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More than four years of ground-based measurements taken at the ARM Eastern North Atlantic (ENA) site between July 2015 and September 2019 have been collected and processed in this study. Monthly and hourly means of clear-sky, all-sky, total cloud fraction (CFT), and single-layered low (CFL) and high (CFH) clouds, the impacts of all scene types on the surface radiation budget (SRB), and their cloud radiative effects (CREs) have been examined. The annual averages of CFT, CFL, and CFH are 0.785, 0.342, and 0.123, respectively. The annual averages of the SW (LW) CREs for all sky, total, low, and high clouds are −56.7 (37.7), −76.6 (48.5), −73.7 (51.4), and −26.8 (13.9) W m−2, respectively, resulting in the NET CREs of −19.0, −28.0, −22.2, and −12.9 W m−2. Comparing the cloud properties and CREs at both ARM ENA and Southern Great Plains (SGP) sites, we found that the clear-sky downwelling SW and LW fluxes at the two sites are similar to each other due to their similar atmospheric background. Compared to SGP, the lower all-sky SW and higher LW fluxes at ENA are caused by its higher CFT and all-sky precipitable water vapor (PWV). With different low cloud microphysical properties and cloud condensation nuclei at the two sites, much higher cloud optical depth at SGP plays an important role in determining its lower SW flux, while Tb and PWV are important for downwelling LW flux at the surface. A sensitivity study has shown that the all-sky SW CREs at SGP are more sensitive to CFT (−1.07 W m−2 %−1) than at ENA (−0.689 W m−2 %−1), with the same conclusion for all-sky LW CREs (0.735 W m−2 %−1 at SGP vs 0.318 W m−2 %−1 at ENA). The results over the two sites shed new light on the impacts of clouds on the midlatitude surface radiation budgets, over both ocean and land.

The Southern Ocean is covered by a large amount of clouds with high cloud albedo. However, as reported by previous climate model intercomparison projects, underestimated cloudiness and overestimated absorption of solar radiation (ASR) over the Southern Ocean lead to substantial biases in climate sensitivity. The present study revisits this long-standing issue and explores the uncertainty sources in the latest CMIP6 models. We employ 10-year satellite observations to evaluate cloud radiative effect (CRE) and cloud physical properties in five CMIP6 models that provide comprehensive output of cloud, radiation, and aerosol. The simulated longwave, shortwave, and net CRE at the top of atmosphere in CMIP6 are comparable with the CERES satellite observations. Total cloud fraction (CF) is also reasonably simulated in CMIP6, but the comparison of liquid cloud fraction (LCF) reveals marked biases in spatial pattern and seasonal variations. The discrepancies between the CMIP6 models and the MODIS satellite observations become even larger in other cloud macro- and micro-physical properties, including liquid water path (LWP), cloud optical depth (COD), and cloud effective radius, as well as aerosol optical depth (AOD). However, the large underestimation of both LWP and cloud effective radius (regional means ∼20% and 11%, respectively) results in relatively smaller bias in COD, and the impacts of the biases in COD and LCF also cancel out with each other, leaving CRE and ASR reasonably predicted in CMIP6. An error estimation framework is employed, and the different signs of the sensitivity errors and biases from CF and LWP corroborate the notions that there are compensating errors in the modeled shortwave CRE. Further correlation analyses of the geospatial patterns reveal that CF is the most relevant factor in determining CRE in observations, while the modeled CRE is too sensitive to LWP and COD. The relationships between cloud effective radius, LWP, and COD are also analyzed to explore the possible uncertainty sources in different models. Our study calls for more rigorous calibration of detailed cloud physical properties for future climate model development and climate projection.

