Journal of Geophysical Research: Atmospheres最新文献

Complex spatial structures in polar mesospheric cloud (PMC) images provide visual clues to the dynamics that occur in the summer mesosphere. In this study, we document one such structure, a PMC front, by analyzing PMC images in the northern hemisphere from the Cloud Imaging and Particle Size (CIPS) instrument onboard the aeronomy of ice in the mesosphere (AIM) satellite. A PMC front is defined as a sharp boundary that separates cloudy and mostly clear regions, and where the clouds at the front boundary are brighter than the clouds in the cloudy region. We explore the environment that supports the formation of PMC fronts using near-coincident temperature and water vapor observations from the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) satellite instrument. A comparison of PMC front locations to near-coincident temperature profiles reveals the presence of inversion layers at PMC altitudes. The adiabatic and superadiabatic topside lapse rates of these temperature inversions indicate that some of the identified inversion layers may have been formed by gravity wave (GW) dissipation. The structure of the squared buoyancy frequency profiles indicates a stable layer or thermal duct that can be associated with large-amplitude mesospheric inversion layers (MILs) that extend large distances. These inversion layers may be conducive to horizontal wave propagation. We hypothesize that ducted GWs may be a formation mechanism of PMC fronts.

Detecting supercooled water clouds (SWCs) is essential for enhancing artificial rainfall, preventing aircraft ice accretion, and developing a better understanding of radiative energy balance. The 1.37 μm channel, known as strong water vapor absorbing, was made polarized in the polarized multi-angle imager (PMAI) onboard FengYun-3G satellite. The infight data shown that the new 1.37 μm polarized channel could be used to detect SWCs. The cloudbow is observed around the 140° scattering angle in the 1.37 μm polarization image, with a maximum polarization reflectance of approximately 0.04–0.06. The indicated water clouds with spherical particles in the high-level altitude could be SWCs. Then, the SWCs detected by 1.37 μm polarized channel is verified using polarized reflectance of other channels, the reflectance difference of channels, and thermal infrared bright temperature. The presence of cloudbow in 1.03 and 1.64 μm channels indicate liquid water cloud. The reflectance difference between 1.03 and 1.64 μm of SWCs agree with characteristic of water cloud. The thermal infrared channels from the imager on the same platform indicate cold cloud with the brightness temperature far below 273.16 K. Therefore, the only use of 1.37 μm polarized channel could perform the identification of SWCs. PMAI provides a powerful tool for monitoring supercooled water clouds.

This study presents an implementation of a new melting layer model in the ZJU-AERO radar observation operator (Accurate and Efficient Radar Operator designed by ZheJiang University). The proposed model utilizes a coated spheroid to represent melting snow and graupel. It consists of three stages–coating, soaking, and melting–to account for the dielectric and density effects of melting particles. The scattering properties of the melting particles are computed with the Invariant-Imbedding T-Matrix (IITM) method, and the results are tabulated as look-up tables for the radar operator. Regarding the parameterization of bulk optical properties, a flux-conservation scheme is employed to estimate the size distribution of melting particles. To demonstrate its flexibility and superiority, the single and bulk scattering properties of our multi-stage melting model are compared against the traditional homogeneous model, which uses the effective medium approximation (EMA). The effectiveness of the multi-stage melting model has also been assessed by mapping model states in the regional mesoscale model of the China Meteorology Administration (CMA-MESO) to radar observations. In the microphysics package of CMA-MESO, the melting process is not explicitly represented, and we assume that melting hydrometeors occur where solid and liquid phases overlap. When compared with observations, the present multi-stage melting model successfully reproduces melting layer signatures, highlighting its potential for microphysic validation, quantitative precipitation estimations, and data assimilation studies.

Nuclear war would cause massive amounts of smoke from its associated explosions and fires that would subsequently inject copious amounts of aerosols into the stratosphere. There would also be considerable changes to the global climate system, threatening human health and sustainability. Our work reveals a reduction of global-scale tropical cyclones (TCs) from a simulation of a nuclear war scenario that could produce ∼150 million tons of smoke. In response to nuclear war, there would be spatially uneven cooling that would result in an anomalous sea surface temperature (SST) gradient pattern. Associated with this would be an anomalous zonal vertical circulation, tending to suppress upward motion and increase vertical wind shear over all TC main development regions, largely explaining the observed TC reduction at regional and global scales. This study improves our understanding of the impact of nuclear war on the TC environment.

