OUCE Oxford University Centre for the Environment “Applying probabilistic climate change information to strategic resource assessment and planning” Funded

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


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).


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.


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


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


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


ClimatePrediction.netClimatePrediction.net


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


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


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)


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


CATCHMOD: water balance model CATCHMOD: water balance model of River Thames basin.of River Thames basin.


River Thames Basin upstream of River Thames Basin upstream of Kingston gauge and GCM grid-boxesKingston gauge and GCM grid-boxes


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)


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


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)


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


Results: Change FactorsResults: Change Factors

PPT (%CF) PET (%CF)Temperature at 2xCO2

PPT vs PET


+ unperturbed HadAM3

* present day

Results: Standard CATCHMODResults: Standard CATCHMOD


Results: CP.net and CATCHMODResults: CP.net and CATCHMOD

Q50

Q50


Results: CP.net and CATCHMODResults: CP.net and CATCHMOD

Q95

Q95


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


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


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.


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.


Smoothed frequency Smoothed frequency distributions and distributions and

CDFs: Q50CDFs: Q50

Uncertainties:•Climate model parameterization•Hydrological model parameterization•No downscaling•No hydrological model structure


Smoothed Smoothed frequency frequency

distributions and distributions and CDFs: Q95CDFs: Q95



Smoothed frequency Smoothed frequency distributions and CDFs: distributions and CDFs:

Q95Q95



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


Frequency distribution Frequency distribution of flows: annual of flows: annual

statisticsstatistics



Frequency distribution Frequency distribution of flows: annual of flows: annual

statisticsstatistics


Documents

OUCE Oxford University Centre for the Environment “Applying probabilistic climate change information to strategic resource assessment and planning” Funded