This study develops a new thin cirrus detection algorithm applicable to overland scenes. The methodology builds from a previously developed overwater algorithm, which makes use of the Geostationary Operational Environmental Satellite 16 (GOES-16) Advanced Baseline Imager (ABI) channel 4 radiance (1.378-μm “cirrus” band). Calibration of this algorithm is based on coincident Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) cloud profiles. Emphasis is placed on rejection of false detections that are more common in overland scenes. Clear-sky false alarm rates over land are examined as a function of precipitable water vapor (PWV), showing that nearly all pixels having a PWV of H removes significant land surface and low-/midlevel cloud false alarms from the overall sample while preserving over 80% of valid cirrus pixels. Additionally, the use of an aggressive PWV layer threshold preferentially removes noncirrus pixels such that the remaining sample is composed of nearly 70% cirrus pixels, at the cost of a much-reduced overall sample size. This study shows that lower-tropospheric clouds are a much more significant source of uncertainty in cirrus detection than the land surface.

Poor representations of aerosols, clouds and aerosol-cloud interactions (ACI) in Earth System Models (ESMs) have long been the largest uncertainties in predicting global climate change. Huge efforts have been made to improve the representation of these processes in ESMs, and key to these efforts is evaluation of ESM simulations with observations. Most well-established ESM diagnostics packages focus on the climatological features; however, they are lack of the process-level understanding and representations of aerosols, clouds, and ACI. In this study, we developed an ESM aerosol-cloud diagnostics package (ESMAC Diags) to facilitate routine evaluation of aerosols, clouds and aerosol-cloud interactions simulated by the Department of Energy’s (DOE) Energy Exascale Earth System Model (E3SM). This paper documents its version 2 functionality (ESMAC Diags v2), which has substantial updates from its version 1 (Tang et al., 2022a). The simulated aerosol and cloud properties have been extensively compared with in-situ and remote-sensing measurements from aircraft, ship, surface and satellite platforms in ESMAC Diags v2. It currently includes six field campaigns and two permanent sites covering four geographical regions: Eastern North Atlantic, Central U.S., Northeastern Pacific and Southern Ocean, where frequent liquid or mixed-phase clouds are present and extensive measurements are available from the DOE Atmospheric Radiation Measurement user facility and other agencies. ESMAC Diags v2 generates various types of single-variable and multi-variable diagnostics, including percentiles, histograms, joint histograms and heatmaps, to evaluate model representation of aerosols, clouds, and aerosol-cloud interactions. Select examples highlighting ESMAC Diags capabilities are shown using E3SM version 2 (E3SMv2). E3SMv2 in general can reasonably reproduces many observed aerosol and cloud properties, with biases in some variables such as aerosol particle and cloud droplet sizes and number concentrations. The coupling of aerosol and cloud number concentrations may be too strong in E3SMv2, possibly indicating a bias in processes that control aerosol activation. Furthermore, the liquid water path adjustment to perturbed cloud droplet number concentration behaves differently in E3SMv2 and observations, which warrants a further study to improve the cloud microphysics parameterizations in E3SMv2.

There has been a growing concern that most climate models predict too frequent precipitation, likely due to lack of reliable sub-grid variability and vertical variations of microphysical processes in low-level warm clouds. In this study, the warm cloud physics parameterizations in the singe-column configurations of NCAR Community Atmospheric Model version 6 and 5 (SCAM6 and SCAM5, respectively) are evaluated using ground-based and airborne observations from the DOE ARM Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign near the Azores islands during 2017–2018. Eight-month SCM simulations show that both SCAM6 and SCAM5 can generally reproduce marine boundary-layer cloud structure, major macrophysical properties, and their transition. The improvement of warm cloud properties from CAM5 to CAM6 physics can be found compared to the observations. Meanwhile, both physical schemes underestimate cloud liquid water content, cloud droplet size, and rain liquid water content, but overestimate surface rainfall. Modeled cloud condensation nuclei (CCN) concentrations are comparable with aircraft observed ones in the summer but overestimated by a factor of two in winter, largely due to the biases in the long-range transport of anthropogenic aerosols like sulfate. We also test the newly recalibrated autoconversion and accretion parameterizations that account for vertical variations of droplet size. Compared to the observations, more significant improvement is found in SCAM5 than in SCAM6. This result is likely explained by the introduction of sub-grid variations of cloud properties in CAM6 cloud microphysics, which further suppresses the scheme sensitivity to individual warm rain microphysical parameters. The predicted cloud susceptibilities to CCN perturbations in CAM6 are within a reasonable range, indicating significant progress since CAM5 which produces too strong aerosol indirect effect. The present study emphasizes the importance of understanding biases in cloud physics parameterizations by combining SCM with in situ observations.

