The bulk circulation associated with convective clouds includes not only a region of updraft and cloudy air but also a region of compensating descent and cloud-free air, and horizontal motions coupling these regions. The Kinematic Representation of Non-rotating Updraft Tori (KRoNUT) model is a simple representation of this entire flow. First, the skill of the KRoNUT in representing flows from a high-resolution full-physics simulation of marine tropical convection is compared to various plume representations of convection. Then the KRoNUT is used to construct bulk descriptions of the dry dynamics of isolated and interacting convective circulations under the influence of advection and diffusion only . Cross sections of advective and diffusive tendencies show that while vertical advection of the vertical wind is the most important advective tendency in clouds, the horizontal component of the convective circulation and advection thereof plays a crucial role in the evolution of circulations in the absence of buoyancy. Strong curvature of the flow near the surface and near the updraft core results in locally strong diffusive tendencies that depend on scale. Cross sections of tendencies from the KRoNUT compare favourably to results from the simulation. Interacting circulations are shown to exhibit a wide range of dynamics, with some cases of interactions leading to unique stability of geometric properties of otherwise evolving flows and some leading to geometric clustering of circulation centres.

Fog is a key part of the hydrological cycle in many regions of the world. Coastal fog specifically influences coastal biomes and impacts maritime activities and commerce. Due to complex interactions with landforms, the spatial distributions of coastal fog frequency defy simple characterization. This study is the first to analyze the summertime spatio-temporal heterogeneity of fog along the Oregon coast, a region which depends on coastal fog for shading and as a hydrological input during its warm, dry summers. Direct measures of fog require expensive optical instrumentation that is not broadly deployed. To increase the spatial coverage of in-situ fog records, we used publicly available coastal imagery data sets to detect fog presence at low cost. The coastal imagery consisted of six camera sites located along central and northern Oregon beaches that spanned a 150 km distance. We analyzed the spatio-temporal patterns of this dataset in conjunction with surface weather metrics from the Newport Municipal Airport, which included a visibility sensor for precise fog measurements. We found that the timing of fog occurrence and the overall fog frequency along the coast varied across the locations. Despite this heterogeneity, we found that the average monthly fog frequency at the camera sites had a moderate linear relationship to different metrics of fog and low cloud presence at the airport, suggesting that the airport fog measurements were a reasonable proxy for relative changes in spatially averaged fog frequency. We also found that when there was fog at a camera site, weather conditions at the airport (high relative humidity, low wind speed and lifting condensation level) tended to be supportive of fog formation. However, we found surface weather conditions at the airport were insufficient for real-time fog detection at any particular site along the coast, highlighting the need for more in-situ fog measurements.

Abstract The discussion of radiative cooling in cloud dynamics rarely considers quantities aside from the total radiative cooling over a cloud layer, which is constant for blackbody clouds in a given environment. However, here we demonstrate using idealized radiative transfer calculations that the total cooling in the upper part of the cloud increases with liquid water path up to ~100 g m− 2, well beyond the threshold liquid water path needed for a cloud to be a blackbody. Moreover, the maximum local cooling rate increases with LWP indefinitely. We then show using high-resolution simulations that the modulation of longwave radiative cooling profiles in blackbody clouds alters the dynamics, free tropospheric entrainment rates, liquid water path, and organization of closed cell stratocumulus clouds. We suggest that the role of longwave radiation in blackbody clouds, particularly in the context of aerosol-cloud interactions, should be given more consideration.

This study examines how different structural choices in bulk microphysics schemes impact the simulation of warm rain initiation. A single liquid category (SLC) approach prognosing up to four moments of a single drop size distribution (DSD) is compared to the traditional two-category, two-moment approach with separate DSDs for cloud and rain (four total prognostic variables). Different methods for calculating tendencies of the prognostic variables from drop collision-coalescence are also tested: a discretized numerical-integration approach, machine learning via neural networks, lookup tables, and traditional power law fits. Relative to simulations using a bin microphysics model, SLC gives smaller error overall than the two-category approach when numerical integration is used to calculate the collision-coalescence tendencies for both. Replacing the numerical integration with a pre-computed lookup table reduces computational cost with little loss of accuracy. However, using fitted power laws with SLC to represent the collision-coalescence tendencies substantially reduces accuracy and leads to an order of magnitude increase in error. It is also demonstrated that with SLC, reasonably accurate solutions are obtained using only three prognostic moments, while a two-moment SLC scheme leads to substantial error. Overall, both the choice of prognostic moments (e.g., SLC vs. two-category) and method to calculate the collision-coalescence tendencies are important to consider for minimizing errors in bulk schemes. SLC with a sufficiently detailed calculation of the collision-coalescence tendencies provides accurate solutions for a reasonable computational cost, providing a viable alternative to the traditional two-category, two-moment approach for bulk microphysics.

