DataSheet_1_Warm bias of cold sea surface temperatures in the East Sea (Japan Sea).pdf
The East/Japan Sea (ES) is regarded as a natural laboratory for predicting future changes in the global Meridional Overturning Circulation (MOC) under warming climates, as the ES MOC (EMOC) changes rapidly in comparison with the global MOC. Specifically, intermediate and deep-water masses of the ES...
Main Authors: | , |
---|---|
Format: | Dataset |
Language: | unknown |
Published: |
2022
|
Subjects: | |
Online Access: | https://doi.org/10.3389/fmars.2022.965346.s001 https://figshare.com/articles/dataset/DataSheet_1_Warm_bias_of_cold_sea_surface_temperatures_in_the_East_Sea_Japan_Sea_pdf/20501259 |
id |
ftfrontimediafig:oai:figshare.com:article/20501259 |
---|---|
record_format |
openpolar |
spelling |
ftfrontimediafig:oai:figshare.com:article/20501259 2023-05-15T18:18:57+02:00 DataSheet_1_Warm bias of cold sea surface temperatures in the East Sea (Japan Sea).pdf Seung-Tae Yoon JongJin Park 2022-08-17T05:19:48Z https://doi.org/10.3389/fmars.2022.965346.s001 https://figshare.com/articles/dataset/DataSheet_1_Warm_bias_of_cold_sea_surface_temperatures_in_the_East_Sea_Japan_Sea_pdf/20501259 unknown doi:10.3389/fmars.2022.965346.s001 https://figshare.com/articles/dataset/DataSheet_1_Warm_bias_of_cold_sea_surface_temperatures_in_the_East_Sea_Japan_Sea_pdf/20501259 CC BY 4.0 CC-BY Oceanography Marine Biology Marine Geoscience Biological Oceanography Chemical Oceanography Physical Oceanography Marine Engineering sea surface temperature argo float data satellite data east sea bias correction outcropping Dataset 2022 ftfrontimediafig https://doi.org/10.3389/fmars.2022.965346.s001 2022-08-17T23:04:49Z The East/Japan Sea (ES) is regarded as a natural laboratory for predicting future changes in the global Meridional Overturning Circulation (MOC) under warming climates, as the ES MOC (EMOC) changes rapidly in comparison with the global MOC. Specifically, intermediate and deep-water masses of the ES are formed in its northern reaches via wind-driven subduction of surface water, and convection from the surface to deep layers during the winter. Accordingly, it is important to investigate the variation of winter sea surface temperatures (SSTs) for characterizing and predicting the EMOC; however, global SST products must be corrected and optimized for the ES, as they fail to incorporate the local marginal sea conditions. Here, a warm bias in cold SST was identified for three SST products, such as optimally interpolated sea surface temperatures (OISSTs), microwave SSTs, and operational SST and sea ice analysis products, suggesting the potential usefulness of a correction method incorporating Argo float data. When comparing OISSTs with 5 m temperature estimates from Argo float data during 2000–2020, under the assumption that the mixed layer depth is deeper than 8 m, a nearly normalized histogram of biases was produced, and the robust warm bias (mean = 0.9°C) was detected in the range of relatively cold SSTs (-2°C to 10°C), yet no significant bias in warm SSTs (> 10°C) was found. To minimize the warm bias in cold SSTs, OISSTs were corrected with an inverse 4 th -order polynomial fitting method. Subsequently, the mean bias between the corrected SSTs and top depth temperatures of Argo float data was significantly reduced to less than 0.1°C. Moreover, the warm bias of cold SSTs resulted in severe underestimations of the outcropping area colder than 1°C over the northern region, as well as the occurrence period of 1°C to 5°C SSTs in the north-western ES. These results highlight the importance of local bias correction for SST products, and it is expected that the newly suggested correction method will improve model ... Dataset Sea ice Frontiers: Figshare |
institution |
Open Polar |
collection |
Frontiers: Figshare |
op_collection_id |
ftfrontimediafig |
language |
unknown |
topic |
Oceanography Marine Biology Marine Geoscience Biological Oceanography Chemical Oceanography Physical Oceanography Marine Engineering sea surface temperature argo float data satellite data east sea bias correction outcropping |
