地球科学最新文献

{"title":"Genetic programming-enabled cross-sectional average velocity prediction in vegetated open channels: a focus on suspended vegetation","authors":"Yecong Liu,&nbsp;Mengyang Liu,&nbsp;Wenxin Huai,&nbsp;Yidan Ai,&nbsp;Zhonghua Yang","doi":"10.1016/j.jhydrol.2025.134079","DOIUrl":"10.1016/j.jhydrol.2025.134079","url":null,"abstract":"<div><div>Exploring the cross-sectional average velocity in open channels with suspended vegetation is significant, as it addresses the need for predicting flow rates and evaluating mass transport in ecological rivers. This research focuses on suspended vegetation and links it to emergent vegetation through water level changes. Utilizing experimental data from various researches, genetic programming (GP), a machine learning (ML) technique, was adopted to search for a robust relationship between the Froude number with energy slope, vegetation density, and relative hang depth. Unlike traditional modeling, GP requires no prior assumptions and successfully developed a Chezy-like predictive model without a predefined structure. The physical sound of this model was thoroughly discussed by analyzing its structural form and the influence of parameters on the Chezy coefficient <em>C</em>. The validation program confirms its superior prediction performance in open channels, regardless of whether the suspended vegetation extends downwards and roots into the riverbed. Additionally, the model is independent of vegetation drag coefficients, making it more computationally convenient and accurate than existing models. This study offers a novel research perspective and practical tool for understanding cross-sectional average flow velocity in the presence of suspended vegetation, with significant implications for hydraulics and environmental engineering.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"662 ","pages":"Article 134079"},"PeriodicalIF":6.3,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144858233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable ensemble learning unveils main aerosol optical properties in predicting cloud condensation nuclei number concentration","authors":"Nan Wang, Yuying Wang, Chunsong Lu, Bin Zhu, Xing Yan, Yele Sun, Jialu Xu, Junhui Zhang, Zhuoxuan Shen","doi":"10.1038/s41612-025-01181-y","DOIUrl":"https://doi.org/10.1038/s41612-025-01181-y","url":null,"abstract":"<p>Variations in cloud condensation nuclei number concentration (<i>N</i>_CCN) significantly influence cloud microphysics, yet direct <i>N</i>_CCN measurements remain challenging. Here, we present an <i>N</i>_CCN ensemble learning (NEL) model utilizing ensemble learning and interpretability analysis on aerosol optical parameters. Validated at two land sites, two ocean sites and one polar site within the Atmospheric Radiation Measurement program, the mean absolute percentage error range of the NEL model across different environments is from 12% to 36%, demonstrating high accuracy. Key findings reveal that aerosol optical parameters can serve as predictors for <i>N</i>_CCN. Aerosol scattering and backscattering coefficients, absorption coefficient, backscatter fraction (BSF), and Ångström exponent (AE) are positively correlated with <i>N</i>_CCN, while single scattering albedo shows negative correlations. <i>N</i>_CCN prediction at land sites is highly sensitive to BSF, largely driven by the backscattering coefficient, as fine particles dominate in these sites. At ocean sites, <i>N</i>_CCN prediction is more sensitive to AE, primarily influenced by the scattering coefficient, due to the higher proportion of larger particles. At the polar site, <i>N</i>_CCN prediction shows sensitivity to both BSF and AE, mainly driven by the scattering coefficient, as polar sites are cleaner and contain larger particles. These differences reflect the variation in particle size and number concentration across different environments.