Mixed-phase clouds modulate the water and energy cycles of high-latitude regions, yet their liquid-ice phase partitioning has long been poorly simulated in climate models. Here, simulations of Arctic mixed-phase clouds by the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) are assessed against large-eddy simulations, satellite data, and ground-based observations during the Cold-Air Outbreaks in the Marine Boundary Layer Experiment field campaign. SCREAM simulates nearly completely frozen clouds, which is attributed largely to the unreasonably strong Wegener-Bergeron-Findeisen (WBF) process that converts liquid to ice excessively and partly to the early over-abundant ice production at cold temperatures from a temperature-deterministic deposition ice nucleation scheme. Assuming no subgrid variation for the WBF process in the original formulation particularly conflicts with the instantaneous saturation adjustment assumption in the condensation scheme that assumes subgrid variability, leading to exaggerated WBF process rates. A proposed simple physically-based improvement on the treatment of subgrid cloud overlap substantially increases supercooled liquid water content and notably improves cloud-top phase partitioning, aligning better with observations. Improvement of supercooled liquid water content also converges with increasing horizontal resolution. The deposition ice nucleation scheme is found responsible for a falsely-produced ice cloud aloft that is not observed, biasing the simulated cloud radiative effects and top-of-atmosphere radiative fluxes. This study identifies key deficiencies in cloud parameterizations that continue to challenge convection-permitting models.

Abstract A process‐oriented calibration framework is developed for the Simplified Higher‐Order Closure (SHOC) turbulence scheme in DOE's Simple Cloud Resolving E3SM Atmospheric Model (SCREAM). This framework leverages machine learning surrogates and observational constraints to efficiently calibrate SHOC adjustable parameters across two convective regimes: clear‐sky dry convective boundary layer and fair‐weather shallow cumulus clouds from ARM observations. We use perturbed‐parameter ensembles of a doubly periodic version of SCREAM to train surrogates and apply Markov Chain Monte Carlo sampling guided by cost functions based on benchmarking large‐eddy simulations and observations to identify optimized parameter sets that perform well in both regimes. The calibrated SHOC parameters substantially improve boundary‐layer turbulence and cloud boundaries, and modeled cloud fraction and radiative effects align better with observations than the default. These results demonstrate that combining multiple process‐specific convective regimes with machine‐learning surrogates can reduce parametric uncertainties and yield a model more faithful to cloud–turbulence interactions.

Abstract Aerosol radiative effects are the largest uncertainty in anthropogenic forcing estimates due to challenges in accurately representing aerosol properties and aerosol‐cloud interactions. In this study, we implement the Advanced Particle Microphysics module (APM) into the Energy Exascale Earth System Model (E3SMv3) to represent evolving size‐resolved aerosol types and mixing state progression. We show E3SMv3‐APM represents well key variations in the sulfate‐nitrate‐ammonium system, on the annual‐mean, with spatial correlations of 0.86, 0.47 and 0.77, respectively, to observed mass concentrations across the US. However, a substantial bias in wintertime nitrate and ammonium is apparent, which is a common bias within global aerosol models. Observed cloud condensation nuclei (CCN) and condensation nuclei concentrations are used to evaluate simulated aerosol microphysical properties. The model successfully captures CCN variability in regions dominated by both natural and anthropogenic sources (normalized mean bias: −0.149; correlation: 0.76). Simulated aerosol optical depth reflects MODIS and AERONET observations. Pre‐industrial (PI) to present‐day (PD) simulations show H 2 SO 4 and NH 3 concentrations rising by factors of 1.1 and 4.5, and a 14‐fold nucleation rate increase at the surface. For the PD simulation, mid‐latitude land regions exhibit enhanced new‐particle formation and growth, while oceanic accumulation‐mode increases reflect long‐range transport and in‐cloud chemistry. The modeled present‐day aerosol direct radiative effect is −0.29 W m −2 , while shortwave and longwave cloud radiative effects are −1.54 W m −2 and 0.15 W m −2 , respectively. Parameterizations that influence autoconversion efficiency and cloud‐droplet number limits play dominant roles in driving the shortwave cloud radiative effect differences between E3SMv3‐APM and E3SMv3.

