Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 4
Articles | Volume 17, issue 4
https://doi.org/10.5194/gmd-17-1651-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-1651-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 4
Development and technical paper
 | 
26 Feb 2024
Development and technical paper |  | 26 Feb 2024

Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on El Niño–Southern Oscillation forecasts

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

Viewed

Total article views: 2,240 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,706 474 60 2,240 76 120
  • HTML: 1,706
  • PDF: 474
  • XML: 60
  • Total: 2,240
  • BibTeX: 76
  • EndNote: 120
Views and downloads (calculated since 31 Jul 2023)
Cumulative views and downloads (calculated since 31 Jul 2023)

Viewed (geographical distribution)

Total article views: 2,240 (including HTML, PDF, and XML) Thereof 2,197 with geography defined and 43 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 29 Oct 2025
Download
Short summary
Parameter estimation is the process that optimizes model parameters using observations, which could reduce model errors and improve forecasting. In this study, we conducted parameter estimation experiments using the CESM and the ensemble adjustment Kalman filter. The obtained initial conditions and parameters are used to perform ensemble forecast experiments for ENSO forecasting. The results revealed that parameter estimation could reduce analysis errors and improve ENSO forecast skills.
Read more
Share