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.
- SourceAvailable from: Armin Köhl[Show abstract] [Hide abstract]
ABSTRACT: We present results from a new synthesis (GECCO2) that covers the years 1948-2011 employing a similar configuration of the Massachusetts Institute of Technology general circulation model as the previous 50-yr (1952-2001) GECCO synthesis. In GECCO2, the resolution was increased, it now includes the Arctic Ocean and a dynamic/thermodynamic sea ice model. The synthesis uses the adjoint method to bring the model into consistency with available hydrographic and satellite data as well as prior estimates of surface fluxes. In comparison to GECCO, GECCO2 provides a better agreement with the assimilated data, however, the estimated flux adjustments remain similar to GECCO. Global heat content changes are in agreement with recent observational estimates and the estimate of the global heatflux is close to a radiative forcing estimate. Both show a clear effect of the radiative forcing from volcanic eruptions and a weak relation to ENSO events. In contrast to GECCO, the importance of the Denmark Strait overflow for the variability of the Atlantic Meridional Overturning Circulation (AMOC) is replaced in GECCO2 by water mass transformation in the subpolar gyre, which is shown to be part of the thermohaline circulation if the overturning is defined as function of density . Heat and freshwater transport estimates in the Atlantic are more consistent with previous estimates than the unconstrained run. Decomposing heat and freshwater transports into overturning and gyre components by averaging on density coordinates demonstrates that in these coordinates the contribution from the gyre circulation largely disappears for heat transport and is reduced for the freshwater transport.Quarterly Journal of the Royal Meteorological Society 03/2014; · 3.33 Impact Factor
- Proceedings of the Institute of Marine Engineering, Science, and Technology. Journal of operational oceanography 12/2014; · 1.50 Impact Factor
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ABSTRACT: Several prototypes of coupled ocean-atmosphere data assimilation (DA) frameworks have been developed or are under development by different groups worldwide. Almost all of these systems, however, tend to analyze the atmosphere and the ocean separately, i.e., coupled single-component DA, thereby limiting the impact of observations across the air-sea interface. While such a framework serves as a valuable ‘intermediate’ step for operational centers such as the NCEP , several fundamental questions remain unanswered. For example, it is not yet clear what will be the potential benefit of using near surface observations (SST, for example) to correct both the ocean and the atmospheric states simultaneously within a coupled framework. At a more fundamental level, the relative differences in accuracy between different coupled frameworks, which can include assimilation of data either within a single-component, within multiple components or allow cross-component interactions, remain to be established. Recently, the Community Earth System Model (CESM; previously known as the Community Climate System Model -CCSM) has been interfaced to a community facility for ensemble data assimilation (Data Assimilation Research Testbed – DART), which allows us to answer such questions critically by examining a suite of coupled system configurations. First a coupled multicomponent DA system is being run in which data is assimilated into each of the respective ocean/atmosphere model components during the assimilation step, and information is exchanged between the model components during the forecast step. Secondly, two coupled single component DA systems are being run in which observations are assimilated into only one of the ocean/atmosphere components during the assimilation step. Work is ongoing to expand the coupled multi-component DA framework to allow for cross-component DA, in which the data will be assimilated into each of the ocean and the atmosphere components but the information immediately transferred to the other component through the ensemble filter, and both components updated simultaneously at each assimilation step. Initial runs are being conducted to evaluate the differences between these coupled system configurations after assimilation experiments of length one year. In this presentation, specific analyses (e.g., development of tropical storms, sea-ice formation around the Arctic) as well as correlations of states with observations across model components will be discussed. Details of the air-sea fluxes (heat, momentum and moisture) and corresponding ocean-atmosphere dynamical feedbacks will also be presented. The knowledge gained through this study is expected to improve our understanding of the fidelity and applicability of coupled ocean-atmosphere DA systems, and potentially their long-term climate prediction capabilities.6th WMO Symposium on Data Assimilation, University of Maryland; 10/2013