The CARIWIG Weather The CARIWIG Weather Generator (WG)Generator (WG)

Phil Jones and Colin HarphamPhil Jones and Colin HarphamClimatic Research UnitClimatic Research Unit

School of Environmental SciencesSchool of Environmental SciencesUniversity of East Anglia, Norwich, UKUniversity of East Anglia, Norwich, UK

SummarySummary• Brief background to the UK Climate Brief background to the UK Climate

Projections project (UKCP09) and why Projections project (UKCP09) and why there was a need for a WGthere was a need for a WG

• Probabilistic Climate Projections and Probabilistic Climate Projections and what’s proposed in CARIWIGwhat’s proposed in CARIWIG

• CARIWIG Weather Generator (WG) CARIWIG Weather Generator (WG) • Where and for how many sites can WG Where and for how many sites can WG

output be produced?output be produced?

Principles for UKCP09Principles for UKCP09• Probabilistic climate prediction systemProbabilistic climate prediction system

– Modelling uncertainty through perturbed physics Modelling uncertainty through perturbed physics ensemblesensembles

– Weighting these using observationsWeighting these using observations– Produces projected ranges for each variable as Produces projected ranges for each variable as

pdfs, but at the monthly scalepdfs, but at the monthly scale– However, users wanted to continue to use impact However, users wanted to continue to use impact

models (e.g. crop model) specific to their sectormodels (e.g. crop model) specific to their sector– UKCP09 developed a Weather Generator (WG) to UKCP09 developed a Weather Generator (WG) to

translate pdfs to the time series climate impacts translate pdfs to the time series climate impacts users wantedusers wanted

– WGs provide possible sequences of future weather WGs provide possible sequences of future weather required by the impacts modelsrequired by the impacts models

Background to Weather GeneratorsBackground to Weather Generators

• Technique developed in hydrology to produce Technique developed in hydrology to produce longer weather sequences with the same longer weather sequences with the same characteristics as the real world weather characteristics as the real world weather

• Developed to aid estimation of the likelihood Developed to aid estimation of the likelihood of very rare extreme eventsof very rare extreme events

• Most recent development has been for Most recent development has been for UKCP09 (jointly between Newcastle and UKCP09 (jointly between Newcastle and CRU/UEA) through web-based tools and CRU/UEA) through web-based tools and delivery of datasets to users, for subsequent delivery of datasets to users, for subsequent use with sector-specific impact modelsuse with sector-specific impact models

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The Newcastle Rainfall Generator• Why is there a need?Why is there a need?

• Relatively short length of rainfall observationsRelatively short length of rainfall observations

• UsesUses• Extending the utility of existing recordsExtending the utility of existing records• Temporal downscaling to user needsTemporal downscaling to user needs

• Not physically basedNot physically based• Conceptual and stochasticConceptual and stochastic

Schematic of the applicationSchematic of the application

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Stochasticrainfall model

Rainfallrunoff model

Rainfall propertiesE.g.Daily variance

Neyman Scott Rectangular Pulses (NSRP) Stochastic Rainfall Model

time• Storm origins arrive in a Storm origins arrive in a

Poisson processPoisson process

time

• Each origin generates a Each origin generates a random number of rain random number of rain cellscells

timein

tens

ity• Each rain cell is a Each rain cell is a

rectangular pulserectangular pulseto

tal i

nten

sity

time

• The total rainfall at The total rainfall at any time is the sum any time is the sum of all active rain cellsof all active rain cells

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Fitting the NSRP model

• Mean daily rainfall amountsMean daily rainfall amounts

• Variance - daily timescaleVariance - daily timescale

• Lag-1 autocorrelationLag-1 autocorrelation

• Dry/dry and wet/wet transition probabilitiesDry/dry and wet/wet transition probabilities

• SkewnessSkewness

• Model fitted monthly by numerical optimizationModel fitted monthly by numerical optimization

Other weather variablesOther weather variables• The WG generates daily rainfall series, and then The WG generates daily rainfall series, and then

meteorological data for the following five variables:-meteorological data for the following five variables:-– Daily mean temperatureDaily mean temperature T T (deg C)(deg C)– Daily temperature rangeDaily temperature range R R (deg C)(deg C)– Vapour PressureVapour Pressure VP VP (Pa)(Pa)– Sunshine durationSunshine duration S S (hrs) (hrs) – Wind SpeedWind Speed W (m/s) W (m/s)

These are then used to calculate These are then used to calculate potential potential evapotranspirationevapotranspiration (PET) using the Penman-Monteith (PET) using the Penman-Monteith method. method.

