Seasonal Climate Forecasting for Applied Use in the Western USA Katherine Hegewisch 1, Renaud Barbero 2, John Abatzoglou 1 1 University of Idaho, Department.

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Presentation transcript:

Seasonal Climate Forecasting for Applied Use in the Western USA Katherine Hegewisch 1, Renaud Barbero 2, John Abatzoglou 1 1 University of Idaho, Department of Geography, Moscow, ID 2 Newcastle University, Civil Engineering and Geosciences, Newcastle, UK Funded by USDA-NIFA Winter (Dec-Feb) Spring (Mar-May) Temperature Colder than Normal Temperature Warmer than Normal Precipitation Wetter than Normal Precipitation Drier than Normal

Applied Use Case: Rangeland Restoration SITUATION Cheatgrass dominated rangelands burn easily Federal $$$ spent each summer fighting rangeland fires RESTORATION After a fire, want to restore native grasses…. but … Restoration success depends on annual weather variations DECISION MAKING Want to know probabilities for restoration success for different methods Have models of seed success dependent on weather Need seasonal forecasts to provide a glimpse of coming water year University of Idaho, Moscow,IDHegewisch, Barbero, Abatzoglou

Applied Use Case: Rangeland Restoration SITUATION Cheatgrass dominated rangelands burn easily Federal $$$ spent each summer fighting rangeland fires RESTORATION After a fire, want to restore native grasses…. but … Restoration success depends on annual weather variations DECISION MAKING Want to know probabilities for restoration success for different methods Have models of seed success dependent on weather Need seasonal forecasts to provide a glimpse of coming water year University of Idaho, Moscow,IDHegewisch, Barbero, Abatzoglou

Applied Use Case: Rangeland Restoration SITUATION Cheatgrass dominated rangelands burn easily Federal $$$ spent each summer fighting rangeland fires RESTORATION After a fire, want to restore native grasses…. but … Restoration success depends on annual weather variations DECISION MAKING Want to know probabilities for restoration success for different methods Hardegree et al. have models of seed success dependent on weather Need seasonal forecasts to provide a glimpse of coming water year University of Idaho, Moscow,IDHegewisch, Barbero, Abatzoglou

NWS Forecast Products 5-Day Weather Forecast 40 MON TUES WED THURS FRI University of Idaho, Moscow,IDHegewisch, Barbero, Abatzoglou

CPC Forecast Products Seasonal Climate Forecast Winter (Dec-Feb) Spring (Mar-May) Temperature Colder than Normal Temperature Warmer than Normal Precipitation Wetter than Normal Precipitation Drier than Normal University of Idaho, Moscow,IDHegewisch, Barbero, Abatzoglou

NWS/CPC Forecast Products Watches/ Warnings 0-2 Days 3-7 Days 6-10 Days Monthly Seasonal 8-14 Days Observed Atmosphere State Initial & Projected State of Atmosphere Initial & Projected Ocean, Land Surface Basis for Forecasts Forecasts Climate Models Weather Models Weather Models Uncertainty In Forecast Small Medium Large University of Idaho, Moscow,IDHegewisch, Barbero, Abatzoglou

NWS/CPC Forecast Products Watches/ Warnings 0-2 Days 3-7 Days 6-10 Days Monthly Seasonal 8-14 Days Observed Atmosphere State Initial & Projected State of Atmosphere Initial & Projected Ocean, Land Surface Basis for Forecasts Forecasts Climate Models Weather Models Weather Models Uncertainty In Forecast Small Medium Large University of Idaho, Moscow,IDHegewisch, Barbero, Abatzoglou

NWS/CPC Forecast Products Watches/ Warnings 0-2 Days 3-7 Days 6-10 Days Monthly Seasonal 8-14 Days Observed Atmosphere State Initial & Projected State of Atmosphere Initial & Projected Ocean, Land Surface Basis for Forecasts Forecasts Climate Models Weather Models Weather Models Uncertainty In Forecast Small Medium Large University of Idaho, Moscow,IDHegewisch, Barbero, Abatzoglou

