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The aerosol–planetary boundary layer (PBL) interaction was proposed as an important mechanism to stabilize the atmosphere and exacerbate surface air pollution. Despite the tremendous progress made in understanding this process, its magnitude and significance still have large uncertainties and vary largely with aerosol distribution and meteorological conditions. In this study, we focus on the role of aerosol vertical distribution in thermodynamic stability and PBL development by jointly using micropulse lidar, sun photometer, and radiosonde measurements taken in Beijing. Despite the complexity of aerosol vertical distributions, cloud-free aerosol structures can be largely classified into three types: well-mixed, decreasing with height, and inverse structures. The aerosol–PBL relationship and diurnal cycles of the PBL height and PM2.5 associated with these different aerosol vertical structures show distinct characteristics. The vertical distribution of aerosol radiative forcing differs drastically among the three types, with strong heating in the lower, middle, and upper PBL, respectively. Such a discrepancy in the heating rate affects the atmospheric buoyancy and stability differently in the three distinct aerosol structures. Absorbing aerosols have a weaker effect of stabilizing the lower atmosphere under the decreasing structure than under the inverse structure. As a result, the aerosol–PBL interaction can be strengthened by the inverse aerosol structure and can be potentially neutralized by the decreasing structure. Moreover, aerosols can both enhance and suppress PBL stability, leading to both positive and negative feedback loops. This study attempts to improve our understanding of the aerosol–PBL interaction, showing the importance of the observational constraint of aerosol vertical distribution for simulating this interaction and consequent feedbacks.

The complex and diverse cloud vertical distribution (CVD) largely impacts radiative and precipitation properties of clouds. Using 10‐year active satellite observations, we classified CVD over the Tibetan Plateau into 12 categories and found that overlapping clouds have less frequency but stronger radiative effect, heating rate and larger precipitation (partly reflecting the seeding effect) compared with single‐layer non‐strong convective clouds. Under a warming climate due to uniform sea surface temperature increase of 4K (quadrupling CO2 increase), extremely high (>10 km) ice clouds will increase, particularly those below the tropopause will increase slightly (largely), accompanied by clear (weak) increases in stratospheric clouds. Simultaneously, a moderate to rapid decrease will occur in clouds below 10 km. Such CVD changes could further exacerbate tropopause warming. The probability of cloud overlap is also likely to increase in warmer climates, thus possibly further causing non‐convective cloud systems with stronger intra‐atmospheric heating, larger precipitation intensity and proportion. Plain Language Summary The diverse vertical morphologies of clouds (cloud vertical distribution, CVD) cause great challenges in accurately simulating radiative and precipitation properties, influencing regional radiation budget. As a robust signature of climate change, CVD significantly impacts cloud feedback and will change dramatically with warming. Therefore, knowledge about the CVD and its potential change are essential, especially on the Tibetan Plateau (TP, with high climate sensitivity). Using 10‐year active satellite observations, we classified clouds into 12 classes and found multi‐layer (overlapping) clouds are less frequent but with stronger radiative effect, atmospheric heating and larger precipitation intensity (proportion) than single‐layer clouds excluding convective clouds. Under a warming climate due to uniform sea surface temperature increase of 4K (quadrupling CO2 increase), clouds (especially ice‐cloud) above 10 km will increase, and those below the tropopause increase slightly (largely) accompanied by clear (weak) increases in the stratospheric clouds. Simultaneously, clouds below 10 km will experience a moderate to rapid decrease. Such CVD changes could exacerbate tropopause warming. In addition, the probability of cloud overlap in warmer climates is also likely to increase due to stronger convective available potential energy, possibly resulting in stronger atmospheric heating and larger precipitation intensity and proportion for non‐convective cloud systems. Key Points Multi‐layer clouds are less frequent on the Tibetan Plateau but with stronger radiation effect and precipitation intensity than single‐layer clouds Increase/decrease in clouds above/below tropopause might be tied to a possible convective available potential energy (CAPE)‐induced switch from general to intense convection The enhanced CAPE‐induced deep convection may increase the likelihood of cloud overlap, further enhancing intra‐atmospheric heating

