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Regional climate downscaling theory. IPCC Fourth Assessment Report ensemble range for annual precipitation change across Yemen by the 2050s under SRES.

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Presentation on theme: "Regional climate downscaling theory. IPCC Fourth Assessment Report ensemble range for annual precipitation change across Yemen by the 2050s under SRES."— Presentation transcript:

1 Regional climate downscaling theory

2 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 Unintelligent downscaling

3 300km 50km 10km 1m Point …what (some think) the climate impacts community needs. What the climate model centres provide… downscaling

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 FamilyMethods DynamicalVariable resolution models Limited Area/ Regional Climate Models (RCMs) StatisticalWeather pattern classification Weather generators Transfer functions


7 NARCCAP RCM domains Source:

8 Verifying regional climate model skill Observed (left column) and RegCM3 simulation (right column) of near surface winds, precipitation and surface temperature for summer 1987-2000. Source: Pal et al. (2007)

9 Comparison of observed (UDEL, left panel) and dynamically downscaled (MMFI, right panel) average winter precipitation (mm/day) for 1980-2004. Source: Verifying regional climate model skill

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 season under 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 Regional climate models StrengthsWeaknesses - Limited area -Variable resolution -Enhanced spatial and temporal resolution compared with GCMs -Responsive to multiple drivers (atmospheric, land-surface) -Multivariate output across domain and levels in the atmosphere -Generates internally consistent maps of change -Results depend on the quality of GCM inputs -As computationally demanding as GCMs -Results depend on domain location and size -Results depend on method of boundary forcing -Technically demanding to set up and run

15 Statistical downscaling methods Applicable to: Sub-grid scales (small islands, point processes) Complex/ heterogeneous environments Extreme events Exotic predictands Transient change/ ensembles

16 A downscaling manifesto (Wigley et al., 1990) Key issues Predictor selection Local variations in predictability Stationarity of scaling Predictor domain GCM biases

17 Weather classification schemes to condition daily surface variables

18 Hubert Horace Lamb (1913-1997)

19 Conditional probabilities of rainfall and mean intensity in the Cotswolds, UK associated with the seven main Lamb Weather Types (LWT), 1891-1910. Key: Anticyclonic (A), Westerly (W), Cyclonic (C), Northery (N), North-westerly (NW), Southerly (S) and Easterly (E) patterns. Weather classification: LWT scheme to condition daily rainfall

20 Weather typing methods Weather typingStrengthsWeaknesses - Subjective classification - Analogues - Fuzzy clusters - Self organising maps - Monte Carlo - Hybrid methods -Enhanced spatial and temporal resolution compared with GCMs -Yields physically interpretable linkages to surface climate -Can be applied to surface climate, air quality, flooding, soil erosion, etc. -Compositing of selected events such as extremes -Results depend on the quality of GCM inputs -Requires a classification scheme -Circulation patterns can be insensitive to radiative forcing -May 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 follows dry-to-wet (p 01 ) = 0.291 wet-to-wet (p 11 ) = 0.654 Therefore it follows (for a two state model) that dry-to-dry (p 00 ) = 1 - p 01 = 0.709 wet-to-dry (p 10 ) = 1 - p 11 = 0.346

23 Precipitation amount distributions Daily precipitation totals at Addis Ababa, Ethiopia 1963-1988 modelled using gamma, fourth root and stretched exponential distributions.

24 EARWIG: A point-n-click weather generator Example 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 Weather generatorStrengthsWeaknesses - Markov chains -Stochastic models -Spell length methods -Neyman-Scott -Mixture models -Enhanced spatial and temporal resolution compared with GCMs -Simultaneous weather generation at multiple sites -Multivariate outputs -Spatial interpolation of model parameters for data sparse regions -Captures variability across different space and time scales -Results depend on the quality of GCM inputs -Arbitrary adjustment of parameters for future climate estimation -Unanticipated effects on secondary variables from changing precipitation parameters

26 Grid boxes of GCM data available for downscaling to sites across the UK. Synoptic controls of Londons urban heat island during the summer of 1995 Transfer function approaches

27 Validation of modelled nocturnal UHI intensity for the summer of 1995 Grey lines denote observations, red the modelled UHI

28 Downscaled maximum daily ozone concentrations for Russell Square, London. Source: Wilby (2008) Validation of modelled ozone concentrations in central London

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 functionsStrengthsWeaknesses - Linear regression -Artificial neural networks -Canonical correlation -Kriging -Enhanced spatial and temporal resolution compared with GCMs -Relatively straightforward to apply -Useful for exotic predictands -Applicable to a wide range of time and space scales -Results depend on the quality of GCM inputs -Observed variance typically underestimated -May assume linearity or normality of data -Poor representation of extreme events -Assumes stationarity of the predictor-predictand relationship(s)

31 Summary – the six eras of downscaling PeriodActivities 1950sOrigins in numerical weather prediction 1980sRationale and proof of concept 1990sMethod refinement and inter-comparison 2000sCharacterising uncertainty Theory into practice 2010s?Towards robust adaptation decision-making

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