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Cold pools formed by precipitating convective clouds are an important source of mesoscale temperature variability. However, their sub‐mesoscale (100 m–10 km) structure has not been quantified, impeding validation of numerical models and understanding of their atmospheric and societal impacts. We assess temperature variability in observed and simulated cold pools using variograms calculated from dense network observations collected during a field experiment and in high‐resolution case‐study and idealized simulations. The temperature variance in cold pools is enhanced for spatial scales between ∼5 and 15 km compared to pre‐cold pool conditions, but the magnitude varies strongly with cold pool evolution and environment. Simulations capture the overall cold pool variogram shape well but underestimate the magnitude of the variability, irrespective of model resolution. Temperature variograms outside of cold pool periods are represented by the range of simulations evaluated here, suggesting that models misrepresent cold pool formation and/or dissipation processes. Plain Language Summary: Cold pools are cool gusty winds beneath thunderstorms that are formed by cooling from rainfall. They have many important impacts in the atmosphere and on society but are difficult to properly simulate in numerical weather models. The variability in cold pool temperature is an understudied feature of cold pools but which is important to represent in numerical models. In this study, we examine cold pool temperature variability from a dense network of surface weather station observations collected during a field campaign, and we compare those observations to numerical simulations of cold pools in a range of environments. We find that cold pools enhance temperature variability for distances greater than ∼5 km but suppress variability on smaller distances, and that the magnitude of cold pool temperature variability is strongly dependent on the environment and cold pool lifetime. We also show that numerical models, even at very high resolutions, are not able to properly simulate the magnitude of cold pool temperature variability. We highlight areas for improvement in numerical models that may help to improve simulations of cold pool variability, including land‐atmosphere interactions, turbulence, and conversion processes between water vapor and condensed water in storms. Key Points: Cold pool impacts on sub‐mesoscale temperature variability are quantified using variograms derived from observations and simulationsCold pools enhance temperature variability on scales between 5 and 15 km, but the magnitude varies strongly with lifetime and environmentHigh‐resolution case‐study and idealized simulations underestimate the magnitude of cold pool variability, irrespective of resolution [ABSTRACT FROM AUTHOR]

Seven years of data collected at the Atmospheric Radiation Measurement's Eastern North Atlantic (ENA) site are analyzed to understand the controls of Cloud Condensation Nuclei (CCN) concentrations in the region. Day‐night differences in the aerosol data as segregated by wind direction demonstrate the aerosol observations to be impacted by local emissions when the wind direction (wdir) is between 90° and 310° (measured clockwise from the North where air is coming from). Data collected during marine conditions (wdir <90° or wdir >310°) show the CCN concentrations to be higher in the summer months as compared to the winter months. CCN budget analysis revealed advection and precipitation scavenging being primarily responsible for modulating the CCN concentrations at the site on monthly timescales, with rain rates driving the precipitation scavenging term. High (greater than 75th percentile) and low (lower than 25th percentile) CCN events were identified for each month to characterize the sub‐monthly variability of CCN concentrations. Low CCN events had thicker clouds, stronger rain rates, and lower reanalysis reported free‐tropospheric aerosol pseudo number concentration at the ENA site as compared to the high CCN events. Analysis of satellite data of air‐parcels 48 hr prior to their arrival at the ENA site demonstrated the air parcels during low CCN events to encounter higher cloudiness, stronger rain rates, and higher cloud top heights as compared to the high CCN events. The results presented herein provide key constraints for model evaluation studies and climatological studies conducted at the ENA site. Plain Language Summary: Aerosols on which water vapor condenses to form cloud droplets are commonly referred to as cloud condensation nuclei (CCN). The mechanisms responsible for changes in CCN concentrations over the open oceans are not fully known. The variability in the CCN concentrations over the Eastern North Atlantic region is examined. Analysis of day‐night differences in aerosol properties with wind direction show the aerosol measurements to be affected by human activity during certain wind conditions. Seven years of data collected when conditions were not impacted by human activity show higher CCN concentrations during summer months as compared to the winter month, and substantial variability within each month. CCN can be locally produced through wave‐breaking, transported to the location, and subsequently removed from the atmosphere as they transition into cloud or rain drops. Causes of seasonal and sub‐monthly changes in these CCN concentrations are examined. The analysis show transport of CCN to the site along with removal due to cloud processing to be the primary mechanisms for causing seasonal changes. In addition, air associated with low CCN concentration conditions at the site encountered higher cloudiness and rain prior to its arrival at the site as compared to that with high CCN concentrations. Key Points: Data suggest aerosol measurements at the Atmospheric Radiation Measurement Eastern North Atlantic site are affected by local emissions when the wind direction is between 90° and 310°Lower ambient wintertime Cloud Condensation Nuclei (CCN) concentrations compared to summer are primarily driven by advection and precipitationCCN variability at sub‐monthly timescales is driven by clouds and precipitation upstream of the site [ABSTRACT FROM AUTHOR]