The climatic and health effects of aerosols are strongly dependent on the intra-annual variations in their loading and properties. While the seasonal variations of regional aerosol optical depth (AOD) have been extensively studied, understanding the temporal variations in aerosol vertical distribution and particle types is also important for an accurate estimate of aerosol climatic effects. In this paper, we combine the observations from four satellite-borne sensors and several ground-based networks to investigate the seasonal variations of aerosol column loading, vertical distribution, and particle types over three populous regions: the Eastern United States (EUS), Western Europe (WEU), and Eastern and Central China (ECC). In all three regions, column AOD, as well as AOD at heights above 800 m, peaks in summer/spring, probably due to accelerated formation of secondary aerosols and hygroscopic growth. In contrast, AOD below 800 m peaks in winter over WEU and ECC regions because more aerosols are confined to lower heights due to the weaker vertical mixing. In the EUS region, AOD below 800 m shows two maximums, one in summer and the other in winter. The temporal trends in low-level AOD are consistent with those in surface fine particle (PM2.5) concentrations. AOD due to fine particles (<0.7 µm diameter) is much larger in spring/summer than in winter over all three regions. However, the coarse mode AOD (>1.4 µm diameter), generally shows small variability, except that a peak occurs in spring in the ECC region due to the prevalence of airborne dust during this season. When aerosols are classified according to sources, the dominant type is associated with anthropogenic air pollution, which has a similar seasonal pattern as total AOD. Dust and sea-spray aerosols in the WEU region peak in summer and winter, respectively, but do not show an obvious seasonal pattern in the EUS region. Smoke aerosols, as well as absorbing aerosols, present an obvious unimodal distribution with a maximum occurring in summer over the EUS and WEU regions, whereas they follow a bimodal distribution with peaks in August and March (due to crop residue burning) over the ECC region.

Despite ongoing efforts, the vertical distribution of aerosols globally is poorly understood. This in turn leads to large uncertainties in the contributions of the direct and indirect aerosol forcing on climate. Using the Global Aerosol Synthesis and Science Project (GASSP) database – the largest synthesised collection of in situ aircraft measurements currently available, with more than 1000 flights from 37 campaigns from around the world – we investigate the vertical structure of submicron aerosols across a wide range of regions and environments. The application of this unique dataset to assess the vertical distributions of number size distribution and cloud condensation nuclei (CCN) in the global aerosol–climate model ECHAM-HAM reveals that the model underestimates accumulation-mode particles in the upper troposphere, especially in remote regions. The processes underlying this discrepancy are explored using different aerosol microphysical schemes and a process sensitivity analysis. These show that the biases are predominantly related to aerosol ageing and removal rather than emissions.

The seasonal and spatial variations of vertical distribution and optical properties of aerosols over China are studied using long-term satellite observations from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) and ground-based lidar observations and Aerosol Robotic Network (AERONET) data. The CALIOP products are validated using the ground-based lidar measurements at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL). The Taklamakan Desert and Tibetan Plateau regions exhibit the highest depolarization and color ratios because of the natural dust origin, whereas the North China Plain, Sichuan Basin and Yangtze River Delta show the lowest depolarization and color ratios because of aerosols from secondary formation of the anthropogenic origin. Certain regions, such as the North China Plain in spring and the Loess Plateau in winter, show intermediate depolarization and color ratios because of mixed dust and anthropogenic aerosols. In the Pearl River Delta region, the depolarization and color ratios are similar to but higher than those of the other polluted regions because of combined anthropogenic and marine aerosols. Long-range transport of dust in the middle and upper troposphere in spring is well captured by the CALIOP observations. The seasonal variations in the aerosol vertical distributions reveal efficient transport of aerosols from the atmospheric boundary layer to the free troposphere because of summertime convective mixing. The aerosol extinction lapse rates in autumn and winter are more positive than those in spring and summer, indicating trapped aerosols within the boundary layer because of stabler meteorological conditions. More than 80 % of the column aerosols are distributed within 1.5 km above the ground in winter, when the aerosol extinction lapse rate exhibits a maximum seasonal average in all study regions except for the Tibetan Plateau. The aerosol extinction lapse rates in the polluted regions are higher than those of the less polluted regions, indicating a stabilized atmosphere due to absorptive aerosols in the polluted regions. Our results reveal that the satellite and ground-based remote-sensing measurements provide the key information on the long-term seasonal and spatial variations in the aerosol vertical distribution and optical properties, regional aerosol types, long-range transport and atmospheric stability, which can be utilized to more precisely assess the direct and indirect aerosol effects on weather and climate.

