Preprints
https://doi.org/10.5194/gmd-2023-124
https://doi.org/10.5194/gmd-2023-124
Submitted as: development and technical paper
 | 
01 Aug 2023
Submitted as: development and technical paper |  | 01 Aug 2023
Status: a revised version of this preprint was accepted for the journal GMD and is expected to appear here in due course.

The 4DEnVar-based land coupled data assimilation system for E3SM version 2

Pengfei Shi, L. Ruby Leung, Bin Wang, Kai Zhang, Samson M. Hagos, and Shixuan Zhang

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 assimilation experiment 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.

Pengfei Shi, L. Ruby Leung, Bin Wang, Kai Zhang, Samson M. Hagos, and Shixuan Zhang

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-124', Anonymous Referee #1, 11 Aug 2023
    • AC4: 'Reply on RC1', Pengfei Shi, 28 Dec 2023
  • RC2: 'Comment on gmd-2023-124', Anonymous Referee #1, 28 Aug 2023
    • AC1: 'Reply on RC2', Pengfei Shi, 28 Dec 2023
  • RC3: 'Comment on gmd-2023-124', Anonymous Referee #2, 13 Sep 2023
    • AC2: 'Reply on RC3', Pengfei Shi, 28 Dec 2023
  • RC4: 'Comment on gmd-2023-124', Anonymous Referee #3, 14 Sep 2023
    • AC3: 'Reply on RC4', Pengfei Shi, 28 Dec 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-124', Anonymous Referee #1, 11 Aug 2023
    • AC4: 'Reply on RC1', Pengfei Shi, 28 Dec 2023
  • RC2: 'Comment on gmd-2023-124', Anonymous Referee #1, 28 Aug 2023
    • AC1: 'Reply on RC2', Pengfei Shi, 28 Dec 2023
  • RC3: 'Comment on gmd-2023-124', Anonymous Referee #2, 13 Sep 2023
    • AC2: 'Reply on RC3', Pengfei Shi, 28 Dec 2023
  • RC4: 'Comment on gmd-2023-124', Anonymous Referee #3, 14 Sep 2023
    • AC3: 'Reply on RC4', Pengfei Shi, 28 Dec 2023
Pengfei Shi, L. Ruby Leung, Bin Wang, Kai Zhang, Samson M. Hagos, and Shixuan Zhang
Pengfei Shi, L. Ruby Leung, Bin Wang, Kai Zhang, Samson M. Hagos, and Shixuan Zhang

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Short summary
Improving climate predictions have profound socio-economic impacts. This study introduces a new land coupled data assimilation (LCDA) system for a coupled climate model. We demonstrate improved simulation of soil moisture and temperature in many global regions and throughout the soil layers. Furthermore, significant improvements are also found in reproducing the time evolution of the 2012 U.S. Midwest drought. The LCDA system provides the groundwork for future predictability studies.