One common process for marine fog formation is cloud base lowering (CBL), which is frequently observed, for example, off the coast of California and in Canada's Grand Banks, as well as other foggy ocean regions. While previous studies have extensively examined the meteorological controls on CBL fog, its microphysical characteristics have received comparatively less attention. We employ a single-column model to investigate the interplay among aerosols, microphysics, and CBL fog evolution under diverse meteorological conditions. We find that lower aerosol concentrations make fog formation more probable but that if fog does form, fog water concentrations are lower. Lower aerosol concentrations lead to earlier fog formation due to faster gravitational settling of larger droplets, which serves to flux moisture downward. Faster gravitational settling (among other mechanisms at low aerosol concentration) also suppresses entrainment at cloud top, which aids in keeping the liquid water path high. However, faster gravitational settling also limits the fog water concentration through faster liquid deposition to the surface. It is these counteracting influences of gravitational settling that appear to cause both prolonged fog duration and suppressed fog water concentration. The relative strength of these counteracting influences depends on the environmental conditions.

AbstractBulk microphysics schemes continue to face challenges due in part to the necessary simplification of hydrometeor properties and processes that is inherent to any parameterization. In all operational bulk schemes, one such simplification is the division of liquid water into two subcategories (cloud and rain) when predicting the evolution of warm clouds. It was previously found that biases in collisional growth in a bulk scheme with these separate liquid water categories can be mitigated with a unified liquid water category in which cloud and rain are contained within the same category. In this study, we examine the effect of artificially separating the liquid water category on other microphysical processes and in more realistic settings. Both our idealized 1D and 3D results show that a unified category bulk scheme is fundamentally better at predicting the timing and intensity of rain from warm‐phase cumulus clouds compared to a traditional (separate) category bulk scheme. This is because a unified category bulk scheme allows a bimodal distribution to exist within one traditional “rain” category, whereas separate category bulk scheme only have one mode per category. This advantage allows the unified bulk scheme to retain the information of the largest droplets even as they fall through a layer of small raindrops. A separate category bulk scheme fails to represent this bimodal feature in comparison.

Coastal fog occurs along many of the world’s west coast continental environments. It is particularly consequential during summer when an increased frequency of fog co-occurs with the seasonal dryness characteristic of most west coast climate systems, for example, in the Pacific coast of North and South America, the southwestern African coast, and southern coastal Europe. Understanding coastal fog formation and effects has consequences for many disciplines, including the physical (e.g., atmospheric science, oceanography), biological (e.g., biogeography, ecophysiology), and socio-ecological realms (e.g., Indigenous cultural knowledge, public safety, economics). Although research practices differ across disciplines, they share many of the challenges needed to advance fog science. For example, coastal fog remains difficult to reliably monitor when, where, and why it occurs, which adds difficulty to understanding fog’s effects on all facets of the integrated coastal system. These shared challenges provide ripe opportunities for interdisciplinary collaboration, a template with past success in advancing fog-related science that can continue to have success in the future. In this perspectives review, we summarize the current status and frontiers of fog-related science from multiple disciplines, leveraging examples primarily drawn from the Pacific Northwest coastal region of the United States to show how interdisciplinary collaboration is needed to continue to advance our collective understanding of coastal fog formation and effects on west coast environments.

