Empirical Statistical Downscaling - VALUE: COST · Empirical Statistical Downscaling Robustness &...
Transcript of Empirical Statistical Downscaling - VALUE: COST · Empirical Statistical Downscaling Robustness &...
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Empirical Statistical Downscaling
Robustness & limitations
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Limitations: Observations
●Can only model variables for which there is sufficient observations. ●Observations are also needed for evaluation of all simulations.●Gridding can provide a means of filling in informations where the variable is a smooth function in space and dependent on geographical character●Time scales limitations – depends on observations and dependencies.
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Quality of observations
●The observations may contain errors.● Instrument● Digitising & logging.
●Homogeneous?● History of observer● Relocation● Environment● Instruments● Practices (time of day)● Quality control?
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Quality flags
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Large-scale pattern
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Why the poor scores?
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Geographical gaps
●Locations with no useful observations of the required variable.●Nothing can replace measurements – but we can nevertheless make som tentative estimates...
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Geographical information
●Expect some geographical dependency●Gridding (geographical regression models) can provide some indication of skill (ANOVA).●Cannot evaluate results where there is no data.●Can test by witholding data from some locations and then compare these with gridded results.●Useful to apply ESD to locations from different regions for testing purposes – robustness.
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Stationarity●This is a limitations for all models (parameterisation)●Depends on the choice of predictors – physics basis...●Can be tested using RCMs/GCMs as pseude-reality.
● Sign in the results suggesting changing character between large and small scale dependency?
●Test: predict past trends in a split sample exercise.
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Spatial coherence/consistency
● Also temporal structure and dependencies between quantiles (consistent PDF).
● Some types of models (analogs) can provide description of a set of variables
● Variable-inter-dependencies depend on choice of predictors.
● Perfect DS should capture large scales. Question about intermediate scales.
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Physical, scale, & internal consistency?
● No downscaling is guaranteed physical consistent.● There should be scale consistency – aggrigation should
give the same answer as the original predictor/GCM.● Fluxes & energy budgets: rate of evaporation &
precipitation. ● Inter-relation between different variables and
spatial/temporal coherency?● Validation: both in terms of statistics & physics.● Remedy: two-way coupling for RCMs.
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MisconceptionsMisconceptions Wrong to regard RCMs as 'physical consistent' Non-stationarity not only an ESD-relevant issue
Latent heat flux at surface/precip: METNOHIRHAM driven by ERA40 - ERA40.
Implications for the large-scale vertical heat flow
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Extreme tails
● The upper tail is often not well captured.● Methods resampling past (analogs) not able to
predict new record-breaking values.● Possible solution: predict the PDFs.
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Scarce information space
● Only a few variables (those observed)● Not a comprehensive picture.
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Changes to local conditions
● Changes in vegetation & landscape.● Pollution or urbanisation.● Need to account for in calibration – depends
on f(.) in y = f(X).
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The important thing to remember
● Testing, evaluating, and assessing.● Independent data.● Sensitivity tests.● Use all relevant information.● Apply method to other locations.● Pseudo-reality.● Look for physics.
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Dependency to large scales
● Cannot correct invalid GCM predictions.● Account for GCM-predicted ranges – use
ensembles.● Ensemble spread = PDF.● Some idea of range.
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Deser et al (2012), Nature Climate Change
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Deser et al (2012), Nature Climate Change
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Uncertainties:
●Large-scales.● Sampling, GCMs, emission scenario,...
●Downscaling.● Independent and different to RCMs.
●Observations.
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ESD & RCMs: compllimentary!
T(2m):ESD & ENSEMBLES
After bias Correction:
Consistent pictureRobust response
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Summary: Caveats associated with ESD
Stationarity & non-stationarity (detrend, split sample, physics, perfect model studies, pseudo-reality)
Signal & predictors (past trends, physics)
Strength of dependency (R2)
Risk of coincidental fit (cross-validation, split sample, stepwise, geographical distribution)
Skillful simulation (common EOFs, diagnostics of PP-fit, past trends, climatology)
Remedy – always test, explore, and validate!Remedy – always test, explore, and validate!