Atmospheric and Oceanic Science Letters最新文献_第7页

Skillful bias correction of offshore near-surface wind field forecasting based on a multi-task machine learning model 基于多任务机器学习模型的海上近地面风场预报灵巧偏差校正

IF 2.3 4区地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES

Atmospheric and Oceanic Science Letters

Pub Date : 2025-01-06 DOI: 10.1016/j.aosl.2025.100590

Qiyang Liu , Anboyu Guo , Fengxue Qiao , Xinjian Ma , Yan-An Liu , Yong Huang , Rui Wang , Chunyan Sheng

Accurate short-term forecast of offshore wind fields is still challenging for numerical weather prediction models. Based on three years of 48-hour forecast data from the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System global model (ECMWF-IFS) over 14 offshore weather stations along the coast of Shandong Province, this study introduces a multi-task learning (MTL) model (TabNet-MTL), which significantly improves the forecast bias of near-surface wind direction and speed simultaneously. TabNet-MTL adopts the feature engineering method, utilizes mean square error as the loss function, and employs the 5-fold cross validation method to ensure the generalization ability of the trained model. It demonstrates superior skills in wind field correction across different forecast lead times over all stations compared to its single-task version (TabNet-STL) and three other popular single-task learning models (Random Forest, LightGBM, and XGBoost). Results show that it significantly reduces root mean square error of the ECMWF-IFS wind speed forecast from 2.20 to 1.25 m s⁻¹, and increases the forecast accuracy of wind direction from 50 % to 65 %. As an explainable deep learning model, the weather stations and long-term temporal statistics of near-surface wind speed are identified as the most influential variables for TabNet-MTL in constructing its feature engineering.

摘要

目前, 数值业务预报模式对沿海站点短期风场的准确预报仍存在挑战. 本研究基于欧洲中期天气预报中心ECMWF-IFS的高分辨率模式未来48小时的预报数据, 构建适用于沿海风场订正的热动力特征, 关键变量的短期和长期统计特征, 引入多任务深度学习模型 (TabNet-MTL) 对山东省14个沿海气象站的风向和风速预报同时进行订正. 相比于多个单任务学习模型 (随机森林, LightGBM, XGBoost和TabNet-STL) , TabNet-MTL模型具有显著的偏差订正优势, 风速预报的均方根误差从2.20 m s⁻¹降低到 1.25 m s⁻¹, 风向预报准确率从50 %提高到65 %.此外, TabNet-MTL模型具有可解释性, 特征重要性表明气象站点和近地面风速统计特征对风场订正的改善具有较大贡献.

对于数值天气预报模式来说，海上风场的短期准确预报仍然是一个挑战。基于欧洲中期天气预报中心综合预报系统全球模式（ECMWF-IFS）在山东省沿海14个海上气象站3年的48小时预报数据，引入了TabNet-MTL模型，该模型显著改善了近地面风向和风速的预报偏差。TabNet-MTL采用特征工程方法，以均方误差作为损失函数，并采用5重交叉验证方法保证训练模型的泛化能力。与单任务版本（TabNet-STL）和其他三种流行的单任务学习模型（Random Forest， LightGBM和XGBoost）相比，它在所有站点不同预测提前期的风场校正方面表现出了卓越的技能。结果表明，该方法将ECMWF-IFS风速预报均方根误差从2.20 m s - 1显著降低到1.25 m s - 1，将风向预报精度从50%提高到65%。作为一种可解释的深度学习模型，气象站和近地面风速的长期时间统计数据是TabNet-MTL特征工程中影响最大的变量。摘要目前, 数值业务预报模式对沿海站点短期风场的准确预报仍存在挑战. 本研究基于欧洲中期天气预报中心ECMWF-IFS的高分辨率模式未来48小时的预报数据,构建适用于沿海风场订正的热动力特征,关键变量的短期和长期统计特征,引入多任务深度学习模型(TabNet-MTL)对山东省14个沿海气象站的风向和风速预报同时进行订正。相比于多个单任务学习模型(随机森林,LightGBM, XGBoost和TabNet-STL), TabNet-MTL模型具有显著的偏差订正优势,风速预报的均方根误差从2.20 s−1降低到1.25年代−1,风向预报准确率从50%提高到65%。此外,TabNet-MTL模型具有可解释性,特征重要性表明气象站点和近地面风速统计特征对风场订正的改善具有较大贡献。

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引用次数: 0

Optimizing the key parameter to accelerate the recovery of AMOC under a rapid increase of greenhouse gas forcing 优化关键参数，在温室气体强迫迅速增加的情况下加速恢复 AMOC

