JRA-55 based surface dataset for driving ocean–sea-ice models (JRA55-do)

We present a new surface-atmospheric dataset for driving ocean–sea-ice models based on Japanese 55-year atmospheric reanalysis (JRA-55), referred to here as JRA55-do. The JRA55-do dataset aims to replace the CORE interannual forcing version 2 (hereafter called the CORE dataset), which is currently u...

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Published in:Ocean Modelling
Main Authors: Tsujino, Hiroyuki, Urakawa, Shogo, Nakano, Hideyuki, Small, R. Justin, Kim, Who M., Yeager, Stephen G., Danabasoglu, Gokhan, Suzuki, Tatsuo, Bamber, Jonathan L., Bentsen, Mats, Böning, Claus W., Bozec, Alexandra, Chassignet, Eric P., Curchitser, Enrique, Dias, Fabio Boeira, Durack, Paul J., Griffies, Stephen M., Harada, Yayoi, Ilicak, Mehmet, Josey, Simon A., Kobayashi, Chiaki, Kobayashi, Shinya, Komuro, Yoshiki, Large, William G., Le Sommer, Julien, Marsland, Simon J., Masina, Simona, Scheinert, Markus, Tomita, Hiroyuki, Valdivieso, Maria, Yamazaki, Dai
Language:unknown
Published: 2023
Subjects:
Online Access:http://www.osti.gov/servlets/purl/1530682
https://www.osti.gov/biblio/1530682
https://doi.org/10.1016/j.ocemod.2018.07.002
id ftosti:oai:osti.gov:1530682
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spelling ftosti:oai:osti.gov:1530682 2023-07-30T04:06:45+02:00 JRA-55 based surface dataset for driving ocean–sea-ice models (JRA55-do) Tsujino, Hiroyuki Urakawa, Shogo Nakano, Hideyuki Small, R. Justin Kim, Who M. Yeager, Stephen G. Danabasoglu, Gokhan Suzuki, Tatsuo Bamber, Jonathan L. Bentsen, Mats Böning, Claus W. Bozec, Alexandra Chassignet, Eric P. Curchitser, Enrique Dias, Fabio Boeira Durack, Paul J. Griffies, Stephen M. Harada, Yayoi Ilicak, Mehmet Josey, Simon A. Kobayashi, Chiaki Kobayashi, Shinya Komuro, Yoshiki Large, William G. Le Sommer, Julien Marsland, Simon J. Masina, Simona Scheinert, Markus Tomita, Hiroyuki Valdivieso, Maria Yamazaki, Dai 2023-06-29 application/pdf http://www.osti.gov/servlets/purl/1530682 https://www.osti.gov/biblio/1530682 https://doi.org/10.1016/j.ocemod.2018.07.002 unknown http://www.osti.gov/servlets/purl/1530682 https://www.osti.gov/biblio/1530682 https://doi.org/10.1016/j.ocemod.2018.07.002 doi:10.1016/j.ocemod.2018.07.002 54 ENVIRONMENTAL SCIENCES 2023 ftosti https://doi.org/10.1016/j.ocemod.2018.07.002 2023-07-11T09:34:29Z We present a new surface-atmospheric dataset for driving ocean–sea-ice models based on Japanese 55-year atmospheric reanalysis (JRA-55), referred to here as JRA55-do. The JRA55-do dataset aims to replace the CORE interannual forcing version 2 (hereafter called the CORE dataset), which is currently used in the framework of the Coordinated Ocean-ice Reference Experiments (COREs) and the Ocean Model Intercomparison Project (OMIP). A major improvement in JRA55-do is the refined horizontal grid spacing (~ 55 km) and temporal interval (3 hr). The data production method for JRA55-do essentially follows that of the CORE dataset, whereby the surface fields from an atmospheric reanalysis are adjusted relative to reference datasets. To improve the adjustment method, we use high-quality products derived from satellites and from several other atmospheric reanalysis projects, as well as feedback on the CORE dataset from the ocean modelling community. Notably, the surface air temperature and specific humidity are adjusted using multi-reanalysis ensemble means. In JRA55-do, the downwelling radiative fluxes and precipitation, which are affected by an ambiguous cloud parameterisation employed in the atmospheric model used for the reanalysis, are based on the reanalysis products. This approach represents a notable change from the CORE dataset, which imported independent observational products. Consequently, the JRA55-do dataset is more self-contained than the CORE dataset, and thus can be continually updated in near real-time. The JRA55-do dataset extends from 1958 to the present, with updates expected at least annually. This paper details the adjustments to the original JRA-55 fields, the scientific rationale for these adjustments, and the evaluation of JRA55-do. The adjustments successfully corrected the biases in the original JRA-55 fields. The globally averaged features are similar between the JRA55-do and CORE datasets, implying that JRA55-do can suitably replace the CORE dataset for use in driving global ocean–sea-ice models. Other/Unknown Material Sea ice SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) Ocean Modelling 130 79 139
institution Open Polar
collection SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy)
op_collection_id ftosti
language unknown
topic 54 ENVIRONMENTAL SCIENCES
spellingShingle 54 ENVIRONMENTAL SCIENCES
Tsujino, Hiroyuki
Urakawa, Shogo
Nakano, Hideyuki
Small, R. Justin
Kim, Who M.
Yeager, Stephen G.
Danabasoglu, Gokhan
Suzuki, Tatsuo
Bamber, Jonathan L.
Bentsen, Mats
Böning, Claus W.
