He et al. (2025) Improving precipitation simulations in CIESM through a new entrainment rate parameterization

Identification

• Journal: npj Climate and Atmospheric Science
• Year: 2025
• Date: 2025-12-12
• Authors: Xin Bo He, Chunsong Lu, Guang J. Zhang, Junjun Li, Lei Zhu, Hengqi Wang, Te Li, Xiaohao Guo, Sinan Gao, Yu‐Hao Lin, Kai Yang, Wenhui Liu
• DOI: 10.1038/s41612-025-01282-8

Research Groups

• State Key Laboratory of Climate System Prediction and Risk Management, China Meteorological Administration Aerosol-Cloud and Precipitation Key Laboratory and Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing, China
• Scripps Institution of Oceanography, La Jolla, CA, USA
• National Institute of Education (NIE), Nanyang Technological University (NTU), Singapore, Singapore
• State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
• Jiangsu Meteorological Observatory, Nanjing, China
• Suzhou Meteorological Bureau, Suzhou, China
• Guangzhou Institute of Tropical and Marine Meteorology of China Meteorological Administration, GBA Academy of Meteorological Research, Guangzhou, China
• Wenzhou Meteorological Bureau, Wenzhou, China

Short Summary

This study develops and implements a new entrainment rate parameterization (HL) for deep convection in the CIESM1.1.0 climate model, demonstrating improved simulations of convective and large-scale precipitation in tropical and subtropical regions compared to the existing Gregory parameterization.

Objective

• To develop a new deep convective entrainment rate parameterization (HL parameterization) based on aircraft observations, implement it into the Community Integrated Earth System Model version 1.1.0 (CIESM1.1.0), and evaluate its impact on precipitation simulations and underlying physical mechanisms compared to the existing Gregory parameterization.

Study Configuration

• Spatial Scale: Global climate model simulations with a horizontal resolution of 1.9° latitude × 2.5° longitude and 30 vertical layers. Analysis primarily focuses on the 30°S-30°N region. Aircraft observations from the Western Pacific (Tropical Ocean Global Atmosphere Coupled Ocean Atmosphere Response Experiment - TOGA-COARE). Satellite precipitation data (TRMM) at 0.5° × 0.5° resolution covering 37°S-40°N.
• Temporal Scale: TOGA-COARE field campaign data from 1992-1993. Model simulations run for 6 model years, with results from the last 5 years used for analysis. TRMM satellite data from 1998-2002.

Methodology and Data

• Models used:
  • Community Integrated Earth System Model version 1.1.0 (CIESM1.1.0).
  • Atmospheric component: Community Atmosphere Model version 5 (CAM5).
  • Deep convection parameterization: Based on Zhang and McFarlane (ZM scheme), improved by Song and Zhang (2018).
  • Shallow cumulus parameterization: University of Washington scheme.
  • Cloud microphysics: MG1.5 double-moment scheme.
  • Land component: Community Land Model version 4.0 (CLM4.0).
• Data sources:
  • Aircraft observations: Tropical Ocean Global Atmosphere Coupled Ocean Atmosphere Response Experiment (TOGA-COARE) field campaign (1992-1993), providing 47 deep convective cloud samples with measurements of cloud droplet spectra, vertical velocity, temperature, and water vapor mixing ratio.
  • Satellite observations: Tropical Rainfall Measuring Mission (TRMM) 3A12 and 3A25 datasets (1998-2002) for validation of convective, large-scale, and total precipitation rates.
  • Reanalysis/Climatology: Prescribed climatological sea surface temperature and sea ice data from the Hadley Center (averaged over 1982-2001).

Main Results

• A new deep convective entrainment rate (HL parameterization) is developed from aircraft observations: λ = 0.27w^-0.10 B^-0.17 RH^2.93, showing a correlation coefficient (R) of 0.8 with observed entrainment rates.
• The HL parameterization simulates overall larger entrainment rates (λ) than the Gregory parameterization, with smaller values below approximately 800 hPa and larger values above 800 hPa.
• In the 30°S-30°N region, the HL parameterization significantly improves deep convective precipitation simulations, reducing the bias relative to TRMM observations from 0.920 mm day^-1 to 0.632 mm day^-1 and the root mean square error (RMSE) from 1.44 mm day^-1 to 1.10 mm day^-1.
• The HL parameterization indirectly reduces shallow convective precipitation and increases large-scale precipitation, leading to a better simulation of the partitioning between convective and large-scale precipitation and a reduction in total precipitation bias.
• The improvement in deep convective precipitation is attributed to the HL parameterization's larger overall entrainment rates, which suppress deep convective cloud development (reduced deep convection occurrence index, lower cloud-top heights, and cloud depths), leading to reduced hydrometeor mixing ratios and precipitation production rates, especially above 800 hPa.
• The increase in large-scale precipitation and decrease in shallow convective precipitation are linked to an increased stratiform cloud fraction (1.04% increase in 30°S-30°N), which reduces net shortwave radiation at the surface (-5.63 W m^-2 difference), weakens atmospheric circulation, and reduces low-level water vapor convergence.
• The HL parameterization yields a global mean Earth Energy Imbalance (EEI) of 0.44 W m^-2, which is closer to observations than the Gregory parameterization (1.82 W m^-2).

Contributions

• Development of a robust, observation-based entrainment rate parameterization for deep convective clouds, addressing a critical uncertainty in climate models.
• Significant improvement in the simulation of deep convective, shallow convective, large-scale, and total precipitation rates in the CIESM1.1.0 model, particularly in tropical and subtropical regions.
• Enhanced ability to simulate the partitioning between convective and large-scale precipitation, a persistent bias in current climate models.
• Detailed elucidation of the macro- and microphysical mechanisms underlying the improved precipitation simulations.
• Improved simulation of the Earth Energy Imbalance (EEI) at the top of the atmosphere, aligning better with observational estimates.
• Provides a valuable reference for future improvements in deep convection schemes within climate models.

Funding

• National Natural Science Foundation of China (42325503, 42405156, 42305091)
• Jiangsu Specially-Appointed Professor (Grant R2024T01)
• National Key Research and Development Program of China (2024YFB3908300)
• Science and Technology Plan Project of Suzhou (2023ss10)
• Guangdong Basic and Applied Basic Research Foundation (2024A1515510021)

Citation

@article{He2025Improving,
  author = {He, Xin Bo and Lu, Chunsong and Zhang, Guang J. and Li, Junjun and Zhu, Lei and Wang, Hengqi and Li, Te and Guo, Xiaohao and Gao, Sinan and Lin, Yu‐Hao and Yang, Kai and Liu, Wenhui},
  title = {Improving precipitation simulations in CIESM through a new entrainment rate parameterization},
  journal = {npj Climate and Atmospheric Science},
  year = {2025},
  doi = {10.1038/s41612-025-01282-8},
  url = {https://doi.org/10.1038/s41612-025-01282-8}
}