PNNL

Abstract

A new land coupled data assimilation (LCDA) system based on the four-dimensional ensemble variational (4DEnVar) method is developed and applied to the fully coupled Energy Exascale Earth System Model version 2 (E3SMv2). The dimension-reduced projection four-dimensional variational (DRP-4DVar) method is employed to implement 4DVar using the ensemble technique instead of the adjoint technique. Monthly mean soil moisture and temperature analyses from a global land reanalysis product are assimilated into the land component of E3SMv2 with a one-month assimilation window along the coupled model trajectory from 1980 to 2016. The coupled simulation is evaluated using multiple metrics, including the cost function, assimilation efficiency index, correlation, root mean square error and bias, and compared with a control simulation without land data assimilation. The LCDA system yields improved simulation of soil moisture and temperature compared with the control simulation, with improvements found throughout the soil layers and in many regions of the global land. Furthermore, significant improvements are also found in reproducing the time evolution of the 2012 U.S. Midwest drought, highlighting the crucial role of land surface in drought lifecycle. The LCDA system is intended to be a foundational resource to investigate land-derived climate predictability for future prediction research by the E3SM community.