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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]

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]

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]

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]

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]

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 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]

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]

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]

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]

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]

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]

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]

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]

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]

The deadly and destructive nature of tropical cyclones (TCs) makes understanding their response to future climate change of the utmost importance. TC genesis hinges on multiple factors, including an initial disturbance. African easterly waves (AEWs) have been shown to serve as such disturbances for TCs developing in the North Atlantic. It is therefore crucial to understand the relationship between AEWs and TCs and how this relationship may be affected by climate change. In this study, we examine the AEW‐TC relationship in historical and future climates using three models from the HighResMIP PRIMAVERA simulations. The AEWs and TCs were tracked in the model data using objective tracking algorithms, and AEW and TC tracks were then matched together if they were close to each other in space and time. The strength of the AEWs was measured using the eddy kinetic energy and the curvature vorticity of the waves. TC strength and intensity were measured using potential intensity and lifetime maximum 10 m windspeed. We found that future changes in the frequency of AEWs are not a good indicator of future TC activity. However, AEW strength, as well as environmental conditions conducive to strong TCs, are good indicators of AEWs that develop into TCs in both historical and future climates. Plain Language Summary: Tropical cyclones (TCs) are both deadly and destructive and it is important to understand how they will respond to future climate change. African easterly waves (AEWs) are disturbances that can develop into TCs that form in the North Atlantic. The relationship between AEWs and TCs is an important part of understanding how TCs will be affected by climate change. In this study, we examine the AEW‐TC relationship in historical and future climates using three climate models. AEWs and TCs were tracked in the model data, and AEW and TC tracks were then matched together if they were close to each other in space and time. The number of AEWs and TCs in the historical and future climates were compared. We found that future changes in the number of AEWs are not a good indicator of future changes in the number of TCs. However, AEW strength, as well as environmental conditions conducive to strong TCs, are good indicators of AEWs that develop into TCs in both historical and future climates. Key Points: Future changes in the frequency of African easterly waves (AEWs) are not a good indicator of future tropical cyclone (TC) activityAEWs that develop into TCs are stronger than non‐developing waves in historical and future climatesWave strength combined with environmental conditions are good indicators of tropical cyclogenesis from waves [ABSTRACT FROM AUTHOR]

In this study, we developed a regional coupled model for the western North Pacific (WNP) and East Asian region. The atmospheric/oceanic component of the coupled model was the Weather Research and Forecasting and the Regional Ocean Modeling System, coupled through the National Center for Atmospheric Research Coupler version 7 (CPL7). An evaluation is provided through the simulation of the climatology and interannual variability of the summer monsoon climate from 1982 to 2015. The coupled model is also compared with a solely atmospheric run, which is driven by the observed sea surface temperature (SST) as the surface boundary. The results show that the coupled model has good capability in modeling the climate in the WNP and East Asian domain, including the climatology of summer precipitation and circulation, the subseasonal (month‐to‐month) variation in the western Pacific subtropical high and associated precipitation, and the seasonal cycle of the East Asian rain belt. One merit of this model is its ability to simulate the northward and eastward shift from early to late summer. With respect to the interannual variability, the model captures the spatial pattern of the leading mode of summer precipitation, with a north–south dipole mode in the studied region. The model also reproduces the larger SST variability in the northern part of this region. The temporal correlation coefficient between the coupled simulation and observation was 0.70 for the precipitation and 0.53 for the SST leading mode. The coupled model also captures the precipitation–SST relationship, which results from reasonably considering the air–sea coupling processes in the coupled run. Plain Language Summary: Due to the resolution of global models is relatively coarse and cannot describe the regional features, especially in the monsoon regions, regional climate model is an essential solution for regional climate simulation and projection. Compared with atmospheric alone model which is driven by prescribed sea surface temperature, regional air‐sea coupled model can describe reasonably the air‐sea coupling processes and has better performance. In this study, an regional coupled model is developed based on the Community Earth System Model coupler version 7 framework. Based on our analysis, the regional coupled model reasonably reproduced most key characteristics in the East Asian monsoon region. In addition to being used in regional climate simulation and projection, it can also be conveniently coupled with global models through the coupler 7. Thus with this useful tool in future we can develop global‐regional two‐way coupling simulation system for climate predictions and projections. Key Points: A regional coupled model is developed for East Asia and western North Pacific region based on Community Earth System Model frameworkClimatology and interannual variability of the summer monsoon climate have been evaluatedThe model can well capture the climatological northward and eastward movements of the western Pacific subtropical high from June to August [ABSTRACT FROM AUTHOR]

Natural aerosols and their interactions with clouds remain an important uncertainty within climate models, especially at the poles. Here, we study the behavior of sea salt aerosols (SSaer) in the Arctic and Antarctic within 12 climate models from CMIP6. We investigate the driving factors that control SSaer abundances and show large differences based on the choice of the source function, and the representation of aerosol processes in the atmosphere. Close to the poles, the CMIP6 models do not match observed seasonal cycles of surface concentrations, likely due to the absence of wintertime SSaer sources such as blowing snow. Further away from the poles, simulated concentrations have the correct seasonality, but have a positive mean bias of up to one order of magnitude. SSaer optical depth is derived from the MODIS data and compared to modeled values, revealing good agreement, except for winter months. Better agreement for aerosol optical depth than surface concentration may indicate a need for improving the vertical distribution, the size distribution and/or hygroscopicity of modeled polar SSaer. Source functions used in CMIP6 emit very different numbers of small SSaer, potentially exacerbating cloud‐aerosol interaction uncertainties in these remote regions. For future climate scenarios SSP126 and SSP585, we show that SSaer concentrations increase at both poles at the end of the 21st century, with more than two times mid‐20th century values in the Arctic. The pre‐industrial climate CMIP6 experiments suggest there is a large uncertainty in the polar radiative budget due to SSaer. Plain Language Summary: Aerosols emitted from the ocean, such as sea salt particles (aerosols), are critical for the climate of polar regions. However, there is still uncertainty in their representation in climate models. The purpose of this work is to evaluate the representation of sea salt aerosols (SSaer) in the Arctic and Antarctic in a recent model inter‐comparison initiative, and to assess the consequences for our understanding of present‐day and future polar climate. We find that the models disagree between them and with observations from ground stations and from space. This suggests that the formulation of sea salt emissions in global models is not adapted for polar regions. With sea ice retreat, SSaer will most likely increase in the future, which makes addressing the current uncertainty an important next step for the scientific community. Key Points: CMIP6 models have a large uncertainty in present day sea salt aerosol abundance at the polesModel performance is degraded closer to the poles suggesting inadequate emission sources within the polar regionsBoth present and future radiative balance at the poles is uncertain because of sea salt aerosols [ABSTRACT FROM AUTHOR]

