Atmospheric Research最新文献

{"title":"Responses of atmospheric inorganic nitrogen deposition to emissions in a polluted region of southern China","authors":"Zhi-Li Chen ,&nbsp;Yan Qiu ,&nbsp;Wei Song ,&nbsp;Xue-Yan Liu","doi":"10.1016/j.atmosres.2025.108026","DOIUrl":"10.1016/j.atmosres.2025.108026","url":null,"abstract":"<div><div>Human-induced nitrogen (N) emissions have enhanced atmospheric N deposition in many polluted regions. However, the spatiotemporal relations between N emissions and deposition remain poorly characterized, which hampers N emission management and effect evaluation in polluted regions. This study investigated dry and wet inorganic N (IN) deposition at representative urban and suburban sites from June 2018 to May 2019 and analyzed historical N emission and deposition data in a N-polluted region of southern China. Dry, wet, and total IN deposition were 14.1 and 6.3, 36.4 and 29.0, and 50.5 and 35.3 kg N ha⁻¹ yr⁻¹ at the urban and suburban sites, respectively. Gaseous N accounted for 79–87 % of dry IN deposition and dry deposition accounted for 18–28 % of total IN deposition. The inter-annual variation of wet IN deposition exhibits an inverted “V” shape: the turning point for ammonium (NH₄⁺) was in 1991 due to the decline in the ratio of ammonia to sulfur dioxide and nitrogen oxide and ammonia emissions; nitrate (NO₃⁻) peaked in 2010, reflecting the benefits of national controls on nitrogen oxide emissions. Consequently, a shift to approximately equal NH₄⁺ and NO₃⁻ deposition occurred in 2010–2020. Spatially, annual IN deposition increased with human-induced land use and N emissions, and about 30 % of the area exceeded the critical load for N deposition. Current emission controls are reducing N emissions and deposition but further mitigation measures are needed, especially broader regional strategies.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"319 ","pages":"Article 108026"},"PeriodicalIF":4.5,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Higher-resolution data improves deep learning-based precipitation nowcasting","authors":"Aofan Gong,&nbsp;Bu Li,&nbsp;Ruiyang Zhou,&nbsp;Fuqiang Tian,&nbsp;Guangheng Ni","doi":"10.1016/j.atmosres.2025.108010","DOIUrl":"10.1016/j.atmosres.2025.108010","url":null,"abstract":"<div><div>Accurate and fine-grained precipitation nowcasting holds paramount importance for weather-dependent decision-making and is facing escalating expectations and challenges. While researchers have notably advanced precipitation nowcasting using deep learning (DL) models with larger sizes and more complicated structures, there is scarce research exploring the potential improvement from employing radar data with higher spatial resolutions—a hundred-meter scale rather than a kilometer scale. To evaluate the improvement of higher-resolution data, two U-Net architecture-based models, one larger and another smaller, were designed and trained with radar data at different spatial resolutions—1000 m, 500 m, and 100 m. Their effectiveness was examined by comparison to two baseline models. The models trained with diverse resolutions of data underwent comparative evaluation through two specific precipitation cases. The results unveil a positive correlation between the precipitation nowcasting performance and the spatial resolution of radar data. Models trained with higher-resolution data demonstrate superior forecasting accuracy, reduced bias, and enhanced spatial alignment between predictions and observations. Higher-resolution data empowers DL models to capture boundaries and local-scale patterns of convective systems more accurately, thereby improving the performance in precipitation nowcasting. More importantly, the comparison indicates that to further promote the performance of DL-based precipitation nowcasting, improving data resolution is more efficient than expanding model size. The use of high-resolution data diminishes computational and development costs by concurrently reducing the size of DL models, underscoring pragmatic benefits for related services. Given limited resources, employing higher-resolution data is recommended for priority consideration over larger-size models.