Hasil untuk "Meteorology. Climatology"

Yufei Wang, Tian Zhou, Xiaokai Song et al.

As the most frequent dust event in the Tarim Basin (TB), persistent floating dust significantly impacts the regional weather and climate. Long-term analysis (2015–2024) showed that the occurrence of persistent floating dust is significantly associated with the presence of the nocturnal low-level jet (NLLJ). To investigate this potential linkage, the Weather Research and Forecasting model with Chemistry (WRF-Chem) was used to simulate the persistent floating dust event accompanied by the NLLJ in the TB from 29 to 31 July 2006. Results indicated that a typical NLLJ occurred during the event, with an easterly jet core (>12 m/s) near 850-hPa facilitating the westward dust transport and accumulation within the TB, as well as strong convergence and vertical uplift on its front side elevating the dust layer height (DLH). Quantification showed that the NLLJ enhanced dust column concentrations (mean maximum > 100 mg/m²) and DLH (mean maximum > 300 m) over the central and western TB, and the cumulative maximum increase in dust emissions exceeded 200 mg/m², in the NLLJ region. Furthermore, nocturnal dust radiative forcing intensified the NLLJ by up to 1 m/s, thereby establishing a positive feedback mechanism. These results reveal the crucial role of the NLLJ in persistent floating dust events and enrich our understanding of such events in the TB.

Alexander Cede, Alexander Cede, Alexander Cede et al.

A technique to determine the radiometric stability of the Earth Polychromatic Imaging Camera (EPIC) and the National Institute of Standards and Technology Advanced Radiometer (NISTAR), the two Earth-viewing instruments operating aboard the Deep Space Climate Observatory (DSCOVR) satellite, which is orbiting the Sun at the Lagrange-1 point, L1, approximately 1.5 million kilometers away from Earth, has been developed and applied. Apart from the satellite’s own measurements, it only uses output from the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate data center (ERA5). This method can be applied to all channels (and not just a subset) and can be repeated periodically to track the instruments’ stability. The method includes the removal of climatological diurnal and seasonal cycles, a multivariate regression fitting with selected ERA5 model output parameters, and referencing the data to the EPIC 551-nm channel, which has been determined to show no drift over the entire mission lifetime together with the NISTAR photodiode channel (200–1,100 nm). The obtained sensitivity changes were very small, ranging from a maximum total degradation of 3% over 10 years in the short UV (<340 nm) to no detectable changes for some channels. For the EPIC UV channels, the derived results were confirmed through a comparison of the EPIC data with radiances from the Ozone Mapping and Profiler Suite (OMPS). We attribute this excellent instrument performance mostly to the L1 orbit, which is not only an ideal location for Earth observation, but is also extremely beneficial (quiet) with respect to instrument performance. At L1, there are only minor temperature variations and much smaller exposure to charged particles from the Sun compared to satellites orbiting the Earth, which are fully or partly inside the Earth’s radiation belts. In this sense, L1 can be considered “observational and instrumental heaven.” The technique described here could only be applied because DSCOVR has two different instruments (EPIC and NISTAR) observing the same Earth flux input. This suggests that it is extremely useful (maybe even essential) to combine imaging instruments (like EPIC) with integrating instruments (like NISTAR) in remote sensing applications.

Minghao Bi, Ke Xu, Riyu Lu

Abstract Summer monsoon breaks can have far-reaching impacts by triggering abnormal weather both locally and remotely. However, their formation mechanisms remain incompletely understood, with intraseasonal oscillations (ISOs) widely recognized as a primary driver. This study investigates South China Sea monsoon break events and finds that 72.9% originate from the Philippine Sea, associated with ISOs, while the residual 27.1% are locally generated, which have yet to be elucidated previously. Our results suggest that these locally generated events are often preceded by anomalously active tropical cyclones, which amplify in situ air–sea interaction, inducing SST cooling that increases large-scale atmospheric stability, thereby promoting monsoon break establishment. Moreover, differing from ISO-related monsoon breaks that increase precipitation over the Yangtze River Valley, locally generated breaks induce significant warming over South China. The diverse mechanisms and climate impacts of monsoon breaks revealed in this study may provide new insights for further improvement of subseasonal predictions.