The planetary boundary layer (PBL) is a critical interface between Earth's surface and atmosphere, influencing atmospheric convection, weather patterns, and air quality. Recognized by the 2017 National Academies of Sciences, Engineering, and Medicine Earth Science Decadal Survey as an Incubation Targeted Observable, high-quality and effective observation of the PBL has become a priority. This paper reviews surface-based and satellite remote sensing techniques for characterizing PBL features, including PBL height (PBLH), boundary layer thermodynamics, turbulence, and boundary layer clouds (BLCs). These elements are treated as interconnected aspects of the PBL system, while recognizing that different instruments retrieve different physical manifestations of the PBL. The review summarizes recent advances and limitations in Micro-Pulse Lidar (MPL), Doppler lidar, Raman lidar, Differential Absorption Lidar (DIAL), ceilometers, wind profilers, GNSS Radio Occultation, radar, and hyperspectral sounders. These observations help trace dynamic processes within the PBL and link PBL structure to broader weather and climate processes. The review also highlights persistent observational gaps over oceans, remote land, and polar regions, where continuous surface-based profiling is sparse. By comparing instrument capabilities, practical limitations, and interpretation issues, this review emphasizes the need for integrated remote sensing approaches and careful definition of the retrieved PBL quantities.

Accurately simulating convective processes in complex terrain remains a critical challenge for global storm-resolving models (GSRMs). This study systematically evaluates moist convective biases in the Regionally Refined Mesh configuration of the U.S. Department of Energy Simple Cloud-Resolving E3SM Atmosphere Model (RRM-SCREAM) using comprehensive observations and large-eddy simulations from the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) campaign in the mountainous area of central Argentina. Comparisons of simulations with high-resolution observations and reanalysis data indicate that RRM-SCREAM effectively captures large-scale meteorological patterns, including regional atmospheric gradients and diurnal variability. However, RRM-SCREAM disproportionately produces smaller precipitation clusters referred to as "popcorn convection," and exaggerated rainfall intensities compared to observations and reference models. Detailed examination of a representative orographic shallow-to-deep convective transition case shows that RRM-SCREAM delays initial shallow convection growth due to lower-tropospheric dryness and sustained convective inhibition, but once triggered, deep convection becomes overly vigorous with excessively strong vertical velocities and elevated cloud ice content, linked to a thermodynamic structure characterized by suppressed low-level moistening and excessive upper-level moisture retention. Our results highlight specific deficiencies in the model representation of convective vertical velocity, cloud microphysical processes, and convective precipitation organization within RRM-SCREAM. Addressing these biases is essential for improving the predictions of convective clouds and precipitation in the global high-resolution atmospheric models.

Abstract. Simulating Liquid Water Path (LWP) during stratocumulus-to-cumulus transition (SCT) remains challenging for storm-resolving models, with biases varying across cloud regimes. We use the storm-resolving DP-EAMxx model and a perturbed-parameter ensemble of three warm-rain microphysical parameters to investigate LWP biases during an SCT event observed in the MAGIC field campaign. Gaussian process emulators trained on observation-derived metrics of mean LWP bias and LWP decorrelation timescale bias are used to identify low-bias parameter combinations within the explored parameter space. While similar parameter constraints are obtained for the stratocumulus (Sc) and transition (Tr) phases, the low-bias parameter combinations for the cumulus (Cu) phase differ substantially, indicating requirement of a stronger reduction in autoconversion and accretion rates for a given prescribed droplet number concentration. Using an overlapping low-bias parameter set from the Sc and Tr phases, the mean LWP bias improves from −31 and −22 g m−2 to −1 and 3 g m−2 in the Sc and Tr phases, respectively, but degrades in the Cu phase. An independent XGBoost model with SHAP attribution, trained using DP-EAMxx-simulated process-level diagnostics and meteorological state, supports the emulator sensitivities: LWP bias in Sc is strongly associated with warm-rain microphysics, whereas dynamical, radiative, and thermodynamic influences become more prominent in Tr and Cu. These results show where a limited set of parameters is effective in improving model performance and where additional sources of uncertainty likely need to be considered across regimes. More broadly, we demonstrate a proof-of-concept observation-constrained framework for the diagnosis of storm-resolving model bias.

