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DOI | 10.1029/2020JD034214 |
Revisiting Online and Offline Data Assimilation Comparison for Paleoclimate Reconstruction: An Idealized OSSE Study | |
Okazaki A.; Miyoshi T.; Yoshimura K.; Greybush S.J.; Zhang F. | |
发表日期 | 2021 |
ISSN | 2169-897X |
卷号 | 126期号:16 |
英文摘要 | Data assimilation (DA) has been applied to estimate the time-mean state, such as annual mean surface temperature for paleoclimate reconstruction. There are two types of DA for this purpose: online-DA and offline-DA. The online-DA estimates both time-mean states (analyses) and initial conditions for subsequent DA cycles, while the offline-DA only estimates the time-mean analyses. If there is sufficiently long predictability in the system of interest compared to the temporal resolution of the observations, online-DA is expected to outperform offline-DA by utilizing information in the initial conditions. However, previous studies failed to show the superiority of online-DA when time-averaged observations are assimilated, and the reason has not been investigated thoroughly. This study compares online-DA and offline-DA and investigates the relation to the predictability using an intermediate complexity general circulation model with perfect-model observing system simulation experiments. The result shows that the online-DA outperforms offline-DA when the length of predictability is longer than the averaging time of the observations. We also found that the longer the predictability, the more skillful the online-DA. Here, the ocean plays a crucial role in extending predictability, which helps online-DA to outperform offline-DA. Interestingly, the observations of near-surface air temperature over land are highly valuable to update the ocean variables in the analysis steps, suggesting the importance of using cross-domain covariance information between the atmosphere and the ocean when online-DA is applied to reconstruct paleoclimate. © 2021. The Authors. |
英文关键词 | atmosphere-ocean coupled data assimilation; climate predictability; data assimilation; last millennium; paleoclimate reconstruction |
来源期刊 | Journal of Geophysical Research: Atmospheres |
文献类型 | 期刊论文 |
条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/237074 |
作者单位 | Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, PA, United States; RIKEN Center for Computational Science, Kobe, Japan; Institute of Industrial Science, The University of Tokyo, Kashiwa, Japan; RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program, Kobe, Japan; RIKEN Cluster for Pioneering Research, Kobe, Japan; Department of Atmospheric and Oceanic Science, University of Maryland, College Park, College Park, MD, United States; Graduate School of Science, Kyoto University, Kyoto, Kyoto, Japan; Application Laboratory, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan |
推荐引用方式 GB/T 7714 | Okazaki A.,Miyoshi T.,Yoshimura K.,et al. Revisiting Online and Offline Data Assimilation Comparison for Paleoclimate Reconstruction: An Idealized OSSE Study[J],2021,126(16). |
APA | Okazaki A.,Miyoshi T.,Yoshimura K.,Greybush S.J.,&Zhang F..(2021).Revisiting Online and Offline Data Assimilation Comparison for Paleoclimate Reconstruction: An Idealized OSSE Study.Journal of Geophysical Research: Atmospheres,126(16). |
MLA | Okazaki A.,et al."Revisiting Online and Offline Data Assimilation Comparison for Paleoclimate Reconstruction: An Idealized OSSE Study".Journal of Geophysical Research: Atmospheres 126.16(2021). |
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