IF 2.3 4区地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES

Atmospheric and Oceanic Science Letters

Pub Date : 2025-01-01 DOI: 10.1016/j.aosl.2024.100509

Haolan Ren , Fei Zheng , Tingwei Cao , Qiang Wang

Atlantic Meridional Overturning Circulation (AMOC) plays a central role in long-term climate variations through its heat and freshwater transports, which can collapse under a rapid increase of greenhouse gas forcing in climate models. Previous studies have suggested that the deviation of model parameters is one of the major factors in inducing inaccurate AMOC simulations. In this work, with a low-resolution earth system model, the authors try to explore whether a reasonable adjustment of the key model parameter can help to re-establish the AMOC after its collapse. Through a new optimization strategy, the extra freshwater flux (FWF) parameter is determined to be the dominant one affecting the AMOC's variability. The traditional ensemble optimal interpolation (EnOI) data assimilation and new machine learning methods are adopted to optimize the FWF parameter in an abrupt 4×CO₂ forcing experiment to improve the adaptability of model parameters and accelerate the recovery of AMOC. The results show that, under an abrupt 4×CO₂ forcing in millennial simulations, the AMOC will first collapse and then re-establish by the default FWF parameter slowly. However, during the parameter adjustment process, the saltier and colder sea water over the North Atlantic region are the dominant factors in usefully improving the adaptability of the FWF parameter and accelerating the recovery of AMOC, according to their physical relationship with FWF on the interdecadal timescale.

摘要

大西洋经向翻转环流 (Atlantic Meridional Overturning Circulation, AMOC) 通过其经向的热量和水团输送, 在气候变化中起着关键作用. 然而, 气候模式模拟未来AMOC在温室气体强迫下的变化存在较大不确定性. 模式参数的不确定性是导致AMOC产生不确定性的主要因素之一. 因此, 本文采用简化的海气耦合模式首先探寻出模式中AMOC的最敏感参数为淡水通量系数 (Freshwater Flux, FWF), 再基于集合最优插值 (Ensemble Optimal Interpolation, EnOI) 探讨通过参数优化减小温室气体强迫下AMOC模拟不确定性的可行方案. 理想试验揭示了, 北大西洋海表温度和海表盐度在温室气体强迫下的增量可以有效地优化FWF, 进而使得AMOC相比默认参数能快速收敛, 减小其在未来气候预估中的不确定性.

大西洋经向翻转环流（AMOC）通过其热量和淡水输送在长期气候变化中起着核心作用，在气候模式中，它可能在温室气体强迫的快速增加下崩溃。以往的研究表明，模型参数的偏差是导致AMOC模拟不准确的主要因素之一。本文利用低分辨率地球系统模型，探讨合理调整关键模型参数是否有助于在AMOC崩溃后重建AMOC。通过一种新的优化策略，确定了额外淡水通量（FWF）参数是影响AMOC变率的主要参数。在突发4×CO2强迫实验中，采用传统的集成最优插值（EnOI）数据同化和新的机器学习方法对FWF参数进行优化，以提高模型参数的自适应性，加速AMOC的恢复。结果表明，在千禧年模拟中突然的4×CO2强迫作用下，AMOC首先崩溃，然后在默认FWF参数下缓慢重建。然而，从年代际时间尺度上看，北大西洋地区较咸、较冷的海水是有效提高FWF参数适应性和加速AMOC恢复的主导因素。摘要大西洋经向翻转环流(大西洋经向翻转环流,大西洋经向翻转环流)通过其经向的热量和水团输送,在气候变化中起着关键作用。“”“”“”“”“”“”英文释义：英文释义：因此,本文采用简化的海气耦合模式首先探寻出模式中大西洋经向翻转环流的最敏感参数为淡水通量系数(淡水通量,FWF),再基于集合最优插值(整体最优插值,EnOI)探讨通过参数优化减小温室气体强迫下大西洋经向翻转环流模拟不确定性的可行方案。理想试验揭示了,北大西洋海表温度和海表盐度在温室气体强迫下的增量可以有效地优化FWF,进而使得大西洋经向翻转环流相比默认参数能快速收敛,减小其在未来气候预估中的不确定性。

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引用次数: 0

Interannual variability of boreal summer intraseasonal oscillation over the northwestern Pacific influenced by the Pacific Meridional Mode 受太平洋经向模式影响的西北太平洋北方夏季季内振荡的年际变化

IF 2.3 4区地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES

Atmospheric and Oceanic Science Letters

Pub Date : 2025-01-01 DOI: 10.1016/j.aosl.2024.100492

Haoyu Zhou, Pang-Chi Hsu, Lin Chen, Yitian Qian

During the boreal summer, intraseasonal oscillations exhibit significant interannual variations in intensity over two key regions: the central-western equatorial Pacific (5°S–5°N, 150°E–150°W) and the subtropical Northwestern Pacific (10°–20°N, 130°E–175°W). The former is well-documented and considered to be influenced by the ENSO, while the latter has received comparatively less attention and is likely influenced by the Pacific Meridional Mode (PMM), as suggested by partial correlation analysis results. To elucidate the physical processes responsible for the enhanced (weakened) intraseasonal convection over the subtropical northwestern Pacific during warm (cold) PMM years, the authors employed a moisture budget analysis. The findings reveal that during warm PMM years, there is an increase in summer-mean moisture over the subtropical northwestern Pacific. This increase interacts with intensified vertical motion perturbations in the region, leading to greater vertical moisture advection in the lower troposphere and consequently resulting in convective instability. Such a process is pivotal in amplifying intraseasonal convection anomalies. The observational findings were further verified by model experiments forced by PMM-like sea surface temperature patterns.