NWS/CPC Forecast Products Watches/ Warnings 0-2 Days 3-7 Days 6-10 Days Monthly Seasonal 8-14 Days Observed Atmosphere State Initial & Projected State of Atmosphere Initial & Projected Ocean, Land Surface Basis for Forecasts Forecasts Climate Models Weather Models Weather Models Uncertainty In Forecast Small Medium Large University of Idaho, Moscow,IDHegewisch, Barbero, Abatzoglou

Climate Models Land-surface snow cover (Winter – Spring) Sea-surface temperatures (SST) (Inter-annual) Land-surface soil moisture (Spring – Summer) Seasonal climate is experienced as a sequence of ‘weather events’ Climate models based on slowly-evolving features in climate system: SST, Soil Moisture, Snow Cover Provide boundary conditions for energy/moisture Have slow response time University of Idaho, Moscow,IDHegewisch, Barbero, Abatzoglou

North American Multi Model Ensemble(NMME) Providing seasonal climate forecasts since models (USA/Canada) with 6-20 runs each (i.e CFSv2) Monthly Forecasts of temperature, precipitation ( up to 7 month lead times) University of Idaho, Moscow,IDHegewisch, Barbero, Abatzoglou

NMME Forecasts: Use Issues Spatial Scale: Forecasts are on ~100 km grids Biases: Forecast statistics differ from observations Frequency: Forecasts are monthly Variables: Forecasts are only avg. T/ P Skill: Forecasts have varying skill University of Idaho, Moscow,IDHegewisch, Barbero, Abatzoglou

Biases: forecasts have biases from observations Spring Temp. Anomaly( o C) Winter Prec. Anomaly (mm) NMME Forecasts: Use Issues University of Idaho, Moscow,IDHegewisch, Barbero, Abatzoglou Observations (gridMET) ( )) vs NMME Hindcasts ( ) vs vs vs vs vs vs -2.4

Biases: forecasts have biases from obs (gridMET) Spring Temp. Anomaly( o C) NMME Hindcasts ( ) Observed Winter Prec. Anomaly (mm) NMME Forecasts: Use Issues University of Idaho, Moscow,IDHegewisch, Barbero, Abatzoglou

Biases: forecasts have biases from obs (gridMET) Spring Temp. Anomaly( o C) NMME Hindcasts ( ) Observed Winter Prec. Anomaly (mm) NMME Forecasts: Use Issues University of Idaho, Moscow,IDHegewisch, Barbero, Abatzoglou Spring Temp. Anomaly( o C) Shift in Mean Of temperatures NMME Hindcasts ( ) Observed

Biases: forecasts have biases from obs (gridMET) Spring Temp. Anomaly( o C) NMME Hindcasts ( ) Observed Winter Prec. Anomaly (mm) NMME Forecasts: Use Issues University of Idaho, Moscow,IDHegewisch, Barbero, Abatzoglou Spring Temp. Anomaly( o C) Change in Spread (Variance) Of temperatures NMME Hindcasts ( ) Observed

Temperature Precipitation Spatially Averaged Correlations For October Hindcasts p<0.05 Significant Skill r > 0.36 Skill: models having varying skill NMME Forecasts: Use Issues University of Idaho, Moscow,IDHegewisch, Barbero, Abatzoglou Skill: comparing downscaled hindcasts( ) vs observations(gridMET)

bcsdNMME: Downscaled NMME Forecasts University of Idaho, Moscow,IDHegewisch, Barbero, Abatzoglou UI Applied Climate Lab Product: bcsdNMME (bias corrected, spatially downscaled NMME) Daily bcsdNMME Monthly bcsdNMME translated to daily (using gridMET daily patterns) Designed for modeling runs Variables: Min/Max Temperature Precipitation Humidity Radiation Wind Speed Resolution: 4-km (1/24-deg) Monthly/Seasonal bcsdNMME Bias Correction of NMME forecasts Scales: 1 to 6 month avg. outlooks Variables: Mean Temperature Precipitation Resolution: 4-km (1/24-deg) Skill Maps Correlation, RMS, Heidke Skill Score Observational Dataset Used: UI gridMET/METDATA (Abatzoglou)

