Parameterization Interactions in Global Aquaplanet Simulations

Abstract

Global climate simulations rely on parameterizations of physical processes that have scales smaller than the resolved ones. In the atmosphere, these parameterizations represent moist convection, boundary layer turbulence and convection, cloud microphysics, longwave and shortwave radiation, and the interaction with the land and ocean surface. These parameterizations can generate different climates involving a wide range of interactions among parameterizations and between the parameterizations and the resolved dynamics. To gain a simplified understanding of a subset of these interactions, we perform aquaplanet simulations with the global version of the Weather Research and Forecasting (WRF) model employing a range (in terms of properties) of moist convection and boundary layer (BL) parameterizations. Significant differences are noted in the simulated precipitation amounts, its partitioning between convective and large‐scale precipitation, as well as in the radiative impacts. These differences arise from the way the subcloud physics interacts with convection, both directly and through various pathways involving the large‐scale dynamics and the boundary layer, convection, and clouds. A detailed analysis of the profiles of the different tendencies (from the different physical processes) for both potential temperature and water vapor is performed. While different combinations of convection and boundary layer parameterizations can lead to different climates, a key conclusion of this study is that similar climates can be simulated with model versions that are different in terms of the partitioning of the tendencies: the vertically distributed energy and water balances in the tropics can be obtained with significantly different profiles of large‐scale, convection, and cloud microphysics tendencies.

A Unified Eddy-Diffusivity/Mass-Flux Approach for Modeling Atmospheric Convection

“…A number of attempts to merge the boundary layer and shallow convection parameterizations have been documented in the literature (e.g., Lock et al 2000;Hourdin et al 2002;Golaz et al 2002a,b;Cheinet and Teixeira 2003;Suselj et al 2012Suselj et al , 2013Suselj et al , 2014Angevine et al 2018;Suselj et al 2019). One such approach, which has become increasingly popular, is based on the eddydiffusivity/mass-flux (EDMF) approximation (Siebesma and Teixeira 2000;Teixeira and Siebesma 2000;Soares et al 2004;Angevine 2005;Siebesma et al 2007;Rio and Hourdin 2008;Pergaud et al 2009;Neggers et al 2009;Neggers 2009;Witek et al 2011a,b;Suselj et al 2012Suselj et al , 2013Neggers 2015;Bhattacharya et al 2018;Tan et al 2018;Suselj et al 2019). Several attempts to combine shallow and deep convection within a consistent framework have also been undertaken (Bechtold et al 2001;Kuang and Bretherton 2006;Hohenegger and Bretherton 2011;D'Andrea et al 2014).…”

Empirical values and assumptions in the convection schemes of numerical models

“…Two different approaches have been proposed that unify the PBL as well as shallow and deep convection. Those approaches are the so-called EDMF framework (e.g., Hourdin et al, 2002;Köhler et al, 2011;Hourdin et al, 2013;Bhattacharya et al, 2018) and third-order turbulent schemes (e.g., H. Guo et al, 2014Guo et al, , 2015. Parameterizations that account for the coexistence of more numerous cloud types within a model grid cell include the use of Markov chains considering a certain number of cloud types (Khouider et al, 2010;Dorrestijn et al, 2013b;Pe-ters et al, 2013) or the use of a probability density function (PDF) (e.g., Plant and Craig, 2008;Sakradzija et al, 2016) , among others.…”

Using Megha‐Tropiques satellite data to constrain humidity in regional convective simulations: A northern Australian test case

“…This set of schemes was used as a control run and several other sensitivity experiments were conducted (shown in Table 3) by changing the cumulus and PBL schemes. Different combinations of cumulus and PBL schemes can produce different model climates (Bhattacharya et al ., 2018) with significant differences in low‐level clouds (Lamraoui et al ., 2019).…”

“…For moist processes, several studies have reported the sensitivity of background moisture in the initial conditions affect convection (Nicholls et al ., 1988; Takemi and Satomura, 2000; Roebber et al ., 2002; Gallus et al ., 2005; Weisman et al ., 2008). Likewise, simulations with different combinations of PBL and cumulus schemes have been used to explore the sensitivity of moisture involving large‐scale dynamics, boundary layer and convection (Bhattacharya et al ., 2018; Lamraoui et al ., 2019). Currently, it is unclear if background humidity errors outperform the variability introduced in simulating moist processes from multiple model physics options.…”