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

Journal Article · 10 January 2024 · Journal of Advances in Modeling Earth Systems

DOI: https://doi.org/10.1029/2023MS003748 · OSTI ID:2281034

Stanford, McKenna W.  ^[1]; Varble, Adam C. ^[2]; Morrison, Hugh  ^[3]

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1. Department of Atmospheric Sciences University of Utah Salt Lake City UT USA, Now at Center for Climate Systems Research Earth Institute Columbia University New York City NY USA
2. Pacific Northwest National Laboratory Richland WA USA
3. National Center for Atmospheric Research Boulder CO USA

Abstract A stochastic horizontal subgrid‐scale mixing scheme is evaluated in ensemble simulations of a tropical oceanic deep convection case using a horizontal grid spacing (Δ _h ) of 3 km. The stochastic scheme, which perturbs the horizontal mixing coefficient according to a prescribed spatiotemporal autocorrelation scale, is found to generally increase mesoscale organization and convective intensity relative to a non‐stochastic control simulation. Perturbations applied at relatively short autocorrelation scales induce differences relative to the control that are more systematic than those from perturbations applied at relatively long scales that yield more variable outcomes. A simulation with mixing enhanced by a constant factor of 4 significantly increases mesoscale organization and convective intensity, while turning off horizontal subgrid‐scale mixing decreases both. Total rainfall is modulated by a combination of mesoscale organization, areal coverage of convection, and convective intensity. The stochastic simulations tend to behave more similarly to the constant enhanced mixing simulation owing to greater impacts from enhanced mixing as compared to reduced mixing. The impacts of stochastic mixing are robust, ascertained by comparing the stochastic mixing ensembles with a non‐stochastic mixing ensemble that has grid‐scale noise added to the initial thermodynamic field. Compared to radar observations and a higher resolution Δ _h  = 1 km simulation, stochastic mixing seemingly degrades the simulation performance. These results imply that stochastic mixing produces non‐negligible impacts on convective system properties and evolution but does not lead to an improved representation of convective cloud characteristics in the case studied here.

• Research Organization: ARM Data Center, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE; USDOE Office of Science (SC), Biological and Environmental Research (BER)
• Grant/Contract Number: AC05-76RL01830; SC0016476; SC0020104
• OSTI ID: 2281034
• Report Number(s): PNNL-SA-183931; e2023MS003748
• Journal Information: Journal of Advances in Modeling Earth Systems, Journal Name: Journal of Advances in Modeling Earth Systems Journal Issue: 1 Vol. 16; ISSN 1942-2466
• Publisher: American Geophysical Union (AGU)Copyright Statement
• Country of Publication: United States
• Language: English