1. Implementing Probabilistic Climate Outlooks within a Seasonal Hydrologic Forecast System Andy Wood and Dennis P. Lettenmaier Department of Civil and Environmental Engineering for Session H21H-06 2004 Annual Fall Meeting, AGU San Francisco December 14

2. MAGIC (or to some, ALCHEMY) at least: spatial disaggregation temporal disaggregation if not also: bias correction variable transformation Hydrologic Prediction using Climate Forecasts OUTLINE  western U.S. forecasting system  use of NCEP CPC seasonal outlooks  CPC downscaling tests  Conclusions climate forecasts at a coarse scale (in space and sometimes time) hydrologic forecasts at a scale suitable for water management application (e.g., relatively finer) Needs Verification

3. Background: W. US Forecast System

4. ESP ENSO/PDO ENSO CPC Official Outlooks Seasonal Forecast Model (SFM) CAS OCN SMLR CCA CA NSIPP-1 dynamical model VIC Hydrolog y Model NOAA NASA UW Seasonal Climate Forecast Data Sources

5. Background: W. US Forecast System Soil Moisture Initial Condition Snowpack Initial Condition

6. Background: W. US Forecast System targeted statisticse.g., runoff volumes monthly hydrographs

7. Background: CPC Seasonal Outlooks e.g., precipitation

8. Background: CPC Seasonal Outlook Use  spatial unit for raw forecasts is the Climate Division (102 for U.S.)  CDFs defined by 13 percentile values (0.025 - 0.975) for P and T are given

9. Background: CPC Seasonal Outlook Use probabilities => anomalies precipitation

10. Approach: CPC Seasonal Outlook Use climate division anomalies => model forcing ensembles we want to test (1) and (2):  testing (2) is easy, using CPC retrospective climate division dataset  testing (1) is more labor-intensive, less straightforward (2) “downscaling” (1) “Shaake Shuffle” CPC monthly climate division anomaly CDFs spatial / temporal disaggregation ensemble formation monthly climate division T & P ensembles daily 1/8 degree Prcp, Tmax and Tmin ensemble timeseries

11. Approach: CPC Seasonal Outlook Use Downscaling Evaluation Question 1: Does hydrologic simulation driven by the downscaled forcings reproduce expected* streamflow mean and variability? *expected = simulated from 1/8 degree observed forcings (Maurer et al.) Spatial Disaggregation  transform CPC climate division retrospective timeseries (1960-99) into monthly anomaly timeseries (%P, delta T)  apply anomalies to 1/8 degree monthly P and T means (from UW COOP- based observed dataset of Maurer et al., 2001)  yields: 1/8 degree monthly P and T timeseries Temporal Disaggregation  daily weather generator creates daily P and T sequences for 1/8 degree grid  scale and shift sequences by month to reproduce monthly 1/8 degree P and T timeseries values

12. Results: CPC-based flow w.r.t. UW obs dataset Answer to Question 1: (will the resulting forcings reproduce expected* streamflow mean and variability?) SURPRISINGLY WELL, BUT NOT QUITE mean std dev

13. Results: CPC-based flow w.r.t. UW obs dataset Additional examples show similar results Mean pretty well reproduced; variability biased mean std dev

14. Results: CPC temp/precip w.r.t. UW obs dataset based on 1960-99

15. Results: CPC temp/precip w.r.t. UW obs dataset based on 1960-99

16. Results: CPC Seasonal Outlook Use Downscaling Evaluation Question 2: Will bias-correction of CPC climate variables correct errors in the derived streamflow simulation? specific to calendar month and climate model grid cell cpc distrib

17. Results: CPC-based flow w.r.t. UW obs dataset Answer to Question 2: (does bias correction help?) YES..........(but) mean std dev

18. Results: CPC-based flow w.r.t. UW obs dataset Additional examples show similar results Mean pretty well reproduced; variability improved mean std dev

19. For more information: www.hydro.washington.edu/Lettenmaier/Projects/fcst/ Conclusions  Our current approach for downscaling CPC seasonal outlooks is adequate from hydrologic perspective, but would be improved by a bias-correction step.  A systematic temperature bias appears to exist in the CPC climate division dataset, relative to the UW 1/8 degree observed climate dataset (aggregated to the CPC C.D. unit). Still needed…  Assessment of ensemble formation step in CPC forecast use.

20. Questions?

21. Framework: Downscaling CPC outlooks downscaling uses Shaake Shuffle (Clark et al., J. of Hydrometeorology, Feb. 2004) to assemble monthly forecast timeseries from CPC percentile values

22. Results: CPC-based flow w.r.t. UW obs dataset Additional examples show similar results Mean pretty well reproduced; variability biased mean std dev

23. Results: CPC temp/precip w.r.t. UW obs dataset based on 1960-99

24. Results: CPC-based flow w.r.t. UW obs dataset Additional examples show similar results Mean pretty well reproduced; variability improved mean std dev