Journal of Geophysical Research: Atmospheres最新文献

We report two gamma-ray glows observed on 22 December 2023, during a winter thunderstorm in Japan, using an array of four radiation detectors. The first glow, detected by one sensor, was quenched by a lightning discharge. The second glow appeared 2–3 min later and was tracked by three other detectors. Radar observations suggest both glows originated from the same thundercloud cell. However, the detection timing of the second glow was inconsistent with simple thundercloud movement, indicating temporal variations in intrinsic glow brightness. A three-dimensional lightning mapping observation suggests that a discharge activity depleted the electric field that generated the first glow and that the electric field having produced the second glow has been rapidly recovered. In addition, the radar observations also detected a descent of the thundercloud core between the two glows, which may have developed an electrified region and the second glow enough to be observed by the detectors. Tracking gamma-ray glows is crucial for understanding electrified regions in thunderclouds and associated gamma-ray glows.

In this study, we present the development and validation of a microwave-based global gross primary productivity (GPP) estimation method, EDVI-GPP, using the microwave emissivity difference vegetation index (EDVI) retrieved from the China's Fengyun-3D satellite for the period 2020–2022. The global EDVI-GPP model incorporates the effect of diffuse radiation from clouds on GPP and utilizes the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm to tune its key parameters of light use efficiency and the seasonality of the vegetation water content index. In situ GPP measurements from 163 eddy covariance flux sites covering 10 major ecosystem types around the globe are used to calibrate and validate the model performance. EDVI-GPP provides daily temporal resolution GPP estimations applicable under both clear and cloudy skies. At 8-day temporal resolution, the coefficients of determination between EDVI-GPP and in situ GPP measurements (R² = 0.51) during 2020–2022 are comparable to several mainstream GPP products (FLUXCOM-GPP, MODIS-GPP, GLASS-GPP, PML-GPP, and GOSIF-GPP), with a reduced bias of −0.46 gC m⁻² day⁻¹. On a global scale, the annual-averaged EDVI-GPP exhibits high spatial consistency (R = 0.71–0.74) with the compared GPP products. The EDVI-GPP model quantifies the mean global GPP as 123.77 ± 1.33 Pg C yr⁻¹ from 2020 to 2022, which is in close agreement with other published estimates. By using the three-cornered hat (TCH) method to evaluate GPP uncertainty, we find that EDVI-GPP exhibits a large uncertainty in agricultural areas. This research incorporates microwave-derived variables into daily GPP estimation on a global scale, providing a less cloud-affected and reliable measurement.

Breaking gravity waves (GWs) are a major source of turbulence and momentum deposition in the middle atmosphere. In spite of the importance of this phenomenon, the small-scale vortex structures associated with wave breaking remain poorly documented under realistic conditions. We perform a three-dimensional compressible simulation of orographic GWs observed above Syowa Station, Antarctica, using 125 m horizontal grid spacing from the ground to the mesosphere. In the 75–80 km region upward-propagating waves break and exhibit numerous horseshoe-shaped vortices whose bends extend obliquely downward. By examining the time evolution of a representative horseshoe-shaped vortex, we identify the mechanism responsible for the downward elongation of the vortex tube: (a) horizontal vorticity aligned with the GW phase lines is amplified as the wave approaches the overturning condition; (b) a convectively driven downward flow displaces the vortex tube downward, creating the initial horseshoe shape; (c) vertical shear associated with the GW tilts and stretches the tube, reinforcing the downward flow. The direction of the initial vortex tube is determined largely by the baroclinic effect of the GW. The present study also addresses the relationship between the vortex tube structure and the mean flow acceleration (0.3–0.4 m s⁻¹ min⁻¹). The vortex tube deformation associated with wave breaking can be interpreted as a manifestation of the irreversible cascade that transfers the GW momentum to the mean flow.