Abstract. Process-level modeling is central to diagnosing and improving atmospheric parameterizations in Earth system models, yet continental single-column and convection-permitting process configurations are often run with prescribed surface fluxes, limiting realism and consistency with fully coupled global simulations. Here we develop, document, and demonstrate a reproducible workflow to enable fully interactive land–atmosphere coupling in both the E3SM single-column model (SCM) and the doubly periodic configuration of SCREAM (DP-SCREAM). The approach leverages a multi-year offline-forced E3SM/ELM integration to provide spun-up land initial conditions and introduces an offline preprocessing method that generates DP-SCREAM–compatible land restart files by extracting and replicating the appropriate ELM grid cell (including its landunit/column/PFT hierarchy) across the doubly periodic domain, avoiding intrusive modifications to ELM I/O. Using daily-initialized two-day hindcast integrations, we apply this framework to two years over GoAmazon and ten years over the Southern Great Plains, and to targeted short process-level cases (CASS and GoAmazon single/double pulse). Interactive land coupling is required to reproduce key near-surface thermodynamic biases characteristic of global coupled simulations, including warm 2-m temperature biases and the elimination of spurious persistent nocturnal fog layers that arise in prescribed-flux configurations. In contrast, prominent cloud and convection process-level biases identified previously (i.e. insufficient deepening of shallow convection, deficits of mid-level congestus, and weak convective organization) are largely insensitive to prescribed versus interactive surface flux treatments, indicating that they are driven primarily by atmospheric model physics and/or dynamics. These results provide both a community-ready capability for coupled land process-level simulations in E3SM and practical guidance on when interactive versus prescribed surface fluxes are appropriate for process-level model evaluation and development.

Global Storm-Resolving Models (GSRMs) are becoming increasingly vital for advancing climate modeling and improving the prediction of extreme weather events. Houston, a coastal region frequently affected by deep convective storms, offers an ideal setting to evaluate the ability of GSRMs to simulate deep convection. This study assesses the performance of the Doubly Periodic Simple Cloud-Resolving E3SM (Energy Exascale Earth System Model) Atmosphere Model (DP-SCREAM) using observations from the TRacking Aerosol Convection interactions ExpeRiment (TRACER) campaign. DP-SCREAM effectively reproduces the diurnal cycles of clouds and precipitation, demonstrating much greater skill than the E3SM single column model. The DP-SCREAM is demonstrated to be applicable to coastal regions, partially due to the forcing data sets already capturing the influence of breezes. DP-SCREAM also replicates biases persistent in the global version of SCREAM: the underrepresentation of boundary layer shallow clouds, a lack of mid-level congestus clouds, and the popcorn convection, characterized by small and disorganized convective cells generating the strongest precipitation. To investigate these issues, two sensitivity experiments were conducted: increasing the mixing length and scaling up the buoyancy flux within the Simplified Higher Order Closure scheme. Increasing the mixing length improved mid-level congestus representation and reduced unrealistic early morning fog occurrence. Enhancing buoyancy flux only marginally improved the bias of underproduced big convective cells. An additional resolution sensitivity test at 0.5 km grid spacing demonstrated that a refined horizontal resolution alone is insufficient to resolve these biases.

Pyrocumulonimbus (pyroCb) clouds, driven by extreme fires under favorable meteorological conditions, can inject smoke into the stratosphere at magnitudes comparable to those of moderate volcanic eruptions, potentially altering the global radiative balance and atmospheric composition. However, simulating pyroCb is particularly challenging in Earth system models. Using the Energy Exascale Earth System Model (E3SM), we developed a novel global multiscale framework to model pyroCb events in California, which includes a high-resolution fire radiative power time series, a one-dimensional plume-rise parameterization, a fire-induced vertical water vapor transport scheme, and a surface wildfire sensible heat flux representation. Our simulation successfully reproduces many pyroCb features, including cloud height, spatiotemporal evolution, and convective intensity in comparison with satellite and ground-based observations. Sensitivity experiments show that realistic pyroCb simulation depends on vertical water vapor transport. These advances provide a basis for future exploration of pyroCb impacts at regional and global scales within climate models.

To investigate the environmental factors controlling the onset and maintenance of afternoon precipitation over tropical rainforests, two contrasting cases are created for large eddy simulations (LES) using GoAmazon observations: one features a single‐pulse rain dissipating quickly in earlier afternoon, while the other shows double pulses lasting until evening. For these two specific cases under consideration, LES confirms that early‐morning relative humidity dominates afternoon rain patterns. The attributed impacts are distinct: moisture determines the rain onset timing while temperature affects the peak rain intensity. Single‐pulse day observes one round of strong precipitation and cold pools, which further suppresses convection. On double‐pulse day, the first precipitation peak results from an intermediate development of congestus clouds, whose detrainment leads to a gradual moistening of the lower‐to‐middle troposphere. This behavior of convection favors a second pulse of stronger precipitation with more convective organization, whose development decouples from surface fluxes and sustains until evening.