摘要

在北半球夏季, 西北太平洋地区的季节内振荡在两个主要区域呈现出显著的年际变率: 一个区域是赤道太平洋中西部 (5°S–5°N, 150°E–150°W), 另一区域为副热带西北太平洋 (10°–20°N, 130°E–175°W). 通过偏相关分析, 揭示了前者受到厄尔尼诺–南方涛动 (ENSO) 的影响, 而后者与太平洋经向模态 (PMM) 有关. 利用水汽方程诊断, 探讨了在PMM暖 (冷) 年期间, 副热带西北太平洋季节内对流活动增强 (减弱) 的物理过程. 结果表明, 在PMM暖年, 副热带西北太平洋地区的季节平均水汽增加与季节内垂直扰动的增强相互作用, 导致了对流层低层水汽垂直输送的增加, 进而引发对流不稳定性的增强, 促使季节内对流活动增强. 这一发现在以PMM海温为驱动的全球环流模式试验中也得到了验证.

在北方夏季，两个关键区域的季内振荡强度表现出显著的年际变化：赤道中西部太平洋（5°S-5°N, 150°E-150°W）和亚热带西北太平洋（10°-20°N, 130°E-175°W）。偏相关分析结果表明，前者文献丰富，被认为受ENSO的影响，而后者受到的关注相对较少，可能受太平洋经向模态（PMM）的影响。为了阐明暖（冷）PMM年副热带西北太平洋季节内对流增强（减弱）的物理过程，作者采用了水分收支分析。结果表明，在温暖的PMM年，亚热带西北太平洋夏季平均湿度增加。这种增加与该区域垂直运动扰动加剧相互作用，导致对流层下层垂直水汽平流增强，从而导致对流不稳定。这种过程是放大季内对流异常的关键。在类似pmm的海面温度模式的强迫下，模式实验进一步验证了观测结果。摘要在北半球夏季,西北太平洋地区的季节内振荡在两个主要区域呈现出显著的年际变率:一个区域是赤道太平洋中西部(5°S-5°N, 150°e - 150°W),另一区域为副热带西北太平洋(10°-20°N, 130°e - 175°W)。通过偏相关分析,揭示了前者受到厄尔尼诺——南方涛动(ENSO)的影响,而后者与太平洋经向模态(PMM)有关。利用水汽方程诊断,探讨了在PMM暖(冷)年期间,副热带西北太平洋季节内对流活动增强(减弱)的物理过程。结果表明,在PMM暖年,副热带西北太平洋地区的季节平均水汽增加与季节内垂直扰动的增强相互作用,导致了对流层低层水汽垂直输送的增加,进而引发对流不稳定性的增强,促使季节内对流活动增强。【中文译文】

{"title":"Interannual variability of boreal summer intraseasonal oscillation over the northwestern Pacific influenced by the Pacific Meridional Mode","authors":"Haoyu Zhou, Pang-Chi Hsu, Lin Chen, Yitian Qian","doi":"10.1016/j.aosl.2024.100492","DOIUrl":"10.1016/j.aosl.2024.100492","url":null,"abstract":"<div><div>During the boreal summer, intraseasonal oscillations exhibit significant interannual variations in intensity over two key regions: the central-western equatorial Pacific (5°S–5°N, 150°E–150°W) and the subtropical Northwestern Pacific (10°–20°N, 130°E–175°W). The former is well-documented and considered to be influenced by the ENSO, while the latter has received comparatively less attention and is likely influenced by the Pacific Meridional Mode (PMM), as suggested by partial correlation analysis results. To elucidate the physical processes responsible for the enhanced (weakened) intraseasonal convection over the subtropical northwestern Pacific during warm (cold) PMM years, the authors employed a moisture budget analysis. The findings reveal that during warm PMM years, there is an increase in summer-mean moisture over the subtropical northwestern Pacific. This increase interacts with intensified vertical motion perturbations in the region, leading to greater vertical moisture advection in the lower troposphere and consequently resulting in convective instability. Such a process is pivotal in amplifying intraseasonal convection anomalies. The observational findings were further verified by model experiments forced by PMM-like sea surface temperature patterns.</div><div>摘要</div><div>在北半球夏季, 西北太平洋地区的季节内振荡在两个主要区域呈现出显著的年际变率: 一个区域是赤道太平洋中西部 (5°S–5°N, 150°E–150°W), 另一区域为副热带西北太平洋 (10°–20°N, 130°E–175°W). 通过偏相关分析, 揭示了前者受到厄尔尼诺–南方涛动 (ENSO) 的影响, 而后者与太平洋经向模态 (PMM) 有关. 利用水汽方程诊断, 探讨了在PMM暖 (冷) 年期间, 副热带西北太平洋季节内对流活动增强 (减弱) 的物理过程. 结果表明, 在PMM暖年, 副热带西北太平洋地区的季节平均水汽增加与季节内垂直扰动的增强相互作用, 导致了对流层低层水汽垂直输送的增加, 进而引发对流不稳定性的增强, 促使季节内对流活动增强. 这一发现在以PMM海温为驱动的全球环流模式试验中也得到了验证.</div></div>","PeriodicalId":47210,"journal":{"name":"Atmospheric and Oceanic Science Letters","volume":"18 1","pages":"Article 100492"},"PeriodicalIF":2.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140399719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Satellite remote sensing reveals overwhelming recovery of forest from disturbances in Asia 卫星遥感揭示了亚洲森林受干扰后的巨大恢复力

