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Regional climate prediction comparisons via statistical upscaling and downscaling

Regional climate prediction comparisons via statistical upscaling and downscaling. Peter Guttorp University of Washington Norwegian Computing Center peter@stat.washington.edu. Outline. Regional climate models Comparing model to data Upscaling Downscaling Results. Acknowledgements.

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Regional climate prediction comparisons via statistical upscaling and downscaling

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  1. Regional climate prediction comparisons via statistical upscaling and downscaling Peter Guttorp University of Washington Norwegian Computing Center peter@stat.washington.edu

  2. Outline • Regional climate models • Comparing model to data • Upscaling • Downscaling • Results

  3. 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

  4. 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)

  5. Data • SMHI synoptic stations in south central Sweden, 1961-2008

  6. Models of climate and weather • Numerical weather prediction: • Initial state is critical • Don’t care about entire distribution, just most likely event • Need not conserve mass and energy • Climate models: • Independent of initial state • Need to get distribution of weather right • Critical to conserve mass and energy

  7. Regional climate models • Not possible to do long runs of global models at fine resolution • Regional models (dynamic downscaling) use global model as boundary conditions and runs on finer resolution • Output is averaged over land use classes • “Weather prediction mode” uses reanalysis as boundary conditions

  8. Comparison of model to data • Model output daily averaged 3hr predictions on (12.5 km)2 grid • Use open air predictions only • RCA3 driven by ERA 40/ERA Interim • Data daily averages point measurements (actually weighted average of three hourly measurements, min and max) • Aggregate model and data to seasonal averages

  9. Upscaling • Geostatistics: predicting grid square averages from data • Difficulties: • Trends • Seasonal variation • Long term memory features • Short term memory features

  10. Long term memory models

  11. A “simple” model space-time trend noise periodic seasonal component seasonal variability

  12. Looking site by site • Naive wavelet-based trend (Craigmile et al. 2004)

  13. Seasonal part

  14. Seasonal variability • Modulate noise • two term Fourier series

  15. 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

  16. Estimated SDFs of standardized noise • Clear evidence of both short and long memory parts FD FEXP

  17. Space-time model • Gaussian white measurement error • Process model in wavelet space • scaling coefficients have mean linear in time and latitude • separable space-time covariance • trend occurs on scales ≥ 2j for some j • obtained by inverse wavelet transform with scales < j zeroed • Gaussian spatially varying parameters

  18. Dependence parameters LTM Short term

  19. Trend estimates

  20. Estimating grid squares • Pick q locations systematically in the grid square • Draw sample from posterior distribution of Y(s,t) for s in the locations and t in the season • Compute seasonal average • Compute grid square average

  21. Downscaling • Climatology terms: • Dynamic downscaling • Stochastic downscaling • Statistical downscaling • Here we are using the term to allow • data assimilation for RCM • point prediction using RCM

  22. Downscaling model (0.91,0.95) smoothed RCM

  23. Comparisons

  24. Reserved stations • Borlänge: Airport that has changed ownership, lots of missing data • Stockholm: One of the longest temperature series in the world. Located in urban park. • Göteborg: Urban site, located just outside the grid of model output

  25. Predictions and data

  26. Spatial comparison

  27. Annual scale Borlänge Stockholm Göteborg

  28. Comments • Nonstationarity • in mean • in covariance • Uncertainty in model output • ”Extreme seasons” where down-and upscaling agree with each other but not with the model output • Model correction approaches

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