U-Net: A deep-learning method for improving summer precipitation forecasts in China

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.摘要本研究应用了名为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.     

Monthly prediction of tropical cyclone activity over the South China Sea using the FGOALS-f2 ensemble prediction system

本研究基于新一代FGOALS-f2动力集合预测系统35年(1981-2015年)的热带气旋历史回报试验对南海台风季(7-11月)热带气旋活动超前10天的月预测技巧进行评估,并对2020年南海台风季热带气旋活动进行了实时月预测尝试.结果表明:FGOALS-f2能较好地预测南海热带气旋路径密度演变特征,预测的热带气旋生成个...     

A spatiotemporal 3D convolutional neural network model for ENSO predictions: A test case for the 2020/21 La Niña conditions

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.摘要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.     

山东2009年10月降水气候背景及前期海温与大气环流特征

利用1951—2009年北半球500hPa高度、北太平洋海温、环流特征量、降水等资料,采用相关分析、合成分析、经验函数正交分解(EOF)、子波分析等多种统计技术,对影响山东2009年10月降水趋势的各种因素进行分析和研究。结果表明:山东10月降水大致存在3种降水分布型;在不同时间尺度的气候背景上,2009年10月山东基本处于一个少雨或由少雨向多雨转换的气候阶段;2009年春季加利福尼亚冷流的减弱,2009年6月开始的厄尔尼诺事件及6月起西太平洋副高持续的偏强、偏西、正常或偏南状态,各种指标均指示山东10月降水偏少的可能性大,预测与实况基本吻合。     

The exceptionally strong and persistent Arctic stratospheric polar vortex in the winter of 2019–2020: 2019-2020冬季北半球超强极涡事件

The Arctic stratospheric polar vortex was exceptional strong, cold and persistent in the winter and spring of 2019–2020. Based on reanalysis data from the National Centers for Environmental Prediction/National Center for Atmospheric Research and ozone observations from the Ozone Monitoring Instrument, the authors investigated the dynamical variation of the stratospheric polar vortex during winter 2019–2020 and its influence on surface weather and ozone depletion. This strong stratospheric polar vortex was affected by the less active upward propagation of planetary waves. The seasonal transition of the stratosphere during the stratospheric final warming event in spring 2020 occurred late due to the persistence of the polar vortex. A positive Northern Annular Mode index propagated from the stratosphere to the surface, where it was consistent with the Arctic Oscillation and North Atlantic Oscillation indices. As a result, the surface temperature in Eurasia and North America was generally warmer than the climatology. In some places of Eurasia, the surface temperature was about 10 K warmer during the period from January to February 2020. The most serious Arctic ozone depletion since 2004 has been observed since February 2020. The mean total column ozone within 60°–90°N from March to 15 April was about 80 DU less than the climatology.摘要2019-2020冬季北极平流层极涡异常并且持续的偏强,偏冷.利用NCEP再数据和OMI臭氧数据, 本文分析了此次强极涡事件中平流层极涡的动力场演变及其对地面暖冬天气和臭氧低值的影响.此次强极涡的形成是由于上传行星波不活跃.持续的强极涡使得2020年春季的最后增温出现时间偏晚.平流层正NAM指数向下传播到地面, 与地面AO指数和NAO指数相一致, 欧亚大陆和北美地面气温均比气候态偏暖, 在欧亚大陆的一些地区, 2020年1月和2月的气温甚至偏高了10K.2020年2月以来北极臭氧出现了2004年以来的最低值, 2020年3-4月60°–90°N的平均臭氧柱总量比气候态偏低了80DU.     

Subseasonal predictability of the transition of the stratospheric polar vortex: A case study in winter 1987/88