</p>","PeriodicalId":19438,"journal":{"name":"npj Climate and Atmospheric Science","volume":"5 1","pages":""},"PeriodicalIF":9.0,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144851661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lifting the profile of deep soil carbon in New Zealand’s managed planted forests","authors":"Loretta G. Garrett,&nbsp;Katherine A. Heckman,&nbsp;Angela R. Possinger,&nbsp;Brian D. Strahm,&nbsp;Jeff A. Hatten,&nbsp;Fiona P. Fields,&nbsp;Steve A. Wakelin","doi":"10.1186/s13021-025-00323-2","DOIUrl":"10.1186/s13021-025-00323-2","url":null,"abstract":"<div><h3>Background</h3><p>Forest soils are a globally significant carbon-store, including in deep layers (&gt; 30 cm depth). However, there is high uncertainty regarding the response of deep soil organic carbon (DSOC) to climate change and the resulting impact on the total OC budget for forest ecosystems. Managed forests have an opportunity to reduce the risk of DSOC loss with climate change, however, the basic understanding of DSOC is lacking. Planted forests in New Zealand are managed with very limited knowledge of DSOC, both in the amount and the capacity of the soil to continue to store carbon with climate change. In this study, we explore DSOC stocks to at least 2 m depth at 15 planted forest sties in New Zealand. We also explore DSOC radiocarbon age and soil mineralogy, then contextualise our results within international SOC datasets and climate change vulnerability frameworks to identify research priorities for New Zealand’s planted forest soils.</p><h3>Results</h3><p>DSOC stocks and soil mineralogy in New Zealand’s planted forests were diverse both horizontally across soil types and vertically throughout the soil profile. Critically, limiting measurements of SOC to the top 30 cm misses more than half of the SOC stocks present to at least 2 m depth (mean 57%; range 33–72%). At depth, mineral-associated OC was the dominant fraction of DSOC (average &gt; 90%) and was on average much older (&gt; 1000 years) than the current planted forest land use (&lt; 100 years).</p><h3>Conclusions</h3><p>This small case study highlights that New Zealand’s planted forests contain substantial stocks of DSOC, much of which is older than the current forest land use. The deep soils were dominated by reactive metals, and although the age of DSOC suggest long-term stability, the large contribution of reactive metal-mediated SOC stabilisation may indicate vulnerability to warming soil temperatures relative to other climate change factors. There is a pressing need to expand soil sampling to greater depths and establish a robust SOC baseline for New Zealand’s planted forests. This is essential for enabling spatial predictions of DSOC dynamics under future climate scenarios, identify the key controls on DSOC persistence, and concomitant impacts on forest ecosystem function and resilience.</p></div>","PeriodicalId":505,"journal":{"name":"Carbon Balance and Management","volume":"20 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://cbmjournal.biomedcentral.com/counter/pdf/10.1186/s13021-025-00323-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144832103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Late Quaternary crustal shortening rate of the Wensu fault-bend fold in the southern Tian Shan, NW China","authors":"Kezhi Zang ,&nbsp;Chuanyong Wu ,&nbsp;Zhan Gao ,&nbsp;Xuezhu Wang ,&nbsp;Haiyang Yuan ,&nbsp;Jinshuo Zhang ,&nbsp;Sihua Yuan ,&nbsp;Xiaohui Yu ,&nbsp;Yunxiao Ma","doi":"10.1016/j.jsg.2025.105536","DOIUrl":"10.1016/j.jsg.2025.105536","url":null,"abstract":"<div><div>N‒S crustal shortening in the Tian Shan shows an obvious eastward decrease, which results in an eastward decrease in the width and uplift height of the topography. However, the highest peak in the Tian Shan region appears in its middle part (the Wensu area) instead of at the expected western end. At present, the kinematic information and N‒S crustal shortening rate of the Wensu foreland thrust system remain poorly constrained, which has led to controversy regarding the deformation characteristics and mechanism of geomorphic growth in this area. In this study, we focused on the kinematics and shortening rate of the Wensu fault-bend fold (WFBF), the frontal structural belt of the Wensu foreland thrust system. On the basis of interpretations of detailed high-resolution remote sensing images, field investigations, surveying of displaced terraces with an unmanned drone, the dating of late Quaternary sediments via OSL and trench excavation, we determined a relatively low N‒S crustal shortening rate of 1.31 ± 0.23 mm/yr over the past 24,000–40,000 years for the WFBF. We suggest that a listric thick-skinned fault geometry at depth results in more vertical uplift components, which is the key factor of significant topographic uplift amplitude in this region.