Wetlands are the largest natural source of methane, yet bottom-up models and top-down models do not agree on global wetland methane emissions. In this study, we use TROPOMI methane data and inverse modeling to estimate the spatial and temporal distribution of global wetland methane emissions during the years 2019–2020 and compare inverse modeling results with an ensemble of 16 bottom-up wetland models from the Global Carbon Project (GCP). We find that our inverse model increases wetland methane emissions near the equator (0$��°�$–15$��°�$) by 7% and decreases emissions in mid- and high-latitude regions (31$��°�$–90$��°�$) by 26% compared to the mean of the GCP models. We also find that our inverse modeling estimate exhibits little seasonality in wetland methane emissions across most tropical wetland regions, even when emissions estimates within the prior include seasonality. This result is consistent with some bottom-up models but not others. For mid- and high-latitude wetland regions (e.g., the West Siberian lowland and Hudson Bay Great Lakes region), the seasonality of our inverse emissions estimate is consistent with most GCP models and suggests wetland methane emissions peak in July. Furthermore, we argue that an inundation map with accurate seasonality is a prerequisite for obtaining a bottom-up methane emission estimate with appropriate seasonality. Overall, many of the bottom-up models examined in this study agree with the magnitude and seasonality of the inverse model in major wetland regions, but there are nonetheless many opportunities to improve convergence between the bottom-up and top-down estimates.

Scanning electron microscopy (SEM) is a critical tool for characterizing the morphology, elemental composition, and size characteristics of atmospheric single particles. Although advancements in computer-controlled technologies have significantly improved analytical throughput (>1,000 particles/hour), the analysis of particulate matter (PM) samples still faces two fundamental challenges: first, determining how many particles need to be analyzed (i.e., the analysis threshold) to ensure statistical representativeness and second, quantifying the data uncertainty caused by the limited number of particles analyzed. Herein, we established an innovative framework addressing both challenges: a multicriteria analysis threshold evaluation system was developed to determine analysis thresholds, and a cyclic overlapping block bootstrap (COBB) method was proposed to quantify data uncertainty arising from finite particle counts. Analysis of 38 PM samples (479,200 particles) encompassing diverse emission sources, urban environments, and seasonal variations revealed that sample complexity dictated analysis thresholds. Environmental samples required higher thresholds (approximately 4,300 particles for active sampling and 5,000 for passive sampling) than source samples (approximately 3,600 particles) primarily due to their more complex composition. COBB analysis demonstrated an inverse correlation between component abundance and relative uncertainty. Notably, trace components (abundance <1.0%) exhibited persistently high uncertainty even with 2,000-particle analyses. This framework establishes systematic methodologies spanning standardized SEM data acquisition to uncertainty quantification, substantially enhancing the scientific rigor, and cross-study comparability of SEM-based atmospheric PM research.

Mesoscale convective system (MCS) is a major contributor to extreme precipitation over East Asia, but their long-term trends remain insufficiently understood. Here, we assess the capability of the weather research and forecasting model, configured as a convection-permitting model (CPM) to simulate MCS characteristics and 23-year trends over East Asian monsoon region from 2001 to 2023 (June–September) by comparing with high-resolution observational data. We employed an MCS tracking method PyFLEXTRKR, which can identify and track MCSs based on precipitation and brightness temperature. The CPM effectively captures key MCS characteristics, including lifetime, lifecycle total precipitation amount, and movement speed. However, it also has systematic biases: the model underestimates MCS size and meso-α MCS frequency while overestimating meso-β MCS occurrence and both mean and maximum MCSs precipitation intensities. Despite these biases, the model captures increasing (decreasing) trends in total and MCS precipitation over Manchuria and eastern China (Taiwan). In contrast, it struggles to reproduce observed total and MCS precipitation trends over North China Plain (NCP) and the Korean Peninsula (KP). These biases stem from the model's inability to capture enhanced moisture transport into East Asia, resulting in an underestimation of low-level moisture over NCP and KP, as indicated by trends in vertically integrated moisture flux and 850 hPa specific humidity. By characterizing systematic and regional model biases, this study lays the groundwork for more reliable CPM-based assessments of MCS responses to climate variability and change.

