Effects of Explicit Convection on Land Surface Air Temperature and Land‐Atmosphere Coupling in the Thermal Feedback Pathway

Abstract

Simulating and understanding continental temperature extremes is a critical issue in Earth System Modeling. Conventional general circulation models are impaired by imperfect cloud and boundary layer parameterization schemes with implications for potentially unrealistic distributions of atmospheric variables and land-atmosphere coupling signals. In this study we examine a modern version of Superparameterization (SP) in the Community Atmosphere Model v.5 to examine impacts of SP on the characteristics of land surface air temperature, thermal land-atmosphere coupling, and the relationship between them. The results show that SP in the Community Atmosphere Model (SPCAM) improves mean land surface air temperature at daily time scales regionally compared to Community Atmosphere Model 5 and better simulates thermal land-atmosphere coupling (soil moisture-surface air temperature coupling) in several well-known hot spots (e.g., the Great Plains, Sahel, and India). Detailed analysis of regional probability distribution functions reveals how the intrinsic relationships of atmosphere variables, land-atmosphere coupling, and temperature extremes are modified by SP. Regression-type metrics are also used to examine global land-atmosphere coupling, with some expected limitations where multiple coupling regimes coexist temporally. Stratifying results by soil moisture percentile proves illuminating in this regard and reveals that SP frequently amplifies land-atmosphere thermal coupling at the dry end of regional coupling regimes.

Superparameterised cloud effects in the EMAC general circulation model (v2.50) – influences of model configuration

“…The superparameterisation uses a simple fixed conversion rate (see Khairoutdinov and Randall, 2003, Appendix D), whereas the cloud scheme uses the formulation of Beheng (1994). Focusing on this aspect, Suzuki et al (2015) has shown that the distribution of precipitation categories (non-precipitating, drizzle, rain) is dependent on its expression, thereby influencing the precipitation rate. Future studies with SP-EMAC should investigate the onset of precipitation for maritime clouds in more detail or should consider using a two-moment microphysical scheme and its coupling to an aerosol submodel.…”

Summertime Near‐Surface Temperature Biases Over the Central United States in Convection‐Permitting Simulations

“…The motivation is multiple: First and foremost, CPMs can improve the simulations of MCSs and other precipitation (Berg et al., 2015; Feng et al., 2018, 2019; Prein et al., 2017a, 2021; Q. Yang et al., 2017). Furthermore, convection‐permitting capabilities, either conventional (Dai et al., 2021) or through cloud super‐parameterization with embedded cloud‐resolving models (Qin et al., 2018; Sun & Pritchard, 2016, 2018), improve representations of land‐atmosphere coupling over well‐known hotspot regions like the US Great Plains (Koster et al., 2004). Given these improvements, one may surmise that CPMs would improve surface climate representation over Central US during summertime.…”

Sensitivity of land-atmosphere coupling strength to perturbations of early-morning temperature and moisture profiles in the European summer

“…From the technical perspective, the coupling strength is influenced by the choice of the data set used for the investigation (Dirmeyer et al, 2018;Ferguson and Wood, 2011) and, in case of models, their configuration such as parameterization schemes (Chen et al, 2017;Milovac et al, 2016;Pitman et al, 2009), initialization (Santanello Jr. et al, 2019) or model resolution (Hohenegger et al, 2009;Knist et al, 2020;J. Sun & Pritchard, 2018;Jian Sun & Pritchard, 2016;Taylor et al, 2013).…”