Empirical values and assumptions in the convection schemes of numerical models

Abstract

Abstract. Convection influences climate and weather events over a wide range of spatial and temporal scales. Therefore, accurate predictions of the time and location of convection and its development into severe weather are of great importance. Convection has to be parameterized in global climate models and Earth system models as the key physical processes occur at scales much lower than the model grid size. This parameterization is also used in some numerical weather prediction (NWP) models when convection is not explicitly resolved. The convection schemes described in the literature represent the physics by simplified models that require assumptions about the processes and the use of a number of parameters based on empirical values. These empirical values and assumptions are rarely discussed in the literature. The present paper examines these choices and their impacts on model outputs and emphasizes the importance of observations to improve our current understanding of the physics of convection. The focus is mainly on the empirical values and assumptions used in the activation of convection (trigger), the transport and microphysics (commonly referred to as the cloud model), and the intensity of convection (closure). Such information can assist satellite missions focused on elucidating convective processes (e.g., the INCUS mission) and the evaluation of model output uncertainties due to spatial and temporal variability of the empirical values embedded into the parameterizations.

Statistics of Isolated Deep Convection Initiation and Its Relation to Topography in the North China Area

“…Given that simulated DCI, especially in complex terrain, needs improvement (Prein et al, 2015;Villalba-Pradas & Tapiador, 2022), relationships established in this study could better inform convection parameterization schemes. A topographical factor could be set to represent the probability of convection occurrence according to the elevation and relief amplitude of each model grid box.…”

Distinct Mixing Regimes in Shallow Cumulus Clouds

“…Fundamental questions about the origin of the in‐cloud air (Heus et al., 2008; Paluch, 1979), how mixing affects droplet size distribution (Khain & Pinsky, 2018; Prabhakaran et al., 2020; Tölle & Krueger, 2014), and how clouds affect the environmental radiative properties (Eytan et al., 2020; Koren et al., 2007; Marshak et al., 2006) are still open. Moreover, a significant part of the discrepancy between climate model results is attributed to cumulus mixing parameterization (Rio et al., 2019; Villalba‐Pradas & Tapiador, 2022). Hence, a better understanding of the mixing processes can significantly improve our climate understanding.…”

Incorporating the Effect of Large‐Scale Vertical Motion on Convection Through Convective Mass Flux Adjustment in E3SMv2

“…The early studies suggested that the large‐scale, low‐level ascending motion, acting as a mechanic lifting mechanism, can help convection overcome the CIN to reach its LFC, and therefore, this dynamical effect of large‐scale vertical velocity has a positive impact on convective cloud development. Several convection schemes have explicitly incorporated the large‐scale vertical velocity in their trigger functions (see review of Villalba‐Pradas and Tapiador (2022)). Kuo (1974) required that large‐scale vertical velocity at 900‐mb be greater than a threshold to ensure that convective instability can be activated by large‐scale flow.…”