The stratospheric polar vortex (SPV), which is an important factor in subseasonal-to-seasonal climate variability and climateprediction, exhibited a remarkable transition from weak in early winter to strong in late winter in 1987/88 (most significant on the interannual timescale during 1979–2019). Therefore, in this study, the subseasonal predictability of this transition SPV case in 1987/88 was investigated using the hindcasts from a selected model (that of the Japan Meteorological Agency) in the Subseasonal-to-Seasonal Prediction project database. Results indicated that the predictability of both weak and strong SPV stages in winter 1987/88, especially near their peak dates, exhibited large sensitivity to the initial condition, which derived mainly from the sensitivity in capturing the 100-hPa eddy heat flux anomalies. Meanwhile, the key tropospheric precursory systems with respect to the occurrence and predictability of this transition SPV case were investigated. The Eurasian teleconnection wave trains might have been a key precursor for the weak SPV stage, while significant tropospheric precursors for the strong SPV stage were not found in this study. In addition, positive correlation (r = 0.41) existed between the forecast biases of the SPV and the NAO in winter 1987/88, which indicates that reducing the forecast biases of the SPV might help to improve the forecasting of the NAO and tropospheric weather.摘要平流层极涡作为冬季次季节尺度上一个重要的可预测性来源, 其强度在1987/88年冬季表现为1979–2019年最显著的转折, 即在前 (后) 冬极端偏弱 (强). 因此在本文中选取这一个例研究了该年冬季平流层极涡在次季节尺度上的可预测性. 结果表明弱极涡和强极涡事件的预测与模式能否准确预测上传行星波的强度紧密相关. 同时, 发现前期对流层欧亚遥相关波列可能是弱极涡事件发生的关键预兆信号. 此外, 模式对平流层极涡强度和北大西洋涛动预测误差之间存在显著正相关关系, 表明模式减少平流层极涡的预测误差可能可以提高北大西洋涛动及相关对流层气候预测.     

Long-term change in low-cloud cover in Southeast China during cold seasons

Southeast China has comparable stratus cloud to that over the oceans, especially in the cold seasons (winter and spring), and this cloud has a substantial impact on energy and hydrological cycles. However, uncertainties remain across datasets and simulation results about the long-term trend in low-cloud cover in Southeast China, making it difficult to understand climate change and related physical processes. In this study, multiple datasets and numerical simulations were applied to show that low-cloud cover in Southeast China has gone through two stages since 1980—specifically, a decline and then a rise, with the turning point around 2008. The regional moisture transport plays a crucial role in low-cloud cover changes in the cold seasons and is mainly affected by the Hadley Cell in winter and the Walker Circulation in spring, respectively. The moisture transport was not well simulated in CMIP6 climate models, leading to poor simulation of the low-cloud cover trend in these models. This study provides insights into further understanding the regional climate changes in Southeast China.摘要中国东南地区在冬春冷季节盛行低云, 对局地能量平衡和水文循环有重要的作用. 本研究使用多套数据和数值模拟结果, 分析这一地区冷季节内低云云量在1980年至2017年的长期变化. 结果表明, 低云云量经历了先下降后上升的趋势变化, 转折点出现在2008年左右. 局地水汽通量输送在影响低云云量的变化中起着至关重要的作用, 其在冬季和春季分别受到哈德莱环流和沃克环流的影响. CMIP6中的气候模式对水汽通量输送的模拟能力欠佳, 影响了对低云云量的模拟结果.     

Impacts of the stratospheric quasi-biennial oscillation on the tropospheric circulation and climate in the Northeast Asia–North Pacific region in early summer

Previous studies have indicated that the stratospheric quasi-biennial oscillation (QBO) has a global impact on winter weather, but relatively less attention has been paid to its effect in summer. Using ERA5 data, this study reports that the QBO has a significant impact on the tropospheric circulation and surface air temperature (SAT) in the extratropics in Northeast Asia and the North Pacific in early summer. Specifically, a QBO-induced mean meridional circulation prevails from Northeast Asia to the North Pacific in the westerly QBO years, exhibiting westerly anomalies in 20°–35°N and easterly anomalies in 35°–65°N from the lower stratosphere to troposphere. This meridional pattern of zonal wind anomalies can excite positive vorticity and thus lead to anomalous low pressure and cyclonic circulation from Northeast Asia to the North Pacific, which in turn cause northerly wind anomalies and decreased SAT in Northeast Asia in June. Conversely, in the easterly QBO years, the QBO-related circulation and SAT anomalies are generally in an opposite polarity to those in the westerly QBO years. These findings provide new evidence of the impact of the QBO on the extratropical climate, and may benefit the prediction of SAT in Northeast Asia in early summer.摘要本文研究了平流层准两年振荡 (QBO) 对东北亚-北太平洋地区初夏对流层环流和地表气温的影响. 在QBO西风位相年, 东北亚至北太平洋地区存在一支由QBO引发的平均经向环流异常, 该经向环流异常可在东北亚至北太平洋地区激发正涡度, 并形成异常气旋式环流. 气旋左侧出现的异常偏北风导致6月东北亚地表气温下降. QBO东风位相年的结果与西风位相年大致相反. 这些结果为QBO对热带外地区天气,气候的影响提供了新的证据, 并为东北亚初夏地表气温的预测提供了新的线索.     