We introduce the latest version of the RegCM regional climate modeling system, RegCM5. Compared to the previous model version (RegCM4) the main new development is the inclusion of the non‐hydrostatic dynamical core from the weather prediction model MOLOCH, which is more accurate and much more computationally stable and efficient than the previous one. In particular, the new dynamical core is best designed for use at convection‐permitting resolutions of a few km. Several physics schemes and coupled model components have also been upgraded compared to the previous version of the model. A set of test simulations for present day climate conditions at parameterized convection and convection permitting resolutions over different European domains is presented for illustrative purposes. Overall, for these simulations RegCM5 exhibits a better performance than RegCM4 for the majority of statistics analyzed, especially at convection‐permitting resolutions. However, this performance depends on the physics schemes used, and further optimization work is under way to fully test the model in different climate settings and reduce current biases. RegCM5 is a freely available, computationally efficient, flexible, and portable Regional Earth System model designed for community use, so that prospective model users are welcome to access its code and use it for different applications. Plain Language Summary: We introduce the latest version of the RegCM regional climate modeling system, RegCM5, in which the main new development, in addition to some physics upgrades, is the inclusion of the non‐hydrostatic dynamical core from the weather prediction model MOLOCH. This makes the model more accurate and much more computationally efficient than the previous version, especially at convection‐permitting resolutions of a few km. A set of test simulations for present day climate conditions at parameterized convection and convection permitting resolutions over different European domains is presented for illustrative purposes. Overall, these simulations show that RegCM5 mostly exhibits a better performance than RegCM4 for most statistics analyzed, especially at convection‐permitting resolutions, although this performance depends on the physics schemes used. Further optimization work is under way to fully test the model in different climate settings and reduce current biases. RegCM5 is a freely available, computationally efficient, flexible, and portable Regional Earth System model designed for community use, so that prospective model users are welcome to access its code and use it for different applications. Key Points: The fifth version of the regional climate modeling system RegCM, has been developed and releasedIllustrative experiments show that the model overall performs better and is much more computationally efficient than the previous versionThe model is a community resource so that prospective users are encouraged to access it and use it [ABSTRACT FROM AUTHOR]

Ural blocking (UB) is a prominent mode of variability of the Northern Hemisphere atmospheric circulation, particularly in fall. It can persist for several days and exert a lagged influence on the wintertime NH circulation, providing predictability at the subseasonal time scale. Using two atmospheric models, we explore how the early winter atmospheric circulation responds to a 2‐week persistent UB anomaly imposed in early November. Experiments are carried out with two different configurations of Barents‐Kara (BK) sea‐ice concentration to examine whether it plays a role in how UB impacts atmospheric variability. In both models, the UB anomaly is followed by a weakening of the stratospheric polar vortex and a negative phase of the North Atlantic Oscillation (NAO), which lasts up to 2 months after the forcing is released. Interestingly, the response is more persistent under low BK sea‐ice conditions, that is, BK sea ice modulates the atmospheric response to UB. Additional experiments with prescribed sea ice concentration anomalies alone suggest that BK sea ice exerts a limited influence on early winter NH atmospheric variability. The response to UB involves a weakening of the polar vortex that persists longer under low BK sea ice, which explains the more persistent response in that configuration. Our study highlights that UB variability in November is a robust precursor for early winter NAO/polar stratosphere anomalies, and this may be more relevant in the context of declining Arctic sea‐ice extent. Provided that climate models accurately capture this teleconnection, it has the potential to improve subseasonal predictions of the NH wintertime climate. Plain Language Summary: Predicting subseasonal fluctuations of the polar stratosphere in winter (or polar vortex) is critical to anticipate weather extreme events over populated areas of the Northern Hemisphere. In this study, we highlight that a persisting high‐pressure system over the Europe/Ural region in fall (known as Ural blocking [UB]) can force a delayed response at the surface several weeks later, that resembles the negative phase of the North Atlantic Oscillation (NAO). This is found in two different atmospheric models in which the UB pattern is imposed during the first 2 weeks of November, using a regional relaxation of the troposphere. We also investigate the response to fall/early winter Barents‐Kara sea ice decline, which has been hypothesized to have a similar impact on the polar vortex/NAO, but we find that it is of secondary importance compared to the influence of UB. Key Points: Two‐week persistent Ural blocking (UB) anomalies induce a weaker polar vortex and negative North Atlantic OscillationRole of Barents‐Kara (BK) sea ice is secondary compared to the influence of UBHowever, BK sea ice modulates the persistence of the response to UB [ABSTRACT FROM AUTHOR]

