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![Page 1: Regional climate downscaling theory. IPCC Fourth Assessment Report ensemble range for annual precipitation change across Yemen by the 2050s under SRES.](https://reader035.fdocuments.us/reader035/viewer/2022062712/56649b58550346318e8d7e55/html5/thumbnails/1.jpg)
Regional climate downscaling theorySource: http://culter.colorado.edu/NWT/site_info/site_info.html
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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”
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300k
m
50km
10km
1m
Poi
nt
…what (some think) the climate impacts community needs.
What the climate model centres provide…
dow
nsca
ling
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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)
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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
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Source: Daniel Caya
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NARCCAP RCM domainsSource: http://www.narccap.ucar.edu/data/domain-plot.png
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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)
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Comparison of observed (UDEL, left panel) and dynamically downscaled (MMFI, right panel) average winter precipitation (mm/day) for 1980-2004. Source: http://www.narccap.ucar.edu/results/ncep-results.html
Verifying regional climate model skill
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How an RCM sees complex topography
Source: Ferranti (2007)
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Heavy rainfall biases (PRUDENCE)
Estimates of return value (in mm) for 1 day, 5 year event for grid cells. Source: Fowler et al. (2007)
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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)
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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)
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Regional Climate Models
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
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Land
Precipitation
TopographyVegetation
Soils
Agg
rega
tion
Dow
nscaling
RCM
GCM
SDS
Climate Model Grid Scale
Statistical downscaling
methods
Applicable to:
• Sub-grid scales (small islands, point processes)
• Complex/ heterogeneous environments
• Extreme events
• Exotic predictands
• Transient change/ ensembles
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A downscaling “manifesto”(Wigley et al., 1990)
Key issues
• Predictor selection
• Local variations in predictability
• Stationarity of scaling
• Predictor domain
• GCM biases
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Weather classification schemes to condition daily surface variables
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Hubert Horace Lamb (1913-1997)
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WESTERLY
ANTICYCLONIC
CYCLONIC
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Rainfall occurrence
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0.70.80.9
A W C N NW S E
Pro
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Rainfall intensity
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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
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Weather typing methods
Weather typing 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
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Key publications reflecting the early development of daily weather generators
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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
Daily precipitation amounts at Addahi-A 1984-1992
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Observed Simulated
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Precipitation amount distributions
Daily precipitation totals at Addis Ababa, Ethiopia 1963-1988 modelled using gamma, fourth root and stretched exponential distributions.
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GAMMA 4TH ROOT STR EXP
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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)
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Weather generator methods
Weather generator 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
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Grid boxes of GCM data available for downscaling to sites across the UK.
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Sea level pressure (normalised)
Heat is
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Wind speed (normalised)
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Synoptic controls of London’s urban heat island during the summer of 1995
Transfer function approaches
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1995
Validation of modelled nocturnal UHI intensity for the summer of 1995
Grey lines denote observations, red the modelled UHI
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Downscaled maximum daily ozone concentrations for Russell Square, London. Source: Wilby (2008)
Maximum 15-minute ozone concentration
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OBS SDSM
Validation of modelled ozone concentrations in central London
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Summer 10-yr mean nocturnal UHI
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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)
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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)
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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