Health impact analyses are increasingly tapping the broad spatial coverage of satellite aerosol optical depth (AOD) products to estimate human exposure to fine particulate matter (PM2.5). We use a forward geophysical approach to derive ground-level PM2.5 distributions from satellite AOD at 1 km2(exp) resolution for 2011 over the northeastern US by applying relationships between surface PM2.5 and column AOD (calculated offline from speciated mass distributions) from a regional air quality model (CMAQ; 12×12 km2(exp) horizontal resolution). Seasonal average satellite-derived PM2.5 reveals more spatial detail and best captures observed surface PM2.5 levels during summer. At the daily scale, however, satellite-derived PM2.5 is not only subject to measurement uncertainties from satellite instruments, but more importantly to uncertainties in the relationship between surface PM2.5 and column AOD. Using 11 ground-based AOD measurements within 10 km of surface PM2.5 monitors, we show that uncertainties in modeled PM2.5∕AOD can explain more than 70 % of the spatial and temporal variance in the total uncertainty in daily satellite-derived PM2.5 evaluated at PM2.5 monitors. This finding implies that a successful geophysical approach to deriving daily PM2.5 from satellite AOD requires model skill at capturing day-to-day variations in PM2.5∕AOD relationships. Overall, we estimate that uncertainties in the modeled PM2.5∕AOD lead to an error of 11 µg m−3(exp) in daily satellite-derived PM2.5, and uncertainties in satellite AOD lead to an error of 8 µg m−3(exp). Using multi-platform ground, airborne, and radiosonde measurements, we show that uncertainties of modeled PM2.5∕AOD are mainly driven by model uncertainties in aerosol column mass and speciation, while model representation of relative humidity and aerosol vertical profile shape contributes some systematic biases. The parameterization of aerosol optical properties, which determines the mass extinction efficiency, also contributes to random uncertainty, with the size distribution being the largest source of uncertainty and hygroscopicity of inorganic salt the second largest. Future efforts to reduce uncertainty in geophysical approaches to derive surface PM2.5 from satellite AOD would thus benefit from improving model representation of aerosol vertical distribution and aerosol optical properties, to narrow uncertainty in satellite-derived PM2.5.

The direct perturbation of anthropogenic aerosols on Earth’s energy balance [i.e., direct aerosol radiative forcing (DARF)] remains uncertain in climate models. In this study, we investigate the uncertainty of DARF associated with aerosol vertical distribution, using simulation results from 14 global models within phase 6 of the Coupled Model Intercomparison Project (CMIP6). The column mass loading for each aerosol species is first normalized to the multimodel average for each model, which is called the mass-normalization process. The unified radiative transfer model and aerosol optical parameter are used, so that the differences in the calculated DARF are solely attributed to the difference in aerosol vertical profiles. The global mean DARF values in 2014 with respect to 1850 before and after mass normalization are −0.77 ± 0.52 and −0.81 ± 0.12 W m−2 respectively, assuming external mixing, which indicates that the intermodel difference in aerosol vertical distribution accounts for ∼20% of the total DARF uncertainty. We further conduct two separate experiments by normalizing aerosol optical depth (AOD) and aerosol single scattering albedo (SSA) profiles, respectively, and find that the vertical distribution of SSA results in larger DARF uncertainty (0.17 W m−2) than that of AOD (0.10 W m−2). Finally, compared with CALIPSO observation, CMIP6 models tend to produce higher aerosol layers. The bias in modeled aerosol profile with respect to CALIPSO leads to stronger DARF, especially for land regions.

