Journal of Geophysical Research: Atmospheres最新文献

The Great Lakes Region sits at the intersection of multiple North American storm tracks. During the cold season (October–March), the regional weather is dominated by extratropical cyclone activity. While these Great Lakes extratropical cyclones (GL ETCs) are getting warmer and holding more moisture with time, there is considerable interannual variability in storm characteristics. To better understand cold-season variability, this study investigates the correlations between GL ETC thermodynamic characteristics and different global teleconnection patterns. Using a database of 886 cyclones identified in ERA5 data, we find that while there is no correlation between teleconnection indices and the number of GL ETCs each year, the Pacific North American and North Pacific Gyre Oscillation (NPGO) indices are significantly correlated with the moisture content in GL ETCs, and the North American Oscillation is correlated with ETC temperature. The Arctic Oscillation is correlated with all thermodynamic characteristics in GL ETCs. Additionally, the relationships between GL ETCs and teleconnection indices are shifting with time, and some teleconnections, like the NPGO, may become more influential to Great Lakes weather in the future.

The mid-latitude North Pacific exhibits a sea fog frequency of 20%–40% in the summer (June–August). Here, we show that the interannual variability of the summer sea fog over the mid-latitude North Pacific region is correlated with the Asian-Pacific Oscillation (APO) driven by the seasonal heating of the Tibetan Plateau. During the positive phase of APO, the sea fog frequency is observed to be higher along with a stronger North Pacific Subtropical High, North Pacific Trough and South Asian High, while the Bering Sea Low and Okhotsk High are weaker. The stronger northward heat and water vapor fluxes in the positive phase of APO associated with the North Pacific Subtropical High lead to a higher air temperature and relative humidity over the mid-latitude North Pacific region. Additionally, this northward warm advection is stronger in the upper boundary layer than the cooling by the sea surface temperature in the lower layer, creating a temperature inversion within the marine boundary layer and hence a higher sea fog frequency over the mid-latitude North Pacific region. Our study shows that the teleconnection of the Tibetan Plateau can be extended to the mid-latitude northern Pacific through the North Pacific Subtropical High.

Brown carbon (BrC) aerosols, primarily emitted from biomass burning events such as wildfires, significantly impact the climate by absorbing sunlight and contributing to atmospheric warming. However, the details of BrC photochemical aging are not fully understood. When exposed to UV radiation, the chemical composition and optical properties of BrC can change, influencing atmospheric radiative forcing. Previous studies suggested that water-soluble BrC in clouds and fog initially experiences enhanced light absorption due to phenolic monomers forming dimers. However, the identities of dimers responsible for the enhancement have neither been identified nor quantified. This study investigates the direct photolysis of a few key phenolic compounds present in BrC under UVA and UVB radiation. In particular, vanillin was used as a model compound with an aim to identify and quantify divanillin, the dimer of vanillin, and evaluate its role in photo-enhancement. Using liquid chromatography-mass spectrometry and UV-visible spectroscopy, we confirmed the formation of phenolic dimers during photolysis. Quantitative analysis of vanillin revealed that while phenolic homodimers formed under both UVA and UVB conditions, with yields peaking at approximately 10% after 5 min, their contribution to enhanced visible-range absorption was minor (about 10%). Our observations indicate that not all dimers contribute equally to photo-enhancement. While divanillin showed negligible absorbance, the demethylated dimer (dimer-$��{\text{CH}}_2�$) correlated better with the observed optical changes. This suggests that differences in molecular structure and conjugation, rather than the dimer backbone alone, are critical for absorbance, highlighting the need to distinguish between different dimers and oligomers in atmospheric models.

