Atmospheric Research最新文献

Snowfall is a critical component of the Earth system and an important indicator and amplifier of climate change. Climate warming is reducing the seasonal snowpack globally, which could have catastrophic consequences for the regions in high dependence on snow for water recharge. However, the climate influences on extreme snowfall events, which significantly impact humans, are still poorly understood. This study uses the Tibetan Plateau snowpack observation dataset combined with NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) climate data to investigate the spatial and temporal characteristics of snowfall, and to explore its response mechanism to climate change and future change trends across the Lower Yarlung Zangbo River (LYZR) in Southeastern Tibetan Plateau (SETP). Annual snowfall decreased at a rate of 1.05 mm per decade (P < 0.05) during the historical period (1960–2014). Relative to 1960–2014, annual snowfall would decline of ∼38 % and ∼ 73 % by the end of the 21st century under the SSP245 and SSP585 scenarios, respectively. At the same time, a general decrease in extreme snowfall averaged the LYZR is expected for historical and future periods, but it showed notable spatial variations. The regional increase in extreme snowfall is mainly distributed along the valley of Brahmaputra-Yarlung Zangbo. In addition, precipitation accounted for 66.5 % of the snowfall variations during the historical period. Meanwhile, according to future warming, temperature would dominate snowfall variations, contributing 56.66 % to 72.92 % during 2015–2100. The projected response of mean and extreme snowfall to climate change indicates that it is a double-edged sword. To scientifically address the risks and challenges posed by snowfall changes in alpine regions, it is imperative to limit future climate warming.

When northern Taiwan is influenced locally, PM_2.5 events occur in specific conditions. If it is also affected by long-range transport from mainland China in the northwest, the probability of these PM_2.5 events increases. The current study applies WRF/CMAQ to study such case but focus on the local. From April 7 to 9, 2019, the Pacific high pressure extended westward, causing the prevailing weak southerly wind near Taiwan. The intensity of the Pacific high pressure weakened, and the wind speed near Taiwan also weakened. Therefore, the PM_2.5 concentration in Taiwan increased. In northern Taiwan, the hourly PM_2.5 concentration will inevitably increase to approximately 70 μg m⁻³. The ISAM of the CMAQ model demonstrates that local contributions to daily PM_2.5 concentrations at the four representative monitoring stations in northern Taiwan are 3.5 to 16.2 μg m⁻³, greater than those from central, southern, and eastern Taiwan, from 1.8 to 6.7 μg m⁻³. We use the integrated process rate (IPR) and integrated reaction rate (IRR) of CMAQ to explore the formation mechanisms of PM_2.5. The main causes of the increase in PM_2.5 concentration are horizontal advection (HADV), aerosol processes (AERO), emissions (EMIS), and a small amount of cloud processes and aqueous chemistry (CLDS). The main source of NO₃⁻ is the reaction between OH and NO₂ during the day and the reaction between N₂O₅ and water vapor at night. The gaseous HNO₃ concentration reaches a peak near noon or soon after. The aerosol ANO₃ concentration is high in the early morning. We also use the Sulfur Tracking Method (STM) of CMAQ to explore the sources of SO₄²⁻. Mainly it is from the local reaction of SO₂ and H₂O₂. Finally, we apply AERO7 in CMAQ to explore the main sources of carbon components. The organic carbon (OC) concentration is much greater than the element carbon (EC) concentration, which indicates a strong local photochemical effect in northern Taiwan. OC mainly comes from low-volatility/semivolatile oxidized combustion OC, followed by low-volatility/semivolatile POA. Similar weather conditions occur in autumn, winter, and spring. When the weak southerly wind was around Taiwan, PM_2.5 in northern Taiwan was noticed.

Dust storms are associated with large amounts of particulate matter (PM) that can have adverse effects on health and the environment. The contribution of natural dust to atmospheric PM levels represents a scientific challenge, especially in areas with close proximity to dust sources. To improve our knowledge in this area, we collected 300 PM filter-samples across five oasis cities on the southern edge of the Taklimakan Desert in 2016, and applied the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) data to quantify the contribution of natural dust to PM on sand dust and non-sand dust days. Research has shown that the particle size distribution of mineral dust in Taklimakan dust aerosols was relatively uniform. On sand dust days, CaSO₄ and Na₂SO₄ were directly emitted from the surface, leading to higher sulfate concentrations in PM. While the increase in the proportions of organic carbon - OC1 and OC2 was attributed to the mixed anthropogenic emissions. On non-sand dust days, SO₄²⁻ concentration was significantly affected by emissions from anthropogenic sources. The automotive composite emissions and coal combustion were the main sources of elemental carbon - EC1. Based on the results of MERRA-2 reanalysis data, natural dust constituted 53 % and 70 % of the total PM_2.5 and PM₁₀, respectively. In this study, we have quantified the contribution of natural dust under different weather conditions and identifying potential sources of PM in oasis cities. This study provides support for the assessment of natural dust and PM prevention in oasis cities.

