOUCEOxford University Centre for the EnvironmentOxford University Centre for the Environment
Frequency distribution Frequency distribution of flows: annual of flows: annual
statisticsstatistics
Uncertainties:•CP.net parameter dependence •No hydrological model•No downscaling•No hydrological model structure
OUCEOUCEOxford University Centre for the EnvironmentOxford University Centre for the Environment
Frequency distribution Frequency distribution of flows: annual of flows: annual
statisticsstatistics
Uncertainties:•CP.net parameter dependence •No hydrological model•No downscaling•No hydrological model structure