1. University of Washington Center for Science in the Earth System
Applied Climate Dynamics Progress
For
ARCs Council Meeting
Tallahassee, FL
October 21, 2007

2. University of Washington west-wide hydrologic forecast system
Almost 250 forecast points
Monthly (biweekly in winter) updates to 12 month max lead
Based on ESP, CPC “official”, and CFS ensembles

3. UW national surface water monitor
Daily updates for soil moisture and runoff
Used in NRCS NWCC weekly snowpack and drought monitor report, and CPC input to drought monitor
Spatial resolution currently ½ degree, increase to 1/8 degree and merger with Princeton University 1/8 degree product for eastern U.S. underway

4. Drought research – multimodel comparison – July 1934 soil moisture as percentiles

7. Multi-Model Ensemble Process Flow:
Break 40-year time series into 40 Januaries, 40 Februaries, etc.
Examine interannual variation of each month’s time series
For each month:
Apply bias correction to all 3 models
Find PCs of the set of 3 models
Mult. Lin. Reg. of obs vs PCs
Ensemble mean = weighted avg of PCs, with weights given by coefs in step 3
Conclusions:
Different models are best at different times
Ensemble is better than the best model at all times
Better correlation with interannual signal → better long-lead-time forecast skill
Better prediction of
summer low flows
Month
Month

8. December-January-February
Evaluation of 15-day ensemble forecast accuracy from Hamill-Whitaker reforecast data set
Input: “reforecast” 15-day 500mb geopotential height anomaly forecast and verification maps
Output: canonical correlation pairs of forecast and verification maps that are patterns of variability for which the forecast has the most skill
December-January-February (DJF):
Leading verification map (top panel) is a mix of Pacific North American and Northern Annular Modes (map shows typical anomalies, contour interval 10m)
Associated forecast map (not shown) similar and weaker
Temporal amplitudes of verification and forecast maps correlated at 0.67
October-November-December:
Leading verification map similar to that for DJF, albeit weaker and more of a Pacific / Polar emphasis
Temporal correlation 0.52
Future work: There are more floods in
Cold and neutral ENSO: stratify reforecast skill
Certain MJO phases: examine observations (reforecast poor at MJO)
October-November-December

9. Regional Climate Modeling
Current work uses MM5 Weather Model
Transition to Weather Research and Forecasting (WRF) Model underway
Nested grids km
Outermost grid by forcing global model
ECHAM5 - Max Planck, Hamburg, model
CCSM3 - NCAR Community Model

10. Fall Precipitation in MM5 forced by ECHAM5
Shift in storm track winds and increased moisture flux increases precipitation along N-S ridges
MM5 forced
By ECHAM5
Increased Westerly Flow
Change in Sep-Oct-Nov Precip (mm/day)
1990s to 2050s
Contours: Change in 500-mb heights

11. Winter Warming in MM5 forced by ECHAM5
Snow-albedo feedback amplifies warming at mid-elevation along terrain
1990s to 2050s DJF
Temperature Change
Difference between MM5 and ECHAM5
Less warming
In MM5
More warming
In MM5
Change in Winter Temperature (degrees C)
Change in Winter Temperature (degrees C)

12. Statistical Downscaling
Empirically relate large-scale spatial pattern to observed 1/8-degree patterns
Fast but lacks potential feedbacks
Applied to 20 IPCC models and 2 scenarios
Method:
Bias-correct global climate model simulation
Spatially downscale to 1/8-degree
Wood et al. (2004), Salathé et al (2007)

13. DJF Results for 2050s Temperature Precipitation Composite of 18 models
Change from to
Temperature
Precipitation °C
mm/day

14. DJF Results for 2050s Temperature Precipitation Composite of 18 models
Change from to
Temperature
Precipitation °C
Percent