Search

In this paper, we examine differences in cloud adjustments (often called rapid adjustments) that occur as a direct result of abruptly increasing the solar constant by 4% or abruptly quadrupling of atmospheric CO2. In doing so, we devise a novel method for calculating the cloud adjustments for the abrupt solar forcing simulations that uses differences between coupled model simulations with abrupt solar and CO2 forcing, in combination with uncoupled, atmosphere‐only, abrupt CO2 forced experiments that have prescribed sea‐surface temperature. Our main findings are that (a) there are substantial differences in the responses of stratocumulus and cumulus clouds to solar and CO2 forcing, which follow the differences in the direct radiative effect that solar and CO2 forcing have at cloud top, and (b) there are differences in the adjustment of the average optical depth of high clouds to solar and CO2 forcing that we speculate are driven by the differences in the vertical profile of radiative heating and differences in the pattern of sea‐surface temperature change (for a fixed global mean temperature). These cloud adjustments contribute significantly to the total net cloud radiative effect, even after 150 years of simulation. Plain Language Summary: In climate change, clouds change due to a variety of mechanisms including surface temperature, dynamical circulations, and radiative forcing. In this paper, we examine the latter: how clouds respond to radiative forcing. We study this topic using climate model simulations where the brightness of the sun is abruptly increased by 4% and compare those with simulations where CO2 concentration is abruptly quadrupled. In doing so we find that, there are differences in the cloud response to changes in solar and CO2 forcing which include the occurrence of thick and thin high cloud, as well as the amount and height of low and mid‐level clouds. Key Points: Increasing CO2 causes a reduction and lowering of mid‐level and low‐level clouds which does not occur from solar forcingThere is large reduction in optically thin high clouds from solar forcing, especially as compared with CO2Even after 150 years adjustments make a significant contribution to the total net cloud radiative effect [ABSTRACT FROM AUTHOR]

The divergence method, a lightweight approach for estimating emission fluxes from satellite images, rests on a few implicit assumptions. This paper explicitly outlines these assumptions by deriving the method from first principles. The assumptions are: the enhanced mass flux is dominated by advection, normal fluxes vanish at the top and bottom of the atmosphere, steady‐state conditions apply, sources are multiplications of temporal and spatial functions, sinks are described as first‐order reactions, and effective wind fields are concentration‐weighted wind fields. No such assumptions have to be made for the background field. A "topography correction term" does not follow from the theory, but is rather shown to be a practical correction for topography‐dependent effective wind speed errors. The cross‐sectional flux method follows naturally from the derived theory, and the methods are compared. Effects of discrete pixels and finite‐difference operations are explored, leading to recommendations, primarily the recommendation to integrate over small regions only to minimize the influence of noise. Numerical examples featuring Gaussian plumes and COSMO‐GHG simulated plumes are provided. The Gaussian plume example suggests that the divergence method might underestimate emissions when assuming only advection in the presence of cross‐wind diffusion. Conversely, the cross‐sectional flux method remains unaffected, provided fluxes are integrated across the entire plume. The COSMO‐GHG example reveals frequent violations of the steady‐state assumption, although the assumption remains valid proximal to the source (<20 km in this example). It is the hope that this paper provides a solid theoretical foundation for the divergence and cross‐sectional flux methods. Plain Language Summary: Power plants, megacities, and other regions can be places where considerable amounts of gases are emitted into the atmosphere. Satellite data is capable of recording the enhanced concentrations in the atmosphere due to these sources. How does one estimate emissions of these sources based on this data? Typically by running an atmospheric inverse model; but one computationally lightweight method gaining popularity is the "divergence method." With this method, one applies a simple mathematical operation to the satellite image multiplied by the horizontal wind speeds—the result of which is related to the emissions. The method has been used before, but here we systematically state the assumptions involved; factors like how gases are transported horizontally and over time, and what happens when we apply the method on pixelated data (such as satellite images). We find that some other analyses in the literature may be partially incorrect. Another popular method (the "cross‐sectional flux") is easily derived from the same theory. Synthetic examples are used to illustrate that various assumptions are not met in realistic data; although the examples also show that one can work around these limitations by a careful application of the methods, and by acquiring more estimates over many days. Key Points: The divergence method for estimating emissions from satellite images is derived from first principlesAssumptions made on the way are made explicit, highlighting discrepancies with existing literatureA comparison is made against the cross‐sectional flux method which follows from the same theory [ABSTRACT FROM AUTHOR]

This paper makes use of a combination of FLEXPART‐WRF simulations forced with ERA5 and the CESM2 model—incorporated in the CMIP6 project—to infer a series of changes over the present century in the behavior of the landfalling atmospheric rivers (ARs) arriving to the Iberian Peninsula. In addition, future changes in the intensity and position of their main moisture sources are studied. In overall terms, there is a noticeable increase in the amount of moisture transported by ARs in the study region, particularly accentuated by the end of the century. However, no significant changes in the number of events are observed. A northward shift of both the mean position of the ARs as well as their main sources of moisture is also detected, particularly for the end of the century, and in the summer and fall months. In relation to the latter, an increase in the contribution of moisture contribution is also observed, quantitatively compatible with Clausius‐Clapeyron amplification. Plain Language Summary: This paper makes use of a combination of simulations forced with reanalysis data and a climate model to infer a series of changes over the present century in the behavior of the landfalling atmospheric river—ARs, regions of intense moisture transport located in the lower layers of the atmosphere—arriving at the Iberian Peninsula. In addition, future changes in the intensity and position of their main moisture sources are studied. In overall terms, there is a noticeable increase in the amount of moisture transported by ARs in the study region, particularly accentuated by the end of the century. However, no significant changes in the number of events are observed. A northward shift of both the mean position of the ARs as well as their main sources of moisture is also detected, particularly for the end of the century, and in the summer and fall months. In relation to the latter, an increase in the contribution of moisture contribution is also observed, in a ratio similar to that expected. Key Points: FLEXPART‐WRF forced with CESM2 model has been able to reproduce the historical conditions of Atmospheric River over the Iberian PeninsulaA northward shift of the main source regions is projected, notable in summer and fall and particularly by the end of the centuryGradual strengthening in the intensity of Atmospheric Rivers is expected, observable from an increase in the amount of moisture transported [ABSTRACT FROM AUTHOR]

An analysis is presented of the precipitation bias and change signal in an ensemble of regional climate model (RCM) (RegCM4) projections driven by multiple general circulation models (GCMs) over China. RegCM4 is driven by five different GCMs for the 120‐year period 1979–2099 at 25 km grid spacing, under the representative concentration pathway RCP8.5. We find that the GCMs and RegCM4 reproduce the general spatial pattern of precipitation over China in all four seasons, with RegCM4 providing greater spatial detail, especially over areas with complex terrain. The spatial patterns of precipitation bias show common features between the GCMs and RegCM4, characterized by an underestimation in the wetter regions, and an overestimation in the drier ones. Systematic increases of precipitation are projected in northern China, most pronounced in the Northwest basins, by both the GCMs and RegCM4 in all seasons except summer, when more mixed results are found. In addition, weak correlations of the projected change patterns are found in summer between the GCMs and nested RegCM4, indicating the greater role played by the representation of local convection processes during this monsoon season. The projections across the RegCM4 experiments show higher consistency and lower spread compared to the GCM ensemble, again indicating that the nested model physics significantly modulates the change signal deriving from the GCM boundary forcing. Plain Language Summary: China is a vulnerable country to climate change due to its dense population, unbalanced social and economic development, shortage of water resources, and fragile ecosystems. How future precipitation will change over the region is of great concern for the general public and decision makers. This paper presents a first analysis of precipitation simulations from a set of five RCM (RegCM4) 21st century climate change projections, driven by coarse resolution general circulation models (GCMs) over China. We find that the spatial patterns of precipitation bias show common features between the GCMs and RegCM4, characterized by a precipitation underestimation in the wetter regions, and an overestimation in the drier ones. Systematic increases of precipitation are projected in north China by both the GCMs and RegCM4 in all seasons except summer, when, weak correlations of the projected change patterns are found between the GCMs and nested RegCM4, indicating the greater role of the representation of local convection processes during this monsoon season. The projections across the RegCM4 experiments show higher consistency and lower spread compared to the GCM ensemble, again indicating that the nested model physics significantly modulates the change signal deriving from the GCM boundary forcing. Key Points: The spatial patterns of bias show common features between the GCMs and RegCM4RegCM4 provides greater spatial detail of present day precipitation simulation compared to the GCMs and finer structures of future changesThe change patterns across the RegCM4 projections show a high correlation, but not always between each pair of driving GCM and RegCM4 [ABSTRACT FROM AUTHOR]

A heavy nocturnal warm sector rainfall occurred over the western coastal region of South China in June 2020, of which the precipitation magnitude was seriously underestimated by the operational TRAMS (Tropical Regional Atmosphere Model System) model. In this study, by improving the configuration of initial conditions (ICs) and lateral boundary conditions (LBCs) in offline nesting, the simulation of the convection initiation (CI) process can be well improved. The CI simulation is found to be sensitive to the vertical resolution in ICs in this case. When the low‐level vertical resolution of ICs is increased, the nocturnal near‐surface cold layer caused by inland mountains can be resolved better, which is necessary to form the convergence line along the coastline. Another important impact of increasing the vertical resolution of ICs is the successful simulation of horizontal convective rolls (HCRs) over the northern South China Sea. The HCRs apparently increase the depth of the warm‐moist marine boundary layer jet (MBLJ), and finally lead to the CI along the coastline. The increased vertical resolutions of LBCs can reduce the discontinuity of simulated moist tongue across the southern lateral boundary of the TRAMS model, which provides more moisture for the warm sector rainfall through MBLJ. LBCs with Higher temporal frequency help to alleviate the western position bias of rain belt by reducing the temporal interpolation error. Our study highlights the importance of finer ICs and LBCs in offline nesting for regional operational NWP systems in the South China region. Plain Language Summary: Warm‐sector heavy rainfall in South China usually develops under weakly forced weather condition, numerical forecasting of its initiation and intensification process is a difficult problem. To investigate its sensitivity to the configuration of initial conditions (ICs) and lateral boundary conditions (LBCs), a simulation study is carried out for a nocturnal warm sector rainfall event happened in western Guangdong. When the low‐level vertical resolution of ICs is increased, the nocturnal near surface cold layer from inland mountains can be resolved better. Another important impact of increasing vertical resolution of ICs is the successful simulation of horizontal convective rolls (HCRs) over the northern South China Sea. The HCRs apparently increase the depth of warm‐moist marine boundary layer jet (MBLJ), and finally lead to the CI along coast line. Additionally, the vertical resolution and the updating frequency of the lateral boundary conditions also have important effects on the simulation of the rainstorm intensification process. This paper emphasizes the importance of accurate ICs and LBCs to improve the predictability of warm‐sector rainfall in South China, which can provide practical reference for the design of operational systems. Key Points: Increasing low‐level vertical resolution of initial conditions is critical to the convection initiation (CI) simulation of warm‐sector rainfall caseThe increased vertical resolutions of lateral boundary conditions (LBCs) can provide more moisture for the CI and upscale convective growth process through marine boundary layer jetHigher temporal frequency LBCs is help to alleviate the western position bias of rain belt by reducing the temporal interpolation error [ABSTRACT FROM AUTHOR]

Gravity waves (GW) carry energy and momentum from the troposphere to the middle atmosphere and have a strong influence on the circulation there. Global atmospheric models cannot fully resolve GWs, and therefore rely on highly simplified GW parametrizations that, among other limitations, account for vertical wave propagation only and neglect refraction. This is a major source of uncertainty in models, and leads to well‐known problems, such as the late break‐up of polar vortex due to the "missing" GW drag around 60°S. To investigate these phenomena, GW observations over Southern Andes were performed during SouthTRAC aircraft campaign. This paper presents measurements from a SouthTRAC flight on 21 September 2019, including 3‐D tomographic temperature data of the infrared limb imager GLORIA (8–15 km altitude) and temperature profiles of the ALIMA lidar (20–80 km altitude). GLORIA observations revealed multiple overlapping waves of different wavelengths. 3‐D wave vectors were determined from the GLORIA data and used to initialize a GW ray‐tracer. The ray‐traced GW parameters were compared with ALIMA observations, showing good agreement between the instruments and direct evidence of oblique (partly meridional) GW propagation. ALIMA data analysis confirmed that most waves at 25–40 km altitudes were indeed orographic GWs, including waves seemingly upstream of the Andes. We directly observed horizontal GW refraction, which has not been achieved before SouthTRAC. Refraction and oblique propagation caused significant meridional transport of horizontal momentum as well as horizontal momentum exchange between waves and the background flow all along the wave paths, not just in wave excitation and breaking regions. Plain Language Summary: Gravity waves (GW) are temperature and wind disturbances in the atmosphere that carry energy and momentum from troposphere to the middle atmosphere and have a strong influence on the circulation there. Global atmospheric models currently cannot adequately represent GW propagation: the facts that GWs can change wavefront orientation (refraction) and travel horizontally (and not just vertically) are typically neglected. This leads to important known model inaccuracies, for example, too low temperatures in southern polar regions. SouthTRAC aircraft measurement campaign observed GWs exited by wind flow over the Southern Andes in September–November 2019. Temperature measurements were conducted with the IR spectrometer GLORIA (provided 3‐D data) and the ALIMA lidar instrument. GLORIA data revealed many overlapping waves of different wavelengths, their propagation further up was investigated using ray‐tracing. Most waves seen by GLORIA were ray‐traced to ALIMA observations where their parameters were confirmed, thus validating our ray‐tracing technique and the two instruments against each other. We directly observed wave propagation in both vertical and horizontal directions and change in horizontal wave orientation (the latter was not seen before SouthTRAC). Due to these phenomena, many GWs carried momentum that had different directions and was deposited in a different location than most models typically predict. Key Points: High‐resolution multi‐instrument measurements of orographic gravity waves (GWs) over the Andes were carried outOblique GW propagation and strong horizontal refraction were observed and analyzed using ray‐tracingSignificant redistribution of horizontal momentum due to horizontal refraction was observed all along the path of wave propagation [ABSTRACT FROM AUTHOR]

