Journal of Atmospheric and Solar-Terrestrial Physics最新文献_第9页

Drought and flood evolution characteristics during winter rapeseed season based on the Z-index in Hubei Province, China 基于z指数的湖北省冬季油菜籽季旱涝演变特征

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2025-09-22 DOI: 10.1016/j.jastp.2025.106646

Yaotian Tian, Enhao Zhang, Yongyuan Huang, Ming Huang, Haoran Shi, Hui Chen

Analyzing drought and flood characteristics is of great significance to ensure production security, disaster prevention, and mitigation. Hubei Province, as a major rapeseed-producing area in China, is crucial for the nation's edible oil supply. However, there were no studies on drought and flood characteristics during rapeseed season in Hubei Province. To study the drought and flood evolution, daily meteorological data from 28 surface weather stations in Hubei Province during the rapeseed season from 1960 to 2019 was adopted to calculate the Z-index. The Mann-Kendall trend test and wavelet analysis were employed. The results indicated that precipitation at the seedling, flowering, ripening, and whole growth stages of rapeseed generally decreased by 4.79, 2.14, 2.58, and 7.38 mm (10 yr)⁻¹, respectively. Precipitation at the budding stage of rapeseed increased by 1.74 mm (10 yr)⁻¹. Based on the Mann-Kendall trend test, the mutation of precipitation at the seedling, budding, flowering, ripening, and whole growth stages of rapeseed began in 1987, 1983, 1961, 1960, and 1968, respectively. Precipitation at the seedling, ripening, and whole growth stages decreased from south to north across Hubei Province. From southeast to northwest of Hubei Province, precipitation at the budding and flowering stages decreased. A shift from drought to flood was observed at the budding stage, whereas a shift from flood to drought occurred at the other stages. Severe drought (15.0 %) and mild drought (20 %) occurred most frequently at the flowering stage. Severe flood occurred most often at the budding stage (16.7 %), moderate flood at the ripening stage (13.3 %), and mild flood at the seedling stage (13.3 %). According to the wavelet analysis, the cycles of drought and flood disasters at the seedling, budding, flowering, ripening, and whole growth stages were 28, 23, 14, 18, and 7 years, respectively. This study provides a scientific basis for predicting drought and flood disasters and guiding rapeseed production in Hubei Province.

分析旱涝特征对保障生产安全、防灾减灾具有重要意义。湖北省作为中国主要的油菜籽产区，对全国的食用油供应至关重要。但目前尚无对湖北省油菜季旱涝特征的研究。利用1960 - 2019年湖北省28个地面气象站的油菜籽季逐日气象资料，计算z指数，研究旱涝演变。采用Mann-Kendall趋势检验和小波分析。结果表明：油菜籽苗期、开花期、成熟期和全生育期降水总体上分别减少4.79、2.14、2.58和7.38 mm (10 yr)−1；油菜出芽期降水量增加1.74 mm (10 yr)−1。根据Mann-Kendall趋势检验，油菜苗期、出芽期、开花期、成熟期和全生育期降水分别在1987年、1983年、1961年、1960年和1968年发生突变。苗期、成熟期和全生育期降水量由南向北递减。湖北从东南向西北，出芽期和开花期降水呈减少趋势。出芽期由干旱向洪水转变，其他阶段由洪水向干旱转变。重度干旱（15.0%）和轻度干旱（20%）发生在花期。出芽期多发生重度洪水（16.7%），成熟期多发生中度洪水（13.3%），苗期多发生轻度洪水（13.3%）。根据小波分析，苗期、出芽期、花期、成熟期和全生育期的旱涝灾害周期分别为28年、23年、14年、18年和7年。该研究为预测湖北省旱涝灾害和指导油菜籽生产提供了科学依据。

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引用次数: 0

Time series analysis of the impact of global warming on Türkiye 全球变暖对土壤影响的时间序列分析

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2025-09-22 DOI: 10.1016/j.jastp.2025.106647