On-road vehicles are significant contributors to air pollution globally, particularly to nitrogen oxides (NO_x) and ozone (O₃). Quantifying their contribution to air quality is crucial to understanding the trends of vehicle emissions as low- and “zero” emission vehicles join the fleet. Modeling on-road emissions is complex due to various factors like fleet activities, traffic patterns, and meteorological conditions. We compare on-road emissions from two mobile models: the National Oceanic and Atmospheric Administration Fuel-based Inventory of Vehicle Emission (FIVE) and the US Environmental Protection Agency Motor Vehicle Emission Simulator (MOVES), finding they contribute 4%–33% to volatile organic compounds (VOCs), NO_x, and fine particulate matter (PM_2.5) in the contiguous United States (CONUS). Using a regional chemical transport model, we assess air quality effects under different emission scenarios. Both emission data sets yield satisfactory performance, with MOVES showing lower biases in ozone (O₃) and PM_2.5 over CONUS, while FIVE performs better at city scales due to higher urban NO_x emissions. In January, on-road emissions increased surface O₃ over western and southern US by up to 9.1%–13.1% but decreased by 2.5% over the northeastern US, while PM_2.5 predictions vary across the US (−85% to 24%). In July, on-road emissions elevate O₃ and PM_2.5 concentrations by 15%–20% across CONUS, except in some west coast cities. They also greatly contribute to nitrogen dioxide (NO₂) by more than 80% near roads and in urban areas. This study highlights the significant impact of on-road emissions on urban air quality and provides insights for improving air quality forecasting and management.

Temperature and water vapor are known to fluctuate on multiple scales. In this study 27 years of airborne measurements of temperature and relative humidity from In-service Aircraft for a Global Observing System (IAGOS) are used to parameterize the distribution of water vapor in the upper troposphere and lower stratosphere. The parameterization is designed to simulate water vapor fluctuations within gridboxes of atmospheric general circulation models (AGCMs) with typical size of a few tens to a few hundred kilometers. The distributions currently used in such models are often not supported by observations at high altitude. More sophisticated distributions are key to represent ice supersaturation, a physical phenomenon that plays a major role in the formation of natural cirrus and contrail cirrus. Here the observed distributions are fitted with a beta law whose parameters are adjusted from the gridbox mean variables. More specifically the standard deviation and skewness of the distributions are expressed as empirical functions of the average temperature and specific humidity, two typical prognostic variables of AGCMs. Thus, the distribution of water vapor is fully parameterized for a use in these models. The new parameterization reproduces the observed distributions with a determination coefficient always greater than 0.917 and with a mean value of 0.997. The parameterization is robust to a selection of various geographical subsets of data and to gridbox sizes varying between 25 and 300 km.

Zinc (Zn) exerts a significant influence on the global environment, terrestrial ecosystems, and human health. The application of Zn isotopes (δ⁶⁶Zn) has been suggested as a potent tool for tracing environmental contamination. However, studies focusing on Zn isotope tracing within the cryosphere areas are notably limited. Here we present the first data set on Zn isotopes in glacial cryoconite, based on observations over a large regional scale in High Asian Mountains (including Tibetan Plateau (TP) and its surroundings of western China). The results showed that glacial cryoconite had a general heavy Zn isotopic signature in various TP locations, with δ⁶⁶Zn values ranging from −0.22‰ to +0.87‰. Employing the MixSIAR model, the overall Zn contribution source to the cryoconite was mineral dust (36%) > coal burning (33%) > non-exhaust traffic emissions (22%) > industrial smelting (10%). We ascertained that anthropogenic sources account for the primary contribution (about 60%–73%) of Zn inputs in all glacial locations, with coal burning emerging as the foremost anthropogenic contributor (mean 33%). Anthropogenic Zn in various TP locations was primarily derived from Zn emissions resulting from coal combustion, though it is also predominantly influenced by industrial smelting source in cryoconite of the Tianshan Mountains. Our results aligned with coal combustion data from the energy inventory of western China, suggesting that regional coal burning likely represents the foremost source of atmospheric Zn pollutant emission and deposition in the High Asia mountain glaciers.