loading page

Revisiting online and offline data assimilation comparison for paleoclimate reconstruction: an idealized OSSE study
  • +2
  • Atsushi Okazaki,
  • Takemasa Miyoshi,
  • Kei Yoshimura,
  • Steven J. Greybush,
  • Fuqing Zhang
Atsushi Okazaki
Pennsylvania State University

Corresponding Author:[email protected]

Author Profile
Takemasa Miyoshi
Author Profile
Kei Yoshimura
University of Tokyo
Author Profile
Steven J. Greybush
Pennsylvania State University
Author Profile
Fuqing Zhang
Pennsylvania State University
Author Profile


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 the time-mean states and the initial conditions for the next DA cycles while the offline-DA only estimates the former. 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 found to be highly valuable to update the ocean variables in the analysis steps, suggesting the importance to use cross-domain covariance information between the atmosphere and the ocean when online-DA is applied to reconstruct paleoclimate.
27 Aug 2021Published in Journal of Geophysical Research: Atmospheres volume 126 issue 16. 10.1029/2020JD034214