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"319 ","pages":"Article 108010"},"PeriodicalIF":4.5,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulation of premonsoon thunderclouds over two climatic regimes: evaluation and dichotomous detection","authors":"Rupraj Biswasharma ,&nbsp;Imlisunup Pongener ,&nbsp;Imolemba Longkumer ,&nbsp;G.Ch. Satanarayana ,&nbsp;Sunil D. Pawar ,&nbsp;V. Gopalakrishnan ,&nbsp;Sanjay Sharma","doi":"10.1016/j.atmosres.2025.108027","DOIUrl":"10.1016/j.atmosres.2025.108027","url":null,"abstract":"<div><div>This study investigates premonsoon (April) thundercloud properties over Northeastern (Kohima, 25.6°N, 94.1°E) and Eastern (Rampurhat, 24.2°N, 87.8°E) India. Thunderclouds were detected using Electric Field Mills (EFM-100) and Lightning Detectors (LD-350), while the WRF model was employed to simulate thundercloud properties. Simulations were performed across all 30 days in each region, regardless of actual thundercloud occurrence, to assess the model's accuracy in differentiating thundercloud and non-thundercloud days. The regional variability of cloud-to-ground (CG) flash density was well represented by the model, with higher flash densities in Rampurhat (mean: 39 × 10⁻⁴ km⁻² h⁻¹) compared to Kohima (mean: 31 × 10⁻⁴ km⁻² h⁻¹), consistent with observations. However, the model exhibited a slight overestimation in Kohima and an underestimation in Rampurhat, with spatiotemporal deviations from observations in both regions. Despite this, the simulations effectively captured regional differences in dynamical parameters (vertical velocity, wind shear) and microphysical properties (mixing ratios: q_ice, q_graupel, q_cloud), with Rampurhat showing higher values overall. Six stability indices were evaluated to determine the most reliable indicator for distinguishing thundercloud/non-thundercloud days. In Kohima, the TT index (≥ 38 °C) was most effective, while in Rampurhat, CAPE (≥ 1680 J kg⁻¹) proved more suitable, suggesting that distinct physical mechanisms drive thundercloud development in these regions. The dichotomous detection results for all 30 days in Kohima (Rampurhat) yielded 14 (21) successful detections, 5 (1) misses, 3 (1) false alarms, and 8 (7) correct rejections. We introduced the Correct Rejection Rate (CRR) to assess the model's performance in detecting non-thundercloud days, which showed better accuracy over Rampurhat than Kohima. The study underscores the heightened complexity of predicting thunderclouds over the hilly terrain of Northeastern India compared to the flatter terrain of Eastern India and offers valuable insights for improving strategies to mitigate lightning hazards.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"320 ","pages":"Article 108027"},"PeriodicalIF":4.5,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143575548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interannual climate anomalies modulate the subseasonal dynamical prediction skill from the regional perspective over Central Southwest Asia","authors":"Shiyu Zhang ,&nbsp;Jing Yang ,&nbsp;Tao Zhu ,&nbsp;Qing Bao","doi":"10.1016/j.atmosres.2025.108023","DOIUrl":"10.1016/j.atmosres.2025.108023","url":null,"abstract":"<div><div>The El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD), as key oceanic boundary conditions, play a pivotal role in modulating regional climate variability. However, their influence on subseasonal dynamical prediction has yet to be fully understood. Focusing on Central Southwest Asia (CSWA), a region urgently needing accurate subseasonal prediction and significantly influenced by ENSO and IOD, this study investigates whether and how these interannual climate anomalies affect regional subseasonal rainfall prediction skills during early boreal winter using state-of-the-art Subseasonal-to-Seasonal (S2S) prediction models. First, the study finds that both deterministic and probabilistic prediction skills for domain-averaged rainfall anomalies and dry/wet events at a 2–4-week lead are significantly enhanced under La Niña and active IOD conditions compared to neutral states, while El Niño conditions show limited enhancement. This asymmetry in the ENSO impact is attributed to the inherent uncertainty in El Niño's influence on CSWA rainfall. Second, the analysis reveals that currently operational models exhibit higher skill in predicting ENSO at a 1-month lead, whereas predictions for IOD are comparatively less accurate. Nonetheless, prediction errors for both strong ENSO and IOD events at a 1-month lead are found to be significantly correlated with rainfall anomaly prediction errors over CSWA during the early boreal winter. This study confirms the significant effect of oceanic boundary conditions on regional subseasonal dynamical predictions and emphasizes the need to improve subseasonal prediction skills related to sea surface temperature variability associated with ENSO and IOD in order to reduce rainfall forecast errors and enhance the reliability of S2S predictions.