Journal of Geophysical Research: Atmospheres最新文献

We investigate the 1D reflectance distribution of shallow cumulus as a function of cloud size, using high-spatial-resolution observations from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Reflectance transects through clouds are compared between Inner Mongolia and Tropical Ocean. The reflectance shows varying degrees of shadowing–illumination effects, depending on transect direction. Perpendicular to the solar azimuth (cross-sun direction), where shadowing–illumination effects are minimal, the reflectance follows a symmetric, bell-shaped decrease from the cloud center to the cloud edge and into the transition zone or radiative halo. The peak reflectance is located slightly off-center for larger clouds, which can be explained by the competition between radiative smoothing and sharpening. Reflectance steadily increases with cloud size in both regions; however, Inner Mongolia clouds are significantly brighter and have larger cloud top height variations for the same chord length (cloud diameter). The size and height of continental clouds also systematically increase with surface temperature, indicating more vigorous convection over warm, dry land characterized by high Bowen ratio. In the halo region, reflectance increases by 20%–25% above its clear-sky value within one chord length of the cloud edge, the brightness enhancement showing little variation with cloud size. The chord-length normalized size of the halo steadily decreases with cloud size, as the absolute halo size does not scale linearly with cloud size. This observation, consistent with large-eddy simulations of the moist buffer around shallow cumulus, suggests that the processes responsible for halo formation have size-independent length scales.

Climate change exacerbates hydroclimatic extremes, yet predicting risks for vulnerable Indo-Pacific populations remains limited by overlooked storm-climate linkages. The Indian Ocean dipole (IOD) and El Niño-Southern Oscillation (ENSO) govern regional droughts, floods, and heat waves. While IOD and ENSO typically synchronize (e.g., positive IOD with El Niño), several positive IODs (pIODs) occur independently or even cooccur with La Niña—events that defy conventional modes and trigger mismatches in forecasts. Current theories predominantly attribute this phase mismatch phenomenon to internal forcing within the Indian Ocean, lacking robust identification of external forcing mechanisms—a critical gap that undermines the reliability of predictive frameworks. Here, we show that active western North Pacific (WNP) tropical cyclones (TCs) induce maritime continent subsidence, triggering anomalous easterly winds and sea surface cooling in the eastern tropical Indian Ocean, thus generating independent pIODs. Our findings identify a critical external forcing mechanism for the ENSO-IOD phase discrepancy, filling a pivotal knowledge gap in current theoretical frameworks. By quantifying TCs' role as synoptic-scale triggers of interannual extremes, we provide a basis for disaster agencies to integrate real-time TC activity into early warning systems.

The collection of dust aerosols (0.1–10 $��\mu �$m) by near-surface obstacles is one of the important processes in atmospheric aerosol dry deposition. However, existing understanding of this process remains limited. For example, most current deposition models assume complete deposition or describe the collection process through empirical models, making it difficult to accurately predict the concentration distribution of dust particles within the near-surface collection layer. In this study, the effects of turbulence around obstacles are considered to improve an existing numerical model for simulating aerosol particle deposition in rough surface environments. The modified numerical model is validated through wind tunnel experiments. Subsequently, the particle collection process on an isolated cylindrical obstacle placed on the ground is investigated using numerical simulations. The effects of different wind conditions, particle properties, and obstacle sizes on the collection process are analyzed. Finally, a fitted relationship between the Stokes number and collection efficiency is proposed. The results show that inertial impaction and interception jointly dominate particle deposition on the windward side of the isolated obstacle, while turbulence-induced impaction governs deposition on the leeward side. Therefore, parameters that affect particle inertia and the turbulent flow around the obstacle have a more significant impact on the particle deposition process. The simulation results agree well with existing experimental data and exhibit lower data dispersion, demonstrating that the numerical model developed in this study can effectively describe the collection of particles by near-surface obstacles. This provides a solid basis for improving atmospheric particle deposition models.

Accurate representation of CO₂ transport is important for reliable flux inversions with global inversion systems. Previous studies have revealed systematic discrepancies between two widely used global transport models, GEOS-Chem and TM5, in simulated CO₂ distributions at mid-latitudes. However, the causes of these discrepancies and potential transport biases in the models remain unclear without constraints of intensive measurements. Here, we drive GEOS-Chem and TM5 with identical surface CO₂ fluxes and perform simulations with and without parameterized convection. By comparing the simulated spatial-temporal distributions of [CO₂] to intensive aircraft and tower measurements collected by the Atmospheric Carbon Transport (ACT)-America project and NOAA's airborne profiling and tower networks across four seasons, we identify potential transport biases in both models over North America. Model discrepancies in vertical transport produce seasonal-mean planetary boundary layer (PBL)–free-troposphere [CO₂] differences of up to ∼4 ppm and PBL height differences of ∼100–400 m. In summer, GEOS-Chem underestimates transport through moist convection but overestimates PBL mixing depth, whereas TM5 exhibits more realistic total vertical mixing (moist convection and PBL depth). In spring and winter, TM5 underestimates PBL mixing while potentially overestimating mixing through moist convection, whereas GEOS-Chem more accurately represents vertical mixing. These transport biases are likely to contribute to discrepancies in the meridional gradient of zonal-mean [CO₂] and propagate into differences in posterior flux estimates from the OCO-2 Model Intercomparison Project inversions using the two transport models over North America. Expanded vertically resolved [CO₂] measurements beyond North America can further advance the current understanding of transport uncertainties at larger spatial scales.

