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.
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.
the Creative Commons Attribution 4.0 License.
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
Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Hohai University, Nanjing, China
College of Oceanography, Hohai University, Nanjing, China
Southern Laboratory of Ocean Science and Engineering (Zhuhai), Zhuhai, China
Yihao Chen
College of Oceanography, Hohai University, Nanjing, China
Xiaojing Li
State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China
Southern Laboratory of Ocean Science and Engineering (Zhuhai), Zhuhai, China
Xunshu Song
State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China
Southern Laboratory of Ocean Science and Engineering (Zhuhai), Zhuhai, China
Related authors
Zikang He, Yiguo Wang, Julien Brajard, Xidong Wang, and Zheqi Shen
The Cryosphere, 19, 3279–3293, https://doi.org/10.5194/tc-19-3279-2025, https://doi.org/10.5194/tc-19-3279-2025, 2025
Short summary
Short summary
Declining Arctic sea ice presents both risks and opportunities for ecosystems, communities, and economic activities. To address prediction errors in dynamical models, we apply machine learning for error correction during prediction (online) or post-processing (offline). Our results show that both methods enhance sea ice predictions, particularly from September to January, with offline corrections outperforming online corrections.
Zikang He, Julien Brajard, Yiguo Wang, Xidong Wang, and Zheqi Shen
EGUsphere, https://doi.org/10.5194/egusphere-2025-212, https://doi.org/10.5194/egusphere-2025-212, 2025
Short summary
Short summary
Climate prediction is challenging due to systematic errors in traditional climate models. We addressed this by training a machine learning model to correct these errors and then integrating it with the traditional climate model to form an AI-physics hybrid model. Our study demonstrates that the hybrid model outperforms the original climate model on both short-term and long-term predictions of the atmosphere and ocean.
Zikang He, Yiguo Wang, Julien Brajard, Xidong Wang, and Zheqi Shen
The Cryosphere, 19, 3279–3293, https://doi.org/10.5194/tc-19-3279-2025, https://doi.org/10.5194/tc-19-3279-2025, 2025
Short summary
Short summary
Declining Arctic sea ice presents both risks and opportunities for ecosystems, communities, and economic activities. To address prediction errors in dynamical models, we apply machine learning for error correction during prediction (online) or post-processing (offline). Our results show that both methods enhance sea ice predictions, particularly from September to January, with offline corrections outperforming online corrections.
Zikang He, Julien Brajard, Yiguo Wang, Xidong Wang, and Zheqi Shen
EGUsphere, https://doi.org/10.5194/egusphere-2025-212, https://doi.org/10.5194/egusphere-2025-212, 2025
Short summary
Short summary
Climate prediction is challenging due to systematic errors in traditional climate models. We addressed this by training a machine learning model to correct these errors and then integrating it with the traditional climate model to form an AI-physics hybrid model. Our study demonstrates that the hybrid model outperforms the original climate model on both short-term and long-term predictions of the atmosphere and ocean.
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
Balmaseda, M., Alves, O., Arribas, A., Awaji, T., Behringer, D., Ferry, N., Fujii, Y., Lee, T., Rienecker, M., Rosati, T., and Stammer, D.: Ocean Initialization for Seasonal Forecasts, Oceanography, 22, 154–159, https://doi.org/10.5670/oceanog.2009.73, 2009. a
