Atmospheric and Oceanic Science Letters最新文献

{"title":"Linkage between the Asian-Pacific Oscillation and winter precipitation over southern China: CMIP6 simulation and projection","authors":"Qiwei Fan ,&nbsp;Botao Zhou","doi":"10.1016/j.aosl.2023.100401","DOIUrl":"10.1016/j.aosl.2023.100401","url":null,"abstract":"<div><p>Based on the simulations of 30 CMIP6 models, this paper evaluates their performance in simulating the linkage between the winter Asian-Pacific Oscillation (APO) and precipitation over southern China (SC). Results show that 12 out of the 30 models can reproduce well the observed inverse relationship featuring a positive APO phase corresponding to a decrease in SC precipitation. Associated with the positive APO phase, an anomalous anticyclonic circulation dominates the southern part of Asia in the upper troposphere, and an anomalous cyclonic circulation prevails particularly in the lower troposphere of the South China Sea and the Malay Archipelago. Accordingly, the East Asian westerly jet (EAWJ) shifts northward, and low-level northeasterly anomalies appear over SC, which yield anomalous descending motion and water vapor flux divergence in SC, respectively, hence decreasing the in-situ precipitation. Using the ensemble of the 12 models, the future relationship between the winter APO and SC precipitation under the SSP5-8.5 scenario was further projected. The projection indicates that the APO connection with SC precipitation will still be significant, but weakened slightly, during the second half of the 21st century as compared to the present. Such a weakening may result from the weaker linkage between SC precipitation and the meridional displacement of the EAWJ.</p></div>","PeriodicalId":47210,"journal":{"name":"Atmospheric and Oceanic Science Letters","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49445854","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":"Enhanced seasonality of surface air temperature over China during the mid-Holocene","authors":"Zhiping Tian,&nbsp;Dabang Jiang","doi":"10.1016/j.aosl.2023.100393","DOIUrl":"10.1016/j.aosl.2023.100393","url":null,"abstract":"<div><p>Using all available simulations performed by climate models participating in PMIP4 (Paleoclimate Modelling Intercomparison Project – Phase 4), the authors quantify the seasonality change of surface air temperature over China during the mid-Holocene (6000 years ago) and the associated physical mechanisms. Relative to the preindustrial period, all 16 models consistently show an enhanced temperature seasonality (i.e., summer minus winter temperature) across China during that interglacial period, with a nationally averaged enhancement of 2.44 °C or 9% for the multimodel mean. The temperature seasonality change is closely related with the seasonal contrast variation of surface energy fluxes mainly due to the mid-Holocene orbital forcing. Specifically, the summer–winter increase in surface net shortwave radiation dominates the intensified temperature seasonality at the large scale of China during the mid-Holocene; the surface net longwave radiation has a minor positive contribution in most of the Tibetan Plateau and eastern China; and both the surface latent and sensible heat fluxes show partial offset effects in most of the country. There are uncertainties in the reconstructed temperature seasonality over China during the mid-Holocene based on the proxy data that can reflect seasonal signals.</p><p>摘要</p><p>利用PMIP4多模式试验数据, 作者量化了中全新世 (距今约6000年) 中国温度季节性变化. 结果表明: 相对于工业革命前期, 所有16个模式一致模拟显示中全新世我国温度季节性 (即夏季与冬季温差) 增强, 平均增幅9%; 这与该时期轨道强迫引起的地表能量通量的季节对比变化密切相关, 其中净短波辐射起主导作用, 净长波辐射作用次之, 感热和潜热为负贡献; 与模拟不同, 重建结果存在不确定性.