Journal of Geophysical Research: Atmospheres最新文献

Aerosol trace element (TE) transport serves as a critical driver of marine TE biogeochemical cycles and climate feedback systems. In the rapidly warming Arctic Ocean (AO), however, the contemporary distribution patterns and decadal variability of aerosol TE deposition remain poorly constrained, representing a critical gap in our understanding of current and future Arctic environmental changes. Here, we present extensive shipboard observations of 13 aerosol TEs across the AO during summer 2024. TE concentrations and the enrichment levels of these anthropogenic TEs rank among the lowest globally observed in the AO. The comparable or even elevated mineral- dominated TE concentrations and deposition fluxes (Al, Fe) in the Central AO than Peripheral AO challenge current dust models, potential influenced by sea ice/snow resuspension. Coal combustion (As, Se), non-exhaust vehicular emissions (Ni, Cr), and metallurgical activities (Zn) emerged as primary anthropogenic sources, with detectable anthropogenic imprint even in mineral-dominated TEs (e.g., Fe, Mn). Decadal comparisons with historical records revealed a near ten-fold reduction in Pb and Cd enrichment, contrasting with a near-doubling of V enrichment driven by intensified Arctic shipping. Moreover, the distinct aerosol Fe/Al fractionation between this study and historical observations likely arises from mixing inputs of anthropogenic Fe-rich particles and permafrost-derived Fe-depleted weathering products, which amplify uncertainties in Fe flux estimations derived from dust proxy approaches. This study provides advance understanding of aerosol TE dynamics in the warming Arctic and provide critical constraints for polar biogeochemical cycles.

The characteristics of Pekeris modes as well as Lamb modes are investigated using the new reanalysis data set JAWARA, which spans over 19 years and covers the entire middle atmosphere. Pekeris modes are a class of global normal modes whose energy is trapped in two height regions that is, around the stratopause and the surface, while the energy of Lamb mode is trapped only at the surface. Statistically significant spectral peaks corresponding to both Pekeris and Lamb modes are detected for seven normal modes. The vertical structures closely match theoretical expectations for most modes. Notably, the geopotential height amplitudes of the Pekeris modes are comparable to or greater than those of the Lamb modes in the mesosphere and lower thermosphere, suggesting an important role for Pekeris modes in the dynamics of this region. On the other hand, the Lamb modes are dominant in the stratosphere.

Organic aerosols (OAs) exhibit non-ideal behaviors that challenge conventional models assuming ideal equilibrium partitioning. This study integrates a unified kinetic framework into WRF-Chem model to handle non-ideal evolution of OAs with considering kinetic mass transfer process with multidirectional interactions (particle surface area, volume, molecular weight) governed by Fick's second law. Simulations in winter of the North China Plain (NCP) reveal that non-ideal treatment enhances condensation of organics species and water vapor, amplifies interactions between OA, aerosol liquid water content (ALWC), and secondary inorganic aerosols (SIA, pSO₄²⁻, pNO₃⁻ and pNH₄⁺). The revised framework reduces mean bias in OA and SIA predictions from normalized mean bias (NMB) of −18.4% to −2.9%, −33.4% to −23.0%, −2.0% to −0.3%, and −35.4% to −30.2%, respectively, achieves better performance in reproducing ALWC with better correlation (from 0.81 to 0.88), and improves PM_2.5 modeling accuracy (NMB from −18.0% to −9.5%) in “2 + 26” city cluster among the NCP. The framework enhances predictions without modifying chemical mechanisms and suggests a potential reductions in direct radiative forcing estimation (−0.77 W/m² among the NCP). The findings advocate urgent integrating non-ideal behavior of OA into air quality models to advance aerosol prediction.

