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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)
Outline
• 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
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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
(1900-2100).
•
•
Apply T across entire grid cell. (CIG)
Interpolate T between centers of grid cells. (UI)
2. Account for sub-grid topography in climate data.
•
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Determine Anomaly Grids using PRISM data.
Downscale to finer spatial resolution.
Bias Correction
• Aggregate PRISM to Climate resolution
raw PRISM grid
(1950-1999)
4-km grid
aggregated
PRISM grid
(1950-1999)
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
T
*
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
interpolated
PRISM grid
aggregated
PRISM grid
*
anomaly grids
(50)
Anomaly Grids
Anomaly Grids
anomaly grid
Regionally
averaged PRISM
cdf
Probability
1
Interpolated
climate grid
De-trended
Regionally
averaged climate
cdf
0
-15
Temperature
25
Downscaled
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
http://www.ipcc.ch/SPM2feb07.pdf
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
Idaho
• 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
economics/policy
– If funded, $2M / per year for 5 years
• Develop junior faculty and make strategic new hires.