Accurate modeling of carbon, nitrogen, and sulfur wet deposition (i.e., through rain, snow, or graupel) flux is important for characterizing and quantifying the role of deposition in global biogeochemical cycles. The simulation of wet deposition of solutes, alongside precipitation rates, in the Community Atmosphere Model version 6 with Chemistry (CAM-chem) has had limited previous evaluation leaving an opportunity to determine its accuracy in simulating precipitation chemistry. Here, we assessed the accuracy of 1° resolution CAM-chem outputs of wet deposition over the contiguous U.S. (CONUS) from 2002 to 2022, comparing model outputs for observed equivalents of sulfate (SO₄²⁻), ammonium (NH₄⁺), nitrate (NO₃⁻), and dissolved organic carbon (DOC) wet deposition with long-term records collected at hundreds of stations across CONUS. After evaluating the temporal, spatial, and quantile differences between modeled and observed wet deposition fluxes, we find the model captures long-term and seasonal patterns but consistently overestimates NO₃⁻, while underestimating SO₄²⁻, NH₄⁺, and DOC wet deposition fluxes. Model-measurement agreement improved at higher deposition flux quantiles and site-specific alignment was strongest for NO₃⁻, and moderate for SO₄²⁻ and NH₄⁺. Low model-measurement agreement for DOC comparisons is likely due to focusing on aerosol contributions. Higher resolution model simulations (∼14 km) resulted in equivalent comparisons as the 1° model, suggesting that wet deposition processes are represented consistently across different model simulations and spatial resolution is not the main driver of inaccuracies of model deposition. Benchmarking modeled deposition outputs is crucial for evaluating CAM-chem's performance and its utility in understanding landscape drivers of deposition chemistry within Earth system models.

During cyclone passing in the summer melt season, low-level atmospheric characteristics (e.g., temperature inversions) over the marginal ice zone (MIZ) exhibit a more pronounced response and distinctive variations than other ice zones, as influenced by abrupt sea ice transitions in the MIZ. Here, we constructed a high-resolution model for the Pacific Arctic Region (PAR) using the polar-optimized Weather Research and Forecasting model (PWRF), applied it to simulate the low-level atmosphere during the 2018 Chinese National Arctic Research Expedition (CHINARE-2018), and were able to accurately reproduce the observed conditions. Simulations, combined with observational data, were used to evaluate and investigate extreme variations in inversions over the MIZ. Analysis of 84 observed inversions revealed that inversion variability increased significantly during cyclone passing, with a specific inversion of extreme variations occurring in the MIZ. Simulation results showed that abrupt sea ice transitions in the MIZ triggered boundary-layer destabilization, setting it apart from other ice zones and leading to these extreme inversion variations. Before the abrupt sea ice transitions occurred, inversions were mainly controlled by warm advection induced by the cyclone, which facilitated the formation of low-level clouds. Following the transitions, increased surface turbulent fluxes destabilized the boundary-layer, triggering strong upward motion. Meanwhile, accumulated moisture and energy promoted convection, lifting the clouds and inversions. Adiabatic cooling and radiative cooling at the cloud-top over the MIZ led to the formation of inversions reaching the highest altitudes and lowest temperatures during CHINARE-2018, fully capping the cloud-top.

Previous studies suggest that a tropical cyclone (TC) may contribute to the genesis of another TC to its east or southeast in the western North Pacific (WNP) through Rossby wave dispersion. However, the influence of a pre-existing TC (PTC) has not been fully clarified in realistic simulations. This study conducted 42 numerical experiments (NPTC) for 1981–2022, in which a PTC was removed by horizontal smoothing from the initial conditions 120 hr before the genesis of a subsequent TC (STC). Compared to control simulations with the PTC (WPTC), the number of STC formations was slightly larger (WPTC: 31, NPTC: 33). The intensities of common STCs are very similar. STCs were typically located to the south or southwest of a subtropical high. The experiments showed wave-like anomaly patterns in the vorticity field southeast of PTCs and in the geopotential height field northeast of the PTCs. Vertical wind shear (VWS), enhanced by PTC outflows, created an unfavorable environment for STC formation and intensification. Reanalysis data supported strong VWS and a similar wave-like pattern. A positive sea surface temperature anomaly over the central equatorial Pacific induced westerly wind anomalies in the southeastern WNP. The spatial collocation of PTCs and STCs was frequently observed when the active eastern convection is expected by the indices of Madden–Julian Oscillation, Boreal Summer Intraseasonal Oscillation, and El Niño–Southern Oscillation. These conditions promote TC genesis farther east, suggesting that such collocation does not imply PTC-induced formation. Thus, impact of PTCs appears less significant than previously assumed.

