1. 1 Arun Kumar Climate Prediction Center 27 October 2010 Ocean Observations and Seasonal-to-Interannual Prediction Arun Kumar Climate Prediction Center NCEP

2. Summary Give an overview of the importance of ocean variability in seasonal climate predictions Give an overview of the importance of ocean variability in seasonal climate predictions  A need for predicting the ocean variability on seasonal time scale A need for predicting the ocean variability on seasonal time scale  Importance of sustained ocean observations for skillful seasonal climate predictionsImportance of sustained ocean observations for skillful seasonal climate predictions 2 Arun Kumar Climate Prediction Center 27 October 2010

3. Reasons for Skillful Atmospheric Predictions 3 Arun Kumar Climate Prediction Center 27 October 2010 Sources for skillful prediction of atmospheric and terrestrial variablesSources for skillful prediction of atmospheric and terrestrial variables –Medium-range weather predictions: Initial conditions –Seasonal predictions: Slowly varying boundary conditions Sea surface temperatureSea surface temperature Soil moistureSoil moisture Sea iceSea ice …

4. Evidence for SST Related Atmospheric Predictability 4 Arun Kumar Climate Prediction Center 27 October 2010 Horel & Wallace, 1981: Planetary-scale atmospheric phenomenon associated with Southern Oscillation. MWR Ropelewski & Halpert, 1987: Global and Regional scale precipitation patterns associated with El Nino/Southern Oscillation (ENSO). MWR Global Influence of ENSO SSTs

5. From Predictability to Predictions For the real-time seasonal prediction of atmospheric and terrestrial climate variability, SST need to be predictedFor the real-time seasonal prediction of atmospheric and terrestrial climate variability, SST need to be predicted –Empirical SST prediction methods –Dynamical SST prediction methods Both methods require an ocean observing system to estimate the historical evolution, as well as the current state of the oceanBoth methods require an ocean observing system to estimate the historical evolution, as well as the current state of the ocean 5 Arun Kumar Climate Prediction Center 27 October 2010

6. Empirical SST Prediction Systems Examples – CPC - Markov model; CDC - Linear inverse model; CPC – Constructed Analog; CPC – Canonical Correlation Analysis;…Examples – CPC - Markov model; CDC - Linear inverse model; CPC – Constructed Analog; CPC – Canonical Correlation Analysis;… All methods need a hindcast (re-forecast) to build up a history of SST predictions, and place a level of confidence in the prediction system. And therefore, require historical ocean observationsAll methods need a hindcast (re-forecast) to build up a history of SST predictions, and place a level of confidence in the prediction system. And therefore, require historical ocean observations Some of the empirical methods have benefited from sub-surface ocean observations, e.g., vertically integrated heat contentSome of the empirical methods have benefited from sub-surface ocean observations, e.g., vertically integrated heat content 6 Arun Kumar Climate Prediction Center 27 October 2010

7. Empirical SST Prediction Systems 7 Arun Kumar Climate Prediction Center 27 October 2010 Xue, Y., et al., 2000: ENSO prediction skill with Markov model: The impact of sea level, J. Climate

8. Dynamical Seasonal Prediction Systems Coupled Ocean-Atmosphere General Circulation Models (CGCM)Coupled Ocean-Atmosphere General Circulation Models (CGCM) – Initialized predictions –Need an initial estimate of the three dimensional state of the ocean (and atmosphere…) – ocean observing system + ocean data assimilation system –Need to put real-time forecasts in a historical context, and hence a set of re-forecasts going back in time – historical ocean analysis (or ocean reanalysis) 8 Arun Kumar Climate Prediction Center 27 October 2010

9. Dynamical Seasonal Prediction Systems Since their advent in ~1990, CGCM based seasonal prediction systems have continued to evolve with improved CGCMs, assimilation methods, and improvements in the ocean observing system (e.g., extension of TAO into Atlantic and Indian Ocean; ARGO; …)Since their advent in ~1990, CGCM based seasonal prediction systems have continued to evolve with improved CGCMs, assimilation methods, and improvements in the ocean observing system (e.g., extension of TAO into Atlantic and Indian Ocean; ARGO; …) 9 Arun Kumar Climate Prediction Center 27 October 2010 D. Behringer

10. Dynamical Seasonal Prediction Systems 10 Arun Kumar Climate Prediction Center 27 October 2010 Saha et al., 2006: The NCEP Climate Forecast System, J. Climate

11. Dynamical Seasonal Prediction Systems Routine seasonal predictions based on CGCMs from many operational centersRoutine seasonal predictions based on CGCMs from many operational centers –ECMWF (European Center for Medium-Range Weather Forecasts) –UKMET –Meteo-France –NOAA-NCEP –BoM (Bureau of Meteorology) –JMA (Japan Meteorological Agency) –BCC (Beijing Climate Center) There is also indication that other modes of ocean variability, e.g., IOD, Atlantic tripole pattern, also a play role in necessitating relevant ocean observationsThere is also indication that other modes of ocean variability, e.g., IOD, Atlantic tripole pattern, also a play role in necessitating relevant ocean observations 11 Arun Kumar Climate Prediction Center 27 October 2010

12. Need for Ocean Observing System for Seasonal Predictions Ocean initializationOcean initialization Analysis and forecast validationAnalysis and forecast validation Improvements in the ocean observing system have had demonstrable positive impact on the seasonal prediction of SSTs and associated global impactsImprovements in the ocean observing system have had demonstrable positive impact on the seasonal prediction of SSTs and associated global impacts 12 Arun Kumar Climate Prediction Center 27 October 2010

13. Some Issues Ocean data assimilation systems lagging behind the available data?Ocean data assimilation systems lagging behind the available data? Adequateness and redundancy in the observational data is hard to quantify…yet at the same time, there are budgetary pressures, and a need for expanded observations for other variablesAdequateness and redundancy in the observational data is hard to quantify…yet at the same time, there are budgetary pressures, and a need for expanded observations for other variables Collaborations between different operational centers and exchange of respective ocean analysis and their assessment would be an extremely useful exercise (e.g., heat content analysis – Yan Xue’s poster)Collaborations between different operational centers and exchange of respective ocean analysis and their assessment would be an extremely useful exercise (e.g., heat content analysis – Yan Xue’s poster) 13 Arun Kumar Climate Prediction Center 27 October 2010

14. Conclusion Seasonal-to-Interannual prediction systems have reached an operational status at many, many centersSeasonal-to-Interannual prediction systems have reached an operational status at many, many centers Seasonal-to-Interannual prediction would be a critical component in the “Global Framework of Climate Services (GFCS)”Seasonal-to-Interannual prediction would be a critical component in the “Global Framework of Climate Services (GFCS)” Ocean observing system is a critical component forOcean observing system is a critical component for –Sustaining the Seasonal-to-Interannual prediction systems, and –For continued improvements in skill of Seasonal-to-Interannual prediction systems 14 Arun Kumar Climate Prediction Center 27 October 2010