Revisiting Online and Offline Data Assimilation Comparison for Paleoclimate Reconstruction: An Idealized OSSE Study
Atsushi Okazaki, Takemasa Miyoshi, Kei Yoshimura, Steven J. Greybush, Fuqing Zhang
Abstract
Abstract 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.