Strong heatwaves with widespread urban-related hotspots over Africa in 2019

2019 was one of the hottest years in recent decades, with widespread heatwaves over many parts of the world, including Africa. However, as a developing and vulnerable region, the understanding of recent heatwave events in Africa is limited. Here, the authors incorporated different climate datasets, satellite observations, and population estimates to investigate patterns and hotspots of major heatwave events over Africa in 2019. Overall, 2019 was one of the years that experienced the strongest heatwaves in terms of intensity and duration since 1981 in Africa. Heatwave hotspots were clearly identified across western-coastal, northeastern, southern, and equatorial Africa, where major cities and human populations are located. The proportion of urban agglomerations (population) exposed to extreme (99th percentile) heatwaves in the Northern Hemisphere and Southern Hemisphere rose from 4% (5 million people) and 15% (17 million people), respectively, in the baseline period of 1981–2010 to 36% (43 million people) and 57% (53 million people), respectively, in 2019. Heatwave patterns and hotspots in 2019 were related to anomalous seasonal change in atmospheric circulation and above-normal sea surface temperature. Without adaptation to minimize susceptibility to the effects of heatwave events, the risks they pose in populated areas may increase rapidly in Africa.摘要2019 年是近几十年来最热的年份之一, 包括非洲在内的全球许多地区都受到大范围的热浪侵袭. 然而, 非洲作为脆弱的发展中地区, 我们对其近年热浪事件的了解非常有限. 本研究中, 我们结合了不同的气候数据集, 卫星观测资料和人口数据, 研究了 2019 年非洲地区主要热浪事件发生的时空特征和热点分布区. 总体而言, 2019 年是非洲地区自 1981 年以来热浪强度最强, 持续时间最久的年份之一. 在主要城市和人口所在的非洲西海岸, 东北部, 南部和赤道地区是热浪发生的热点区. 位于赤道以北的非洲地区, 暴露于极端 (第 99 个百分位) 热浪的城市人口比例从 1981–2010 年基准期的 4% (500 万人) 上升至2019 年的 36% (4300 万人). 位于赤道以南地区, 暴露于极端热浪的城市人口则从基准期的15% (1700 万人) 上升至57% (5300 万人). 2019 年的热浪时空特征和热点分布与大气环流的季节变化异常和海温的暖异常有关. 如果不及时采取适应措施以尽量减少人口对热浪事件影响的敏感性, 热浪对非洲人口稠密地区构成的风险可能会迅速增加.     

Distribution of lightning spatial modes and climatic causes in China

This paper investigates the distribution of spatial modes of cloud-to-ground (CG) lightning activity across China's land areas during the period 2010–20 and their possible causes based on the CG lightning dataset of the China National Lightning Detection Network. It is found that the first empirical orthogonal function mode (EOF1) occupies 32.86% of the total variance of the summer CG lightning anomaly variation. Also, it exhibits a negative–positive–negative meridional seesaw pattern from north to south. When the SST of the East Pacific and Indian Ocean warms abnormally and the SST of the Northwest Pacific becomes abnormally cold, a cyclonic circulation is stimulated in the Yellow Sea, East China Sea, and tropical West Pacific region of China. As the water vapor continues to move southwards, it converges with the water vapor deriving from the Bay of Bengal in South China, and ascending motion strengthens here, thus enhancing the CG lightning activity of this area. Affected by the abnormal high pressure, the corresponding CG lightning activities in North China and Northeast China are relatively weak. The ENSO phenomenon is the climate driver for the CG lightning activity occurring in land areas of China.摘要本文利用中国气象局国家雷电监测网 (CNLDN) 的地闪观测数据集, 分析了2010–2020年中国陆地区域地闪空间模态分布特征及其可能的气候成因. 研究发现, 夏季地闪第一模态的方差贡献率为32.86%, 其分布从北到南呈现出“−+−”的经向跷跷板模式. 当东太平洋和印度洋的海温异常增暖, 西北太平洋的海温异常变冷时, 在中国黄海, 东海及热带西太平洋地区激发出气旋性环流. 随着水汽南下至华南地区, 与来自孟加拉湾的水汽汇合, 上升运动在此加强, 从而使得该地区的雷电活动增强. 表明厄尔尼诺-南方涛动 (ENSO) 现象, 是发生在中国陆地区域的地闪活动的气候驱动因子.     

