Search Results - "Varble, Adam C."

The role of sub-cloud mesoscale convergence in sculpting convective updraft width and depth.

Authors: Marquis, James N. Varble, Adam C. Feng, Zhe et al.

Source: Journal of the Atmospheric Sciences; 2026, Vol. 83 Issue 4, p1-23, 23p

Influences of the South American Low-Level Jet on the Convective Environment in Central Argentina Using a Convection-Permitting Simulation.

Authors: Sasaki, Clayton R. S. Rowe, Angela K. McMurdie, Lynn A. et al.

Source: Monthly Weather Review; Feb2024, Vol. 152 Issue 2, p629-648, 20p

Large Spatiotemporal Variability in Aerosol Properties over Central Argentina during the CACTI Field Campaign.

Authors: Fast, Jerome D. Varble, Adam C. Fan Mei et al.

Source: Atmospheric Chemistry & Physics Discussions. 5/16/2024, p1-34. 34p.

Evaluation of E3SM Simulated Aerosols and Aerosol‐Cloud Interactions Across GCM and Convection‐Permitting Scales.

Authors: Huang, Meng Ma, Po‐Lun Varble, Adam C. et al.

Source: Journal of Advances in Modeling Earth Systems; Dec2025, Vol. 17 Issue 12, p1-21, 21p

Indications of a Decrease in the Depth of Deep Convective Cores with Increasing Aerosol Concentration during the CACTI Campaign.

Authors: Veals, Peter G. Varble, Adam C. Russell, James O. H. et al.

Source: Journal of the Atmospheric Sciences; Mar2022, Vol. 79 Issue 3, p705-722, 18p, 2 Color Photographs, 1 Chart, 9 Graphs, 1 Map

Low-Level Mesoscale and Cloud-Scale Interactions Promoting Deep Convection Initiation.

Authors: Marquis, James N. Varble, Adam C. Robinson, Paul et al.

Source: Monthly Weather Review; Aug2021, Vol. 149 Issue 8, p2473-2495, 23p, 2 Diagrams, 6 Graphs, 6 Maps

Utilizing a Storm-Generating Hotspot to Study Convective Cloud Transitions: The CACTI Experiment.

Authors: Varble, Adam C. Nesbitt, Stephen W. Salio, Paola et al.

Source: Bulletin of the American Meteorological Society; Aug2021, Vol. 102 Issue 8, pE1597-E1620, 24p

Prescribing the aerosol effective radiative forcing in the Simple Cloud-Resolving E3SM Atmosphere Model v1.

Authors: Mahfouz, Naser Beydoun, Hassan Mülmenstädt, Johannes et al.

Source: Atmospheric Chemistry & Physics. 2025, Vol. 25 Issue 21, p15105-15120. 16p.

Evaluation of Liquid Cloud Albedo Susceptibility in E3SM Using Coupled Eastern North Atlantic Surface and Satellite Retrievals.

Authors: Varble, Adam C. Po-Lun Ma Christensen, Matt W. et al.

Source: Atmospheric Chemistry & Physics Discussions. 5/23/2023, p1-39. 39p.

Opinion: A Critical Evaluation of the Evidence for Aerosol Invigoration of Deep Convection.

Authors: Varble, Adam C. Igel, Adele L. Morrison, Hugh et al.

Source: Atmospheric Chemistry & Physics Discussions. 5/23/2023, p1-31. 31p.

The Impacts of Rotational Mixing on the Precipitation Simulated by a Convection Permitting Model.

Authors: Hagos, Samson Feng, Zhe Varble, Adam C. et al.

Source: Journal of Advances in Modeling Earth Systems; May2025, Vol. 17 Issue 5, p1-17, 17p

Machine learning reveals strong grid-scale dependence in the satellite Nd–LWP relationship.

Authors: Christensen, Matthew W. Geiss, Andrew Varble, Adam C. et al.

Source: Atmospheric Chemistry & Physics. 2026, Vol. 26 Issue 1, p59-76. 18p.

Aerosol-Induced Closure of Marine Cloud Cells: Enhanced Effects in the Presence of Precipitation.

Authors: Christensen, Matthew W. Peng Wu Varble, Adam C. et al.

Source: Atmospheric Chemistry & Physics Discussions. 11/1/2023, p1-35. 35p.

Evaluation of a Stochastic Mixing Scheme in the Deep Convective Gray Zone Using a Tropical Oceanic Deep Convection Case Study.

Authors: Stanford, McKenna W. Varble, Adam C. Morrison, Hugh

Source: Journal of Advances in Modeling Earth Systems. Jan2024, Vol. 16 Issue 1, p1-23. 23p.

Earth System Model Aerosol–Cloud Diagnostics (ESMAC Diags) package, version 2: assessing aerosols, clouds, and aerosol–cloud interactions via field campaign and long-term observations.

Authors: Tang, Shuaiqi Varble, Adam C. Fast, Jerome D. et al.

Source: Geoscientific Model Development. 2023, Vol. 16 Issue 21, p6355-6376. 22p.

No Warm-Phase Invigoration of Convection Detected during GoAmazon.

Authors: ÖKTEM, RUSEN ROMPS, DAVID M. VARBLE, ADAM C.

Source: Journal of the Atmospheric Sciences; Oct2023, Vol. 80 Issue 10, p2345-2364, 20p

Can GCMs represent cloud adjustments to aerosol–cloud interactions?

Authors: Mülmenstädt, Johannes Ackerman, Andrew S. Fridlind, Ann M. et al.

Source: EGUsphere; 3/28/2024, p1-36, 36p

General circulation models simulate negative liquid water path-–droplet number correlations, but anthropogenic aerosols still increase simulated liquid water path.

Authors: Mülmenstädt, Johannes Gryspeerdt, Edward Dipu, Sudhakar et al.

Source: EGUsphere; 1/9/2024, p1-29, 29p

Can general circulation models (GCMs) represent cloud liquid water path adjustments to aerosol–cloud interactions?

Authors: Mülmenstädt, Johannes Ackerman, Andrew S. Fridlind, Ann M. et al.

Source: Atmospheric Chemistry & Physics. 2024, Vol. 24 Issue 23, p13633-13652. 20p.

Atmospheric Radiation Measurement (ARM) airborne field campaign data products between 2013 and 2018.

Authors: Mei, Fan Comstock, Jennifer M. Pekour, Mikhail S. et al.

Source: Earth System Science Data. 2024, Vol. 16 Issue 11, p5429-5448. 20p.