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1.
Sci Data ; 9(1): 691, 2022 Nov 11.
Artículo en Inglés | MEDLINE | ID: mdl-36369203

RESUMEN

Investigating oceanic variations at multiple spatial and temporal scales is vital for an in-depth understanding of the ocean response to global climate change. However, the available observational datasets contain uncertainties and deficiencies that leave them insufficient for investigating global ocean variability with long temporal scales and/or meso spatial scales. Here, we present a daily and century-long (1901-2010) global oceanic simulation dataset with high resolution (1/10° horizontal resolution and 55 vertical layers) forced by 6-hour atmospheric data from ERA-20C. Preliminary evaluations demonstrate that this simulation can realistically reproduce the large-scale global ocean circulation and capture the essential features of global surface mesoscale eddies. This long-running high-resolution simulation dataset provides temporally highly resolved oceanic and flux variables. Together with its good performance in simulating the global oceanic state, this eddy-resolving simulation has the potential to help toward a better understanding of ocean variability at multiple spatial and temporal scales.

2.
Sci Rep ; 10(1): 12791, 2020 Jul 30.
Artículo en Inglés | MEDLINE | ID: mdl-32732943

RESUMEN

Coherent oceanic mesoscale eddies with unique dynamical structures have great impacts on ocean transports and global climate. Eddy kinetic energy (EKE), derived from time-dependent circulation, is commonly used to study mesoscale eddies. However, there are three deficiencies of EKE when focusing on the analysis of coherent mesoscale eddies. Here, we propose a comprehensive concept-Lagrangian EKE (LEKE) as an additional metric which is a combination of gridded EKE calculated in Eulerian framework and tracked coherent mesoscale eddies in Lagrangian framework. Evidences suggest that LEKE can make up these deficiencies as an effective supplement. In this study, regional application over Northwestern Pacific Ocean is taken as an example. It clearly demonstrates that LEKE reveals more accurate and detailed characteristics of both cyclonic and anticyclonic eddies than EKE when coherent mesoscale eddies are the specific focus, such as the variation rates of kinetic energy during the eddy propagation, spatial-temporal differences of kinetic energy between cyclonic and anticyclonic eddies. Overall, using LEKE to analyze coherent mesoscale eddies gives the rise to understand the spatial-temporal contrasts between eddies with different polarities, and provides a new perspective to recognize the crucial role played by coherent mesoscale eddies in the ocean.

3.
Sci Bull (Beijing) ; 64(3): 198-204, 2019 Feb 15.
Artículo en Inglés | MEDLINE | ID: mdl-36659618

RESUMEN

The Atlantic Multidedal Oscillation (AMO) is a prominent mode of sea surface temperature variability in the Atlantic and incurs significant global influence. Most coupled models failed to reproduce the observed 50-80-year AMO, but were overwhelmed by a 10-30-year AMO. Here we show that the 50-80-year AMO and 10-30-year AMO represent two different AMO regimes. The key differences are: (1) the 50-80-year AMO involves transport of warm and saline Atlantic water into the Greenland-Iceland-Norwegian (GIN) Seas prior to reaching its maximum positive phase, while such a transport is weak for the 10-30-year AMO; (2) the zonality of atmospheric variability associated with the 50-80 year AMO favors the transport of warm and saline water into the GIN Seas; (3) the disappearance of Pacific variability weakens the zonality of atmospheric variability and the transport of warm and saline water into the GIN Seas, leading to the weakening of the 50-80-year AMO. In contrast, the 10-30-year AMO does not show dependence on the variability in Pacific and in the GIN Seas and may be an Atlantic-intrinsic mode. Our results suggest that differentiating these AMO regimes and a better understanding of the cross-basin connections are essential to reconcile the current debate on the nature of AMO and hence to its reliable prediction, which is still lacking in most of coupled models.

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