IF 2.3 4区地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES

Atmospheric and Oceanic Science Letters

Pub Date : 2025-01-01 DOI: 10.1016/j.aosl.2024.100511

Yiying Zhu , Hesong Wang , Anzhi Zhang

Forest ecosystems play key roles in mitigating human-induced climate change through enhanced carbon uptake; however, frequently occurring climate extremes and human activities have considerably threatened the stability of forests. At the same time, detailed accounts of disturbances and forest responses are not yet well quantified in Asia. This study employed the Breaks For Additive Seasonal and Trend method—an abrupt-change detection method—to analyze the Enhanced Vegetation Index time series in East Asia, South Asia, and Southeast Asia. This approach allowed us to detect forest disturbance and quantify the resilience after disturbance. Results showed that 20 % of forests experienced disturbance with an increasing trend from 2000 to 2022, and Southeast Asian countries were more severely affected by disturbances. Specifically, 95 % of forests had robust resilience and could recover from disturbance within a few decades. The resilience of forests suffering from greater magnitude of disturbance tended to be stronger than forests with lower disturbance magnitude. In summary, this study investigated the resilience of forests across the low and middle latitudes of Asia over the past two decades. The authors found that most forests exhibited good resilience after disturbance and about two-thirds had recovered to a better state in 2022. The findings of this study underscore the complex relationship between disturbance and resilience, contributing to comprehension of forest resilience through satellite remote sensing.

摘要

目前对于亚洲森林在应对气候变化和人类活动干扰方面的研究相对较少. 本研究利用BFAST突变检测方法, 分析了东亚, 南亚和东南亚的增强植被指数 (EVI) 长时间序列中检测到的森林扰动和恢复情况. 结果显示, 2000年至2022年期间, 约20%的森林经历了扰动, 且受到扰动的森林面积呈增加趋势, 东南亚国家受扰动的影响更为严重. 在扰动事件发生后, 95%的森林具有较好的恢复能力, 能够在受到扰动后的一段时间后恢复过来, 其中约有三分之二的森林在2022年时已经恢复到了较扰动前更好的状态.

森林生态系统通过增强碳吸收，在减缓人为引起的气候变化方面发挥关键作用；然而，频繁发生的极端气候和人类活动已经严重威胁到森林的稳定性。与此同时，亚洲对干扰和森林反应的详细描述尚未得到很好的量化。本研究采用一种突变检测方法——累加季节和趋势断裂法，对东亚、南亚和东南亚的植被指数增强时间序列进行了分析。这种方法使我们能够检测森林扰动并量化扰动后的恢复力。结果表明：2000 - 2022年，20 %的森林受到干扰，并呈增加趋势，其中东南亚国家受干扰的影响更为严重。具体而言，95% %的森林具有强大的恢复能力，可以在几十年内从干扰中恢复。扰动较大的森林恢复力往往比扰动较小的森林更强。总之，本研究调查了过去二十年来亚洲中低纬度地区森林的恢复力。作者发现，大多数森林在受到干扰后表现出良好的恢复能力，大约三分之二的森林在2022年恢复到更好的状态。本研究的发现强调了干扰与恢复力之间的复杂关系，有助于通过卫星遥感了解森林的恢复力。本研究利用BFAST突变检测方法,分析了东亚,南亚和东南亚的增强植被指数(以)长时间序列中检测到的森林扰动和恢复情况。结果显示, 2000年至2022年期间, 约20%的森林经历了扰动, 且受到扰动的森林面积呈增加趋势, 东南亚国家受扰动的影响更为严重. 在扰动事件发生后, 95%的森林具有较好的恢复能力, 能够在受到扰动后的一段时间后恢复过来, 其中约有三分之二的森林在2022年时已经恢复到了较扰动前更好的状态.

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引用次数: 0

Impact of ocean data assimilation on the seasonal forecast of the 2014/15 marine heatwave in the Northeast Pacific Ocean 海洋数据同化对东北太平洋 2014/15 年海洋热浪季节性预报的影响

IF 2.3 4区地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES

Atmospheric and Oceanic Science Letters

Pub Date : 2025-01-01 DOI: 10.1016/j.aosl.2024.100498

Tiantian Tang, Jiaying He, Huihang Sun, Jingjia Luo

A remarkable marine heatwave, known as the “Blob”, occurred in the Northeast Pacific Ocean from late 2013 to early 2016, which displayed strong warm anomalies extending from the surface to a depth of 300 m. This study employed two assimilation schemes based on the global Climate Forecast System of Nanjing University of Information Science (NUIST-CFS 1.0) to investigate the impact of ocean data assimilation on the seasonal prediction of this extreme marine heatwave. The sea surface temperature (SST) nudging scheme assimilates SST only, while the deterministic ensemble Kalman filter (EnKF) scheme assimilates observations from the surface to the deep ocean. The latter notably improves the forecasting skill for subsurface temperature anomalies, especially at the depth of 100–300 m (the lower layer), outperforming the SST nudging scheme. It excels in predicting both horizontal and vertical heat transport in the lower layer, contributing to improved forecasts of the lower-layer warming during the Blob. These improvements stem from the assimilation of subsurface observational data, which are important in predicting the upper-ocean conditions. The results suggest that assimilating ocean data with the EnKF scheme significantly enhances the accuracy in predicting subsurface temperature anomalies during the Blob and offers better understanding of its underlying mechanisms.