The spatial pattern of intermodel spread of surface temperature (TS‐SPREAD) is similar to the absolute error of modeled surface temperature relative to observations, implying the possibility of using intermodel spread to understand the model biases. The regions with maximum TS‐SPREAD are located in the northern polar (NP) and southern polar (SP) regions, and regions with the large orographic features, such as the Tibetan Plateau (TP). We find: (a) Winter amplification exists in the TS‐SPREAD over both the NP and SP regions, but the maximum TS‐SPREAD over the TP happens during spring and summer with weak seasonality there. (b) Surface energy balance analyses show salient land‐sea contrast in the interlinkage between the physical processes. (c) Over the Arctic Ocean and the Southern Ocean, the maximum spreads of sea‐ice‐induced surface albedo appear during summer when the TS‐SPREADs are the weakest because the effect of surface albedo is offset by the heat storage in the oceans. (d) Through the interseasonal linkage of the heat storage term, i.e., the summer‐storing‐winter‐releasing of oceanic heat content, the summer surface albedo may indirectly contribute to the winter amplification of the TS‐SPREAD over the polar oceans. Such interseasonal linkage of the processes does not exist over the land areas. A method of distinguishing the roles of the local and nonlocal dynamical processes in the contribution of the clear‐sky downward longwave radiation to the TS‐SPREAD is also provided. Plain Language Summary: Understanding the origin of climate model biases is essential to the projection of future climate change, and it is very hard task for the climate modeling community. Here, we show that there exists similarity in the spatial patterns of the intermodel spread, mean absolute errors, and absolute mean errors of surface temperature simulations in climate ensembles. Therefore, we may use the intermodel spread to help understand the mean model biases. We find the regions with maximum surface temperature spread (TS‐SPREAD) are mainly located in the northern polar (NP) and southern polar (SP) regions, and regions with large orographic feature, such as the Tibetan Plateau, indicating the polar and topographic amplifications of TS‐SPREAD in climate models. In the paper, we reveal the process‐chains that lead to the spatial pattern and seasonality of amplified TS‐SPREAD over these regions. We suggest that sea‐ice contributes to the TS‐SPREAD over the NP and SP oceans, not in a direct way, but by its coupling with ocean storage, interseasonal heat release through turbulent fluxes, and also the horizontal heat transport, both across the border of and inside the polar regions. The findings may help climate modelers to improve their models. Key Points: The pattern‐similarity among intermodel spread, mean absolute bias, and absolute mean bias enable us to use the former to understand the latter twoThe interseasonal linkage between surface albedo, heat storage, turbulent fluxes, and dynamical transport causes winter amplification over polar oceansA method is provided to determine the local versus nonlocal contributions to surface downward longwave radiation [ABSTRACT FROM AUTHOR]

This is a summary of the papers published in the Special Section titled "Fast Physics in Climate Models: Parameterization, Evaluation, and Observation" (https://agupubs.onlinelibrary.wiley.com/doi/toc/10.1002/(ISSN)2169‐8996.FASTPHYS1), as requested by the JGR‐Atmosphere Chief Editor, Professor Minghua Zhang. Key Points: Motivation for this special section is presentedPapers in this special issue are summarizedFuture challenges facing fast physics in climate models are discussed [ABSTRACT FROM AUTHOR]

Turbulence in very stable boundary layers is typically unsteady and intermittent. The study implements a stochastic modeling approach to represent unsteady mixing possibly associated with intermittency of turbulence and with unresolved fluid motions such as dirty waves or drainage flows. The stochastic parameterization is introduced by randomizing the mixing lengthscale used in a Reynolds average Navier‐Stokes (RANS) model with turbulent kinetic energy closure, resulting in a stochastic unsteady RANS model. The randomization alters the turbulent momentum diffusion and accounts for sporadic events of possibly unknown origin that cause unsteady mixing. The paper shows how the proposed stochastic parameterization can be integrated into a RANS model used in weather‐forecasting and its impact is analyzed using neutrally and stably stratified idealized numerical case studies. The simulations show that the framework can successfully model intermittent mixing in stably stratified conditions, and does not alter the representation of neutrally stratified conditions. It could thus present a way forward for dealing with the complexities of unsteady flows in numerical weather prediction or climate models. Plain Language Summary: Limited computer resources lead to a simplified representation of unresolved small‐scale processes in weather forecasting and climate models, through parameterization schemes. Among the parameterized processes, turbulent fluxes exert a critical impact on the exchange of heat, water and carbon between the land and the atmosphere. Turbulence theory was, however, developed for homogeneous and flat terrain, with stationary conditions. At nighttime or in cold environment, turbulence is typically non‐stationary, weak and intermittent and the classical theory fails. Part of the intermittent mixing is due to turbulence enhancement by small‐scale wind variability. In the following, a random modeling approach is used to enhance turbulent mixing due to small‐scale wind variability and intermittency of mixing. The proposed approach is shown to be a viable approach to represent the effect of small‐scale variability of mixing for different atmospheric flow conditions. Key Points: A stochastic parameterization of turbulence is implemented in a Reynolds average Navier‐Stokes (RANS) model to represent unsteady mixingThe introduced stochastic perturbations of the mixing length enable the simulation of intermittent turbulence in the stable boundary layerThe stochastic unsteady RANS model does not alter the simulation of neutral conditions [ABSTRACT FROM AUTHOR]

We conduct a global assessment of the spatial heterogeneity of cloud phase within the temperature range where liquid and ice can coexist. Single‐shot Cloud‐Aerosol Lidar with Orthogonal Polarization lidar retrievals are used to examine cloud phase at scales as fine as 333 m, and horizontal heterogeneity is quantified according to the frequency of switches between liquid and ice along the satellite's path. In the global mean, heterogeneity is greatest between −15 and −4°C with a peak at −5°C, when small patches of ice are prevalent within liquid‐dominated clouds. Heterogeneity "hot spots" are typically found over the extratropical continents, whereas phase is relatively homogeneous over the Southern Ocean and the eastern subtropical ocean basins, where supercooled liquid clouds dominate. Even at a fixed temperature, heterogeneity undergoes a pronounced annual cycle that, in most places, consists of a minimum during autumn or winter and a maximum during spring or summer. Based on this spatial and temporal variability, it is hypothesized that heterogeneity is affected by the availability of ice nucleating particles. These results can be used to improve the representation of subgrid‐scale heterogeneity in general circulation models, which has the potential to reduce longstanding model biases in cloud phase partitioning and radiative fluxes. Plain Language Summary: At temperatures where ice and liquid can coexist within clouds, climate models tend to produce too much ice and too little liquid compared to satellite observations. This bias is likely caused by the assumption that liquid and ice are uniformly mixed, which results in the rapid conversion of liquid to ice for thermodynamic reasons. To reduce this bias, models need to account for the spatial heterogeneity ("patchiness") of liquid and ice that exists in the real atmosphere. The goal of this paper is to quantify this spatial heterogeneity using satellite‐based lidar observations of cloud phase. We find small pockets of ice in liquid‐dominated clouds to be more common than small pockets of liquid in ice‐dominated clouds. The greatest heterogeneity is found over the midlatitude continents, whereas phase is relatively uniform over the Southern Ocean and other maritime regions with extensive low cloud cover. In the mid and high latitudes, cloud phase tends to be more heterogeneous during spring and summer and more homogeneous during autumn and winter. These results can be used in the future to improve model representations of the thermodynamic processes responsible for biases in cloud phase. Key Points: Cloud phase heterogeneity is greatest at −5°C, when small ice patches form in majority‐liquid cloudsCloud phase is relatively homogeneous over the Southern Ocean and heterogeneous over the northern continentsFor a fixed temperature, extratropical phase heterogeneity is generally greatest during local spring and summer [ABSTRACT FROM AUTHOR]

The November 2001 Solar Proton Event (SPE) is one of the strongest events in the era of satellite observations. However, no observational case study of this exceptional event's impact on atmospheric chemistry has been reported. In this paper, we use satellite‐based observations from Optical Spectrograph and Infrared Imaging Systems (OSIRIS) to quantify the SPE impact on middle atmospheric O3 in the southern hemisphere during summertime conditions. The results show a relatively modest, yet detectable, O3 depletion in the upper stratosphere and lower mesosphere. Compared to the observations, the Whole Atmosphere Community Climate Model (WACCM‐D) simulates somewhat lower O3 levels before the event but captures well the relative ozone depletion. The largest depletion is seen on November 6th, after the Geostationary Operational Environment Satellite observed the peak proton fluxes. On this day, the O3 depletion was observed and simulated from the pole to 55°S geographic latitude. The daily polar cap (poleward of 60°S geographic latitude) averaged O3 profiles show a maximum depletion of 16.6 ± 2.2% at 1 hPa and 18.8 ± 3.3% at 1.5 hPa altitude, by OSIRIS and WACCM‐D, respectively. After the SPE, an enhancement in NOx is simulated by the results of the model within altitudes of the observation, which is well correlated with the observed and modeled O3 depletion. Challenges related to the detection of SPE impact on O3 in the summer hemisphere are discussed. We find that a careful analysis of simulation results can be essential when isolating the SPE impact from background variation. Plain Language Summary: The 4th of November 2001 solar proton event (SPE) is one of the strongest events in the era of satellite observations. However, no direct satellite‐based observations have been reported for this particular event. Here, we have used observations from the Optical Spectrograph and Infrared Imaging Systems (OSIRIS) instrument, onboard the Odin satellite, to study the impact of this SPE on middle atmospheric ozone in the southern polar region. As OSIRIS is dependent on sunlight to measure ozone, we study the impact in the southern hemisphere during summertime. The observations are compared to a state‐of‐the‐art global climate model that can simulate the atmosphere with meteorological dynamics, coupled with chemistry and radiation such as solar UV and proton events. The results show a modest but detectable upper stratosphere and lower mesospheric ozone depletion in the south polar region. Comparison between observation and model reveals the model has lower ozone levels before the event but captures well the ozone depletion after the SPE. Key Points: We analyze Optical Spectrograph and Infrared Imaging Systems/Odin observations from the southern summer pole and compare them to Whole Atmosphere Community Climate Model simulationsA modest but detectable upper stratospheric and lower mesospheric O3 depletion is seenThe simulated NOx enhancement shows good correlation with the observed and modeled O3 depletion [ABSTRACT FROM AUTHOR]

Non‐hydrostatic km‐scale weather and climate models show significant improvements in simulating clouds and precipitation, especially of convective nature. However, even km‐scale models need to parameterize some physical processes and are thus subject to the corresponding parameter uncertainty. Systematic calibration has the advantage of improving model performance with transparency and reproducibility, thus benefiting model intercomparison projects, process studies, and climate‐change scenario simulations. In this paper, the regional atmospheric climate model COSMO v6 is systematically calibrated over the Tropical South Atlantic. First, the parameters' sensitivities are evaluated with respect to a set of validation fields. Five of the most sensitive parameters are chosen for calibration. The objective calibration then closely follows a methodology previously used for regional climate simulations. This includes simulations considering the interaction of all pairs of parameters, and the exploitation of a quadratic‐form metamodel to emulate the simulations. In the current set‐up with 5 parameters, 51 simulations are required to build the metamodel. The model is calibrated for the year 2016 and validated in two different years using slightly different model setups (domain and resolution). Both years demonstrate significant improvements, in particular for outgoing shortwave radiation, with reductions of the bias by a factor of 3–4. The results thus show that parameter calibration is a useful and efficient tool for model improvement. Calibrating over a larger domain might help to further improve the overall performance, but could potentially also lead to compromises among different regions and variables, and require more computational resources. Key Points: A systematic calibration method is applied to improve the performance of a km‐resolution regional climate model over the tropical AtlanticCloud‐related model performance at the km‐scale is significantly improved through systematic calibrationThe calibrated parameter setting is robust among different years [ABSTRACT FROM AUTHOR]

The formation of the Aralkum (Aral Desert), following the severe desiccation of the former Aral Sea since the 1960s, has created what may be regarded as one of the world's most significant anthropogenic dust sources. In this paper, focusing on dust emission and transport patterns from the Aralkum, the dust life‐cycle has been simulated over Central Asia using the aerosol transport model COSMO‐MUSCAT (COnsortium for Small‐scale MOdelling‐MUltiScale Chemistry Aerosol Transport Model), making use of the Global Surface Water data set to take into account the sensitivity to changes in surface water coverage over the region between the 1980s (the "past") and the 2010s (the "present"). Over a case study 1‐year period, the simulated dust emissions from the Aralkum region increased from 14.3 to 27.1 Tg year−1 between the past and present, an increase driven solely by the changes in the surface water environment. Of these simulated modern emissions, 14.5 Tg are driven by westerly winds, indicating that regions downwind to the east may be worst affected by Aralkum dust. However a high degree of interannual variability in the prevailing surface wind patterns ensures that these transport patterns of Aralkum dust do not occur every year. Frequent cloud cover poses substantial challenges for observations of Central Asian dust: in the Aralkum, over two‐thirds of the yearly emissions are emitted under overcast skies, dust which may be impossible to observe using traditional satellite or ground‐based passive remote sensing techniques. Furthermore, it is apparent that the pattern of dust transport from the Aralkum under clear‐sky conditions is not representative of the pattern under all‐sky conditions. Plain Language Summary: Since the 1960s the Central Asian lake that used to be known as the Aral Sea has almost completely dried out, due to human activity. This environmental disaster has created a new desert known as the Aralkum (the "Aral Desert"), which now has a size of 245 km × 245 km across. Dried lakes such as the Aralkum can be very effective sources of wind‐driven atmospheric dust. The soils of the Aralkum are also contaminated with agricultural chemicals from nearby croplands, making the Aralkum a major regional threat to human health. Using an atmospheric computer model, we explore the consequences of the new Aralkum for the patterns of atmospheric dust and their potential impacts in Central Asia. We find that the new Aralkum has contributed an extra 7% per year to the total dust quantity over Central Asia, however due to thick cloud cover over two thirds of this dust from the Aralkum cannot be seen by Earth‐observing satellites. The wind patterns over the Aralkum vary from year to year, so while our simulations predict that most of the Aralkum's dust is transported to the east during the simulation year, during other years plenty more dust will be transported elsewhere. Key Points: The impact of changes in surface water coverage over the Aralkum (the former Aral Sea) for dust emission and transport is investigatedThere is a high degree of interannual variability in the directions of dust‐emitting winds over the AralkumOver two thirds of Aralkum dust activity occurs under thick cloud cover, limiting the possibility of it being observed by satellites [ABSTRACT FROM AUTHOR]