Zhihao Sun, Jiarui Yang, Yanbing Qi et al.

Farmland carbon pools are pivotal to the terrestrial carbon cycle. Studying soil organic carbon density (SOCD) spatial heterogeneity is crucial for uncovering regional carbon balance mechanisms and optimizing sequestration strategies. Focusing on county-scale SOCD heterogeneity, this study aids sustainable farmland management, carbon sequestration, and spatial carbon pool modeling. Based on the 2020 farmland soil organic carbon (SOC) analysis survey data. The SOCD and spatial pattern of farmland in Chengcheng County, Shaanxi Province were estimated and analysed by combining GIS and GPS technologies. Optimal Parameter Geodetector (OPGD) was used to detect spatial heterogeneity and quantitatively attribute the influencing factors of SOCD in farmland in Chengcheng. The results showed that the SOCD of farmland in Chengcheng, ranged from 1.10 to 1.79 kg m ^−2 , with an average of 1.39 kg m ^−2 . The SOC stock was 151.96 × 10 ^4 t. Both values were lower than the national average and much lower than those in European and American countries. Between the north-west and south-east of the plough layer, Chengcheng’s farmland’s SOCD clearly distinguishes two distinct sections. The average farmland SOCD in the north-west is lower than that in the south-east by 0.27 kg m ^−2 . The explanatory power of temperature, population density and DEM all exceeded 0.3, and the explanatory power of the interaction between them exceeded 0.6. This implies that the combined influences of nature and human activity are responsible for the spatial patterns of SOCD in farming at the county scale in the Weibei dry loess plateau.

Farahnaz Fazel-Rastgar, Venkataraman Sivakumar, Masoud Rostami et al.

Abstract Using data from the MERRA-2 database, the study identifies a clear warming trend in August, with significant peaks in the annual average air temperature in 2010 (28.3 °C) and 2021 (27.9 °C) in the Caspian Sea. Concurrently, the Caspian Sea’s water levels have declined by approximately 16 cm annually from 1992 to 2025, totaling a drop of around 2.04 m, driven by higher evaporation rates linked to rising temperatures. Heat extremes are observed, with maximum daily air temperatures reaching 30.3 °C and minimums of 26.8 °C in August 2021, representing deviations of 2.6 °C and 2.4 °C from the climatological mean. The most intense event exceeded 2.63 °C for the maximum 2 m air temperatures in August 2021 and 2.16 °C above the climatological mean for an extended 12 days. Furthermore, the frequency of heatwaves has increased, with two major heatwave events occurring in 2010 and 2021. The study also highlights significant changes in sea surface temperature, Chlorophyll concentrations, and sea salt levels, with higher sea salt concentrations in 2021 correlating with lower Chlorophyll levels. Spatial analysis reveals regional temperature variations, with warmer temperatures in the southeast in 2010 and cooler conditions in the eastern regions in 2021. Additionally, the study observed a shift in the subtropical jet stream, which weakened and shifted eastward in 2021 compared to 2010. The jet stream split around the high-pressure system, transitioning from zonal to meridional flow. These changes in atmospheric circulation, characterized by dry conditions and reduced low-level relative humidity in 2021 (5–10% drier than in 2010), contributed to the extreme heatwave events. The findings highlight the role of atmospheric circulation patterns, including the development of high-pressure systems and shifts in the jet stream, in driving temperature extremes and heatwaves in the Caspian Sea region, with significant implications for future climate variability and ecosystem impacts. The results underline the need for further research and mitigation efforts to address the ongoing warming, heatwaves, and water level decline in the Caspian Sea, which pose significant ecological and socio-economic challenges. Marine heatwaves in the Caspian Sea have been causing significant ecological disruptions.

