Preprints
https://doi.org/10.5194/acp-2020-1245
https://doi.org/10.5194/acp-2020-1245

  11 Jan 2021

11 Jan 2021

Review status: this preprint is currently under review for the journal ACP.

Evaluation of the CMIP6 marine subtropical stratocumulus cloud albedo and its controlling factors

Bida Jian1, Jiming Li1, Guoyin Wang2, Yuxin Zhao1, Yarong Li1, Jing Wang1, Min Zhang3, and Jianping Huang1 Bida Jian et al.
  • 1Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou, Gansu, China
  • 2Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences, Fudan University, Shanghai, China
  • 3Inner Mongolia Institute of Meteorological Sciences, Hohhot, Inner Mongolia, China

Abstract. The cloud albedo at the subtropical marine subtropical stratocumulus regions has a key role in regulating the regional energy budget. Based on 12 years of monthly data from multiple satellite datasets, the long-term, monthly and seasonal cycle averaged cloud albedo at five stratocumulus regions were investigated to inter-compare the atmosphere-only simulations of Phase 5 and 6 of the Coupled Model Inter-comparison Project (AMIP5 and AMIP6). Statistical results showed that the long-term regressed cloud albedos were underestimated in most AMIP6 models compared with the satellite-driven cloud albedos, and the AMIP6 models produced a similar spread of AMIP5 at all regions. The monthly mean and seasonal cycle of cloud albedo of AMIP6 ensemble mean showed better correlation with the satellite-driven observation than that of AMIP5 ensemble mean, however, fail to reproduce the values and amplitude in some regions. By employing the Modern-Era Retrospective Analysis for Research and Applications Version 2 data, this study estimated the relative contributions of different aerosols and meteorological factors on the marine stratocumulus cloud albedo under different cloud liquid water path (LWP) conditions. The multiple regression models can explain ~60 % of the changes in the cloud albedo. Under the monthly mean LWP ≤ 60 g m−2, dust and black carbon dominantly contributed to the changes in the cloud albedo, while sulfate aerosol contributed the most under the condition of 60 g m−2 < LWP ≤ 120 g m−2. These results suggest that the parameterization of cloud-aerosol interactions is critical for accurately simulating the cloud albedo in models.

Bida Jian et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2020-1245', Anonymous Referee #1, 23 Jan 2021
  • RC2: 'Comment on acp-2020-1245', Anonymous Referee #2, 09 Feb 2021

Bida Jian et al.

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Short summary
We evaluate the performance of the AMIP6 model in simulating cloud albedo over marine subtropical regions and the impacts of different aerosol types and meteorological factors on the cloud albedo based on multiple satellite datasets and reanalysis data. The results show that AMIP6 shows moderate improvement over AMIP5 in simulating the monthly variation of cloud albedo, and changes in different aerosol types and meteorological factors can explained ~60 % of the changes in the cloud albedo.
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