The Western North Atlantic Ocean (WNAO) is a complex land‐ocean‐atmosphere system that experiences a broad range of atmospheric phenomena, which in turn drive unique aerosol transport pathways, cloud morphologies, and boundary layer variability. This work, Part 2 of a 2‐part paper series, provides an overview of the atmospheric circulation, boundary layer variability, three‐dimensional cloud structure, and precipitation over the WNAO; the companion paper (Part 1) focused on chemical characterization of aerosols, gases, and wet deposition. Seasonal changes in atmospheric circulation and sea surface temperature explain a clear transition in cloud morphologies from small shallow cumulus clouds, convective clouds, and tropical storms in summer, to stratus/stratocumulus and multilayer cloud systems associated with winter storms. Synoptic variability in cloud fields is estimated using satellite‐based weather states, and the role of postfrontal conditions (cold‐air outbreaks) in the development of stratiform clouds is further analyzed. Precipitation is persistent over the ocean, with a regional peak over the Gulf Stream path, where offshore sea surface temperature gradients are large and surface fluxes reach a regional peak. Satellite data show a clear annual cycle in cloud droplet number concentration with maxima (minima) along the coast in winter (summer), suggesting a marked annual cycle in aerosol‐cloud interactions. Compared with satellite cloud retrievals, four climate models qualitatively reproduce the annual cycle in cloud cover and liquid water path, but with large discrepancies across models, especially in the extratropics. The paper concludes with a summary of outstanding issues and recommendations for future work. Key Points: Atmospheric circulation and sea surface temperature drive large seasonal changes in precipitation, surface fluxes, and cloud typesSynoptic activity in winter yields the highest seasonal rain rates, low‐cloud occurrence, and cloud droplet number concentrationsClimate models simulate a wide range of low‐cloud properties, with improved results for models with more sophisticated turbulence schemes [ABSTRACT FROM AUTHOR]

According to recent field studies, almost half of the New Particle Formation (NPF) events occur aloft, in a residual layer, near the top of the boundary layer. Therefore, measurements of the meteorological parameters, precursor gas concentrations, and aerosol loadings conducted at the ground level are often not representative of the conditions where the NPFs take place. This paper presents new measurements obtained during the Turbulent Flux Measurements of the Residual Layer Nucleation Particles, conducted at the Southern Great Plains research site. Vertical turbulent fluxes of 3–10 nm‐sized particles were measured using a sonic anemometer and two condensation particle counters with nominal cutoff diameters of ≥ $\ge $3 nm and ≥ $\ge $10 nm mounted at the top of the 10‐m telescoping tower. Aerosol number size distribution (5–300 nm) was determined through the ground‐based Scanning Mobility Particle Sizers. The size selected (15–50 nm) particle hygroscopicity was derived with the Humidified Tandem Differential Mobility Analyzer. The ground level observations were supplemented by vertically‐resolved measurements of horizontal and vertical wind speeds and aerosol backscatter. The data analysis suggests that (a) turbulent flux measurements of 3–10 nm particles can distinguish between near‐surface and residual‐layer small particle events; (b) sub‐50 nm particles had a hygroscopicity value of 0.2, suggesting that organic compounds dominate atmospheric nanoparticle chemical composition at the site; and (c) current methodologies are inadequate for estimating the dry deposition velocity of sub‐10 nm particles because it is not feasible to measure particle concentration very near the surface, in the diffusion sublayer. Plain Language Summary: The atmosphere is filled with microscopic particles, termed "aerosols." Many of these particles are produced through a complex process known as new particle formation or nucleation. As the particle concentration in the atmosphere has been shown to influence Earth's radiative balance and human health, the formation of new particles from the gas phase is a topic of interest for both climate and air quality sciences. Researchers have identified some factors that influence nucleation events, though none of these factors can explain and predict nucleation consistently. There is evidence that roughly half of the nucleation events detected at ground level originate above the surface, where the process is favored by colder temperatures, lower preexisting particle concentrations, and higher relative humidity. Therefore, ground level measurements may not always be representative of the conditions under which nucleation takes place. The inability to distinguish between ground level and elevated nucleation events is contributing to inconsistencies in our understanding of the factors that influence nucleation. Here, we show that vertical turbulent fluxes of 3–10 nm‐sized particles can be used to distinguish between near‐surface and residual‐layer nucleation. We also show that the existing methodology is inadequate for the estimation of particle dry deposition velocity. We believe these findings will help to better represent aerosols in regional and global climate models. Key Points: Near‐surface/residual‐layer particle nucleation events can be identified by positive/negative turbulent fluxes of 3–10 nm sized particlesHygroscopic diameter growth factor measurements show that organics are dominating sub‐50 nm composition at the Southern Great Plains siteThe eddy‐covariance measurements are inadequate for the estimation of sub‐10 nm particle deposition velocity in most environments [ABSTRACT FROM AUTHOR]

The Grid‐point Atmospheric Model of the IAP LASG version 3 (GAMIL3) has been developed by upgrading the horizontal resolution, methods of parallel computation, boundary layer scheme, aerosol parameterization, convective parameterization, stratocumulus cloud fraction scheme, land component, and coupler, as well as tuning some moist physical parameters with large uncertainties. Its performance is evaluated, and the results show significant improvements compared with the previous version, GAMIL2. The simulated performance of mean states is notably enhanced, including the energy budget terms at the top of the atmosphere (TOA) and surface, shortwave/longwave cloud radiative forcing (SWCF/LWCF), precipitation, zonal wind, low‐level temperature, 500‐hPa geopotential height, and snow cover fraction in the Northern Hemisphere. The characteristics of internal variability are captured well, such as the frequency band and active areas of quasi‐biweekly (QBW) oscillation, spectral power of convectively coupled equatorial waves (CCEWs), Madden‐Julian Oscillation (MJO) eastward propagation, and heat flux response to El Niño‐Southern Oscillation (ENSO), and these variabilities are generally strengthened in GAMIL3. In addition, the anthropogenic aerosol climate effects are weakened when using the forcings recommended by CMIP6. Plain Language Summary: The Coupled Model Intercomparison Project (CMIP) provides a multimodel platform for research in climate change originating from unforced variability or in response to changes in anthropogenic forcings. The Atmospheric Model Intercomparison Project (AMIP) experiment, which is part of the sixth phase of CMIP (CMIP6), is designed to evaluate the atmospheric component in the climate system. The GAMIL3 is the atmospheric component of Flexible Global‐Ocean‐Atmosphere‐Land System Model Grid‐point version 3 (FGOALS‐g3), one of the CMIP6 models, and it has been developed from GAMIL2. This paper presents a description of the GAMIL3 model and its upgrades, the performance of the simulated mean state and internal variability, and the anthropogenic aerosol climate effects. This description is useful for understanding climate variability and change. Key Points: This paper presents GAMIL3 and the results of its AMIP simulationsOverall performance is improved, and climate variabilities are strengthenedAnthropogenic aerosol effects using the CMIP6 protocol are weaker than those from the model's default approach [ABSTRACT FROM AUTHOR]

Coupled ocean‐atmosphere models still suffer from systematic biases in simulating the sea‐surface temperature (SST) in the equatorial Pacific. Like in many current generations of climate models, the Chinese Academy of Sciences (CAS) Earth System Model (CAS‐ESM2‐0) simulated warmer SST in the eastern Pacific and colder SST in the central to western Pacific at the equator. We analyzed the physical processes causing these biases. We find that model biases in surface heat flux are a consequence, not the cause, of the SST bias. Ocean currents are implicated as the cause. The biases in ocean currents come from both the ocean model itself and the bias in surface winds in the atmospheric model. We show that the bias of ocean currents in the standalone ocean model causes warming in the region of positive precipitation bias in the standalone atmospheric model. When the two models are coupled, the precipitation bias is greatly amplified. Anomalous equatorial winds as atmospheric Rossby‐gravity wave response to precipitation induce additional bias in ocean currents that transport heat in the upper ocean. The cold equatorial SST bias in the central and western Pacific is found to be due to temperature advection by the anomalous zonal currents, while the warm bias in the eastern Pacific is due to advection by the anomalous zonal and meridional currents as well as downwelling. Results from the analysis offer insights into the causes of the SST biases in coupled models in the equatorial Pacific. Plain Language Summary: The systematic bias of simulated sea‐surface temperature (SST) in the equatorial Pacific is a well‐known problem in coupled ocean‐atmosphere models. This paper uses CAS‐ESM2‐0 as a case study to investigate such a problem. We found that the SST bias in this model is caused by ocean dynamics rather than the surface heat flux. The bias in ocean currents comes from both the standalone ocean model and amplification by bias in the atmospheric model. The precipitation bias in the atmospheric model causes the Rossby‐gravity wave response of the surface wind to force additional bias in ocean currents. The cold equatorial SST bias in the central and western Pacific in CAS‐ESM2‐0 is found to be due to temperature advection by the anomalous zonal currents, while the warm bias in the eastern Pacific is due to advection by the anomalous zonal and meridional currents as well as downwelling. Key Points: The annual equatorial Pacific SST bias in CAS‐ESM2‐0 is caused by ocean dynamics rather than surface heat fluxBias in ocean currents is contributed by both the standalone ocean model and atmospheric surface windsBias in atmospheric precipitation impacts surface winds as equatorial Rossby‐gravity wave response to latent heating [ABSTRACT FROM AUTHOR]

Energetic particle precipitation (EPP) impact on the middle atmospheric ozone chemistry potentially plays an important role in the connection between space weather and Earth's climate system. A variant of the Whole Atmosphere Community Climate Model (WACCM‐D) implements a detailed set of ionospheric D‐region chemistry instead of a simple parameterization used in the earlier WACCM versions. This allows WACCM‐D to capture the impact of EPP in more detail. Here, we validate the ion chemistry of the WACCM‐D by analyzing the middle atmospheric ozone response to the EPP forcing during well‐known solar proton events (SPEs). We use a multi‐satellite approach to derive the middle atmospheric sensitivity for the SPE forcing as a statistical relation between the solar proton flux and the consequent ozone change. An identical sensitivity analysis is carried out for the WACCM‐D model results, enabling one‐to‐one comparison with the results derived from the satellite observations. Our results show a good agreement in the sensitivity between satellites and the WACCM‐D for nighttime conditions. For daytime conditions, we find a good agreement for the satellite data sets that include the largest SPEs (maximum proton flux >104 pfu). However, for those satellite data‐sets with only minor and moderate SPEs, WACCM‐D tends to underestimate the sensitivity in daytime conditions. In summary, the WACCM‐D with its full ion chemistry and transportation demonstrates a realistic representation of the SPE sensitivity of ozone, and thus provides a conservative platform for long‐term EPP impact studies. Plain Language Summary: In this paper, we test the atmospheric chemistry included in the most recent version of a state of the art climate model. For this test, we use measurements from four satellite instruments to derive the atmospheric ozone sensitivity to solar proton forcing as a statistical relation. We derive the same relation from the climate model simulations and perform a one‐to‐one comparison with the satellite results. Our comparisons show a good agreement between the simulated and observed sensitivity for nighttime conditions. For daytime conditions, we find a good agreement only when the strongest proton events are included. If we consider only weak and moderate proton events, the climate model tends to underestimate the sensitivity compared to the satellites. We conclude that, overall, the chemistry included in the model provides a realistic representation of the atmospheric impact from protons. This is important for long‐term simulations of the space weather influences on climate. Key Points: Atmospheric ozone sensitivity to solar proton events is derived from multiple satellites and Whole Atmosphere Community Climate Model (WACCM‐D) modelA good agreement exists between nighttime data sets. For daytime, there is agreement only when largest, major solar proton events (SPEs) are includedThe results indicate WACCM‐D likely provides a realistic and/or conservative estimate of ozone sensitivity to SPEs [ABSTRACT FROM AUTHOR]