Previous studies have found that low-level Arctic clouds often persist for long periods even in the face of very low surface cloud condensation nuclei (CCN) concentrations. Here, we investigate whether these conditions could occur due to continuous entrainment of aerosol particles from the free troposphere (FT). We use an idealized large eddy simulation (LES) modeling framework, where aerosol concentrations are low in the boundary layer (BL) but increased up to 50× in the free troposphere. We find that the tests with higher tropospheric aerosol concentrations simulated clouds, which persisted for longer and maintained higher liquid water paths (LWPs). This is due to direct entrainment of the tropospheric aerosol into the cloud layer, which results in a precipitation suppression from the increase in cloud droplet number and in stronger cloud-top radiative cooling, which causes stronger circulations maintaining the cloud in the absence of surface forcing. Together, these two responses result in a more well-mixed boundary layer with a top that remains in contact with the tropospheric aerosol reservoir and can maintain entrainment of those aerosol particles. The surface aerosol concentrations, however, remained low in all simulations. The free-tropospheric aerosol concentration necessary to maintain the clouds is consistent with concentrations that are frequently seen in observations.

Data for Sterzinger and Igel (2023) "Simulated Idealized Arctic Cloud Sensitivity to Above Cloud CCN Concentrations" all_data.tar.gz contains the processed horizontally-averaged data used to plot the figures in the paper. data_processing.tar.gz contains the scripts to process the 4-D data output from the model and namelists in https://doi.org/10.5281/zenodo.7991355

The overarching objective of this project was to further our understanding of low-level Arctic cloud behavior in ultra-low aerosol concentration environments. To this end, we used a combination of modeling and analysis of DOE ARM datasets to understand the frequency of aerosol-limited cloud dissipation events, to understand the microphysical processes that lead to aerosol-limited dissipation, and to understand how above-cloud aerosol particles can work to maintain low-level clouds that exist in boundary layers with ultra-low aerosol concentrations. We found that aerosol-limited dissipation is unlikely to occur at the DOE long-term observation facilities at North Slope Alaska and Oliktok Point but possibly does occur at higher latitudes in the Arctic. At these higher latitudes, aerosol concentrations are frequently observed to be higher, often substantially higher, than in the boundary layer. These reservoirs can be sufficient to maintain low-level clouds. When they are insufficient, clouds appear to dissipate due to a depletion of liquid water through precipitation that is reinforced by a shutdown of the dynamical processes that support these clouds.

Bin and bulk schemes are the two primary methods to parameterize cloud microphysical processes. This study attempts to reveal how their structural differences (size-resolved vs. moment-resolved) manifest in terms of cloud and precipitation properties. We use a bulk scheme, the Arbitrary Moment Predictor (AMP), which uses process parameterizations identical to those in a bin scheme but predicts only moments of the size distribution like a bulk scheme. As such, differences between simulations using AMP's bin scheme and simulations using AMP itself must come from their structural differences. In one-dimensional kinematic simulations, the overall difference between AMP (bulk) and bin schemes is found to be small. Full-microphysics AMP and bin simulations have similar mean liquid water path (mean percent difference < 4%), but AMP simulates significantly lower mean precipitation rate (-35%) than the bin scheme due to slower precipitation onset. Individual processes are also tested. Condensation is represented almost perfectly with AMP, and only small AMP-bin differences emerge due to nucleation, evaporation, and sedimentation. Collision-coalescence is the single biggest reason for AMP-bin divergence. Closer inspection shows that this divergence is primarily a result of autoconversion and not of accretion. In full microphysics simulations, lowering the diameter threshold separating cloud and rain category in AMP from 80 to 50 mu m reduces the largest AMP-bin difference to similar to 10%, making the effect of structural differences between AMP (and perhaps triple-moment bulk schemes generally) and bin even smaller than the parameterization differences between the two bin schemes.Plain Language Summary There are two primary ways to predict how clouds form and evolve. In a model grid box, bulk schemes typically predict the evolution of just the total number and mass of cloud droplets, whereas bin schemes not only keep track of total amount but also the number of droplets of different sizes. Bulk schemes are more computationally efficient than bin schemes, but bin schemes are usually assumed to be more accurate. This study aims to reveal how such differences affect their prediction of clouds. Our results show that small droplets colliding and combining to form raindrops is the only process that leads to large differences between bin and bulk schemes. Other individual processes, including droplet activation, condensation, evaporation, and sedimentation, contribute substantially less to differences between the two schemes. These results suggest that the advantages of using a bin scheme may not be as large as previously thought

