Journal of Atmospheric and Solar-Terrestrial Physics最新文献

{"title":"The PC index value as a standard for estimation of the magnetic substorms power","authors":"Oleg Troshichev,&nbsp;Alexander Nikolaev","doi":"10.1016/j.jastp.2025.106591","DOIUrl":"10.1016/j.jastp.2025.106591","url":null,"abstract":"<div><div>The polar cap magnetic activity <em>PC</em> index was introduced as a measure of the magnetic activity generated in the Earth's polar caps by the solar wind influence. At present the <em>PC</em> index is regarded as a proxy of the solar wind energy input into the magnetosphere in course of solar wind – magnetosphere coupling (IAGA Resolutions, 2013, 2021). It implies that <em>PC</em> index can be used as a standard for estimation of various magnetospheric and ionospheric characteristics in course of magnetosphere disturbances. This paper demonstrates possibility to monitor the magnetic substorm progression at latitudes from 55° to 85° CGLat in form of maps of the geomagnetic <em>δH</em> and <em>δD</em> components spatial distribution for different values of <em>PC</em> index.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"274 ","pages":"Article 106591"},"PeriodicalIF":1.8,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144571374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Response of PM2.5 concentration to climate variability and climate change prediction in China","authors":"Wan Li ,&nbsp;Hujia Zhao ,&nbsp;Changshuang Wang ,&nbsp;Peng Wang ,&nbsp;Dong Han ,&nbsp;Chengyu Wang ,&nbsp;Wenjing Yuan ,&nbsp;Shuanglu Bo","doi":"10.1016/j.jastp.2025.106588","DOIUrl":"10.1016/j.jastp.2025.106588","url":null,"abstract":"<div><div>Air pollution, particularly fine particulate matter (PM_2.5), poses severe environmental and public health challenges. Understanding the complex interactions between PM_2.5 concentrations and meteorological factors is crucial for effective air quality management and policy development. However, existing predictive models often struggle to capture the nonlinear and spatiotemporal dependencies of PM_2.5 variations, limiting their interpretability and accuracy. To address these gaps, this study developed a machine learning-based model using extensive historical environmental and meteorological data to analyze the nonlinear response of near-surface PM_2.5 concentrations to multiple spatiotemporal drivers. A monthly-scale prediction model was established, integrating CMIP6 climate projections under different emission scenarios, projected emission inventories, and multi-source auxiliary data. The findings reveal that PM_2.5 concentrations are highest in winter and lowest in summer, with significant seasonal and regional variations from 2015 to 2023. The model demonstrated strong predictive performance, particularly over the North China Plain and Northeast urban agglomerations (<em>R</em> = 0.825), though performance was weaker over the Tibetan Plateau. Key meteorological factors influencing PM_2.5 concentrations include specific humidity, 500 hPa wind, and short-wave radiation. Under future climate scenarios, PM_2.5 concentrations in South and East China are projected to decline during 2025–2030, while Northern China may experience seasonal increases under low-emission scenarios. Regional climate changes, such as increased precipitation and wind speeds in certain areas, further influence PM_2.5 concentration patterns. This study provides a novel, data-driven approach to quantifying the impact of meteorological fluctuations on PM_2.5 variations, offering valuable insights for air quality forecasting and policy formulation under different climate scenarios.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"274 ","pages":"Article 106588"},"PeriodicalIF":1.8,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sequential evolution of ionospheric TEC anomalies and acoustic-gravity wave precursors associated with the February 8, 2025, Mw 7.6 Cayman Islands earthquake","authors":"Karan Nayak ,&nbsp;Rosendo Romero-Andrade ,&nbsp;Gopal Sharma ,&nbsp;Roberto Colonna","doi":"10.1016/j.jastp.2025.106582","DOIUrl":"10.1016/j.jastp.2025.106582","url":null,"abstract":"<div><div>This study investigates seismo-ionospheric anomalies associated with the Mw 7.6 Cayman Islands earthquake of February 8, 2025, utilizing GNSS-derived ionospheric Total Electron Content (TEC) parameters, including the Ionospheric Disturbance Index (IDI), Rate of TEC Index (ROTI), Acoustic-Gravity Wave (AGW) oscillations, spatial TEC anomaly maps, and Regional Similarity Index (SI). A structured precursor hierarchy was identified, starting with early AGW disturbances several hours before significant ionospheric perturbations, followed sequentially by deviations in SI, IDI increases, and eventual ROTI turbulence. Spatial TEC anomaly maps demonstrated pronounced negative and positive anomalies closely centered around the epicenter region prior to the earthquake, while co-seismic TEC enhancements clearly correlated with seismic wave propagation. Correlation analysis between indices confirmed a progressive and systematic evolution of ionospheric anomalies, highlighting AGWs as crucial early precursors. Geomagnetic filtering (Dst/Kp) ensured the isolation of seismic-driven ionospheric disturbances from solar and geomagnetic influences. These findings robustly validate GNSS-TEC indices as reliable indicators of seismo-ionospheric processes and emphasize their potential role in earthquake precursor monitoring and hazard mitigation strategies.