Representation of aerosol-cloud interactions (ACI) remains one of the largest uncertainties in climate models and our understanding of climate change. Using multisource cloud, aerosol, and meteorology data during summer of 2015–2024, this study investigates ACI from the perspective of aerosol size (denoted by Ångström exponent, AE) over the ocean and land in eastern China. Our findings reveal that at a fixed cloud water path, the cloud droplet effective radius (CER) increases with the aerosol index (AI) under high-AE conditions (fine-mode aerosols), while CER decreases with increasing AI when AE is below 1.4 (coarse-mode aerosols) in both regions. We interpret the opposite correlations as arising from aerosol size-dependent regulation of cloud-nucleating ability, which leads to distinct dominant cloud microphysical processes. Over land, smaller aerosols with lower cloud-nucleating ability lead to weaker competition for water vapor and the collision-coalescence process becomes dominant due to the enhanced turbulence as aerosols increase. Conversely, activation efficiency is significantly stronger for coarse-mode aerosols over the ocean and the competition effect becomes the dominant process. In addition, the dominant aerosol size decreases as cloud top pressure increases over land, leading to a transition in the CER-AI relationships from negative to positive. The link between lower cloud tops and finer aerosols is consistent with the enhanced radiative stabilization induced by a higher proportion of fine aerosols (often light-absorbing). In contrast, AE values over the ocean remain consistently low, resulting in persistent negative correlations. Despite variations in meteorological conditions, the opposite correlations under dominant coarse- and fine-mode aerosol conditions still exist.

The Arctic atmosphere in the winter months of the Northern Hemisphere is influenced by a stratospheric polar vortex, characterized by strong westerly circumpolar winds and extremely low temperatures. These vortices impact surface weather in various ways, and their dynamics may be affected by recent anthropogenic climate change. However, key aspects of these dynamics, such as the processes of energy and helicity cascading, remain unclear. In this study, we propose a novel approach to studying polar vortex dynamics that considers kinetic helicity. Mainly using ERA5 reanalysis data but also Aeolus Level 2C wind data, we examine the evolution of kinetic helicity in the stratosphere over the Arctic region during winter 2022/23. Our focus is on the formation and stability of the polar vortex, as well as sudden stratospheric warmings (SSWs). We find that the polar vortex is strongly helical, with the sign of kinetic helicity depending on the evolutionary stage of the vortex. Our analysis of the kinetic energy and kinetic helicity spectra reveals the presence of dual cascades during vortex formation. The spectral properties of the stratosphere over the Arctic change seasonally and as a function of the magnitude of the kinetic energy and helicity, with spectra being steeper during polar vortex activity. Similar correlations are absent for the spectra in the troposphere. Finally, we found that the evolution of kinetic enstrophy and helicity can predict the occurrence of SSWs with an accuracy of approximately 8 days.

Satellite remotely sensed (RS) soil moisture (SM) is commonly assimilated to leverage land surface modeling. However, the improvement in deep soil simulation remains challenging as limited by the relative shallow penetration depth (< 5 cm). With advancements in satellite RS technologies like P-band sensors, retrieving deeper SM has become increasingly feasible. Here, we demonstrate the potentially added value of enriched deep soil moisture information in land surface data assimilation (DA) by using ground-based SM observations from a dense monitoring network in the central Tibetan Plateau. Specifically, a cost function-based multi-layer SM DA framework is developed to optimize soil texture profiles and organic matter content. A series of DA experiments were conducted to explore the optimal assimilation and optimization depths. The results suggest that assimilation of top 20 cm SM is adequate to reasonably optimize key soil parameters for all soil layers, and thereby improves SM estimates to the depth of 40 cm. This improvement can further propagate into soil temperature profiles and surface flux (e.g., evapotranspiration) estimates. Besides, the performance of deep SM DA is found insensitive to assimilation frequency varying up to 5 days, highlighting the promise and feasibility of regional land DA with 0–20 cm SM products, which are readily accessible from future satellite missions.

Wildfires burn surface vegetation and release lignin-derived compounds, such as methoxyphenols. Methoxyphenols in the atmosphere are pivotal in modulating climate change, secondary particle pollution, and human health risks. However, the quantity of methoxyphenol emissions from vegetation due to wildfires and their spatiotemporal trends remain unclear on a global scale. Here, we established a global atmospheric emission inventory of methoxyphenol due to wildfires at 0.25° × 0.25° resolution from 2001 to 2020, and clarified their global spatial distribution and seasonal variations. The annual emissions of methoxyphenol released by wildfires globally range from 798.6 to 1385.3 Gg, with Africa being the region with the highest emissions. Among the cumulative emissions of methoxyphenols, the emission of methyl syringol is the highest, reaching 2809.6 Gg. Simultaneously, alkyl and ketone substituents have higher emissions than those of acid substances. For land cover types, the proportion of methoxyphenols emitted from the burning of deciduous broadleaf forests is the highest, possibly because the fallen leaves provide ample fuel for the fire. These findings depict the global methoxyphenol pollution caused by wildfires, providing a foundation for further exploration of the methoxyphenols-mediated environmental, climate, and health effects from wildfires.