Deep convective updraft invigoration via indirect effects of increased aerosol number concentration on cloud microphysics is frequently cited as a driver of correlations between aerosol and deep convection properties. Here, we critically evaluate the theoretical, modeling, and observational evidence for warm- and cold-phase invigoration pathways. Though warm-phase invigoration is plausible and theoretically supported via lowering of the supersaturation with increased cloud droplet concentration in polluted conditions, the significance of this effect depends on substantial supersaturation changes in real-world convective clouds that have not been observed. Much of the theoretical support for cold-phase invigoration depends on unrealistic assumptions of instantaneous freezing and unloading of condensate in growing, isolated updrafts. When applying more realistic assumptions, impacts on buoyancy from enhanced latent heating via fusion in polluted conditions are largely canceled by greater condensate loading. Many foundational observational studies supporting invigoration have several fundamental methodological flaws that render their findings incorrect or highly questionable. Thus, much of the evidence for invigoration has come from numerical modeling, but different models and setups have produced a vast range of results. Furthermore, modeled aerosol impacts on deep convection are rarely tested for robustness, and microphysical biases relative to observations persist, rendering many results unreliable for application to the real world. Without clear theoretical, modeling, or observational support, and given that enervation rather than invigoration may occur for some deep convective regimes and environments, it is entirely possible that the overall impact of cold-phase invigoration is negligible. Substantial mesoscale variability of dominant thermodynamic controls on convective updraft strength coupled with substantial updraft and aerosol variability in any given event are poorly quantified by observations and present further challenges to isolating aerosol effects. Observational isolation and quantification of convective invigoration by aerosols is also complicated by limitations of available cloud condensation nuclei and updraft speed proxies, aerosol correlations with meteorological conditions, and cloud impacts on aerosols. Furthermore, many cloud processes, such as entrainment and condensate fallout, modulate updraft strength and aerosol–cloud interactions, varying with cloud life cycle and organization, but these processes remain poorly characterized. Considering these challenges, recommendations for future observational and modeling research related to aerosol invigoration of deep convection are provided.

In this study, a marine fog event that occurred from 0000 to 1800 UTC 7 September 2018 near Canada's Grand Banks is used to investigate the sensitivity of simulated fog properties to six model parameters found primarily in the microphysics scheme. To do so, we ran a large suite of regional simulations that spanned the life cycle of the fog event using the Regional Atmospheric Modeling System (RAMS). We randomly selected parameter combinations for the simulation suite and used Gaussian process regression to emulate the response of a variety of simulated fog properties to the parameters. We find that the microphysics shape parameter, which controls the relative width of the droplet size distribution, and the aerosol number concentration have the greatest impact on fog in terms of spatial extent, duration, and surface visibility. In general, parameters that reduce mean fall speed of droplets and/or suppress drizzle formation lead to reduced visibility in fog but also delayed onset, shorter lifetimes, and reduced spatial extent. The importance of the distribution width suggests a need for better characterization of this property for fog droplet distributions and better treatment of this property in microphysics schemes.

An intercomparison between 10 single-column (SCM) and 5 large-eddy simulation (LES) models is presented for a radiation fog case study inspired by the Local and Non-local Fog Experiment (LANFEX) field campaign. Seven of the SCMs represent single-column equivalents of operational numerical weather prediction (NWP) models, whilst three are research-grade SCMs designed for fog simulation, and the LESs are designed to reproduce in the best manner currently possible the underlying physical processes governing fog formation. The LES model results are of variable quality and do not provide a consistent baseline against which to compare the NWP models, particularly under high aerosol or cloud droplet number concentration (CDNC) conditions. The main SCM bias appears to be toward the overdevelopment of fog, i.e. fog which is too thick, although the inter-model variability is large. In reality there is a subtle balance between water lost to the surface and water condensed into fog, and the ability of a model to accurately simulate this process strongly determines the quality of its forecast. Some NWP SCMs do not represent fundamental components of this process (e.g. cloud droplet sedimentation) and therefore are naturally hampered in their ability to deliver accurate simulations. Finally, we show that modelled fog development is as sensitive to the shape of the cloud droplet size distribution, a rarely studied or modified part of the microphysical parameterisation, as it is to the underlying aerosol or CDNC.

