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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.
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]
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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]
ISSN:21699275
DOI:10.1029/2020JC016406