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Seasonal to Decadal Predictability

Seasonal to Decadal Predictability. presented by Andrew Wittenberg with Tony Rosati, Shaoqing Zhang, You-Soon Chang, Rich Gudgel, Bill Stern, Tom Delworth, Gabe Vecchi, Whit Anderson, Fanrong Zeng, Rong Zhang. Key Questions.

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Seasonal to Decadal Predictability

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  1. Seasonal to Decadal Predictability presented by Andrew Wittenberg with Tony Rosati, Shaoqing Zhang, You-Soon Chang, Rich Gudgel, Bill Stern, Tom Delworth, Gabe Vecchi, Whit Anderson, Fanrong Zeng, Rong Zhang

  2. Key Questions • What seasonal-to-decadal predictability exists within the climate system? • What mechanisms impart predictability? • internal variability (e.g. ENSO, AMOC; slow ocean adjustment)‏ • natural forcings (solar variability, recent volcanoes)‏ • anthropogenic forcings (GHGs, aerosols) • Dependence on models/obs/initialization systems? • Societal relevance?

  3. Ensemble Coupled Data Assimilation (ECDA)‏ • Temporally-evolving PDF of climate state, subject to observations • Multivariate: maintains physical balance among state variables (T-S, geostrophy)‏ • Nonlinear evolution of climate PDF • Simultaneous adjustment of all components • Coupled initialization: minimizes shock to forecast model • Initial conditions for seasonal-decadal predictions • Validation for predictions and model development • Up-to-date analysis publicly available on GFDL website • http://www.gfdl.noaa.gov/ocean-data-assimilation • Active participation in CLIVAR/GSOP intercomparisons 2008 OAR Outstanding Paper Award: S. Zhang, M. J. Harrison, A. Rosati, and A. Wittenberg (MWR 2007)‏

  4. New assimilation dramatically improves ENSO forecasts NINO3 anomaly correlation 3D-variational assimilation Forecast Lead (months)‏ 1 3 5 7 9 11 0.6 J F M A M J J A S O N D Forecast Start Month Ensemble Coupled Data Assimilation Forecast Lead (months)‏ 1 3 5 7 9 11 GFDL participating in CTB/NCEP/ National MME, IRI and APCC J F M A M J J A S O N D Forecast Start Month

  5. Assimilation research to improve initialization • Multi-model ECDA: to mitigate model biases • Coupled model parameter estimation within ECDA • ECDA in high resolution CGCM • Impact of full-depth ARGO profilers

  6. Decadal potential predictability: Atlantic • How well does assimilation constrain the AMOC? • Sources of AMOC predictability? • Observing system: • Role of various components? • Impact of nonstationarity on AMOC predictability? Idealized “perfect model” experiments: ARGO outperforms XBT network, in both assimilation and forecast skill

  7. GFDL Decadal Prediction Research (in support of IPCC AR5)‏ • Does observational initialization enhance decadal climate projections? • ECDA (for initial conditions )‏ • Coupled reanalysis, 1970-present • “Workhorse” CM2.1 model from IPCC AR4 [2009] • Decadal hindcasts, 1980-present (10 members)‏ • Decadal predictions, 2001 onward (10 members) • Experimental high-res model (if scientifically warranted) [2010] • Decadal predictions, 2001 onward (10 members) • CM3 model for IPCC AR5 [2010, tentative] • Decadal predictions, 2001 onward (10 members)‏

  8. Summary • Developing advanced assimilation using coupled climate models • Detecting climate change, with estimates of uncertainty • Improving understanding of predictability at seasonal-to-decadal time scales • Foundation for development of a NOAA capability for decadal predictions

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