Submitted as: development and technical paper
31 Jul 2023
Submitted as: development and technical paper |  | 31 Jul 2023
Status: this preprint is currently under review for the journal GMD.

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 et al.

Status: open (extended)

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 reply

Zheqi Shen et al.

Zheqi Shen et al.


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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.