</div></div>","PeriodicalId":50035,"journal":{"name":"Journal of Structural Geology","volume":"200 ","pages":"Article 105536"},"PeriodicalIF":2.9,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144840641","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":"From Coastal Retreat to Seaward Growth: Emergent Behaviors From Paired Community Beach Nourishment Choices","authors":"J. Lorenzo-Trueba,&nbsp;A. Janoff,&nbsp;O. Thomas,&nbsp;D. Jin,&nbsp;P. Hoagland,&nbsp;A. Ashton","doi":"10.1029/2025EF006352","DOIUrl":"https://doi.org/10.1029/2025EF006352","url":null,"abstract":"<p>Coastal communities often address shoreline erosion through beach nourishment, adding externally sourced sand to widen beaches for recreation and property protection. While nourishment enhances beachfront property values, the need for periodic maintenance creates interdependencies where the actions of neighboring communities affect local shoreline dynamics. Using a coupled model of two neighboring communities, we examine the interplay between community nourishment decisions and the redistribution of nourishment sand. We find that the value a community places on wider beaches not only influences their propensity to nourish, but also their and their neighbors' nourishment efficiency and net benefits. Communities that nourish more frequently tend to have lower nourishment efficiency, as sand is redistributed alongshore, benefiting less-active neighbors at their expense. A 20-year New Jersey case study confirms that communities that nourish more have lower nourishment efficiencies, including instances where less wealthy communities nourish significantly more, enabling wealthier neighbors to enjoy higher efficiencies—suggesting that such dynamics may already be shaping real-world coastal outcomes. In future scenarios, we simulate the effects of rising sand costs and accelerated erosion due to sea-level rise under coordinated and non-coordinated planning methods, finding that less wealthy communities experience a higher risk of beachfront property loss under non-coordination, exacerbating disparities in coastal management. These findings underscore the importance of inter-community cooperation in optimizing economic and environmental outcomes in beach nourishment strategies.</p>","PeriodicalId":48748,"journal":{"name":"Earths Future","volume":"13 8","pages":""},"PeriodicalIF":8.2,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2025EF006352","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144843273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Effects of Spatially Distributed Soil Properties on Hydrological Modeling via the Noah-MP Land Model","authors":"Rui Qian,&nbsp;Yuanhao Fang,&nbsp;Xingnan Zhang,&nbsp;Guo-Yue Niu","doi":"10.1029/2024JD043143","DOIUrl":"https://doi.org/10.1029/2024JD043143","url":null,"abstract":"<p>Soil hydraulic properties (SHPs) are crucial in modeling hydrological and ecological processes across scales. However, most land surface models (LSMs) use SHPs based on land cover and soil types, thus neglecting the inherent heterogeneity in SHPs. We hypothesize that spatially distributed SHPs can enhance the performance of LSMs in modeling ecohydrological processes. In this study, we performed regional simulations to evaluate the effects of 3-dimensional spatially distributed SHPs (3D SHPs) on Noah-MP-simulated hydrological fluxes. The simulations were conducted at a 3-hourly and 0.1° resolution from 1981 to 2018 over mainland China. Compared with the default lookup table soil parameters, the use of 3D SHPs enhanced not only the accuracy of the simulated runoff and evapotranspiration (ET) but also the ability of Noah-MP to reliably characterize the spatial patterns and vertical interactions of soil moisture in various climate zones. Moreover, 3D SHPs increase the mean Kling-Gupta efficiency (KGE) for runoff (from 0.32 to 0.67) and ET (from 0.65 to 0.74) and decrease the mean root mean square error (RMSE) (from 0.067 to 0.037) for surface soil moisture (SSM) in these climate zones. The KGE is better suited for assessing the fit of runoff and ET, and RMSE is more appropriate for capturing the bias in SSM. The improvements in KGE (runoff: from 0.40 to 0.74) and RMSE (SSM: from 0.051 to 0.019) are more significant in humid climate zones. This study highlights the importance of heterogeneous soil properties for the overall performance of Noah-MP.