Offshore wind power plays a vital role in the expanding renewable energy landscape of the Northeast US. Accurate prediction of wind and turbulence is essential for optimizing the efficiency and reliability of offshore wind farms; however, boundary layer flows in the offshore environment can be complex. To address this challenge, our study uses an advanced three-dimensional planetary boundary-layer (PBL) scheme within the Weather Research and Forecasting (WRF) model to predict hub-height wind and turbulent kinetic energy (TKE) for the Northeast US offshore region. We conducted a comprehensive evaluation of sub-kilometric, gray-zone mesoscale WRF simulations with measurements of hub-height wind and TKE, buoyancy and wind shear taken at the Woods Hole Oceanographic Institution's Air-sea Interaction Tower (ASIT). Our evaluation for different synoptic forcings, clustered based on self-organizing map analysis, revealed a good agreement between the predicted and observed hub-height wind speed, while the hub-height TKE evaluation had mixed performance. TKE was generally overpredicted in stable conditions, especially for strongly forced nodes with predominant southerly flow. In contrast, the model performed well in weakly forced, northerly flow regimes, regardless of stability. The overprediction of TKE under stable conditions was linked to misclassification of atmospheric stability, a known limitation in stable boundary layer parameterizations.

The memories of soil moisture (SM) and soil temperature (ST) modulate the effect of land surface on climate prediction on monthly and longer timescales. Based on a Lagrangian-based understanding of SM and ST memories, this study explores the characteristics of SM and ST memories using ERA5-Land reanalysis data, observations, land surface model and WRF model. The results show that SM and ST memories are longer in North China, Northeast China, Northwest China and Qinghai-Tibet Plateau than in other regions of China, even exceeding 10 months. In the southern part of China, memories are short, approximately 0–6 months. SM memories have similar spatial distributions at different soil depths over 12 months, as do ST memories. The WRF-simulated memories show generally consistent spatial patterns with ERA5-Land but are lower in some regions. Moreover, SM and ST anomalies mainly persist in the form of subsequent SM and ST anomalies, respectively. The relationships between the memories and land surface fluxes exhibit distinct characteristics in arid and wet regions. The memories of SM anomalies are longer in frozen soil, non-flood season and the arid-humid transition zone. Additionally, SM and ST memories are characterized with clear monthly and decadal variations, which may be related to some oceanic and atmospheric patterns. This study advances the understanding of critical processes linking land conditions before a certain time to atmosphere thereafter.

We present a supervised machine learning (ML) framework to automatically identify new particle formation (NPF) events and analyze key atmospheric factors associated with their occurrence and growth. We applied ML to detect NPF events using start time and particle concentrations across size ranges, while identifying atmospheric variables including ambient temperature, relative humidity, solar radiation intensity (SRI), wind speed, wind direction, boundary layer height, total organics, sulfate, nitrate, total surface area concentration, sulfur dioxide, and turbulent kinetic energy (TKE). We analyzed a 6-year data set from the Atmospheric Radiation Measurement at the Southern Great Plains (SGP) site in Oklahoma, USA. Using long-term ground-based measurements, we identified NPF events and applied Random Forest Classifiers, which achieved 90%–95% prediction accuracy. Feature importance analysis highlighted SRI, relative humidity, and ambient temperature as the most influential variables, contributing normalized importances of 28%, 17%, and 10%. Partial Dependence Plots (PDPs) indicated that higher SRI and lower relative humidity were critical in promoting NPF formation at SGP. Seasonally, NPF events were more frequent in winter (42.1%) and spring (35.5%), and least in summer (4.0%). Particle growth rates also exhibited a seasonal variation, with the lowest in winter (below 2 nm hr⁻¹) and highest in late spring and early summer (exceeding 5 nm hr⁻¹). Temperature, turbulent kinetic energy, and aerosol properties were the primary factors of growth rate variability. This study advances predictive modeling of NPF, offers insights for future campaign deployments, and demonstrates the effectiveness of ML in understanding the formation and growth of atmospheric aerosols.