Long-lead streamflow forecasts: 2. An approach based on ensemble climate forecasts Andrew W. Wood, Dennis P. Lettenmaier, Alan.F. Hamlet University of Washington NWS/OGP Climate Prediction Assessments Workshop Alexandria, VAOct, 2002

Overview Research Objective To produce monthly to seasonal snowpack, streamflow, runoff & soil moisture forecasts for continental scale river basins Underlying rationale/motivation 1.Global numerical weather prediction / climate models exploit SST – atmosphere teleconnections 2.Hydrologic models add soil-moisture / snowpack influence on future hydrologic conditions and streamflow (persistence)

Experimental Applications Columbia River Basin Summer 2001 drought East Coast, Summer 2000 drought

1. Downscaling 2. VIC hydrologic simulations UW Experimental West-wide hydrologic prediction system climate model T & P output NCEP, NSIPP, CCM, MPI *ESP as baseline (note: not using official tercile forecasts, yet) Real-time Ensemble Forecasts Ensemble Hindcasts (for bias-correction and preliminary skill assessment) West-wide forecast products streamflow soil moisture, snowpack tailored to application sectors fire, power, recreation * ESP extended streamflow prediction (unconditional climate forecasts run from current hydrologic state) current work

Variable Infiltration Capacity (VIC) Model

Simulations start of month 0end of month 6 Forecast Products streamflow soil moisture runoff, snowpack VIC model spin-up VIC forecast ensemble climate forecast information (from climate model output, or terciles) VIC climatology ensemble 1-2 years back NCDC meteorol. station obs. up to 2-4 months from current LDAS/other meteorol. forcings for remaining spin-up data sources NOTE: In using climate model output, BIAS is a major obstacle.

climate model forecast meteorological outputs ~1.9 degree resolution (T62) monthly total P, avg T Use 3 step approach:1) statistical bias correction 2) downscaling 3) hydrologic simulation General S/I Forecasting Approach  hydrologic (VIC) model inputs  streamflow, soil moisture, snowpack, runoff 1/8-1/4 degree resolution daily P, Tmin, Tmax

Models: Global Spectral Model (GSM) ensemble forecasts from NCEP/EMC forecast ensembles available near beginning of each month, extend 6 months beginning in following month each month: 210 ensemble members define GSM climatology for monthly Ptot & Tavg 20 ensemble members define GSM forecast

One Way Coupling of GSM and VIC models a) bias correction: climate model climatology  observed climatology b) spatial interpolation: GSM ( deg.)  VIC (1/8 deg) c) temporal disaggregation (via resampling of observed patterns): monthly  daily a. b. c. T GSM T OBS

Bias Example: NCEP Global Spectral Model (GSM) Bias is removed at the monthly GSM-scale from the meteorological forecasts

GSM Bias Example (cont): for one cell over Ohio River basin biases in monthly Precip & Temperature can be too large for use as hydrologic simulation inputs

UW climate model downscaling and bias adjustment approach 1. bias correction - percentile-based mapping of model output to climate model-scale observations (i.e., spatially averaged, temporally aggregated) 2. downscaling - interpolation of monthly anomalies to 1/8 degree, application to long term 1/8 degree observed means 3. disaggregation – by resampling observed daily sequences

GSM Bias Example (cont): after procedure, most monthly biases removed

Comparison with Dynamical Downscaling PCM: DOE Parallel Climate Model (2.8 degree resolution) RCM: PNNL Regional Climate Model (1/2 degree resolution) Forecasting approach after dynamical downscaling Forecasting approach used HISTORICAL climate scenario from PCM, for 20 year period

Downscaling Method Comparisons Domain and Model resolutions

Downscaling Method Comparisons Precipitation downscaled vs. observed ( averages) pcm rcm OBS Methods – PCM vs RCM IInterpolation SD Statistical (Spatial) Downscaling alone BCSDBias-correction and SD

CRB Initial Conditions (percentile)

CRB May forecast hindcast “observed” forecast medians

CRB May forecast basin avg. soil moisture

CRB May Forecast Streamflow

CRB May Forecast cumulative flow averages forecast medians

Summary Comments about Approach Climate-hydrology forecast model system has potential  only if model biases are addressed  should be compared with current forecast practices, and with other experimental approaches  performs as well as dynamical downscaling approach, and is simpler to implement Critical needs  access to quality met data during spin-up period  ability to demonstrate / assess skill quantitatively, hopefully aided by what we learn from retrospective assessments (hindcasts)

Sample Results from Recent Work Current Objectives  Implement climate-hydrology model forecast system over western U.S. domain  Assess skill of approach with respect to traditional standards such as ESP and climatology, using retrospective analysis

Recent results: streamflow Columbia R. Basin hindcast analysis GSM- and ESP-derived ensembles for , all years using RMSE-skill score wrt. climatology Results Both ensembles show skill (from initial conditions), but ESP outperforms GSM in most locations (in figure, larger circle = higher skill) Explanation Poor precipitation simulation in GSM JAN forecasts RMSE-Skill Score JAN forecast of FEB-JUL flow

Continuing Research Develop a framework for use with ESP, multiple models GSM CCM3 NSIPP COLA ECHAMESP seasonal forecast skill profiles seasonal forecast skill profile specific to basin & season best model(s) forecast ensemble develop logic for using/discarding/combining model/ESP forecasts associate forecast with reliability discussion based on skill profiles of component model and/or ESP multiple models can be used - e.g., skill-weighted super ensembles ESP is unconditional resampling of observed climate

Progress and schedule taskcurrentFALL domain Columbia (CRB) California Colorado, Great Basin, Rio Grande hindcast ensemble analysis NCEP, ESP NSIPP, CCM, MPI real time ensemble forecast NCEP, ESP, NSIPP, CCM, MPI multi-model ensemble test for CRB, NCEP+ESP all domain / all models official tercile forecasts NCEP, (ESP), NSIPP, CCM, MPI