Preprints
https://doi.org/10.5194/gmd-2023-113
https://doi.org/10.5194/gmd-2023-113
Submitted as: development and technical paper
 | 
31 Jul 2023
Submitted as: development and technical paper |  | 31 Jul 2023
Status: a revised version of this preprint was accepted for the journal GMD and is expected to appear here in due course.

Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on ENSO forecast

Zheqi Shen, Yihao Chen, Xiaojing Li, and Xunshu Song

Abstract. This study investigates parameter estimation (PE) to enhance climate forecasts of a coupled general circulation model by adjusting the background vertical diffusivity coefficients in its ocean component. These parameters were initially identified through sensitivity experiments and subsequently estimated by assimilating the sea surface temperature and temperature-salinity profiles. This study expands the coupled data assimilation system of the Community Earth System Model (CESM) and the ensemble adjustment Kalman filter (EAKF) to enable parameter estimation. PE experiments were performed to establish balanced initial states and adjusted parameters for forecasting the El Nino-Southern Oscillation (ENSO). Comparing the model states between the PE experiment and a state estimation (SE) experiment revealed that PE can significantly reduce the uncertainty of these parameters and improve the quality of analysis. The forecasts obtained from PE and SE experiments further validate that PE has the potential to improve the forecast skill of ENSO.

Zheqi Shen, Yihao Chen, Xiaojing Li, and Xunshu Song

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-113', Anonymous Referee #1, 17 Aug 2023
    • AC1: 'Reply on RC1', Zheqi Shen, 12 Nov 2023
  • RC2: 'Comment on gmd-2023-113', Anonymous Referee #2, 20 Oct 2023
    • AC2: 'Reply on RC2', Zheqi Shen, 13 Nov 2023
  • AC3: 'Major revisions have been made', Zheqi Shen, 13 Nov 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-113', Anonymous Referee #1, 17 Aug 2023
    • AC1: 'Reply on RC1', Zheqi Shen, 12 Nov 2023
  • RC2: 'Comment on gmd-2023-113', Anonymous Referee #2, 20 Oct 2023
    • AC2: 'Reply on RC2', Zheqi Shen, 13 Nov 2023
  • AC3: 'Major revisions have been made', Zheqi Shen, 13 Nov 2023
Zheqi Shen, Yihao Chen, Xiaojing Li, and Xunshu Song
Zheqi Shen, Yihao Chen, Xiaojing Li, and Xunshu Song

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Short summary
Parameter estimation is the process that optimizes the model parameters using observations, which could reduce the model errors and improve the forecast. In this study, we conducted parameter estimation experiments using the CESM and ensemble Kalman filter. The obtained initial conditions and parameters are used to perform ensemble forecast experiment for ENSO forcast. The results revealed that parameter estimation could reduce analysis errors and improve ENSO forecast skills.