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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 on theme: "Seasonal Climate Forecasting for Applied Use in the Western USA Katherine Hegewisch 1, Renaud Barbero 2, John Abatzoglou 1 1 University of Idaho, Department."— Presentation transcript:

1 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

2 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

3 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

4 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

5 NWS Forecast Products 5-Day Weather Forecast 40 MON TUES WED THURS FRI 35 32 41 30 39 28 27 32 35 University of Idaho, Moscow,IDHegewisch, Barbero, Abatzoglou

6 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

7 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

8 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

9 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

10 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

11 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

12 North American Multi Model Ensemble(NMME) Providing seasonal climate forecasts since 2011 8 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

13 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

14 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) (1981-2010)) vs NMME Hindcasts (1981-2010) 1981 1982 1983 -0.10 vs -0.19 -0.01 vs 0.53 0.01 vs 0.24 0.65 vs 1.3 2.8 vs 3.8 -0.5 vs -2.4

15 Biases: forecasts have biases from obs (gridMET) Spring Temp. Anomaly( o C) NMME Hindcasts (1981-2010) Observed Winter Prec. Anomaly (mm) NMME Forecasts: Use Issues University of Idaho, Moscow,IDHegewisch, Barbero, Abatzoglou

16 Biases: forecasts have biases from obs (gridMET) Spring Temp. Anomaly( o C) NMME Hindcasts (1981-2010) 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 (1981-2010) Observed

17 Biases: forecasts have biases from obs (gridMET) Spring Temp. Anomaly( o C) NMME Hindcasts (1981-2010) 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 (1981-2010) Observed

18 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(1981-2010) vs observations(gridMET)

19 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)

20 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)

21 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)

22 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

23 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

24 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?

25 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

26 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

27 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

28 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

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