Anna S Jensen, Karl Rittger, Mark S Raleigh

The seasonal mountain snowpack of the Western US (WUS) is a key water resource to millions of people and an important component of the regional climate system. Impurities at the snow surface can affect snowmelt timing and rate through snow radiative forcing (RF), resulting in earlier streamflow, snow disappearance, and less water availability in dry months. Predicting the locations, timing, and intensity of impurities is challenging, and little is known concerning whether snow RF has changed over recent decades. Here we analyzed the relative magnitude and spatio-temporal variability of snow RF across the WUS at three spatial scales (pixel, watershed, regional) using remotely sensed RF from spatially and temporally complete (STC) MODIS data sets (STC-MODIS Snow Covered Area and Grain Size/MODIS Dust Radiative Forcing on Snow) from 2001 to 2022. To quantify snow RF impacts, we calculated a pixel-integrated metric over each snowmelt season (1st March–30th June) in all 22 years. We tested for long-term trend significance with the Mann–Kendall test and trend magnitude with Theil–Sen’s slope. Mean snow RF was highest in the Upper Colorado region, but notable in less-studied regions, including the Great Basin and Pacific Northwest. Watersheds with high snow RF also tended to have high spatial and temporal variability in RF, and these tended to be near arid regions. Snow RF trends were largely absent; only a small percent of mountain ecoregions (0.03%–8%) had significant trends, and these were typically decreasing trends. All mountain ecoregions exhibited a net decline in snow RF. While the spatial extent of significant RF trends was minimal, we found declining trends most frequently in the Sierra Nevada, North Cascades, and Canadian Rockies, and increasing trends in the Idaho Batholith. This study establishes a two-decade chronology of snow impurities in the WUS, helping inform where and when RF impacts on snowmelt may need to be considered in hydrologic models and regional hydroclimate studies.

Wen Li, Zhibin Sun, Zhaoai Yan et al.

The near-surface atmospheric electrostatic field plays a pivotal role in comprehending the global atmospheric circuit model and its influence on climate change. Prior to delving into the intricate interplay between solar activities, geological activities, and atmospheric electric field, a comprehensive examination of the diurnal fair atmospheric electric field’s baseline curve within a specific region is essential. Based on the atmospheric electric field network monitoring in Yunnan Province in the year 2022, this study systematically investigated the distribution of the atmospheric electric field under both fair-weather and disturbed weather conditions at a quadrilateral array encompassing Chuxiong Station, Mouding Station, Lufeng Station, and Dali Station. The primary focus was on elucidating the variations in the daily variation curves of fair atmospheric electric fields and conducting a comparative analysis with the Carnegie curves. The possible reasons for the differences among them are also discussed in this study, but more observational evidence is required to confirm the specific causes in the future.

Chen-Hao Chuang, Yi-Huan Hsieh, Pin-Yen Liu et al.

This study analyzes the influences of the mid-level vortex on the formation of Tropical Cyclone Toraji (2013). A rare case of a tropical cyclone that formed near Taiwan involved a mid-level vortex that was a remnant of Tropical Cyclone Kong-Rey (2013). The piecewise potential vorticity inversion method is applied to examine the contribution of the mid-level vortex to the low-level wind field under quasi-balanced conditions. Numerical sensitivity experiments are conducted to quantify the importance of the mid-level vortex on Toraji formation, in which the mid-level vortex is removed with different removing factors (percentages) from the initial field. The results indicate that mid-level positive potential vorticity anomalies significantly contribute to the low-level positive vorticity before Toraji formation. Furthermore, when the removing factors increase in the sensitivity experiments, either the intensity of the simulated low-level vortex or the development trend of pre-Toraji decreases. However, there is no significant relationship between the convection’s magnitude and the intensity of the mid-level vortex. The main difference comes from the mid-level vortex’s intensity, which would result in a greater high-level warm core structure and cause stronger vertical mass flux. In summary, the mid-level vortex plays a critical role in the formation of Toraji. It provides a favorable environment for forming the pre-Toraji vortex by maintaining a high-level warm-core structure, leading to the formation of Toraji.