This study presents a comprehensive climatological benchmarking of tropical cyclones (TCs) generated by AI-based global weather prediction models. Using all TC events from the North Atlantic and Western Pacific basins between 2020 and 2025, we assess the ability of two AI models (Pangu-Weather and Aurora) to reproduce observed TC track density, climatology of storm characteristics, and physical consistency with TC theory. By comparing AI-simulated TCs with ERA5 reanalysis, we benchmark the distributions of intensity, size, forward speed, and evaluate the model's ability to credibly simulate extratropical transition. Results show that both Pangu and Aurora perform well in reproducing storm track density, forward speed distribution, and outer size distribution. Aurora shows an improved performance in simulating storm intensity compared to Pangu, with less bias in the distribution of minimum central pressure and maximum wind speed. However, both models overestimate the distribution of storm inner size (radius of maximum winds), especially for extreme events. AI models capture the relative frequency and temporal evolution of extratropical transition patterns with reasonable accuracy. The AI-simulated TCs are also less likely to conform to gradient wind balance compared to ERA5, indicating that the AI TCs may not be physically realistic in many cases. This benchmarking identifies systematic biases that can guide future corrections and support extended applications of AI models for TC hazard and risk assessment. Our work establishes a foundation for future studies using AI weather models in the context of TC climatological and hazard research.

A set of gridded, satellite-based, precipitation products has been used to assess the performance of 46 Coupled Model Intercomparison Project (CMIP6) atmospheric-only simulations and 5 reanalyses over the Southern Ocean (SO) on daily timescales, in terms of total precipitation and variance, frequency and intensity of wet days, and seasonal changes. Besides the expected “too frequent, too light” precipitation biases in most models and reanalyses over the region, our study reveals other undocumented features such as notorious peak shifts and shape changes in the frequency distributions from 35°–50° to 50°–65° bands, which do not occur in satellite estimates. We also evaluated snowfall over mid to high latitudes and found that models have a substantial bias in frequency and intensity of snow days (>1 mm/day). The fraction of snow to total precipitation is substantially smaller for AMIP, but due to a rainfall overestimation rather than a deficit in snowfall. The intercomparison of the 7 precipitation data sets that employ remote-sensing observations (including GPCPv3.2 and IMERGv7) is characterized by large uncertainties, which are additionally discussed. Using previous studies based on in situ data over the SO as well as CloudSat, we can also infer biases in most satellite data sets.

Jet drop production is believed to be an important contributor to submicron sea spray aerosol (SSA), although whether it is the dominant source remains uncertain. To clarify this critical issue, we approached the problem from two perspectives. First, it is found that the rupture of a sub-100 μm bubble (diameter < 100 μm) generates only 3–4 jet drops. Second, as sub-100 μm bubbles ascend, their size distributions would remain relatively unchanged. Based on the above results and model calculations, it is concluded that sub-100 μm bubbles in the ocean are unlikely to be a dominant source of submicron sea spray aerosols. Moreover, the production rate of jet drops from sub-100 μm bubbles decreases with increasing temperature, with no significant change in drop size. These findings would help elucidate the role of jet drop production in sea spay aerosols generation and material transfer through sea air interface.

Soils have been identified as an important HONO source, yet field studies on HONO fluxes remain limited, and the impact of soil heterogeneity on HONO fluxes has not yet been systematically evaluated. Here, an automated dynamic chamber system equipped with dual measurement chambers (MC1, MC2) was employed to measure HONO fluxes from wheat fields in the Huaihe River Basin, with alternating measurements of gas concentrations inside the chambers achieved via solenoid valve switching. Laboratory and field evaluations confirmed the feasibility of this flux measurement system. Two MCs were separated by 7 m, covering a wheat field at the seedling stage with low seedling coverage. The field results revealed similar diurnal variation patterns of HONO fluxes in MC1 and MC2, with higher fluxes observed at noon, and their mean values were 1.06 and 1.41 ng N m⁻² s⁻¹, respectively. Correlation analysis indicated that photosensitized conversion of NO₂ on soil surfaces and microbial processes contribute comparably to HONO flux in the MC1, whereas microbial processes play a relatively more important role in the MC2. The higher inorganic nitrogen levels in the MC2 further suggest that enhanced nitrification may have increased HONO emissions. Further analysis showed that when the HONO flux difference between MC1 and MC2 is most significant, it may be caused by temperature-induced variations in soil microbial processes, while the flux difference on some dates is likely associated with changes in NO₂ × J(NO₂). Our study provides the field evidence for understanding the spatial heterogeneity of HONO emissions from agricultural soils.