</p>","PeriodicalId":15986,"journal":{"name":"Journal of Geophysical Research: Atmospheres","volume":"130 16","pages":""},"PeriodicalIF":3.4,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144843502","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":"A hybrid process-data driven framework for real-time hydrological forecasting with interpretable deep learning","authors":"Feilin Zhu ,&nbsp;Ou Zhu ,&nbsp;Mingyu Han ,&nbsp;Weifeng Liu ,&nbsp;Xuning Guo ,&nbsp;Tiantian Hou ,&nbsp;Lingqi Zhao ,&nbsp;Chengjing Xu ,&nbsp;Ping-an Zhong","doi":"10.1016/j.jhydrol.2025.134082","DOIUrl":"10.1016/j.jhydrol.2025.134082","url":null,"abstract":"<div><div>Hydrological forecasting is vital for water resource management and flood risk mitigation. However, traditional models often struggle with inherent uncertainties and complex nonlinear relationships. This study proposes an innovative modelling framework for enhancing real-time hydrological forecasting by integrating process-driven and data-driven models into a hybrid system. By combining the Xinanjiang (XAJ) hydrological model with the Long Short-Term Memory networks (LSTM) deep learning model, this approach captures both physical and nonlinear relationships in hydrological processes. The hybrid model employs Particle Swarm Optimization and Bayesian Optimization for parameter calibration, ensuring optimal model performance. The LSTM model functions as a post-processing module, correcting residuals from the XAJ simulations and refining initial predictions to generate final forecasts. Bayesian inference quantifies the uncertainty associated with the hybrid model’s predictions, providing valuable probabilistic insights for water resource management. Additionally, Shapley Additive Explanations (SHAP) enhance the interpretability of the model’s decision-making process, increasing its practical applicability. Numerical experiments using data from the Chitan Reservoir Basin in southeastern China validate the framework’s effectiveness. Seven performance metrics assess the accuracy of deterministic forecasts and the reliability and sharpness of probabilistic forecasts. Comparative experiments across different flood events and forecast lead times indicate that the XAJ-LSTM hybrid model demonstrates superior performance compared to standalone models, particularly at longer lead times. It achieves lower forecast errors, narrower prediction intervals, and higher confidence in the forecasts. The interpretability analysis reveals the influence of different residuals on the model’s predictions, providing valuable insights into the hydrological processes. By combining the complementary strengths of process-driven and data-driven models, this framework offers improved accuracy, reliability, sharpness, and interpretability of hydrological forecasting, contributing to more reliable and informed water resource management.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"662 ","pages":"Article 134082"},"PeriodicalIF":6.3,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144858304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing runoff prediction accuracy of deep learning model using baseflow separation method and timestamp information","authors":"Chen Jin ,&nbsp;Qin Xu ,&nbsp;Xing Chen ,&nbsp;Jing Cai ,&nbsp;Han Meng","doi":"10.1016/j.jhydrol.2025.134044","DOIUrl":"10.1016/j.jhydrol.2025.134044","url":null,"abstract":"<div><div>In deep learning-based runoff prediction methods, the encoder-decoder framework, with its flexible input–output mapping mechanism, effectively incorporates future precipitation forecast data, significantly improving the stability and reliability of multi-step predictions. In light of this, we utilized a novel rainfall-runoff model based on the Transformer architecture, termed RR-Former, to investigate strategies for improving multi-step ahead prediction accuracy of rainfall-runoff processes. This study integrated baseflow separation and Fast Fourier Transform into the RR-Former model, resulting in a dual-branch forecasting model, FBS-RR-Former, designed to decouple high and low-frequency information. Research findings indicate that the FBS-RR-Former model outperforms RR-Former in multi-step ahead predictions ranging from 1 to 7 days. The strategy of separating high and low-frequency information during prediction effectively enhances the model’s stability and accuracy in long-term forecasting. In exploring temporal feature enhancement mechanisms, this study systematically compared two paradigms for encoding temporal information: time feature embedding and timestamp information modeling (TimeSter). Unlike the former, which offers limited assistance for runoff prediction, TimeSter comprehensively improved the model’s performance in multi-step forecasts. Although TimeSter cannot entirely replace the role of other meteorological data, such as temperature, the FBS-RR-Former model, driven solely by timestamp information and precipitation data, outperformed models powered by all meteorological variables in semi-humid regions with minimal snow influence. Finally, we simulated potential forecast precipitation biases by introducing varying degrees of noise into precipitation data for the forecast periods. The FBS-RR-Former model exhibited robust performance across different noise scenarios, maintaining its ability to produce meaningful predictions even under strong noise conditions.