</p></div>","PeriodicalId":47210,"journal":{"name":"Atmospheric and Oceanic Science Letters","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48357010","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":"Subseasonal transition of Barents–Kara sea-ice anomalies in winter related to the reversed warm Arctic–cold Eurasia pattern","authors":"Yijia Zhang ,&nbsp;Zhicong Yin ,&nbsp;Huijun Wang","doi":"10.1016/j.aosl.2023.100392","DOIUrl":"10.1016/j.aosl.2023.100392","url":null,"abstract":"<div><p>Subseasonal reversal of the warm Arctic–cold Eurasia pattern (WACE) could trigger an extreme cold/warm transition in winter and sandstorms in spring over eastern China. An associated subseasonal transition of the sea-ice anomaly also occurs in the Barents–Kara seas (BKS) driven by such remarkable high-latitude atmospheric pattern reversals. Under a warm Arctic and enhanced Ural high, abnormal downward turbulent heat flux and increased downward infrared radiation in the BKS are conducive to sea ice melting. The surface southerly wind drives the sea ice to drift from the thin to perennial ice area and further enlarges the open ocean surface. The opposite mechanism occurs in the opposite phase of WACE, causing positive BKS sea-ice anomalies. When WACE reverses on the subseasonal scale, the above mechanisms occur in early and late winter, respectively, resulting in a significant subseasonal transition of BKS sea-ice anomalies. More importantly, in the last decade, with a more frequent reversal of WACE, the subseasonal transition between early winter and late winter in BKS sea ice has enhanced. The findings of this study establish a comprehensive schematic of the subseasonal reversal of WACE and contribute to better understanding and predicting extreme climate in eastern China.</p></div>","PeriodicalId":47210,"journal":{"name":"Atmospheric and Oceanic Science Letters","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46278742","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":"More extreme-heat occurrences related to humidity in China","authors":"Wenyue He ,&nbsp;Huopo Chen","doi":"10.1016/j.aosl.2023.100391","DOIUrl":"10.1016/j.aosl.2023.100391","url":null,"abstract":"<div><p>The co-occurrence of day and night compound heat extremes has attracted much attention because of the amplified socioeconomic and human health impacts. Based on ERA5 hourly reanalysis data, this study characterized and compared extreme day–night compound humid-heat/high-temperature events (CHHEs/CHTEs) in China as well as the associated impacts. Results indicated that the spatial patterns of summer mean extreme CHHEs are consistent with those of extreme CHTEs, except in northwestern China. A greater magnitude of these two types of events dominates over southern China, but the high-frequency centers are mainly observed over northern China. Significant increasing trends in frequency are captured nationwide, but with much stronger trends detected in northern and western China. Further analysis shows that the anomalies of humidity play a more important role than those of temperature in the occurrence of extreme CHHEs in most parts of China, but particularly in eastern regions. Since 1961, the human population and land areas of China have experienced strongly increasing compound heat extremes, with a faster rate of exposure to extreme CHHEs than to extreme CHTEs. This study highlights the importance of understanding regional changes in humidity when considering heat stress in the future.</p></div>","PeriodicalId":47210,"journal":{"name":"Atmospheric and Oceanic Science Letters","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47344271","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":"Impact of the Asian–Pacific Oscillation on early autumn precipitation over Southeast China: CMIP6 evaluation and projection","authors":"Changji Xia ,&nbsp;Wei Hua ,&nbsp;Yu Zhang ,&nbsp;Guangzhou Fan","doi":"10.1016/j.aosl.2023.100389","DOIUrl":"10.1016/j.aosl.2023.100389","url":null,"abstract":"<div><p>This study evaluated the capability of 32 models of phase 6 of the Coupled Model Intercomparison Project in modeling the influence of the preceding August Asian–Pacific Oscillation (APO) on subsequent early autumn (September) precipitation over Southeast China and associated atmospheric anomalies, as well as its future projection during 2021–2040 (near-term), 2041–2060 (mid-term), and 2081–2100 (long-term) under different Shared Socioeconomic Pathways (SSPs: SSP2-4.5 and SSP5-8.5). Results indicated that two-thirds of the individual models yielded positive correlations between the APO and Southeast China precipitation that conformed to the observations. On the basis of the capability to