BUOYANCY FLUX AS A METRIC FOR OCEAN–ATMOSPHERE INTERACTION ANALYSES ON DIFFERENT TIME SCALES

The article provides a comprehensive review of publications, both in journals and monographs, devoted to the metric of ocean–atmosphere interaction – the buoyancy flux. This method of assessing the tendency to vertical mixing in the ocean, derived theoretically in the second half of the XX century, is currently used. The buoyancy flux allows not only a qualitative but also a quantitative assessment of ocean-atmosphere interaction through heat, moisture, and radiative fluxes on different time scales. It can be used to account for the contribution of different components of the ocean-atmosphere interaction to the dynamics of the upper mixed layer of the ocean: radiative fluxes, heat fluxes, and freshwater fluxes. To assess the ability of land bodies to absorb or emit CO2 as a function of synoptic atmospheric dynamics, and even to estimate the variability of upwelling that affects the socio-economic situation in coastal areas. The review summarises scientific papers using buoyancy flux for various purposes, presents the first paper to introduce the metric, and summarises methodologies that offer promise in this line of research based on current ocean and atmospheric data. Forty-two English-language papers and 5 publications by Russian researchers have been analysed.

Authors

Institutions

Publication Details

Journal
Journal of Oceanological Research
Published
2024-12-29
DOI
https://doi.org/10.29006/1564-2291.jor-2024.52(4).9
Primary Topic
Oceanographic and Atmospheric Processes
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

BUOYANCY FLUX AS A METRIC FOR OCEAN–ATMOSPHERE INTERACTION ANALYSES ON DIFFERENT TIME SCALES

Natalia Tilinina, S. K. Gulev, P. K. Mironenkov
Journal of Oceanological Research
Oceanographic and Atmospheric Processes
article

BUOYANCY FLUX AS A METRIC FOR OCEAN–ATMOSPHERE INTERACTION ANALYSES ON DIFFERENT TIME SCALES

Natalia Tilinina, S. K. Gulev, P. K. Mironenkov
article en

Abstract

The article provides a comprehensive review of publications, both in journals and monographs, devoted to the metric of ocean–atmosphere interaction – the buoyancy flux. This method of assessing the tendency to vertical mixing in the ocean, derived theoretically in the second half of the XX century, is currently used. The buoyancy flux allows not only a qualitative but also a quantitative assessment of ocean-atmosphere interaction through heat, moisture, and radiative fluxes on different time scales. It can be used to account for the contribution of different components of the ocean-atmosphere interaction to the dynamics of the upper mixed layer of the ocean: radiative fluxes, heat fluxes, and freshwater fluxes. To assess the ability of land bodies to absorb or emit CO2 as a function of synoptic atmospheric dynamics, and even to estimate the variability of upwelling that affects the socio-economic situation in coastal areas. The review summarises scientific papers using buoyancy flux for various purposes, presents the first paper to introduce the metric, and summarises methodologies that offer promise in this line of research based on current ocean and atmospheric data. Forty-two English-language papers and 5 publications by Russian researchers have been analysed.

Journal of Oceanological ResearchVol. 52(4)
Lomonosov Moscow State University (RU), Institute of Oceanology. PP Shirshov Russian Academy of Sciences (RU)
Russian Science Foundation
Life below water
Openalex Percentile: Top 74%
Oceanographic and Atmospheric Processes
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.