</div></div>","PeriodicalId":362,"journal":{"name":"Journal of Hydrology","volume":"662 ","pages":"Article 134044"},"PeriodicalIF":6.3,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144858375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hundred-Meter-Scale In Situ Observations Reveal Joint Impact of Humidity and Wind on Raindrop Microphysics","authors":"Zitong Liu,&nbsp;Ang Zhou,&nbsp;Kun Zhao,&nbsp;Hongyan Luo,&nbsp;Long Wen,&nbsp;Qing Lin,&nbsp;Yinghui Lu,&nbsp;Hao Huang,&nbsp;Shuguang Wang,&nbsp;Gang Chen,&nbsp;Zhengwei Yang,&nbsp;Chunsheng Zhang,&nbsp;Zhi Tao,&nbsp;Shirui Zhang","doi":"10.1029/2025GL116977","DOIUrl":"https://doi.org/10.1029/2025GL116977","url":null,"abstract":"<p>The mechanisms linking raindrop size distributions (DSDs) to environmental conditions remain poorly understood, limiting their practical application. We develop a unique fine-scale vertical in situ data set to reveal the evolution of near-surface DSDs and quantify how environmental factors modulate raindrop microphysics. Near-surface raindrop breakup is identified as a common feature during the East Asian summer, with an average threshold diameter of 1.16 mm for breakup initialization. Further analysis reveals that relative humidity and wind speed exert opposing influences on raindrop microphysical processes, with coalescence favored in humid monsoon environments and breakup intensified within typhoon outer rainbands. By incorporating empirical relationships between these two environmental factors and microphysical processes, we derive observational constraints that significantly reduce biases in near-surface rainfall estimates. For heavy rainfall cases the bias is reduced by up to 75%. These findings improve understanding of raindrop microphysics in boundary layer and help improve quantitative precipitation estimation.</p>","PeriodicalId":12523,"journal":{"name":"Geophysical Research Letters","volume":"52 16","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2025GL116977","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144832423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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":"Influence of the IMF Elevation Angle on Midday Auroral Intensity During Northward IMF","authors":"Shengting Zhu,&nbsp;Xiaoli Luan,&nbsp;Jiuhou Lei,&nbsp;Su Zhou","doi":"10.1029/2025GL117831","DOIUrl":"https://doi.org/10.1029/2025GL117831","url":null,"abstract":"<p>Using global ultraviolet imager auroral observations, we have investigated the possible role of the IMF elevation angle (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>ϕ</mi>\u0000 </mrow>\u0000 <annotation> $phi $</annotation>\u0000 </semantics></math>), which is determined by IMF B_x and B_z, on the midday auroral intensity for northward B_z conditions. The results show that in summer and equinoxes, the peak midday auroral intensity enhances by 11%–19% from non-favorable conditions, when <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>ϕ</mi>\u0000 </mrow>\u0000 <annotation> $phi $</annotation>\u0000 </semantics></math> favors tail lobe reconnection in each hemisphere. Larger absolute and relative difference also occurred in the polar cap, especially in summer. We propose that these <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>ϕ</mi>\u0000 </mrow>\u0000 <annotation> $phi $</annotation>\u0000 </semantics></math> effects contribute to stronger midday auroral intensity due to lobe reconnection, which are associated with special B_x polarities (i.e., B_x &lt; 0 and B_x &gt; 0 in the northern and southern hemispheres, respectively). In addition, when favorable <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>ϕ</mi>\u0000 </mrow>\u0000 <annotation> $phi $</annotation>\u0000 </semantics></math> (or B_x) condition occurs in one hemisphere, we find no evident midday aurora enhancement in the conjugate hemisphere in statistics. This suggests the lobe reconnection generally dominates in one hemisphere for both solstice and equinoctial periods.</p>","PeriodicalId":12523,"journal":{"name":"Geophysical Research Letters","volume":"52 16","pages":""},"PeriodicalIF":4.6,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2025GL117831","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144832425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}