reproduce the significantly positive relationship between the APO and Southeast China precipitation, three models were chosen as the “best” model ensemble (BMME). The BMME effectively simulated both the APO-associated precipitation and the atmospheric anomalies, and outperformed the ensemble of the remaining 29 models in terms of the positive correlation between the APO and Southeast China precipitation, and the negative correlations between the meridional displacement of the East Asian jet (EAJ) and the APO and Southeast China precipitation. In general, during three future time periods under both SSPs, the BMME projected persistent negative correlations between the APO and EAJ, and the APO–Southeast China precipitation and EAJ–Southeast China precipitation relationships were projected to weaken. However, considerable discrepancies were evident among the changes projected by the individual models; only the projected changes in the APO–EAJ relationship showed good model agreement.</p><p>摘要</p><p>本文对32个CMIP6模式对8月亚洲–太平洋涛动 (APO) 与我国东南初秋 (9月) 降水及大气环流联系的模拟能力进行了评估, 并就SSP2-4.5和SSP5-8.5情景下, 未来2021–2040年 (近期) , 2041–2060 (中期) 和2081–2100 (长期) 期间二者联系的变化进行了预估. 基于模式对APO与我国东南初秋降水之间显著正相关关系的再现能力, 选取3个模式作为“最优”模式集合 (BMME) . 研究表明, BMME较好地模拟了与APO相关的我国东南初秋降水和大气环流异常, 且在再现APO与我国东南部降水的正相关关系, 以及东亚高空急流 (EAJ) 经向位移与APO和我国东南部初秋降水之间的负相关关系方面均优于单个模式. 总体而言, 未来不同SSP情景下尽管APO与EAJ之间仍呈负相关关系, 但APO与我国东南初秋降水以及EAJ与我国东南降水的关系将呈减弱确实. 此外, 不同模式预估结果之间存在明显差异, 仅对未来APO-EAJ关系的预估表现出较好的一致性.</p></div>","PeriodicalId":47210,"journal":{"name":"Atmospheric and Oceanic Science Letters","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44272923","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 machine learning approach to quality-control Argo temperature data","authors":"Qi Zhang ,&nbsp;Chenyan Qian ,&nbsp;Changming Dong","doi":"10.1016/j.aosl.2022.100292","DOIUrl":"10.1016/j.aosl.2022.100292","url":null,"abstract":"<div><p>A machine learning approach is proposed to identify temperature outliers from Argo float profiles as a complementary procedure to current Argo quality control. A machine learning unsupervised classification (i.e., the Gaussian mixture model, GMM) is applied to cluster the Argo data into classes to construct convex hulls with the smallest polygons encompassing all the data points. Good or bad temperature data are identified as within or outside the polygons based on point-in-polygon analysis implemented by the ray casting algorithm. The South China Sea was selected as an example and results showed that the proposed approach could identify more than 70% of the profiles containing the outliers and mark the outliers automatically at the same time. This highlights the potential of the proposed methodology to be a good complementary quality control method.</p><p>摘要</p><p>本文提出了一种基于机器学习的Argo浮标温度异常值检测方法. 该方法采用机器学习无监督算法高斯混合模型对Argo浮标数据进行聚类分析, 并构建包围所有数据点的最小多边形的凸包. 基于射线投影算法实现点在多边形内分析, 通过自动识别数据点位于凸包内外来判断该数据点数据质量的好坏. 本文采用南海区域Argo浮标数据对该方法进行测试, 结果表明该方法可以识别70%以上的包含异常值的温度剖面, 同时自动标记出各异常值点.</p></div>","PeriodicalId":47210,"journal":{"name":"Atmospheric and Oceanic Science Letters","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45689706","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":"U-Net: A deep-learning method for improving summer precipitation forecasts in China","authors":"Qimin Deng ,&nbsp;Peirong Lu ,&nbsp;Shuyun Zhao ,&nbsp;Naiming Yuan","doi":"10.1016/j.aosl.2022.100322","DOIUrl":"10.1016/j.aosl.2022.100322","url":null,"abstract":"<div><p>A deep-learning method named U-Net was applied to improve the skill in forecasting summer (June–August) precipitation for at a one-month lead during the period 1981–2020 in China. The variables of geopotential height, soil moisture, sea level pressure, sea surface temperature, ocean salinity, and snow were considered as the model input to revise the seasonal prediction of the Climate Forecast System, version 2 (CFSv2). Results showed that on average U-Net reduced the root-mean-square error of the original CFSv2 prediction by 49.7% and 42.7% for the validation and testing set, respectively. The most improved areas were Northwest, Southwest, and Southeast China. The anomaly same sign percentages and temporal and spatial correlation coefficients did not present significant improvement but maintained the comparable performances of CFSv2. Sensitivity experiments showed that soil moisture is the most crucial factor in predicting summer rainfall in China, followed by geopotential height. Due to its advantages in handling small training dataset sizes, U-Net is a promising deep-learning method for seasonal rainfall prediction.