Arif Ozbek, Mehmet Bilgili

According to the assessments of the Intergovernmental Panel on Climate Change (IPCC), Türkiye, located within the Mediterranean basin, is among the regions most susceptible to the adverse impacts of climate change. This heightened vulnerability is largely attributed to its geographic location, climatic characteristics, and socio-economic structure, which together amplify the risks associated with rising temperatures and increasing climate variability. In the present study, monthly mean air temperature data for Türkiye, recorded by the Turkish State Meteorological Service between 1970 and 2022 (TSMS dataset), were analyzed in combination with reanalysis-based satellite observations obtained from the ERA5 (ERA5 dataset). These historical records formed the foundation for developing temperature projections extending to the year 2050. To achieve this, two complementary time-series forecasting approaches were applied: the Long Short-Term Memory (LSTM) deep-learning model, known for its ability to capture nonlinear dependencies and long-range temporal patterns, and the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model, a classical statistical method suitable for handling seasonality and trend components in climate data. The projection results revealed that Türkiye's mean temperature anomaly relative to the 1970–1980 baseline period is expected to rise by approximately 2.52 °C when based on in-situ observational data, and by about 3.48 °C when derived from ERA5 reanalysis estimates. These findings consistently indicate a significant warming trajectory, regardless of the dataset or modeling approach applied.

根据政府间气候变化专门委员会（IPCC）的评估，位于地中海盆地内的t rkiye是最容易受到气候变化不利影响的地区之一。这种脆弱性的增加主要归因于其地理位置、气候特征和社会经济结构，这些因素共同放大了与气温上升和气候变率增加相关的风险。在本研究中，结合ERA5 （ERA5数据集）获得的基于再分析的卫星观测数据，分析了土耳其国家气象局1970 - 2022年记录的 rkiye月平均气温数据（TSMS数据集）。这些历史记录为发展到2050年的温度预测奠定了基础。为了实现这一目标，采用了两种互补的时间序列预测方法：长短期记忆（LSTM）深度学习模型，以其捕获非线性依赖关系和长期时间模式的能力而闻名，以及季节自回归综合移动平均（SARIMA）模型，这是一种经典的统计方法，适用于处理气候数据中的季节性和趋势成分。预估结果显示，与1970-1980年基线期相比，基于原位观测资料的 rkiye平均温度距平预计将上升约2.52℃，而基于ERA5再分析估计的距平预计将上升约3.48℃。无论采用何种数据集或建模方法，这些发现一致表明一个显著的变暖轨迹。

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引用次数: 0

Long-term projections of global, northern hemisphere, and arctic sea ice concentration using statistical and deep learning approaches 使用统计和深度学习方法的全球、北半球和北极海冰浓度的长期预测

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2025-09-20 DOI: 10.1016/j.jastp.2025.106634

Mehmet Bilgili , Engin Pinar , Md. Najmul Mowla , Tahir Durhasan , Muhammed M. Aksoy

The accelerating decline in sea ice concentration (SIC) poses significant challenges for global climate regulation, maritime navigation, and arctic ecosystem stability. This study develops and evaluates two advanced time-series forecasting models, seasonal autoregressive integrated moving average (SARIMA) and long short-term memory (LSTM) networks, to project SIC trends through 2050 across three spatial domains: the globe, the northern hemisphere, and the arctic. Utilizing the ERA5 reanalysis dataset (1970–2024) from the European center for medium-range weather forecasts (ECMWF), the models capture seasonal cycles and complex temporal dependencies to enable robust long-term projections. Comparative analysis demonstrates that SARIMA effectively models periodic fluctuations, while LSTM excels at learning nonlinear dependencies inherent in SIC dynamics. Performance metrics, including mean absolute percentage error (MAPE), root mean square error (RMSE), and correlation coefficient (R), confirm the high accuracy of both models, with SARIMA showing superior capability in representing structured seasonal patterns. Projections indicate a persistent decline in SIC, with arctic concentrations decreasing from 55.60% in 2023 to approximately 46.84% by 2050, underscoring the pronounced effects of arctic amplification. These results provide valuable insights for climate modeling, arctic policy formulation, and the development of adaptive navigation strategies in a rapidly changing polar environment.

