regional climate prediction comparisons via statistical upscaling and downscaling
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Regional climate prediction comparisons via statistical upscaling and downscaling. Peter Guttorp University of Washington Norwegian Computing Center [email protected]. Outline. Regional climate models Comparing model to data Upscaling Downscaling Results. Acknowledgements. - PowerPoint PPT PresentationTRANSCRIPT
Regional climate prediction comparisons
via statistical upscaling and downscaling
Peter GuttorpUniversity of Washington
Norwegian Computing Center
Outline
Regional climate modelsComparing model to dataUpscalingDownscalingResults
Acknowledgements
Joint work with Veronica Berrocal, University of Michigan, and Peter Craigmile, Ohio State University
Temperature data from the Swedish Meteorological and Hydrological Institute web site
Regional model output from Gregory Nikulin, SMHI
Climate and weather
Climate change [is] changes in long-term averages of daily weather.
NASA: Climate and weather web site
Climate is what you expect; weather is what you get.
Heinlein: Notebooks of Lazarus Long (1978)
Climate is the distribution of weather.
AMSTAT News (June 2010)
Data
SMHI synoptic stations in south central Sweden, 1961-2008
Models of climate and weather
Numerical weather prediction:Initial state is criticalDon’t care about entire distribution, just most likely eventNeed not conserve mass and energy
Climate models:Independent of initial stateNeed to get distribution of weather rightCritical to conserve mass and energy
Regional climate models
Not possible to do long runs of global models at fine resolutionRegional models (dynamic downscaling) use global model as boundary conditions and runs on finer resolutionOutput is averaged over land use classes“Weather prediction mode” uses reanalysis as boundary conditions
Comparison of model to data
Model output daily averaged 3hr predictions on (12.5 km)2 gridUse open air predictions onlyRCA3 driven by ERA 40/ERA InterimData daily averages point measurements (actually weighted average of three hourly measurements, min and max)Aggregate model and data to seasonal averages
Upscaling
Geostatistics: predicting grid square averages from dataDifficulties:TrendsSeasonal variationLong term memory featuresShort term memory features
Long term memory models
A “simple” model
space-time trend
periodic seasonalcomponent
noise
seasonalvariability
Looking site by site
Naive wavelet-based trend (Craigmile et al. 2004)
Seasonal part
Seasonal variability
Modulate noise two term Fourier series
Both long and short memory
Consider a stationary Gaussian process with spectral density
Examples:B(f) constant: fractionally differenced process (FD)B(f) exponential: fractional exponential process (FEXP) (log B truncated Fourier series)
Short term memory Long term
memory
Estimated SDFs of standardized noise
Clear evidence of both short and long memory parts
FD
FEXP
Space-time model
Gaussian white measurement errorProcess model in wavelet space
scaling coefficients have mean linear in time and latitude separable space-time covariancetrend occurs on scales ≥ 2j for some jobtained by inverse wavelet transform with scales < j zeroed
Gaussian spatially varying parameters
Dependence parameters
LTM
Short term
Trend estimates
Estimating grid squares
Pick q locations systematically in the grid squareDraw sample from posterior distribution of Y(s,t) for s in the locations and t in the seasonCompute seasonal averageCompute grid square average
Downscaling
Climatology terms:Dynamic downscalingStochastic downscalingStatistical downscaling
Here we are using the term to allow•data assimilation for RCM•point prediction using RCM
Downscaling model
smoothed RCM
(0.91,0.95)
Comparisons
Reserved stations
Borlänge: Airport that has changed ownership, lots of missing dataStockholm: One of the longest temperature series in the world. Located in urban park.Göteborg: Urban site, located just outside the grid of model output
Predictions and data
Spatial comparison
Annual scale
Borlänge
Stockholm
Göteborg
Comments
Nonstationarityin meanin covariance
Uncertainty in model output”Extreme seasons” where down-and upscaling agree with each other but not with the model outputModel correction approaches