We present a comparison of atmospheric transport model (ATM) simulations for carbonyl sulfide (COS), within the framework of the atmospheric tracer transport model intercomparison project "TransCom‐COS." Seven ATMs participated in the experiment and provided simulations of COS mixing ratios over the years 2010–2018, using state‐of‐the‐art surface fluxes for various components of the COS budget: biospheric sink, oceanic source, sources from fire and industry. The main goal of TransCom‐COS is to investigate the impact of the transport uncertainty and emission distribution in simulating the spatio‐temporal variability of tropospheric COS mixing ratios. A control case with seasonal surface fluxes of COS was constructed. The results indicate that the COS mixing ratios are underestimated by at least 50 parts per trillion (ppt) in the tropics, pointing to a missing tropical source. In summer, the mixing ratios are overestimated by at least 50 ppt above 40°N, pointing to a likely missing sink in the high northern latitudes. Regarding the latitudinal profile, the model spread is greater than 60 ppt above 40°N in boreal summer. Regarding the seasonal amplitude, the model spread reaches 50 ppt at 6 out of 15 sites, compared to an observed seasonal amplitude of 100 ppt. All models simulated a too late minimum by at least 2–3 months at two high northern‐latitude sites, likely owing to errors in the seasonal cycle in the ocean emissions. This study highlighted the shortcomings in the COS global budget that need to be resolved before using COS as a photosynthesis tracer. Plain Language Summary: In this study, we evaluate the state‐of‐the‐art fluxes for various components of the carbonyl sulfide (COS) budget: biospheric sink, oceanic source, sources from fire and industry. A control case with seasonal surface fluxes of COS was constructed. Seven atmospheric transport models provided simulations of COS mixing ratios. Then, the simulated mixing ratios were evaluated against atmospheric measurements at several surface sites. Results show that all models fail to capture the observed latitudinal distribution and that the model spread is small compared to the model‐observation mismatch. In summer, the overestimated mixing ratios above 40°N point to a likely missing sink in the high northern latitudes. The underestimated mixing ratios in the tropics point to a missing tropical source. This study highlighted the shortcomings in the COS global budget that need to be resolved before using COS as a photosynthesis tracer. Key Points: The model‐observation mismatch suggests there is a missing source in the tropics and a missing sink in the high northern latitude in summerAt northern latitude sites, the model spread in seasonal amplitude reaches 50 ppt compared to a mean seasonal amplitude of about 100 pptThe diurnal rectifier effect is small, decreasing the seasonal amplitude by up to 20% at continental sites [ABSTRACT FROM AUTHOR]

Twenty historical and future Coordinated Regional Climate Downscaling Experiment ensemble for Europe simulations are bias corrected to investigate the future changes in the daily maximum wind speed over Scandinavia. We use quantile mapping to adjust the wind for the historical period (1985–2014) and quantile delta mapping for two future periods (2041–2070, 2071–2100, RCP8.5) with the 3‐km spatial resolution NORA3 hindcast as the reference data set. Decomposing the variance, we find that most of the inter‐model spread in the bias and the response to climate change is due to the regional climate model over land and mainly to the general climate model over sea. On average, the mean daily maximum wind speed is projected to increase everywhere except along the western coast of Norway and Denmark. In summer, we see an opposite sign over Sweden and Finland. The Norwegian Sea experiences weaker mean and high wind whereas the Baltic Sea experiences a strengthening. Bias correction influences the amplitude of the response, not the response pattern. Wind speed distributions can have different shapes in the future, with, for example, a flattening of the distribution off the coast of Norway with more frequent weak and very strong winds. Apart from a few locations in Norway, we find an increase in the number of days in the local highest historical wind category. Overall, summer exhibits opposite signals compared to the three other seasons. At country scale, the sign of the change in the mean and 98th percentile varies greatly depending on the region and among the simulations, especially for Norway. Plain Language Summary: The Coordinated Regional Climate Downscaling Experiment ensemble for Europe consists in running regional climate models (RCMs) to improve the spatial resolution and climate simulation of global climate models outputs. In the present study, we use 20 of these simulations, named downscalings, to investigate future changes in the daily maximum wind speed over Scandinavia. As models are not perfect, they present some biases that we correct using quantile mapping, a method adjusting the whole wind speed distribution toward a reference distribution. The reference chosen is the Norwegian hindcast NORA3 that has a 3‐km spatial resolution. We show the added value of downscaling and that the bias pattern over land is mainly driven by the RCM. However, the bias correction does not modify the patterns of the future changes in wind speed. On average, the mean daily maximum wind speed will increase in Sweden, Finland, and southeastern Norway whereas it will decrease over Denmark and the Norwegian coast. Although the changes are small, more than 80% of the countries' area has the same response sign, except Norway. Over most of Scandinavia, the number of days within the category of largest historical local wind increases in the future. As in previous studies, we find large model uncertainties in the future projections. Key Points: The variance in the bias of the mean daily maximum wind speed mainly comes from the choice of the regional climate modelThe ensemble mean change by 2071–2100 shows a moderate increase in the mean wind speed over Scandinavian land except the Norwegian coastBias correction modifies the amplitude of the future wind speed changes, not their pattern [ABSTRACT FROM AUTHOR]

