In clouds containing both liquid and ice that have temperatures between -3C and -8C, liquid droplets collide with large ice crystals, freeze, and shatter, producing a plethora of small ice splinters. This process, known as Hallett-Mossop rime splintering, can cause clouds to reflect less sunlight and to have shorter lifetimes. Here, we use a novel suite of five global cloud-resolving models, which break up the Earth’s atmosphere into columns with 2-4 km horizontal edges, to show that this microscale process has global implications. Simulations that include Hallett-Mossop rime splintering have reduced cumulus cloud cover over the Southern Ocean and reflect 12 Wm^(-2) less sunlight back to space over the same region, better matching satellite observed radiative fluxes. We evaluate simulated clouds using high-resolution visible images from the Himawari satellite, and radar reflectivities and two-dimensional images of cloud particles from the SOCRATES aircraft campaign. Cumulus clouds from simulations with Hallett-Mossop rime splintering included have more realistic cloud morphology, cloud vertical structure and ice crystal properties. We show that Hallett-Mossop rime splintering is an important control on cumulus cloud cover and cloud radiative effects over the Southern Ocean, and that including it in simulations improves model performance. We also demonstrate the key role that global cloud-resolving models can play in detangling the effects of clouds on Earth’s climate across scales, making it possible to translate the behavior of tiny cloud particles (10^(-8) m^2) to their impact on the radiative budget of the massive Southern Ocean basin (10^(14) m^2).

Controls on pristine aerosol over the Southern Ocean (SO) are critical for constraining the strength of global aerosol indirect forcing. Observations of summertime SO clouds and aerosols in synoptically varied conditions during the 2018 SOCRATES aircraft campaign reveal novel mechanisms influencing pristine aerosol-cloud interactions. The SO free troposphere (3-6 km) is characterized by widespread, frequent new particle formation events contributing to much larger concentrations (≥ 1000 mg-1) of condensation nuclei (diameters > 0.01 μm) than in typical sub-tropical regions. Synoptic-scale uplift in warm conveyor belts and sub-polar vortices lifts marine biogenic sulfur-containing gases to free-tropospheric environments favorable for generating Aitken-mode aerosol particles (0.01-0.1 μm). Free-tropospheric Aitken particles subside into the boundary layer, where they grow in size to dominate the sulfur-based cloud condensation nuclei (CCN) driving SO cloud droplet number concentrations (Nd ~ 60-100 cm-3). Evidence is presented for a hypothesized Aitken-buffering mechanism which maintains persistently high summertime SO Nd against precipitation removal through CCN replenishment from activation and growth of boundary layer Aitken particles. Nudged hindcasts from the Community Atmosphere Model (CAM6) are found to underpredict Aitken and accumulation mode aerosols and Nd, impacting summertime cloud brightness and aerosol-cloud interactions and indicating incomplete representations of aerosol mechanisms associated with ocean biology.

Cirrus clouds of various thicknesses and radiative characteristics extend over much of the tropics, especially around deep convection. They can be difficult to observe due to their high altitude and sometimes small optical depths. They are also difficult to simulate in conventional global climate models, which have coarse grid spacings and simplified parameterizations of deep convection and cirrus formation. We investigate the representation of tropical cirrus in global storm-resolving models (GSRMs), which have higher spatial resolution and explicit convection and could more accurately represent cirrus cloud processes. This study uses GSRMs from the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains (DYAMOND) project. The aggregate life cycle of tropical cirrus is analyzed using joint albedo and outgoing longwave radiation (OLR) histograms to assess the fidelity of models in capturing the observed cirrus cloud populations over representative tropical ocean and land regions. The proportions of optically-thick deep convection, anvils, and cirrus vary across models and are reflected in the vertical distribution of cloud cover and top-of-atmosphere radiative fluxes. Model differences in cirrus populations, likely driven by subgrid processes such as ice microphysics, dominate over regional differences between convectively-active tropical land and ocean locations.

We design a new strategy to load-balance high-intensity sub-grid atmospheric physics calculations restricted to a small fraction of a global climate simulation’s domain. We show why the current parallel load balancing infrastructure of CESM and E3SM cannot efficiently handle this scenario at large core counts. As an example, we study an unusual configuration of the E3SM Multiscale Modeling Framework (MMF) that embeds a binary mixture of two separate cloud-resolving model grid structures that is attractive for low cloud feedback studies. Less than a third of the planet uses high-resolution (MMF-HR; sub-km horizontal grid spacing) relative to standard low-resolution (MMF-LR) cloud superparameterization elsewhere. To enable MMF runs with Multi-Domain CRMs, our load balancing theory predicts the most efficient computational scale as a function of the high-intensity work’s relative overhead and its fractional coverage. The scheme successfully maximizes model throughput and minimizes model cost relative to precursor infrastructure, effectively by devoting the vast majority of the processor pool to operate on the few high-intensity (and rate-limiting) HR grid columns. Two examples prove the concept, showing that minor artifacts can be introduced near the HR/LR CRM grid transition boundary on idealized aquaplanets, but are minimal in operationally relevant real-geography settings. As intended, within the high (low) resolution area, our Multi-Domain CRM simulations exhibit cloud fraction and shortwave reflection convergent to standard baseline tests that use globally homogenous MMF-LR and MMF-HR. We suggest this approach can open up a range of creative multi-resolution climate experiments without requiring unduly large allocations of computational resources.