Low‐level mixed‐phase clouds are important for Earth's climate but are poorly represented in climate models. A one‐moment microphysics scheme from Seiki and Roh (2020, https://doi.org/10.1175/JAS-D-19-0266.1) improves the representation of supercooled water and verifies it with a single‐column model. We evaluate the performance of this scheme using a global cloud‐system‐resolving simulation. We show that the scheme has several major improvements over the original scheme on which it is based, which underestimated the generation of supercooled droplets. The new scheme suppresses the original scheme's tendency to overestimate the conversion of cloud water to rain, vapor to cloud ice, and cloud water to cloud ice. It greatly improves the previously underestimated production of low‐level mixed‐phase clouds at middle‐to‐high latitudes, particularly over the ocean at the middle latitudes of the Southern Hemisphere. It also increases the lifetime of liquid clouds, thus improving the simulation of low‐level liquid clouds in western coastal regions of the tropics. The temperature dependency of the ratio of mass fraction of liquid cloud to the sum of ice and liquid clouds, F, reveals that mixed‐phase clouds statistically develop in a much wider range of temperature (−30°C ∼ 0°C), which supports the development of more mixed‐phase clouds in our simulation. The change to a wider range of F at given temperature is expected to be important, because it allows more complex feedback processes to arise from different cloud phase regimes. An improved simulation in seasonal variation of shortwave radiation and its cloud radiative effect are also identified. Plain Language Summary: Low‐level mixed‐phase clouds are important for the Earth's radiation budget because they persistently develop especially over the Southern Ocean, and reflect sunlight back to space. However, most climate models have suffered from simulating those mixed‐phase clouds, which has been a major source of uncertainty to project a future climate change. This paper reveals that an improved modeling of mixed‐phase clouds leads to better representation of the global distribution and seasonal change of mixed‐phase clouds considerably, which also results in an improved simulation of the Earth's radiation budget. The present result is capable of contributing to improve not only our climate model, but current climate models worldwide. Key Points: We use a global cloud‐system‐resolving simulation to analyze a one‐moment microphysics scheme for representing mixed‐phase low‐level cloudsWe show that the new scheme improves the representation of supercooled water and increases the lifetime of liquid cloudsThe new scheme also better represents seasonal changes in reflection of solar radiation, compared with the previous schemes [ABSTRACT FROM AUTHOR]

Upward lightning occurs generally from tall structures. The mechanism of the initiation of upward lightning is not yet fully understood. Upward lightning can be classified into two categories based on either the absence or the presence of other lightning activity prior to the upward flash. This work proposes an explanation of how upward lightning flashes can be triggered by nearby lightning activity. It is generally thought that the lightning activity prior to the flash will intensify the electric field at the tip of the tall structures. However, to the best of our knowledge, no attempt has been made to evaluate theoretically this hypothesis. In this paper, we derive analytically the electric field enhancement on the ground (or at the top of tall structures) based on different triggering scenarios. These fields are later used in a simplified corona model to evaluate if they are able to trigger upward lightning. It is shown that both slow processes such as leader propagation and faster return strokes can trigger an upward negative flash from relatively short structures of a few tens of meters, even without any slowly varying background electric field. This study confirms theoretically experimental observations and it provides new insights into the mechanisms of initiation of upward flashes from tall structures. Key Points: Analytical formulas are derived to describe the electrostatic field changes associated with horizontal and vertical leadersThe derived formulas are used to evaluate the electric fields in different scenarios leading to the triggering of an upward flashUpward lightning can be triggered by nearby lightning activity, either during leader propagation phase, or after the return stroke phase [ABSTRACT FROM AUTHOR]

Changes in concentrations of greenhouse gases lead to changes in radiative fluxes throughout the atmosphere. The value of this change, the instantaneous radiative forcing, varies across climate models, due partly to differences in the distribution of clouds, humidity, and temperature across models and partly due to errors introduced by approximate treatments of radiative transfer. This paper describes an experiment within the Radiative Forcing Model Intercomparision Project that uses benchmark calculations made with line‐by‐line models to identify parameterization error in the representation of absorption and emission by greenhouse gases. Clear‐sky instantaneous forcing by greenhouse gases is computed using a set of 100 profiles, selected from a reanalysis of present‐day conditions, that represent the global annual mean forcing from preindustrial times to the present day with sampling errors of less than 0.01 W m−2. Six contributing line‐by‐line models agree in their estimate of this forcing to within 0.025 W m−2 while even recently developed parameterizations have typical errors 4 or more times larger, suggesting both that the samples reveal true differences among line‐by‐line models and that parameterization error will be readily identifiable. Agreement among line‐by‐line models is better in the longwave than in the shortwave where differing treatments of the water vapor continuum affect estimates of forcing by carbon dioxide and methane. The impacts of clouds on instantaneous radiative forcing are estimated from climate model simulations, and the adjustment due to stratospheric temperature changes estimated by assuming fixed dynamical heating. Adjustments are large only for ozone and for carbon dioxide, for which stratospheric cooling introduces modest nonlinearity. Key Points: Mean clear‐sky instantaneous radiative forcing by greenhouse gases is computed with six benchmark models using 100 atmospheric profilesSampling error is several times smaller than the level of disagreement among models, which is itself smaller than parameterization errorThe impacts of clouds and stratospheric adjustment are roughly estimated; adjustments are large only for carbon dioxide and ozone [ABSTRACT FROM AUTHOR]

Surface wind speed has great impacts on the economy, environment, and society around the world. Based on 24 global climate models (GCMs) from Coupled Model Intercomparison Project Phase 6 (CMIP6), this paper assesses the historical surface wind speed over China, and quantifies the advancements of CMIP6 over CMIP5. In addition, future changes of surface wind speed under the Shared Socioeconomic Pathway scenarios of 1‐2.6, 2‐4.5, and 5‐8.5 (SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5) are also provided, by using 18 out of the 24 models with all three scenarios. Results show that multimodel ensemble mean of CMIP6 can well capture the spatial distributions of the annual, summer, and winter surface wind speed and generally performs better than both the median of the individual CMIP6 GCMs and multimodel ensemble mean of CMIP5 in monthly, seasonal, and annual wind climatology. However, CMIP6 GCMs fail to reproduce the observed decreasing trends, in terms of magnitude and/or sign. Meanwhile, the annual cycle of the surface wind speed simulated by CMIP6 GCMs falsely demonstrates the maximum in winter instead of spring. In the context of global warming, surface wind speed is projected to decrease in most parts of China in the middle and late 21st century under the three scenarios. Moreover, the trends of wind speed averaged over China will decrease significantly in both annual and winter under all three scenarios, but it will increase significantly in summer under SSP5‐8.5. Key Points: CMIP6 can well capture the spatial distribution and climatology of surface wind speed over China and outperforms CMIP5 in generalAmong the individual CMIP6 models, CAMS‐CSM1‐0 performs best in reproducing the present‐day surface wind speed over ChinaSurface wind speed is projected to decrease in the mid‐ and late‐21st century under SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5 scenarios [ABSTRACT FROM AUTHOR]

The mechanism of the generation of reaction‐diffusion waves in the mesopause region (80–90 km) has been studied analytically. These waves are the propagating phase fronts arising in the oscillations of O, O3, H, OH, and HO2 concentrations. They appear in the presence of horizontal eddy diffusion in zonal direction when mesospheric photochemistry responds subharmonically (with a period of 2 days) to diurnal variations of solar radiation. The photochemical system in the mesopause region is a nonlinear oscillator which can be roughly described by a system of two differential nonautonomous equations with power law nonlinearity which was derived in our earlier papers. To model the wave propagation, we have considered a continuous chain of oscillators with diffusion coupling and with the phase of external periodic forcing depending linearly on spatial coordinates. It was found that the reaction‐diffusion waves are caused by specific "wind" type transport appearing in the equations for the amplitudes of 2‐day photochemical oscillations of O and H concentrations due to the zonal inhomogeneity of the external forcing. The obtained expression for the wave propagation velocity fully confirmed the earlier numerical results that the magnitude of the velocity is proportional to the diffusion coefficient and the gradient of the external forcing phase. The wave propagation direction is determined by the definite phase relations specified by internal parameters of the system only. Key Points: We study the 2‐day photochemical oscillations and their phase fronts zonally traveling under the action of eddy diffusionWaves are caused by "wind" type transport appearing in equations for oscillation amplitudes due to zonal inhomogeneity of solar radiationThe wave propagation direction is determined by definite phase relations of 2‐day photochemical oscillations [ABSTRACT FROM AUTHOR]

This study assesses the benefit of convective‐scale data assimilation (DA) for model initialization using well‐known functional relationships between lightning flash rate and vertical velocity. Based on the relationships, a lightning DA scheme to update model kinematic states was implemented in the Weather Research and Forecasting Data Assimilation (WRFDA) three‐dimensional variational (3DVar) system. This scheme combines total lightning observations with model‐based prescribed vertical velocity profiles to retrieve kinematic information through a DA scheme. With the availability of space‐borne lightning imagers in recent years, total lightning observations from the Lightning Mapping Imager (LMI) on board the FY‐4A geostationary satellite were assimilated in combination with radar DA. A detailed analysis of the impact of the lightning DA scheme on convective precipitation forecasting was conducted using a squall line case over Beijing on 13 July 2017. The assimilation of LMI data provides added benefits to the assimilation of radar radial winds by reducing wind errors and strengthening convergence along the squall line in the analysis. Although the microphysical states are identical due to the assimilation of reflectivity, the lightning DA scheme helps in promoting updraft developments at lightning observation locations, which improves the representation of mixed‐phase convection. The quantitative verification of short‐term convective forecasts indicated that the lightning DA adds value to the radar DA by improving the precipitation forecast skill. The lightning DA scheme was evaluated further for a heavy rainfall case in 2018 over the Beijing metropolitan area and revealed overall similar forecast improvements for composite reflectivity and accumulated rainfall. Plain Language Summary: Lightning flashes are closely related to the upward air motions in thunderstorms and hence are indicative of strong wind convergence. Currently, lightning imagers on board the geostationary satellites provide increased availability of lightning data over broad regions and can improve weather forecasting accuracy. This paper describes how the space‐borne lightning observations could be employed to update model kinematic states and improve convective precipitation forecasting. Key Points: A lightning data assimilation (DA) scheme to update model kinematic states was developed using a three‐dimensional variational (3DVar) systemThe event data from Lightning Mapping Imager (LMI) aboard the FY‐4A geostationary satellite were assimilated to reflect lightning horizontal dimensionThe new lightning DA scheme improves the convective analysis and storm forecasting in two severe convective cases [ABSTRACT FROM AUTHOR]

It is challenging to develop observationally based spatial estimates of meteorology in Alaska and the Yukon. Complex topography, frozen precipitation undercatch, and extremely sparse in situ observations all limit our capability to produce accurate spatial estimates of meteorological conditions. In this Arctic environment, it is necessary to develop probabilistic estimates of precipitation and temperature that explicitly incorporate spatiotemporally varying uncertainty and bias corrections. In this paper we exploit the recently developed ensemble Climatologically Aided Interpolation (eCAI) system to produce daily historical estimates of precipitation and temperature across Alaska and the Yukon Territory at a 2 km grid spacing for the time period 1980–2013. We extend the previous eCAI method to address precipitation gauge undercatch and wetting loss, which is of high importance for this high‐latitude region where much of the precipitation falls as snow. Leave‐one‐out cross‐validation shows our ensemble has little bias in daily precipitation and mean temperature at the station locations, with an overestimate in the daily standard deviation of precipitation. The ensemble is statistically reliable compared to climatology and can discriminate precipitation events across different precipitation thresholds. Long‐term mean loss adjusted precipitation is up to 36% greater than the unadjusted estimate in windy areas that receive a large fraction of frozen precipitation, primarily due to wind induced undercatch. Comparing the ensemble mean climatology of precipitation and temperature to PRISM and Daymet v3 shows large interproduct differences, particularly in precipitation across the complex terrain of southeast and northern Alaska. Plain Language Summary: It is challenging to create spatial maps of precipitation and temperature in Alaska and the Yukon. The rugged terrain and extreme conditions, particularly snow and wind, along with the extremely sparse station networks, limit our ability to estimate historical conditions. Thus, it is critical to understand the uncertainty in our spatial estimates of meteorological conditions. Here we develop a new estimate of uncertainty for historical meteorology and include corrections to precipitation measurements for errors due to snowfall and wind. We show that there are large differences between several independent mapping efforts. We then demonstrate that our ensemble product skillfully predicts the observed probability of occurrence of multiple precipitation events sizes (the ensemble is statistically reliable). Finally, our precipitation corrections increase precipitation estimates by more than 30% in some portions of Alaska. Key Points: It is difficult to estimate the spatial variability in meteorology across the Arctic due to complex topography, sparse observations, and frozen precipitationWe developed a probabilistic approach to estimate precipitation and temperature, including precipitation undercatchThere are large uncertainties in our precipitation and temperature estimates over much of Alaska [ABSTRACT FROM AUTHOR]

This work is the first of a two‐part study that aims to develop a computationally efficient bias correction framework to improve surface PM2.5 forecasts in the United States. Here, an ensemble‐based Kalman filter (KF) technique is developed primarily for nonrural areas with approximately 500 surface observation sites for PM2.5 and applied to three (GEOS‐Chem, WRF‐Chem, and WRF‐CMAQ) chemical transport model (CTM) hindcast outputs for June 2012. While all CTMs underestimate daily surface PM2.5 mass concentration by 20–50%, KF correction is effective for improving each CTM forecast. Subsequently, two ensemble methods are formulated: (1) the arithmetic mean ensemble (AME) that equally weights each model and (2) the optimized ensemble (OPE) that calculates the individual model weights by minimizing the least‐square errors. While the OPE shows superior performance than the AME, the combination of either the AME or the OPE with a KF performs better than the OPE alone, indicating the effectiveness of the KF technique. Overall, the combination of a KF with the OPE shows the best results. Lastly, the Successive Correction Method (SCM) was applied to spread the bias correction from model grids with surface PM2.5 observations to the grids lacking ground observations by using a radius of influence of 125 km derived from surface observations, which further improves the forecast of surface PM2.5 at the national scale. Our findings provide the foundation for the second part of this study that uses satellite‐based aerosol optical depth (AOD) products to further improve the forecast of surface PM2.5 in rural areas by performing statistical analysis of model output. Plain Language Summary: Air quality forecasting plays an important role in informing the general public and decision‐makers on reducing exposure to air pollution. Air quality models simulating atmospheric constituents such as particulate matter with a diameter less than 2.5 μm (PM2.5) are often used to provide daily forecasts. However, these models are subject to large error and uncertainty as a result of the incomplete representation of the real atmosphere. Here, we develop a computationally efficient framework to improve model forecasts by performing bias correction on model outputs. We focus on nonrural areas in the continental United States and show that our technique improves model forecasts of surface PM2.5. In a companion paper, we focus on the application of satellite data to improve PM2.5 forecasting in rural areas. Key Points: The chemical transport models (GEOS‐Chem, WRF‐Chem, and WRF‐CMAQ) show systematically low bias of PM2.5Model output postprocessing with surface data and the ensemble Kalman filter technique improves the PM2.5 forecast at both local and urban scaleThe Successive Correction Method extends the PM2.5 forecast improvement from the local to regional scale [ABSTRACT FROM AUTHOR]