海冰浓度的加速下降对全球气候调节、海上航行和北极生态系统的稳定提出了重大挑战。本研究开发并评估了两个先进的时间序列预测模型，即季节自回归综合移动平均（SARIMA）和长短期记忆（LSTM）网络，以预测到2050年全球、北半球和北极三个空间域的SIC趋势。利用欧洲中期天气预报中心（ECMWF）的ERA5再分析数据集（1970-2024），这些模式捕捉季节周期和复杂的时间依赖性，从而实现可靠的长期预测。对比分析表明，SARIMA有效地模拟了周期波动，而LSTM在学习SIC动力学中固有的非线性依赖方面表现出色。包括平均绝对百分比误差（MAPE）、均方根误差（RMSE）和相关系数(R)在内的性能指标证实了两种模型的高准确性，其中SARIMA在表示结构化季节模式方面表现出卓越的能力。预估表明SIC持续下降，北极浓度从2023年的55.60%下降到2050年的约46.84%，强调了北极放大的显著影响。这些结果为气候建模、北极政策制定以及在快速变化的极地环境中自适应导航策略的发展提供了有价值的见解。

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引用次数: 0

Raindrop size distribution in stratiform precipitation: Insights from spectral bin simulations over the high-altitude cloud physics observatory, Western Ghats 层状降水中的雨滴大小分布：来自西高止山脉高空云物理观测站的光谱仓模拟的见解

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2025-09-17 DOI: 10.1016/j.jastp.2025.106643

Sumit Kumar , E.A. Resmi , Dharmadas Jash , Sachin Patade , R.K. Sumesh , Anusha Andrews , Nita Sukumar , A.R. Aswini , Gayatri Kulkarni

An understanding of the Raindrop Size Distributions (RSD) derived from the bin microphysics scheme is vital for improving rainfall estimates and elucidating cloud microphysical processes over the complex terrain of the Western Ghats. This study investigates the structure and evolution of RSD using simulations from the Weather Research and Forecasting (WRF) model with the fast version of the spectral bin microphysics scheme (SBM-Fast), and co-located observations from the micro rain radar and ceilometer at the High-Altitude Cloud Physics Observatory (HACPO), Munnar. Three prolonged stratiform events (>2 h) are analysed to examine key microphysical signatures of melting layer structure, raindrop growth, and vertical variation in RSD. MRR and model results indicate a clear increase in RSD below the melting layer (∼4.8 km). The SBM shows an enhanced rainwater mixing ratio beneath the melting layer, in agreement with observed RSD growth. Above the melting layer, graupel and snow aloft contribute to the growth of raindrops. The model tends to overpredict mid-sized raindrops in the range 1–2 mm near the surface, leading to an overestimation in rainfall for the stratiform case. The enhanced particle growth and subsequent hydrometeor loading were supported by vertical wind with higher updrafts, as captured by the model simulation.

从bin微物理方案中得到的雨滴大小分布（RSD）的理解对于改善降雨估计和阐明西高止山脉复杂地形上的云微物理过程至关重要。本文利用气象研究与预报（WRF）快速版谱仓微物理方案（SBM-Fast）模式的模拟，以及Munnar高空云物理观测站（HACPO）微雨雷达和ceilometer的同步观测资料，研究了RSD的结构和演变。分析了三个延长的层状事件（>2 h），以检查熔化层结构、雨滴生长和RSD垂直变化的关键微物理特征。MRR和模式结果表明，熔融层以下（~ 4.8 km）的RSD明显增加。SBM显示熔融层下雨水混合比增强，与观测到的RSD增长一致。在融化层之上，高空的霰和雪有助于雨滴的生长。该模式倾向于高估地表附近1-2毫米范围内的中等大小雨滴，导致对层状降水的高估。正如模型模拟所捕捉到的那样，颗粒生长的增强和随后的水成物装载是由垂直风和更高的上升气流支持的。

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引用次数: 0

IMF By-driven electric field disturbances over the equator during northward IMFs: PPEF responses 北向IMF期间赤道上空由IMF驱动的电场扰动：PPEF响应

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2025-09-17 DOI: 10.1016/j.jastp.2025.106642