This sequel study continues to develop a strategic framework for the global Planetary Boundary Layer Height (PBLH) analysis and monitoring using the National Aeronautics and Space Administration (NASA) Goddard Earth Observing System (GEOS) data assimilation (DA) system. The framework supports the assessment of PBLH from multiple observing systems, including radiosonde, Global Navigation Satellite System-Radio Occultation (GNSS-RO), spaceborne lidars: Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and Cloud-Aerosol Transport System (CATS), ground-based lidar: NASA Micro-Pulse Lidar Network (MPLNET), and ground-based radar: networks of radar wind profilers (GRWP), using either consistent or inconsistent PBLH definitions from the GEOS model. A comprehensive evaluation over a 27-day period (23 August –18 September 2015) is performed to quantify the PBLH Observation minus Forecast bias and Root Mean Square Deviation across data types. Although radiosonde, GNSS-RO, and GRWP PBLH are assessed using consistent model definitions, lidar-based PBLH is compared using inconsistent ones due to current model limitations. The results underscore the importance of using physically and instrumentally consistent model PBLH with corresponding PBLH observations. They further demonstrate that robust quality control and thinning procedures tailored to each observation type are critical, particularly when model definition and PBLH observations are inconsistent. The results also highlight notable discrepancies among two space-based lidar PBLH data sets, especially over the ocean, which the implementation of corresponding lidar-based model PBLH and advanced PBLH retrieval algorithms are expected to reduce. The developed framework enables a robust evaluation of current and future PBLH data sets and serves as a foundation for an effective assimilation strategy.

Temperature and air humidity are critical environmental factors regulating photosynthesis and biogenic volatile organic compound (BVOC) emissions from plants, which influence air quality and climate change. While previous research has demonstrated the impacts of temperature on photosynthesis and BVOCs, the effects of humidity and the combined effects of temperature and humidity remain understudied. Here we discuss the discrete and synergistic impacts of temperature and humidity on ponderosa pine trees. We used a portable photosynthesis system coupled to a proton-transfer-reaction mass spectrometer to quantify leaf-level changes in photosynthesis, stomatal conductance, and emissions as a function of both temperature and humidity. Results demonstrate that all BVOC emissions investigated increased with temperature, regardless of humidity, in agreement with literature. To our knowledge, we present the first direct observation of temperature-dependent methyl vinyl ketone and methacrolein emissions from ponderosa pines. We find that elevated humidity enhances the baseline emissions of many BVOCs. Increasing relative humidity from 30% to 50% resulted in basal emission rates increasing between 1.7-fold for sesquiterpenes and 2.9-fold for 2-methyl-3-buten-2-ol. Our results may help explain field observations where BVOC emissions are decoupled from temperature under some conditions, and we clearly illustrate the need for further investigations on BVOC humidity sensitivity over larger (e.g., ecosystem) scales and on other plant species to improve chemical transport model predictions.

Black carbon (BC) from maritime emissions plays a critical role by influencing radiation, cloud processes, and atmospheric dynamics in the marine atmosphere. These impacts depend on BC concentration and mixing state with other aerosol components. However, in situ observations of BC over oceans remain scarce, and the influence of the marine environment on the evolution of BC mixing state is not well understood. Here, we present shipborne measurements aboard the research sailing yacht S/Y Eugen Seibold during 10 Atlantic Ocean cruises. The data set spans 1,120 of measurement hours from near-coastal regions to remote ocean areas. In oceanic regions extending from tens to thousands of kilometers offshore, 1-min averaged BC concentrations were typically around 100 ng m⁻³, suggesting a well-mixed marine background. Despite the relatively small variability in BC mass concentrations, the mixing state of BC exhibits substantial differences between nearshore and remote oceanic regions. High number fractions (>50%) of BC particles without core-shell morphologies, characterized by BC externally attached to non-BC materials, were observed in near-coastal regions and decreased to ∼20% in remote oceanic regions. In ship-impacted regions, small freshly emitted BC particles tend to coagulate with other aerosol particles and forming non-core-shell attached structures, while high relative humidity (RH > 85%) tends to promote the formation of thick coatings. Our results provide new insights into climate-relevant properties and underscore the importance of coagulation and hygroscopic processing for the mixing state of BC in the marine atmosphere.