Climate models struggle to accurately represent the highly reflective boundary layer clouds overlying the remote and stormy Southern Ocean. We use in-situ aircraft observations from the Southern Ocean Clouds, Radiation and Aerosol Transport Experimental Study (SOCRATES) to evaluate Southern Ocean clouds in a cloud-resolving large-eddy simulation (LES) and two coarse resolution global atmospheric models, the CESM Community Atmosphere Model (CAM6) and the GFDL global atmosphere model (AM4), run in a nudged hindcast framework. We develop six case studies from SOCRATES data which span the range of observed cloud and boundary layer properties. For each case, the LES is run once forced purely using reanalysis data (‘ERA5-based’) and once strongly nudged to an aircraft profile (‘Obs-based’). The ERA5-based LES can be compared with the global models, which are also nudged to reanalysis data, and is better for simulating cumulus. The Obs-based LES closely matches an observed cloud profile and is useful for microphysical comparisons and sensitivity tests, and simulating multi-layer stratiform clouds. We use two-moment Morrison microphysics in the LES and find that it simulates too few frozen particles in clouds occurring within the Hallett-Mossop temperature range. We modify the Hallett-Mossop parameterization so that it activates within boundary layer clouds and we achieve better agreement between observed and simulated microphysics. The nudged GCMs achieve reasonable supercooled liquid water dominated clouds in most cases but struggle to represent multi-layer stratiform clouds and to maintain liquid water in cumulus clouds. CAM6 has low droplet concentrations in all cases and underestimates stratiform cloud-driven turbulence.

Cirrus control the longwave radiative budget of the tropics. For the first time, we quantify the variability in cirrus properties and longwave cloud radiative effects (CREs) that arises from using different bulk ice microphysical parameterizations within a single global storm-resolving model. We run five-day meteorologically-nudged simulations with four commonly-used microphysics schemes (M2005, Thompson, P3 and SAM1MOM) and evaluate them with satellite products and in situ observations. Tropical average longwave CRE varies over 20 W m$^{-2}$ between schemes. Within the Thompson scheme, rapid autoconversion of cloud ice to snow leads to deficient anvil cirrus even with radiatively active snow. SAM1MOM, which uses saturation adjustment for cloud ice, also has deficient anvil cirrus. M2005 and P3 simulate cirrus with realistic frozen water path, and P3 best reproduces observed longwave CRE. Even in those schemes, ice crystal number concentrations commonly hit limiters and lack the observed variability and dependence on frozen water content.

The extension of a cloud-resolving model, the System for Atmospheric Modeling (SAM), to global domains is described. The resulting global model, gSAM, is formulated on a latitude-longitude grid. It uses an anelastic dynamical core with a single reference profile (as in SAM), but its governing equations differ somewhat from other anelastic models. For quasi-hydrostatic flows, they are isomorphic to the primitive equations in pressure coordinates, but with the globally uniform reference pressure playing the role of actual pressure. As a result, gSAM can exactly maintain steady zonally symmetric baroclinic flows that have been specified in pressure coordinates, produces accurate simulations when initialized or nudged with global reanalyses, and has a natural energy conservation equation, despite the drawbacks of using the anelastic system to model global scales. gSAM employs a novel treatment of topography using a type of immersed boundary method, the Quasi-Solid Body Method (QSBM), where the instantaneous flow velocity is forced to stagnate in grid cells inside prescribed terrain. The results of several standard tests designed to evaluate accuracy of global models with and without topography as well as results from real-Earth simulations are presented.

Climate models rely on parameterizations of a variety of processes in the atmospheric physics, but a common concern is that the temporal resolution is too coarse to consistently resolve the behavior that individual parameterizations are designed to capture. This study examines timescales numerically derived from the Morrison-Gettelman (MG2) microphysics as implemented within the Energy Exascale Earth System Model, version 1 (E3SMv1). Numerically-relevant timescales in MG2 are derived by computing the eigenspectrum of its Jacobian. These timescales are found to often be smaller than the default 5 min timestep used for MG2. The fast timescales are then heuristically connected to individual microphysics processes. By substepping a few particular rain processes within MG2, the time discretization error for those processes was significantly reduced with minimal additional expense to the overall microphysics. While this improvement has a substantial effect on the target processes and on the vertical distribution of stratiform-derived rain within E3SMv1, the overall model climate is found to not be sensitive to MG2 time step.

