Progress in Downscaling Climate Change Scenarios in Idaho Brandon C. Moore.
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Transcript of Progress in Downscaling Climate Change Scenarios in Idaho Brandon C. Moore.
![Page 1: Progress in Downscaling Climate Change Scenarios in Idaho Brandon C. Moore.](https://reader035.fdocuments.in/reader035/viewer/2022062714/56649d3b5503460f94a16127/html5/thumbnails/1.jpg)
Progress in Downscaling Climate Change Scenarios in Idaho
Brandon C. Moore
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Outline
• Datasets
• Methodologies
• Downscaling at the University of Idaho
• Results
• Summary
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Datasets• GCM • Observed
8 km
~220 km
8 km
Problems: (1) GCM too coarse for local assessment (2) GCM biases in climatology (spatially and temporally)
(3) Regional climate variability (topography, water)
4 km
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Datasets
• Which datasets should we use?– Climatology– Historic trends– Extremes– Multi-model
• Ensemble• Weighted-average
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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
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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
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Relative to Climate Scenarios
1°K
• 1K uncertainty in downscaling is comparable to the spread of the models around 2050.
• Not as significant at 2100
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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
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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
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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
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Change in Cool Season (Oct-May) PrecipitationPercent Change Late 21st Century SRES-A1B vs. Late 20th Century 20C3M
MME
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Change in Snowfall (SWE%)Percent Change Late 21st Century SRES-A1B vs. Late 20th Century 20C3M
MME
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Projections of annual temperature trends for the state of Idaho
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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
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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.