Global reanalyzes like ERA5 accurately capture atmospheric processes at spatial scales of O(10) $\mathcal{O}(10)$ km or larger. By downscaling ERA5 with large‐eddy simulation (LES), LES can provide details about processes at spatio‐temporal scales down to meters and seconds. Here, we present an open‐source Python package named the "Large‐eddy simulation and Single‐column model—Large‐Scale Dynamics," or (LS)2D in short, designed to simplify the downscaling of ERA5 with doubly‐periodic LES. A validation with observations, for several sensitivity experiments consisting of month‐long LESs over Cabauw (the Netherlands), demonstrates both its usefulness and limitations. The day‐to‐day variability in the weather is well captured by (LS)2D and LES, but the setup under‐performs in conditions with broken or near overcast clouds. As a novel application of this modeling system, we used (LS)2D to study surface solar irradiance variability, as this quantity directly links land‐surface processes, turbulent transport, and clouds, to radiation. At a horizontal resolution of 25 m, the setup reproduces satisfactorily the solar irradiance variability down to a timescale of seconds. This demonstrates that the coupled LES‐ERA5 setup is a useful tool that can provide details on the physics of turbulence and clouds, but can only improve on its host reanalysis when applied to meteorological suitable conditions. Plain Language Summary: Modern global weather models are accurate in predicting atmospheric processes at scales of around 10 km or larger, but are less good at predicting smaller scale processes, like for example, the interaction between solar radiation, individual clouds, and the resulting clouds shadows that are cast onto the land surface. High spatio‐temporal resolution research models are able to capture these smaller scale processes, but require a coupling to a weather model to account for the day‐to‐day variability in our weather. In this paper, we present a framework to couple large to small scale models, and demonstrate both the benefits and challenges of using this coupled model setup. The coupled setup excels in capturing the aforementioned high frequency interactions between small clouds and surface solar radiation. However, the chaotic nature of broken to overcast clouds is proven difficult to represent. The coupled model setup is published as open‐source code, and is therefore freely available to the research community. Key Points: We developed an open‐source Python package named (LS)2D, designed to downscale the ERA5 reanalysis with turbulence and cloud‐resolving large‐eddy simulation (LESs)One month long experiments with (LS)2D and MicroHH over the Netherlands demonstrate both the skill and limitations of the coupled setupCapturing high‐frequency interactions between clouds and surface solar irradiance requires high resolution (O $\mathcal{O}$(10) m) LES [ABSTRACT FROM AUTHOR]

Predicting the evolution of burned area, smoke emissions, and energy release from wildfires is crucial to air quality forecasting and emergency response planning yet has long posed a significant scientific challenge. Here we compare predictions of burned area and fire radiative power from the coupled weather/fire‐spread model WRF‐Fire (Weather and Research Forecasting Tool with fire code), against simpler methods typically used in air quality forecasts. We choose the 2019 Williams Flats Fire as our test case due to a wealth of observations and ignite the fire on different days and under different configurations. Using a novel re‐gridding scheme, we compare WRF‐Fire's heat output to geostationary satellite data at 1‐hr temporal resolution. We also evaluate WRF‐Fire's time‐resolved burned area against high‐resolution imaging from the National Infrared Operations aircraft data. Results indicate that for this study, accounting for containment efforts in WRF‐Fire simulations makes the biggest difference in achieving accurate results for daily burned area predictions. When incorporating novel containment line inputs, fuel density increases, and fuel moisture observations into the model, the error in average daily burned area is 30% lower than persistence forecasting over a 5‐day forecast. Prescribed diurnal cycles and those resolved by WRF‐Fire simulations show a phase offset of at least an hour ahead of observations, likely indicating the need for dynamic fuel moisture schemes. This work shows that with proper configuration and input data, coupled weather/fire‐spread modeling has the potential to improve smoke emission forecasts. Plain Language Summary: Predicting wildfire growth and smoke behavior is an important and historically difficult task. Here we compare a coupled fire‐weather model to current practices used in air quality forecasting in their ability to predict daily wildfire spread and fire intensity for the 2019 Williams Flats Fire. We also evaluate the fire‐weather model against high‐resolution satellite and aircraft measurements in ways previously unexplored. We find that including fire‐fighting efforts in our model makes the biggest improvements toward accurate wildfire forecasting. When we account for fire‐fighting efforts, fuel moisture maps, and increased fuel loads, our coupled fire‐weather model significantly outperforms simpler methods with reasonable computational time. Despite this, modeled fire intensity tends to lead observations by at least an hour. Optimal configuration options and avenues for forecasting improvements are discussed. Key Points: Adding containment lines into our wildfire model made the biggest difference in improving accuracy of daily burned area predictionsAccounting for containment, fuel moisture content maps, and increased fuel density consistently increased prediction skill above persistenceModeled fire radiative power leads observations by at least an hour, but with similar accuracy to prescribed diurnal cycles [ABSTRACT FROM AUTHOR]