Ram Singh , Tarun Kumar Pant , S. Sripathi , Diptiranjan Rout , Ankush Bhaskar , Danny E. Scipion

This article provides the first evidence of equatorial and low-latitude ionospheric electric field disturbances driven by quasi-periodic variations in the interplanetary magnetic field (IMF) By component, under a constant northward IMF Bz (∼+15 nT), solar wind dynamic pressure (∼8 nPa), solar wind velocity (∼450 km/s), and positive Sym-H (+10 nT). The virtual height of the F layer (h’F), measured by ionosondes at opposite longitudes in the American (Jicamarca, 11.9°S, −76.0°E; Fortaleza, 3.9°S, −38.52°E) and Indian (Trivandrum, 8.5°N, 77.0°E; Tirunelveli, 8.73°N, 77.7°E) sectors, shows electric field disturbances with opposite polarities during the day and night, driven by changes in the IMF. During northward IMF Bz conditions, vertical E × B plasma drifts measured by the Jicamarca Incoherent Scatter Radar (ISR), along with equatorial electrojet (EEJ) observations, reveal westward electric field disturbances on the dayside. Conversely, on the nightside, ionosonde measurements at Trivandrum and Tirunelveli show eastward electric field perturbations. Notably, quasi-periodic electric field fluctuations with prominent periodicities of approximately 15 min are observed in both the IMF By and ionospheric parameters, such as dh'F/dt and EEJ, in both local time sectors. These fluctuations are most likely driven by modulations in high-latitude disturbance polar (DP2) currents. SuperDARN ionospheric convection maps display enhanced convection, rotation, and expansion, which appear to influence the equatorial electric field. The presence of northward Bz (NBZ) currents and reverse convection patterns likely contributes to the observed westward electric field perturbations at low latitudes.

本文首次提供了由行星际磁场（IMF）准周期变化驱动的赤道和低纬度电离层电场扰动的证据。通过分量，在恒定的北行星际磁场Bz （~ +15 nT）、太阳风动压（~ 8 nPa）、太阳风速度（~ 450 km/s）和正symm - h （+10 nT）下。在美洲（Jicamarca, 11.9°S,−76.0°E; Fortaleza, 3.9°S,−38.52°E）和印度（Trivandrum, 8.5°N, 77.0°E; Tirunelveli, 8.73°N, 77.7°E）扇区，由电离层仪在相反经度测量的F层虚拟高度（h 'F）显示，在IMF变化的驱动下，白夜间的电场扰动具有相反的极性。在向北的IMF Bz条件下，Jicamarca非相干散射雷达（ISR）测量的垂直E × B等离子体漂移，以及赤道电喷流（EEJ）观测显示，白天侧的西向电场干扰。相反，在夜晚，Trivandrum和Tirunelveli的电离探空仪测量显示向东的电场扰动。值得注意的是，在两个当地时间扇区的IMF By和电离层参数（如dh'F/dt和EEJ）中都观察到具有大约15分钟显著周期性的准周期电场波动。这些波动很可能是由高纬度扰动极流（DP2）的调制引起的。SuperDARN电离层对流图显示对流、旋转和膨胀增强，这似乎影响了赤道电场。北向Bz （NBZ）流和反向对流模式的存在可能是低纬度观测到的西向电场扰动的原因。

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引用次数: 0

Enhanced estimation of seasonal irradiance patterns: The adapted time synchronous average method 季节辐照度模式的增强估计：适应时间同步平均法

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2025-09-16 DOI: 10.1016/j.jastp.2025.106620

Mohammed Telidjane , Benaoumeur Bakhti

Accurately separating the cyclic pattern from background noise in irradiance data is critical for various applications, including monitoring photovoltaic panels. However, seasonality, evident as amplitude modulation in the irradiance waveform, poses a significant challenge to existing methods like Time Synchronous Averaging (TSA) and Cepstral Editing (CE). These methods struggle to effectively remove the influence of seasonality on the estimated cyclic pattern. This work proposes a novel method called the Adapted Time Synchronous Average (ATSA) to address these limitations. The new approach is inspired by the application of cyclostationary tools used in mechanical signals of rotating machinery. ATSA employs a three-step approach: signal normalization to mitigate seasonality, TSA estimation using the normalized data, and adaptive filtering for signal reconstruction. Our analysis demonstrates that ATSA outperforms TSA and CE in separating the cyclic pattern from the stochastic noise component in irradiance data.