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"319 ","pages":"Article 108023"},"PeriodicalIF":4.5,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143528686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of snow cover on the surface energy balance and its contribution to the extreme cold wave in Spain after snowstorm Filomena","authors":"Pablo Fernández-Castillo ,&nbsp;Carlos Román-Cascón ,&nbsp;Carlos Yagüe","doi":"10.1016/j.atmosres.2025.108015","DOIUrl":"10.1016/j.atmosres.2025.108015","url":null,"abstract":"<div><div>Snow cover can significantly alter the energy fluxes that take part on the surface energy balance (SEB), which subsequently affects surface air temperature. However, studies of the influence of snow cover on the SEB are scarce due to limited observational data and usually constrained to high altitudes or mountainous areas. In this study, the SEB is analysed under two differentiated land surface conditions: with and without snow cover, for a non-mountainous region in the midlatitudes. This is done in order to analyse the physical processes leading to one of the most extreme and disruptive cold waves of the present century in Spain. The cold wave took place in January 2021 after historical snowstorm Filomena left widespread snowfall amounts of 30–50 cm in the interior of Spain. By means of analysis of in-situ observational data from a micrometeorological tower and synoptic fields, we determine the contribution of synoptic and microscale processes to the extremely cold surface air temperatures recorded. The SEB analysis reveals the decisive role of snow cover in the cold wave. Surface albedo increased from 20 % to 80 %, leading to a 60 % decrease in the surface net radiative flux. Together with weak turbulence conditions due to weak synoptic forcing, this allowed extraordinarily low surface air temperatures. Thus, this event stands out from the typical pattern leading to cold waves in this region, characterised by cold air advection. This study highlights the importance of having observational data of micrometeorological variables, which have proven very insightful into the physical mechanisms contributing to the cold wave.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"320 ","pages":"Article 108015"},"PeriodicalIF":4.5,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143562134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards understanding the extreme flood–drought abrupt alternation over Southeast China in late summer 2019","authors":"Shaolei Tang ,&nbsp;Jing-Jia Luo","doi":"10.1016/j.atmosres.2025.108024","DOIUrl":"10.1016/j.atmosres.2025.108024","url":null,"abstract":"<div><div>The abrupt alternation between floods and droughts can have catastrophic impacts on local communities. In 2019, Southeast China experiences an extreme flood-drought abrupt alternation (FDAA), characterized by record-breaking flood in July and extreme drought in August. This study investigates the atmospheric circulations driving this FDAA and identifies the key mechanisms involved. Both the flood in July and the drought in August are closely associated with cyclonic circulation anomalies, but with distinct locations and dynamics. In July, a cyclonic anomaly over the East China Sea causes moisture convergence and ascending motions, leading to heavy rainfall. In contrast, two cyclonic anomalies in August—one over Northeast China and the other over the Western North Pacific (WNP)—induce northerly winds, moisture divergence, and descending motions, resulting in rainfall deficits. The July cyclonic anomaly is linked to positive soil moisture anomalies over Eastern Europe, which, through cooling surface air temperature, triggers eastward-propagating wave trains that induce cyclonic anomalies over the East China Sea. The August anomalies, on the other hand, are connected to North Atlantic and tropical Indian/Pacific SST anomalies. North Atlantic SST anomalies induce the anomalous Northeast China cyclone through modifying overlying atmosphere and triggering eastward-propagating wave trains. Significant positive IOD and warm SST anomalies over the central Pacific collaboratively trigger cyclonic circulation anomalies over the WNP through modifying the Walker circulation and the Hadley circulation. Our findings highlight the importance of understanding intraseasonal changes in tropical and midlatitude forcings to improve predictions and mitigate the impacts of compound climate disasters.