Journal of Geophysical Research: Atmospheres最新文献

Based on seven reanalysis data sets and three atmospheric river (AR) detection algorithms, we investigate the dominant interdecadal modes of East Asian (EA) summer atmospheric rivers (ARs) from 1940 to 2024. The first mode exhibits a monopole pattern characterized by coherent AR enhancement, associated with a low-level anomalous anticyclone and an intensified western North Pacific subtropical high (WNPSH). The anomalous anticyclone is maintained by the meridional wave train induced from the western tropical Pacific under the anomalous Indo-West Pacific Walker circulation triggered by the negative sea surface temperature anomalies (SSTAs) over the southern Indian Ocean (IO), which is closely linked to the IO Basin mode. The second mode presents a zonal dipole pattern featured by opposite AR anomalies, corresponding to a pair of low-level anomalous cyclonic and anticyclonic circulations and the northeastward WNPSH retreat. The dipole circulation is mainly sustained by the meridional wave train excited from the Maritime Continent (MC) when cold tropical eastern Pacific SSTAs and warm MC SSTAs generate an anomalous Pacific Walker circulation, accompanied by the combined effect of a wave train from the mid-latitudes. The second mode is significantly modulated by the Pacific Decadal Oscillation. Particularly, the dominant EA AR mode varied from the monopole to dipole modes around 1977/1978 due to changes in oceanic forcing from the Indian to the Pacific Ocean, leading to a shift in EA AR frequency from an increasing trend to a relatively stable state.

Sulfur and nitrogen wet deposition is governed by anthropogenic emissions and precipitation regimes. However, the impacts of precipitation characteristics on wet deposition SO₄²⁻, NO₃⁻ and NH₄⁺ and its hysteresis to precursor emission controls remain inadequately quantified. Multiyear (2010–2024) rainwater chemistry, air pollutants, and meteorological parameters were monitored in Wuhan. Random forest models were applied to weather-normalized sulfur and nitrogen wet deposition. Trend analysis using the normalized data revealed that 1-unit reduction in precursor emissions declined 0.14–0.48-unit sulfur and nitrogen wet deposition. This hysteresis was driven partly by enhanced wet scavenging associated with increased light rain frequency, as the scavenging ratios of atmospheric sulfur and nitrogen compounds increased by 1.9%–10.5% yr⁻¹ from 2016 (2019) to 2024. Light rain with a rate ≤16.5, 17.3, and 6.8 mm d⁻¹ most strongly influenced wet deposition trends of NH₄⁺, NO₃⁻, and SO₄²⁻, respectively, identified by the SHapley Additive exPlanations approach. The light rain frequency rose by 2.6%–3.7% yr⁻¹ during 2010–2024, annually adding 0.036 kg N ha⁻¹ yr⁻¹ to nitrogen deposition since 2010 and 0.003 kg S ha⁻¹ yr⁻¹ to sulfur deposition since 2014. This study demonstrates how light rain critically governs atmospheric sulfur and nitrogen source-sink dynamics amid shifting emissions and climates.

We present a method for retrieving cloud optical depth that applies 3D radiative transfer to utilize the combination of polarimetry and multi-spectral imagery that is newly available from satellite missions such as the Plankton, Aerosol, Cloud, Ocean, Ecosystem (PACE) Mission. Due to the approximate spectral invariance of scattering by clouds, this combination of measurements is sensitive to the mean number of scattering events experienced by visible radiation. Using a hierarchy of synthetic cloud fields ranging from idealized cloud geometries, stochastically generated cloud fields with idealized microphysics, and those produced by Large Eddy Simulations (LES), we demonstrate that the combination of visible reflectance and the mean number of scattering events skillfully predicts the in-cloud mean of the optical depth at both 8 and 1 km resolution, with coefficients of determination exceeding 0.92 and 0.86, respectively. Further out-of-sample testing on LES cloud fields show that a multi-linear regression trained on stochastically generated cloud fields reduces the relative root-mean-square error from 29% under the plane-parallel homogeneous assumption to less than 14% at 6 km resolution. Biases in 1 km resolution retrievals of trade cumulus are reduced from −74% to −40%. Uncertainties from instrumentation and atmospheric correction add up to 20% additional uncertainty in cloud optical depth for the LES cloud fields. With this method, the PACE mission can provide the first global estimate of cloud optical depth that accounts for cloud heterogeneity.

