Search NASA⌕ Search

DOE OSTI · 2504014

An Improved Convection Parameterization with Detailed Aerosol–Cloud Microphysics for a Global Model

Abstract

Abstract A new microphysical treatment that includes aerosol–cloud interactions and secondary ice production (SIP) mechanisms is implemented in the convection scheme of the Community Atmosphere Model, version 6 (CAM6). The approach is to embed a 1D Lagrangian parcel model in the bulk convective plume of the existing deep convection parameterization. Aerosol activation, growth processes including collision/coalescence, and three processes of SIP mechanisms, two of which are normally overlooked in atmospheric models, are represented in this embedded parcel model. These microphysical processes are treated with a hybrid bin/bulk scheme and a high spatial and temporal resolution for the integration of the embedded parcel in 1D, allowing vertical velocity to determine the microphysical evolution following the in-cloud motion during ascent. Simulations of an observed case (Midlatitude Continental Convective Clouds Experiment) of a mesoscale convective system in Oklahoma, United States, with a single-column model (SCAM) version of CAM, are compared with aircraft in situ and ground-based observations of microphysical properties from the convection and precipitation. Results from the validation show the new microphysical scheme has a good representation of the ice initiation in the bulk convective plume, including the known and empirically quantified pathways of primary and secondary initiation, with benefits for the accuracy of properties of its supercooled cloud liquid. The sensitivity simulations and use of tagging tracers for the validated simulation confirm that the newly included SIP mechanisms are of paramount importance for convective microphysics and can be successfully treated in the global model.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jadav, Arti [Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden] (ORCID:0000000163845957), Waman, Deepak [Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden, Department of Troposphere, Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, Germany], Pant, Chandra Shekhar [Department of Hydro and Renewable Energy, Indian Institute of Technology Roorkee, Roorkee, India], Patade, Sachin [Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden], Gautam, Martanda [Johannes Gutenberg University, Mainz, Germany], Phillips, Vaughan [Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden], Bansemer, Aaron [Dynamical and Physical Meteorology Section, National Center for Atmospheric Research, Boulder, Colorado], Barahona, Donifan [Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland], Storelmov, Trude [Department of Geosciences, University of Oslo, Oslo, Norway]. 2025-01-01. An Improved Convection Parameterization with Detailed Aerosol–Cloud Microphysics for a Global Model. https://doi.org/10.1175/jas-d-23-0175.1

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↗