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"319 ","pages":"Article 108024"},"PeriodicalIF":4.5,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Possible relationships between the intra-seasonal variation of winter temperature in Northwest China and Arctic sea ice","authors":"Jing Liu ,&nbsp;Lijuan Chen ,&nbsp;Ying Liu ,&nbsp;Daquan Zhang ,&nbsp;Hai Zhi","doi":"10.1016/j.atmosres.2025.107973","DOIUrl":"10.1016/j.atmosres.2025.107973","url":null,"abstract":"<div><div>The increasing impacts on regional climate of sea ice anomalies have spatio-temporal uncertainties, this study investigates the possible relationships between Arctic sea ice anomalies and intra-seasonal evolution of winter temperature in Northwest China (with a particular emphasis on Xinjiang). The reanalysis dataset during 1990–2022 from the National Centers for Environmental Prediction and the National Center for Atmospheric Research are utilized, with multiple statistical methods including season-reliant empirical orthogonal function (EOF) and regression analysis being employed. The results show that the first and second EOF modes of intra-seasonal temperature evolution in winter over Xinjiang are characterized by in-phase and out-of-phase variations, explaining 32 % and 19 % variance, respectively. The in-phase (out-of-phase) evolution of intra-seasonal temperature is affected by the in-phase (out-of-phase) variations of sea ice in the Kara Sea-East Siberia Sea-Chukchi Sea (KEsC) and the Barents Sea (BS) during the preceding October. The reduction in the KEsC sea ice triggers upward-propagating wave energy anomalies, which is favorable for the wave train shaping as a meridional dipole pattern from the Ural Mountains to Central Asia in early winter through the stratosphere-troposphere coupling, thereby leading to temperature anomalies in Xinjiang during the following December and January. The BS sea ice anomalies favor the formation and maintenance of a North Atlantic Oscillation-like pattern, the wave energy disperses downstream and enhances in February, triggering a Scandinavian teleconnection pattern-like wave train. This leads to circulation anomalies over Xinjiang, further causes temperature anomalies in February. In summary, the KEsC and BS sea ice in October serves as key precursor signals impacting the intra-seasonal evolution of winter temperature in Xinjiang, with the significant impacts of sea ice in these two key regions on intra-seasonal temperature variations occurring at different times.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"319 ","pages":"Article 107973"},"PeriodicalIF":4.5,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification and feature analysis of thunderstorm based on AGRI and LMI of Fengyun-4A satellite","authors":"Yang Zhao,&nbsp;Lixia Bi,&nbsp;Xiangzhen Kong","doi":"10.1016/j.atmosres.2025.108016","DOIUrl":"10.1016/j.atmosres.2025.108016","url":null,"abstract":"<div><div>In this study, the thunderstorms (TS) cloud top height (CTH) data of the advanced geosynchronous radiation imager of the Fengyun-4 A (FY-4 A, a geostationary satellite) were quality-controlled using the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data, and the lightning data of the LMI of the FY-4 A were processed by the cluster analysis method. The convective systems in China's land and adjacent sea areas were identified from the CTH data, and the CTH data of TS was corrected. The CTH and lightning activity characteristics of TS and overshooting convections (OC) and their relationship were analyzed. TS and OC occurred more often over land areas than over sea areas. The two had similar spatial distribution characteristics: they were mainly concentrated at low latitudes, and the CTH of both showed a noticeable decreasing trend with increasing latitude. Most TS had CAPE^1/2 (updraft speed) values between 15 ms⁻¹ and 50 ms⁻¹ and CTH between 11 km and 17.5 km. Most OC had CAPE^1/2 values between 20 ms⁻¹ and 50 ms⁻¹ and CTH between 15 km and 19 km. For both TS and OC, small updrafts generated low flash extent density values and large minimum flash area values, while large updrafts produced high flash extent density and small minimum flash area.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"318 ","pages":"Article 108016"},"PeriodicalIF":4.5,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical investigation of the role of Saharan dust on the anomalous electrical structure of a thunderstorm over Corsica","authors":"Christelle Barthe,&nbsp;Pierre Tulet,&nbsp;Sybille de Sevin,&nbsp;Inès Vongpaseut,&nbsp;Sylvain Coquillat","doi":"10.1016/j.atmosres.2025.107988","DOIUrl":"10.1016/j.atmosres.2025.107988","url":null,"abstract":"<div><div>This study analyzes the role of dust particles on the formation of an anomalous charge structure in a thunderstorm observed on 14 October 2016 around Corsica. The cloud-resolving model Meso-NH with explicit aerosol-microphysics coupling is used to simulate realistically the interactions between the storm and the dust plume. This reference simulation shows no liquid water content in the convective region below −10 °C, which is conducive to only positive charging of the graupel according to traditional liquid water content temperature diagrams of the non-inductive charging mechanism. Differential hydrometeor fall speeds lead to a positive charge region below a negative charge region at the cloud scale, the so-called anomalous charge structure.