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"274 ","pages":"Article 106582"},"PeriodicalIF":1.8,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trends and variability in global ocean heat content time series data for the period 2005–2035","authors":"Mehmet Bilgili","doi":"10.1016/j.jastp.2025.106586","DOIUrl":"10.1016/j.jastp.2025.106586","url":null,"abstract":"<div><div>Approximately 90 % of the surplus heat generated by anthropogenic greenhouse gas (GHG) emissions is absorbed by the oceans, positioning ocean heat content (OHC) as a fundamental indicator for assessing the progression of climate change. Accurate knowledge of OHC changes at both global and regional levels offers valuable insights into the scope of global warming and its effects on sea-level rise, weather patterns, and ecosystems, and helps refine predictions in climate science. This study models monthly average OHC at 0–700 m and 0–2000 m layers across major ocean basins (Global, Southern Hemisphere, Northern Hemisphere, Pacific, Atlantic, and Indian Oceans) using the Seasonal Autoregressive Integrated Moving Average (SARIMA) method. Utilizing data from 2005 to 2023, the study provides forecasts up to 2035. The results demonstrate that the SARIMA models have correlation coefficients (R) above 0.8544, mean absolute error (MAE) values less than 0.932 × 10²² J, and root mean square error (RMSE) values under 1.222 × 10²² J, indicating highly accurate trends and satisfactory agreement with the observed data. Projected increases in OHC anomalies by 2035 highlight a continued warming trend, particularly in the upper ocean layers.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"274 ","pages":"Article 106586"},"PeriodicalIF":1.8,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aerosol impact on precipitation and cold pools over an arid region","authors":"J. Sandeep ,&nbsp;K. Gayatri ,&nbsp;P. Murugavel ,&nbsp;Prabhakaran Thara","doi":"10.1016/j.jastp.2025.106581","DOIUrl":"10.1016/j.jastp.2025.106581","url":null,"abstract":"<div><div>This study investigates aerosol impacts in the primary and secondary convective cells and their associated cold pools resulting from the convective outflow over the dry and arid regions of the Indian peninsula. A convective event observed with C-band polarimetric radar is analysed through several numerical simulations, focusing on the impact of aerosol on rainfall and cold pool characteristics. Control simulations were conducted with low, moderate and high cloud condensation nuclei (CCN). Additional sensitivity experiments introduced more ice nuclei particles (INP) in both the primary and secondary convection areas with supercooled liquid water content. The introduction of more INP in all experiments resulted in more ice crystals, snow, hail, as well as an enhancement of high-intensity rainfall. In the primary convection area, the addition of INP led to enhanced mass flux, snow, hail, and melting of hail, which contributed to enhanced area averaged rainfall (&gt;1 mm) and an increase in the cold pool area. However, no significant change was observed in the speed and depth of the primary cold pool depending on the low/high INP simulation. Addition of INP in the secondary convection area did not notably affect the strength or area of the cold pool. Overall, the study highlights that the spatial features of convection and cold pools are modified by the introduction of additional ice forming aerosols.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"274 ","pages":"Article 106581"},"PeriodicalIF":1.8,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global response of stratospheric gravity waves to Madden-Julian Oscillation","authors":"Brentha Thurairajah ,&nbsp;Chihoko Y. Cullens ,&nbsp;Scott L. England","doi":"10.1016/j.jastp.2025.106584","DOIUrl":"10.1016/j.jastp.2025.106584","url":null,"abstract":"<div><div>The Madden-Julian Oscillation (MJO) is a dominant mode of intraseasonal variability in the tropical troposphere. While the MJO is a slow-moving oscillation that does not propagate to high altitude, it's impacts on the broader spectrum of waves can be seen throughout the middle atmosphere. The influence of this ∼30–90 