Latent Heat Flux Sensitivity to Sea Surface Temperature: Regional Perspectives

Kumar, Bipin; Cronin, Meghan F.; Joseph, Sudheer; Ravichandran, M.; Sureshkumar, N.

doi:10.1175/jcli-d-16-0285.1

Cited by 34 publications

(21 citation statements)

References 31 publications

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“…As the sensible heat flux is proportional to the difference of temperature between the surface air temperature and the sea surface temperature, variations of the surface temperature across the front have been taken into account in the AROME estimates of the sensible heat flux by rescaling it with the measured surface temperature. In the latent heat flux, we neglect the influence of variations of the surface temperature, which is less than 4 W m −2 around the Svalbard region (Kumar et al, ).…”

Section: Datamentioning

confidence: 99%

Observations of Turbulence at a Near‐Surface Temperature Front in the Arctic Ocean

et al. 2020

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High-resolution ocean temperature, salinity, current, and turbulence data were collected at an Arctic thermohaline front in the Nansen Basin. The front was close to the sea ice edge and separated the cold and fresh surface melt water from the warm and saline mixed layer. Measurements were made on 18 September 2018, in the upper 100 m, from a research vessel and an autonomous underwater vehicle. Destabilizing surface buoyancy fluxes from a combination of heat loss to the atmosphere and cross-front Ekman transport by down-front winds reduced the potential vorticity in the upper ocean. Turbulence structure in the mixed layer was generally consistent with turbulence production through convection by heat loss to atmosphere and mechanical forcing by moderate winds. Conditions at the front were favorable for forced symmetric instability, a mechanism drawing energy from the frontal geostrophic current. A clear signature of increased dissipation from symmetric instability could not be identified; however, this instability could potentially account for the increased dissipation rates at the front location down to 40 m depth that could not be explained by the atmospheric forcing. This turbulence was associated with turbulent heat fluxes of up to 10 W m −2 , eroding the warm and cold intrusions observed between 30 and 60 m depth. A Seaglider sampled across a similar frontal structure in the same region 10 days after our survey. The submesoscale-to-turbulence-scale transitions and resulting mixing can be widespread and important in the Atlantic sector of the Arctic Ocean. Plain Language SummaryOn 18 September 2018, high-resolution temperature, salinity, current, and turbulence data were collected near a surface temperature and salinity front in the Nansen Basin north of Svalbard, within the framework of the Nansen Legacy Project. Measurements were performed from the research icebreaker Kronprins Haakon and using an autonomous underwater vehicle. The front separates warm and salty Atlantic-origin waters from cold and fresh Arctic-origin waters. Energetic turbulence in the upper 30 m was consistent with convection by heat loss to the atmosphere and mechanical forcing by moderate winds at the surface of the ocean. At the front, the conditions were favorable for forced symmetric instability, a mechanism drawing energy from the large-scale current. The contribution to turbulence from this instability could not be clearly identified but can be potentially important. Such observations linking 1-3 km scales to turbulence in the Arctic Ocean are rare. Turbulence at a front in the Arctic has consequences on the heat and nutrient transport from the slope to the deep Arctic Basin. A Seaglider sampled across a similar frontal structure in the same region end of September 2018. Observed frontal structure and mixing processes can be common in the Atlantic sector of the Arctic Ocean.

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Section: Datamentioning

confidence: 99%

Observations of Turbulence at a Near‐Surface Temperature Front in the Arctic Ocean

et al. 2020

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“…Additionally, Kumar et al (2017) found that the winds are always originated at sea in tropical regions. The authors also pointed out that large-scale winds blow from regions with cold SST towards regions with warmer SST and, therefore, the surface air is more humid and colder than SST.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…Also, a good SST representation with the models is important. Performing a global analysis of the LHF sensitivity to variations in SST focused on tropical regions, Kumar et al (2017) have found that the sensitivity of variables related directly to LHF (WIND and Q) and SST give rise to dynamic (WIND) and thermodynamic (Q) feedbacks, and that this interaction favors making LHF sensitive to SST mainly in the tropics, where the increase in SST causes changes in the WIND (Q), resulting in a decrease/increase in LHF.…”

