Journal of Geophysical Research: Atmospheres最新文献

Variable falling-snow deposition caused by near-surface turbulence in complex terrain is an important factor contributing to snow cover heterogeneity. A simple falling-snow deposition model is often needed for hydrological, climatic, and land surface studies. Here, we use the Large Eddy Simulation Model S-ARPS (Snow Advanced Regional Prediction System) to simulate falling-snow deposition over single three-dimensional (3D) hills with different obstacle Reynolds numbers, and over a real complex terrain area at Namtso under different wind conditions. An EOF (Empirical Orthogonal Function) method is applied to the LES data to establish a simple prediction model for snow deposition. For single 3D hills, the accuracy of the EOF-based falling-snow deposition model reaches as high as 78%, and for the Namtso terrain 80%. The EOF-based model presented in this study is mathematically simple and practically easy to implement in comparison to machine-learning and large-eddy simulation models for application to climatic and hydrological studies, which universality can be expanded with further vorticity to spatial mode studies.

Based on 3 years of summertime radar observations in East China, this study quantifies the relationship between polarimetric radar signatures (PRSs) and retrieved raindrop size distributions (RSDs) in heavy-rainfall-producing convection. Multiple PRSs, including the 30-dBZ and 40-dBZ echo-tops, the integrated intensities of $��Z_{\text{DR}}�$ and $��K_{\text{DP}}�$ columns, the maximum graupel and hail height, as well as the changes in $��Z_H�$ and $��Z_{\text{DR}}�$ within the warm-cloud layer, can to some extent indicate the mean raindrop size, number concentration, and rain rate at the low level (1-km level). A Multi-Layer Perceptron model is designed and trained to preliminarily predict these RSD parameters using the PRSs as inputs. These RSD parameters are generally reproduced and the correlations between predicted and observed values are above 0.8. The study clearly demonstrates the quantitative constraints of PRSs on low-level RSDs in heavy-rainfall-producing convection. Such microphysical constraints have potential applications for polarimetric radar in the prediction of RSDs and rainfall intensity.

Several studies have presented methods for estimating daytime or nighttime atomic oxygen (O) from measurements made by the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument on the Thermosphere, Ionosphere, Mesosphere Energetics and Dynamics satellite. In the present study, we describe algebraic formulas adapted from the standard SABER Version 2 method for estimating daytime and nighttime O based on the assumption that ozone in the upper mesosphere is in equilibrium. We highlight uncertainties in the calculations that lead to global mean differences ranging up to a factor of two or more in the nighttime O concentration. Important considerations are the choice of which reactions are included in the ozone budget and the values of uncertain parameters used in the model of emissions from vibrationally excited hydroxyl. SABER observations cover almost all local times. Since O is long-lived in the upper mesosphere, the dominant source of diurnal variation is transport by tidal winds. Even when accounting for this transport, there are appreciable differences between daytime and nighttime O. Daytime O is higher than is consistent with the nighttime O that is estimated from several methods. The estimated daytime O has a strong and nearly linear dependence on ozone while the nighttime O is sensitive to multiple parameters in the model of OH airglow emissions. The day/night discrepancy supports other evidence that the SABER daytime mesospheric ozone has large uncertainties and needs to be updated.

China is the largest methane (CH₄) emitter globally, with the Yangtze River Delta (YRD) region recognized as a major emission hotspot. However, due to the scarcity of in situ observations and the complex spatiotemporal variability of these sources, significant uncertainties remain in regional CH₄ emission estimates. To address this, we conducted continuous atmospheric CH₄ concentration measurements from 1 June 2023, to 31 May 2024, at a central YRD site. Using an atmospheric transport model and a Bayesian inversion framework, we quantified monthly and sub-monthly CH₄ emissions from different source categories, with a focus on waste treatment (including both landfill and wastewater). The results reveal the following key findings: (a) Substantial discrepancies were found between prior and posterior emissions across all categories. At the city scale, posterior annual CH₄ emissions were estimated to be 87.4%, 64.6%, 109.5%, and 91.9% of prior emissions for all categories, waste treatment, rice paddy + wetland, and other sources, respectively, with waste treatment contributing the largest uncertainty. (b) Strong seasonal biases were observed, with waste treatment emissions peaking in August and reaching a minimum in March (a 2.6-fold variation), while rice paddy emissions were overestimated in May and underestimated in August by a factor of two. (c) CH₄ emissions from waste treatment exhibited high temperature sensitivity, increasing by 29%–31% per 10°C rise. Under future warming scenarios, waste treatment CH₄ emission factors (EFs) will increase by up to 121.3% under SSP5-8.5 by the end of the century (2091–2100), relative to 2023–2024 levels. In contrast, atmospheric pressure showed negligible influence (3.2% per 1 hPa) on waste treatment CH₄ emissions. (d) More additional observations (i.e., satellite or multiple sites) are strongly suggested to resolve the prior spatial pattern of emissions, especially for fossil fuel-related sources.

Understanding the extent and impact of small-scale island wakes on the atmospheric boundary layer (ABL) is essential for advancing local meteorology. Using the high-resolution WRF-PALM nested model, we quantitatively evaluated the ABL disturbances caused by small-scale islands. Sensitivity experiments with and without islands, conducted during both daytime and nighttime, were used to investigate the wake structures, their effects on near-surface meteorology, and the underlying physical mechanisms. The results reveal notable diurnal variations in the wake structure. At night, orographic dynamics dominate, creating a wind sheltering effect in the leeward area, reducing wind speed, and forming a weak wind wake with downward motion near the surface. This reduces the upward kinematic turbulent sensible heat flux, inhibiting latent heat flux from the sea. During the day, thermal and dynamic forces contribute to a more complex wake structure, with accelerated airflows flanking the low-speed wake on both sides. The convergence of wind streams induces upward motion and enhances wind-driven mixing, thereby increasing turbulent sensible heat exchange within the wake zone. Even small islands induce substantial horizontal disturbances in the ABL, with the temperature wake at night extending 5.3 times the island's area and 5.9 times its length, reaching more than 15 km downstream. Thermal effects dominate over dynamic effects in influencing vertical ABL disturbances, increasing the disturbance heights of wind speed, potential temperature, and water vapor to 445 m, 665 m, and 675 m, respectively. These findings reveal the meteorological significance of small-island wakes and their essential role in shaping local boundary-layer processes.

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.