As the Arctic climate rapidly warms, there is a critical need for understanding variability and change in the Arctic carbon cycle, but sparse spatial coverage of observations has hindered progress. This work analyzes measurements of atmospheric CO2 in the Arctic from long‐term on‐ice measurements (the O‐Buoy Network), as well as coastal observatories from 2009 to 2016. The on‐ice measurements showed smaller seasonal amplitudes than coastal observatories, in contrast to the general observation of poleward increases of seasonal cycle amplitude. Average on‐ice measurements were also lower than their coastal counterparts during winter and spring, contradicting the expectation that CO2 increases poleward in boreal winter. We compared the observations to CO2 simulated in an updated version of GEOS‐Chem 3‐D chemical transport model, which includes new tracers of airmass history and CO2 sources and sinks. The model reproduced the observed features of the seasonal cycle and showed that terrestrial biosphere fluxes and synoptic transport explain most CO2 variability (both synoptic and interannual) over the Arctic Ocean surface. The polar airmass partially isolates the Arctic Ocean surface air from terrestrial CO2 exchange, which explains the reduced seasonal cycle amplitude and winter maxima. All Arctic coastal sites had similar CO2 interannual variability, particularly in summer, which was largely reproduced by the model. The interannual variability observed over sea ice, however, was distinct from the coastal sites and not reproduced by the model. Air‐sea CO2 exchange in and around sea ice, which was once thought to be negligible, may be an important driver of interannual variability over the Arctic Ocean. Plain Language Summary: The Arctic is undergoing immense biogeochemical change, due to rapid warming in recent decades. These rapid changes contribute to uncertainty in the Arctic carbon cycle and the physical processes impacting carbon fluxes at these latitudes. In this work, we analyzed and modeled atmospheric CO2 concentrations obtained over Arctic sea ice and compared them with CO2 concentrations measured at coastal observatories. We found that CO2 concentrations over sea ice do not follow the general observed behavior that seasonal cycle amplitudes of CO2 (dominated by photosynthesis and respiration of the terrestrial biosphere) are increasing poleward as our global climate continues to warm. We also found that the interannual growth of CO2 is different between the coast and over sea ice. Our findings suggest that another physical process (likely gas exchange in and around sea ice) may be impacting atmospheric CO2 concentrations year‐to‐year at the highest latitudes and obtaining more observations over sea ice will add to knowledge of this sparsely observed region. Key Points: Atmospheric CO2 mixing ratio observations over Arctic Ocean sea ice are examined and interpreted using a chemical transport modelSeasonal cycle amplitudes of atmospheric CO2 are smaller, on average, over sea ice than at their coastal counterpartsGas exchange in and around sea ice likely influences the spatial gradients and interannual variability of CO2 over Arctic sea ice [ABSTRACT FROM AUTHOR]

Clouds cast shadows on the surface and locally enhance solar irradiance by absorbing and scattering sunlight, resulting in fast and large solar irradiance fluctuations on the surface. Typical spatiotemporal scales and driving mechanisms of this intra‐day irradiance variability are not well known, hence even 1 day ahead forecasts of variability are inaccurate. Here, we use long‐term, high‐frequency solar irradiance observations combined with satellite imagery, numerical simulations, and conceptual modeling to show how irradiance variability is linked to the cloud size distribution. Cloud shadow sizes are distributed according to a power law over multiple orders of magnitude, deviating only from the cloud size distribution due to cloud edge transparency at scales below ≈750 m. Locally cloud‐enhanced irradiance occurs as frequently as shadows, and is similarly driven mostly by boundary layer clouds, but distributed over a smaller range of scales. We reconcile studies of solar irradiance variability with those on clouds, which brings fundamental understanding to what drives irradiance variability. Our findings have implications not only for weather and climate modeling, but also for solar energy and photosynthesis by vegetation, where detailed knowledge of surface solar irradiance is essential. Plain Language Summary: The amount of sunlight received at a given location on the Earth's surface varies on many temporal and spatial scales. Clouds have a large influence on the daily and local scale by creating alternating patterns of shadows and extra bright sunlight, which can fluctuate back and forth multiple times per minute. Weather and climate models do not capture these details, because they are necessarily simplified in order to save computational time. We aim to learn what the typical time and spatial scales of these shadows and sunny patterns are, and where they originate from. Our study is based on 10 yr of detailed sunlight observations, satellite imagery of clouds, and cloud‐resolving atmospheric modeling. We find that horizontal scales in sunlight variability are almost identical to those of clouds. Only at the smallest scales, where clouds are so small that they become transparent, do the observations of sunlight variability deviate from cloud sizes. These observations can be reproduced with a simple model based on the size distributions of clouds. This knowledge could help guide those who try to include sunlight variability in a simple way into their analysis and modeling of, for example, solar energy and photosynthesis. Key Points: Surface solar irradiance variability scales with a power law over multiple orders of magnitude, set by the distribution of cloud sizesBroken and scattered boundary layer clouds cause most of the intra‐day solar irradiance variabilityCloud shadow size can be constructed from cloud size by accounting for cloud edge transparency with an approximate length scale of 25 m [ABSTRACT FROM AUTHOR]

Clouds over tropical oceans are an important factor in the Earth's response to increased greenhouse gas concentrations, but their representation in climate models is challenging due to the small‐scale nature of the involved convective processes. We perform two 4‐year‐long simulations at kilometer‐resolution (3.3 km horizontal grid spacing) with the limited‐area model COSMO over the tropical Atlantic on a 9,000 × 7,000 km2 domain: A control simulation under current climate conditions driven by the ERA5 reanalysis, and a climate change scenario simulation using the Pseudo‐Global Warming approach. We compare these results to the changes projected in the CMIP6 scenario ensemble. Validation shows a good representation of the annual cycle of albedo, in particular for trade‐wind clouds, even compared to the ERA5 reanalysis. Also, the vertical structure and annual cycle of the intertropical convergence zone (ITCZ) is accurately simulated, and the simulation does not suffer from the double ITCZ problem commonly present in global climate models (GCMs). The response to global warming differs between the COSMO simulation and the analyzed GCMs. While both exhibit an overall weakening of the Hadley circulation, the narrowing of the ITCZ (known as the deep‐tropics squeeze) is not so pronounced in the kilometer‐resolution simulation, likely due to the absence of a double ITCZ bias. Also, there is a more pronounced intensification of the ITCZ at the equator in the kilometer‐resolution COSMO simulation, and a stronger associated increase in the anvil cloud fraction. Plain Language Summary: Tropical clouds are an important component of the climate system as they can either amplify or dampen human‐induced climate change. However, predicting how clouds will change in a warming climate is challenging due to their small‐scale nature. We perform high‐resolution atmospheric climate simulations over the tropical Atlantic to study what we can learn about tropical clouds from this relatively novel type of models. We validate the simulation under current climate conditions and find a good representation of tropical clouds in comparison to coarser climate models. In particular, the high‐resolution simulation does not suffer from the double intertropical convergence zone (ITCZ) problem commonly present in global climate models. We then study how tropical clouds will change in a warming climate and find differences between our high‐resolution simulation and coarser climate simulations. The tropical overturning (Hadley) circulation weakens in both model types, but the narrowing of the ITCZ (known as the deep‐tropics squeeze) is not so pronounced in the high‐resolution simulation, likely due to the absence of a double ITCZ bias. Key Points: We perform a km‐resolution climate simulation over the tropical Atlantic for current and future climate conditions using the Pseudo‐Global Warming approachWe find an accurate representation of the annual cycle of shallow cumulus clouds and a realistic structure of the intertropical convergence zone (ITCZ), without double ITCZThe ITCZ shows a stronger central intensification and weaker narrowing with warming than in the global climate models [ABSTRACT FROM AUTHOR]