With their extensive coverage, marine low clouds greatly impact global climate. Presently, marine low clouds are poorly represented in global climate models, and the response of marine low clouds to changes in atmospheric greenhouse gases and aerosols remains the major source of uncertainty in climate simulations. The eastern North Atlantic (ENA) is a region of persistent but diverse subtropical marine boundary layer clouds, whose albedo and precipitation are highly susceptible to perturbations in aerosol properties. In addition, the ENA is periodically impacted by continental aerosols, making it an excellent location to study the cloud condensation nuclei (CCN) budget in a remote marine region periodically perturbed by anthropogenic emissions, and to investigate the impacts of long-range transport of aerosols on remote marine clouds. The Aerosol and Cloud Experiments in Eastern North Atlantic (ACE-ENA) campaign was motivated by the need of comprehensive in situ measurements for improving the understanding of marine boundary layer CCN budget, cloud and drizzle microphysics, and the impact of aerosol on marine low cloud and precipitation. The airborne deployments took place from 21 June to 20 July 2017 and from 15 January to 18 February 2018 in the Azores. The flights were designed to maximize the synergy between in situ airborne measurements and ongoing long-term observations at a ground site. Here we present measurements, observation strategy, meteorological conditions during the campaign, and preliminary findings. Finally, we discuss future analyses and modeling studies that improve the understanding and representation of marine boundary layer aerosols, clouds, precipitation, and the interactions among them.

This study uses machine learning methods, specifically the random forest (RF), on a radar-based mesoscale convective system (MCS) tracking dataset to classify the five types of linear MCS morphology in the contiguous United States during the period 2004-2016. The algorithm is trained using radar- and satellite-derived spatial and morphological parameters, and reanalysis environmental information from 5-yr manually identified nonlinear and five linear MCS modes. The algorithm is then used to automate the classification of linear MCSs over 8 years with high accuracy, providing a systematic, long-term climatology of linear MCSs. Results reveal that nearly 40% of MCSs are classified as linear MCSs, in which half of the linear events belong to the type of system having a leading convective line. The occurrence of linear MCSs shows large annual and seasonal variations. On average, 113 linear MCSs occur annually during the warm season (through March to October), with most of these events clustered from May through August in the central eastern Great Plains. MCS characteristics, including duration, propagation speed, orientation, and system cloud size, have large variability among the different linear modes. The systems having a trailing convective line and the systems having a back-building area of convection typically move more slowly and have higher precipitation rate, and thus have higher potential in producing extreme rainfall and flash flooding. Analysis of the environmental conditions associated with linear MCSs show that the storm-relative flow is of most importance in determining the organization mode of linear MCSs.