Roland P. H. Berton

A simple 2D non-stationary parcel model has been elaborated by coupling kinematics and microphysics of ice crystals, for the purpose of retrieving realistic trajectories in a cirrus uncinus, with a hooked-shape head or cap, a trail or virga, and observed lifetimes. Prescribed kinematics and ambient physics includes vertical profiles of horizontal winds and updraft, and of air temperature and humidity. Modelled microphysics combines mass variation by deposition/sublimation of water vapour, radiative transfer and ventilation due to friction with ambient air. Upon changing the altitude of the updraft base, other parameters being unchanged, two distinct regimes of motions are found: setting the base at an altitude of 10 km yields a steady, non-oscillatory motion, with a hooked cap and a tail, typical of a cirrus uncinus, whereas fixing the base at a lower altitude (7–9 km), yields a self-sustained damped oscillatory motion with a long time period (7–10 hours) and a long wavelength (70–200 km), depending on supersaturation level and radiative conditions. This latter phenomenon may be related to the turrets occurring with “long-lasting” cirrus or to the Mesoscale Uncinus Complex (MUC) reported in published works. The sensitivity to the ice crystal size is analysed in order to estimate the spreading of trajectories, and the impact of radiative transfer is examined on the oscillatory regime at the light of three contrasting examples. A simple analytic model with kinematics and constant updraft is aimed at an outline of salient features and derivation of characteristic time- and spatial scales of the phenomena.

Qunyong Wu, Yuhang Wang, Haoyu Sun et al.

Atmospheric pollution is a critical issue in public health systems. The simulation of atmospheric pollution dispersion in urban blocks, using CFD, faces several challenges, including the complexity and inefficiency of existing CFD software, time-consuming construction of CFD urban block geometry, and limited visualization and analysis capabilities of simulation outputs. To address these challenges, we have developed a prototype system that couples 3DGIS and CFD for simulating, visualizing, and analyzing atmospheric pollution dispersion. Specifically, a parallel algorithm for coordinate transformation was designed, and the relevant commands were encapsulated to automate the construction of geometry and meshing required for CFD simulations of urban blocks. Additionally, the Fluent-based command flow was parameterized and encapsulated, enabling the automatic generation of model calculation command flow files to simulate atmospheric pollution dispersion. Moreover, multi-angle spatial partitioning and spatiotemporal multidimensional visualization analysis were introduced to achieve an intuitive expression and analysis of CFD simulation results. The result shows that the constructed geometry is correct, and the mesh quality meets requirements with all values above 0.45. CPU and GPU parallel algorithms are 13.3× and 25× faster than serial. Furthermore, our case study demonstrates the developed system’s effectiveness in simulating, visualizing, and analyzing atmospheric pollution dispersion in urban blocks.

Zhaohui Xiong, Sichun Long, Maoqi Liu et al.

In the context of anomalous global climate change and the frequent occurrence of droughts and floods, studying trends in the conversion rate between precipitable water vapor (PWV) and actual precipitation in a certain region can help in analyzing the causes of these natural disasters. This paper examines the variation trend in the conversion rate between PWV and actual precipitation on a monthly scale in Hubei from 1960 to 2020. To estimate historical PWV data, we propose a new method for estimating PWV using water vapor pressure based on the RF algorithm. The new method was evaluated by radiosonde data and improved the accuracy by 1 mm over the traditional method in Hubei. Based on this method, we extrapolate the monthly average PWV in Hubei from 1960 to 2020 and analyze the conversion rate between PWV and precipitation during this period. Our results showed that there was no obvious cyclical pattern in the conversion rate in either the longitude or latitude directions. In Hubei, where the topography varies significantly in the longitude direction, the conversion rate is influenced by topography, with the smallest conversion rate being in the transition zone between the mountainous region of western Hubei and the Jianghan Plain. In the latitudinal direction, the conversion rate decreases with increasing latitude.