Direct assimilation of Dual‐frequency Precipitation Radar (DPR) data of the Global Precipitation Measurement (GPM) core satellite is challenging mainly due to its long revisiting intervals relative to the time scale of precipitation, and precipitation location errors. This study explores a method for improving precipitation forecasts using GPM DPR through model parameter estimation. We developed a 28 km mesh global atmospheric data assimilation system that integrates the Nonhydrostatic ICosahedral Atmospheric Model (NICAM) and Local Ensemble Transform Kalman Filter (LETKF) coupled with a satellite radar simulator. Using the NICAM‐LETKF and GPM DPR observations, this study estimates a model cloud physics parameter corresponding to snowfall terminal velocity. To overcome the difficulties of long revisiting intervals and precipitation location errors, we propose a parameter estimation method based on a two‐dimensional histogram known as the contoured frequency by temperature diagram (CFTD). Parameter estimation effectively mitigated the gap between simulated and observed CFTD, resulting in improved 6 hr precipitation forecasts. Plain Language Summary: Direct assimilation of satellite‐borne radar data into weather forecasting models is challenging mainly due to its long revisiting intervals and precipitation location errors. This study explores a method for improving precipitation forecasts using the satellite radar data for optimizing an uncertain parameter in a weather forecasting model. We estimated a model cloud physics parameter corresponding to snowfall terminal velocity. Parameter estimation effectively mitigated the gap between simulated and observed radar reflectivity, resulting in improved 6 hr precipitation forecasts. Key Points: Direct assimilation of GPM DPR reflectivity is challenging due to its long revisiting intervals relative to the time scale of precipitationA new model parameter estimation approach based on a reflectivity‐temperature histogram is used to improve global precipitation forecastsParameter estimation of snow terminal velocity mitigated the gap between simulated and observed reflectivity, resulting in improved forecasts [ABSTRACT FROM AUTHOR]

The parameterization scheme that represents gravity waves due to convection in LMDz‐6A, the atmospheric components of the IPSL coupled climate model (IPSLCM6), is directly compared to Strateole‐2 balloon observations made in the lower tropical stratosphere from November 2019 to February 2020. The input meteorological fields necessary to run the parameterization offline are extracted from the ERA5 reanalysis and correspond to the instantaneous meteorological conditions found underneath the balloons. In general, we find a fair agreement between measurements of the momentum fluxes due to waves with periods less than 1 hr and the parameterization. The correlation of the daily values between the observations and the results of the parameterization is around 0.4, which is statistically elevated considering that we analyze around 600 days of data and surprisingly good considering that the parameterization has not been tuned: the scheme is just the standard one that helps producing a quasi‐biennial oscillation (QBO) in the IPSLCM6 model. Online simulations also show that the measured values of momentum fluxes are well representative of the zonally and averaged values of momentum fluxes needed in LMDz‐6A to simulate a QBO. The observations also show that longer waves with periods smaller than a day carry about twice as much flux as waves with periods smaller than an hour, which is a challenge since the low period waves that make the difference are potentially in the "gray zone" of most climate models. Plain Language Summary: In most large‐scale atmospheric models, gravity wave (GW) parameterizations are based on well‐understood but simplified theories and parameters which are keyed to reduce systematic errors on the planetary scale winds. In the equatorial regions, the most challenging errors concern the quasi‐biennial oscillation. Although it has never been verified directly, it is expected that the parameterizations tuned this way should transport a realistic amount of momentum flux in both the eastward and westward directions when compared to direct observations. Here, we show that it is the case, to a certain extent, using constant‐level balloon observations at 20 km altitude. The method consists in comparing directly, each day and at the location of the balloon the measured momentum fluxes and the estimation of a GW parameterization using observed values of the large‐scale meteorological conditions of wind, temperature, and precipitation. Key Points: A non‐orographic parameterization tuned to produce a realistic tropical quasi‐biennial oscillation is used to predict in‐situ observationsParameterized gravity waves (GWs) needed in large‐scale models have realistic amplitudes in the tropical lower stratosphereDay‐to‐day variations of the estimated GW momentum fluxes correlate well with observations [ABSTRACT FROM AUTHOR]

