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Understanding Downscaled
Climate Scenarios
over Idaho
Brandon Moore and Von P. Walden
University of Idaho
(with lots of input from Eric Salathe, UW CIG)
• Purpose for downscaling climate for Idaho
• Description of the downscaling method
– U of I (where differs from CIG [denoted by *])
– Discussion of the choices involved
A priori assumption of stationarity of variability
Length of the historical record
Method of detrending
Interpretation of GCM grids - Interpolation?
What downscaled output looks like for Idaho
Data availability - web service
Current work (precipitation, snow cover extent)
Future work
Purpose for Downscaling Climate Data
for Idaho
• Universities
– Increasing demand for data
• State and Federal Agencies
– Current project with IDWR
– DEQ - Governor’s Climate “Initiative”
– USGS, USFS, Bureau of Rec, USDA, etc …
Purpose for Downscaling Climate Data
for Idaho
• Examples
– M.S. Student looking at changes in fire risk in
upper Great Basin (A. Kuchy, S. Bunting)
– Faculty interested in modeling changes in
hydrology in the Palouse Region (C. Harris)
– Hydrologic changes in the upper Snake due to
climate change (R. Qualls)
– Interest from foresty faculty, …
Description of Downscaling Method
1. Account for differences between model and obs.
Determine Bias Correction between climate and
observational data (1950-1999).
Apply Bias Correction to entire Climate dataset
Apply T across entire grid cell. (CIG)
Interpolate T between centers of grid cells. (UI)
2. Account for sub-grid topography in climate data.
Determine Anomaly Grids using PRISM data.
Downscale to finer spatial resolution.
Bias Correction
• Aggregate PRISM to Climate resolution
raw PRISM grid
4-km grid
PRISM grid
2.5-degree grid
Bias Correction
• For each climate grid cell:
– Determine and remove long-term trend in full time-series
(1900-2100) of climate data by applying a 2nd degree
polynomial fit to the data.
Bias Correction
• For each climate grid cell:
– Compute T between de-trended climate data and
observational data
• Determine mean difference between de-trended climate data and
observation data for 1950-1999
Bias Correction
• For each climate grid cell:
– Add T back onto de-trended data to shift the climate data to
location as raw climate data but without the trend
Bias Correction
Anomaly Grids
• Compute Anomaly Grid
(“perturbation factor”)*
– Interpolate aggregated PRISM data to
PRISM resolution using same schema
as climate interpolation
– Difference raw PRISM grid and
interpolated PRISM (Difference grids as
anomalies for 50 years)
raw PRISM grid
PRISM grid
PRISM grid
anomaly grids
Anomaly Grids
Anomaly Grids
anomaly grid
averaged PRISM
climate grid
averaged climate
climate grid
Downscaled Data for Idaho
• Using three models selected by CIG as spanning
the range of potential change:
– low (GISS)
– medium (ECHAM)
– high (IPSL)
• Two climate change scenarios:
– A2 - aggressive use of fossil fuels
– B1 - more ecologically friendly
Differences in April (1990/99 - 2090/99)
ECHAM5 A2 Tmax
ECHAM5 B1 Tmax
ECHAM5 A2 Tmin
ECHAM5 B1 Tmin
IPSL A2 Tmin
IPSL A2 Tmin
U of I Climate Data Website
U of I Climate Data Website
Current Research
• Temperature downscaling is nearly completed.
• Precipitation downscaling is in progress.
• Predicting future snow cover extent over Idaho
based on relationship between historical snow
images and past climate model output. Then
impose future climate change.
– Visualizations
• Animations of snow cover forecasted to 2100 with Snow Water
Equivalence (SWE) and Thermometer indicators
• Spatial depiction of trends of temperature and precipitation in
• Applying climate change scenarios to hydrologic
models in small to medium-sized watersheds.
Future Research
• New EPSCoR Research Infrastructure
Improvement (RII) proposal,
“Water in a Changing Climate”
– Connect with CIG
– Focus on Snake River Basin
• Connection between surface and ground water
– Interactions of hydrology with biology and
– If funded, $2M / per year for 5 years
• Develop junior faculty and make strategic new hires.