Robust Estimates of Earth System Predictability of the First Kind Using the CESM2 Multiyear Prediction System (CESM2-MP)
Kim, Yong-Yub ; Lee, June-Yi ; Chikamoto, Yoshimitsu ; Timmermann, Axel ; Lee, Sun-Seon ; Kwon, Eun Young ; Park, Wonsun ; Hasan, Nahid A. ; Bethke, Ingo ; Fransner, Filippa ; Karwat, Alexia ; Subrahmanian, Abhinav R. ; Franzke, Christian L. E.
Année de publication
2025
Here, we present a new seasonal-to-multiyear Earth prediction system, Community Earth System Model, version 2, multiyear prediction system (CESM2-MP), based on the CESM2. A 20-member ensemble that assimilates oceanic temperature and salinity anomalies provides the initial conditions for 5-yr predictions from 1960 to 2020. We analyze skills using pairwise ensemble statistics, calculated among individual members (IMs), and compare the results with those obtained from the more commonly used ensemble-mean (EM) approach. This comparison is motivated by the fact that an EM of a nonlinear dynamical system generates, unlike reality, a heavily smoothed trajectory, akin to the evolution of a slow manifold. However, for most autonomous nonlinear systems, the EM does not even represent a solution of the underlying physical equations, and it should therefore not be used as an estimate of the expected trajectory. The IM-based approach is less sensitive to ensemble size than EM-based skill computations, and its estimates of attainable prediction skills are closer to the actual skills. Using IM-based statistics helps to unravel the physics of predicted patterns in the CESM2-MP and their relationship to ocean-atmosphere-land interactions and climate modes of variability. Furthermore, the IM-based method emphasizes predictability of the first kind, which is associated with initial error sensitivity. In contrast, the EM-based method is more sensitive to the predictability of the second kind, which is associated with the external forcing and time-varying boundary conditions. Calculating IM-based skills for the CESM2-MP provides new insights into the sources of predictability originating from ocean initial conditions, helping to delineate and quantify the forecast limits of internal climate variability.</div>
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