A New Ensemble‐Based Approach to Correct the Systematic Ocean Temperature Bias of CAS‐ESM‐C to Improve Its Simulation and Data Assimilation Abilities.
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| Title: | A New Ensemble‐Based Approach to Correct the Systematic Ocean Temperature Bias of CAS‐ESM‐C to Improve Its Simulation and Data Assimilation Abilities. |
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| Authors: | Du, Mengjiao1,2,3, Zheng, Fei1,4,5 zhengfei@mail.iap.ac.cn, Zhu, Jiang1,3,5, Lin, Renping1, Yang, Haipeng6, Chen, Quanliang2 |
| Source: | Journal of Geophysical Research. Oceans. Dec2020, Vol. 125 Issue 12, p1-23. 23p. |
| Subject Terms: | *Ocean-atmosphere interaction, *Computer simulation of climate change, Ocean temperature measurement, Atmospheric models, Computer simulation of climatology, Statistical bias |
| Abstract: | Over the past several decades, many efforts have been devoted to increasing the simulation performance of climate models, but significant biases remain that hinder the performance of coupled systems. Hence, bias correction is regarded not only as a useful tool for improving climate simulations but also as an important step before data assimilation, which depends on the hypothesis of unbiasedness. In this study, using sea temperature climatological data, a new ensemble‐based approach is proposed for correcting the biases of the sea temperature in CAS‐ESM‐C. Through analyzing the results of the proposed bias correction method with various intensities and time windows, its performance in suppressing the simulation biases of ocean fields is evaluated. The simulation biases of atmospheric variables are also reduced via air‐sea interactions, which will improve the ocean simulation performance. Additional benefits can be realized by applying the bias correction method. For example, a superior simulation of climate variabilities in a coupled model, such as ENSO (El Niño‐Southern Oscillation), is realized due to the improvement of climatological fields. The ability to assimilate various ocean observations is also significantly improved with a better background mean state. Plain Language Summary: Coupled model is the usual tool for climate prediction and research, but there are still many unignorable simulation biases, especially global sea temperature bias has a greater impact on simulation and prediction under air‐sea interactions. So, we proposed a new ensemble‐based correcting approach for the systematic sea temperature bias and applied it to a climate system model, which use sea temperature climatology data as a "weak" observation to correct the model bias. Through comparing the results of different test designs, the reasonable bias approach could be based on the improvement of model biases, then provide more accurate climate mean states for ENSO simulations and background fields for ocean data assimilation. Key Points: A new ensemble‐based approach is proposed for correcting the biases of sea temperature in CAS‐ESM‐C using climatological dataThe simulation biases of atmospheric and oceanic fields are all significantly reduced through air‐sea interactionsThe benefits of improving the simulation of climate variations and the ability to assimilate various ocean observations are explored [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Geophysical Research. Oceans is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
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| Header | DbId: 8gh DbLabel: GreenFILE An: 147789230 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A New Ensemble‐Based Approach to Correct the Systematic Ocean Temperature Bias of CAS‐ESM‐C to Improve Its Simulation and Data Assimilation Abilities. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Du%2C+Mengjiao%22">Du, Mengjiao</searchLink><relatesTo>1,2,3</relatesTo><br /><searchLink fieldCode="AR" term="%22Zheng%2C+Fei%22">Zheng, Fei</searchLink><relatesTo>1,4,5</relatesTo><i> zhengfei@mail.iap.ac.cn</i><br /><searchLink fieldCode="AR" term="%22Zhu%2C+Jiang%22">Zhu, Jiang</searchLink><relatesTo>1,3,5</relatesTo><br /><searchLink fieldCode="AR" term="%22Lin%2C+Renping%22">Lin, Renping</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Yang%2C+Haipeng%22">Yang, Haipeng</searchLink><relatesTo>6</relatesTo><br /><searchLink fieldCode="AR" term="%22Chen%2C+Quanliang%22">Chen, Quanliang</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Geophysical+Research%2E+Oceans%22">Journal of Geophysical Research. Oceans</searchLink>. Dec2020, Vol. 125 Issue 12, p1-23. 23p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Ocean-atmosphere+interaction%22">Ocean-atmosphere interaction</searchLink><br />*<searchLink fieldCode="DE" term="%22Computer+simulation+of+climate+change%22">Computer simulation of climate change</searchLink><br /><searchLink fieldCode="DE" term="%22Ocean+temperature+measurement%22">Ocean temperature measurement</searchLink><br /><searchLink fieldCode="DE" term="%22Atmospheric+models%22">Atmospheric models</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation+of+climatology%22">Computer simulation of climatology</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+bias%22">Statistical bias</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Over the past several decades, many efforts have been devoted to increasing the simulation performance of climate models, but significant biases remain that hinder the performance of coupled systems. Hence, bias correction is regarded not only as a useful tool for improving climate simulations but also as an important step before data assimilation, which depends on the hypothesis of unbiasedness. In this study, using sea temperature climatological data, a new ensemble‐based approach is proposed for correcting the biases of the sea temperature in CAS‐ESM‐C. Through analyzing the results of the proposed bias correction method with various intensities and time windows, its performance in suppressing the simulation biases of ocean fields is evaluated. The simulation biases of atmospheric variables are also reduced via air‐sea interactions, which will improve the ocean simulation performance. Additional benefits can be realized by applying the bias correction method. For example, a superior simulation of climate variabilities in a coupled model, such as ENSO (El Niño‐Southern Oscillation), is realized due to the improvement of climatological fields. The ability to assimilate various ocean observations is also significantly improved with a better background mean state. Plain Language Summary: Coupled model is the usual tool for climate prediction and research, but there are still many unignorable simulation biases, especially global sea temperature bias has a greater impact on simulation and prediction under air‐sea interactions. So, we proposed a new ensemble‐based correcting approach for the systematic sea temperature bias and applied it to a climate system model, which use sea temperature climatology data as a "weak" observation to correct the model bias. Through comparing the results of different test designs, the reasonable bias approach could be based on the improvement of model biases, then provide more accurate climate mean states for ENSO simulations and background fields for ocean data assimilation. Key Points: A new ensemble‐based approach is proposed for correcting the biases of sea temperature in CAS‐ESM‐C using climatological dataThe simulation biases of atmospheric and oceanic fields are all significantly reduced through air‐sea interactionsThe benefits of improving the simulation of climate variations and the ability to assimilate various ocean observations are explored [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Geophysical Research. Oceans is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1029/2020JC016406 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 1 Subjects: – SubjectFull: Ocean-atmosphere interaction Type: general – SubjectFull: Computer simulation of climate change Type: general – SubjectFull: Ocean temperature measurement Type: general – SubjectFull: Atmospheric models Type: general – SubjectFull: Computer simulation of climatology Type: general – SubjectFull: Statistical bias Type: general Titles: – TitleFull: A New Ensemble‐Based Approach to Correct the Systematic Ocean Temperature Bias of CAS‐ESM‐C to Improve Its Simulation and Data Assimilation Abilities. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Du, Mengjiao – PersonEntity: Name: NameFull: Zheng, Fei – PersonEntity: Name: NameFull: Zhu, Jiang – PersonEntity: Name: NameFull: Lin, Renping – PersonEntity: Name: NameFull: Yang, Haipeng – PersonEntity: Name: NameFull: Chen, Quanliang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2020 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 21699275 Numbering: – Type: volume Value: 125 – Type: issue Value: 12 Titles: – TitleFull: Journal of Geophysical Research. Oceans Type: main |
| ResultId | 1 |