Bryan, F.: Parameter sensitivity of primitive equation ocean general circulation models, J. Phys. Ocean, 17, 970–985, 1987. a
Chen, Y., Shen, Z., Tang, Y., and Song, X.: Ocean data assimilation for the initialization of seasonal prediction with the Community Earth System Model, Ocean Modell., 183, 102194, https://doi.org/10.1016/j.ocemod.2023.102194, 2023. a, b, c, d
Cheng, L. and Kitade, Y.: Quantitative evaluation of turbulent mixing in the Central Equatorial Pacific, J. Oceanogr., 70, 63–79, 2014. a
Evensen, G., Dee, D. P., and Schröter, J.: Parameter Estimation in Dynamical Models, vol. 516 of NATO Science Series, Springer, Dordrecht, 373–398, https://doi.org/10.1007/978-94-011-5096-5, 1998. a
Fujii, Y., Nakaegawa, T., Matsumoto, S., Yasuda, T., Yamanaka, G., and Kamachi, M.: Coupled Climate Simulation by Constraining Ocean Fields in a Coupled Model with Ocean Data, J. Climate, 22, 5541–5557, https://doi.org/10.1175/2009JCLI2814.1, 2009. a
Gao, C. and Zhang, R.-H.: The roles of atmospheric wind and entrained water temperature (Te) in the second-year cooling of the 2010–12 La Niña event, Clim. Dynam., 48, 597–617, 2017. a
Gao, Y., Tang, Y., Song, X., and Shen, Z.: Parameter Estimation Based on a Local Ensemble Transform Kalman Filter Applied to El Niño–Southern Oscillation Ensemble Prediction, Remote Sens., 13, 3923, https://doi.org/10.3390/rs13193923, 2021. a
Garcia, H., Boyer, T., Baranova, O., Locarnini, R., Mishonov, A., Grodsky, A. E., Paver, C., Weathers, K., Smolyar, I., Reagan, J., Seidov, D., and Zweng, M.: World ocean atlas 2018: Product documentation, edited by: Mishonov, A., 1, 1–20, https://www.ncei.noaa.gov/sites/default/files/2022-06/woa18documentation.pdf (last access: 18 April 2021), 2019. a, b
Gaspari, G. and Cohn, S.: Construction of correlation functions in two and three dimensions, Q. J. Roy. Meteor. Soc., 125, 723–757, 1999. a
Gouretski, V. and Reseghetti, F.: On depth and temperature biases in bathythermograph data: Development of a new correction scheme based on analysis of a global ocean database, Deep-Sea Res. Pt. I, 57, 812–833, 2010. a
Gregg, M. C.: Variations in the Intensity of Small-Scale Mixing in the Main Thermocline, J. Phys. Oceanogr., 7, 436–454, 1977. a
Han, G., Wu, X., Zhang, S., Liu, Z., and Li, W.: Error Covariance Estimation for Coupled Data Assimilation Using a Lorenz Atmosphere and a Simple Pycnocline Ocean Model, J. Climate, 26, 10218–10231, https://doi.org/10.1175/JCLI-D-13-00236.1, 2013. a
Hu, X.-M., Zhang, F., and Nielsen-Gammon, J. W.: Ensemble-based simultaneous state and parameter estimation for treatment of mesoscale model error: A real-data study, Geophys. Res. Lett., 37, L08802, https://doi.org/10.1029/2010GL043017, 2010. a
Huang, B., Liu, C., Banzon, V., Freeman, E., Graham, G., Hankins, B., Smith, T., and Zhang, H.: Improvements of the daily optimum interpolation sea surface temperature (DOISST) version 2.1, J. Climate, 34, 8, 2923–2939, https://doi.org/10.1175/JCLI-D-20-0166.1, 2021. a
Jin, E. K., Kinter, J. L., Wang, B., Park, C.-K., Kang, I.-S., Kirtman, B. P., Kug, J.-S., Kumar, A., Luo, J.-J., Schemm, J., Shukla, J., and Yamagata, T.: Current status of ENSO prediction skill in coupled ocean–atmosphere models, Clim. Dynam., 31, 647–664, https://doi.org/10.1007/s00382-008-0397-3, 2008. a
Jochum, M.: Impact of latitudinal variations in vertical diffusivity on climate simulations, J. Geophys. Res., 114, C01010, https://doi.org/10.1029/2008JC005030, 2009. a, b
Jochum, M. and Potemra, J.: Sensitivity of tropical rainfall to Banda Sea diffusivity in the community climate system model, J. Climate, 21, 6445–6454, 2008. a