</p><p>摘要</p><p>本研究应用了名为U-Net的深度学习方法来提高中国夏季 (6–8月) 降水的预报技能, 预报时段为1981–2020年, 预报提前期为一个月. 将位势高度场, 土壤湿度, 海平面气压, 海表面温度, 海洋盐度和青藏高原积雪等变量作为模型输入, 本文对美国NCAR气候预报系统第2版 (CFSv2) 的季节性预报结果进行了修正. 结果显示, 在验证集和测试集上, U-Net平均将原CFSv2预测的均方根误差分别减少了49.7%和42.7%. 预报结果改善最大的地区是中国的西北,西南和东南地区. 然而, 同号率和时空相关系数没有得到明显改善, 但仍与CFSv2的预测技巧持平. 敏感性实验表明, 土壤湿度是预测中国夏季降雨的最关键因素, 其次是位势高度场. 本研究显示了U-Net模型在训练小样本数据集方面的优势, 为我国汛期季节性降雨预测提供了一种有效的深度学习方法.</p></div>","PeriodicalId":47210,"journal":{"name":"Atmospheric and Oceanic Science Letters","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49004690","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 spatiotemporal 3D convolutional neural network model for ENSO predictions: A test case for the 2020/21 La Niña conditions","authors":"Lu Zhou ,&nbsp;Chuan Gao ,&nbsp;Rong-Hua Zhang","doi":"10.1016/j.aosl.2023.100330","DOIUrl":"10.1016/j.aosl.2023.100330","url":null,"abstract":"<div><p>Many coupled models are unable to accurately depict the multi-year La Niña conditions in the tropical Pacific during 2020–22, which poses a new challenge for real-time El Niño–Southern Oscillation (ENSO) predictions. Yet, the corresponding processes responsible for the multi-year coolings are still not understood well. In this paper, reanalysis products are analyzed to examine the ocean–atmosphere interactions in the tropical Pacific that have led to the evolution of sea surface temperature (SST) in the central-eastern equatorial Pacific, including the strong anomalous southeasterly winds over the southeastern tropical Pacific and the related subsurface thermal anomalies. Meanwhile, a divided temporal and spatial (TS) 3D convolution neural network (CNN) model, named TS-3DCNN, was developed to make predictions of the 2020/21 La Niña conditions; results from this novel data-driven model are compared with those from a physics-based intermediate coupled model (ICM). The prediction results made using the TS-3DCNN model for the 2020–22 La Niña indicate that this deep learning–based model can capture the two-year La Niña event to some extent, and is comparable to the IOCAS ICM; the latter dynamical model yields a successful real-time prediction of the Niño3.4 SST anomaly in late 2021 when it is initiated from early 2021. For physical interpretability, sensitivity experiments were designed and carried out to confirm the dominant roles played by the anomalous southeasterly wind and subsurface temperature fields in sustaining the second-year cooling in late 2021. As a potential approach to improving predictions for diversities of ENSO events, additional studies on effectively combining neural networks with dynamical processes and mechanisms are expected to significantly enhance the ENSO prediction capability.</p><p>摘要</p><p>2020–22年间热带太平洋经历了持续性多年的拉尼娜事件, 多数耦合模式都难以准确预测其演变过程, 这为厄尔尼诺-南方涛动(ENSO)的实时预测带来了很大的挑战. 同时, 目前学术界对此次持续性双拉尼娜事件的发展仍缺乏合理的物理解释, 其所涉及的物理过程和机制有待于进一步分析. 本研究利用再分析数据产品分析了热带东南太平洋东南风异常及其引起的次表层海温异常在此次热带太平洋海表温度(SST)异常演变中的作用, 并构建了一个时空分离(Time-Space)的三维(3D)卷积神经网络模型(TS-3DCNN)对此次双拉尼娜事件进行实时预测和过程分析. 通过将TS-3DCNN与中国科学院海洋研究所(IOCAS)中等复杂程度海气耦合模式(IOCAS ICM)的预测结果对比, 表明TS-3DCNN模型对2020–22年双重拉尼娜现象的预测能力与IOCAS ICM相当, 二者均能够从2021年初的初始场开始较好地预测2021年末 El Niño3.4区SST的演变. 此外, 基于TS-3DCNN和IOCAS ICM的敏感性试验也验证了赤道外风场异常和次表层海温异常在2021年末赤道中东太平洋海表二次变冷过程中的关键作用. 未来将神经网络与动力 模式模式间的有效结合, 进一步发展神经网络与物理过程相结合的混合建模是进一步提高ENSO事件预测能力的有效途径.