在辐照度数据中准确地从背景噪声中分离循环模式对于各种应用至关重要，包括监测光伏板。然而，季节性，如辐照度波形中的幅度调制，对时间同步平均（TSA）和倒谱编辑（CE）等现有方法提出了重大挑战。这些方法难以有效地消除季节性对估计周期型的影响。这项工作提出了一种称为自适应时间同步平均（ATSA）的新方法来解决这些限制。新方法的灵感来自于在旋转机械的机械信号中使用的循环静止工具的应用。ATSA采用三步方法：信号归一化以减轻季节性，使用归一化数据进行TSA估计，以及自适应滤波进行信号重建。我们的分析表明，在从辐照数据中的随机噪声成分中分离循环模式方面，ATSA优于TSA和CE。

引用次数: 0

Impact of climatic factors on volume aerosol size distribution over Northern India 气候因子对印度北部气溶胶体积大小分布的影响

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2025-09-15 DOI: 10.1016/j.jastp.2025.106633

Nabin Sharma , Sishir Dahal , Sunil Kumar Chaurasiya , Sarvan Kumar , Kalpana Patel

The correlation between climatic factors and the volume aerosol size distribution (V-ASD), which was extracted from AERONET data for 5 years at five monitoring sites throughout Northern India, has been investigated. Fine-mode aerosols (<0.58 μm) showed negative correlations with rainfall (RF) and relative humidity (RH), where RH enhances hygroscopic growth, making particles more prone to removal by precipitation scavenging. Depending on the station, the correlation between Wind Speed (WS) and V-ASD varied. The pre-monsoon period was marked by greater WS and Boundary Layer Height, which improved aerosol dispersion and mixing, but the monsoon period (June–September) regularly raised RF and RH, according to seasonal changes. At most sites, coarse-mode aerosols (>0.58 μm) showed a positive correlation with temperature, whereas fine-mode particles showed negative correlations with both temperature and relative humidity. Coarse particle development was greatly impacted by temperature-driven processes, especially at Gandhi College and Kanpur. These results demonstrate the intricate relationships that exist in North India between regional climatic factors and aerosol size distributions.

从印度北部5个监测点的AERONET数据中提取了5年的气溶胶体积大小分布（V-ASD），研究了气候因子与气溶胶体积大小分布之间的相关性。细模气溶胶（<0.58 μm）与降雨量（RF）和相对湿度（RH）呈负相关，其中相对湿度增强吸湿性生长，使颗粒更容易被降水清除。风速（WS）与V-ASD之间的相关性因气象站的不同而不同。季风前期WS和边界层高度增大，有利于气溶胶的扩散和混合，但季风期（6 - 9月）根据季节变化，RF和RH有规律地升高。在大多数站点，粗模态气溶胶（>0.58 μm）与温度呈正相关，而细模态气溶胶与温度和相对湿度均呈负相关。粗颗粒的发育很大程度上受到温度驱动过程的影响，特别是在甘地学院和坎普尔。这些结果表明，在印度北部，区域气候因子与气溶胶大小分布之间存在着复杂的关系。

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引用次数: 0

Bayesian-optimised hybrid machine learning model for coastal wind gust prediction in a marine-influenced atmospheric boundary layer 海洋影响大气边界层中海岸阵风预测的贝叶斯优化混合机器学习模型

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2025-09-14 DOI: 10.1016/j.jastp.2025.106629

Mohammad Reza Chalak Qazani , Mahmood Al-Bahri , Muhammad Zakarya , Falah Y.H. Ahmed , Amirhossein Mohajerzadeh , Saeid Hosseini , Mehdi Moayyedian , Zoran Najdovski , Houshyar Asadi

