1 Arun Kumar Climate Prediction Center 27 October 2010 Ocean Observations and...
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![Page 1: 1 Arun Kumar Climate Prediction Center 27 October 2010 Ocean Observations and Seasonal-to-Interannual Prediction Arun Kumar Climate Prediction Center NCEP.](https://reader035.fdocuments.net/reader035/viewer/2022062803/56649f515503460f94c75180/html5/thumbnails/1.jpg)
1 Arun Kumar Climate Prediction Center 27 October 2010
Ocean Observations and Seasonal-to-Interannual Prediction
Arun Kumar
Climate Prediction Center
NCEP
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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
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Reasons for Skillful Atmospheric Predictions
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• 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
• …
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Evidence for SST Related Atmospheric Predictability
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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
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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
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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
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Empirical SST Prediction Systems
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Xue, Y., et al., 2000: ENSO prediction skill with Markov model: The impact of sea level, J. Climate
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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)
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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; …)
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D. Behringer
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Dynamical Seasonal Prediction Systems
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Saha et al., 2006: The NCEP Climate Forecast System, J. Climate
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Dynamical Seasonal Prediction Systems
• 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
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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
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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)
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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
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