Rapid intensification (RI) of tropical cyclones (TCs) is challenging to understand and predict. This study investigates the dynamics and energetics of TCs that underwent RI and non-RI in the Bay of Bengal (BoB) from 2005 to 2023. Eulerian and quasi-Lagrangian frameworks were employed to study the energetics of TCs. Results reveal that the quasi-Lagrangian captures the intensity and its variabilities more robustly during RI than the Eulerian framework, particularly highlighting the rapid increase in EKE (Eddy Kinetic Energy). Energy budget analysis shows that warm BoB regions serve as primary energy sources for RI, with the baroclinic conversion term emerging as the dominant driver of EKE during intensification. Additionally, the study shows that RI and non-RI TC trajectories are statistically distinct, with RI TCs showing more northward movement and non-RI TCs predominantly moving westward. Analysis of composite sea surface temperature (SST) anomalies reveals correlations with RI TC tracks, indicating a positive anomaly during RI phases. RI events are associated with more negative Surface Latent Heat Flux anomalies along the cyclone track, suggesting more substantial ocean-to-atmosphere heat transfer. Moreover, RI TCs experience weaker vertical wind shear than non-RI events, further supporting RI. These distinctions underscore the relevance of RI and the role of energetics emphasizing the importance of baroclinic conversion in driving cyclone intensification. This comparative analysis between these frameworks sheds light on the complexities of cyclone energetics and accentuates the quasi-Lagrangian framework's efficacy in capturing subtle yet critical variations in RI dynamics.

Uncertainties in aerosol–cloud interactions (ACI) remain significant. Recent studies report an anti-Twomey phenomenon, where cloud droplet effective radius (R_e) exhibits a positive correlation with aerosol optical depth (AOD), contrary to the classical Twomey effect. Using long-term satellite and reanalysis data over East Asia, we found a strong positive R_e-AOD correlation. Further analysis reveals that AOD, as a column-integrated quantity, lacks aerosol vertical information and cannot fully represent cloud condensation nuclei (CCN), potentially misleading the interpretation of the Twomey effect. Consequently, we analyzed aerosol vertical profiles and introduced a metric, Elevated Aerosol Ratio (EA_ratio), defined as the ratio of aerosol concentration integrals above and below clouds. Higher EA_ratio values correlated with a stronger anti-Twomey phenomenon, while lower values aligned with the classical Twomey effect. Notably, the anti-Twomey phenomenon primarily appeared under low liquid water path (LWP) conditions: as LWP increased, both the anti-Twomey phenomenon and the influence of EA_ratio diminished. Moreover, the marine anti-Twomey phenomenon is linked to pollution. Elevated aerosol layers in heavily polluted nearshore areas—driven by anthropogenic emissions and atmospheric dispersion—distort the R_e-AOD relationship. Additionally, while coarse aerosols suppress marine anti-Twomey phenomenon, the EA_ratio-based analysis remains valid when excluding their influence as much as possible. Our findings demonstrate that the observed anti-Twomey phenomenon is primarily a statistical artifact arising from the lack of vertical co-location between aerosols and clouds. This mismatch biases the satellite-based quantification of the Twomey effect when column-integrated AOD is used as a proxy for CCN.

Accurate representation of mesoscale cellular convective (MCC) cloud morphology over the Southern Ocean is essential for improving climate model parametrizations and reducing projection uncertainties given their highly variable radiative impacts. In this study, we investigate three potential drivers of the organization and transition from closed-to open-cell MCC clouds over the Southern Ocean under post-frontal conditions: (a) sea surface temperature (SST) including its gradients, (b) cloud ice production processes, and (c) microphysical latent cooling. Using a convection-permitting configuration of the WRF model and building on findings from Part I, we evaluate how each driver shapes the morphological evolution of MCCs. Perturbed physics experiments reveal that while SST is not the primary driver of the closed-to-open MCCs transition, it influences cloud cellular morphology in two ways: warmer SST deepens the boundary layer and enhances precipitation, whereas colder SST suppresses boundary layer mixing, leading to reduced cloud cover. Enhanced ice production plays a key role in MCCs organization, driving cloud “break-up” by increasing precipitation formation. Cloud evaporative cooling significantly affects MCCs organization, likely by affecting negative cloud buoyancy, allowing clouds to grow deeper and drying the boundary layer. These findings highlight critical processes that govern MCCs behavior and provide valuable insights for improving the representation of shallow clouds in climate models, ultimately aiding efforts to reduce uncertainties in climate sensitivity projections.