</div><div>To disentangle between the radiative and microphysical role of dust particles on cloud structure, additional simulations are performed. First, using a climatological extinction of dust particles over Corsica, the microphysical structure of the storm is similar to the one of the reference simulation, excluding the radiative role of dust particles as a factor controling the charge structure in this case. Secondly, a low, constant and homogeneous ice nuclei particle concentration profile is imposed in the microphysics scheme. It enables the presence of supercooled water at temperatures below −20 °C, allowing the negative charging of the graupel at higher levels of the cloud, and potentially leading to a normal tripole. It highlights the role of desert dust as ice nuclei in the depletion of liquid water content in the mixed phase region of the cloud, which could lead to an anomalous charge structure.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"319 ","pages":"Article 107988"},"PeriodicalIF":4.5,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determining the influence of meteorological, environmental, and anthropogenic activity variables on the atmospheric CO2 concentration in the arid and semi-arid regions: A case study in the Middle East","authors":"Seyed Mohsen Mousavi ,&nbsp;Naghmeh Mobarghaee Dinan ,&nbsp;Korous Khoshbakht ,&nbsp;Saeed Ansarifard ,&nbsp;Oliver Sonnentag ,&nbsp;Amir Naghibi","doi":"10.1016/j.atmosres.2025.108009","DOIUrl":"10.1016/j.atmosres.2025.108009","url":null,"abstract":"<div><div>Climate change and the resulting warming caused by human activities, such as releasing greenhouse gases (GHG), notably carbon dioxide (CO₂), are paramount concerns in the contemporary era. Therefore, it is essential to understand spatial and temporal variations of atmospheric carbon dioxide concentration (XCO₂) on national and international scales and the role of controlling variables. This research examines the role of controlling variables on XCO₂ by utilizing spatiotemporal modeling, correlation analysis, and machine learning methods to determine the sinks and sources of CO₂ and their changes in the Middle East (ME). First, XCO₂ data from the OCO-2 satellite spanning from 2015 to 2021 were employed for spatiotemporal analysis. The temporal analysis of XCO₂ showed peak levels during spring (May) and the lowest levels during summer (September). Additionally, the spatial dispersion of XCO₂ revealed significant spatial heterogeneity influenced by land cover. Areas with high vegetation abundance and suitable weather conditions exhibited minimum XCO₂ levels, while vice versa occurred. Next, the correlation analysis between the principal controlling variables and XCO₂ revealed that, except for vegetation abundance and anthropogenic CO₂ emissions, they exhibited predominantly negative and positive correlations throughout the year, respectively. However, the correlation patterns for temperature, precipitation, humidity, and wind speed varied across different months, showing both negative and positive relationships depending on the month. Recognizing that simple correlation alone cannot determine which variables played the most significant role in XCO₂ each month, this study employed machine learning and Permutation Feature Importance (PFI) techniques. The results showed that, except for March and September, when precipitation and wind speed respectively had the most significant influence on determining XCO₂, air temperature played the dominant role in other months. Additionally, the average monthly results revealed that air temperature, wind speed, precipitation, and vegetation abundance, respectively, played the most significant roles in XCO₂. In contrast, CO₂ emissions had the most negligible impact. These results highlight the considerable influence of meteorological and environmental variables on regulating XCO₂ levels and distributions throughout the ME. By elucidating the seasonality of the carbon cycle and identifying key XCO₂ drivers over the ME, this study provides valuable insights to guide regional climate change mitigation policies and further analysis of vulnerable semi-arid ecosystems.</div></div>","PeriodicalId":8600,"journal":{"name":"Atmospheric Research","volume":"319 ","pages":"Article 108009"},"PeriodicalIF":4.5,"publicationDate":"2025-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}