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Enhancing the Scale and Relevance of Seasonal Climate Forecasts -

Enhancing the Scale and Relevance of Seasonal Climate Forecasts -. N. Ward - IRI acknowledgments to colleagues at IRI and partners this presentation with L. Sun and A. Robertson. Advancing knowledge of scales Space scales Weather within climate Methods for information creation

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Enhancing the Scale and Relevance of Seasonal Climate Forecasts -

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  1. Enhancing the Scale and Relevance of Seasonal Climate Forecasts - N. Ward - IRI acknowledgments to colleagues at IRI and partners this presentation with L. Sun and A. Robertson • Advancing knowledge of scales • Space scales • Weather within climate • Methods for information creation • Pure dynamical systems • Model output statistics • Empirical predictors, lead-time issues • Issues for practical improvement Climate Prediction and Agriculture: Advances and Challenges, WMO, Geneva, May 11th, 2005

  2. Collaborative Work in Regions

  3. Skill of Model Hindcasts Using Observed SST

  4. Part 1: Advances in Understanding of Predictability at Smaller Spatial and Temporal scales (a) Space Scales

  5. Example of driving a Regional Climate Model with output from a Global Climate Model. Surface Wind at One Time Step DYNAMICAL DOWNSCALING

  6. RSM Precipitation Forecast from Jan for Feb-Mar-Apr (Avg of 10 ensemble) 800 700 600 500 Precipitation Forecast (mm) 400 300 200 Correlation 0.79 100 0 0 200 400 600 800 1000 1200 Precipitation Observed (mm)

  7. Regional models can represent influence on local climate from detailed landscape – e.g. elevation, land cover type …

  8. Even in this situation, how to estimate predictability at the field scale? Quantifying decline in skill at smaller scales: General: Barnston et al NE Brazil example: Sun et al

  9. Leading pattern • of small-scale • rainfall anomalies • over Ceara • Observed • Regional Model • Hypothesis: • Local physiography • induces systematic • variability features

  10. Contingency tables for 3 subregions of Ceara State at local scales (FMA 1971-2000) Contingency tables for 3 subregions of Ceara State at local scales (FMA 1971-2000) Contingency tables for 3 subregions of Ceara State at local scales (FMA 1971-2000) RSM RSM RSM RSM RSM RSM RSM RSM RSM OBS OBS OBS B B B Coast Coast Coast B B B N N N A A A 5 5 5 3 3 3 2 2 2 N N N 3 3 3 4 4 4 3 3 3 A A A 2 2 2 3 3 3 5 5 5

  11. Statistical Downscaling Results for Sri Lanka, 1951-80 Verification Map shows correlation skill (shading) along with contours of elevation

  12. Statistical Downscaling Results for Senegal, 1968-2002 Verification Map shows correlation skill (red positive) for Seasonal rainfall (upper) And NDVI (lower)

  13. Large-scale predictability doescascade into predictability at smaller spatial scalesThere is need to represent the localphysiographic forcing to bestestimate the small scale seasonal climate

  14. Part 1: Advances in Understanding of Predictability at Smaller Spatial and Temporal scales (b) Weather within Climate

  15. … … Predictability of weather statisticsthrough the season

  16. Predictability of the interannual variability of weather statistics over Ceara, NE Brazil Blue = Observed, Pink dash = Predicted by RSM (no statistical correction) Number of Dry spells Longest Dry spell Number of Days without rain

  17. Model Simulation vs. Observation Seasonal Rainfall total R=0.84 Drought Index R=0.74 Flooding Index R=0.84 Weather Index R=0.69

  18. Illustration of concepts in statistical downscaling to weather series: (From a study using the Hidden Markov Model approach) Rainfall states (S Georgia / N Florida, USA) Rainfall occurrence probability Average rainfall amount on wet days (from parameters of mixed exponential distribution) HMM rainfall parameters “learned” from the data

  19. Estimated state sequence1924-1998March to August seasonality, sub-seasonal and interannual variability

  20. Estimated state sequence forMarch-May rainfall in Kenya March April May - “dry” state (#3, yellow) tends to occur in March - “wet” states (#1, green), (#2, blue) tend to occur in April–May To get rainfall sequence: P(Rt | St)

  21. Predictability of seasonal means doescascade into predictability of weather statistics through the seasonRainfall onset involves the specific timingof a set of weather events. The limit of forecastingthe specific timing of weather events is about 2 weeksHowever, it is reasonable to think that information about the likelihood of a set of weather events over a certain time-period could be provided in situations where there is strong SST forcing on the large-scale circulationFurthermore, the possibility for projecting forward information about large-scale intraseasonal structuresis open to further analysis

  22. Part 2: Tools for Prediction

  23. Precipitation Forecast FMA 2004, using persisted SST Note: not the raw model output - already an element of statistical transformation of model output

  24. Statistical Transformation/Downscaling Methods can be applied to the output of all categories of dynamical prediction systems EOF 1 of 850mb Oct-Dec zonal wind from GCM (ECHAM4) GCM was driven with observed SST 1950-1980 To be used as predictor for observed 20kmx20km rainfall over Sri Lanka

  25. Statistical Downscaling Results for Sri Lanka, 1951-80 Verification Map shows correlation skill (shading) along with contours of elevation

  26. Units are correlation skill • Statistical Downscaling to NDVI • Using a GCM with Sept SST to • predict December vegetation • (about 25km resolution) • across East Africa 1982-1998 • Spatial variations in skill may reflect • variations in climate predictability • variations in climate-NDVI coupling • Hypotheses to explore using RCMs. Contours are elevation Corrected high resolution NDVI provide by USGS Time series of area-average predicted NDVI over NE Kenya (r=0.76)

  27. Climate Predictability Tool (CPT)

  28. Example of Reservoir Inflow in Ceara, NE Brazil Probabilistic forecasts based on 2 SST indices in July of previous year Model trained on 1912-1992 data Annual Inflow Forecast Year

  29. Part 3: Some Further Key Issues for Practical Improvement in SI Prediction Systems Lead-time (SST development) Land surface (initial conditions, interaction) Presence of Low-frequency Climate

  30. Early example of 2-tier GCM forecast experiments using persisted SSTA – Sahel Seasonal Rainfall Total Sensitivity of skill to SST development from April to June UKMO model, results published early 1990s

  31. Example of Reservoir Inflow in Ceara, NE Brazil Probabilistic forecasts based on 2 SST indices in July of previous year Model trained on 1912-1992 data Annual Inflow Forecast Year

  32. Exploring Enhancement of Predictability from Global Initial Soil Moisture Conditions

  33. The NCEP RSM Land Module

  34. Enhancing the Scale and Relevance of Seasonal Climate Forecasts - N. Ward - IRI acknowledgments to colleagues at IRI and partners this presentation with L. Sun and A. Robertson • Advancing knowledge of scales • Space scales • Weather within climate • Methods for information creation • Pure dynamical systems • Model output statistics • Empirical predictors, lead-time issues • Issues for practical improvement Climate Prediction and Agriculture: Advances and Challenges, WMO, Geneva, May 11th, 2005

  35. Reservoir Management Tool Input: Probability Seasonal Forecasts and Reservoir System Properties Output: Properties of Reservoir operation With and without Seasonal forecasts

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