In this study, spatial and temporal variations of spring compound dry and hot events (CDHEs) over northern Asia (NA) during 1950–2020 and the related possible mechanisms are investigated on the interannual timescale. The standardized compound event indicator (SCEI) is used to represent compound dry and hot conditions over NA, which is validated by the observed summer case of CDHEs over western Russia in 2010. The first empirical orthogonal function (EOF1) mode of the SCEI over NA presents a monopole pattern, while the EOF2 mode shows an east-west dipole pattern. Possible mechanism analysis indicates that the CDHEs tend to be modulated by local high-pressure anomalies, accompanied by reduced cloud cover and more solar radiation. The high-pressure anomalies can lead to warm temperature and water vapor divergence, which synergistically contributes to the occurrence of CDHEs. Further analysis shows that the EOF1 mode is jointly affected by the Scandinavian (SCAND) pattern and the Arctic Oscillation (AO); while the EOF2 mode is influenced by the Atlantic-Eurasian (AEA) teleconnection. Moreover, the North Atlantic tripolar pattern of sea surface temperature (SST) can influence the EOF1 mode through changing the AO. And the diagonal tripolar SST pattern over the North Atlantic affects the EOF2 mode via altering the AEA pattern. The influence of the SST patterns is further confirmed by numerical experiments using the Community Atmospheric Model version CAM-5.3.

While air pollution due to fine particulate matter (PM_2.5) has been effectively controlled in China; the photochemical pollution characterized by elevated ozone (O₃) has emerged as a major concern for air quality improvement. Except for ozone, Lhasa is one of the cleanest cities in China with the lowest annual PM_2.5 concentration in 2017. The levels of major air pollutants in Lhasa are much lower than those of other cities in the eastern region of China, especially in May, when the O₃ concentration peaks. This study was based on multi-source observations in combination with the Goddard Earth Observing System coupled with chemistry (GEOS-Chem) and the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) models to explore the causes of O₃ pollution in Lhasa in May 2023. The results indicated that during the high O₃ episodes, the concentrations of O₃ precursors were low in Lhasa. Surrounding cities in other parts of the Tibetan Plateau also experienced high ozone concentrations despite being in different airsheds, suggesting that the O₃ pollution in Lhasa was caused by regional transport rather than purely local emissions. Stratospheric intrusion events modulated by the westerly jet led to elevated ozone in the troposphere above Lhasa City. The results of the GEOS-Chem model indicated that horizontal advection, turbulence, diffusion, and other effects led to high concentrations of ozone in the near-surface above Lhasa. Vertical transport was the dominant factor leading to ozone concentration increases during high ozone days, with a contribution of 6.33 Gg/day. In addition, high-altitude air masses with a maximum altitude of over 8000 m, were observed arriving in Lhasa during the high ozone days. This study revealed that stratospheric intrusions have a greater contribution to the high O₃ concentration in Lhasa in spring and provided a scientific basis for mitigating O₃ pollution in the plateau cities.

The climatic disparities between urban cores and surrounding rural areas have become increasingly pronounced due to rapid urbanization. The vapor pressure deficit (VPD) serves as a critical meteorological indicator reflecting atmospheric aridity, and identifying the contributions of urban and rural climate factors on the urban-rural VPD disparities plays an important role in understanding urban climate and ecosystem development patterns. This study introduces a quantitative method, utilizing partial differential equations, to assess the relative contributions of two crucial meteorological factors—air temperature (T) and relative humidity (RH)—toward urban-rural differences in VPD. Analyzing daily scale data from 116 pairs of urban and rural stations across mainland China for the period 2000–2017, we derived several key findings: Urban areas generally exhibit higher VPD than surrounding rural areas, with a relative difference in annual VPD of about 21.6 %, and the disparity increases over time. Furthermore, the influence of urbanization on VPD responds differently across seasons and geographical conditions. The simulated trends in VPD urban-rural differences using the partial differential method align closely with the actual observed trends, validating the effectiveness of our quantitative analysis approach. The VPD urban-rural disparity shows greater sensitivity to shifts in RH, with sensitivity coefficients exceeding 1.5 times to changes in T in most cases. RH is the primary factor causing VPD urban-rural differences in mainland China, and the relative contribution of RH is about 2.5 times of T. The patterns of sensitivity and the contribution of factors exhibit notable variation among subregions. This study enhances the understanding of how urbanization affects regional climate and provides insights for assessing the environmental impacts of future urban expansion.