Air pollution in India is complex due to the multitude of sources and varying topography, rendering the interplay between meteorology and emission sources significant. To address this challenge, this work presents an integrated methodology for PM_2.5 source apportionment in Bhopal, central India, combining dispersion-normalized positive matrix factorization (DN-PMF) with a machine-learning interpretability approach using Random Forest and SHAP (RF-SHAP). DN-PMF improves conventional source identification by incorporating air dilution effects, yielding refined source contributions for nine factors in Bhopal. Seasonal factor contributions peaked during periods with a lower boundary layer height, such as secondary sulfate during the winter season (21.3 μg m⁻³, 31.7%). The COVID-19 lockdowns, a quasi-natural emissions reduction experiment, led to a decrease in aerosol contributions from industrial, residential and traffic-related sources. However, during this period, crop residue burning was exposed as a major anthropogenic contributor, which together with unfavorable meteorology resulted in increased mean PM_2.5 (50.6 ± 24.3 μg m⁻³) during the lockdowns compared to the reference period (36.7 ± 9.7 μg m⁻³). Using RF-SHAP the influence of meteorology and emission sources in driving secondary inorganic aerosol formation was examined. Secondary nitrate and residential fuel were identified as key contributors to exceedances of the Indian National Ambient Air Quality Standards (60 μg m⁻³, 24-hr average) at the study site. Integrating DN-PMF with RF-SHAP (driver analysis) enhanced source attribution by linking source contributions with their driving factors, establishing a framework for assessing pollution dynamics. This framework can help strengthen improved air quality initiatives in India, including the national Smart Cities Mission.

Abnormally elevated ozone (O₃) is observed during compound heatwave and drought (CHWD), posing severe environmental and socioeconomic threats. Response of O₃ to CHWD is complicated by the vegetation-atmosphere feedbacks through influencing biogenic emissions and stomatal deposition. Here, we employed a regional meteorology-chemistry-vegetation coupled model, integrated with optimized drought emission algorithm and interactive dry deposition scheme, to investigate the vegetation-atmosphere feedbacks and their effects on O₃ pollution during CHWD. Analysis shows that CHWD have intensified in the northern hemisphere, with more frequent occurrence, stronger intensity and longer duration compared to the average climatology. Unusually elevated O₃ levels and exceedance frequency by more than 20% compared to normal conditions were observed during CHWD in the United States, western Europe and China. Model results indicate that heatwaves and droughts jointly lead to 10%–24% increase of summertime biogenic volatile organic compound emissions in vegetated regions, except in the severely drought-affected areas. Furthermore, extremely hot and dry conditions induce stomatal closure and suppress plant growth, inhibiting O₃ stomatal removal by water-stressed vegetation. It is estimated that such intricate vegetation-atmosphere feedbacks substantially exacerbate O₃ pollution during CHWD, with equal importance to that of increased photochemical rates. Our findings offer a novel perspective on the interactions between climate, vegetation, and chemistry regarding compound extreme weather events.

Ice cores from Mt. Logan, the second highest peak in North America located in the St. Elias mountains in southwest Yukon, Canada, have provided conflicting accumulation records, thus the hydroclimate response to changing atmospheric conditions in the highest elevation regions is not well constrained. Here, we present the accumulation record from the new 325 m Mt. Logan ice core drilled at 5,334 m asl on the summit plateau in May 2022. Multi-parameter annual layer counting, confirmed with radionuclide and volcanic tephra measurements, extends to 1911 CE, associated with a depth of 257 m. The thinning-corrected annual accumulation record reveals an average rate of 3.0 m water equivalent per year (m weq a⁻¹) from 1912 to 2020 CE, greater than six times higher than the previous estimate of ∼0.41 m weq a⁻¹ from the 2002 Mt. Logan Prospector Russell Col core. Correlation analysis between the annual accumulation record and regional climate data sets (e.g., Japanese 55-year Reanalysis, weather stations) reveal a strong positive relationship with warm-season total precipitable water and temperature. Thus, we suggest interdecadal precipitation variability on Mt. Logan is at least partially driven by warm-season atmospheric water vapor loading, potentially related to atmospheric temperature responses associated with the warm-season Alaska Blocking Index. Further, the record reveals a statistically significant increase in accumulation of 0.13 m weq per decade since 1970. These results reveal a drastically different Mt. Logan ice core record and provide a new warm-season perspective on drivers of high-elevation accumulation variability in the North Pacific.