</p></div>","PeriodicalId":47210,"journal":{"name":"Atmospheric and Oceanic Science Letters","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48586843","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":"Wave forecast in the Atlantic Ocean using a double-stage ConvLSTM network","authors":"Lin Ouyang ,&nbsp;Fenghua Ling ,&nbsp;Yue Li ,&nbsp;Lei Bai ,&nbsp;Jing-Jia Luo","doi":"10.1016/j.aosl.2023.100347","DOIUrl":"10.1016/j.aosl.2023.100347","url":null,"abstract":"<div><p>Accurate forecasting of ocean waves is of great importance to the safety of marine transportation. Despite wave forecasts having been improved, the current level of prediction skill is still far from satisfactory. Here, the authors propose a new physically informed deep learning model, named Double-stage ConvLSTM (D-ConvLSTM), to improve wave forecasts in the Atlantic Ocean. The waves in the next three consecutive days are predicted by feeding the deep learning model with the observed wave conditions in the preceding two days and the simultaneous ECMWF Reanalysis v5 (ERA5) wind forcing during the forecast period. The prediction skill of the <span>d</span>-ConvLSTM model was compared with that of two other forecasting methods—namely, the wave persistence forecast and the original ConvLSTM model. The results showed an increasing prediction error with the forecast lead time when the forecasts were evaluated using ERA5 reanalysis data. The <span>d</span>-ConvLSTM model outperformed the other two models in terms of wave prediction accuracy, with a root-mean-square error of lower than 0.4 m and an anomaly correlation coefficient skill of ∼0.80 at lead times of up to three days. In addition, a similar prediction was generated when the wind forcing was replaced by the IFS forecasted wind, suggesting that the <span>d</span>-ConvLSTM model is comparable to the Wave Model of European Centre for Medium-Range Weather Forecasts (ECMWF-WAM), but more economical and time-saving.</p><p>摘要</p><p>海浪预报对海上运输安全至关重要. 本研究提出了一种涵盖物理信息的深度学习模型Double-stage ConvLSTM (D-ConvLSTM) 以改进大西洋的海浪预报. 将D-ConvLSTM模型与海浪持续性预测和原始ConvLSTM模型的预测技巧进行对比. 结果表明, 预测误差随着预测时长的增加而增加. D-ConvLSTM模型在预测准确度方面优于前二者, 且第三天预测的均方根误差低于0.4 m, 距平相关系数约在0.8. 此外, 当使用IFS预测风替代再分析风时, 能够产生相似的预测效果. 这表明D-ConvLSTM模型的预测能力能够与ECMWF-WAM模式相当, 且更节省计算资源和时间.</p></div>","PeriodicalId":47210,"journal":{"name":"Atmospheric and Oceanic Science Letters","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44406695","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":"3D DBSCAN detection and parameter sensitivity of the 2022 Yangtze river summertime heatwave and drought","authors":"Zhenchen Liu ,&nbsp;Wen Zhou ,&nbsp;Yuan Yuan","doi":"10.1016/j.aosl.2022.100324","DOIUrl":"10.1016/j.aosl.2022.100324","url":null,"abstract":"<div><p>Spatially and temporally accurate event detection is a precondition for exploring the mechanisms of climate extremes. To achieve this, a classical unsupervised machine learning method, the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm, was employed in the present study. Furthermore, the authors developed a 3D (longitude–latitude–time) DBSCAN-based workflow for event detection of targeted climate extremes and associated analysis of parameter sensitivity. The authors applied this 3D DBSCAN-based workflow in the detection of the 2022 summertime Yangtze extreme heatwave and drought based on the ERA5 reanalysis dataset. The heatwave and drought were found to have different development and migration patterns. Synoptic-scale heatwave extremes appeared over the northern Pacific Ocean at the end of June, extended southwestwards, and covered almost the entire Yangtze River Basin in mid-August. By contrast, a seasonal-scale drought occurred in mid-July over the continental area adjacent to the Bay of Bengal, moved northeastwards, and occupied the entire Yangtze River Basin in mid-September. Event detection can provide new insight into climate mechanisms while considering patterns of occurrence, development, and migration. In addition, the authors also performed a detailed parameter sensitivity analysis for better understanding of the algorithm application and result uncertainties.</p><p>摘要</p><p>极端气候事件的精准识别是机理分析的重要前提. 本研究借助无监督机器学习中经典的DBSCAN密度聚类算法, 发展了在三维 (经度-纬度-时间) 空间内进行目标事件识别和参数敏感性分析的研究方案. 在2022年长江全域高温伏秋旱事件识别中的应用表明, 本次天气尺度极端热浪和季节尺度重旱事件的产生发展, 空间传播模式不同. 天气尺度热浪信号自6月底从北太平洋向西南方向延伸, 直至8月中旬覆盖长江全域; 季节重旱信号于7月中旬从孟加拉湾陆面区域向东北向延伸, 直至9月中旬覆盖长江全域. 同时, 本研究中亦进行了相关参数敏感性的详细分析, 对算法应用, 结果理解亦有帮助.</p></div>","PeriodicalId":47210,"journal":{"name":"Atmospheric and Oceanic Science Letters","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45194770","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}