We used the observations from the east coast of the USA and model simulations to elicit marine atmospheric surface layer (MASL) processes that controlled the spatial variability of electromagnetic ducting conditions in the near‐shore (60 km) marine environment during CASPER‐East field campaign. The estimated evaporation duct height (EDH) showed an average decrease of ∼2.5 m over every 10 km distance from the shore. The surface layer stability conditions chiefly determined the duct heights. While the mean duct height remained ∼10 m under unstable conditions, deeper (>20 m) evaporation ducts were frequently evident (∼40% of the time) during stable MASL conditions, which were responsible for the observed near‐shore EDH variability. Episodes of warm offshore flow and advection of hot and humid air from the Gulfstream region created ideal environmental conditions for stable MASL development and thus deeper ducts over the coastal ocean. A detailed case study of warm and humid Gulfstream air advection over the cooler coastal ocean is presented to elucidate the temporal evolution of the surface layer stability and associated changes in the spatial distribution of the evaporation duct height. Key Points: Stable conditions in the nearshore CASPER‐East region can be caused by both offshore flow and flow from the Gulf StreamThe near‐shore region of Duck, North Carolina, showed a gradual decrease of evaporation duct height from the shore ∼2.5 m for every 10 kmHigh‐evaporation ducts are associated with stable stratifications in the coastal marine atmospheric surface layer [ABSTRACT FROM AUTHOR]

The spread in global mean precipitation among climate models is explored in two ensembles using the complementary perspectives of surface evaporation and energy budgets. Models with higher global mean precipitation have stronger oceanic evaporation, driven by drier near‐surface air. The drier surface conditions occur alongside increases in near‐surface temperature and moisture at 925 hPa, which point to stronger boundary layer mixing. Correlations suggest that the degree of lower tropospheric mixing explains 18%–49% of the intermodel precipitation variance. To test this hypothesis, the degree of mixing is indirectly varied in a single‐model experiment by adjusting the relative humidity threshold that controls low‐cloud fraction. Indeed, increasing lower tropospheric mixing results in more global mean precipitation. Energetically, increased precipitation rates are associated with more downwelling longwave radiation to the surface and weaker sensible heat fluxes. These results highlight how lower‐tropospheric processes must be better constrained to reduce the precipitation discrepancy among climate models. Plain Language Summary: Climate models exhibit a spread in their simulation of the present‐day global mean precipitation rate; a fundamental climate statistic whose spread is surprisingly understudied. This 13% spread compares with the expected change in the global mean precipitation rate in a warmer climate scenario. Complex precipitation physics can make understanding what processes control the global mean precipitation rate across climate models inherently difficult. We find that the degree of mixing within the lower 1‐km of the atmosphere (lower‐tropospheric mixing) controls a large fraction of the spread in global mean precipitation across models. We also show linkages between the lower tropospheric mixing and the energy budget framework that is typically used to understand the global mean precipitation rate. Our results highlight a local scale process (mixing) that controls and impacts a global scale climate statistic (global mean precipitation). They also suggest that future attempts to bridge satellite observations and climate model output can potentially help reduce the existing spread and bias among climate models. Key Points: Fifth Coupled Model Intercomparison Project Atmospheric Model Intercomparison Project simulations disagree on the magnitude of the present‐day global mean precipitation rate by 13%Lower tropospheric mixing explains as much as 49% of the intermodel varianceMost (up to 75%) of the spread in column atmospheric energy happens at the surface [ABSTRACT FROM AUTHOR]