摘要

2013年底至2016年初, 东北太平洋上发生了历史上罕见的极端海洋热浪事件 (称为“Blob”事件) , 形成了从海表延伸至海洋深处300m的强烈且持续的海温暖异常. 本文利用南京信息工程大学全球气候预测系统1.0版本 (NUIST-CFS 1.0) , 采用两种海洋资料同化方案, 探究海洋资料同化差异对这一极端海洋热浪事件季节预测的影响. 本文采用的一种同化方案为仅同化海表面温度 (Surface sea temperature, SST) 的SST-nudging方案, 而另一种方案为在前一种方案的基础上加入确定性集合卡尔曼滤波 (Deterministic Ensemble Kalman Filter, DEnKF) , 同化更多海洋观测数据的EnKF方案. 主要结论为, 利用EnKF方案可显著提高对“Blob”期间次表层温度异常预测的准确性, 这主要源于EnKF方案在预测次表层的水平和垂直热传输方面表现出色. 该研究有助于更好地理解海洋热浪事件潜在物理机制及其季节预测水平.

2013年底至2016年初，东北太平洋出现了一次引人注目的海洋热浪，被称为“Blob”，从表面延伸到300米深处，显示出强烈的温暖异常。本研究采用基于南京信息工程大学全球气候预报系统（NUIST-CFS 1.0）的两种同化方案，探讨了海洋资料同化对此次海洋极端热浪季节预报的影响。海表温度（SST）推动方案只同化海表温度，而确定性集合卡尔曼滤波（EnKF）方案同化从表层到深海的观测。后者显著提高了对地下温度异常的预测能力，特别是在100-300 m深度（下层），优于海温推动方案。它在预测低层的水平和垂直热输送方面都表现出色，有助于改进对团团期间低层变暖的预测。这些改进源于地下观测资料的同化，这对预测上层海洋条件很重要。结果表明，利用EnKF方案吸收海洋数据显著提高了预测Blob期间地下温度异常的精度，并有助于更好地理解其潜在机制。摘要2013年底至2016年初,东北太平洋上发生了历史上罕见的极端海洋热浪事件(称为“Blob”事件),形成了从海表延伸至海洋深处300米的强烈且持续的海温暖异常。本文利用南京信息工程大学全球气候预测系统1.0版本(NUIST-CFS 1.0),采用两种海洋资料同化方案,探究海洋资料同化差异对这一极端海洋热浪事件季节预测的影响。本文采用的一种同化方案为仅同化海表面温度(海表面温度、风场的SST-nudging方案,而另一种方案为在前一种方案的基础上加入确定性集合卡尔曼滤波(确定性合奏卡尔曼滤波器,DEnKF),同化更多海洋观测数据的EnKF方案。主要结论为,利用EnKF方案可显著提高对“Blob”期间次表层温度异常预测的准确性,这主要源于EnKF方案在预测次表层的水平和垂直热传输方面表现出色。该研究有助于更好地理解海洋热浪事件潜在物理机制及其季节预测水平.

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引用次数: 0

Impacts of the annual cycle of background SST in the tropical Pacific on the phase and amplitude of ENSO 热带太平洋背景海温年周期对厄尔尼诺/南方涛动相位和振幅的影响

IF 2.3 4区地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES

Atmospheric and Oceanic Science Letters

Pub Date : 2025-01-01 DOI: 10.1016/j.aosl.2024.100496

Song Jiang , Congwen Zhu , Ning Jiang

The dominant annual cycle of sea surface temperature (SST) in the tropical Pacific exhibits an antisymmetric mode, which explains 83.4% total variance, and serves as a background of El Niño–Southern Oscillation (ENSO). However, there is no consensus yet on its anomalous impacts on the phase and amplitude of ENSO. Based on data during 1982–2022, results show that anomalies of the antisymmetric mode can affect the evolution of ENSO on the interannual scale via Bjerknes feedback, in which the positive (negative) phase of the antisymmetric mode can strengthen El Niño (La Niña) in boreal winter via an earlier (delayed) seasonal cycle transition and larger (smaller) annual mean. The magnitude of the SST anomalies in the equatorial eastern Pacific can reach more than ±0.3℃, regulated by the changes in the antisymmetric mode based on random sensitivity analysis. Results reveal the spatial pattern of the annual cycle associated with the seasonal phase-locking of ENSO evolution and provide new insight into the impact of the annual cycle of background SST on ENSO, which possibly carries important implications for forecasting ENSO.