Accurate prediction of wind gusts is crucial for applications in aviation, coastal and marine operations, and atmospheric dynamics research. This study presents a novel model combining a Sequencing Block and a Layer Perceptron (MLP) optimised using Bayesian Optimisation (B-MLP) to enhance the precision of coastal atmospheric wind gust forecasts. The model is validated using a 13-year dataset (January 2010 to March 2023) from Muscat International Airport, a coastal site influenced by Gulf of Oman sea–land breeze interactions. The Sequencing Block is designed and developed to capture the optimal arrangement of dataset segmentation using atmospheric and boundary layer parameters, thereby enhancing the model's predictive accuracy. The B-MLP model's efficacy is compared against traditional methods, including Decision Tree (DT) and Support Vector Regression (SVR), demonstrating a substantial enhancement in forecast quality. The B-MLP model achieves a correlation coefficient of 0.817 between actual and forecasted wind gusts, outperforming DT and SVR by notable margins in both accuracy and error reduction. The newly proposed model is validated using a 13-year dataset (January 2010 to March 2023) from Muscat International Airport, a coastal site influenced by Gulf of Oman sea–land breeze interactions, to prove its robustness and applicability on a 1-day ahead prediction horizon. The proposed B-MLP model improves forecast accuracy and offers a scalable solution for atmospheric boundary layer studies, marine safety applications, and real-time meteorological data analysis.

准确预测阵风对于航空、沿海和海洋作业以及大气动力学研究的应用至关重要。为了提高沿海大气阵风预报的精度，提出了一种结合序列块和层感知器（MLP）的新模型，该模型采用贝叶斯优化（B-MLP）进行优化。该模型使用马斯喀特国际机场的13年数据集（2010年1月至2023年3月）进行了验证，马斯喀特国际机场是受阿曼湾海陆风相互作用影响的沿海站点。测序块的设计和开发是为了捕捉使用大气和边界层参数的数据集分割的最佳安排，从而提高模型的预测精度。将B-MLP模型的有效性与传统方法（包括决策树（DT）和支持向量回归（SVR））进行了比较，结果表明B-MLP模型在预测质量方面有了实质性的提高。B-MLP模型在实际阵风和预测阵风之间的相关系数为0.817，在精度和误差减少方面都明显优于DT和SVR。新提出的模型使用马斯喀特国际机场的13年数据集（2010年1月至2023年3月）进行验证，马斯喀特国际机场是一个受阿曼湾海陆风相互作用影响的沿海站点，以证明其在提前1天预测范围内的稳健性和适用性。提出的B-MLP模式提高了预报精度，并为大气边界层研究、海洋安全应用和实时气象数据分析提供了可扩展的解决方案。

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引用次数: 0

Variability in the thermospheric neutral mass density: A multiple model comparison 热层中性质量密度的变率：多模式比较

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2025-09-12 DOI: 10.1016/j.jastp.2025.106630

Yongliang Zhang, Patrick B. Dandenault, Larry J. Paxton, Robert Schaefer, Clayton Cantrall, Hyosub Kil, Rafael Mesquita, Matthew E. Zuber

Variability in the thermospheric neutral mass density in LEO/VLEO altitudes has been investigated using outputs from five models (MSIS2.0, HASDM, WACCM-X, TIEGCM, and WAM-IPE) under different geophysical conditions: geomagnetically quiet, moderate storm and super storm. These models are selected to represent empirical, assimilation, and physics-based methods. We compared the global neutral mass density distribution and the time variations in the densities using equatorial and polar orbits at three fixed LEO/VLEO altitudes (100, 200, and 300 km) from the five models. Our key findings from the analyses are: (1) there are significant systematic biases among the model results; (2) WACCM-X, TIEGCM and HASDM peak densities are roughly consistent with each other during a super storm. However, their UT differences are up to a half day; (3) WAM-IPE and MSIS-2.0 models tend to give lower densities than other models; (4) the geomagnetic activity impact on neutral mass densities increases with altitude and it is negligible at 100 km altitude, becomes evident at 150 km, and is significant at 200 km; (5) geomagnetic storms tend to reduce the biases among the model densities The systematic biases among models are likely due to the different parameterizations, drivers and boundary conditions used in the models. A systematic evaluation of the models using multiple and cross-calibrated ground truth data sets is needed to fully address the biases and offer the insight required to improve the models.