Methanesulfonic acid (MSA), an acid molecule with properties similar to that of sulfuric acid (SA), has attracted increasing attention for its role in driving atmospheric new particle formation (NPF). Currently, ethanolamine (MEA) is recognized as the most promising atmospheric organic amine for promoting the formation of MSA-based clusters. Given the complexity of multi-component aerosol nucleation mechanisms, it is essential to explore the potential of other gaseous substances participating in the MSA-MEA-driven NPF. Formic acid (FA), the most abundant organic acid in both the atmospheric gas phase and particulate phase, warrants urgent investigation regarding its potential role in aerosol nucleation. Herein, we investigate the enhancement potential of FA on MSA-MEA-driven NPF within the troposphere. The results indicate that the enhancing effect of FA emerges in the lower troposphere (T ≤ 278.15 K) when [FA] ≥ 10¹⁰ cm⁻³. The enhancement strength, R_FA, increases with decreasing temperature and increasing FA concentration, reaching a factor of up to 21.54 at 258.15 K, and is most significant in regions with high MEA emissions. At 258.15 K, when [FA] ≥ 10¹¹ cm⁻³, FA can directly promote the MSA-MEA-FA growth pathway, rather than merely acting as a catalyst for the MSA-MEA pathway. Consequently, the ternary MSA-MEA-FA nucleation mechanism may play a crucial role in the NPF processes in cities with severe Industrial pollution, forested areas, industrial zones rich in volatile compounds, cold oceans, and polar regions.

Over recent decades, North African dust storms have undergone marked variability, reflecting complex interactions between regional climate processes and environmental change. Using four decades (1984–2023) of visibility-based observational records, we examine regional and seasonal trends in dust storm frequency across the Sahel and the Sahara, capturing their distinct dust dynamics. Results reveal a significant decline in dust activity in both regions, most pronounced during pre-monsoon (MAM) and monsoon (JJA) seasons in the Sahel, and during post-monsoon (SON) and dry season (DJF) in the Sahara. Integrating surface observations with local meteorology (precipitation, surface wind speed, vegetation) and climate indices (AMO, NAO, MEI), we find the Atlantic Multidecadal Oscillation (AMO) as the primary driver, with region-specific effects: in the Sahel, AMO-driven warming and rainfall increase vegetation, suppressing dust; in the Sahara, AMO intensifies the Saharan Heat Low (SHL) and elevates temperatures, modulating dust through atmospheric stability and wind patterns. Local meteorology further differentiates responses, with precipitation and Leaf Area Index (LAI) dominating dust variability in the Sahel, while SHL strength and surface winds are most influential in the Sahara. By explicitly separating the Sahel and Sahara and integrating multiple drivers, this study provides a more spatially resolved understanding of dust–climate link and suggests continued declines in North African dust storm activity under future warming. These findings offer critical constraints for improving dust emission projections in climate models.

In recent years, machine-learning (ML) models trained on reanalysis data have rivaled physics-based forecast models in terms of performance skill for global weather forecasting. With increased rollout stability, the question of how these models perform for subseasonal to seasonal (S2S, week 3–8) forecasting has emerged. In this study we run a large set of subseasonal hindcasts over 2004–2023 to evaluate two ML weather forecast models at the S2S time scale, SFNO-HENS (Nvidia, fully ML) and NeuralGCM (Google Research, hybrid). Corresponding hindcasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) are used as a baseline for comparison to a physics-based model. Because our focus is on predicting moisture transport over the Western United States between October and March, we evaluate the models' prediction skill for the Madden-Julian Oscillation (MJO) and its associated teleconnections in the North Pacific. We find that both ML models are competitive with the ECWMF model, with comparable skill in predicting the North Pacific large-scale circulation and the MJO at week 3 and beyond. Even though overall the mid-latitude subseasonal prediction skill remains low, the ML models exhibit interesting behavior such as a realistic propagation of the MJO across the Maritime Continent and realistic teleconnections. A SFNO-HENS sensitivity experiment with altered initial conditions in the tropics demonstrates the stability of the model, and it illustrates the capability of ML models to represent important physical processes of the atmosphere at the S2S time scale.

