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.
- Journal of The Atmospheric Sciences - J ATMOS SCI. 01/1986; 43(6):606-632.
- [show abstract] [hide abstract]
ABSTRACT: Interannual variability in the subsurface tropical Indian Ocean (TIO) is studied using three independent data sets: satellite derived sea-level data, an ocean general circulation model simulation, and in situ upper-ocean temperature data. It is found that significant interannual variability in the TIO is confined to the north of 15°S. Unlike the Pacific Ocean, the dominant modes of interannual variability in the Indian Ocean do not show co-variability between the surface and the subsurface. In contrast to the sea-surface temperature variability dominated by the El Niño and Southern Oscillation, subsurface variability is governed by the Indian Ocean Dipole in the TIO. The dominant mode of the interannual variability in the subsurface TIO is characterized by a dipole. Its evolution is controlled by equatorial ocean dynamics forced by zonal winds in the equatorial region. The subsurface dipole provides the delayed time required to reverse the phase of the surface dipole in the following year. The second dominant mode of interannual variability shows the interesting quasi-biennial tendency. It is found that the turnabout of the phase of the subsurface dipole leads to the quasi-biennial behavior of the TIO. Analysis of in situ subsurface temperature data substantiates this finding.Deep Sea Research Part II: Topical Studies in Oceanography. 01/2002;
- [show abstract] [hide abstract]
ABSTRACT: The relationship between the flux of outgoing longwave radiation (OLR) estimated from satellite observations and precipitation is investigated using monthly OLR data from the NOAA polar-orbiting satellites and the merged analysis of precipitation of Xie and Arkin for the 8-yr period from July 1987 to June 1995. The mean annual cycle of OLR in the Tropics is dominated by changes in cloudiness and exhibits a strong negative correlation with precipitation, while in the extratropics the strongest influence on the annual cycle of OLR is surface temperature and a positive correlation with precipitation is found. However, the anomaly of OLR exhibits a negative correlation with precipitation over most of the globe. The regression coefficient relating the anomaly of precipitation to that of OLR is spatially inhomogeneous and seasonally dependent but can be expressed with high accuracy as a globally uniform linear function of the local mean precipitation. Based on these results, a new technique is developed to estimate monthly precipitation over the globe from OLR data. First, the mean annual cycle of precipitation is calculated from the merged analysis of precipitation for the 8-yr period. The precipitation anomaly is then estimated from the OLR anomaly field using the coefficient value appropriate for the mean annual cycle of precipitation at each location. Finally, the total precipitation is estimated as the sum of the mean annual cycle and the anomaly. Verification tests showed that this estimate, which is referred to here as the OLR-based precipitation index (OPI), is able to represent large-scale precipitation with globally uniform and temporally stable high quality, similar to geostationary satellite IR-based estimates over the Tropics and to estimates based on microwave scattering observations over extratropical areas. The OPI estimates are then produced for the 22-yr period from 1974 to 1995 and are used to investigate the annual and interannual variability of global precipitation. The mean distribution and seasonal variations as observed in the 22-yr set of OPI estimates agree well with those of several published long-term means of precipitation estimated from station observations, and the interannual variability in precipitation associated with the El Niño-Southern Oscillation phenomenon resemble those found in previous studies but with additional details, particularly over ocean areas.Journal of Climate - J CLIMATE. 01/1998; 11(2):137-164.