摘要

基于1982–2022年资料分析, 本文发现, 热带太平洋海温年循环的主导模态为反对称模式, 是ENSO发展的背景场. 然而, 其对ENSO相位和振幅的异常影响尚未可知. 反对称模态异常可以通过Bjerknes反馈影响ENSO的年际变化, 其正 (负) 异常可以通过更早 (更晚) 的季节循环转变时间和更大 (更小) 的年平均值增强冬季El Niño (La Niña) 的强度. 根据随机敏感性实验分析, 与反对称模态变化有关的赤道中东太平洋海温异常可达±0.3℃以上. 研究结果为背景海温年循环对ENSO的影响提供了新的见解, 这可能对ENSO的预测具有重要意义.

热带太平洋海表温度（SST）的主导年周期表现为反对称模式，解释了83.4%的总方差，并为El Niño-Southern涛动（ENSO）提供了背景。然而，其对ENSO相位和振幅的异常影响尚未达成共识。基于1982—2022年的数据，结果表明，反对称模态异常通过Bjerknes反馈影响ENSO的年际演变，其中反对称模态的正（负）相位通过更早（更晚）的季节周期转变和更大（更小）的年平均值增强了北方冬季El Niño （La Niña）。根据随机敏感性分析，赤道东太平洋海温异常的幅度可达±0.3℃以上，受反对称模态变化的调节。结果揭示了ENSO演变的年周期空间格局与季节锁相关系，为背景海温年周期对ENSO的影响提供了新的认识，这可能对ENSO的预测具有重要意义。摘要基于1982 - 2022年资料分析,本文发现,热带太平洋海温年循环的主导模态为反对称模式,是ENSO发展的背景场。【中文译文】反对称模态异常可以通过Bjerknes反馈影响ENSO的年际变化,其正(负)异常可以通过更早(更晚)的季节循环转变时间和更大(更小)的年平均值增强冬季厄尔尼诺(拉尼娜)的强度。±0.3℃。“”“”“”“”“”“”“”“”“”

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引用次数: 0

Application of the improved dung beetle optimizer, muti-head attention and hybrid deep learning algorithms to groundwater depth prediction in the Ningxia area, China 改进的蜣螂优化器、多头注意力和混合深度学习算法在中国宁夏地区地下水深度预测中的应用

IF 2.3 4区地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES

Atmospheric and Oceanic Science Letters

Pub Date : 2025-01-01 DOI: 10.1016/j.aosl.2024.100497

Jiarui Cai , Bo Sun , Huijun Wang , Yi Zheng , Siyu Zhou , Huixin Li , Yanyan Huang , Peishu Zong

Due to the lack of accurate data and complex parameterization, the prediction of groundwater depth is a challenge for numerical models. Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas. In this study, two new models are applied to the prediction of groundwater depth in the Ningxia area, China. The two models combine the improved dung beetle optimizer (DBO) algorithm with two deep learning models: The Multi-head Attention–Convolution Neural Network–Long Short Term Memory networks (MH-CNN-LSTM) and the Multi-head Attention–Convolution Neural Network–Gated Recurrent Unit (MH-CNN-GRU). The models with DBO show better prediction performance, with larger R (correlation coefficient), RPD (residual prediction deviation), and lower RMSE (root-mean-square error). Compared with the models with the original DBO, the R and RPD of models with the improved DBO increase by over 1.5%, and the RMSE decreases by over 1.8%, indicating better prediction results. In addition, compared with the multiple linear regression model, a traditional statistical model, deep learning models have better prediction performance.

摘要

本研究将两个新模型应用于位于中国西北干旱半干旱区的宁夏地区地下水深度预测. 这两个模型将改进的蜣螂优化 (DBO) 算法与两个深度学习模型相结合, 即多头注意力-卷积神经网络-长短期记忆网络和多头注意力-回旋神经网络-门控递归单元. 带有DBO的模型预测结果表现出更大的相关系数 (R) , 残差预测偏差 (RPD) 和较低的均方根误差 (RMSE) , 预测结果更好. 此外, 与DBO模型相比, 改进后的DBO模型的R和RPD增加了1.5%以上, RMSE降低了1.8%以上, 表明预测结果更好. 与传统的统计模型多元线性回归模型相比, 深度学习模型具有更好的预测性能.

由于缺乏准确的数据和复杂的参数化，地下水深度的预测对数值模型来说是一个挑战。机器学习可以有效地解决这一问题，并在许多地区的地下水深度预测中被证明是有用的。本文将两个新模型应用于宁夏地区地下水深度的预测。这两种模型将改进的蜣螂优化器（DBO）算法与多头注意卷积神经网络-长短期记忆网络（MH-CNN-LSTM）和多头注意卷积神经网络-门控循环单元（MH-CNN-GRU）两种深度学习模型相结合。DBO模型预测效果较好，相关系数R、残差预测偏差RPD较大，均方根误差RMSE较低。与原始DBO模型相比，改进DBO模型的R和RPD提高了1.5%以上，RMSE降低了1.8%以上，表明预测效果较好。此外，与传统的统计模型多元线性回归模型相比，深度学习模型具有更好的预测性能。这两个模型将改进的蜣螂优化(DBO)算法与两个深度学习模型相结合,即多头注意力——卷积神经网络——长短期记忆网络和多头注意力——回旋神经网络——门控递归单元。带有DBO的模型预测结果表现出更大的相关系数(R),残差预测偏差(RPD)和较低的均方根误差(RMSE),预测结果更好。此外,与DBO模型相比,改进后的DBO模型的R和RPD增加了1.5%以上,RMSE降低了1.8%以上,表明预测结果更好。与传统的统计模型多元线性回归模型相比, 深度学习模型具有更好的预测性能.