day oscillation on stratospheric gravity waves (GWs) is not just confined to the tropics but extends to high-latitudes. In this study, we use long-term (1979–2018) reanalysis data to investigate the global stratospheric GW response to MJO during the boreal and austral winter months of December, January, February (DJF) and June, July, August (JJA), respectively. In DJF, the MJO GW relative anomalies are in general significant in both hemispheres during all eight MJO phases. In JJA, the GW relative anomalies are stronger in the winter hemisphere compared to the summer hemisphere. In the low- and mid-latitudes, the GW response in the troposphere depends on the strength of the convection, while the stratospheric response is related to the zonal wind response and filtering of upward propagating GWs. The strength and spatial pattern of GW anomalies in the high-latitude winter hemisphere in MJO Phases 4 and 7 may be connected to the dissipation of MJO related poleward propagating planetary waves.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"274 ","pages":"Article 106584"},"PeriodicalIF":1.8,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the influence of anthropogenic forcing on meteorological drought pattern in East Africa: An analysis using CMIP6 models","authors":"Tamirat Yohannes ,&nbsp;Jinhua Yu ,&nbsp;Mulualem Abera ,&nbsp;Philemon King’uza ,&nbsp;Jonah Kazora ,&nbsp;Xuyu Zhang","doi":"10.1016/j.jastp.2025.106569","DOIUrl":"10.1016/j.jastp.2025.106569","url":null,"abstract":"<div><div>This study evaluated the performance of twelve Global Climate Models (GCMs) from CMIP6 in simulation of precipitation and drought over East Africa (EA) from 1981 to 2014. We employed different statistical and drought indices methods to identify systematic bias and to select the best ranking of models to reduce uncertainties. The evaluation was preformed against with Observed data from Climate Hazard Group InfraRed precipitation with stations (CHIRPS) and Climate Research Unit (CRU). Based on the mean MAM seasonal models and observed CHIRPS data, evaluated through statistical metrics, the results show that the top preforming models: CMCC-CM2-HR4, CanESM5, GFDL-ESM4, and TaiESM1 better captured precipitation over EA precipitation, with bias ranging from 12 to 39 % during the historical period. The study highlights the importance of understanding the influences of human-induced climate change on MAM droughts. The study uses two drought indices, its aims to detect changes in drought patterns and attribute these changes to anthropogenic climate forcing. Historical simulations, including all types of forcing, successfully replicate observed spatial trends. The Standardized Precipitation Evapotranspiration Index (SPEI) shows a drying trend in most parts, except the south and southeast of Tanzania and Kenya. The drying trend is more pronounced in the simulations that include all types of forcing, highlighting the significant impact of human-induced climate change on drought patterns in EA. This outcome strongly suggests that human-induced climate change significantly contributes to the intensification of MAM seasonal drought in EA. More comprehensive detection work is carried out using the signal's fingerprint of both ALL and NAT, which are projected onto the observation and evaluated against the background noise from control simulations without external forcing. The trend pattern signal of ALL is observed SPEI and shows a stronger signal. However, the NAT signal is not detected in SPEI. The findings reveal that changes in precipitation due to human activity are predominant in the south and southeast, while changes in temperature due to human activity are more evident in northern East Africa.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"274 ","pages":"Article 106569"},"PeriodicalIF":1.8,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144549667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Observations of traveling ionospheric disturbances over Ukraine during geospace storm using single GNSS receiver","authors":"Sergii Panasenko ,&nbsp;Vadym Skipa ,&nbsp;Kateryna Aksonova ,&nbsp;Oleksandr Bogomaz ,&nbsp;Igor Domnin","doi":"10.1016/j.jastp.2025.106555","DOIUrl":"10.1016/j.jastp.2025.106555","url":null,"abstract":"<div><div>We proposed a simplified technique for detecting large-scale traveling ionospheric disturbances (TIDs) based on data from a single GNSS receiver. This method enables an approximate determination of the zonal or meridional propagation of TIDs when satellite trajectories are predominantly aligned along parallels or meridians. We identified large-scale TIDs during the passage of the sunrise solar terminator and enhanced auroral activity. These disturbances exhibited dominant periods of approximately 60 min, with amplitudes ranging from 0.2 to 0.25 TECU (1 TECU = <span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>16</mn></mrow></msup></mrow></math></span> <span><math><msup><mrow><mi>m</mi></mrow><mrow><mo>−</mo><mn>2</mn></mrow></msup></math></span>). TIDs induced by space weather variations mainly propagated along the meridians, whereas the propagation direction for TIDs generated by the sunrise solar terminator could not be determined. Comparison with incoherent scatter data revealed good agreement in the periods and observation intervals of the TIDs. Furthermore, incorporating GNSS data from the European dense GNSS receiver network validated the inferred directions of TID propagation.