Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Geosci. Model Dev., 10, 4647-4664, 2017
https://doi.org/10.5194/gmd-10-4647-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Development and technical paper
21 Dec 2017
Effectiveness and limitations of parameter tuning in reducing biases of top-of-atmosphere radiation and clouds in MIROC version 5
Tomoo Ogura1, Hideo Shiogama1, Masahiro Watanabe2, Masakazu Yoshimori3, Tokuta Yokohata1, James D. Annan4, Julia C. Hargreaves4, Naoto Ushigami5, Kazuya Hirota2, Yu Someya2, Youichi Kamae6, Hiroaki Tatebe7, and Masahide Kimoto2 1National Institute for Environmental Studies, Tsukuba, Ibaraki, Japan
2Atmosphere and Ocean Research Institute, University of Tokyo, Kashiwa, Chiba, Japan
3Faculty of Environmental Earth Science, Global Institution for Collaborative Research and Education, and Arctic Research Center, Hokkaido University, Sapporo, Hokkaido, Japan
4BlueSkiesResearch.org.uk, Settle, North Yorkshire, UK
5Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
6Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
7Japan Agency for Marine-Earth Science and Technology, Yokohama, Kanagawa, Japan
Abstract. This study discusses how much of the biases in top-of-atmosphere (TOA) radiation and clouds can be removed by parameter tuning in the present-day simulation of a climate model in the Coupled Model Inter-comparison Project phase 5 (CMIP5) generation. We used output of a perturbed parameter ensemble (PPE) experiment conducted with an atmosphere–ocean general circulation model (AOGCM) without flux adjustment. The Model for Interdisciplinary Research on Climate version 5 (MIROC5) was used for the PPE experiment. Output of the PPE was compared with satellite observation data to evaluate the model biases and the parametric uncertainty of the biases with respect to TOA radiation and clouds. The results indicate that removing or changing the sign of the biases by parameter tuning alone is difficult. In particular, the cooling bias of the shortwave cloud radiative effect at low latitudes could not be removed, neither in the zonal mean nor at each latitude–longitude grid point. The bias was related to the overestimation of both cloud amount and cloud optical thickness, which could not be removed by the parameter tuning either. However, they could be alleviated by tuning parameters such as the maximum cumulus updraft velocity at the cloud base. On the other hand, the bias of the shortwave cloud radiative effect in the Arctic was sensitive to parameter tuning. It could be removed by tuning such parameters as albedo of ice and snow both in the zonal mean and at each grid point. The obtained results illustrate the benefit of PPE experiments which provide useful information regarding effectiveness and limitations of parameter tuning. Implementing a shallow convection parameterization is suggested as a potential measure to alleviate the biases in radiation and clouds.

Citation: Ogura, T., Shiogama, H., Watanabe, M., Yoshimori, M., Yokohata, T., Annan, J. D., Hargreaves, J. C., Ushigami, N., Hirota, K., Someya, Y., Kamae, Y., Tatebe, H., and Kimoto, M.: Effectiveness and limitations of parameter tuning in reducing biases of top-of-atmosphere radiation and clouds in MIROC version 5, Geosci. Model Dev., 10, 4647-4664, https://doi.org/10.5194/gmd-10-4647-2017, 2017.
Publications Copernicus
Download
Short summary
Present-day climate simulated by coupled ocean atmosphere models exhibits significant biases in top-of-atmosphere radiation and clouds. This study shows that only limited part of the biases can be removed by parameter tuning in a climate model. The results underline the importance of improving parameterizations in climate models based on cloud process studies. Implementing a shallow convection parameterization is suggested as a potential measure to alleviate the biases.
Present-day climate simulated by coupled ocean atmosphere models exhibits significant biases in...
Share