bcsdNMME: Downscaled NMME Forecasts University of Idaho, Moscow,IDHegewisch, Barbero, Abatzoglou UI Applied Climate Lab Product: bcsdNMME (bias corrected, spatially downscaled NMME) Daily bcsdNMME Monthly bcsdNMME translated to daily (using gridMET daily patterns) Designed for modeling runs Variables: Min/Max Temperature Precipitation Humidity Radiation Wind Speed Resolution: 4-km (1/24-deg) Monthly/Seasonal bcsdNMME Bias Correction of NMME forecasts Scales: 1 to 6 month avgs Variables: Mean Temperature Precipitation Resolution: 4-km (1/24-deg) Skill Maps Correlation, RMSE, Heidke Skill Score Observational Dataset Used: UI gridMET/METDATA (Abatzoglou)

bcsdNMME: Downscaled NMME Forecasts University of Idaho, Moscow,IDHegewisch, Barbero, Abatzoglou UI Applied Climate Lab Product: bcsdNMME (bias corrected, spatially downscaled NMME) Daily bcsdNMME Monthly bcsdNMME translated to daily (using gridMET daily patterns) Designed for modeling runs Variables: Min/Max Temperature Precipitation Humidity Radiation Wind Speed Resolution: 4-km (1/24-deg) Monthly/Seasonal bcsdNMME Bias Correction of NMME forecasts Scales: 1 to 6 month avg. outlooks Variables: Mean Temperature Precipitation Resolution: 4-km (1/24-deg) Skill Maps Correlation, RMS, Heidke Skill Score Observational Dataset Used: UI gridMET/METDATA (Abatzoglou)

Increased Resolution: Forecasts on 4-km grids Raw forecasts, ~ 100-km bcsdNMME, 4-km bcsdNMME: Downscaled NMME Forecasts University of Idaho, Moscow,IDHegewisch, Barbero, Abatzoglou

bcsdNMME: Downscaled NMME Forecasts Provides Skill Maps: correlation, RMSE,HSS Forecast skill varies by month, model and lead-time Skill stronger for temperature than precipitation Correlation Coefficient University of Idaho, Moscow,IDHegewisch, Barbero, Abatzoglou Feb. Temperature (1-mo lead) Feb. Precipitation (1-mo lead) No skill 100% skill

bcsdNMME: Downscaled NMME Forecasts Summer(Jun-Aug) Temperature University of Idaho, Moscow,IDHegewisch, Barbero, Abatzoglou Correlation ( r ) skill for Multi-Model Mean NMME Hindcasts (Barbero et al, in prep) r Low Skill in Central Valley, CA: inability of models to forecast thermal inversions?

bcsdNMME: Downscaled NMME Forecasts University of Idaho, Moscow,IDHegewisch, Barbero, Abatzoglou Correlation ( r ) skill for Multi-Model Mean NMME Hindcasts (Barbero et al, in prep) r Summer (Jun-Aug) Precipitation Low Skill in SW: inability of models to forecast monsoonal processes

bcsdNMME: Downscaled NMME Forecasts University of Idaho, Moscow,IDHegewisch, Barbero, Abatzoglou Correlation ( r ) skill for Multi-Model Mean NMME Hindcasts (Barbero et al, in prep) r Winter (Dec-Feb) Precipitation Low Skill around circle: inability of models to simulate path of moisture transport

bcsdNMME: Downscaled NMME Forecasts University of Idaho, Moscow,IDHegewisch, Barbero, Abatzoglou Correlation ( r ) skill for Multi-Model Mean NMME Hindcasts (Barbero et al, in prep) r Winter (Dec-Feb) Precipitation Skill maps have fine resolution features showing added value over coarse resolution analysis

Conclusions Forecasts have potential to inform decision making in rangeland restoration/rehabilitation Climate forecasts are different than weather forecasts NOAA’s NMME seasonal forecasts have some use issues Bias-corrected, downscaled NMME forecasts available each month from UI Applied Climate Lab (Abatzoglou) monthly/daily versions 4-km resolution University of Idaho, Moscow,IDHegewisch, Barbero, Abatzoglou lots of ecological variables skill maps