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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]

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]

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]

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]

Abstract: Observations for the 2005–2008 time period from three Southeastern Aerosol Research and Characterization (SEARCH) air quality monitoring sites are examined for diurnal and seasonal variation in concentrations of gaseous elemental mercury (GEM), gaseous oxidized mercury (GOM), and particle bound mercury (HgP < 2.5 μm). The sites are located at 1) a suburban-coastal location near Pensacola, Florida (OLF), 2) an urban location in Birmingham, Alabama (BHM), and 3) a rural location west-northwest of Atlanta, Georgia (YRK). Average concentrations of GEM at both OLF and YRK are 1.35 ng m−3, whereas at BHM it is 2.12 ng m−3. All sites show increase in GEM concentration during the morning hours (0.023 and 0.011 ng m−3hr−1 at OLF and YRK between 6 and 10 AM, 0.038 ng m−3hr−1 at BHM between 5 and 10 AM) due to downward mixing of higher concentrations from the residual layer, after which OLF and YRK show negligible variation compared to decrease in concentration at BHM (2.3–1.9 ng m−3 from 10 AM to 6 PM). All sites show seasonal variation of GEM with enhanced concentrations found in winter and spring. Average GOM concentrations are 4.26, 8.55, and 78.2 pg m−3 at OLF, YRK, and BHM, respectively. Seasonally, GOM values are enhanced during fall and spring. All sites undergo a sinusoidal daytime variation of GOM that peaks in the afternoon, while BHM additionally exhibits an early morning enhancement likely caused by vertical mixing. The average HgP concentrations at OLF, YRK, and BHM are 2.49, 4.43, and 39.5 pg m−3, respectively. At OLF, vertical mixing causes an early morning increase in HgP concentration followed by an afternoon decline during all seasons. A daytime increase in HgP is found at YRK for all seasons, while at BHM, nocturnal accumulation followed by a daytime decline is also found for most seasons except winter. In winter, concentrations increase due to vertical mixing in the morning and then decline as the boundary layer grows. Boundary layer processes appear to play an important role in the seasonal and diurnal variation of Hg species and further investigation utilizing a boundary layer process model is warranted. [Copyright &y& Elsevier]

Abstract: In this investigation, the impact of soil moisture on the precipitation amount and distribution in model simulations for West Africa was assessed. This included the examination of the dependence of precipitation on soil moisture, boundary layer parameters or convective conditions. For this purpose, different soil moisture fields from the rainy season of the year 2006 in West Africa were collected. These included three analysis fields, satellite measurements, and model output from the African Monsoon Multidisciplinary Analysis (AMMA) Land Surface Model Intercomparison Project. The different soil moisture fields were analyzed and compared first; large differences in the mean values, the north–south gradients, and the heterogeneity were revealed. The distributions were taken to initialize five convection-permitting model runs performed with the limited-area model COSMO (Consortium for Small-scale Modeling), forced by reanalyses from the European Centre for Medium-Range Weather Forecasts. A case study of a period with intense convective activity in the Sudanian and Sahelian zone was selected for the simulations. The simulated precipitation was compared with data from two satellite-based precipitation analyses and with forecasts from two global models of lower resolution. The model spread encompassed the precipitation analyses in the southern subdomain, while the latter lay far above the model results in the northern subdomain. The different soil moisture fields also influenced the turbulent fluxes at the surface, the boundary layer state and height as well as convective instability. Standard deviation of the simulations was highest for convective available potential energy, while the impact on convective inhibition was less pronounced. Only a weak negative relation of precipitation to soil moisture or conditional instability could be discerned. In this case, dry soil favored a higher boundary layer with better developed convective cells contributing to larger precipitation sums. The spread between the five precipitation forecasts suggests that a high-resolution limited-area ensemble, built by applying atmospheric variations as well as variations of the initial land surface conditions, will be a proper method to improve the prediction of mesoscale convective systems and convective precipitation for West Africa. [Copyright &y& Elsevier]

The S-L (She and Leveque) scaling law, also named the hierarchical similarity theory, has been extensively tested for the turbulence made in the laboratory, but seldom been tested for the turbulence in the atmospheric boundary layer (ABL). In this paper, the S-L scaling law is applied to the turbulence in the ABL observed under unstably stratified conditions and over different types of underlying surfaces. The results of analyses show that over this type of homogeneous and flat underlying surface, such as the underlying surface in HUBEX (Huaihe River Basin Energy and Water Cycle Experiment), vertical speed and temperature fields well satisfy the S-L scaling law. For the turbulence over the homogeneous but rather rough underlying surface of forest and under unstably stratified conditions in PFRD (Park Falls Ranger District of the Chequamegon National Forest, Wisconsin, USA), the analyses show that the vertical speed and temperature fields sometimes conform sometimes do not conform to the S-L scaling law. However, at a time, either both of the vertical speed and temperature fields conform to the S-L scaling law, or both of them do not. Horizontal speed fields in both of the field experiments do not satisfy the S-L scaling law. The new explanation of the above-mentioned phenomena is given. [ABSTRACT FROM AUTHOR]

An observing network has been established along the United States west coast that provides up to 20 years of observations to support early warning, preparedness and studies of atmospheric rivers (ARs). The Hydrometeorology Testbed–West Legacy Observing Network, a suite of upper air and surface observing instruments, is now an official National Oceanic and Atmospheric Administration (NOAA) observing system with real-time data access provided via publicly available websites. This regional network of wind profiling radars and co-located instruments also provides observations of boundary layer processes such as complex-terrain flows that are not well depicted in the current operational rawindsonde and radar networks, satellites, or in high-resolution models. Furthermore, wind profiling radars have been deployed ephemerally for projects or campaigns in other areas, some with long records of observations. Current research uses of the observing system data are described as well as experimental products and services being transitioned from research to operations and applications. We then explore other ways in which this network and data library provide valuable resources for the community beyond ARs, including evaluation of high-resolution numerical weather prediction models and diagnosis of systematic model errors. Other applications include studies of gap flows and other terrain-influenced processes, snow level, air quality, winds for renewable energy and the predictability of cloudiness for solar energy industry. [ABSTRACT FROM AUTHOR]

Based on the ARPS (Advanced Regional Prediction System) at 2 km grid spacing, the convective initiation (CI) of cells that evolved into a squall line on the southern flank of a Meiyu front in East China is investigated. The initiations of four convective cells, denoted CI-A through CI-D, are reasonably captured in the simulation. For CI-A and CI-B, locally enhanced convergence bands associated with boundary layer horizontal convective rolls (HCRs) play a crucial role in determining the exact locations of CIs, whereas a cold outflow boundary from earlier frontal precipitation contributes to additional convergence forcing. For CI-C, initiation occurs directly over the gust front, with the frontal convergence providing the main forcing. CI-D occurs south of and sufficiently far from the gust front, and is mainly forced by the HCR circulations. With surface heat flux turned off in the model, CI is much delayed. Therefore, surface heating increases the convective instability of air south of the front and causes the development of HCRs; it also enhances the gust front convergence by mixing higher southwesterly momentum toward the surface. When the condensation process is turned off, HCRs and/or gust frontal forcing are still able to lift the low-level air to super-saturation where CI is expected. [ABSTRACT FROM AUTHOR]