The accurate representation of interactions between clouds and planetary boundary layer (PBL) is a persistent challenge in climate models, critical for simulating surface energy budget. The emergence of kilometer‐grid‐scale global storm resolving models (GSRMs) offers the potential for enhanced details of PBL processes in these complex interactions. This study evaluates the representation of PBL‐coupled and decoupled clouds in nine GSRM simulations against extensive ground‐based observations by the Department of Energy Atmospheric Radiation Measurement (ARM) program, across six sites encompassing diverse regimes such as marine and continental environments in tropics and midlatitude. By differentiating coupling based on the relative positions between cloud bases and PBL tops, our analysis focuses on the simulation of PBL height, cloud frequency, position and vertical extent. The GSRMs generally exhibit commendable agreements with observed cloud structures and PBL diurnal cycles across different ARM sites. In contrast to the relatively consistent representation of decoupled clouds, discrepancies exist between the simulated and the observed coupled clouds, particularly in areas of intense convection, for example, over tropical rainforests and mountainous regions. These biases are probably associated with the models' tendency to underestimate the boundary layer humidity and the frequency of coupled clouds within different ranges of PBL heights. This study underscores the importance for continuous improvements in the representation of boundary layer and convection within these global kilometer‐grid‐scale models.

This study employs an explainable machine learning (ML) framework (XGBoost‐SHapley Additive exPlanations analysis) to investigate controlling factors on cloud liquid water path (LWP) using EPCAPE observations near the California coast. Aerosols are found to be the dominant factor explaining LWP variability, surpassing meteorological factors (MFs). By isolating aerosol effects from meteorological influences, the ML reveals a negative linear relationship between LWP and cloud droplet number concentration ( N d ) in log space, likely driven by entrainment drying via evaporation‐entrainment feedback. This aligns with the negative regime of the inverted‐V relationship reported in previous studies, while no positive LWP responses are found due to a limited number of precipitating cases in EPCAPE. Furthermore, the sensitivity of LWP to N d shows a non‐linear dependence on MFs like moisture contrast between surface and free troposphere and lower‐tropospheric stability. This occurs due to the interplay between the MFs' direct effects on entrainment drying and indirect effects through LWP adjustments.

Abstract This study assesses a 40‐day 3.25‐km global simulation of the Simple Cloud‐Resolving E3SM Model (SCREAMv0) using high‐resolution ground‐based observations from the Atmospheric Radiation Measurement (ARM) Green Ocean Amazon (GoAmazon) field campaign. SCREAMv0 reasonably captures the diurnal timing of boundary layer clouds yet underestimates the boundary layer cloud fraction and mid‐level congestus. SCREAMv0 well replicates the precipitation diurnal cycle, however it exhibits biases in the precipitation cluster size distribution compared to scanning radar observations. Specifically, SCREAMv0 overproduces clusters smaller than 128 km, and does not form enough large clusters. Such biases suggest an inhibition of convective upscale growth, preventing isolated deep convective clusters from evolving into larger mesoscale systems. This model bias is partially attributed to the misrepresentation of land‐atmosphere coupling. This study highlights the potential use of high‐resolution ground‐based observations to diagnose convective processes in global storm resolving model simulations, identify key model deficiencies, and guide future process‐oriented model sensitivity tests and detailed analyses.

Accurate simulations of boundary layer cloud processes remain challenging in Earth system modeling. Observations are essential to evaluate and improve models of such processes. This study introduces a comprehensive validation framework for a satellite-based detection algorithm of continental shallow cumulus (ShCu) clouds during the daytime, which was initially developed using ground-based observations of stereo cameras at the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains site (J. Tian, Zhang, Klein, & Schumacher, 2021, , 2022, ). To validate this algorithm, the framework employs ground-based ceilometer measurements from North Alabama (NA) where ShCu populations are prevalent. This study first generates clear-sky surface reflectance maps at NA and identifies ShCu pixels with a detection threshold using Geostationary Operational Environmental Satellite (GOES) reflectance data. The obtained cloud fractions (CFs) are then compared against CFs from a ground-based ceilometer, considering factors such as observed area differences, satellite parallax issue, and systematic biases. We found that with a detection threshold (triangle R) of 0.05, the ShCu detection algorithm is effective for NA, enabling the reproduction of hourly ShCu CFs using GOES. Our framework is straightforward and easily repeatable to evaluate the effectiveness of a triangle R threshold for detecting ShCu clouds in various geographic regions where ceilometers are deployed. This satellite detection of ShCu provides a crucial regional context for ground-based measurements, facilitating the tracking of convection initiation and its coupling with land surface conditions. Integrating localized ground-based and regional satellite data will enhance our ability to conduct thorough studies of cloud morphology and land-atmosphere interactions in North Alabama.

