In climate reconstructions by data assimilation, the sensitivities to both proxies and prior estimates need to be taken into account because models are uncertain and proxies are limited spatiotemporally. This study examines these sensitivities using multiple climate model simulations and different combinations of proxies (corals, ice cores, and tree-ring cellulose). Experiments were conducted based on an offline data assimilation approach. These experiments show annual variations in the global distribution of surface air temperature and precipitation range from 850 to 2000. The results indicate that standard deviations of surface air temperature and precipitation amount during the entire period differ by up to 50% due to prior estimates. Experiments with different types of proxies show that the El Niño-like distribution of positive anomalies in the central to eastern tropical Pacific can be reproduced adequately in experiments with corals, but not in experiments without corals. The correlation coefficient of the NINO.3 index from 1971 to 2000 between experiments with corals and the Japanese 55-year Reanalysis (JRA-55) were 0.79 at maximum, while the correlation coefficient between experiments without corals and JRA-55 were 0.20 at maximum.

Water isotopes measured in Antarctic ice cores enable reconstruction at the first order of the past temperature variations. However, the seasonality of the precipitation and episodic events, including synoptic-scale disturbances, influence the isotopic signals recorded in ice cores. In this study, we adopted an isotope-enabled atmospheric general circulation model from 1981 to 2010 to investigate variations in climatic factors in δ18O of precipitation (δ18Op) at Dome Fuji, East Antarctica. The Southern Annular Mode (SAM), the primary mode of atmospheric circulation in the southern mid-high latitudes, significantly contributes to the isotope signals. Positive δ18Op anomalies, especially in the austral winter, are linked to the negative polarity of the SAM, which weakens westerly winds and increases the southward inflow of water vapor flux. Daily variations in temperature and δ18Op in Dome Fuji are significantly small in the austral summer, and their contribution to the annual signals is limited. The isotope signals driven by the SAM are a locational feature of Dome Fuji, related to the asymmetric component of the large-scale atmospheric pattern.

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.