GFDL’s Coupled Ensemble Data Assimialtion System, 1980-2006 Coupled Reanalysis and Its Impact on ENSO Forecasts

• Main Authors: S. Zhang, A. Rosati, M. J. Harrison, R. Gudgel, W. Stern
• Other Authors: The Pennsylvania State University CiteSeerX Archives
• Format: Text
• Language: English
• Published: 2008
• Subjects: Sea ice
• Online Access: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.184.7734; http://wcrp.ipsl.jussieu.fr/Workshops/Reanalysis2008/Documents/G4-434_ea.pdf

A coupled data assimilation (CDA) system, consisting of an ensemble filter applied to GFDL’s global fully-coupled climate model (CM2), has been developed to facilitate the detection and prediction of seasonal-to-multidecadal climate variability and climate trends. The assimilation provides a self-consistent, temporally-continuous estimate of the coupled model state and its uncertainty, in the form of discrete ensemble members which can be used directly to initialize probabilistic climate forecasts without initial shocks. Then 1976-2006 real oceanic observations (XBTs,ARGOs,CTDs,MRBs,OSDs,MBTs and SSTs) and atmospheric (NCAR/NCEP) reanalyses were assimilated into the coupled ensemble system to form 24 member atmosphere/ocean/land/sea-ice state estimates. This talk focuses on the obtained oceanic reanalysis and its impact on ENSO forecasts. Hindcast statistics show this ensemble climate state estimate and prediction system improved ENSO forecast skills dramatically. This happens mainly because the self-consistent ensemble initial conditions from this coupled assimilation system make all components of the coupled model stay in a physically-balanced state, which help model dynamics project the initial signals onto a seasonal-interannual time scale. 1 Description of GFDL’s CDA system Viewing the evolution of climate states as a continuous