Development of a four-dimensional variational coupled data assimilation system for enhanced analysis and prediction of seasonal to interannual climate variations
ABSTRACT A four-dimensional variational (4D-VAR) data assimilation system using a coupled ocean-atmosphere global model has been successfully developed with the aim of better defining the dynamical states of the global climate on seasonal to interannual scales. The application of this system to state estimations of climate processes during the 1996–1998 period shows, in particular, that the representations of structures associated with several key events in the tropical Pacific and Indian Ocean sector (such as the El Niño, the Indian Ocean dipole, and the Asian summer monsoon) are significantly improved. This fact suggests that our 4D-VAR coupled data assimilation (CDA) approach has the potential to correct the initial location of the model climate attractor on the basis of observational data. In addition, the coupling parameters that control the air-sea exchange fluxes of mass, momentum, and heat become well adjusted. Such an initialization using the 4D-VAR CDA approach allows us to make a roughly 1.5-year lead time prediction of the 1997–1998 El Niño event. These results demonstrate that our 4D-VAR CDA system has the ability to enhance forecast potential for seasonal to interannual phenomena.
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ABSTRACT: We carry out the first attempt to apply an adjoint method to a coupled general circulation model (CGCM) toward enhancing a skill in seasonal climate modeling. Focusing on 10-day mean errors of a CGCM output, we optimize the oceanic initial conditions together with the bulk adjustment factors by employing a four-dimensional variational data assimilation approach. We perform 9-month-long assimilation experiments independently every 6 months between January 1990 and March 2000. When using the optimized values for the initial conditions and the adjustment factors, a set of 9-month-long, 10-member ensemble simulation always displays realistic seasonal cycle and its interannual modulations over the tropical Indian Ocean (e.g., growing, mature, and decaying phases of the Indian Ocean Dipole Mode events). The optimized values of the bulk adjustment factors primarily reduce the model biases in climatological fields, while the optimization of the oceanic initial conditions largely contributes to a realistic representation of the interannual modulations of seasonal cycle. In the overlapped seasons (i.e., January–March and July–September), the ensemble mean states derived from two experiments show only slight differences in seasonal climate variations over most of the Indian Ocean. These results validate that our assimilation approach is generally effective for advancing a seasonal climate modeling and for obtaining a realistic analysis that is compatible between atmosphere and ocean.Journal of Geophysical Research 01/2009; 114. · 3.17 Impact Factor
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ABSTRACT: Time-varying air–sea coupled processes in the central to eastern equatorial Pacific associated with strong El Niño development during the 1997–1998 period are examined using a newly developed reanalysis dataset obtained from four-dimensional variational ocean–atmosphere coupled data assimilation experiments. The time series of this data field exhibits realistic features of El Niño evolution. Our analysis indicates that resonance between eastward-propagating oceanic downwelling Kelvin waves and the seasonal rise of sea-surface temperature (SST) in the central to eastern equatorial Pacific generates relatively persistent high SST conditions accompanied by a deeper thermocline and more relaxed easterly winds than usual. The surface condition resulting from the wave-seasonal SST resonance represents a preconditioned state that leads to an enhancement in incident downwelling Kelvin waves to levels sufficient to induce large-amplitude unstable coupled waves in the central to eastern equatorial region. Heat balance estimates using our reanalysis dataset suggest that the unstable coupled waves are categorized within the intermediate regime of coupled Kelvin and Rossby waves and have the potential to grow rapidly. We argue that the seasonal resonance and the unstable coupled waves should play crucial roles in the development of the largest historical El Niño event, which was recorded between late 1997 and early 1998.Deep Sea Research Part I: Oceanographic Research Papers. 01/2009;
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ABSTRACT: The Geophysical Fluid Dynamics Laboratory has developed an ensemble coupled data assimilation (ECDA) system based on the fully coupled climate model, CM2.1, in order to provide reanalyzed coupled initial conditions that are balanced with the climate prediction model. Here, we conduct a comprehensive assessment for the oceanic variability from the latest version of the ECDA analyzed for 51 years, 1960–2010. Meridional oceanic heat transport, net ocean surface heat flux, wind stress, sea surface height, top 300 m heat content, tropical temperature, salinity and currents are compared with various in situ observations and reanalyses by employing similar configurations with the assessment of the NCEP’s climate forecast system reanalysis (Xue et al. in Clim Dyn 37(11):2511–2539, 2011). Results show that the ECDA agrees well with observations in both climatology and variability for 51 years. For the simulation of the Tropical Atlantic Ocean and global salinity variability, the ECDA shows a good performance compared to existing reanalyses. The ECDA also shows no significant drift in the deep ocean temperature and salinity. While systematic model biases are mostly corrected with the coupled data assimilation, some biases (e.g., strong trade winds, weak westerly winds and warm SST in the southern oceans, subsurface temperature and salinity biases along the equatorial western Pacific boundary, overestimating the mixed layer depth around the subpolar Atlantic and high-latitude southern oceans in the winter seasons) are not completely eliminated. Mean biases such as strong South Equatorial Current, weak Equatorial Under Current, and weak Atlantic overturning transport are generated during the assimilation procedure, but their variabilities are well simulated. In terms of climate variability, the ECDA provides good simulations of the dominant oceanic signals associated with El Nino and Southern Oscillation, Indian Ocean Dipole, Pacific Decadal Oscillation, and Atlantic Meridional Overturning Circulation during the whole analyzed period, 1960–2010.Climate Dynamics 40(3-4). · 4.23 Impact Factor