The planetary boundary layer (PBL) height (PBLH) is an important parameter for various meteorological and climate studies. This study presents a multi-structure deep neural network (DNN) model, which can estimate PBLH by integrating the morning temperature profiles and surface meteorological observations. The DNN model is developed by leveraging a rich dataset of PBLH derived from long-standing radiosonde records augmented with high-resolution micro-pulse lidar and Doppler lidar observations. We access the performance of the DNN with an ensemble of 10 members, each featuring distinct hidden-layer structures, which collectively yield a robust 27-year PBLH dataset over the southern Great Plains from 1994 to 2020. The influence of various meteorological factors on PBLH is rigorously analyzed through the importance test. Moreover, the DNN model's accuracy is evaluated against radiosonde observations and juxtaposed with conventional remote sensing methodologies, including Doppler lidar, ceilometer, Raman lidar, and micro-pulse lidar. The DNN model exhibits reliable performance across diverse conditions and demonstrates lower biases relative to remote sensing methods. In addition, the DNN model, originally trained over a plain region, demonstrates remarkable adaptability when applied to the heterogeneous terrains and climates encountered during the GoAmazon (Green Ocean Amazon; tropical rainforest) and CACTI (Cloud, Aerosol, and Complex Terrain Interactions; middle-latitude mountain) campaigns. These findings demonstrate the effectiveness of deep learning models in estimating PBLH, enhancing our understanding of boundary layer processes with implications for improving the representation of PBL in weather forecasting and climate modeling.

To help bridge science topics related to land-atmosphere interactions, we organized a virtual special issue in this journal (Agricultural and Forest Meteorology [AFM]) entitled, "Land-Atmosphere Interactions: Integrating Surface Flux with Boundary Layer Measurements." The motivation for the special issue was driven by existing disciplinary barriers between research areas that all address land-atmosphere interactions. In particular, it addressed research silos between those who study features of the land surface, surface fluxes (including water, energy, and trace gases), atmospheric boundary layer growth and thermodynamics, and atmospheric composition and aerosols. The special issue sought to bring these communities together to integrate multiple observations across the soil-vegetation-atmosphere continuum with the aim of 1) improving broader understanding of land-atmosphere interactions, feedbacks, and coupling, 2) fostering new collaborations between atmospheric and surface flux scientists, and 3) identifying new paths for integrative research. Here, we provide an overview and synthesis of the special issue.

AbstractUnderstanding interactions between low clouds and land surface fluxes is critical to comprehending Earth's energy balance, yet their relationships remain elusive, with discrepancies between observations and modeling. Leveraging long‐term field observations over the Southern Great Plains, this investigation revealed that cloud‐land interactions are closely connected to cloud‐land coupling regimes. Observational evidence supports a dual‐mode interaction: coupled stratiform clouds predominate in low sensible heat scenarios, while coupled cumulus clouds dominate in high sensible heat scenarios. Reanalysis data sets, MERRA‐2 and ERA‐5, obscure this dichotomy owing to a shortfall in representing boundary layer clouds, especially in capturing the initiation of coupled cumulus in high sensible heat scenarios. ERA‐5 demonstrates a relatively closer alignment with observational data, particularly in capturing relationships between cloud frequency and latent heat, markedly outperforming MERRA‐2. Our study underscores the necessity of distinguishing different cloud coupling regimes, essential to the understanding of their interactions for advancing land‐atmosphere interactions.

Abstract The new generation of heterogeneous CPU/GPU computer systems offer much greater computational performance but are not yet widely used for climate modeling. One reason for this is that traditional climate models were written before GPUs were available and would require an extensive overhaul to run on these new machines. In addition, even conventional “high–resolution” simulations don't currently provide enough parallel work to keep GPUs busy, so the benefits of such overhaul would be limited for the types of simulations climate scientists are accustomed to. The vision of the Simple Cloud‐Resolving Energy Exascale Earth System (E3SM) Atmosphere Model (SCREAM) project is to create a global atmospheric model with the architecture to efficiently use GPUs and horizontal resolution sufficient to fully take advantage of GPU parallelism. After 5 years of model development, SCREAM is finally ready for use. In this paper, we describe the design of this new code, its performance on both CPU and heterogeneous machines, and its ability to simulate real‐world climate via a set of four 40 day simulations covering all 4 seasons of the year.