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引用次数: 0

Arctic sea-ice extent: No record minimum in 2023 or recent years 北极海冰范围：2023 年或近年不会出现最低纪录

IF 2.3 4区地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES

Atmospheric and Oceanic Science Letters

Pub Date : 2025-01-01 DOI: 10.1016/j.aosl.2024.100499

Ola M. Johannessen , Tor I. Olaussen

Arctic sea-ice extent reaches its minimum each year in September. On 11 September 2023 the minimum was 4.969 million square kilometers (mill.km²). This was not a record low, which occurred in 2012, when the minimum was 4.175 mill.km², 0.794 mill.km² less than the minimum in 2023. However, the ice extent had decreased by 0.432 mill.km² compared with 2022. Nevertheless, the summer melting in 2023 was remarkably less than expected when considering the strong heat waves in the atmosphere and ocean, with record temperatures set around the world. In general, there is a high correlation between the long-term decrease in sea-ice extent and the increasing CO₂ in the atmosphere, where the increase of CO₂ in recent decades explains about 80% of the decrease in sea ice in September, while the remainder is caused by natural variability.

北极海冰面积在每年9月达到最低点。2023年9月11日的最小值为496.9万平方公里。这并不是2012年的最低记录，当时的最低记录是4175万辆。平方公里，0.794平方米。小于2023年的最小值。海冰面积减少了0.432 mm。与2022年相比。然而，考虑到大气和海洋中的强烈热浪，2023年的夏季融化明显低于预期，世界各地都创下了创纪录的温度。总的来说，海冰范围的长期减少与大气中二氧化碳的增加之间存在高度的相关性，其中近几十年来二氧化碳的增加解释了9月份海冰减少的80%左右，而其余部分是由自然变率引起的。

引用次数: 0

Increase in the variability of terrestrial carbon uptake in response to enhanced future ENSO modulation 未来厄尔尼诺/南方涛动调制增强时陆地碳吸收变化的增加

IF 2.3 4区地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES

Atmospheric and Oceanic Science Letters

Pub Date : 2025-01-01 DOI: 10.1016/j.aosl.2024.100508

Younong Li , Li Dan , Jing Peng , Qidong Yang , Fuqiang Yang

El Niño–Southern Oscillation (ENSO) is a major driver of climate change in middle and low latitudes and thus strongly influences the terrestrial carbon cycle through land–air interaction. Both the ENSO modulation and carbon flux variability are projected to increase in the future, but their connection still needs further investigation. To investigate the impact of future ENSO modulation on carbon flux variability, this study used 10 CMIP6 earth system models to analyze ENSO modulation and carbon flux variability in middle and low latitudes, and their relationship, under different scenarios simulated by CMIP6 models. The results show a high consistency in the simulations, with both ENSO modulation and carbon flux variability showing an increasing trend in the future. The higher the emissions scenario, especially SSP5-8.5 compared to SSP2-4.5, the greater the increase in variability. Carbon flux variability in the middle and low latitudes under SSP2-4.5 increases by 30.9% compared to historical levels during 1951–2000, while under SSP5-8.5 it increases by 58.2%. Further analysis suggests that ENSO influences mid- and low-latitude carbon flux variability primarily through temperature. This occurrence may potentially be attributed to the increased responsiveness of gross primary productivity towards regional temperature fluctuations, combined with the intensified influence of ENSO on land surface temperatures.

摘要

ENSO是中低纬度地区气候系统的主要驱动因素, 对陆地碳循环有重要影响. 本研究基于10个CMIP6地球系统模式, 分析了不同情景下ENSO变率与中低纬度地区总初级生产力变率的关系. 结果显示, 未来ENSO变率和总初级生产力变率在未来多数模式均显示为增加. 在未来情境下(2051-2100年), 中低纬度地区的总初级生产力变率较历史时期(1951–2000年)增加了30.9%(SSP2-4.5), 58.2%(SSP5-8.5). 进一步分析表明, ENSO主要通过温度影响中低纬度碳通量变率. 这种现象可能归因于总初级生产力对温度的响应增强, 以及ENSO对陆地表面温度的影响.