To investigate the cloud phase and macrophysical properties over the Southern Ocean (SO), the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF2) was installed on the Australian icebreaker research vessel (R/V) Aurora Australis during the Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) field campaign (41 to 69∘ S, 60 to 160∘ E) from October 2017 to March 2018. To examine cloud properties over the midlatitude and polar regions, the study domain is separated into the northern (NSO) and southern (SSO) parts of the SO, with a demarcation line of 60∘ S. The total cloud fractions (CFs) were 77.9 %, 67.6 %, and 90.3 % for the entire domain, NSO and SSO, respectively, indicating that higher CFs were observed in the polar region. Low-level clouds and deep convective clouds are the two most common cloud types over the SO. A new method was developed to classify liquid, mixed-phase, and ice clouds in single-layered, low-level clouds (LOW), where mixed-phase clouds dominate with an occurrence frequency (Freq) of 54.5 %, while the Freqs of the liquid and ice clouds were 10.1 % (most drizzling) and 17.4 % (least drizzling). The meridional distributions of low-level cloud boundaries are nearly independent of latitude, whereas the cloud temperatures increased by ∼8 K, and atmospheric precipitable water vapor increased from ∼5 mm at 69∘ S to ∼18 mm at 43∘ S. The mean cloud liquid water paths over NSO were much larger than those over SSO. Most liquid clouds occurred over NSO, with very few over SSO, whereas more mixed-phase clouds occurred over SSO than over NSO. There were no significant differences for the ice cloud Freq between NSO and SSO. The ice particle sizes are comparable to cloud droplets and drizzle drops and well mixed in the cloud layer. These results will be valuable for advancing our understanding of the meridional and vertical distributions of clouds and can be used to improve model simulations over the SO.

The mei-yu season, typically occurring from mid-June to mid-July, on average, contributes to 32% of the annual precipitation over the Yangtze–Huai River Valley (YHRV) and represents one of the three heavy-rainfall periods in China. Here, we briefly review the large-scale background, synoptic pattern, moisture transport, and cloud and precipitation characteristics of the mei-yu frontal systems in the context of the ongoing Integrative Monsoon Frontal Rainfall Experiment (IMFRE) field campaign. Phase one of the campaign, IMFRE-I, was conducted from 10 June to 10 July 2018 in the middle reaches of the YHRV. Led by the Wuhan Institute of Heavy Rain (IHR) with primary support from the National Natural Science Foundation of China, IMFRE-I maximizes the use of our observational capacity enabled by a suite of ground-based and remote sensing instruments, most notably the IHR Mesoscale Heavy Rainfall Observing System (MHROS), including different wavelengths of radars, microwave radiometers, and disdrometers. The KA350 (Shanxi King-Air) aircraft participating in the campaign is equipped with Ka-band cloud radar and different probes. The comprehensive datasets from both the MHROS and aircraft instruments are combined with available satellite observations and model simulations to answer the three scientific questions of IMFRE-I. Some highlights from a previously published special issue are included in this review, and we also briefly introduce the IMFRE-II field campaign, conducted during June–July 2020, where the focus was on the spatiotemporal evolutions of the mei-yu frontal systems over the middle and lower reaches of the YHRV.

The surface radiative imbalance has large impacts on the long-term trends and year-to-year variability of Arctic sea ice. Clouds are believed to be a key factor in regulating this radiative imbalance, whose underlying processes and mechanisms, however, are not well understood. Compared with observations, the Community Earth System Model version 1 (CESM1) is known to underestimate Arctic cloud liquid water. Here, the following hypothesis is proposed and tested: this underestimation is caused by an overactive Wegener–Bergeron–Findeisen (WBF) process in model as too many supercooled liquid droplets are scavenged by ice crystals via deposition. In this study, the efficiency of the WBF process in CESM1 was reduced to investigate the Arctic climate response, and differentiate the responses induced by atmosphere–ocean–sea ice coupling and global warming. By weakening the WBF process, CESM1 simulated liquid cloud fractions increased, especially in winter and spring. The cloud response resulted in increased downwelling longwave flux and decreased shortwave flux at the surface. Arctic clouds and radiation in simulations with reduced WBF efficiency show a better agreement with satellite retrievals. In addition, both coupling and global warming amplify the cloud response to a less efficient WBF process, due to increased relative humidity and enhanced evaporation, respectively. As a response, the sea ice tends to melt over the North Atlantic Ocean, most likely caused by a positive feedback process between clouds, radiation and sea ice during non-summer months. These results improve our understanding of large-scale effects of the WBF process and the role of cloud liquid water in the Arctic climate system.