Recent increases in computing power mean that atmospheric models for numerical weather prediction are now able to operate at grid spacings of the order of a few hundred meters, comparable to the dominant turbulence length scales in the atmospheric boundary layer. As a result, models are starting to partially resolve the coherent overturning structures in the boundary layer. In this resolution regime, the so‐called boundary layer "gray zone," neither the techniques of high‐resolution atmospheric modeling (a few tens of meters resolution) nor those of traditional meteorological models (a few kilometers resolution) are appropriate because fundamental assumptions behind the parameterizations are violated. Nonetheless, model simulations in this regime may remain highly useful. In this paper, a newly formed gray zone boundary layer community lays the basis for parameterizing gray zone turbulence, identifies the challenges in high‐resolution atmospheric modeling and presents different gray zone boundary layer models. We discuss both the successful applications and the limitations of current parameterization approaches, and consider various issues in extending promising research approaches into use for numerical weather prediction. The ultimate goal of the research is the development of unified boundary layer parameterizations valid across all scales. Key Points: The grid resolution of atmospheric models has become fine enough that are able to partially resolve turbulent motions in the boundary layerThis resolution regime comprises the turbulence "gray zone" where the assumptions behind traditional parameterizations are no longer validWe review the current efforts by modelers to overcome the gray zone problem in order to provide useful simulations at high resolutions [ABSTRACT FROM AUTHOR]

The location of the Earth Polychromatic Imaging Camera (EPIC) aboard the Deep Space Climate Observatory (DSCOVR) offers a global view of the Earth and captures its atmosphere at different locations and time of the day. In this paper, we take advantage of that unique feature to study the daytime variability of cloud fraction in a seasonal and zonal context. We observe that the ensemble behavior over ocean has a distinctive convex shape, with higher cloud fractions at early morning and late afternoon, while no such pattern is seen over land. Unique perspectives are obtained by analyzing the cloud fraction of the globe as a whole and separately for each hemisphere, and by studying the effect of the viewing zenith angle on cloud fraction retrievals. Plain Language Summary: Images from the Earth Polychromatic Imaging Camera instrument aboard the Deep Space Climate Observatory satellite are unique compared to those from other satellites since the left edge of the image corresponds to sunrise, the right edge to sunset, and the middle to noon. That capability of capturing different parts of the day in a single picture allows us to study the daytime behavior of cloud fraction in new ways. When conducting separate analysis over ocean and land, we find that cloud fraction over ocean is generally higher in the early morning and late afternoon and minimum during the central hours of the day. However, the behavior over land is generally flat with only a very slight increase in the afternoon. Key Points: EPIC observes the sunlit side of the Earth with a frequency between 1 and 2 hr providing information on cloudiness at different local timesFour years of EPIC global cloud fraction was analyzed as a function of season and latitudeThe daytime evolution of cloudiness over ocean exhibits a clear convex shape, while it has less pronounced features over land [ABSTRACT FROM AUTHOR]

Decadal‐scale trends in aerosol and cloud properties provide important ways for understanding aerosol‐cloud interactions. In this paper, by using MODIS products (2003–2017), we analyze synergetic trends in aerosol properties and warm cloud properties over global ocean. Cloud droplet number concentration (CDNC) and aerosol parameters (aerosol optical depth, angstrom exponent, and aerosol index) show consistent decreasing trend over East Coast of the United States (EUS), west coast of Europe (WEU), and east coast of China (EC), and no significant trend in liquid water path is found over these regions during the period 2003–2017. Over regions with significant long‐term trends of aerosol loading and CDNC (e.g., EUS and WEU), the sensitivity of CDNC to aerosol loading based on the long‐term trend is closer to those derived from ground and aircraft observations and larger than those derived from instantaneous satellite observations, providing an alternative way for quantifying aerosol‐cloud interactions. A clear shift in the normalized probability density function of CDNC between the first 5 years (2003–2007) and the last 5 years (2013–2017) is found, with a decrease of around 50% in the occurrence frequency of high CDNC (>400 cm−3) over EUS and WEU. The relative variances of cloud droplet effective radius generally decrease with decreasing aerosol loading, providing large‐scale evidence for the effects of anthropogenic aerosols on the dispersion of cloud droplet size distribution. The long‐term satellite data sets provide great opportunities for quantifying aerosol‐cloud interactions and further confronting these interactions in climate models in the future. Key Points: Three major pollutant outflow regions show consistent decrease of CDNC, AOD, angstrom exponent, and aerosol indexThe sensitivity of CDNC to aerosol loading based on the long‐term trend is closer to those derived from ground and aircraft observationsThe relative variances of cloud droplet effective radius generally decrease with decreasing aerosol loading [ABSTRACT FROM AUTHOR]

The year 2019 marks the thirtieth anniversary of the development of the first regional climate model (RCM), and here an overview is provided of the progress in regional modeling research and of the main challenges lying ahead. RCMs were primarily developed to provide fine‐scale climate information for impact studies, but they have evolved into general and multipurpose modeling tools. Among the main achievements in RCM research the focus is on: the development of community RCMs applicable to a wide variety of studies and regional contexts; the increase of model simulation length up to centennial scales and spatial resolutions up to convection‐permitting scales (few kilometers), leading to a better understanding of regional to local climate change signals; the development of fully coupled Regional Earth System Models; the inception of intercomparison projects culminating in the international Coordinated Regional Climate Downscaling Experiment; the extensive use of RCM simulations for impact assessments; and the involvement of the scientific community from developing countries in climate modeling research. Among the outstanding issues in need of more attention are the Added Value of using this downscaling technique; various technical aspects concerning RCM simulations; and uncertainties in RCM‐based climate projections. Future directions in RCM research are discussed, with highlight on: transition to convection‐permitting modeling systems; further development of Regional Earth System Models including the human component; next phase of the Coordinated Regional Climate Downscaling Experiment project; and use of RCMs in the distillation of actionable information for contribution to climate service activities. A brief historical overview of regional climate modeling is also presented. Key Point: The paper reviews accomplishments, outstanding issues, and future research directions in regional climate modeling [ABSTRACT FROM AUTHOR]

Vb cyclones typically emerge in the Western Mediterranean and propagate to the Northeast into Central Europe. This paper explores the temporal characteristics of Vb cyclone occurrence based on cyclone tracks identified at the atmospheric levels of Z700 and sea level pressure, using JRA‐55 reanalysis data for the period 1959–2015. The risk of Vb occurrence was significantly high in the 1960s and has remained at a lower level since then. Vb cyclones do not occur fully randomly according to a Poisson point process. Eleven well‐separated and distinct clusters as well as 11 hiatus periods are identified, with average occurrence rates of 21.5 and 5.2 yrea−1, respectively. During the event of Vb, the large‐scale atmospheric circulation is changed into a state favoring the development of successive Vb cyclones. Clustering is very prominent in the case of Genoan Vb cyclones in summer as well as those Vb cyclones developing over the Iberian Peninsula or the North African Coast in winter. Superposition of the polar and the subtropical jet stream over the Western Mediterranean is identified as a main feature at the onset of Vb cyclones. Vb cyclone occurrence appears to be synchronized with the Northern Atlantic Oscillation (NAO; at Z500) and Arctic Oscillation (AO; at Z1000). Clusters have occurred when both NAO and AO were negative. This relation applies to Western Mediterranean cyclones not following a Vb track as well, however to a much weaker extent. In contrast, Vb cyclone frequency was particularly low from 1988 to 1997 during a sustained positive phase of both NAO and AO. Key Points: In the past 50 years, the frequency of Vb cyclones was high in the 1960s and lower since thenVb cyclones were strongly synchronized with NAO and AO; high frequency clusters occurred when NAO and AO were negativeThe coupling of the polar and the subtropical jet stream over the Western Mediterranean is a driving mechanism of the onset of Vb cyclones [ABSTRACT FROM AUTHOR]

We introduce a new climate model (GISS E2.2) that has been specially optimized for the middle atmosphere and whose output is being contributed to the CMIP6 archive. The top of the model is at a geopotential altitude of 89 km, and parameterizations of moist convection and various forms of gravity wave drag based on tropospheric processes are chosen specifically for this optimization. We first evaluate the model in its configuration as a coupled atmosphere‐chemistry model with respect to its simulation of the mean state of the middle atmosphere, from the mesosphere down through the upper troposphere/lower stratosphere. Then we assess its use as a coupled atmosphere‐ocean climate model by exploring its mean ocean climatology. To evaluate its variability, we report on its simulation of the primary modes in the troposphere, stratosphere, and ocean. Two climate change simulations are presented, the responses to instantaneous increases of 2xCO2 and 4xCO2, run with two different ocean models. Sensitivity studies are performed to illustrate the effect of parameterizations on the model results. We compare these results to the lower vertical resolution/top GISS Model E2.1, whose output has also been submitted to CMIP6. The different choices made for these models are explored. It is shown that important improvements in the circulation above and below the tropopause can be obtained when attention is paid to representation of middle atmosphere processes in climate model development. Plain Language Summary: A new "high‐top" GISS climate model with results submitted to the CMIP6 archive is described, and the model development is explained. The model was optimized to produce a realistic middle atmosphere, which involved its simultaneous assessment along with its simulation of tropospheric climate. In particular, the interplay between the parameterization of convection in the troposphere and the dynamics of the middle atmosphere requires appreciation of the needs of both regions. Key Points: This article presents the new GISS "high‐top" climate modelIt discusses the advantages of a higher model top and increased vertical resolutionIt emphasizes the need for the middle atmosphere to be a focus in climate model development [ABSTRACT FROM AUTHOR]

Future changes in Sahel precipitation are uncertain because of large differences among projections from various models. In order to explore this uncertainty, we use a storyline approach which seeks to identify alternative plausible evolutions of Sahel precipitation and their driving factors. By analyzing projections from the CMIP6 climate models, we show that changes in North Atlantic and in Euro‐Mediterranean temperatures explain up to 60% of the central Sahel precipitation change uncertainty. We then construct several storylines of Sahel precipitation change based on future plausible changes in North Atlantic and Euro‐Mediterranean temperatures. In one storyline, an amplified warming of both the North Atlantic and the Euro‐Mediterranean areas promotes a northward shift of the West African monsoon, increasing precipitation over the central Sahel, while, in the opposite storyline, a moderate warming in both regions is associated with a small change in precipitation over the central Sahel and a decrease in precipitation over the western Sahel, at the end of the 21st century. These results indicate that Sahel precipitation uncertainty will not be substantially reduced unless the uncertainty in the future warming of the North Atlantic and the Euro‐Mediterranean areas is constrained. Plain Language Summary: Variations in the strength of the West African Monsoon (WAM) circulation have societal impacts on around 80 million people from Senegal to Chad. However, future projections of the WAM precipitation are uncertain for the end of the 21st century because of strong differences from one climate model to another. We show here that sources of uncertainty in Sahel precipitation depend on changes in North Atlantic and Euro‐Mediterranean temperature, relative to changes in global mean surface air temperature. We show that differences in how climate models simulate the future warming over the North Atlantic and Euro‐Mediterranean area explain a large proportion of the uncertainty in precipitation change over the central Sahel. We provide a method that could be helpful at selecting models for impact studies, based on their sensitivity to climate change over the North Atlantic ocean and Mediterranean Sea. Key Points: Future changes in Sahel precipitation are uncertainChanges in North Atlantic and Euro‐Mediterranean temperatures explain up to 60% of the Sahel precipitation change uncertaintyUncertainty in changes in Sahel precipitation is associated with uncertainty in the future northward shift of the Saharan heat low [ABSTRACT FROM AUTHOR]

