Articles | Volume 17, issue 4
https://doi.org/10.5194/gmd-17-1651-2024
https://doi.org/10.5194/gmd-17-1651-2024
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

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Cited articles

Aksoy, A., Zhang, F., and Nielsen-Gammon, J. W.: Ensemble-based simultaneous state and parameter estimation with MM5, Geophys. Res. Lett., 33, L12801, https://doi.org/10.1029/2006GL026186, 2006. a
Anderson, J. L.: A local least squares framework for ensemble filtering, Mon. Weather Rev., 131, 634–642, 2003. a
Anderson, J. L., Hoar, T. J., Raeder, K., Liu, H., Collins, N., Torn, R. D., and Avellano, A.: The Data Assimilation Research Testbed: A Community Facility, B. Am. Meteorol. Soc., 90, 1283–1296, 2009. a
Annan, J.: Parameter estimation using chaotic time series, Tellus A, 57, 709–714, 2005. a
Annan, J. D. and Hargreaves, J. C.: Efficient parameter estimation for a highly chaotic system, Tellus A, 56, 520–526, 2004. a
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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.
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