Due to limited resolution and inaccurate physical parameterizations, weather and climate models consistently develop biases compared to the observed atmosphere. These biases are problematic for forecasting on timescales from medium-range weather to centennial-scale climate. Using the FV3GFS model at coarse resolution, we propose a method of machine learning corrective tendencies from a hindcast simulation nudged towards an observational analysis. We show that a random forest can predict the nudging tendencies from this hindcast simulation using only the model state as input. This random forest is then coupled to FV3GFS, adding corrective tendencies of temperature, specific humidity and horizontal winds at each timestep. The coupled model shows no signs of instability in year-long simulations and has significant reductions in short-term forecast error for 500hPa height, surface pressure and near-surface temperature. Furthermore, the root mean square error of the annual-mean precipitation is reduced by about 20%.

Pervasive cirrus clouds in the upper troposphere and tropical tropopause layer (TTL) influence the climate by altering the top-of-atmosphere radiation balance and stratospheric water vapor budget. These cirrus are often associated with deep convection, which global climate models must parameterize and struggle to accurately simulate. By comparing high-resolution global storm-resolving models from the Dynamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains (DYAMOND) intercomparison that explicitly simulate deep convection to satellite observations, we assess how well these models simulate deep convection, convectively generated cirrus, and deep convective injection of water into the TTL over representative tropical land and ocean regions. The DYAMOND models simulate deep convective precipitation, organization, and cloud structure fairly well over land and ocean regions, but with clear intermodel differences. All models produce frequent overshooting convection whose strongest updrafts humidify the TTL and are its main source of frozen water. Inter-model differences in cloud properties and convective injection exceed differences between land and ocean regions in each model. We argue that global storm-resolving models can better represent tropical cirrus and deep convection in present and future climates than coarser-resolution climate models. To realize this potential, they must use available observations to perfect their ice microphysics and dynamical flow solvers.

Global atmospheric ‘storm-resolving’ models with horizontal grid spacing of less than 5~km resolve deep cumulus convection and flow in complex terrain. They promise to be reference models that could be used to improve computationally affordable coarse-grid global climate models across a range of climates, reducing uncertainties in regional precipitation and temperature trends. Here, machine learning of nudging tendencies as functions of column state is used to correct the physical parameterization tendencies of temperature, humidity, and optionally winds, in a real-geography coarse-grid model (FV3GFS with a 200 km grid) to be closer to those of a 40-day reference simulation using X-SHiELD, a modified version of FV3GFS with a 3 km grid. Both simulations specify the same historical sea-surface temperature fields. This methodology builds on a prior study using a global observational analysis as the reference. The coarse-grid model without machine learning corrections has too little cloud, causing too much daytime heating of land surfaces that creates excessive surface latent heat flux and rainfall. This bias is avoided by learning downwelling radiative flux from the fine-grid model. The best configuration uses learned nudging tendencies for temperature and humidity but not winds. Neural nets slightly outperform random forests. Forecasts of 850 hPa temperature gain 18 hours of skill at 3-7 day leads and time-mean precipitation patterns are improved 30% by applying the ML correction. Adding machine-learned wind tendencies improves 500 hPa height skill for the first five days of forecasts but degrades time-mean upper tropospheric temperature and zonal wind patterns thereafter. The figure shows maps of 30-day time-mean precipitation pattern difference from the fine-grid reference for prognostic simulations: (a) 200 km baseline(no machine learning correction) (b) Using random forest correction and (c) neural net correction for temperature. humidity and surface radiation corrections. RMSE is the root mean squared precipitation difference from the reference, which is 30% less for the two machine-learning corrected simulations compared to the baseline. (d) Bar charts of the land-mean, ocean-mean and global-mean precipitation biases for these three configurations, showing the machine-learning corrected simulations remove a high bias of land surface precipitation in the baseline simulation.

Sub-kilometer processes are critical to the physics of aerosol-cloud interaction but have been dependent on parameterizations in global model simulations. We thus report the strength of aerosol-cloud interaction in the Ultra-Parameterized Community Atmosphere Model (UPCAM), a multiscale climate model that uses coarse exterior resolution to embed explicit cloud resolving models with enough resolution (250-m horizontal, 20-m vertical) to quasi-resolve sub-kilometer eddies. To investigate the impact on aerosol-cloud interactions, UPCAMâ\euro™s simulations are compared to a coarser multi-scale model with 3 km horizontal resolution. UPCAM produces cloud droplet number concentrations ($N_\mathrm{d}$) and cloud liquid water path (LWP) values that are higher than the coarser model but equally plausible compared to observations. Our analysis focuses on the Northern Hemisphere midlatitude oceans, where historical aerosol increases have been largest. We find similarities in the overall radiative forcing from aerosol-cloud interactions in the two models, but this belies fundamental underlying differences. The radiative forcing from increases in LWP is weaker in UPCAM, whereas the forcing from increases in $N_\mathrm{d}$ is larger. Surprisingly, the weaker LWP increase is not due to a weaker increase in LWP in raining clouds, but a combination of weaker increase in LWP in non-raining clouds and a smaller fraction of raining clouds in UPCAM. The implication is that as global modeling moves towards finer than storm-resolving grids, nuanced model validation of ACI statistics conditioned on the existence of precipitation and good observational constraints on the baseline probability of precipitation will become key for tighter constraints and better conceptual understanding.