Section: Discussionmentioning

confidence: 99%

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The Surface Wind Influence on the Heat Fluxes Variability on the South Atlantic

Lindemann

Ávila-Díaz

Pezzi

et al. 2021

Preprint

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An adequate representation by models and reanalyzes is fundamental since the coverage by observational data on the oceans is still limited. Therefore, this paper aims to evaluate the influence of the wind near the surface on the heat fluxes during the southern winter and summer seasons. Datasets from Coupled Model Intercomparison Project Phase 5 (CMIP5) and reanalyzes were used, in comparison to Objectively Analyzed Air - sea Fluxes (OAFlux) for the South Atlantic Ocean (SAO) during 1980-2005. Results point out an overestimation on the CMIP5 models and reanalyzes to reproduce the heat flux latent and sensible fluxes of SAO, mainly at medium and high latitudes. One possibility may be related the underestimating of surface wind speed, causing an impacts on the heat exchange between ocean and atmosphere. It was also possible to verify that the representation of heat flux, specific humidity, and air and ocean temperatures shows small biases (Mean Bias Error (MBE) to specific humidity (±5 kg.kg-1) and sensible heat flux (±10 W.m-2)). To the test Root Mean Square Error (RMSE)-observations Standard deviation Ratio (RSR), air temperature values are less than 1 °C, and for the wind with values greater than 2 m.s-1. There is less precision of CMIP5 models than OAFlux, resulting in low correlation values (between -0.3 and 0.3). On the other hand, the reanalyzes show small biases in air and ocean temperatures (between ±1 °C) and significant correlations (above 0.9) with the best performances for the NCEP and ERA5.

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“…Considering the high correlation between SST and ocean LHF [51,52], we decided to maintain the high spatial resolution of SST4 dataset to increase spatial heterogeneity. To generate the monthly global ocean LHF products from 2003 to 2007, we used the bilinear interpolation method to interpolate other monthly input data (U, T a , q) to 4 km spatial resolution over 2003-2007.…”

Section: Satellite and Reanalysis Inputs To The Lhf Modelmentioning

confidence: 99%

Estimation of High-Resolution Global Monthly Ocean Latent Heat Flux from MODIS SST Product and AMSR-E Data

Chen

Yao

Zhao

et al. 2020

Advances in Meteorology

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Accurate estimation of satellite-derived ocean latent heat flux (LHF) at high spatial resolution remains a major challenge. Here, we estimate monthly ocean LHF at 4 km spatial resolution over 5 years using bulk algorithm COARE 3.0, driven by satellite data and meteorological variables from reanalysis. We validated the estimated ocean LHF by multiyear observations and by comparison with seven ocean LHF products. Validation results from monthly observations at 96 widely distributed buoy sites from three buoy site arrays (TAO, PIRATA, and RAMA) indicated a bias of less than 7 W/m2 with R2 of more than 0.80 (p<0.01) and with a King–Gupta efficiency (KGE) of over 0.84. Our estimated ocean LHF also performs well in simulating annual variability and predicting between-site variability, as indicated by a bias of lower than 6 W/m2 and an R2 of more than 0.84 (p<0.01). Overall, the average KGE for estimated ocean LHF increased by 18%–23% compared to other LHF products, indicating robust LHF estimation performance. Importantly, our estimated annual ocean LHF has similar global spatial distribution compared to other LHF products, although there are general differences in LHF values due to the difference in the models and the spatial resolution.

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Latent Heat Flux Sensitivity to Sea Surface Temperature: Regional Perspectives

Cited by 34 publications

References 31 publications

Observations of Turbulence at a Near‐Surface Temperature Front in the Arctic Ocean

Observations of Turbulence at a Near‐Surface Temperature Front in the Arctic Ocean

The Surface Wind Influence on the Heat Fluxes Variability on the South Atlantic

Estimation of High-Resolution Global Monthly Ocean Latent Heat Flux from MODIS SST Product and AMSR-E Data

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