spellingShingle |
Oceanography Marine Biology Marine Geoscience Biological Oceanography Chemical Oceanography Physical Oceanography Marine Engineering sea surface temperature argo float data satellite data east sea bias correction outcropping Seung-Tae Yoon JongJin Park DataSheet_1_Warm bias of cold sea surface temperatures in the East Sea (Japan Sea).pdf |
topic_facet |
Oceanography Marine Biology Marine Geoscience Biological Oceanography Chemical Oceanography Physical Oceanography Marine Engineering sea surface temperature argo float data satellite data east sea bias correction outcropping |
description |
The East/Japan Sea (ES) is regarded as a natural laboratory for predicting future changes in the global Meridional Overturning Circulation (MOC) under warming climates, as the ES MOC (EMOC) changes rapidly in comparison with the global MOC. Specifically, intermediate and deep-water masses of the ES are formed in its northern reaches via wind-driven subduction of surface water, and convection from the surface to deep layers during the winter. Accordingly, it is important to investigate the variation of winter sea surface temperatures (SSTs) for characterizing and predicting the EMOC; however, global SST products must be corrected and optimized for the ES, as they fail to incorporate the local marginal sea conditions. Here, a warm bias in cold SST was identified for three SST products, such as optimally interpolated sea surface temperatures (OISSTs), microwave SSTs, and operational SST and sea ice analysis products, suggesting the potential usefulness of a correction method incorporating Argo float data. When comparing OISSTs with 5 m temperature estimates from Argo float data during 2000–2020, under the assumption that the mixed layer depth is deeper than 8 m, a nearly normalized histogram of biases was produced, and the robust warm bias (mean = 0.9°C) was detected in the range of relatively cold SSTs (-2°C to 10°C), yet no significant bias in warm SSTs (> 10°C) was found. To minimize the warm bias in cold SSTs, OISSTs were corrected with an inverse 4 th -order polynomial fitting method. Subsequently, the mean bias between the corrected SSTs and top depth temperatures of Argo float data was significantly reduced to less than 0.1°C. Moreover, the warm bias of cold SSTs resulted in severe underestimations of the outcropping area colder than 1°C over the northern region, as well as the occurrence period of 1°C to 5°C SSTs in the north-western ES. These results highlight the importance of local bias correction for SST products, and it is expected that the newly suggested correction method will improve model ... |
format |
Dataset |
author |
Seung-Tae Yoon JongJin Park |
author_facet |
Seung-Tae Yoon JongJin Park |
author_sort |
Seung-Tae Yoon |
title |
DataSheet_1_Warm bias of cold sea surface temperatures in the East Sea (Japan Sea).pdf |
title_short |
DataSheet_1_Warm bias of cold sea surface temperatures in the East Sea (Japan Sea).pdf |
title_full |
DataSheet_1_Warm bias of cold sea surface temperatures in the East Sea (Japan Sea).pdf |
title_fullStr |
DataSheet_1_Warm bias of cold sea surface temperatures in the East Sea (Japan Sea).pdf |
title_full_unstemmed |
DataSheet_1_Warm bias of cold sea surface temperatures in the East Sea (Japan Sea).pdf |
title_sort |
datasheet_1_warm bias of cold sea surface temperatures in the east sea (japan sea).pdf |
publishDate |
2022 |
url |
https://doi.org/10.3389/fmars.2022.965346.s001 https://figshare.com/articles/dataset/DataSheet_1_Warm_bias_of_cold_sea_surface_temperatures_in_the_East_Sea_Japan_Sea_pdf/20501259 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
doi:10.3389/fmars.2022.965346.s001 https://figshare.com/articles/dataset/DataSheet_1_Warm_bias_of_cold_sea_surface_temperatures_in_the_East_Sea_Japan_Sea_pdf/20501259 |
op_rights |
CC BY 4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.3389/fmars.2022.965346.s001 |
_version_ |
1766195739627618304 |