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Regional climate downscaling theory

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Presentation on theme: "Regional climate downscaling theory"— Presentation transcript:

1 Regional climate downscaling theory

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…
300km 50km downscaling 10km 1m …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
Family Methods Dynamical Variable resolution models Limited Area/ Regional Climate Models (RCMs) Statistical Weather 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 Source: 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) for Source:

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
Strengths Weaknesses 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 ( )

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
Strengths Weaknesses 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 (p01) = 0.291 wet-to-wet (p11) = 0.654 Therefore it follows (for a two state model) that dry-to-dry (p00) = 1 - p01 = 0.709 wet-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 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
Strengths Weaknesses 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 Transfer function approaches
Synoptic controls of London’s urban heat island during the summer of 1995 Grid 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 functions Strengths Weaknesses 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
Period Activities 1950s Origins in numerical weather prediction 1980s Rationale and proof of concept 1990s Method refinement and inter-comparison 2000s Characterising uncertainty Theory into practice 2010s? Towards robust adaptation decision-making

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