Observations and simulations show that an increase in aerosol concentration typically leads to an increase in liquid water path in precipitating stratocumulus clouds due to precipitation suppression, but once precipitation is fully suppressed, further increases in aerosol concentration typically lead to a reduction in liquid water path due to enhanced evaporation at cloud top. The increased evaporation is typically attributed directly to the presence of smaller, more numerous cloud droplets. However, observations suggest that the evaporation rate is primarily controlled by the entrainment mixing rate rather than the droplet properties at the tops of stratocumulus clouds. As such, aerosol-induced changes to droplet properties should not directly lead to faster evaporation. Our simulations suggest instead that the smaller, more numerous droplets enhance the cloud top maximum radiative cooling rate, which in turn increases the entrainment rate and speeds evaporation. Our results highlight that unlike integrated radiative cooling, maximum radiative cooling continues to increase with increasing liquid water path and remains sensitive to droplet properties at high liquid water path. As such, the role of radiation in driving aerosol-cloud interactions may need additional consideration in the future.

Abstract Warm rain collision‐coalescence has been persistently difficult to parameterize in bulk microphysics schemes. We use a flexible bulk microphysics scheme with bin scheme process parameterizations, called AMP, to investigate reasons for the difficulty. AMP is configured in a variety of ways to mimic bulk schemes and is compared to simulations with the bin scheme upon which AMP is built. We find that an important limitation in traditional bulk schemes is the use of separate cloud and rain categories. When the drop size distribution is instead represented by a continuous distribution, the simulation of cloud‐to‐rain conversion is substantially improved. We also find large sensitivity to the threshold size to distinguish cloud and rain in traditional schemes; substantial improvement is found by decreasing the threshold from 40 to 25 μm. Neither the use of an assumed functional form for the size distribution nor the choice of predicted distribution moments has a large impact on the ability of AMP to simulate rain production. When predicting four total moments of the liquid drop size distribution, either with a traditional two‐category, two‐moment scheme with a reduced size threshold, or a four‐moment single‐category scheme, errors in the evolution of mass and the cloud size distribution are similar, but the single‐category scheme has a substantially better representation of the rain size distribution. Optimal moment combinations for the single‐category approach are investigated and appear to be linked more to the information content they provide for constraining the size distributions than to their correlation with collision‐coalescence rates.

Mixed-phase clouds are ubiquitous in the Arctic. These clouds can persist for days and dissipate in a matter of hours. It is sometimes unknown what causes this sudden dissipation, but aerosol-cloud interactions may be involved. Arctic aerosol concentrations can be low enough to affect cloud formation and structure, and it has been hypothesized that, in some instances, concentrations can drop below some critical value needed to maintain a cloud. We use observations from a Department of Energy ARM site on the northern slope of Alaska at Oliktok Point (OLI), the Arctic Summer Cloud Ocean Study (ASCOS) field campaign in the high Arctic Ocean, and the Integrated Characterisation of Energy, Clouds, Atmospheric state, and Precipitation at Summit - Aerosol Cloud Experiment (ICECAPS-ACE) project at the NSF (National Science Foundation) Summit Station in Greenland (SMT) to identify one case per site where Arctic boundary layer clouds dissipated coincidentally with a decrease in surface aerosol concentrations. These cases are used to initialize idealized large eddy simulations (LESs) in which aerosol concentrations are held constant until, at a specified time, all aerosols are removed instantaneously - effectively creating an extreme case of aerosol-limited dissipation which represents the fastest a cloud could possibly dissipate via this process. These LESs are compared against the observed data to determine whether cases could, potentially, be dissipating due to insufficient aerosol. The OLI case's observed liquid water path (LWP) dissipated faster than its simulation, indicating that other processes are likely the primary drivers of the dissipation. The ASCOS and SMT observed LWP dissipated at similar rates to their respective simulations, suggesting that aerosol-limited dissipation may be occurring in these instances. We also find that the microphysical response to this extreme aerosol forcing depends greatly on the specific case being simulated. Cases with drizzling liquid layers are simulated to dissipate by accelerating precipitation when aerosol is removed while the case with a non-drizzling liquid layer dissipates quickly, possibly glaciating via the Wegener-Bergeron-Findeisen (WBF) process. The non-drizzling case is also more sensitive to ice-nucleating particle (INP) concentrations than the drizzling cases. Overall, the simulations suggest that aerosol-limited cloud dissipation in the Arctic is plausible and that there are at least two microphysical pathways by which aerosol-limited dissipation can occur.