Patchaya Tobarameekul, Supawon Sangsuradet, Patcharin Worathanakul

The objectives of the research were to develop synthesis and estimation of each factor on carbon dioxide adsorption of advanced functional zeolite NaY material derived from bagasse ash and rice husk ash with different crystallization temperatures and weight percentages of zinc by the ion exchange method. The adsorbents were tested in a packed bed reactor at different temperatures and flow rates of carbon dioxide. The Minitab program was used to estimate the effects of each factor on carbon dioxide adsorption properties. The results showed that extracted silicon dioxide from bagasse ash and rice husk ash could be successfully used as raw material for zeolite NaY synthesis with a crystallization temperature of 298.15 K. The zeolite NaY crystalline structure was well-preserved after ion exchange. The highest capacity of carbon dioxide adsorption was at 10.33 mmol/g with zeolite 5B298-373-1. The results of the Minitab program showed that the carbon dioxide adsorption decreased with increasing crystallization temperature and carbon dioxide flow rate parameters. However, the increased weight percentage of zinc loading on zeolite NaY resulted in better carbon dioxide adsorption. The factors of the types of adsorbents and adsorption temperature showed interaction with each other.

Deeptija Pandey, Amar Deep Tiwari, Vimal Mishra

Floods cause enormous damage to infrastructure and result in the loss of human lives. Most river basin-scale floods in India occur during the summer monsoon season. While extreme precipitation events and floods are projected to increase under the warming climate, it remains unclear how the frequency of the worst flood that occurred during the observed period will change in the future. Using the observations and simulations conducted using the Variable Infiltration Capacity (VIC) model, we identified the worst flood during the observed period of 1971–2012 in the Mahanadi River basin. The observed worst flood at Basantpur occurred in August 2003, exceeding 33,000 m3/s and a return period of 132 years. Kantamal station of the Mahanadi River basin experienced the worst flood in 2008 with a magnitude of 20,000 m3/s and a return period of 64 years. Multi-day extreme precipitation upstream of the gauge stations driven by the moisture transport from the Bay of Bengal caused the observed worst floods in the basin. We used bias-corrected projections from the six global climate models (GCMs) that participated in the coupled model intercomparison project 6 (CMIP6) under the SSP1-2.6 and SSP5-8.5 scenarios to estimate the projected changes in the frequency and magnitude of floods. The frequency of the worst observed floods is projected to rise at both stations under the projected future climate. In addition, the magnitude of floods of 10, 20, and 50-year return periods are projected to rise at both stations in the Mahanadi basin. The projected rise in the frequency of the worst floods and extreme precipitation in the basin will pose challenges for agriculture and infrastructure.

Farzad Khezri, Mohsen Irandoost, Navid Jalalkamali et al.

Abstract The Urmia Lake Basin as the world's second largest salt lake has experienced severe drought during recent years. The purpose of this study is to analyse the bivariate characteristics of drought (i.e., duration and severity) using two indices including SPI (standard precipitation index) and SPImod (modified SPI) associated with copula functions. For this purpose, rainfall data of six stations were used for the period of 1971–2017. At first, the characteristics of drought were extracted using the two indices. Then, through coding in the MATLAB software environment, eight families of Archimedean copula functions were applied. The simultaneous return period and conditional and Kendall returns were also investigated. The result showed that the Joe copula function was the best predictor for the analysis of both intensity and duration of drought for the study area. The correlation coefficients of Spearman, linear correlation and Tau Kendall computed for the SPI of stations were >0.65, >0.72 and >0.48, respectively, while all of them were significant. At a given critical probability level, t, the value of the Kendall return period was much greater than the standard return period, so that this difference increased with increasing t value. The results obtained from the time series of indices indicated that at least 40% of the months were dry, and the severity of droughts in the Urmia station was much higher than other stations during the studied period. Moreover, SPImod to a large extent eliminates the disadvantages of conventional SPI and takes into account seasonal variations of precipitation in the calculation of SPI.

Z. Şen

Heather Simon, Barron H. Henderson, R. Chris Owen et al.

