[M-GI26] Data assimilation: A fundamental approach in geosciences

[MGI26-P01] Climate reconstruction with observation errors estimated by innovation statistics

★Invited Papers

*Atsushi Okazaki¹, Shunji Kotsuki³, Diego Saúl Carrió Carrió⁴, Kei Yoshimura² (1.Hirosaki University, 2.The University of Tokyo, 3.Chiba University, 4.The University of the Balearic Islands)

Keywords:Paleoclimate reconstruction, Data assimilation, Observation error estimation, Climate proxies

Data assimilation (DA) has been successfully applied to reconstruct paleoclimate. DA combines model simulations and climate proxies based on their error sizes. Therefore, the error information is crucial for DA to work optimally. However, they have been treated rather crudely in the previous studies, especially when the proxies are assimilated directly. This study aims at reconstruction skill improvement by estimating observation errors accurately. For this purpose, we conducted offline data assimilation experiments for the last 100 years. Here, we assimilated stable water isotope ratios recorded in ice cores, tree ring cellulose, and corals. The observation errors were estimated by innovation statistics. We found that the estimated observation errors improved the reconstruction skill 10~15% with metrics of correlation and coefficient of efficiency. In the presentation, we will first show the reconstruction skills' sensitivity to the observation errors that are used in DA. Then, we will show that the observation errors can be estimated correctly using innovation statistics. Lastly, we will show the impact of estimating observation errors on reconstruction skills with DA.