Mei-yu fronts are often accompanied with prominent diabatic heating due to the development of frontal clouds and rain bands. The direct effect and relative importance of diabatic heating on the spatiotemporal morphology of Mei-yu fronts however remain unclear. Here a new frontogenesis function is derived to isolate the effect of diabatic heating and this function is then applied to the latest high resolution reanalysis product ERA5 to conduct an initial survey of dynamic and thermodynamic processes driving the intensity and structure evolutions of three typical Mei-yu fronts. It is found that the direct effect of latent heating (moisture depletion) is always frontogenetical (frontolytical) in the pre-frontal and frontal zone throughout the lifecycle of the front with latent heating (moisture depletion) in general dominates the front intensification (dissipation). Tilting is another critical process that turns the vertical gradient of equivalent potential temperature into horizontal gradient leading to frontogenesis during the front development stage, and, after the release of convective instability ahead of the front, flattens the front surface leading to frontolysis during the front decaying stage. Therefore titling maintains consistently positive contributions to the evolution of a Mei-yu front and its importance depends highly on the convective intensity near the front. The deformation effect appears frontogenetical but carries a much smaller weight compared to diabatic heating, moisture depletion and tilting. The structure evolutions of the three Mei-yu fronts studied here exhibit two distinct patterns: “bending and breaking”, and “moving and rotating”. Both patterns are dominated by the front propagation (i.e., frontogenesis through air parcels), further highlighting the critical roles played by diabatic heating, moisture depletion and titling in determining the spatiotemporal characteristics of Mei-yu fronts. As these three processes are all closely tied to updrafts, clouds and precipitation near the fronts, improved representations of clouds and convection are needed to accurately depict their feedbacks to frontal evolutions and ultimately achieve better predictions of heavy rainfall and floods associated with Mei-yu fronts.

Phase Two of the Integrative Monsoon Frontal Rainfall Experiment (IMFRE-II) was conducted over the middle and lower reaches of the Yangtze River during the period 16 June to 19 July 2020. This paper provides a brief overview of the IMFRE-II field campaign, including the multiple ground-based remote sensors, aircraft probes, and their corresponding measurements during the 2020 mei-yu period, as well as how to use these numerous datasets to answer scientific questions. The highlights of IMFRE-II are: (1) to the best of our knowledge, IMFRE-II is the first field campaign in China to use ground-based, airborne, and spaceborne platforms to conduct comprehensive observations over the middle and lower reaches of the Yangtze River; and (2) seven aircraft flights were successfully carried out, and the spectra of ice particles, cloud droplets, and raindrops at different altitudes were obtained. These in-situ measurements will provide a “cloud truth” to validate the ground-based and satellite-retrieved cloud and precipitation properties and quantitatively estimate their retrieval uncertainties. They are also crucial for the development of a warm (and/or cold) rain conceptual model in order to better understand the cloud-to-rain conversion and accretion processes in mei-yu precipitation events. Through an integrative analysis of ground-based, aircraft, and satellite observations and model simulations, we can significantly improve our cloud and precipitation retrieval algorithms, investigate the microphysical properties of cloud and precipitation, understand in-depth the formation and dissipation mechanisms of mei-yu frontal systems, and improve cloud microphysics parameterization schemes and model simulations.

Summertime barotropic high pressure in the Arctic and its induced warmer and wetter atmosphere over sea ice are suggested to be important contributors to September sea ice loss on interannual and interdecadal time scales in the past decades. Using ERA5 and other reanalysis data, we find that atmospheric warming and moistening in the Arctic, synchronized by high latitude atmospheric circulation variability, work in tandem to melt sea ice through increasing downwelling longwave radiation at the surface. To what extent this atmosphere-longwave radiation-sea ice relationship can be captured in CMIP5 and 6 remains unknown and thus addressing this question is the objective of this study. To achieve this goal, we construct a process-oriented metric emphasizing the statistical relationship between atmospheric temperature and humidity with sea ice, which can effectively rank and differentiate the performance of 30 CMIP5 climate models in reproducing the observed connection. Based on our evaluation, we suggest that most available models in CMIP5 and 6 have a limitation in reproducing the full strength of the observed atmosphere–sea ice connection. This limitation likely stems from a weak impact of downwelling longwave radiation in linking sea ice with circulation associated with the weak sensitivity of the temperature and humidity fields to circulation variability in the Arctic. Thus, further efforts should be devoted to understanding the sources of these models’ limitations and improve skill in simulating the effects of atmospheric circulation in coupling temperature, humidity, surface radiation and sea ice together during Arctic summer.