Aerosol concentrations in the Arctic can get quite low, and recent work has shown that low aerosol concentrations could affect Arctic cloud formation and structure. Arctic mixed-phase clouds have been observed to persist for days at a time and dissipate suddenly, and it has been hypothesized that some instances of cloud dissipation are caused by aerosol concentrations falling below some critical value required to sustain the cloud. We found three cases - from a Department of Energy ARM site on the north slope of Alaska, the ICECAPS-ACE project at the NSF Summit Station in Greenland, and the ASCOS field campaign - where clouds are observed to dissipate coincidentally with a drop of surface aerosol concentration. These cases were used to initialize idealized large eddy simulations in which aerosol concentrations were held constant at observed values before being immediately removed. The resulting simulations are considered to be the fastest possible aerosol-limited dissipation. Comparing simulated liquid water path (LWP) to observations, we find that the ARM case dissipated much faster than our simulations, indicating that the observed dissipation was not driven by lack of available aerosol. The Summit Station and ASCOS simulations dissipate (with respect to LWP) at approximately the same rate as observations, which suggests aerosol-limited dissipation may indeed be occurring in these cases. Furthermore, we find that the microphysical response to aerosol removal varies between the specific cases we simulate. Simulations where the cloud produces constant liquid drizzle dissipate, within 3-4 hours, via an acceleration of precipitation once aerosols are removed. Conversely, the case with a non-precipitating liquid layer dissipates more quickly (< 2 hours), possibly by glaciation via the Wegener-Bergeron-Findeisen (WBF) in which ice grows and precipitates at the expense of liquid droplets. The simulations suggest that aerosol-limited dissipation in the Arctic is plausible, and we present two microphysical pathways by which this dissipation can occur.

Clear-sky periods across the high latitudes have profound impacts on the surface energy budget and lower atmospheric stratification; however an understanding of the atmospheric processes leading to low-level cloud dissipation and formation events is limited. A method to identify clear periods at Utqiaġvik (formerly Barrow), Alaska, during a 5-year period (2014–2018) is developed. A suite of remote sensing and in situ measurements from the high-latitude observatory are analyzed; we focus on comparing and contrasting atmospheric properties during low-level (below 2 km) cloud dissipation and formation events to understand the processes controlling clear-sky periods. Vertical profiles of lidar backscatter suggest that aerosol presence across the lower atmosphere is relatively invariant during the periods bookending clear conditions, which suggests that a sparsity of aerosol is not frequently a cause for cloud dissipation on the North Slope of Alaska. Further, meteorological analysis indicates two active processes ongoing that appear to support the formation of low clouds after a clear-sky period: namely, horizontal advection, which was dominant in winter and early spring, and quiescent air mass modification, which was dominant in the summer. During summer, the dominant mode of cloud formation is a low cloud or fog layer developing near the surface. This low cloud formation is driven largely by air mass modification under relatively quiescent synoptic conditions. Near-surface aerosol particles concentrations changed by a factor of 2 around summer formation events. Thermodynamic adjustment and increased aerosol presence under quiescent atmospheric conditions are hypothesized as important mechanisms for fog formation.

Many factors are at play in determining the amount and distribution of mountain snowfall that is predicted by weather models; among them is the influence of assumed ice habit on snowfall distribution. Ice habit is necessarily greatly simplified in microphysics schemes and uncertainty remains in how best to model ice processes. In this study we simulate a Sierra Nevada snowfall event driven by an extratropical cyclone in February 2014. We have simulated the storm with four fixed habit types as well as with an ice habit scheme that is variable in time and space. In contrast to some previous studies, we found substantially smaller sensitivity of total accumulated precipitation amount and negligible changes in spatial distribution to the ice habit specification. The reason for smaller sensitivity seems to be linked to strong aggregation of ice crystals in the model. Nonetheless, while changes in total accumulated precipitation were small, changes in accumulated ice hydrometeors were larger. The variable-habit simulation produced up to 37% more ice precipitation than any of the fixed-habit simulations with an average increase of 14%. The variable-habit simulation led to a maximization of ice growth in the atmosphere and, subsequently, ice accumulation at the surface. This result points to the potential importance of accounting for the time and space variation of ice crystal properties in simulations of orographic precipitation.