This study uses Las Vegas near-road measurements of carbon monoxide (CO) and nitrogen oxides (NO_x) to test the consistency of onroad emission constraint methodologies. We derive commonly used CO to NO_x ratios (∆CO:∆NO_x) from cross-road gradients and from linear regression using ordinary least squares (OLS) regression and orthogonal regression. The CO to NO_x ratios are used to infer NO_x emission adjustments for a priori emissions estimates from EPA’s MOtor Vehicle Emissions Simulator (MOVES) model assuming unbiased CO. The assumption of unbiased CO emissions may not be appropriate in many circumstances but was implemented in this analysis to illustrate the range of NO_x scaling factors that can be inferred based on choice of methods and monitor distance alone. For the nearest road estimates (25 m), the cross-road gradient and ordinary least squares (OLS) agree with each other and are not statistically different from the MOVES-based emission estimate while ∆CO:∆NO_x from orthogonal regression is significantly higher than the emitted ratio from MOVES. Using further downwind measurements (i.e., 115 m and 300 m) increases OLS and orthogonal regression estimates of ∆CO:∆NO_x but not cross-road gradient ∆CO:∆NO_x. The inferred NO_x emissions depend on the observation-based method, as well as the distance of the measurements from the roadway and can suggest either that MOVES NO_x emissions are unbiased or that they should be adjusted downward by between 10% and 47%. The sensitivity of observation-based ∆CO:∆NO_x estimates to the selected monitor location and to the calculation method characterize the inherent uncertainty of these methods that cannot be derived from traditional standard-error based uncertainty metrics.

C. Rodríguez‐Puebla, A. H. Encinas, Susana Nieto et al.

Samuël Weber, Dalia Salameh, Alexandre Albinet et al.

Receptor-oriented models, including positive matrix factorization (PMF) analyses, are now commonly used to elaborate and/or evaluate action plans to improve air quality. In this context, the SOURCES project has been set-up to gather and investigate in a harmonized way 15 datasets of chemical compounds from PM₁₀ collected for PMF studies during a five-year period (2012−2016) in France. The present paper aims at giving an overview of the results obtained within this project, notably illustrating the behavior of key primary sources as well as focusing on their statistical robustness and representativeness. Overall, wood burning for residential heating as well as road transport were confirmed to be the two main primary sources strongly influencing PM₁₀ loadings across the country. While wood burning profiles, as well as those dominated by secondary inorganic aerosols, present a rather good homogeneity among the sites investigated, some significant variabilities were observed for primary traffic factors, illustrating the need to better characterize the diversity of the various vehicle exhaust and non-exhaust emissions. Finally, natural sources, such as sea salts (widely observed in internal mixing with anthropogenic compounds), primary biogenic aerosols and/or terrigenous particles, were also found as non-negligible PM₁₀ components at every investigated site.

Li Tiancheng, Ren Qing-dao-er-ji, Qiu Ying

Hazards of sandstorm are increasingly recognized and valued by the general public, scientific researchers, and even government decision-making bodies. This paper proposed an efficient sandstorm prediction method that considered both the effect of atmospheric movement and ground factors on sandstorm occurrence, called improved naive Bayesian-CNN classification algorithm (INB-CNN classification algorithm). Firstly, we established a sandstorm prediction model based on the convolutional neural network algorithm, which considered atmospheric movement factors. Convolutional neural network (CNN) is a deep neural network with convolution structure, which can automatically learn features from massive data. Then, we established a sandstorm prediction model based on the Naive Bayesian algorithm, which considered ground factors. Finally, we established a sandstorm prediction model based on the improved naive Bayesian-CNN classification algorithm. Experimental results showed that the prediction accuracy of the sandstorm prediction model based on INB-CNN classification algorithm is higher than that of others and the model can better reflect the law of sandstorm occurrence. This paper used two algorithms, naive Bayesian algorithm and CNN algorithm, to identify and diagnose the strength of sandstorm in Inner Mongolia and found that combining the two algorithms, INB-CNN classification algorithm had the greatest success in predicting the occurrence of sandstorms.

B. Albrecht, C. Bretherton, D. Johnson et al.