In this study, we investigate whether a better representation of precipitation in the Amazon basin arises through an explicit representation of convection and whether it is related to the representation of organized systems. In addition to satellite data, we use ensemble simulations of the ICON‐NWP model at storm‐resolving (2.5–5.0 km) scales with explicit convection (E‐CON) and coarse resolutions, with parameterized convection (P‐CON). The main improvements in the representation of Amazon precipitation by E‐CON are in the distribution of precipitation intensity and the spatial distribution in the diurnal cycle. By isolating precipitation from organized convective systems (OCS), it is shown that many of the well simulated precipitation features in the Amazon arise from the distribution of these systems. The simulated and observed OCS are classified into 6 clusters which distinguish nocturnal and diurnal OCS. While the E‐CON ensembles capture the OCS, especially their diurnal cycle, their frequency is reduced compared to observations. Diurnal clusters are influenced by surface processes such as cold pools, which aid to the propagation of OCS. Nocturnal clusters are rather associated with strong low‐level easterlies, possibly related to the Amazonian low‐level jet. Our results also show no systematic improvement with a twofold grid refinement and remaining biases related to stratiform features of OCS suggest that yet unresolved processes play an important role for correctly representing precipitating systems in the Amazon. Plain Language Summary: The Amazon basin is a relevant element of the Earth system because it influences the global water and carbon cycle, as well as it constitutes a unique ecosystem. Over this important region, conventional climate models do not simulate basic features of rainfall given their inability to resolve this physical process due to their coarse spatial resolution. In this study, we use high‐resolution simulations that allow an explicit representation of such physical process (moist convection) and compare them with a set of coarse‐resolution simulations and observed precipitation. We find that improvements in the representation of Amazon rainfall, such as the distribution of light and high intensity rain rates, as well as the spatial variability of the diurnal cycle, are explained by the explicit representation of moist convection. Moreover, these improvements arise from the representation of big and organized systems that produce intense rainfall (OCS). We find that particular environmental conditions are associated with the OCS according to their time of occurrence. Diurnal OCS are mainly influenced by interactions with the surface, while nocturnal OCS are related to strong low‐level winds. Some of the remaining discrepancies with observed OCS do not show improvements when refining the grid by a factor of two. Key Points: An explicit representation of convection enables the emergence of organized systems (OCS) leading to improved simulations of Amazon rainfallPropagating cold‐pools and strong low‐level easterlies are related to the occurrence of diurnal and nocturnal OCS, respectivelySystematic biases in the size, intensity and nocturnal precipitation phase of OCS are insensitive to a twofold refinement in resolution [ABSTRACT FROM AUTHOR]

Fundamental understanding of the climate responses to solar variability is obscured by the large and complex climate variability. This long‐standing issue is addressed here by examining climate responses under an extreme quiet sun (EQS) scenario, obtained by making the sun void of all magnetic fields. It is used to drive a coupled climate model with whole atmosphere and ocean components. The simulations reveal significant responses, and elucidate aspects of the responses to changes of troposphere/surface forcing and stratospheric forcing that are similar and those that are different. Planetary waves (PWs) play a key role in both regional‐scale responses and the mean circulation changes. Intermediate scale stationary waves and regional climate respond to solar forcing changes in the troposphere and stratosphere in a similar way, due to similar subtropical wind changes in the upper troposphere. The patterns of these changes are similar to those found in a warming climate, but with opposite signs. Responses of the largest scale PW during northern hemisphere and southern hemisphere winters differ, leading to hemispheric differences in the interplay between dynamical and radiative processes. The analysis exposes remarkable general similarities between climate responses in EQS simulations and those under nominal solar minimum conditions, even though the latter may not always appear to be statistically significant. Plain Language Summary: Understanding how climate may change under different solar conditions is both interesting and important. However it is difficult to clearly identify solar signal from the very large climate variability on broad time scales. In this study, we tackle this problem by providing a lower bound of the solar minimum condition according to our current understanding of solar physics. By specifying this extremely low solar minimum condition in a climate model that takes into consideration of the effects of ocean and middle atmosphere, we are able to identify significant climate responses, which are very different between the northern and southern hemispheres. We gain an understanding of the processes driving these responses, including how the lower and upper atmospheric processes may enhance/offset each other. By comparing these climate responses to those under nominal solar minimum conditions, we expose climate patterns that are hidden under the large climate variability in the latter. Key Points: Climate simulations under extreme quiet sun conditions reveal significant responsesHemispheric differences in the interplay between dynamical and radiative processes identifiedQuantify tropospheric/surface and stratospheric responses that are similar or different [ABSTRACT FROM AUTHOR]

The impact of autumn Arctic sea ice reduction on the winter climate in Eurasia has been widely studied, but the role of the atmospheric circulation of the preceding summer has been mostly neglected. This study investigates a linkage between the summer Arctic circulation anomaly and the ensuing East Asian Winter Monsoon (EAWM) variability. The Arctic circulation anomaly, characterized by an anomalous anti‐cyclone occupying the Arctic Ocean and its marginal seas, can strongly melt Arctic sea ice in the Kara Sea‐Laptev Sea‐East Siberian Sea‐Chukchi Sea‐Beaufort Sea through dynamic and thermodynamic processes. The combination of the summer anti‐cyclone (cyclone) anomaly and light (heavy) autumn sea ice leads to an enhanced (weakened) EAWM. By introducing the coupling relationship between summer circulation and autumn sea ice into numerical experiments, the Community Atmospheric Model version 4 model has a good capability for capturing EAWM variability. Nevertheless, the dominant features of EAWM variability cannot be reproduced when the autumn sea ice mismatches the preceding summer circulation in the model. Therefore, the preceding summer circulation may modulate the impact of sea ice on Eurasia to some extent, which may increase uncertainties in the impact of Arctic sea ice loss on winter atmospheric circulation variability. Plain Language Summary: The enhanced East Asian Winter Monsoon (EAWM) leads to cold winter and has a significant impact on the economy, agriculture, and the lives of more than one billion people in East Asia, and the reduced autumn Arctic sea ice is generally believed to influence EAWM variability with great uncertainties due to strong atmospheric internal variability and impacts of other factors. In this study, through observational analysis and large ensemble model simulation experiments, a coupling relationship between the summer Arctic anti‐cyclone anomaly and the Arctic sea ice and their synergistic effect on EAWM variability are investigated. The summer anti‐cyclone anomaly over the Arctic corresponds to light sea ice and enhanced EAWM. Model experiments show that the summer initial conditions may modulate the lag impact of sea ice boundary conditions on winter circulation. Therefore, it is important to consider the synergistic effect of initial conditions and boundary conditions for predicting EAWM and winter surface temperature. Key Points: The Arctic anti‐cyclone (cyclone) anomaly in summer is a great predictor of the East Asian Winter Monsoon (EAWM)The combination of summer anti‐cyclone (cyclone) anomaly and light (heavy) autumn sea ice leads to an enhanced (weakened) EAWMFailure to consider the synergistic effect of initial conditions and sea ice may be one reason for the uncertainty of sea ice effect [ABSTRACT FROM AUTHOR]

