Shuhei Masuda

TL;DR: In this article, a multi-reanalysis ensemble is used to estimate the signal-to-noise ratio (SNR) of the ocean state and to estimate uncertainty levels.

TL;DR: Computer simulations reveal a fast teleconnection between changes in the surface air-sea heat flux off the Adélie Coast of Antarctica and the bottom-water warming in the North Pacific, which can occur within four decades, rather than the centuries that conventional mechanisms have suggested.

TL;DR: In this paper, the authors calculated basin-scale and global ocean decadal temperature change rates from the 1990s to the 2000s for waters below 3000 m. The global heat content (HC) change estimated from the temperature change rate below 3000m was 0.8 × 1022 J decade−1; a value that cannot be neglected for precise estimation of the global heat balance.

Abstract: [1] 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 Nino, 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 Nino event. These results demonstrate that our 4D-VAR CDA system has the ability to enhance forecast potential for seasonal to interannual phenomena.

TL;DR: An overview of the temporal and spatial characteristics of OHC variability and change as represented by an ensemble of dynamical and statistical ocean reanalyses (ORAs) is presented in this article.