In this study, more than 4 years of ground-based observations and retrievals were collected and analyzed to investigate the seasonal and diurnal variations of single-layered MBL (with three subsets: nondrizzling, virga, and rain) cloud and drizzle properties, as well as their vertical and horizontal variations. The annual mean drizzle frequency was ~55%, with ~70% in winter and ~45% in summer. The cloud-top (cloud-base) height for rain clouds was the highest (lowest), resulting in the deepest cloud layer, i.e., 0.8 km, which is 4 (2) times that of nondrizzling (virga) clouds. The retrieved cloud-droplet effective radii rc were the largest (smallest) for rain (nondrizzling) clouds, and the nighttime values were greater than the daytime values. Drizzle number concentration Nd and liquid water content LWCd were three orders and one order lower, respectively, than their cloud counterparts. The rc and LWCc increased from the cloud base to zi ≈ 0.75 by condensational growth, while drizzle median radii rd increased from the cloud top downward the cloud base by collision–coalescence. The adiabaticity values monotonically increased from the cloud top to the cloud base with maxima of ~0.7 (0.3) for nondrizzling (rain) clouds. The drizzling process decreases the adiabaticity by 0.25 to 0.4, and the cloud-top entrainment mixing impacts as deep as upper 40% of the cloud layers. Cloud and drizzle homogeneities decreased with increased horizontal sampling lengths. Cloud homogeneity increases with increasing cloud fraction. These results can serve as baselines for studying MBL cloud-to-rain conversion and growth processes over the Azores.

To investigate the cloud properties over the Southern Ocean (SO), the MARCUS field campaign (41 to 69º S; 60 to 160º E) was conducted from October 2017 to March 2018, using ship-based measurements. To examine cloud properties over the mid-latitude and Polar regions, the study domain is separated into northern (NSO) and southern (SSO) parts of the SO with a demarcation line of 60° S. The total cloud fractions (CFs) were 77.9 %, 67.6 %, and 90.3 % for the entire domain, NSO and SSO, respectively, indicating that higher CFs were observed in the Polar region. Low-level clouds, deep cumulus, and shallow cumulus clouds are the three most common cloud types over the SO. For single-layered low-level clouds, mixed-phase clouds dominate with an occurrence frequency (Freq) of 54.5 %, while the Freq of the liquid and ice clouds were 10.1 % (most drizzling) and 17.4 % (least drizzling). The meridional distributions of low-level cloud boundaries are nearly independent of latitude, whereas the cloud temperatures increased ~ 8 K and atmospheric precipitable water vapor increased from ~5 mm at 69º S to ~18 mm at 43º S. The mean cloud liquid water paths over NSO were much larger than those over SSO. Most liquid clouds occurred over NSO with very few over SSO, whereas more mixed-phase clouds occurred over SSO than over NSO. There were no significant differences for ice cloud Freq between NSO and SSO. These results will be valuable for advancing our understanding of the meridional and vertical distributions of clouds and can be used to improve model simulations over the SO.

