Predictability & Prediction of Seasonal Climate over North America

1. Predictability & Prediction of Seasonal Climate over North America Lisa Goddard, Simon Mason, Ben Kirtman, Kelly Redmond, Randy Koster, Wayne Higgins, Marty Hoerling, Alex Hall, Jerry Meehl, Tom Delworth, Nate Mantua, Gavin Schmidt(US CLIVAR PPAI Panel) NOAA 31st Annual Climate Diagnostics and Prediction Workshop

2. Potential predictability Research forecasts operational Time Series of Prediction Skill (Courtesy of Arun Kumar & Ants Leetmaa) (1) Understand the limit of predictability (2) Identify conditional predictability (e.g. state of ENSO or Indian Ocean) (3) Document the expected skill to judge potential utility of the information for decision support (4) Set a baseline for testing improvements to prediction tools and methodologies (5) Set a target for real-time predictions.

3. Real-time prediction skill…North America, 1-month lead, seasonal terrestrial climate • Provide a template for verification - What are the best metrics? Best for who? - Pros & cons of current metrics - Can we capture important aspects of variability (e.g. trends, drought periods)? • Estimate skill of real-time forecasts - How predictable is N. America climate? - Benefit of multi-model ensembling? • Provide baseline against which we can judge future advances - How best to archive/document for future comparison? - Are we missing something? (i.e. statistical models)

4. Forecast Data Dynamical models (single): • CCCma – Canadian Centre for Climate Modeling and Analysis • KMA – Korean Meteorological Agency • MGO- Main Geophysical Observatory, Russia • NASA/GMAO-National Aeronautics and Space Administration, USA • RPN – Canadian Meteorological Centre • ECHAM4.5 – MPI (run at IRI) • CCM3.6 – NCAR (run at IRI) • ECMWF –European Center for Medium Range Weather Forecasts • Meteo-France – Meteorological Service, France • LODYC- Laboratoire d'Océanographie Dynamique et de Climatologie, France • Met Office – UK Meteorological Office • MPI – Max Planc Institute for Meteorology, Germany • CERFACS – European Centre for Research and Advanced Training in Scientific Computing, France • INGV-Instituto Nazionale di Geofisica e Vulcanolgia, Italy • NOAA-CFS – National Oceanic Atmospheric Administration, USA Multi-Model of dynamical models (simple average) Statistical models (from CPC): CCA, OCN (others?) Multi-Model of dynamical + statistical models

5. Forecast Data

6. Forecast Data: JJA & DJF (1981-2001)

7. Verification Data & Metrics OBSERVATIONAL DATA: 2.5x2.5 deg • 2m T: CRU-TSv2.0 (1901-2002) • Precipitation: CMAP (1979-2004) VERIFICATION MEASURES Metrics consistent with WMO - SVS for LRF (Standardised Verification System for Long Range Forecasts) • Deterministic information : - MSE & its decomposition - correlation, mean bias, & variance ratio • Probabilistic information: - Reliability diagrams, regionally accumulated - ROC areas for individual grid boxes

8. Mean Squared Error

9. Mean Squared Error Pro: * Gives some estimate of uncertainty in forecast (i.e. RMSE). Con: * Can not infer frequency of large errors unless precise distributional assumptions are met. Recommendation: * Perhaps simple graph or table showing frequency of errors of different magnitudes would be appropriate.

10. Correlation : TemperatureDJF 1981-2001

11. Correlation : TemperatureJJA 1981-2001

12. Correlation : PrecipitationDJF 1981-2001

13. Correlation : PrecipitationJJA 1981-2001

14. Correlation Pros: * Commonly used; familiar * Gives simple overview of where models are likely to have skill or not Con: * Merely measure of association, not of forecast accuracy Recommendation: * Avoid deterministic metrics

16. Example Ensemble forecasts of above-median March – May rainfall over north-eastern Brazil

18. 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 ROC Areas : DJF TemperatureBELOW-NORMAL

19. 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 ROC Areas : DJF TemperatureABOVE-NORMAL

20. 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 ROC Areas : JJA TemperatureABOVE-NORMAL

21. 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 ROC Areas : JJA TemperatureBELOW-NORMAL

22. 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 ROC Areas : DJF PrecipitationABOVE-NORMAL

23. 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 ROC Areas : DJF PrecipitationBELOW-NORMAL

24. 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 ROC Areas : JJA PrecipitationABOVE-NORMAL

25. 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 ROC Areas : JJA PrecipitationBELOW-NORMAL

26. ROC Areas Pros: * Can treat probabilistic forecasts * Can be provided point-wise * Can distinguish ‘asymmetric’ skill Cons: * Fails to address reliability

27. RELIABILITY

28. RELIABILITY

29. Reliability Pros: * Treats probabilistic forecasts * Relatively easy to interpret * Provides most relevant information on usability of forecast information over time Cons: * Difficult to provide for individual grid points, especially for short time samples

30. Temperature Trends over North America %-Area Covered by “Above-Normal”

31. Temperature Trends over North America %-Area Covered by “Above-Normal”

32. 1 2 3 4 1 2 3 4 Observed Precipitation over North America1998-2001 JJA DJF Anomalies relative to1981-1997 Percent difference relative to 1981-1997 Frequency (# years out of 4)for precipitation in BN category

33. 4 in 4 3 in 4 2 in 4 1 in 4 Frequency of Below-Normal PrecipitationJJA 1998-2001 OBSERVATIONS

34. 4 in 4 3 in 4 2 in 4 1 in 4 Frequency of Below-Normal PrecipitationDJF 1998-2001 OBSERVATIONS

35. Summary • What’s an appropriate template? - Skill metrics should be flexible (i.e. user defined “events”, categories, thresholds) - Probabilistic forecasts must be treated probabilistically!!! • How are we doing? - Could be better. Encouraging performance estimates by some measures, but inadequate performance on important aspects of climate variability. - Missing elements necessary for seasonal prediction? • Baseline??