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Progress in Downscaling Climate Change Scenarios in Idaho

Progress in Downscaling Climate Change Scenarios in Idaho. Brandon C. Moore. Outline. Datasets Methodologies Downscaling at the University of Idaho Results Summary. GCM. Observed. Datasets. 8 km. 8 km. 4 km. ~220 km. Problems: (1) GCM too coarse for local assessment

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Progress in Downscaling Climate Change Scenarios in Idaho

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  1. Progress in Downscaling Climate Change Scenarios in Idaho Brandon C. Moore

  2. Outline • Datasets • Methodologies • Downscaling at the University of Idaho • Results • Summary

  3. GCM Observed Datasets 8 km 8 km 4 km ~220 km Problems: (1) GCM too coarse for local assessment (2) GCM biases in climatology (spatially and temporally) (3) Regional climate variability (topography, water)

  4. Datasets • Which datasets should we use? • Climatology • Historic trends • Extremes • Multi-model • Ensemble • Weighted-average

  5. Methodology • Delta Method • Apply simple scaling factor; easy to use • Assumes fixed step change; higher stat. moments unchanged • Bias Correction Spatial Downscaling • Wood et al, 2004; Maurer, 2007 • Constructed Analog • Hidalgo et al, 2008

  6. Bias Correction Spatial Downscaling (BCSD) • Aggregate gridded OBS to GCM resolution** (= 1K) • Remove trend • Generate CDF of observed and GCM data** • Q-Q mapping approach • Z-score approach (mean and variability) • Add trend back in • Resample/interpolate** to finer resolution • Apply spatial factor to account for subgrid topography

  7. Relative to Climate Scenarios • 1K uncertainty in downscaling is comparable to the spread of the models around 2050. • Not as significant at 2100 1°K

  8. Construct Analog • Aggregate observed to GCM resolution • Apply a bias correction on the GCM • Determine 30 “best” synoptic patterns based on pattern RMSE • Must be chosen within 45 day window of target date • Determine regression coefficients at coarse resolution • Apply regression coefficients to fine-scale patterns

  9. Downscaling at UI • Construct Analog • Downscaled daily • Tmax, Tmin, Prcp, 10m winds, RHmax, RHmin • Late 20th century (1971-2000) • Late 21st century (2081-2100); A1B • Spatial resolution: 8km • 13 Global Circulation Models • Additional scenarios/time slices to come

  10. Downscaling at UI • Modified BCSD • Downscaled monthly; moving toward daily • 20th and 21st century • Tmax, Tmin, Prcp • Spatial resolution: 4km • 3 models, 2 scenarios • Additional models and scenarios to be completed

  11. Change in Cool Season (Oct-May) PrecipitationPercent Change Late 21st Century SRES-A1B vs. Late 20th Century 20C3M MME

  12. Change in Snowfall (SWE%)Percent Change Late 21st Century SRES-A1B vs. Late 20th Century 20C3M MME

  13. Projections of annual temperature trends for the state of Idaho

  14. Summary • Multiple downscaled climate scenarios for ensemble runs • 2 methods • Multiple models • Multiple resolutions • Future work • Validation • Methods publication • Prepare for AR5 • Meeting in Portland next week

  15. References • Hidalgo, H., Dettinger, M., and Cayan, D., 2008. Downscaling with constructed analogues—Daily precipitation and temperature fields over the United States: California Energy Commision PIER Final Project Report CEC-500-2007-123, 48 p. • Maurer, E. P., 2007. Uncertainty in hydrologic impacts of climate change in the Sierra Nevada, California under two emissions scenarios, Climatic Change, Vol. 82, No. 3-4, 309-325. • Wood, A. W., L. R. Leung, V. Sridhar, and D. P. Lettenmaier, 2004. Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Climatic Change, 62, 189-216.

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