The uncertainty of the climatic effect of Black carbon (BC) remains large. One critical uncertainty source that needs to be captured is BC aging. Here we use the Community Atmosphere Model version 6 (CAM6) configured with the four‐mode version of the Modal Aerosol Module (MAM4) to evaluate the modeled BC aging process with recent laboratory and in‐situ measurements over China. As revealed by the comparison of BC aging timescale and number fraction of aged BC against recent measurements, the modeled condensation aging timescale is estimated to be about 0.8 hr (17%) faster than the chamber measurement, and the diurnal variations of modeled BC aging degree are typically higher than observations mainly due to the fast increase in modeled BC aging degree during daytime. Further analysis shows that the condensation aging dominates (>70%) BC aging across China. More specifically, the condensation of secondary organic aerosol (SOA) vapor contributes most to BC aging over China. Slowing down BC aging increases the modeled surface BC concentration over remote Western China and BC burden, but hardly changes surface BC concentration over Eastern China. Our results suggest that BC aging representation in the MAM4 needs to be further improved toward slowing down the BC aging rate, especially the condensation aging by SOA, to improve the BC simulation over remote areas and its impact on BC transport in MAM4. Plain Language Summary: Black carbon (BC) has a warming effect on climate. Currently, significant divergences in simulated BC burden and optical properties have been shown among global climate models (GCMs), and hence BC radiative forcing remains largely uncertain. BC aging is one of the key processes that contribute to uncertainties in BC simulation. In this study, we evaluate the BC aging process in a global climate model with recent laboratory and in‐situ measurements over China. Our study illustrates that BC aging is too fast in the model compared with observations, and the dominant process for modeled BC aging is SOA vapor condensation over China. We further show how different BC aging rates might affect modeled surface BC concentration and BC burden. Our results suggest that further measurements are needed to better understand BC aging mechanism to improve BC aging in GCMs. Key Points: BC aging is too fast in MAM4 compared against measured BC condensation aging timescale and number fraction of aged BCSOA vapor condensation plays a dominant role in modeled BC aging process over ChinaSlowing down BC aging increases surface BC over remote Western China and BC burden, but hardly changes surface BC over Eastern China [ABSTRACT FROM AUTHOR]

Most of China's carbon sink inversion research uses global atmospheric transport models to assimilate natural fluxes, which quantifies the biosphere and ocean carbon budget with a relative coarse spatial resolution and long timescale from a weekly or monthly perspective. Toward high‐resolution inversion of CO2 fluxes, a novel carbon flux forecast model was developed in this study, which was then further used to carry out carbon assimilation based on a regional chemical transport model (CMAQ) at higher spatial (64 × 64 km2) and temporal (1 hr) resolutions. An Ensemble Kalman Smoother was applied as the assimilation algorithm and further extended to assimilate surface CO2 observations. Concentrations and fluxes were simultaneously assimilated as state variables to help reduce the uncertainty in the initial CO2 fields with the joint data assimilation framework (JDAS). In general, the posterior fluxes reproduced the seasonal, daily and hourly variation effectively, demonstrating the ability to fully absorb and utilize observations. Moreover, the influence of the choice of assimilation window on the carbon flux inversion was assessed via sensitivity experiments, revealing 36 hr to be the optimum length. Evaluation of the prior and posterior flux simulations also indicated that JDAS offers reasonable improvements, making it suitable for fine‐scale flux optimization and estimation. In addition, the posterior biosphere estimation in mainland China (−682 TgC yr−1) tends to be the optimal mathematical solution under current sparse observation coverage with daytime photosynthetic uptake, which likely leads to the overestimation of the optimized CO2 sink. This study serves as a basis for future regional and urban assessment. Plain Language Summary: Top‐down methods are used increasingly to constrain the carbon budget under the Monitoring and Verification Support framework by the United Nations Framework Convention on Climate Change. Different to previous attempts to assimilate China's carbon sink from a weekly or monthly perspective, we developed a novel regional carbon inversion system based on Community Multiscale Air Quality (CMAQ) and Ensemble Kalman Smoother with surface observations toward high‐resolution inversion of CO2 fluxes. Besides the forward, fine‐scale simulation by CMAQ, a flux forecast model with diurnal variation capability was also introduced to facilitate illustrating the hourly evolution of ensemble fluxes. The results indicate that our posterior fluxes reproduced the seasonal, daily and hourly variation effectively, which suggests the method provides a promising tool for future monitoring of the effectiveness of progress toward carbon neutrality. Key Points: An Ensemble Kalman Smoother‐based regional carbon data assimilation system was developed with Community Multiscale Air Quality to resolve fine‐scale CO2 flux variabilityThe seasonal, daily and hourly variations of posterior fluxes were reproduced effectivelySensitivity experiments further highlighted the valuable impact of the choice of assimilation window [ABSTRACT FROM AUTHOR]

