1 Arun Kumar Climate Prediction Center 27 October 2010 Ocean Observations and...

1 Arun Kumar Climate Prediction Center 27 October 2010

Ocean Observations and Seasonal-to-Interannual Prediction

Arun Kumar

Climate Prediction Center

NCEP

Summary

• Give an overview of the importance of ocean variability in seasonal climate predictions

• A need for predicting the ocean variability on seasonal time scale

• Importance of sustained ocean observations for skillful seasonal climate predictions


Reasons for Skillful Atmospheric Predictions


• Sources for skillful prediction of atmospheric and terrestrial variables

– Medium-range weather predictions: Initial conditions

– Seasonal predictions: Slowly varying boundary conditions

• Sea surface temperature

• Soil moisture

• Sea ice

• …

Evidence for SST Related Atmospheric Predictability


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

From Predictability to Predictions

• For 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 ocean


Empirical SST Prediction Systems

• 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 observations

• Some of the empirical methods have benefited from sub-surface ocean observations, e.g., vertically integrated heat content


Empirical SST Prediction Systems


Xue, Y., et al., 2000: ENSO prediction skill with Markov model: The impact of sea level, J. Climate

Dynamical Seasonal Prediction Systems

• 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)



• 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; …)


D. Behringer



Saha et al., 2006: The NCEP Climate Forecast System, J. Climate


• Routine 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 observations


Need for Ocean Observing System for Seasonal Predictions

• Ocean initialization

• Analysis and forecast validation

• Improvements in the ocean observing system have had demonstrable positive impact on the seasonal prediction of SSTs and associated global impacts


Some Issues

• 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 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)


Conclusion

• Seasonal-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)”

• Ocean 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


1 Arun Kumar Climate Prediction Center 27 October 2010 Ocean Observations and...

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