Extreme stratospheric wave activity has been suggested to be connected to surface temperature anomalies, but some key processes are not well understood. Using observations, we show that the stratospheric events featuring weaker‐than‐normal wave activity are associated with increased North American (NA) cold extreme risks before and near the event onset, accompanied by less frequent atmospheric river (AR) events on the west coast of the United States. Strong stratospheric wave events, on the other hand, exhibit a tropospheric weather regime transition. They are preceded by NA warm anomalies and increased AR frequency over the west coast, followed by increased risks of NA cold extremes and north‐shifted ARs over the Atlantic. Moreover, these links between the stratosphere and troposphere are attributed to the vertical structure of wave coupling. Weak wave events show a wave structure of westward tilt with increasing altitudes, while strong wave events feature a shift from westward tilt to eastward tilt during their life cycle. This wave phase shift indicates vertical wave coupling and likely regional planetary wave reflection. Further examinations of CMIP6 models show that models with a degraded representation of stratospheric wave structure exhibit biases in the troposphere during strong wave events. Specifically, models with a stratospheric ridge weaker than the reanalysis exhibit a weaker tropospheric signal. Our findings suggest that the vertical coupling of extreme stratospheric wave activity should be evaluated in the model representation of stratosphere‐troposphere coupling. Plain Language Summary: It is challenging to predict cold spells beyond 2 weeks due to chaotic weather systems. Taking into account stratospheric variability may push predictions beyond this limit. Previous studies have suggested that a stratospheric circulation pattern is connected to surface temperature. Strong and weak stratospheric wave events are defined based on the phase of this pattern. In observations, we find more frequent cold spells over North America and less precipitation over the west coast before and around weak wave events. On the other hand, strong wave events correspond to significant weather changes. Warmer weather over North America and more precipitation over the west coast occur before strong wave events, while more frequent cold spells over North America take place around 10 days later. We reveal that the weather changes during strong wave events correspond to a transition in the vertical structure of planetary waves. State‐of‐the‐art climate models capture the overall linkage between stratospheric wave events and surface conditions. We further illustrate that the models incapable of simulating a realistic stratospheric wave pattern tend to show larger biases in the troposphere during strong wave events. Our findings urge the need to improve the stratospheric wave representation in climate models. Key Points: Increased risks of cold spells over North America follow strong stratospheric wave events or precede weak stratospheric wave eventsThe vertical wave coupling during strong wave events features a shift of vertical wave phase line from westward tilt to eastward tiltIntermodel spread in stratospheric wave structure correlates with tropospheric circulation anomalies during strong wave events [ABSTRACT FROM AUTHOR]

The use of standard aerosol models for atmospheric correction over inland and nearshore coastal waters generates large uncertainties due to their inability to appropriately characterize aerosols in those regions. To address this problem, we developed new aerosol models using data acquired from 1,475 Aerosol Robotic Network (AERONET) sites distributed in inland and coastal regions throughout the globe. We established spatially gridded (5° × 5°) aerosol models on a climatological monthly scale, with a total of 3,207 new aerosol models developed for 310 grid cells worldwide. The monthly aerosol model for each grid was derived using mean monthly values of microphysical aerosol properties from AERONET data. The optical properties (i.e., single scattering albedo and Ångström coefficient) of our global gridded aerosol models demonstrated considerable spatial and seasonal dynamics and deviated substantially from the aerosol models currently utilized by the National Aeronautics and Space Administration (NASA). We built associated lookup tables for all aerosol models and incorporated them into two atmospheric correction algorithms: the combined near‐infrared and shortwave infrared algorithm (NIR‐SWIR) and the Atmospheric Correction algorithm for inLand and Nearshore Coastal waters (ACLANC). For both algorithms, the accuracies of atmospherically corrected satellite remote sensing reflectance (Rrs) improved markedly when the new aerosol models were used, and improvements were noted across all spectral bands and across all six inland and nearshore water bodies used in this study. Our new aerosol models can be easily applied to all ocean color satellite missions to obtain accurate Rrs products for inland and coastal waters worldwide. Plain Language Summary: The use of standard aerosol models for atmospheric correction over inland and nearshore coastal waters generates large uncertainties due to their inability to characterize aerosols in those regions appropriately. This issue also represents the most challenging task of quantitative remote sensing of the inland and coastal environments. To address this problem, we developed new aerosol models using data acquired from 1,475 Aerosol Robotic Network sites distributed in inland and coastal regions throughout the globe. We established spatially gridded (5° × 5°) aerosol models on a climatological monthly scale, with a total of 3,207 new aerosol models developed for 310 grid cells worldwide. Key Points: Develop new aerosol models using data from 1,475 global Aerosol Robotic Network sitesEstablish spatially gridded aerosol models on a climatological monthly scaleAccuracies of the Rrs product improved markedly using new aerosol models [ABSTRACT FROM AUTHOR]

Infrasound—that is, acoustic waves with frequencies below the threshold of human hearing—has historically been used to detect and locate distant explosive events over global ranges (≥1,000 km). Simulations over these ranges have traditionally relied on large‐scale, synoptic meteorological information. However, infrasound propagation over shorter, local ranges (0–100 km) may be affected by smaller, mesoscale meteorological features. To identify the effects of these mesoscale meteorological features on local infrasound propagation, simulations were conducted using the Weather Research and Forecasting (WRF) meteorological model to approximate the meteorological conditions associated with a series of historical, small‐scale explosive test events that occurred at the Big Black Test Site in Bovina, Mississippi. These meteorological conditions were then incorporated into a full‐wave acoustic model to generate meteorology‐informed predictions of infrasound propagation. A series of WRF simulations was conducted with varying degrees of horizontal resolution—1, 3, and 15 km—to investigate the spatial sensitivity of these infrasound predictions. The results illustrate that convective precipitation events demonstrate potentially observable effects on local infrasound propagation due to strong, heterogeneous gradients in temperature and wind associated with the convective events themselves. Therefore, to accurately predict infrasound propagation on local scales, it may be necessary to use convection‐permitting meteorological models with a horizontal resolution ≤4 km at locations and times that support mesoscale convective activity. Plain Language Summary: Infrasound—defined as sound waves below the threshold of human hearing—can travel up to thousands of kilometers and is often generated by explosive events. Because of these characteristics, infrasound has historically been used to detect and locate distant explosions, such as those associated with volcanoes and nuclear testing. However, smaller infrasound sources—such as infrastructure—produce signals that travel much shorter distances and have not been studied as thoroughly. For global infrasound propagation (≥1,000 km), large‐scale meteorology is the main driver, but for local infrasound propagation (0–100 km), smaller‐scale (mesoscale) meteorology may be a primary influence. This study investigates the effects of mesoscale meteorology on local infrasound propagation. Several simulations were first conducted with a high‐resolution numerical weather model. Next, the output from these simulations was fed into an acoustic model, generating high‐resolution, weather‐informed predictions of local infrasound propagation. The main finding from these simulations is that convective precipitation events noticeably influence local infrasound propagation. This indicates that to accurately predict local infrasound propagation, it may be necessary to use high‐resolution weather models that can explicitly represent convective activity. Key Points: The effects of mesoscale meteorological features on local infrasound propagation were simulated with meteorological and acoustic modelsA simulated convective precipitation event extended the range of detectable infrasonic signal near the surface by several kilometersMeteorological models that explicitly represent convection may be necessary for accurate predictions of local infrasound propagation [ABSTRACT FROM AUTHOR]

This study contributes to the communal effort to improve understanding of sea spray generation and transport. For the first time, laboratory‐derived sea spray generation functions (SSGFs) are parameterized in the Meso‐NH mesoscale atmospheric model and are field tested. Formulated from the MATE19 laboratory experiments (Bruch et al., 2021, https://doi.org/10.1007/s10546-021-00636-y) the two SSGFs are driven by the upwind component of the wave‐slope variance 〈Sx2〉 $\langle {S}_{x}^{2}\rangle $ (herein B21A), or both 〈Sx2〉 $\langle {S}_{x}^{2}\rangle $ and the wind friction velocity cubed u∗3 ${u}_{\ast }^{3}$ (herein B21B). In this first attempt to incorporate the SSGFs in Meso‐NH, the simulations are run without a wave model, and the wave‐wind SSGFs are assumed wind‐dependent. Model evaluation is achieved with a new set of sea spray and meteorological measurements acquired over the 0.1–22.75 μm radius range and U10 1–20 m s−1 wind speeds onboard R/V Atalante during the 25 day SUMOS field campaign in the Bay of Biscay. The B21B SSGF offers particularly good sensitivity to a wide range of environmental conditions over the size range, with an average overestimation by a factor 1.5 compared with measurements, well below the deviations reported elsewhere. B21A also performs well for larger droplets at wind speeds above 15 m s−1. Associated with airflow separation and wave breaking, wave‐slope variance allows to represent multiple wave scales and to scale sea spray generation in the laboratory and the field. Using Meso‐NH simulations we find that sea spray may be transported inland and to altitudes well above the marine atmospheric boundary layer. Plain Language Summary: The effects of sea spray on weather and climate remain poorly understood as a result of sparse measurements and large uncertainties in the generation flux. With the aim of improving sea spray transport in atmospheric models, two sea spray generation functions derived from the MATE19 laboratory campaign are parameterized in the Meso‐NH mesoscale atmospheric model. The simulations are run over the Bay of Biscay in February–March 2021, and are compared with super‐micron sea spray concentrations measured during the SUMOS field campaign. Results show that the laboratory‐derived generation functions allow accurate predictions of sea spray concentrations. Furthermore, simulations show that sea spray droplets can be transported far over land, and high into the atmosphere, highlighting their availability to contribute to a wide range of atmospheric processes. Key Points: With comparable wave‐slope variance behavior in laboratory and field conditions we use laboratory‐derived sea spray generation functions in the fieldParameterized with laboratory generation functions and validated using field measurements, Meso‐NH yields accurate sea spray concentrationsBy populating the atmosphere beyond 2.5 km altitude and 100 km inland, sea spray can intervene in a range of weather and cimate processes [ABSTRACT FROM AUTHOR]

Entrainment and associated mixing (i.e., entrainment‐mixing) have been shown to impact drop size distributions. However, most past studies have focused on warm clouds and have not considered the impacts on mixed phase clouds (i.e., those containing liquid and ice particles). This study characterizes the impacts of entrainment‐mixing on mixed phase cloud properties over the Southern Ocean using in situ observations. By taking advantage of strong correlations between droplet clustering and entrainment‐mixing, a clustering metric is used as a proxy to assess the degree of mixing. This maximizes the available sample size for a statistical analysis of entrainment‐mixing impacts on mixed phase properties. A positive relationship is found between the magnitude of droplet clustering and large ice concentrations (those with maximum dimensions greater than ∼300 μm), suggesting entrainment‐mixing enhances the Wegener‐Bergeron‐Findeisen (WBF) process. Particle size distributions are averaged over different ranges of liquid (liquid water content (LWC)) to total water content (TWC) ratio. Since the ratio is expected to transition from 1 to 0 during glaciation, differences in the distributions provide insight into the relation of entrainment‐mixing to mixed phase cloud evolution. Mixed phase samples with the greatest large ice concentrations occur at LWC/TWC < 0.4 in low clustering regions. However, these samples are relatively few, whereas high clustering regions have a greater frequency of samples with LWC/TWC < 0.4. This suggests sublimation/vapor sinks associated with entrainment can counteract the enhanced WBF. In high clustering regions, distributions of small droplets are relatively constant and large droplets (>30 μm) are preferentially removed as LWC/TWC transitions from 1 to 0. Plain Language Summary: Mixed phase clouds (i.e., liquid and ice particles within the same sample volume) have been notoriously difficult to represent in weather and climate models. To improve simulations, continued exploration of mixed phase properties and processes using high resolution observations is necessary. This study uses in situ observations to explore the potential impacts of entrainment and the associated mixing (i.e., entrainment‐mixing) on mixed phase microphysical properties. In situ observations are taken primarily from low‐level clouds over the Southern Ocean acquired during the Southern Ocean Cloud‐Radiation Aerosol Transport Experimental Study. This study takes advantage of the fact that cloud drop clustering is strongly correlated with entrainment‐mixing. Namely, drop clustering is used as a proxy variable to diagnose entrainment‐mixing and the associated impacts on mixed phase clouds. Results suggest entrainment‐mixing can result in an enhanced Wegener‐Bergeron‐Findeisen (WBF) process, as seen by a positive relationship between drop clustering and large ice concentrations. However, the enhanced WBF process can be offset by sufficiently strong entrainment‐mixing. Key Points: A strong relationship between drop clustering and entrainment‐mixing exists on large spatial scales (0.1–1.0 km)Drop clustering is positively correlated with large ice (maximum dimension exceeding 300 μm) number concentrations in mixed phase clouds [ABSTRACT FROM AUTHOR]

This study evaluates high latitude stratiform mixed‐phase clouds (SMPC) in the atmosphere model of the Energy Exascale Earth System Model version 2 (EAMv2) by utilizing one‐year‐long ground‐based remote sensing measurements from the Atmospheric Radiation and Measurement (ARM) program. A nudging approach is applied to model simulations for a constrained comparison with the ARM observations. Observed and modeled SMPCs are sampled and collocated to address the difference in data resolution, so that we can consistently evaluate their macrophysical properties at the North Slope of Alaska (NSA) site in the Arctic and the McMurdo (AWR) site in the Antarctic. We found that EAMv2 overestimates SMPC frequency of occurrence at both sites. However, the model captures the observed larger cloud frequency of occurrence at the NSA site. For collocated SMPCs, the annual statistics of observed cloud macrophysics are generally reproduced at the NSA site, while at the AWR site, there are larger biases. Compared to the AWR site, the lower cloud top and cloud base and the warmer cloud top temperature observed at NSA are well simulated. On the other hand, simulated cloud phase is substantially biased. The model largely overestimates liquid water path, and the ice water path is underestimated at NSA, but at AWR, the liquid water path is frequently underestimated due to the dominance of snow in Antarctic SMPCs. As a result, the observed hemispheric difference in cloud phase partitioning is misrepresented in EAMv2. This study implies that additional model development is needed for high latitude mixed‐phase clouds. Plain Language Summary: Stratiform mixed‐phase clouds (SMPCs) contain both liquid droplets and ice crystals at temperatures between −40° and 0°C. These clouds are ubiquitous at high latitudes and can largely impact global climate change. This study evaluates how well the SMPCs are simulated in the newly released second version of the Energy Exascale Earth System Model atmosphere model (EAMv2) by comparing simulated cloud properties to ground‐based observations. Observed cloud properties are obtained at two high latitude sites in the Arctic and Antarctic, respectively. The meteorological states in model simulations are constrained toward reality to allow a consistent comparison between the model and observations. We find that EAMv2 reasonably simulates the more frequent occurrences of SMPCs, the higher cloud top and cloud base, and the warmer cloud top temperature at the Arctic site than at the Antarctic site, consistent with the observations. However, the model overly simulates the SMPC occurrences at both sites compared to observations. Biases in simulated cloud boundaries are also found, particularly in the Antarctic. Moreover, the simulated cloud phase partitioning, the fraction of liquid‐phase water to ice‐phase water, is substantially biased. This study implies the requirement of additional model development for high latitude mixed‐phase clouds. Key Points: Stratiform mixed‐phase clouds sampled from the Energy Exascale Earth System Model version 2 nudged simulation are consistently evaluated with ground‐based remote sensing dataCloud boundary properties and their hemispheric difference are better simulated than the cloud phaseCloud phase is largely biased, with an opposite hemispheric difference feature in supercooled liquid fraction compared to observations [ABSTRACT FROM AUTHOR]

