期刊
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
卷 126, 期 16, 页码 -出版社
AMER GEOPHYSICAL UNION
DOI: 10.1029/2020JD034214
关键词
data assimilation; paleoclimate reconstruction; last millennium; atmosphere-ocean coupled data assimilation; climate predictability
资金
- JSPS via KAKEN [17K14397, 18H03794, 19F19024]
- RIKEN
- Penn State Center for Advanced Data Assimilation and Predictability Techniques (ADAPT)
Online data assimilation performs better than offline data assimilation when the predictability of the system exceeds the averaging time of observations. The ocean plays a crucial role in extending predictability, aiding online data assimilation to outperform offline data assimilation. Moreover, the observations of near-surface air temperature over land are highly valuable in updating ocean variables, highlighting the importance of utilizing cross-domain covariance information between the atmosphere and the ocean in paleoclimate reconstruction.
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.
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