Karspeck, A. R., Danabasoglu, G., Anderson, J., Karol, S., Collins, N., Vertenstein, M., Raeder, K., Hoar, T., Neale, R., Edwards, J., and Craig, A.: A global coupled ensemble data assimilation system using the Community Earth System Model and the Data Assimilation Research Testbed, Q. J. Roy. Meteor. Soc., 144, 2404–2430, 2018. a
Kivman, G. A.: Sequential parameter estimation for stochastic systems, Nonlin. Processes Geophys., 10, 253–259, https://doi.org/10.5194/npg-10-253-2003, 2003. a
Kondrashov, D., Sun, C., and Ghil, M.: Data Assimilation for a Coupled Ocean–Atmosphere Model. Part II: Parameter Estimation, Mon. Weather Rev., 136, 5062–5076, https://doi.org/10.1175/2008MWR2544.1, 2008. a
Kug, J.-S., Kang, I.-S., and Choi, D.-H.: Seasonal climate predictability with Tier-one and Tier-two prediction systems, Clim. Dynam., 31, 403–416, https://doi.org/10.1007/s00382-007-0264-7, 2008. a
Kunze, E., Firing, E., Hummon, J. M., Chereskin, T. K., and Thurnherr, A. M.: Global Abyssal Mixing Inferred from Lowered ADCP Shear and CTD Strain Profiles, J. Phys. Oceanogr., 36, 1553–1576, 2006. a
Large, W. G., Mcwilliams, J. C., and Doney, S. C.: Oceanic vertical mixing: A review and a model with a nonlocal boundary layer parameterization, Rev. Geophys., 32, 363–403, 1994. a
Ledwell, J. R., Watson, A. J., and Law, C. S.: Mixing of a tracer in the pycnocline, J. Geophys. Res.-Oceans, 103, 21499–21529, 1998. a
Liu, T., Song, X., Tang, Y., Shen, Z., and Tan, X.: ENSO predictability over the past 137 years based on a CESM ensemble prediction system, J. Climate, 35, 763–777, https://doi.org/10.1175/JCLI-D-21-0450.1, 2022. a
Liu, Y., Liu, Z., Zhang, S., Rong, X., Jacob, R., Wu, S., and Lu, F.: Ensemble-based parameter estimation in a coupled GCM using the adaptive spatial average method, J. Climate, 27, 4002–4014, 2014b. a
MacKinnon, J. and Winters, K.: Subtropical catastrophe: Significant loss of low-mode tidal energy at 28.9°, Geophys. Res. Lett., 32, L15605, https://doi.org/10.1029/2005GL023376, 2005. a
Menemenlis, D., Fukumori, I., and Lee, T.: Using Green’s Functions to Calibrate an Ocean General Circulation Model, Mon. Weather Rev., 133, 1224–1240, https://doi.org/10.1175/MWR2912.1, 2005. a
Mulholland, D. P., Laloyaux, P., Haines, K., and Balmaseda, M. A.: Origin and Impact of Initialization Shocks in Coupled Atmosphere–Ocean Forecasts*, Mon. Weather Rev., 143, 4631–4644, https://doi.org/10.1175/MWR-D-15-0076.1, 2015. a
Munk, W. H.: Abyssal recipes, Deep-Sea Res. Ocean. Abstr., 13, 707–730, https://doi.org/10.1016/0011-7471(66)90602-4, 1966. a
Navon, I.: Practical and theoretical aspects of adjoint parameter estimation and identifiability in meteorology and oceanography, Dynam. Atmos. Oceans, 27, 55–79, 1998. a
Neale, R. B., Gettelman, A., Park, S., Conley, A. J., Kinnison, D., Marsh, D., Smith, A. K., Vitt, F., Morrison, H., and Cameronsmith, P.: Description of the NCAR Community Atmosphere Model (CAM 5.0), Natl. Cent. for Atmos, Land Model, Tech. Note NCAR/TN-486+STR, 2010. a
Penny, S. G. and Hamill, T. M.: Coupled Data Assimilation for Integrated Earth System Analysis and Prediction, B. Am. Meteorol. Soc., 98, ES169–ES172, https://doi.org/10.1175/BAMS-D-17-0036.1, 2017. a
Rayner, N., Parker, D. E., Horton, E., Folland, C. K., Alexander, L. V., Rowell, D., Kent, E. C., and Kaplan, A.: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century, J. Geophys. Res.-Atmos., 108, 4407, https://doi.org/10.1029/2002JD002670, 2003. a, b
Shen, Z.: Data and code for gmd-2023-113 “Parameter estimation for ocean background vertical diffusivity coefficients in the Community Earth System Model (v1.2.1) and its impact on ENSO forecast”, Zenodo [code and data set], https://doi.org/10.5281/zenodo.8115394, 2023. a