利用5种模式（MSIS2.0、HASDM、WACCM-X、TIEGCM和WAM-IPE）在不同地球物理条件下（地磁安静、中风暴和超级风暴）的输出，研究了低空/超低空热层中性质量密度的变化。选择这些模型来代表经验、同化和基于物理的方法。我们比较了5种模式在3个固定的LEO/VLEO高度（100、200和300 km）使用赤道和极地轨道的全球中性质量密度分布和密度的时间变化。分析的主要发现有：(1)模型结果存在显著的系统偏差；(2)超级风暴期间WACCM-X、TIEGCM和HASDM的峰值密度基本一致。然而，他们的UT差异高达半天；(3) WAM-IPE和MSIS-2.0模型给出的密度较其他模型低；(4)地磁活动对中性质量密度的影响随海拔高度增大而增大，在100 km高度可忽略，在150 km高度显著，在200 km高度显著；(5)地磁风暴会减小模型密度间的偏差，而模型间的系统偏差可能是由于模型所采用的参数化、驱动因素和边界条件不同造成的。需要使用多个和交叉校准的地面真值数据集对模型进行系统评估，以充分解决偏差并提供改进模型所需的洞察力。

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引用次数: 0

Spatially heterogeneous wetting and climatic drivers of precipitation variability in arid and semi-arid Northwest China since 1960 1960年以来中国西北干旱半干旱地区降水变异的空间非均质湿润和气候驱动因素

IF 1.9 4区地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS

Journal of Atmospheric and Solar-Terrestrial Physics

Pub Date : 2025-09-12 DOI: 10.1016/j.jastp.2025.106632

Hao Wu , Xinyan Li , Zuohui Cai , Yao Chen

This study analyzes the spatiotemporal evolution of precipitation across the arid and semi-arid regions of Northwest China from 1960 to 2020, focusing on long-term trends, regional disparities, and climatic drivers. Piecewise linear regression reveals a sharp wetting transition around 2000, characterized by rising precipitation frequency and intensity. However, this trend is spatially heterogeneous. Fuzzy clustering reveals four distinct change patterns that correspond with key geographic subregions. Before 2000, increases were concentrated in the arid Northern Tianshan (NT) and Tarim Basin (TB); after 2000, semi-arid Northeastern Tibetan Plateau (NETP) and Loess Plateau (LP) became dominant contributors. Precipitation become more seasonally balanced, potentially easing drought stress. Yet, extreme precipitation events have intensified, particularly in arid regions, posing escalating risks to the fragile ecosystems. Slow feature analysis isolates dominant low-varying modes, revealing that NT and NETP are primarily influenced by El Niño-Southern Oscillation, with a two-year lag in NETP. LP is modulated by the East Asian summer monsoon. TB is predominantly affected by the Eurasian wave train pattern and equatorial Indian Ocean sea surface temperature anomalies. These results highlight the complex and regionally varied hydroclimatic change across Northwest China, urgently calling for tailored adaptation and water management strategies.

本文分析了1960 - 2020年中国西北干旱半干旱区降水的时空演变特征，重点分析了长期趋势、区域差异和气候驱动因素。分段线性回归表明，2000年前后出现了急剧的湿润过渡，降水频率和强度均有所上升。然而，这种趋势在空间上是异质的。模糊聚类揭示了与关键地理分区相对应的四种不同的变化模式。降水变得更加季节性平衡，可能缓解干旱压力。然而，极端降水事件有所加剧，特别是在干旱地区，对脆弱的生态系统构成了越来越大的风险。慢特征分析分离出主要的低变化模式，揭示了NT和NETP主要受El Niño-Southern振荡的影响，NETP滞后2年。低压受东亚夏季风调制。结核主要受欧亚波列型和赤道印度洋海面温度异常的影响。这些结果凸显了中国西北地区水文气候变化的复杂性和区域性差异，迫切需要制定有针对性的适应和水管理策略。

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