Revisiting Online and Offline Data Assimilation Comparison for Paleoclimate Reconstruction: An Idealized OSSE Study
Revisiting Online and Offline Data Assimilation Comparison for Paleoclimate Reconstruction: An Idealized OSSE Study
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DOI：
10.1029/2020jd034214
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发表时间：
2020-11
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影响因子：
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通讯作者：
A. Okazaki;T. Miyoshi;K. Yoshimura;S. Greybush;Fuqing Zhang
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文献类型：
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作者：
A. Okazaki;T. Miyoshi;K. Yoshimura;S. Greybush;Fuqing Zhang
数据同化（Data assimilation， DA）被用于估算时间平均状态，如年平均地表温度，用于古气候重建。用于此目的的数据处理有两种类型：在线数据处理和离线数据处理。在线数据分析估计时间平均状态（分析）和后续数据分析周期的初始条件，而离线数据分析仅估计时间平均分析。如果与观测的时间分辨率相比，感兴趣的系统具有足够长的可预测性，则通过利用初始条件下的信息，在线数据分析有望优于离线数据分析。然而，以往的研究未能显示在线数据分析在吸收时间平均观测值时的优势，其原因也没有得到充分的研究。本研究比较了在线- DA和离线- DA，并利用中等复杂环流模式和完美模式观测系统模拟实验研究了两者与可预测性的关系。结果表明，当可预测性的长度大于观测的平均时间时，在线数据分析优于离线数据分析。我们还发现，预测时间越长，在线数据分析就越熟练。在这里，海洋在扩展可预测性方面起着至关重要的作用，这有助于在线数据分析优于离线数据分析。有趣的是，在分析步骤中，陆地近地表空气温度的观测对于更新海洋变量非常有价值，这表明在应用在线数据分析重建古气候时，使用大气和海洋之间的跨域协方差信息的重要性。
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.