Atmospheric hydrogen peroxide (H₂O₂) is a critical oxidant that influences atmospheric chemistry, playing a pivotal role in the cycling of hydroperoxyl (HO₂) and hydroxyl (OH) radicals, ozone (O₃) formation, and sulfate aerosol production. However, the current understanding of its concentration level, sources and atmospheric effects are still poor. This study investigates the drivers of elevated H₂O₂ concentrations during autumn in Beijing using comprehensive field observations from October to November 2021. The averaged H₂O₂ concentration in the urban boundary layer of Beijing is 0.26 ± 0.03 ppb, peaking at 16:00. By integrating Random Forest Regression and relative incremental reactivity analysis, we systematically examine the influence of meteorological factors, trace gases, and photochemical reactions on H₂O₂ concentrations. The result showed the significant contributions of temperature (T) and photochemical processes to H₂O₂ production, while identifying key inhibitors such as nitrogen monoxide (NO). Additionally, we explore the role of H₂O₂ in sulfate formation during haze pollution episodes, finding that although H₂O₂-mediated oxidation contributes to sulfate production, it is not the dominant pathway during the campaign. These findings underscore the complex interplay between meteorological factors, trace gases, and multiphase reactions in regulating H₂O₂ concentrations and cycling, providing valuable insights into the dynamics of atmospheric oxidation processes and offer guidance for mitigating air quality issues in urban boundary layer.

Mixed-phase clouds modulate the water and energy cycles of high-latitude regions, yet their liquid-ice phase partitioning has long been poorly simulated in climate models. Here, simulations of Arctic mixed-phase clouds by the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) are assessed against large-eddy simulations, satellite data, and ground-based observations during the Cold-Air Outbreaks in the Marine Boundary Layer Experiment field campaign. SCREAM simulates nearly completely frozen clouds, which is attributed largely to the unreasonably strong Wegener–Bergeron–Findeisen (WBF) process that converts liquid to ice excessively and partly to the early over-abundant ice production at cold temperatures from a temperature-deterministic deposition ice nucleation scheme. Assuming no subgrid variation for the WBF process in the original formulation particularly conflicts with the instantaneous saturation adjustment assumption in the condensation scheme that assumes subgrid variability, leading to exaggerated WBF process rates. A proposed simple physically-based improvement on the treatment of subgrid cloud overlap substantially increases supercooled liquid water content and notably improves cloud-top phase partitioning, aligning better with observations. Improvement of supercooled liquid water content also converges with increasing horizontal resolution. The deposition ice nucleation scheme is found responsible for a falsely-produced ice cloud aloft that is not observed, biasing the simulated cloud radiative effects and top-of-atmosphere radiative fluxes. This study identifies key deficiencies in cloud parameterizations that continue to challenge convection-permitting models.

Over recent decades, North African dust storms have undergone marked variability, reflecting complex interactions between regional climate processes and environmental change. Using four decades (1984–2023) of visibility-based observational records, we examine regional and seasonal trends in dust storm frequency across the Sahel and the Sahara, capturing their distinct dust dynamics. Results reveal a significant decline in dust activity in both regions, most pronounced during pre-monsoon (MAM) and monsoon (JJA) seasons in the Sahel, and during post-monsoon (SON) and dry season (DJF) in the Sahara. Integrating surface observations with local meteorology (precipitation, surface wind speed, vegetation) and climate indices (AMO, NAO, MEI), we find the Atlantic Multidecadal Oscillation (AMO) as the primary driver, with region-specific effects: in the Sahel, AMO-driven warming and rainfall increase vegetation, suppressing dust; in the Sahara, AMO intensifies the Saharan Heat Low (SHL) and elevates temperatures, modulating dust through atmospheric stability and wind patterns. Local meteorology further differentiates responses, with precipitation and Leaf Area Index (LAI) dominating dust variability in the Sahel, while SHL strength and surface winds are most influential in the Sahara. By explicitly separating the Sahel and Sahara and integrating multiple drivers, this study provides a more spatially resolved understanding of dust–climate link and suggests continued declines in North African dust storm activity under future warming. These findings offer critical constraints for improving dust emission projections in climate models.

Droughts of different durations affect water resources and ecosystems in distinct ways. Human activities have been confirmed to contribute to the increased occurrence of droughts; however, the dependence of these impacts on the durations of drought, and whether they differ between drylands and humid regions, remains insufficiently understood. This study investigates the human influence on droughts of different durations using the Standardized Precipitation Evapotranspiration Index (SPEI), derived from multi-source observations and four sets of Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model simulations. The results show that human activities cause an intensification of long-term droughts, particularly in drylands. This is primarily attributed to rising greenhouse gas (GHG) emissions, with both GHGs and aerosols exerting stronger impacts on droughts in drylands than in humid regions, though aerosols partly offset the intensifying effect. GHGs contribute to more extreme multi-year droughts over drylands by amplifying temperature-induced water demand, whereas aerosols reduce drought occurrence in drylands by enhancing precipitation, in contrast to their precipitation-suppressing effects in humid areas in the past decades.