Identifying the sources of seasonal predictability based on climate memory analysis and variance decomposition

Abstract

It is well recognized that climate predictability has three origins: (i) climate memory (inertia of the climate system) that accumulated from the historical conditions, (ii) responses to external forcings, and (iii) dynamical interactions of multiple processes in the climate system. However, how to systematically identify these predictable sources is still an open question. Here, we combine a recently developed Fractional Integral Statistical Model (FISM) with a Variance Decomposition Method (VDM), to systematically estimate the potential sources of predictability. With FISM, one can extract the memory component from the considered variable. For the residual parts, VDM can then be applied to extract the slow varying covariance matrix, which contains signals related to external forcings and dynamical interactions of multiple processes in climate. To demonstrate the feasibility of this new method, we analyzed the seasonal predictability in observational monthly surface air temperatures over China from 1960 to 2017. It is found that the climate memory component contributes a large portion of the seasonal predictability in the temperature records. After removing the memory component, the residual predictability stems mainly from teleconnections, i.e., in summer the residual predictability is closely related to sea surface temperature anomalies (SSTA) in the eastern tropical Pacific and the northern Indian Ocean. Our results offer the potential of more skillful seasonal predictions compared with the results obtained using FISM or VDM alone.