The Matuyama‐Brunhes reversal of Earth's magnetic dipole field took place 0.78 Ma ago, and detailed temporally resolved paleomagnetic data are available for this period. A geomagnetic reversal is expected to impact the cosmic ray flux, which in turn affects atmospheric ionization rates. To assess the magnitude of this effect, three preexisting models are used in tandem to yield 3D time series of the atmospheric ionization rates for the entire globe during the reversal. Specifically the time variable paleomagnetic field data is input into a particle trajectory calculator to produce geomagnetic cutoff rigidity maps for the entire globe. This is combined with an empirical model of the local interstellar cosmic ray spectrum and ionization yield functions based on Monte Carlo atmospheric particle simulations to produce novel time variable 3D atmospheric ionization rates during the reversal. As the dipole field weakens, the atmospheric ionization increases at low latitudes. The increase is ca. 25% at the surface and up to a factor of 5 in the upper atmosphere. Globally, ionization rates increase around 13% at the surface and up to a factor of 2 in the upper atmosphere, whereas polar regions are largely unaffected. Finally, the change in ionization due to the solar 11‐year cycle is greatly affected by the reversal. The relative change in atmospheric ionization between solar‐minimum and solar‐maximum varies between 2 and two orders of magnitude. Plain Language Summary: The Matuyama‐Brunhes reversal of Earth's magnetic dipole field took place 0.78 Ma ago, and detailed paleomagnetic observations make it possible to reconstruct the magnetic field during this period. A geomagnetic reversal is expected to impact the cosmic ray flux through the atmosphere, which in turn affects the amount of ion‐pairs that are produced in it. This is potentially important for a range of atmospheric processes which depend on the electrical properties of the atmosphere. To assess how much the Matuyama‐Brunhes reversal affects atmospheric ionization rates, we feed the magnetic field data into a series of models to obtain the ion‐pair production rate throughout the atmosphere. We find that as the dipole field weakens, the atmospheric ionization increases toward the equator. The increase is ca. 25% at the surface and up to a factor of 5 in the upper atmosphere. Globally, ionization rates increase around 13% at the surface and up to a factor of 2 in the upper atmosphere, whereas polar regions are largely unaffected. Finally, the change in ionization due to the solar 11‐year cycle is greatly affected by the reversal. The relative change in atmospheric ionization between solar‐minimum and solar‐maximum varies between 2 and two orders of magnitude. Key Points: During the course of a geomagnetic reversal, global ionization rates are heightenedThe effect is most prominent at central latitudes, and increases with altitudeThe amplitude of the solar 11‐year ionization modulation is highly dependent on the dipole strength [ABSTRACT FROM AUTHOR]

Using hindcasts produced by a coupled climate model, this study evaluates whether the model can forecast the observed spatiotemporal complexity in the El Niño−Southern Oscillation (ENSO) during the period 1982−2011: the eastern Pacific (EP), central Pacific‐I (CP‐I) and ‐II (CP‐II) types of El Niño, and the multi‐year evolution events of El Niño occurred in 1986–1988 (i.e., 1986/87/88 El Niño) and La Niña occurred in 1998–2000 (i.e., 1998/99/00 La Niña). With regard to the spatial complexity, it is found that the CP‐I type of El Niño is the easiest to hindcast, the CP‐II is second, and the EP is most difficult to hindcast as its amplitude is significantly underestimated in the model used here. The model deficiency in hindcasting the EP El Niño is related to a warm bias in climatological sea surface temperatures (SSTs) in the tropical eastern Pacific. This warm bias is related to model biases in the strengths of the Pacific Walker circulation and South Pacific high, both of which are notably weaker than observed. As for the temporal complexity, the model successfully hindcasts the multi‐year evolution of the 1998/99/00 La Niña but fails to accurately hindcast the 1986/87/88 El Niño. This contrasting model performance in hindcasting multi‐year events is found to be related to a cold bias in climatological SSTs in the tropical central Pacific. This cold bias result enables the model La Niña, but not El Niño, to activate intrabasin tropical‒subtropical interactions associated with the Pacific Meridional Mode that produce the multi‐year evolution pattern. Plain Language Summary: The El Niño−Southern Oscillation (ENSO) events differ considerably from one event to another. These differences commonly appear in its spatial structure (three different types of El Niño as EP, CP‐I, and CP‐II types) and temporal evolution (single‐vs. multi‐year evolution types). By evaluating climate model hindcasts of these different spatiotemporal types of events during the period 1982–2011, this study finds that the CP‐I El Niño is the easiest to hindcast, followed by the CP‐II El Niño, while the EP El Niño is the most difficult to hindcast. In addition, it is found that the model has a better hindcast performance for single‐year events than multi‐year ones and is better able to hindcast multi‐year La Niña than multi‐year El Niño. The warm and cold biases in the model that impair the model's ability to hindcast the different sorts of ENSO are also identified. The findings of this study will benefit the ENSO forecast and climate model communities. Key Points: A coupled atmosphere‐ocean model's ability to forecast spatiotemporal complexity in ENSO is evaluated by analyzing its hindcastsThe EP type of ENSO is the most difficult to hindcast due to the model's warm bias in the tropical eastern Pacific that degrades amplitude forecastsMulti‐year El Niño is more difficult to hindcast than multi‐year La Niña due to the model's cold bias in the tropical central Pacific [ABSTRACT FROM AUTHOR]

Detection and attribution experiments are designed for the causal diagnosis of features in the climate system, including trends in mean climate and extreme events. While several detection and attribution data sets now exist, the coarse resolution of the climate models used (∼100‐km) often hinders their application to topographically complex regions like Aotearoa New Zealand and small island nations. The coarse atmospheric resolution may also be detrimental for simulating certain features of the atmospheric circulation, including the jets, blocking and cyclones. To address this, here we introduce a new set of climate model runs consisting of high‐resolution atmospheric simulations from the Conformal Cubic Atmospheric Model (CCAM) non‐hydrostatic global model. The variable‐resolution grid employed by CCAM enables targeted high‐resolution simulations over New Zealand (12‐km) and intermediate resolution over the wider South Pacific region (12–35‐km). Simulations from the historical experiment (years 1982–2021), consisting of ten initial condition ensemble members, are presented and evaluated here. The evaluation focuses on the representation of the large‐scale atmospheric circulation over the Southern Hemisphere including the jet streams, storm tracks, cyclones, blocking and teleconnections, as well as more localized temperature and precipitation variability and extremes specifically over New Zealand. While certain biases are highlighted and discussed for the large‐scale atmospheric circulation, CCAM is found to perform especially well for various precipitation and temperature‐based extreme indices at smaller scales across New Zealand, generally outperforming state‐of‐the‐art reanalysis and coarser resolution global atmospheric models. These results support further application of the CCAM ensemble for studying weather and climate extremes in attribution studies. Plain Language Summary: The coarse resolution of climate model output is particularly problematic for small island nations or regions with complex terrain, like New Zealand. Here we present a new set of climate model simulations based on a stretched grid global atmospheric model which enhances the resolution over New Zealand (12‐km) and the South Pacific region (12–35‐km). A comprehensive evaluation of the model is presented, including for the jets, storm tracks, cyclones, blocking, teleconnections, and localized extremes and variability. While certain biases are highlighted at the large scale, the model ensemble is shown to perform particularly well at simulating the variability and extremes of temperature and precipitation over New Zealand. Key Points: A new perturbed initial condition ensemble of historical high‐resolution climate model simulations is presented targeting New ZealandThe jets, storm tracks, cyclones, blocking, teleconnections, and localized extremes and variability are evaluatedThe ensemble is compared against various CMIP6 and AMIP models showing encouraging results over New Zealand [ABSTRACT FROM AUTHOR]

The Horn of Africa drylands (HAD) are among the most vulnerable regions to hydroclimatic extremes. The two rainfall seasons—long and short rains—exhibit high intraseasonal and interannual variability. Accurately simulating the long and short rains has proven to be a significant challenge for the current generation of weather and climate models, revealing key gaps in our understanding of the drivers of rainfall in the region. In contrast to existing climate modeling and observation‐based studies, here we analyze the HAD rainfall from an observationally‐constrained Lagrangian perspective. We quantify and map the region's major oceanic and terrestrial sources of moisture. Specifically, our results show that the Arabian Sea (through its influence on the northeast monsoon circulation) and the southern Indian Ocean (via the Somali low‐level jet) contribute ∼80% of the HAD rainfall. We see that moisture contributions from land sources are very low at the beginning of each season, but supply up to ∼20% from the second month onwards, that is, when the oceanic‐origin rainfall has already increased water availability over land. Further, our findings suggest that the interannual variability in the long and short rains is driven by changes in circulation patterns and regional thermodynamic processes rather than changes in ocean evaporation. Our results can be used to better evaluate, and potentially improve, numerical weather prediction and climate models, and have important implications for (sub‐)seasonal forecasts and long‐term projections of the HAD rainfall. Plain Language Summary: The drylands in the Horn of Africa (HAD) have suffered a series of droughts in recent years with the consecutive failure of both the major rainfall seasons. So far, the rainfall in this region has been studied using observations and outputs from global climate models. In contrast, here, we employ a moisture tracking model, which traces individual parcels of moisture‐carrying air parcels from their destination (the HAD region) to their sources (land areas within and outside the HAD, and the oceans). We find that the majority of the moisture which causes rainfall in the HAD region comes from the oceans. However, in recent years, we see that land contributions are gaining importance in affecting water availability in the region. Key Points: Indian Ocean contributes majority of the moisture to rainfall in the Horn of Africa drylandsThe role of vegetation and land contribution to rainfall during the short rains is increasingMoisture contributions are driven more by atmospheric circulation changes rather than increased evaporation from source regions [ABSTRACT FROM AUTHOR]

Climate projections in the North Atlantic region suffer from great uncertainties, and projections of precipitation are given with a large spread. Some of this uncertainty is related to projections of the North Atlantic warming hole (NAWH). The Community Earth System Model version 2 (CESM2) projects a relatively strong and extensive NAWH, with future sea surface cooling extending to Northern Scandinavia. This study investigates the relatively large winter precipitation decrease projected by CESM2 in the northeastern North Atlantic region, reinforced in a regional model. Three future scenarios from CESM2 are dynamically downscaled with the Weather Research and Forecast model. A methodology to separate convective and orographic from stratiform precipitation is applied to explore the physical mechanisms. Changes in stratiform precipitation closely relate to storm‐track changes, which varies between the scenarios. Convective precipitation decreases by up to 50% over the Norwegian Sea at the end of the century, which is robust across the scenarios. This is explained by the underlying reduced sea surface temperatures of the NAWH, leading to reduced evaporation and reduced convective activity and intensity. The orographic precipitation maximum over the Scandinavian mountains is shifted upstream, likely affected by increased static stability and flow blocking, which also relates to the NAWH. This shift is possibly also explained by more frequent rain versus snow, as well as reduced cross‐barrier wind speeds. This study contributes to highlight the importance of focusing future research efforts on the NAWH, in order to constrain future climate projections in this region. Plain Language Summary: How precipitation patterns and amounts will change in the future is of high societal importance, for example, for hydropower, drinking water, irrigation, industry, and structural design. Given the large uncertainties involved in future climate projections this is still difficult to quantify. One uncertainty is related to climate model predictions of important ocean circulation features in the North Atlantic, which further affect projections of sea surface temperatures. The Community Earth System Model version 2 projects a large area of sea surface cooling in the North Atlantic toward the end of the century. In our study we point to possible connections between this cooling and future decreases in winter precipitation in the northeastern North Atlantic region. This is mostly associated with a reduction in convective precipitation, which is dependent on the temperature of the sea surface. In this area the Scandinavian mountains contribute significantly to the precipitation received through orographic effects. We also find a future upstream shift in the maxima of orographic precipitation, which is indicated to be also (partly) connected to the sea surface cooling. These findings may help in understanding the large spread in future precipitation projections in this region and should urge for more research toward constraining them. Key Points: Winter convective precipitation decreases by up to 50% toward the end of the century in the northeastern North Atlantic regionThis is associated with reduced sea surface temperatures (the North Atlantic warming hole), reduced evaporation and convectionFuture upstream shift in orographic precipitation distribution is likely affected by increasing static stability associated with the NAWH [ABSTRACT FROM AUTHOR]