Shen, Z., Zhong, Q., and Chen, Z.: Parameter estimation using adaptive observations towards maximum total variance reduction with ensemble adjustment Kalman filter, Front. Climate, 4, 850386, https://doi.org/10.3389/fclim.2022.850386, 2022. a
Smith, R., Dukowicz, J., and Malone, R.: Parallel ocean general circulation modeling, Phys. D, 60, 38–61, 1992. a
Smith, R., Jones, P., Briegleb, B., Bryan, F., Danabasoglu, G., Dennis, J., Dukowicz, J., Eden, C., Fox-Kemper, B., Gent, P., Hecht, M., Jayne, S., Jochum, M., Large, W., Lindsay, K., Maltrud, M., Norton, N., Peacock, S., Vertenstein, M., and Yeager, S.: The Parallel Ocean Program (POP) Reference Manual, LAUR-01853, 141, 1–140, 2010. a, b, c
Song, X., Li, X., Zhang, S., Li, Y., Chen, X., Tang, Y., and Chen, D.: A new nudging scheme for the current operational climate prediction system of the National Marine Environmental Forecasting Center of China, Acta Oceanol. Sin., 41, 51–64, https://doi.org/10.1007/s13131-021-1857-4, 2022. a
Stammer, D., Balmaseda, M., Heimbach, P., Köhl, A., and Weaver, A.: Ocean data assimilation in support of climate applications: Status and perspectives, Annu. Rev. Mar. Sci., 8, 491–518, 2016. a
Tang, Y., Kleeman, R., Moore, A. M., Weaver, A., and Vialard, J.: The use of ocean reanalysis products to initialize ENSO predictions: initialization of ENSO prediction, Geophys. Res. Lett., 30, 1694, https://doi.org/10.1029/2003GL017664, 2003. a
Tong, M. and Xue, M.: Simultaneous Estimation of Microphysical Parameters and Atmospheric State with Simulated Radar Data and Ensemble Square Root Kalman Filter. Part II: Parameter Estimation Experiments, Mon. Weather Rev., 136, 1649–1668, https://doi.org/10.1175/2007MWR2071.1, 2008a. a
Tong, M. and Xue, M.: Simultaneous Estimation of Microphysical Parameters and Atmospheric State with Simulated Radar Data and Ensemble Square Root Kalman Filter. Part I: Sensitivity Analysis and Parameter Identifiability, Mon. Weather Rev., 136, 1630–1648, https://doi.org/10.1175/2007MWR2070.1, 2008b. a
Wu, X., Zhang, S., Liu, Z., Rosati, A., Delworth, T. L., and Liu, Y.: Impact of Geographic-Dependent Parameter Optimization on Climate Estimation and Prediction: Simulation with an Intermediate Coupled Model, Mon. Weather Rev., 140, 3956–3971, https://doi.org/10.1175/MWR-D-11-00298.1, 2012. a
Wu, X., Han, G., Zhang, S., and Liu, Z.: A study of the impact of parameter optimization on ENSO predictability with an intermediate coupled model, Clim. Dynam., 46, 711–727, 2016. a
Zhang, S., Liu, Z., Rosati, A., and Delworth, T.: A study of enhancive parameter correction with coupled data assimilation for climate estimation and prediction using a simple coupled model, Tellus A, 64, 10963, https://doi.org/10.3402/tellusa.v64i0.10963, 2012. a, b
Zhang, S., Liu, Z., Zhang, X., Wu, X., and Deng, X.: Coupled data assimilation and parameter estimation in coupled ocean–atmosphere models: a review, Clim. Dynam., 54, 5127–5144, https://doi.org/10.1007/s00382-020-05275-6, 2020. a, b
Zhao, Y., Liu, Z., Zheng, F., and Jin, Y.: Parameter Optimization for Real-World ENSO Forecast in an Intermediate Coupled Model, Mon. Weather Rev., 147, 1429–1445, https://doi.org/10.1175/MWR-D-18-0199.1, 2019. a, b, c
Zhu, Y. and Zhang, R. H.: An Argo‐Derived Background Diffusivity Parameterization for Improved Ocean Simulations in the Tropical Pacific, Geophys. Res. Lett., 45, 1509–1517, 2018. a
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.
Parameter estimation is the process that optimizes model parameters using observations, which...