Search NASASearch

DOE OSTI · 2560729

Implementation and Evaluation of Physics-Driven Dynamic Entrainment-Mixing Parameterization in a Climate Model and Its Impact on Low-Cloud Simulation

Abstract

The turbulent entrainment-mixing process in the Community Earth System Model version 1.2 (CESM1.2) is assumed to follow the extremely inhomogeneous entrainment-mixing. However, different entrainment-mixing scenarios can occur in real clouds. To address this deficiency, a unifying parameterization that represents different entrainment-mixing processes is implemented and evaluated in CESM1.2. The results indicate that the homogeneous mixing degree values simulated by the new parameterization in CESM1.2 are predominantly greater than 50%, suggesting a tendency toward homogeneous mixing. Compared to the extremely inhomogeneous mixing mechanism, the new parameterization increases the cloud droplet number concentration (Nc). More importantly, the new parameterization improves low-cloud fraction (CLDLOW) simulation in Northwest Pacific (NWP) and Southeast Pacific (SEP) regions, with relative improvements of 2.95% and 4.17%, respectively. Furthermore, the improvements reach up to 44.6% and 16.2% in the NWP and SEP regions, respectively, when considering the relationship between N c and CLDLOW. Further analysis reveals that the new parameterization enhances cloud optical depth, longwave radiative cooling effect, net condensation rate, cloud water mixing ratio, lower-troposphere stability, and CLDLOW by increasing N c . Additionally, these results underscore the importance of improving entrainment-mixing parameterization in climate models.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

He, Xin [Nanjing Univ. of Information Science and Technology (China)] (ORCID:0009000533931199), Lu, Chunsong [Nanjing Univ. of Information Science and Technology (China)] (ORCID:0000000289670371), Liu, Yangang [Brookhaven National Laboratory (BNL), Upton, NY (United States)] (ORCID:0000000302380468), Zhu, Yannian [Nanjing Univ. (China)] (ORCID:0000000283711830), Peng, Yiran [Tsinghua Univ., Beijing (China)] (ORCID:0000000236865458), Zhu, Lei [Nanjing Univ. of Information Science and Technology (China)] (ORCID:0000000222383941), Xu, Xiaoqi [Nanjing Joint Institute for Atmospheric Sciences (China)] (ORCID:0000000205722833), Luo, Shi [Civil Aviation Flight University of China, Chengdu (China)] (ORCID:0000000283262676), Wang, Hengqi [Chinese Academy of Sciences (CAS), Beijing (China)] (ORCID:0000000246683391), Li, Te [Jiangsu Meteorological Observatory, Nanjing (China)], Li, Junjun [Nanjing Univ. of Information Science and Technology (China)] (ORCID:0000000320226018), Wang, Hao [Nanjing Univ. (China)] (ORCID:0000000302313943), Gao, Sinan [China Meteorological Administration, Guangzhou (China)] (ORCID:0000000215379215), Lin, Yuhao [Nanjing Univ. of Information Science and Technology (China)]. 2025-03-25. Implementation and Evaluation of Physics-Driven Dynamic Entrainment-Mixing Parameterization in a Climate Model and Its Impact on Low-Cloud Simulation. https://doi.org/10.1029/2024jd041918

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Water4Energy Step-1 Band-M Ready-to-Train Samples for TVA Weeks-to-Years Prediction, Version 0

AI-ready Band-M (monthly) labelled training pack for the Water4Energy Genesis Task-1 project on weeks-to-years prediction of Tennessee Valley temperature and precipitation. The deposit includes leakage-aware issue-time samples (samples_M_v0.nc; N=486), train-only scalers, issue-time split table, supporting monthly panels, and Python generation scripts to recreate the pack from the companion Tier-1 raw observation collection (https://doi.org/10.13139/ORNLNCCS/3398576). Each sample pairs a 12-month lookback of teleconnection indices and SST box anomalies with TVA-mean ERA5 anomaly targets (t2m, tp, msl) at leads 1–3 months.

54 ENVIRONMENTAL SCIENCES

Multi-Angle Snowflake Camera, particle analysis

The c1 level data product for the Mutli-Angle Snowflake Camera contains snowflake fall speeds and particle size, among other analysis for images associated with each hydrometeor.

54 ENVIRONMENTAL SCIENCES

Multi-Angle Snowflake Camera, time bins

The c1 level data product for the Mutli-Angle Snowflake Camera contains snowflake fall speeds and particle size, among other analysis for images associated with each hydrometeor.

54 ENVIRONMENTAL SCIENCES