Mesoscale convective systems (MCSs) play an important role in water and energy cycles as they produce heavy rainfall and modify the radiative profile in the tropics and midlatitudes. An accurate representation of MCSs’ rainfall is therefore crucial in understanding their impact on the climate system. The V06B Integrated Multisatellite Retrievals from Global Precipitation Measurement (IMERG) half-hourly precipitation final product is a useful tool to study the precipitation characteristics of MCSs because of its global coverage and fine spatiotemporal resolutions. However, errors and uncertainties in IMERG should be quantified before applying it to hydrology and climate applications. This study evaluates IMERG performance on capturing and detecting MCSs’ precipitation in the central and eastern United States during a 3-yr study period against the radar-based Stage IV product. The tracked MCSs are divided into four seasons and are analyzed separately for both datasets. IMERG shows a wet bias in total precipitation but a dry bias in hourly mean precipitation during all seasons due to the false classification of nonprecipitating pixels as precipitating. These false alarm events are possibly caused by evaporation under the cloud base or the misrepresentation of MCS cold anvil regions as precipitating clouds by the algorithm. IMERG agrees reasonably well with Stage IV in terms of the seasonal spatial distribution and diurnal cycle of MCSs precipitation. A relative humidity (RH)-based correction has been applied to the IMERG precipitation product, which helps reduce the number of false alarm pixels and improves the overall performance of IMERG with respect to Stage IV.

Aerosols have significant and complex impacts on regional climate in East Asia. Cloud‐aerosol‐precipitation interactions (CAPI) remain most challenging in climate studies. The quantitative understanding of CAPI requires good knowledge of aerosols, ranging from their formation, composition, transport, and their radiative, hygroscopic, and microphysical properties. A comprehensive review is presented here centered on the CAPI based chiefly, but not limited to, publications in the special section named EAST‐AIRcpc concerning (1) observations of aerosol loading and properties, (2) relationships between aerosols and meteorological variables affecting CAPI, (3) mechanisms behind CAPI, and (4) quantification of CAPI and their impact on climate. Heavy aerosol loading in East Asia has significant radiative effects by reducing surface radiation, increasing the air temperature, and lowering the boundary layer height. A key factor is aerosol absorption, which is particularly strong in central China. This absorption can have a wide range of impacts such as creating an imbalance of aerosol radiative forcing at the top and bottom of the atmosphere, leading to inconsistent retrievals of cloud variables from space‐borne and ground‐based instruments. Aerosol radiative forcing can delay or suppress the initiation and development of convective clouds whose microphysics can be further altered by the microphysical effect of aerosols. For the same cloud thickness, the likelihood of precipitation is influenced by aerosols: suppressing light rain and enhancing heavy rain, delaying but intensifying thunderstorms, and reducing the onset of isolated showers in most parts of China. Rainfall has become more inhomogeneous and more extreme in the heavily polluted urban regions.

The mei-yu season over the Yangtze–Huai Rivers basin, typically occurring from mid-June to mid-July, is one of three heavy-rainfall periods over China and can contribute 50% of the annual precipitation. In this study, the first and second heaviest daily precipitation events at the Wuhan station have been selected to represent typical mei-yu and pre-mei-yu precipitation events where the differences in the atmospheric thermodynamic characteristics, precipitation nature, influencing systems, and mechanisms are investigated. During the mei-yu case, moist air mainly came from the South China Sea. Precipitation occurred south of the mei-yu front where abundant moisture and favorable thermodynamic conditions were present. The main influencing systems include a stable blocking pattern and strong and stable western Pacific subtropical high in the midtroposphere, and a small yet intense mesoscale cyclonic vortex in the low troposphere. Rainfall in Wuhan was continuous, caused by a well-organized convective line. A heavy rainband was located along the narrow band between the elongated upper-level jet (ULJ) and the low-level jet (LLJ) where the symmetric instability was found in the midtroposphere near Wuhan. Quite differently, for the pre-mei-yu precipitation case, moist air primarily came from the Beibu Gulf and the Bay of Bengal. Precipitation happened in the low-level convective instability region, where a short-wave trough in the midtroposphere and a mesoscale cyclonic vortex in the low-troposphere were found. Precipitation in Wuhan showed multiple peaks associated with independent meso-β-scale convective systems. A rainstorm occurred at the exit of the LLJ and the right entrance of the ULJ, where convective instability exited in the mid- to low troposphere.