Quantifying the influence of sea spray on air‐sea fluxes under high‐wind conditions is challenging due to a variety of factors. Among existing models, the so‐called bulk air‐sea flux model is commonly used in meteorological applications due to its simplicity, which often involves strong but untested assumptions on spray‐mediated heat fluxes and feedback effects. For example, a common assumption is to treat each droplet size as an independent contribution; that is, it does not interact with other sizes. Thus, the interactions between different size classes of spray are often neglected. In this study, we focus on the polydispersity of the spray size distribution and investigate the appropriateness of assuming an independent contribution from different spray size classes. We implement direct numerical simulations (DNS) with Lagrangian tracking of spray droplets. Based on DNS results, the bulk spray model fails to capture the interactions between different sizes that are observed directly from the droplet and feedback statistics in DNS. Thus, assuming independent contributions from spray droplets results in significant overestimates on the total heat fluxes. We further test different representative sizes of a spray size distribution. We find that the volume‐weighted representative size is capable of predicting the droplet‐modified temperature and humidity fields and generally captures the vertical profiles of spray‐mediated and interfacial heat fluxes. The results indicate that the computation of spray‐mediated fluxes can be simplified in large‐scale parameterizations. Key Points: Directly adding all contributions from spray droplets in bulk models may introduce overestimates on spray‐mediated heat fluxesWe improved the overestimation issue in bulk model for the total heat flux by considering the effects of spray time scalesUsing the volume‐weighted mean size as a representative size could simplify the quantifying of air‐sea fluxes in an Eulerian framework [ABSTRACT FROM AUTHOR]

ABSTRACT Forecasting tropical cyclone ( TC) intensity changes over land is complicated by interactions of various surface and atmospheric features. Due to generally unfavorable conditions, many TCs weaken and decay soon after landfall. In some cases, TCs may also transition to extratropical cyclones (ETs). Despite the absence of oceanic forcing, a number of TCs have been observed to maintain or increase strength inland, termed 'tropical cyclone maintenance or intensification' (TCMIs). This study identifies the environments and characteristic features of TCMIs and explores physical processes that may help to produce an atmosphere conducive for tropical systems. The objectives are to compile an inland TC dataset over a 30-year period, quantify TC traits that may relate to maximum strength over land, and analyse surface and atmospheric conditions leading up to intensification. Of 227 inland TCs globally, 45 maintained or increased strength inland: 17 cold-core ( ET), 16 warm-core ( TCMI), and 12 hybrid cases. Analysis of synoptic conditions indicates that TCs persist when low-level temperature gradients are weak. Soil moisture gradients were in the vicinity of the cyclones at the time of intensification and may be forcing the TCMIs via increased surface latent heat flux ( LHF). The area-averaged LHF threshold is found to be around 70 W m−2 for TCMI occurrence. In the 2 weeks leading up to each TCMI, the LHF tends to be higher than average over the intensification regions and provides further evidence of land surface forcing. [ABSTRACT FROM AUTHOR]

Bulk and size‐resolved particle concentrations were measured at 2.1 and 6.5 m above the soil surface during an intense dust deposition event that occurred in June 2006 in Niger. Bulk concentration measurements were performed using two tapered element oscillating microbalance instruments, and the size‐resolved particle concentrations (from 0.3 to 20 μm) using two optical particle counters. The deposition fluxes derived from the bulk concentrations and those derived from the size‐resolved ones are in very good agreement. The largest deposition fluxes are recorded when the dust concentrations are maximal. The temporal evolution of the dust deposition flux follows that of the dust concentration. This is not the case of the dry deposition velocities that are most of the time controlled by the wind friction velocity. The results also show that large particles are strongly sensible to rebound when the wind friction velocity is the highest. Size‐resolved dry deposition velocities are compared with the rare measurements of dust deposition velocities over bare sandy soils and are confronted to existing parameterizations of the dry deposition velocity. The parameterization of Zhang and Shao (2014, https://doi.org/10.5194/acp‐14‐12429‐2014) appears to be the only one able to reproduce satisfyingly the measured dry deposition velocities on sandy soils in the 1‐ to 10‐μm particle size range. Indeed, unlike others, this scheme considers the desert sandy surfaces as rough surfaces, allowing the interception of dust particles by the sand grains and/or by the small roughness elements present on the surface. Key Points: A dust deposition event allows the determination of dust deposition fluxes and dry deposition velocities using the flux gradient methodThe time evolution of dry deposition velocities is mainly controlled by the wind friction velocity and, for large particles, by reboundMeasured dry deposition velocities are in better agreement with parameterizations that consider bare sandy surfaces as rough surfaces [ABSTRACT FROM AUTHOR]