The surface mass balance (SMB) of large polar ice sheets and of snow and ice surfaces in general are incompletely understood because of the complexity of processes involved. One such process, drifting and blowing snow, has only been considered in a very simplified way in current meteorological and climatological models. To address this problem, the CRYOWRF model has been developed, a coupled model between the meteorological Weather Research and Forecasting model (WRF) and the snow model SNOWPACK, augmented by a detailed treatment of drifting and blowing snow. Applying CRYOWRF to the SMB of Antarctica, we find that the model reproduces measurements of SMB with similar errors than current models. Drifting and blowing snow and its sublimation play a particularly important role, especially in regions of strong katabatic winds. The CRYOWRF simulations are also in line with satellite estimates of blowing snow frequency. There is a need to further consolidate results by simulations with a higher grid resolution and by including more measurements of SMB contributions from snow fall to transport and sublimation. Plain Language Summary: Assessing current and predicting future sea level rise in connection with the general fate of our snow and ice masses on Earth requires understanding snow precipitation in extreme environments and the dynamics of snow on the surface. Over large parts of Antarctica, drifting and blowing snow and sublimation, which is the phase change of ice back to atmospheric vapor, are the only surface ablation processes and need therefore to be well quantified. With a new model, that shows similar performance to other models, we find that drifting and blowing snow and its sublimation play an important role for the snow mass balance especially in regions with strong winds. This has consequences not only for the snow mass balance alone but for the whole ice sheet dynamics as well as for estimating precipitation in these extreme environments. Key Points: Evaluation of the coupled Weather Research and Forecasting (WRF) and SNOWPACK model CRYOWRF, including a blowing snow module, shows reasonable representation of blowing snowBetter representation of atmospheric conditions at a station level by CRYOWRF compared to WRFBlowing snow is an important component of the Antarctic surface mass balance, especially in the katabatic regions of the continent [ABSTRACT FROM AUTHOR]

Planetary boundary layer (PBL) schemes parameterize unresolved turbulent mixing within the PBL and free troposphere (FT). Previous studies reported that precipitation simulation over the Amazon in South America is quite sensitive to PBL schemes and the exact relationship between the turbulent mixing and precipitation processes is, however, not disentangled. In this study, regional climate simulations over the Amazon in January–February 2019 are examined at process level to understand the precipitation sensitivity to PBL scheme. The focus is on two PBL schemes, the Yonsei University (YSU) scheme, and the asymmetric convective model v2 (ACM2) scheme, which show the largest difference in the simulated precipitation. During daytime, while the FT clouds simulated by YSU dissipate, clouds simulated by ACM2 maintain because of enhanced moisture supply due to the enhanced vertical moisture relay transport process: (a) vertical mixing within PBL transports surface moisture to the PBL top, and (b) FT mixing feeds the moisture into the FT cloud deck. Due to the thick cloud deck over Amazon simulated by ACM2, surface radiative heating is reduced and consequently the convective available potential energy is reduced. As a result, precipitation is weaker from ACM2. Two key parameters dictating the vertical mixing are identified, p, an exponent determining boundary layer mixing and λ, a scale dictating FT mixing. Sensitivity simulations with altered p, λ, and other treatments within YSU and ACM2 confirm the precipitation sensitivity. The FT mixing in the presence of clouds appears most critical to explain the sensitivity between YSU and ACM2. Plain Language Summary: Predictions of weather and climate in terms of clouds and precipitation over the Amazon in South America are quite uncertain. This uncertainty has been largely attributed to errors in the planetary boundary layer (PBL) scheme, which represents turbulent mixing. A lack of understanding of the relationship between turbulence, clouds, and precipitation processes prevents us from improving PBL representation in models to achieve better weather and climate simulations. This study disentangles the turbulence/clouds/precipitation relationship, and identifies the root cause of model errors in PBL schemes using regional climate simulations over the Amazon. Two PBL schemes, the Yonsei University (YSU) scheme, and the asymmetric convective model v2 (ACM2) scheme, are examined, which show the largest difference in the simulated precipitation. The main difference between the two PBL schemes is the dissipation (YSU) or maintenance (ACM2) of clouds during daytime above the boundary layer, which modulates surface heating and consequently precipitation. The maintenance of a thick cloud deck over the Amazon in ACM2, is caused by enhanced vertical transport of moisture from the surface to above the boundary layer. Such an improved understanding of the turbulence/clouds/precipitation relationship allow us to propose potential solutions to improve PBL schemes in weather and climate models. Key Points: Disentangle turbulence/cloud/precipitation processes over Amazon and reveal root cause for sensitivity to planetary boundary layer (PBL) schemes using the Weather Research and Forecasting modelFree troposphere (FT) mixing becomes prominent in the presence of clouds, which in turn supports maintenance of the FT clouds that would otherwise dissipateStronger vertical moisture relay transport in asymmetric convective model v2 (ACM2) PBL scheme supports thicker FT clouds, leading to reduced heating and precipitation [ABSTRACT FROM AUTHOR]

This study aims to detect atmospheric rivers (ARs) around the world by developing a deep‐learning ensemble method using AR catalogs of the ClimateNet data set. The ensemble method, based on 20 semantic segmentation algorithms, notably reduces the bias of the testing data set, with its intersection over union score being 1.7%–10.1% higher than that of individual algorithms. This method is then applied to the Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets to quantify AR frequency and its related precipitation in the historical period (1985–2014) and future period (2070–2099) under the Shared Socioeconomic Pathways 5–8.5 warming scenario. The six key regions, which are distributed in different continents of the globe and greatly influenced by ARs, are particularly highlighted. The results show that CMIP6 multi‐model mean with the deep‐learning ensemble method reasonably reproduces the observed AR frequency. In most key regions, both heavy precipitation (90–99 percentile) and extremely heavy precipitation (>99 percentile) are projected to increase in a warming climate mainly due to the increased AR‐related precipitation. The AR contributions to future heavy and extremely heavy precipitation increase range from 145.1% to 280.5% and from 36.2% to 213.5%, respectively, indicating that ARs should be taken into account to better understand the future extreme precipitation changes. Plain Language Summary: Atmospheric rivers (ARs) play a key role in determining the global water cycle and regional precipitation in midlatitudes. Identifying their occurrences and possible changes is therefore critical to understanding the hydroclimate and its change. However, there exist different AR detection tools applying different methods or using different thresholds. The disparities between the outputs of these methods are large and often difficult to reconcile. This study first develops a deep‐learning ensemble method to identify ARs and then applies it to study the AR frequency and its related precipitation in the state‐of‐the‐art climate model simulations for historical and future climates. The results show that AR frequency is reasonably well reproduced by the multi‐model mean. More importantly, it is projected to increase in the future at both the global and regional scales. The six key regions, in which precipitations are robustly influenced by ARs, show that both heavy and extremely heavy precipitation are projected to increase, primarily due to the enhanced AR‐related precipitation. Key Points: An ensemble method with multiple deep‐learning algorithms is established to detect atmospheric rivers (ARs)The ensemble method outperforms individual deep‐learning algorithms in AR detectionThe increase in AR‐related precipitation largely determines the future increase in heavy and extremely heavy precipitation in midlatitudes [ABSTRACT FROM AUTHOR]

Strong East Asian winter monsoon (EAWM) can enhance the Maritime Continent rainfall (MCR) via frequent southward penetration of cold air along the coast of East Asia. However, such impact diminished after the late‐1990s, primarily due to the weakened covariation between the Siberian high and Aleutian low (AL). During 1979–1998, the co‐variability between the Siberian high and AL facilitates a strong east‐west pressure gradient over East Asia. The resultant northerlies invade southward and facilitate the enhanced MCR. In contrast, such covariation is weak during 2000–2018, and it impedes the southward invasion of northerly winds and diminishes EAWM's impact. Further inspections indicate that the weakened covariation is primarily caused by the reduced variability of the AL. The underlying mechanism is confirmed by the historical simulation of version 6 Model for Interdisciplinary Research on Climate (MIROC6) that participates in phase 6 of the Coupled Model Intercomparison Project and numerical experiments with European Center‐Hamburg atmospheric model version 6.3 (ECHAM6). Plain Language Summary: East Asian winter monsoon (EAWM) is an important extratropical system to drive the variations of the Maritime Continent rainfall (MCR) via frequent southward penetration of cold air along the coast of East Asia. However, the EAWM's impact becomes diminished after the late‐1990s, and it is primarily rooted in the weakened covariation between the Siberian high (SH) and Aleutian low (AL). During 1979–1998, the co‐variability between the SH and AL causes a strong east‐west pressure gradient over East Asia. The resultant northerly winds invade southward along the coast of East Asia and facilitate the enhanced MCR. In contrast, during 2000–2018, the covariation is weak and diminishes EAWM's impact. Further analysis indicates that it is the weakened AL variability that determines the weakening of the covariation between the SH and AL. The phenomenon and the underlying mechanism are confirmed by the climate models. This study highlights the interdecadal variations of the EAWM in linking the Asian winter climate. Key Points: The impact of the East Asian winter monsoon on the Maritime Continent rainfall diminished after the late‐1990sIt arises from the weakened Siberian high–Aleutian low covariation that weakens northerly winds reaching the tropicsThe phenomenon and mechanisms are reproduced and validated by numerical experiments with two climate models [ABSTRACT FROM AUTHOR]

Regional atmospheric models struggle to maintain supercooled liquid in mixed‐phase clouds during polar cold‐air outbreaks (CAOs). Previous studies focused on the parameterization of aerosol, microphysics and turbulence to understand the origin of this widespread model bias. This study investigates the role of macrophysics parameterizations (MacP) in the simulation of mixed‐phase clouds. Km‐scale simulations are performed for a large number of CAO cases over Norway, for which continuous ground observations were collected at one site over 6 months. We use a novel analysis that attributes the cloud‐radiative errors to deficiencies in specific cloud regimes. We show that the MacP matters for cloud‐radiative effects in CAOs, but that it is probably not the primary cause of the lack of liquid water in simulated mixed‐phase clouds. Of all the MacP sensitivities explored in this study, the prognostic representation of both liquid and ice shows most promise in increasing the liquid water path. A newly proposed hybrid MacP with prognostic frozen and diagnostic liquid cloud fraction reproduces some of the benefits of the prognostic scheme at reduced cost and complexity. The two‐moment microphysics scheme in this study produces too large precipitation particles. Reducing the snow deposition rate decreases the precipitation particle sizes and largely improves the liquid water path. Simulations are less sensitive to reduced riming rates. Plain Language Summary: Cloud droplets and ice crystals often coexist in polar clouds, even at temperatures well below the freezing level. These clouds play an important role in the climate system, since they reflect more sunlight back to space than completely frozen clouds. Therefore it is important that climate and weather forecasting models are thoroughly evaluated in their ability to produce these so‐called mixed‐phase clouds. This study compares simulations of polar clouds with detailed cloud observations over Norway. We confirm that simulations produce clouds that are nearly completely frozen, while observations clearly show both liquid and frozen clouds. We test sensitivities of the simulations to specific model components to assess potential avenues for model improvement. Of all the tests performed, the ones affecting the simulated snowflake sizes seem most promising in allowing both liquid and frozen cloud to coexist. Key Points: The role of cloud‐fraction parameterizations in mixed‐phase simulations during cold‐air outbreaks is investigatedCloud‐fraction parameterizations matter for cloud‐radiative effects, but have limited impact on the liquid water contentSimulations with reduced snow depositional growth improve liquid water path and precipitation size distributions [ABSTRACT FROM AUTHOR]

The Southern Ocean (SO) clouds exert a significant influence on the Earth's radiation budget. We analyzed ship‐based remote sensing observations of SO clouds over a 5‐month long Department of Energy (DOE) ARM Measurements of Aerosols, Radiation and Clouds over the Southern Oceans (MARCUS) field campaign to better understand cloud phase variability. We developed a method to classify eight categories of hydrometeors (ice, liquid, mixed phase, rain, drizzle, snow, aerosols, and clear sky) based on measurements of lidar, radar and radiosondes. Cloud thermodynamic phases (liquid, ice and mixed phase) at coarser scales were further derived to compare with the DOE Energy Exascale Earth System Model version 1 (E3SMv1) simulation and the Earth Model Column Collaboratory (EMC2) instrument simulator. For a scale‐aware comparison with climate model simulations, we found that spatially averaging the raw remote sensing data (e.g., backscatter and reflectivity) results in increased cloud cover and cloud liquid, whereas, directly averaging cloud phase from higher to lower resolution maintains clear air regions and is thus recommended for in‐cloud frequency comparisons. For cloud thermodynamic phases in stratiform clouds, the E3SMv1 underestimates cloud ice and overestimates cloud liquid at temperatures between −40°C and 0°C. When latitudes increase, both observations and simulations show a transition of dominant phase from liquid to ice for cloud tops as well as for the entire cloud columns, but the model underestimation of ice phase is more severe at higher latitudes. Such model bias is unlikely caused by spatial scale differences or lack of heterogeneity in cloud vertical structure in the simulation. Plain Language Summary: The Southern Ocean region of Earth consists of vast open waters with extensive cloud cover throughout the year. This cloud cover prevents the ocean water below it from absorbing solar heat and maintains global ocean and atmospheric circulation patterns. Thus, it is important to improve our understanding and our ability to model this cloud cover to better predict future changes in the Earth's climate. Here, we analyzed atmospheric data obtained by a ship traversing the open waters between Australia and Antarctica for nearly 5 months. We examined observed cloud properties such as cloud ice and liquid proportions and compared them with global climate model results. We found that the model simulates more liquid and less ice at lower temperatures when compared with observations. Such model bias becomes larger at higher latitudes, indicating that Earth's surface may absorb more solar radiation than the model estimates, especially around Antarctica. Both the observations and the model show that ice clouds are common at higher latitudes whereas liquid clouds are common at lower latitudes. Key Points: Cloud phase with respect to temperature and latitude over the Southern Ocean was identified using 5‐month ship‐based measurementsCompared with observed cloud phase, the E3SMv1 model underestimates ice and overestimates liquid phase between −40°C and 0°CObservations and simulations show similar transition from liquid to ice‐dominated cloud columns and cloud top layers from 43°S to 70°S [ABSTRACT FROM AUTHOR]