Idealized large-eddy simulations (LESs) with prescribed heterogeneous land surface heat fluxes are performed to study the impact of the heterogeneity length scale and background wind speed on the development of shallow cumulus and the subsequent transition to congestus/deep convection. We study the impact of land surface heterogeneity in an atmosphere that favors shallow convection but is also conditionally unstable with respect to deeper convection. We find that before the convection transition, larger and thicker shallow cumulus clouds are attached to moisture pools near the PBL top over patches with low evaporative fraction (referred to as “DRY”). This feature is attributable to a surface-induced secondary circulation whose development depends on the heterogeneity size and the background wind speed. With large patches (≥5 km) under zero ambient wind, the secondary mesoscale circulation promotes the vertical transport of moisture forming a moisture pool over DRY patches, while with smaller patches, no such circulation develops. The influence of the background wind on the secondary circulation is strong such that any wind stronger than 2 m s−1 effectively eliminates the impact of surface heterogeneity on the PBL and brings no secondary circulation. This is because the triggered secondary circulation is not strong enough to withstand the imposed background wind. Based on these, we propose two criteria for the convection transition, namely, that the heterogeneity length scale is greater than 5 km and that the background wind speed is less than Uc0, where Uc0 is the near-surface cross-patch wind speed of the secondary circulation under zero background wind for a given patch size and is about 1.5 m s−1 in our cases.

A Simplified Land Model (SLM) that uses a minimalist set of parameters with a single-layer vegetation and multilevel soil structure has been developed distinguishing canopy and undercanopy energy budgets. The primary motivation has been to design a land model for use in the System for Atmospheric Modeling (SAM) cloud-resolving model to study land-atmosphere interactions with a sufficient level of realism. SLM uses simplified expressions for the transport of heat, moisture, momentum, and radiation in soilvegetation system. The SLM performance has been evaluated over several land surface types using summertime tower observations of micrometeorological and biophysical data from three AmeriFlux sites, which include grassland, cropland, and deciduous-broadleaf forest. In general, the SLM captures the observed diurnal cycle of surface energy budget and soil temperature reasonably well, although reproducing the evolution of soil moisture, especially after rain events, has been challenging. The SLM coupled to SAM has been applied to the case of summertime shallow cumulus convection over land based on the Atmospheric Radiation Measurements (ARM) Southern Great Plain (SGP) observations. The simulated surface latent and sensible heat fluxes as well as the evolution of thermodynamic profiles in convective boundary layer agree well with the estimates based on the observations. Sensitivity of atmospheric boundary layer development to the soil moisture and different land cover types has been also examined.

Clouds represent a key uncertainty in future climate projection. While explicit cloud resolution remains beyond our computational grasp for global climate, we can incorporate important cloud effects through a computational middle ground called the Multi-scale Modeling Framework (MMF), also known as Super Parameterization. This algorithmic approach embeds high-resolution Cloud Resolving Models (CRMs) to represent moist convective processes within each grid column in a Global Climate Model (GCM). The MMF code requires no parallel data transfers and provides a self-contained target for acceleration. This study investigates the performance of the Energy Exascale Earth System Model-MMF (E3SM-MMF) code on the OLCF Summit supercomputer at an unprecedented scale of simulation. Hundreds of kernels in the roughly 10K lines of code in the E3SM-MMF CRM were ported to GPUs with OpenACC directives. A high-resolution benchmark using 4600 nodes on Summit demonstrates the computational capability of the GPU-enabled E3SM-MMF code in a full physics climate simulation.

Advances in numerical modeling of cloud dynamics are driving a need for improved land model prediction at convective storm scales. In this study, satellite and ground‐based vegetation remote sensing data were combined with land model experiments to more accurately characterize land surface spatial heterogeneity in the Community Land Model (CLM4.0). The new subgrid classification of plant functional types (PFT) and leaf area index (LAI) enables consistent comparison between models and ground‐based flux measurements in the U.S. southern Great Plains. Errors in vegetation data sets (inferred from comparison between 250 m satellite and ground‐based LAI), while large, had less impact on the simulated characteristics of spatial heterogeneity compared to errors in model representation of surface energy partitioning (between latent and sensible heat flux) and its relationship to LAI. Predicted spatial heterogeneity in surface energy partitioning was enhanced after replacing soil and stomatal resistance parameters with a new set that better predicts the observed relationship to LAI. These modifications increase the number of smaller (mesoscale) dry land patches having higher sensible heat flux. The parameter experiments suggest that vegetation state and processes (transpiration) act to broaden the size spectrum of surface heat flux heterogeneity, which can influence clouds and convective initiation. Moreover, improvements in vegetation input data and model parameters had partially compensating effects on surface flux heterogeneity, indicating the importance of evaluating input data and parameterizations together to improve prediction at higher spatial resolutions.

Many climate models exhibit a dry and warm bias over the central U.S during the summer months, including the Energy Exascale Earth System Model (E3SM) and its multiscale Modelling Framework (MMF) configuration. Understanding the causes of this bias is important to shine a light on this common model error and reduce the uncertainty in future projections. In this study, we use E3SMv2 and E3SM-MMF to assess how parameterized and resolved convection affect temperature and precipitation biases over the Southern Great Plains site of the Atmospheric Radiation Measurement program. Both configurations overestimate near-surface temperature and underestimate precipitation at the ARM SGP site. The bias is associated with a lack of low-level clouds during days without precipitation and too much incoming solar radiation causing the surface to warm. Low-level cloud fraction in E3SM-MMF during the non-precipitating days is lower in comparison to E3SMv2 and observation, consistent with the larger warm bias. We also find that the underestimated precipitation can be characterized as “too frequent, too weak” in E3SMv2 and “too rare, too intense” in E3SM-MMF. These deficiencies conspire to sustain the warm and dry bias over the central US.