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"274 ","pages":"Article 106555"},"PeriodicalIF":1.8,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel attention-based deep learning model for accurate PM2.5 concentration prediction and health impact assessment","authors":"Ravi Shanker Pathak ,&nbsp;Vinay Pathak ,&nbsp;Amit Rai","doi":"10.1016/j.jastp.2025.106583","DOIUrl":"10.1016/j.jastp.2025.106583","url":null,"abstract":"<div><div>Air pollution is a significant global health hazard, especially in developing, low-income countries with limited resources to address its impacts. Among pollutants, PM2.5 is particularly concerning due to its challenging containment and severe health implications. This study proposes a novel attention augmented hybrid deep learning (DL) model in multi-directed mode to predict the PM2.5 level accurately. The attention mechanism taps the long-term temporal dependencies in the latent vector space. Moreover, convolutional neural network and long short-term memory-based hybrid DL model focuses on short-term temporal dependencies in the feature space. The proposed model dynamically adjusts the focus with alignment score for efficient representation of the dataset, thereby outperforming standard deep learning benchmarks by 4.28 % compared to RNN, 10.5 % compared to LSTM, and 5.7 % compared to GRU. The utilization of ensemble technique in multi-directed mode enables the model to address the complex data dependencies. Subsequently, Bayesian hyperparameter optimization revealed that lower learning rates (1.60 × 10⁻⁶) combined with tanh activation functions and increased dense nodes yielded optimal performance. Additionally, quantitative healthcare impact assessment indicates that improved prediction accuracy potentially reduces direct healthcare economic burden by $82.4 million USD. This research provides a robust framework for PM2.5 forecasting that supports enhanced public health risk management and policy implementation.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"274 ","pages":"Article 106583"},"PeriodicalIF":1.8,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tracking the future of global N2O gas emissions with data-driven forecasts","authors":"Ganime Tuğba Önder","doi":"10.1016/j.jastp.2025.106577","DOIUrl":"10.1016/j.jastp.2025.106577","url":null,"abstract":"<div><div>Forecasting methods are widely used to make accurate decisions before uncertainties or potential problems arise in the future. This research examines the independent performances of traditional statistical Seasonal Autoregressive Integrated Moving Average Model (SARIMA) and deep learning models Long-Short Term Memory Neural Network (LSTM) and Gated Recurrent Unit (GRU) forecasting models in order to forecast the progress of global N₂O (Nitrous Oxide) emissions to 2050. The monthly N₂O emission values between 2001 and 2024 were used to forecast levels up to 2050. The forecast results and actual values were evaluated with R², RMSE, MSE, NSE, MAE and MAPE% error scales. The findings showed that all three methods were successful in forecasting global N₂O gas emissions, but SARIMA model (0.9998 R², 0.011 RMSE, 0.0001 MSE, 1.000 NSE, 0.004 MAE and 0.006 MAPE%) was the method that best fit the available data and produced forecasts with the least error. The results obtained predicted that N₂O emissions could be 8.16 % higher than current levels by 2050. The year 2050 is an important date determined as the global net zero emission target. The models in this study provide a concrete and important contribution to understanding the future course of N₂O emissions and the relationship with the net zero target. It can be used as a guide in the processes of companies to achieve their environmental policies and sustainability goals within the scope of state policies and environmental regulation reporting, when it is desired to increase energy efficiency by reducing emission values, and when it is necessary to calculate climate change risks.</div></div>","PeriodicalId":15096,"journal":{"name":"Journal of Atmospheric and Solar-Terrestrial Physics","volume":"274 ","pages":"Article 106577"},"PeriodicalIF":1.8,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}