PET can only be calculated if all variables are available.PET can only be calculated if all variables are available.We will additionally look into using simpler PET formulae We will additionally look into using simpler PET formulae

such as Thornthwaite, if some variables haven’t been such as Thornthwaite, if some variables haven’t been measuredmeasured

How the rest of the WG is fittedHow the rest of the WG is fitted• Uses station series and requires at least 30 years of Uses station series and requires at least 30 years of

datadata• All non-rainfall variables are normalized dependent All non-rainfall variables are normalized dependent

upon the day being either wet or dryupon the day being either wet or dry• Regression relationships then used to estimate T and Regression relationships then used to estimate T and

DTR dependent on the rainfall state on the current DTR dependent on the rainfall state on the current and previous day (WW,DD,WD,DW and DDD) and previous day (WW,DD,WD,DW and DDD) maintaining both observed intervariable and lag-1 maintaining both observed intervariable and lag-1 correlations. The remaining variables then fitted correlations. The remaining variables then fitted similarly (dependent on rainfall, the state, T and similarly (dependent on rainfall, the state, T and DTR). These relationships are referred to DTR). These relationships are referred to intervariable relationships (IVRs)intervariable relationships (IVRs)

• The regression relationships then used to generate The regression relationships then used to generate many more sequencesmany more sequences

• If regression relationships are weak more noise If regression relationships are weak more noise (random numbers) is added (random numbers) is added

Perturbing the WGPerturbing the WG• Differences/Ratios of changes in precipitation (mean, Differences/Ratios of changes in precipitation (mean,

daily variance, proportion of dry days, skewness and daily variance, proportion of dry days, skewness and daily autocorrelation), temperature, DTR (mean and daily autocorrelation), temperature, DTR (mean and variance) and sunshine, wind speed and vapour variance) and sunshine, wind speed and vapour pressure (all just mean changes) used to modify the pressure (all just mean changes) used to modify the WG parametersWG parameters

• These changes will all be pre-calculated for all the These changes will all be pre-calculated for all the sites, for all GCM/RCM simulations and for future sites, for all GCM/RCM simulations and for future time slices and emission scenariostime slices and emission scenarios

• Intervariable relationships (IVRs) from the present Intervariable relationships (IVRs) from the present world all remain the same. This is because there is world all remain the same. This is because there is little faith in the way these are modelled in climate little faith in the way these are modelled in climate models (all other methods of downscaling assume models (all other methods of downscaling assume this, they just don’t say they do)this, they just don’t say they do)

A single set of change factorsA single set of change factors

All these parameters need to be calculated from the Climate Model output, so daily sequences for at least 20 years of control climate are required

What are the daily series What are the daily series like?like?

Year Month Day Rain Tmin Tmax Vap Press Rel Hum Wind Sun PET(mm) (deg C) (deg C) (hPa) (0:1) (ms-1) (-) (mm)

3001 1 1 0 -0.09 1.84 6.48 1 0.88 4.87 03001 1 2 0 -1.37 2.09 6.97 1 0.76 3.33 03001 1 3 2.9 -0.99 7.22 7.1 0.93 3.05 2.4 0.133001 1 4 0.2 1.97 4.47 7.44 0.97 3.33 2.95 0.043001 1 5 0.9 -1.76 5.11 5.42 0.79 4.15 1.23 0.713001 1 6 0 1.47 5.57 7.13 0.91 5.31 2.42 0.263001 1 7 6.6 1.83 8.81 7.72 0.87 4.41 0.06 0.543001 1 8 3.1 3.93 11.15 8.36 0.8 5.74 1.77 0.873001 1 9 0 4.41 7.99 8.09 0.85 6.13 0.93 0.733001 1 10 0.6 -0.69 5.78 6.84 0.93 5.03 0.01 0.473001 1 11 4.4 3.08 6.11 12.05 1 7.42 1.73 03001 1 12 9.4 0.7 6.53 4.87 0.62 7.83 0.01 1.723001 1 13 2.9 2.17 6.21 7.71 0.94 5.94 3.38 0.223001 1 14 0.9 5.21 5.3 8.75 0.99 5.7 2.46 0.02

All years labelled from 3001 onwards, so shouldn’t be confused with real years.

How will it be used within CARIWIG• List of sites where the WG can be developed• Choose site, RCM, emissions scenario, future period• WG tool will then locate the necessary cached data• 100 30-year sequences are output as a minimum, for

both the control and the future period• Put the WG output through the impact model for the

sector• Additionally you can assess changes in extremes

between the control and future climate using the ETCCDI software. This is a standard way of looking at how extremes of temperature and precipitation have and might change

• ETCCDI – Expert Team on Climate Change, Detection and Indices (joint team between CLIVAR and WMO)

Locations where can the WG be run?• Depends on sufficient length of daily weather data• Cuba (~15 sites)• Dominican Republic (4), Suriname (4)• Trinidad/Tobago (3), Grenada (3)• Barbados (2)• Belize (1-2)• Antigua (1), Caymans (1), Dominica (1), Guyana (1),

Jamaica (1)• At this time unsure if records long enough for St

Lucia, St Kitts and St Vincent

• About 40 sites in total• So far we’ve only produced series for Cuba and Belize

ConclusionsConclusions• Limited to sites with enough data between Limited to sites with enough data between

the years 1971 and 2010the years 1971 and 2010• Generated sequences for both the control Generated sequences for both the control

period and the chosen futureperiod and the chosen future• Need to undertake this for more than one Need to undertake this for more than one

Climate Model and RCM combinationClimate Model and RCM combination• The pdfs and WG approach will enable users The pdfs and WG approach will enable users

to look at worst cases, quantifying the to look at worst cases, quantifying the potential risk through the impact model for potential risk through the impact model for their sectortheir sector