A competitive neural network known as the self-organizing map (SOM) is used to objectively identify synoptic patterns in the North American Regional Reanalysis (NARR) for warm-season (April–September) precipitation events over the Southern and Northern Great Plains (SGP/NGP) from 2007 to 2014. Classifications for both regions demonstrate contrast in dominant synoptic patterns ranging from extratropical cyclones to subtropical ridges, all of which have preferred months of occurrence. Precipitation from deterministic Weather Research and Forecasting (WRF) Model simulations run by the National Severe Storms Laboratory (NSSL) are evaluated against National Centers for Environmental Prediction (NCEP) Stage IV observations. The SGP features larger observed precipitation amount, intensity, and coverage, as well as better model performance than the NGP. Both regions’ simulated convective rain intensity and coverage have good agreement with observations, whereas the stratiform rain (SR) is more problematic with weaker intensity and larger coverage. Further evaluation based on SOM regimes shows that WRF bias varies with the type of meteorological forcing, which can be traced to differences in the diurnal cycle and properties of stratiform and convective rain. The higher performance scores are generally associated with the extratropical cyclone condition than the subtropical ridge. Of the six SOM classes over both regions, the largest precipitation oversimulation is found for SR dominated classes, whereas a nocturnal negative precipitation bias exists for classes featuring upscale growth of convection.

The summer mean and extreme rainfall over the middle reaches of Yangtze River (MRYR) are underestimated in many models from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Our earlier work has identified three synoptic-scale circulation patterns associated with extreme rainfall events over the MRYR during early summer. It is verified here that the presence of these patterns indeed increases the likelihood of the occurrence of extreme rainfall over the MRYR. This relationship between the synoptic-scale circulation pattern and extreme rainfall is reproduced by only a subset of CMIP5 models, where the underestimated frequency of the corresponding synoptic-scale circulation patterns partly explains the underestimated frequency of extreme rainfall thus the summer total over the MRYR. Our analysis also reveals that a few models could “accidentally” simulate a realistic rainfall total and probability distribution of daily rain rate over the MRYR region during summer by generating their own model-dependent synoptic-scale circulation patterns that are not typically seen in observations. These findings suggest that a projection of future changes in extreme rainfall over the MRYR will be better constrained dynamically if we use a subset of models that can reproduce the “rainfall-circulation” relationship, given the diverse response of different circulation patterns to radiative forcing changes in the atmosphere. The results presented here also demonstrate the importance of tracking biases of synoptic-scale circulations in understanding model deficiencies in precipitation simulation, in addition to investigating problems in model physics such as cumulus schemes and microphysics parameterization. Nevertheless, these results are based on the analyses of extreme rainfall in one region and one season, further research covering other regions/seasons is needed.

A record of global single-layered ice cloud properties has been generated using the CloudSat and CALIPSO Ice Cloud Property Product (2C-ICE) during the period 2007–2010. These ice cloud properties are used as inputs for the NASA Langley modified Fu–Liou radiative transfer model to calculate cloud radiative heating rate profiles and are compared with the NASA CERES observed top-of-atmosphere fluxes. The radiative heating rate profiles calculated in the CloudSat/CALIPSO 2B-FLXHR-LIDAR and CCCM_CC products are also examined to assess consistency and uncertainty of their properties using independent methods. Based on the methods and definitions used herein, single-layered ice clouds have a global occurrence frequency of ~ 18%, with most of them occurring in the tropics above 12 km. Zonal mean cloud radiative heating rate profiles from the three datasets are similar in their patterns of SW warming and LW cooling with small differences in magnitude; nevertheless, all three datasets show that the strongest net heating (> + 1.0 K day−1) occurs in the tropics (latitude ~ 50°). Differences in radiative heating rates are also assessed based on composites of the 2C-ICE ice water path (IWP) and total column water vapor (TCWV) mixing ratio to facilitate model evaluation and guide ice cloud parameterization improvement. Positive net cloud radiative heating rates are maximized in the upper troposphere for large IWPs and large TCWV, with an uncertainty of 10–25% in the magnitude and vertical structure of this heating.