With advances in modeling approaches and the application of satellite and ground-based data in dust-related research, our understanding of the dust cycle has significantly improved in recent decades. However, two aspects of the dust cycle, namely the vertical profiles and diurnal cycles, are not yet adequately understood, mainly due to the sparsity of direct observations. Measurements of backscattering caused by atmospheric aerosols have been ongoing since 2014 at the King Abdullah University of Science and Technology (KAUST) campus using a micro-pulse lidar (MPL) with a high temporal resolution. KAUST is located on the eastern coast of the Red Sea and currently hosts the only operating lidar system in the Arabian Peninsula. We use the data from the MPL together with other collocated observations and high-resolution simulations (with 1.33 km grid spacing) from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to study the following three aspects of dust over the Red Sea coastal plains. Firstly, we compare the model-simulated surface winds, aerosol optical depth (AOD), and aerosol size distributions with observations and evaluate the model performance in representing a typical large-scale dust event over the study site. Secondly, we investigate the vertical profiles of aerosol extinction and concentration in terms of their seasonal and diurnal variability. Thirdly, we explore the interactions between dust aerosols and land/sea breezes, which are the most influential components of the local diurnal circulation in the region. The WRF-Chem model successfully reproduced the diurnal profile of surface wind speed, AOD, and dust size distributions over the study area compared to observations. The model also captured the onset, demise, and height of a large-scale dust event that occurred in 2015, as compared to the lidar data. The vertical profiles of aerosol extinction in different seasons were largely consistent between the MPL data and WRF-Chem simulations along with key observations and reanalyses used in this study. We found a substantial variation in the vertical profile of aerosols in different seasons and between daytime and nighttime, as revealed by the MPL data. The MPL data also identified a prominent dust layer at ∼5–7 km during the nighttime, which likely represents the long-range transported dust brought to the site by the easterly flow from remote inland deserts. The sea breeze circulation was much deeper (∼2 km) than the land breeze circulation (∼1 km), but both breeze systems prominently affected the distribution of dust aerosols over the study site. We observed that sea breezes push the dust aerosols upwards along the western slope of the Sarawat Mountains. These sea breezes eventually collide with the dust-laden northeasterly trade winds coming from nearby inland deserts, thus causing elevated dust maxima at a height of ∼1.5 km above sea level over the mountains. Moreover, the sea and land breezes intensify dust emissions from the coastal region during the daytime and nighttime, respectively. Our study, although focused on a particular region, has broader environmental implications as it highlights how aerosols and dust emissions from the coastal plains can affect the Red Sea climate and marine habitats.

The direct perturbation of anthropogenic aerosols on Earth’s energy balance [i.e., direct aerosol radiative forcing (DARF)] remains uncertain in climate models. These uncertainties critically depend on aerosol optical properties, primarily aerosol optical depth (AOD), single scattering albedo (SSA), and the asymmetry factor g. In this study, we investigate the intermodel spread of DARF across 14 global models within phase 6 of the Coupled Model Intercomparison Project (CMIP6), using unified radiative transfer calculation and aerosol optical parameter assumptions. The global mean DARF for clear sky in 2014 with respect to 1850 is estimated as −0.77 ± 0.52 W m−2 assuming an externally mixed state and −0.68 ± 0.53 W m−2 for an internally mixed state. We further conduct a quantitative analysis and find that globally, for the external mixing assumption, AOD is the dominant factor, whose intermodel spread results in 36% of the total DARF uncertainty. For the internal mixing assumption, SSA becomes the major factor, which also leads to 36% DARF uncertainty. The g parameter and aerosol vertical distribution combined contribute to ∼30% of the DARF uncertainty. Regionally, DARF uncertainty is typically more sensitive to SSA where the absorbing aerosol fraction is high, such as South Asia and central Africa. Substantial differences between model-averaged and observed aerosol optical parameters are still noticed, with external mixing in general yielding closer agreement with observations. Our results highlight the importance of aerosol scattering and absorption properties in DARF estimation.

Elucidating the variation of allocation pattern of ecosystem net primary productivity (NPP) and its underlying mechanisms is critically important for understanding the changes of aboveground and belowground ecosystem functions. Under optimal partitioning theory, plants should allocate more NPP to the organ that acquires the most limiting resource, and this expectation has been widely used to explain and predict NPP allocation under changing precipitation. However, confirmatory evidence for this theory has mostly come from observed spatial variation in the relationship between precipitation and NPP allocation across ecosystems, rather than directly from the influences of changing precipitation on NPP allocation within systems. We performed a 6-yr five-level precipitation manipulation experiment in a semiarid steppe to test whether changes in NPP allocation can be explained by the optimal partitioning theory, and how water requirement of plant community is maintained if NPP allocation is unaltered. The 30 precipitation levels (5 levels × 6 yr) were divided into dry, nominal, and wet precipitation ranges, relative to historical precipitation variation over the past six decades. We found that NPP in both aboveground (ANPP) and belowground (BNPP) increased nonlinearly as precipitation increased, while the allocation of NPP to BNPP (f BNPP) showed a concave quadratic relationship with precipitation. The declined f BNPP as precipitation increased in the dry range supported the optimal partitioning theory. However, in the nominal range, NPP allocation was not influenced by the changed precipitation; instead, BNPP was distributed more in the surface soil horizon (0–10 cm) as precipitation increased, and conversely more in the deeper soil layers (10–30 cm) as precipitation decreased. This response in root foraging appears to be a strategy to satisfy plant water requirements and partially explains the stable NPP allocation patterns. Overall, our results suggest that plants can adjust their vertical BNPP distribution in response to drought stress, and that only under extreme drought does the optimal partitioning theory strictly apply, highlighting the context dependency of the adaption and growth of plants under changing precipitation.