El Niño-Southern涛动（ENSO）是中低纬度地区气候变化的主要驱动因素，通过陆气相互作用对陆地碳循环产生强烈影响。预计ENSO调制和碳通量变率在未来都将增加，但它们之间的联系仍需要进一步研究。为探讨未来ENSO调制对碳通量变率的影响，本研究利用10个CMIP6地球系统模式，分析了不同情景下ENSO调制与中低纬度地区碳通量变率的关系。模拟结果显示出较高的一致性，ENSO调制和碳通量变率在未来都呈现出增加的趋势。排放情景越高，特别是SSP5-8.5与SSP2-4.5相比，变率的增加越大。1951-2000年，在SSP2-4.5条件下，中低纬度地区的碳通量变率比历史水平增加了30.9%，而在SSP5-8.5条件下，碳通量变率增加了58.2%。进一步分析表明，ENSO主要通过温度影响中低纬度碳通量变率。这种现象可能归因于总初级生产力对区域温度波动的响应性增强，以及ENSO对陆地表面温度的影响加剧。。本研究基于10个CMIP6地球系统模式,分析了不同情景下ENSO变率与中低纬度地区总初级生产力变率的关系。“”“”“”“”“”“”在未来情境下(2051 - 2100年),中低纬度地区的总初级生产力变率较历史时期(1951 - 2000年)增加了30.9% (ssp2 - 4.5)、58.2% (ssp5 - 8.5)。http://www.tingclass.ac.cn/cn/或http://www.tingclass.ac.cn/cn/中文意思是：“我的意思是我的意思是我的意思是我的意思是我的意思是我的意思。”

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引用次数: 0

Role of the Greenland Sea ice anomaly in the late-spring drought over Northwest China 格陵兰海冰异常在中国西北晚春干旱中的作用

IF 2.3 4区地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES

Atmospheric and Oceanic Science Letters

Pub Date : 2025-01-01 DOI: 10.1016/j.aosl.2024.100488

Yang Liu , Huopo Chen

Drought across Northwest China in late spring has exerted a vital effect on the local climate and agricultural production, and has been alleviated during the past decades. This study explored the influence of the preceding Arctic sea ice on the May drought in Northwest China caused by the precipitation deficit. Further analysis indicated that when the Greenland Sea ice concentration is abnormally high during February to April, the dry conditions in Northwest China tend to be alleviated. The increase of sea ice in the Greenland Sea can excite a meridional circulation, which causes sea surface temperature (SST) anomalies in the North Atlantic via the sea–air interaction, manifested as significant warm SST anomalies over the south of Greenland and the subtropical North Atlantic, but negative SST anomalies over the west of the Azores. This abnormal SST pattern maintains to May and triggers a zonal wave train from the North Atlantic through Scandinavia and Central Asia to Northwest China, leading to abnormal cyclones in Northwest China. Consequently, Northwest China experiences a more humid climate than usual.

摘要

晚春西北地区干旱的发生对西北地区气候和农业生产等具有重要的影响, 但在近几十年, 西北干旱状况呈现出缓解的趋势. 本文研究了前期北极海冰异常对5月中国西北地区 (降水短缺引起的) 干旱异常的影响. 进一步研究表明, 二至四月格陵兰海海冰偏多时, 西北地区干旱有所缓解. 偏多的格陵兰海海冰可激发出经向环流异常, 环流异常通过海–气相互作用在北大西洋产生海温异常, 主要表现为格陵兰岛以南和北大西洋副热带海温偏高, 亚速尔群岛以西海温偏低. 这种海温异常可持续到5月, 并引发从北大西洋经斯堪的纳维亚半岛和中亚至中国西北地区的纬向波列, 并在西北地区产生气旋环流异常, 从而导致该地区水汽辐合, 干旱状况有所缓解.

中国西北地区晚春干旱对当地气候和农业生产产生了重要影响，并在过去几十年中得到了缓解。本研究探讨了前期北极海冰对降水亏缺引起的西北地区5月干旱的影响。进一步分析表明，当2 ~ 4月格陵兰海冰浓度异常高时，西北地区干旱状况趋于缓解。格陵兰海海冰的增加激发了经向环流，经向环流通过海气相互作用引起北大西洋海温异常，表现为格陵兰岛南部和北大西洋副热带海温异常明显，而亚速尔群岛西部海温异常为负。这种海温异常型持续到5月，并触发北大西洋经斯堪的纳维亚和中亚至中国西北的纬向波列，导致中国西北出现异常气旋。因此，中国西北地区的气候比平时更加潮湿。摘要晚春西北地区干旱的发生对西北地区气候和农业生产等具有重要的影响, 但在近几十年, 西北干旱状况呈现出缓解的趋势. 本文研究了前期北极海冰异常对5月中国西北地区 (降水短缺引起的) 干旱异常的影响. 进一步研究表明, 二至四月格陵兰海海冰偏多时, 西北地区干旱有所缓解. 偏多的格陵兰海海冰可激发出经向环流异常, 环流异常通过海–气相互作用在北大西洋产生海温异常, 主要表现为格陵兰岛以南和北大西洋副热带海温偏高, 亚速尔群岛以西海温偏低. 这种海温异常可持续到5月, 并引发从北大西洋经斯堪的纳维亚半岛和中亚至中国西北地区的纬向波列, 并在西北地区产生气旋环流异常, 从而导致该地区水汽辐合, 干旱状况有所缓解.

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