2 “Unintelligent downscaling” IPCC Fourth Assessment Report ensemble range for annual precipitation change across Yemen by the 2050s under SRES A2 emissions (left: driest model; right: wettest model). Data source: Climate Wizard
3 What the climate model centres provide… 300km50kmdownscaling10km1m…what (some think) the climate impacts community needs.Point
4 Justification for downscaling ...studies of the impacts of projected global warming on a regional scale...necessitates the development and application of scenarios to specific problems... Cohen (1990)...Even if global climate models in the future are run at high resolution there will remain the need to ’downscale’ the results from such models to individual sites or localities for impact studies... DOE (1996)...‘downscaling’ techniques, [are] commonly used to address the scale mismatch between coarse resolution global climate model (GCM) output and the regional or local catchment scales required for climate change impact assessment and hydrological modelling... Fowler & Wilby (2007)
5 A typology of downscaling methods FamilyMethodsDynamicalVariable resolution modelsLimited Area/ Regional Climate Models (RCMs)StatisticalWeather pattern classificationWeather generatorsTransfer functions
8 Verifying regional climate model skill Observed (left column) and RegCM3 simulation (right column) of near surface winds, precipitation and surface temperature for summerSource: Pal et al. (2007)
9 Verifying regional climate model skill Comparison of observed (UDEL, left panel) and dynamically downscaled (MMFI, right panel) average winter precipitation (mm/day) forSource:
10 How an RCM sees complex topography Source:Ferranti (2007)
11 Heavy rainfall biases (PRUDENCE) Estimates of return value (in mm) for 1 day, 5 year event for grid cells. Source: Fowler et al. (2007)
12 Uncertainty in projections (PRUDENCE ) Estimates of percent change in the 1-day 5-year and 10-day 5-year return values, respectively, for each RCM and each seasonunder the SRES A2 2071–2100 emissions scenario for Southeast England (SEE)Source:Fowler & Ekstrom (2009)
13 PRECIS: DIY regional downscaling PRECIS model projections of changes in summer monsoon rainfall by the 2080s, under SRES A2 and B2 emissions scenarios. Source: Kumar et al. (2006)
14 Regional Climate Models StrengthsWeaknessesLimited areaVariable resolutionEnhanced spatial and temporal resolution compared with GCMsResponsive to multiple drivers (atmospheric, land-surface)Multivariate output across domain and levels in the atmosphereGenerates internally consistent maps of changeResults depend on the quality of GCM inputsAs computationally demanding as GCMsResults depend on domain location and sizeResults depend on method of boundary forcingTechnically demanding to set up and run
19 Weather classification: LWT scheme to condition daily rainfall Conditional probabilities of rainfall and mean intensity in the Cotswolds, UK associated with the seven main Lamb Weather Types (LWT),Key:Anticyclonic (A), Westerly (W), Cyclonic (C), Northery (N), North-westerly (NW), Southerly (S) and Easterly (E) patterns.
20 Weather typing methods StrengthsWeaknessesSubjective classificationAnaloguesFuzzy clustersSelf organising mapsMonte CarloHybrid methodsEnhanced spatial and temporal resolution compared with GCMsYields physically interpretable linkages to surface climateCan be applied to surface climate, air quality, flooding, soil erosion, etc.Compositing of selected events such as extremesResults depend on the quality of GCM inputsRequires a classification schemeCirculation patterns can be insensitive to radiative forcingMay not capture intra- type variations in surface weather
21 Key publications reflecting the early development of daily weather generators
22 Precipitation occurrence process The transition probabilities for Cambridge, UK are as followsdry-to-wet (p01) = 0.291wet-to-wet (p11) = 0.654Therefore it follows (for a two state model) thatdry-to-dry (p00) = 1 - p01 = 0.709wet-to-dry (p10) = 1 - p11 = 0.346
23 Precipitation amount distributions Daily precipitation totals at Addis Ababa, Ethiopia modelled using gamma, fourth root and stretched exponential distributions.
24 A “point-n-click” weather generator EARWIG:A “point-n-click” weather generatorExample screen for the Environment Agency Rainfall and Weather Impacts Generator (EARWIG). The software is based on the Neyman-Scott Rectangular Pulse (NSRP) weather generator. See: Kilsby et al. (2007)
25 Weather generator methods StrengthsWeaknessesMarkov chainsStochastic modelsSpell length methodsNeyman-ScottMixture modelsEnhanced spatial and temporal resolution compared with GCMsSimultaneous weather generation at multiple sitesMultivariate outputsSpatial interpolation of model parameters for data sparse regionsCaptures variability across different space and time scalesResults depend on the quality of GCM inputsArbitrary adjustment of parameters for future climate estimationUnanticipated effects on secondary variables from changing precipitation parameters
26 Transfer function approaches Synoptic controls of London’s urban heat island during the summer of 1995Grid boxes of GCM data available for downscaling to sites across the UK.
27 Validation of modelled nocturnal UHI intensity for the summer of 1995 Grey lines denote observations, red the modelled UHI
28 Validation of modelled ozone concentrations in central London Downscaled maximum daily ozone concentrations for Russell Square, London. Source: Wilby (2008)
29 Uncertainty in UHI due to GCM output Twenty-first century nocturnal urban heat island intensity in London downscaled from four GCMs under SRES A2 emissions. Source: Wilby (2008)
30 Transfer function methods Transfer functionsStrengthsWeaknessesLinear regressionArtificial neural networksCanonical correlationKrigingEnhanced spatial and temporal resolution compared with GCMsRelatively straightforward to applyUseful for exotic predictandsApplicable to a wide range of time and space scalesResults depend on the quality of GCM inputsObserved variance typically underestimatedMay assume linearity or normality of dataPoor representation of extreme eventsAssumes stationarity of the predictor-predictand relationship(s)
31 Summary – the six eras of downscaling PeriodActivities1950sOrigins in numerical weather prediction1980sRationale and proof of concept1990sMethod refinement and inter-comparison2000sCharacterising uncertaintyTheory into practice2010s?Towards robust adaptation decision-making
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