Tropical cyclone (TC) Khanun in 2017 was simulated in this study by the Weather Research and Forecasting (WRF) model. The observation-validated simulation data were used to examine how asymmetric rotational and environmental flows played the roles in determining the contraction of the radius of maximum kinetic energy of symmetric rotational flow ($K_{\mathrm{\psi s}}$). The radius of maximum $K_{\mathrm{\psi s}}$ was contracted rapidly before rapid intensification (RI) and moved inward slowly, then barely moved, and moved inward slowly again during RI.

The conversion from kinetic energy of asymmetric rotational flow ($K_{\mathrm{\psi a}}$) to $K_{\mathrm{\psi s}}$ was induced by advection of asymmetric rotational tangential wind by asymmetric divergent radial wind. The conversion and convergence of inward flux of $K_{\mathrm{\psi s}}$ led to the rapid contraction before RI. During RI, in additional to horizontal and vertical flux convergence of $K_{\mathrm{\psi s}}$, the conversion from environmental kinetic energy ($K_e$) to $K_{\mathrm{\psi s}}$ through the interaction between $K_{\mathrm{\psi s}}$ and symmetric radial environmental flow was important for the second slow contraction.

The Qinghai-Tibetan Plateau (QTP) is recognized as the world's largest and highest plateau, characterized by intricate topography and underlying surfaces. Within this region, volatile meteorological conditions and severe synoptic systems, including thunderstorms, turbulence, sandstorms, and notably low-level wind shear (LLWS), present safety hazards to aviation operations. Therefore, a comprehensive examination of wind shear at typical QTP airports is essential. Xining Caojiapu International Airport (ZLXN) serves as a crucial transportation hub in the northeastern QTP. The frequent incidence of LLWS events due to the airport's unique geographical and meteorological features makes it an ideal location for investigating LLWS on the plateau. This study analyzed 80 pilot reports collected from 2016 to 2021 to elucidate the spatial and temporal characteristics of LLWS events at this plateau-valley airport. Subsequently, reanalysis data and observations from ground automated weather observing systems (AWOS), a geostationary satellite, a Doppler weather radar (DWR), and a Doppler wind lidar (DWL) were comprehensively employed to investigate the underlying meteorological factors and weather patterns associated with these LLWS events. The findings revealed that LLWS events at ZLXN are predominantly triggered by convective systems, followed by cold fronts and downward momentum transportation, with a smaller proportion being induced by orographic winds and turbulence. Analysis of four representative LLWS events utilizing high-resolution DWL measurements provided insights into the types of LLWS generated and their potential impacts on aircraft operations. Conceptual models, aimed at establishing a foundation for the forecasting and warning of local LLWS, were also proposed based on multiple data sources.

To improve forecast skill of extreme weather events, it is of primary importance to construct accurate initial conditions for a convective-scale ensemble prediction system (EPS). The traditional initial perturbation schemes, e.g., dynamic downscaling, fail to capture the amplitude or structure of convective-scale forecast errors accurately, especially near steep terrain or meso- and small-scale systems during weather system's rapid change. In this study, we developed a new initial perturbation optimizing technique, namely the cosine analysis constraint method. This method is then used to improve the downscaled initial perturbations by introducing smaller-scale information from the analysis increments, generated from data assimilation. We demonstrate the feasibility of the cosine analysis constraint method in the China Meteorological Administration (CMA) convective-scale EPS. Using the control experiment (CTRL) without any modification to the initial perturbation scheme as a reference, we designed the cosine analysis constraint experiment (CONS) and compared it with CTRL. We selected a case study of convective precipitation and two groups of one-month experiments were initialized to verify the feasibility of the new method and exclude case dependence. The results of the one-month test show that adopting the cosine analysis constraint method to optimize the initial perturbations can effectively enhance the consistency between the ensemble mean and the corresponding reanalysis field (regarded as observations). In the case study, the larger horizontal distribution of precipitation spread in CONS indicated the location of convective precipitation more effectively, which is important for operational weather forecasting. The significant effect of the moisture process was confirmed, especially in CONS. The verification results of the entire study domain were significantly improved after the initial perturbations were rescaled. Overall, the forecast skill of meteorological fields at different pressure levels and the extreme precipitation of smaller-scale convective systems were enhanced, which illustrated the potential of the cosine analysis constraint method to improve the quality of initial perturbations in CMA convective-scale EPS.