Deep convection associated with tropical cyclones leads to stratosphere‐troposphere exchange (STE), which affects the upper‐tropospheric ozone concentrations in the vicinity of the cyclones. This study estimates the ozone enhancements over India due to the North Indian Ocean (NIO) cyclones‐driven STE. Indicators such as stratospheric fraction and potential vorticity calculated using the reanalysis data sets suggest that roughly 70% of the cyclones show anomalously high stratospheric intrusions. Aircraft observations over different locations across India also show elevated ozone concentrations in the mid‐to‐upper troposphere on cyclone days. Further, ozone and stratospheric ozone tracer concentrations from Goddard Earth Observing System‐Chemistry simulations and the Copernicus Atmosphere Monitoring Service reanalysis data sets show up to 40 ppb of excess upper tropospheric ozone over India, of which stratospheric ozone accounts for roughly 60%. Stratospheric intrusion due to the Bay of Bengal and the Arabian Sea cyclones affected the upper tropospheric ozone amounts over North and South India, respectively. The stratospheric ozone was observed to propagate downwards into the troposphere, often reaching ∼600 hPa and, in some cases, even the surface. Plain Language Summary: This study estimates the increase in the amount of ozone in the upper half of the lower atmosphere ("upper troposphere") during the North Indian Ocean (NIO) cyclones. Ozone in the upper atmosphere ("stratosphere") protects the Earth from harmful ultraviolet radiation but affects human and ecosystem health when present in the lower atmosphere ("troposphere"). Generally, the ozone in the lower atmosphere occurs due to chemical reactions of gases and sometimes due to the transport from the ozone‐rich upper atmosphere. However, there is a barrier between the upper and the lower atmospheres, also known as the tropopause. Using observations and atmospheric models, we study the penetration of ozone from the upper atmosphere to the lower atmosphere, during the NIO cyclones which disturb the separation barrier. We studied 20 cyclones and found that they disturb the barrier between the upper and the lower atmosphere significantly over India and a large amount of ozone moves between them, so much so that it almost doubles the ozone concentration in the upper half of the lower atmosphere. A warmer world in the future could lead to more cyclones which could increase ozone in the lower atmosphere. Key Points: Stratosphere‐troposphere exchange (STE) over India due to the North Indian Ocean cyclones is studied using reanalysis data and model outputEnhanced stratospheric airmass (∼20%) and ozone (∼40 ppb) were evident in the upper troposphere and lower stratosphereSTE due to the Bay of Bengal and Arabian Sea cyclones influence ozone amplification over northern and southern India, respectively [ABSTRACT FROM AUTHOR]

Climate models are widely used to project future climate changes and analyze underlying mechanisms. However, it remains unknown whether the earlier precipitation projections match the subsequent observations in recent decades, which is the scientific basis for the confidence of precipitation projections and associated impacts. Here, we find models in the Third (TAR), Fourth (AR4), and Fifth (AR5) Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC) skillfully project the subsequent climatological changes in global mean land precipitation for 2002–2020, 2008–2020, and 2014–2020 (relative to 1980–1999 climatology) from several to 10 years ahead, respectively. Skillful regional projections are mainly found in the northern mid to high latitudes. IPCC models are less skillful in projecting subsequent changes in land precipitation at regional scales than at global scales, which is likely to be at least partly explained by the lower signal‐to‐noise ratio. Plain Language Summary: As an important tool, climate models are widely used to project future climate changes and analyze underlying mechanisms. The precondition of this is that the projections of climate models are credible. The most convincing way of validating the credibility of climate projections is to quantify the extent to which past projections agree with subsequent observations. Here, we provide a comprehensive analysis on the skill of past climate models in projecting the subsequent precipitation changes over global land. Results show that models have robust skills in projecting the subsequent climatological changes in global mean land precipitation from several to 10 years ahead. Relative to global scale land, the models are less skillful in projecting the subsequent changes in precipitation at regional scale, although they exhibit some skills in northern mid‐ to high‐latitudes. Key Points: Models show robust skills in projecting the subsequent climatological changes in global mean land precipitation from several to 10 years aheadRelative to global scale, models are less skillful in projecting the subsequent climatological changes in precipitation at regional scaleThe near‐term precipitation projections are similar under various emission scenarios [ABSTRACT FROM AUTHOR]

The Mediterranean region (MR) is one of the climate change hot spots, posing serious threats to society. The insufficient resolution of the global climate models limits their capability to resolve the complex topography over MR, resulting in a large bias in climate variables. This study examines the performance of four Coupled Model Intercomparison Project phase six models from the High‐Resolution Model Intercomparison Project (HighResMIP) in reproducing the Mediterranean climate. A special focus is put on the role of oceanic and atmospheric model horizontal resolution and spread among the models. Various aspects, relevant to air‐sea interactions and precipitation are examined, including the mean, extremes, and associated mechanisms. All HighResMIP models reasonably well capture the large‐scale oceanic and atmospheric characteristics, but there is a sizable model‐to‐model difference. The Hadley Centre Global Environment Model and European Centre for Medium‐Range Weather Forecasts generally perform better than the other models at comparable atmospheric and oceanic horizontal resolutions. A finer oceanic resolution improves the representation not only of oceanic characteristics (i.e., sea surface temperature, SST) but also of the atmospheric processes (wind and precipitation). Likewise, increasing atmospheric model resolution benefits various atmospheric and ocean characteristics. Over some sub‐basins of the Mediterranean Sea, the intensification of surface wind speed results in the deepening of the ocean's mixed layer leading to cooling, indicating negative feedback in Wind‐SST. In contrast, in other regions, the warmer SST results in the intensification of wind (positive feedback in Wind‐SST). Plain Language Summary: This study made an effort to examine the performance of four Coupled Model Intercomparison Project phase six models from the High‐Resolution Model Intercomparison Project for the mean climate and its extremes and associated mechanisms. The Hadley Centre Global Environment Model and European Centre for Medium‐Range Weather Forecasts models generally perform better than the other models at comparable atmospheric and oceanic horizontal resolutions. It is found that, in general, the increasing resolution improves the performance of the model for most of the aspects. Yet, selecting the best‐performing model and tuning the model having unsatisfactory performance is vital. The improvement in the ocean characteristics by increasing atmospheric model resolution and vice versa highlights the importance of fine‐scale air‐sea interaction processes. Key Points: The Hadley Centre Global Environment Model and European Centre for Medium‐Range Weather Forecasts perform better than Euro‐Mediterranean Center on Climate Change and Max Planck Institute Earth System Model at comparable atmospheric and oceanic horizontal resolutionsRefinement of oceanic/atmospheric resolution improves the characteristics of the corresponding as well as the other climate componentsThe inter‐model spread and the response to increasing resolution are associated with different feedback processes between sea surface temperature and wind [ABSTRACT FROM AUTHOR]