Bozec, Alexandra
Chassignet, Eric P.
Curchitser, Enrique
Dias, Fabio Boeira
Durack, Paul J.
Griffies, Stephen M.
Harada, Yayoi
Ilicak, Mehmet
Josey, Simon A.
Kobayashi, Chiaki
Kobayashi, Shinya
Komuro, Yoshiki
Large, William G.
Le Sommer, Julien
Marsland, Simon J.
Masina, Simona
Scheinert, Markus
Tomita, Hiroyuki
Valdivieso, Maria
Yamazaki, Dai
JRA-55 based surface dataset for driving ocean–sea-ice models (JRA55-do)
topic_facet 54 ENVIRONMENTAL SCIENCES
description We present a new surface-atmospheric dataset for driving ocean–sea-ice models based on Japanese 55-year atmospheric reanalysis (JRA-55), referred to here as JRA55-do. The JRA55-do dataset aims to replace the CORE interannual forcing version 2 (hereafter called the CORE dataset), which is currently used in the framework of the Coordinated Ocean-ice Reference Experiments (COREs) and the Ocean Model Intercomparison Project (OMIP). A major improvement in JRA55-do is the refined horizontal grid spacing (~ 55 km) and temporal interval (3 hr). The data production method for JRA55-do essentially follows that of the CORE dataset, whereby the surface fields from an atmospheric reanalysis are adjusted relative to reference datasets. To improve the adjustment method, we use high-quality products derived from satellites and from several other atmospheric reanalysis projects, as well as feedback on the CORE dataset from the ocean modelling community. Notably, the surface air temperature and specific humidity are adjusted using multi-reanalysis ensemble means. In JRA55-do, the downwelling radiative fluxes and precipitation, which are affected by an ambiguous cloud parameterisation employed in the atmospheric model used for the reanalysis, are based on the reanalysis products. This approach represents a notable change from the CORE dataset, which imported independent observational products. Consequently, the JRA55-do dataset is more self-contained than the CORE dataset, and thus can be continually updated in near real-time. The JRA55-do dataset extends from 1958 to the present, with updates expected at least annually. This paper details the adjustments to the original JRA-55 fields, the scientific rationale for these adjustments, and the evaluation of JRA55-do. The adjustments successfully corrected the biases in the original JRA-55 fields. The globally averaged features are similar between the JRA55-do and CORE datasets, implying that JRA55-do can suitably replace the CORE dataset for use in driving global ocean–sea-ice models.
author Tsujino, Hiroyuki
Urakawa, Shogo
Nakano, Hideyuki
Small, R. Justin
Kim, Who M.
Yeager, Stephen G.
Danabasoglu, Gokhan
Suzuki, Tatsuo
Bamber, Jonathan L.
Bentsen, Mats
Böning, Claus W.
Bozec, Alexandra
Chassignet, Eric P.
Curchitser, Enrique
Dias, Fabio Boeira
Durack, Paul J.
Griffies, Stephen M.
Harada, Yayoi
Ilicak, Mehmet
Josey, Simon A.
Kobayashi, Chiaki
Kobayashi, Shinya
Komuro, Yoshiki
Large, William G.
Le Sommer, Julien
Marsland, Simon J.
Masina, Simona
Scheinert, Markus
Tomita, Hiroyuki
Valdivieso, Maria
Yamazaki, Dai
author_facet Tsujino, Hiroyuki
Urakawa, Shogo
Nakano, Hideyuki
Small, R. Justin
Kim, Who M.
Yeager, Stephen G.
Danabasoglu, Gokhan
Suzuki, Tatsuo
Bamber, Jonathan L.
Bentsen, Mats
Böning, Claus W.
Bozec, Alexandra
Chassignet, Eric P.
Curchitser, Enrique
Dias, Fabio Boeira
Durack, Paul J.
Griffies, Stephen M.
Harada, Yayoi
Ilicak, Mehmet
Josey, Simon A.
Kobayashi, Chiaki
Kobayashi, Shinya
Komuro, Yoshiki
Large, William G.
Le Sommer, Julien
Marsland, Simon J.
Masina, Simona
Scheinert, Markus
Tomita, Hiroyuki
Valdivieso, Maria
Yamazaki, Dai
author_sort Tsujino, Hiroyuki
title JRA-55 based surface dataset for driving ocean–sea-ice models (JRA55-do)
title_short JRA-55 based surface dataset for driving ocean–sea-ice models (JRA55-do)
title_full JRA-55 based surface dataset for driving ocean–sea-ice models (JRA55-do)
title_fullStr JRA-55 based surface dataset for driving ocean–sea-ice models (JRA55-do)
title_full_unstemmed JRA-55 based surface dataset for driving ocean–sea-ice models (JRA55-do)
title_sort jra-55 based surface dataset for driving ocean–sea-ice models (jra55-do)
publishDate 2023
url http://www.osti.gov/servlets/purl/1530682
https://www.osti.gov/biblio/1530682
https://doi.org/10.1016/j.ocemod.2018.07.002
genre Sea ice
genre_facet Sea ice
op_relation http://www.osti.gov/servlets/purl/1530682
https://www.osti.gov/biblio/1530682
https://doi.org/10.1016/j.ocemod.2018.07.002
doi:10.1016/j.ocemod.2018.07.002
op_doi https://doi.org/10.1016/j.ocemod.2018.07.002
container_title Ocean Modelling
container_volume 130
container_start_page 79
op_container_end_page 139
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