Improvement of the global prediction system of the COVID-19 pandemic based on the ensemble empirical mode decomposition (EEMD) and autoregressive moving average (ARMA) model in a hybrid approach 基于集合经验模态分解和自回归-移动平均模型的 COVID-19 流行病全球预测系统预测结果改进

In 2020, the COVID-19 pandemic spreads rapidly around the world. To accurately predict the number of daily new cases in each country, Lanzhou University has established the Global Prediction System of the COVID-19 Pandemic (GPCP). In this article, the authors use the ensemble empirical mode decomposition (EEMD) model and autoregressive moving average (ARMA) model to improve the prediction results of GPCP. In addition, the authors also conduct direct predictions for those countries with a small number of confirmed cases or are in the early stage of the disease, whose development trends of the pandemic do not fully comply with the law of infectious diseases and cannot be predicted by the GPCP model. Judging from the results, the absolute values of the relative errors of predictions in countries such as Cuba have been reduced significantly and their prediction trends are closer to the real situations through the method mentioned above to revise the prediction results out of GPCP. For countries such as El Salvador with a small number of cases, the absolute values of the relative errors of prediction become smaller. Therefore, this article concludes that this method is more effective for improving prediction results and direct prediction.摘要2020年, 新型冠状病毒肺炎 (COVID-19) 在世界范围内迅速传播.为准确预测各国每日新增发病人数, 兰州大学开发了 COVID-19 流行病全球预测系统 (GPCP). 在本文的研究中, 我们使用集合经验模态分解 (EEMD) 模型和自回归-移动平均 (ARMA) 模型对 GPCP 的预测结果进行改进, 并对发病人数较少或处于发病初期, 不完全符合传染病规律, GPCP 模型无法预测的国家进行直接预测.从结果来看, 使用该方法修正预测结果, 古巴等国家预测误差均大幅下降, 且预测趋势更接近真实情况.对于萨尔瓦多等发病人数较少的国家直接进行预测, 相对误差较小, 预测结果较为准确.该方法对于改进预测结果和直接预测均较为有效.     

An overview of soil moisture drought research in China: Progress and perspective

Soil moisture drought (SMD) directly affects agricultural yield and land water resources. Understanding and predicting the occurrences and evolution of SMD are of great importance for a largely agricultural country such as China. Compared to other drought categories, SMD receives less attention due to the lack of long-term soil moisture datasets. In recent decades, SMD research has been greatly developed in China, benefiting from increased ground and satellite measurements along with state-of-the-art land surface models. Here, the authors provide a brief overview of the recent progress in SMD research in China, focus on historical drought identification and its prediction, and then raise some future perspectives. Based on historical SMD studies, drought frequency has increased overall and drought duration has been prolonged since the 1950s for China as a whole, but they both show substantial temporal variations at the regional scale. Research on SMD prediction has mainly relied on the statistical relationship between soil moisture and climate variables. Few studies based on the dynamical approach in seasonal drought prediction have highlighted the importance of initial conditions and atmospheric forcing datasets. Given the importance of SMD in agricultural practice and water resource management in China, it is necessary to emphasize the following: 1) conducting research on multiple time scales (e.g., from days to the centurial time scale) and cross-regional drought identification research; and 2) developing a SMD prediction system that takes advantage of climate prediction systems, land surface models, and multisource soil moisture datasets.摘要论文回顾了中国土壤湿度干旱 (SMD) 历史重建和季节预测研究进展, 并对未来研究进行了展望. 自1950s年代以来, 全国整体干旱频率增加, 持续时间延长, 且有明显区域特征. SMD预测多是利用土壤湿度与气候变量之间的统计关系, 而少量基于动力学方法的干旱预测研究强调了初始条件和大气强迫数据对季节尺度干旱预测的重要性. 本论文提出: 1) 加强多时间尺度, 跨区域的SMD研究; 2)联合气候预测系统, 陆面模式和多源土壤湿度数据研制SMD预测系统.     

A deep learning–based U-Net model for ENSO-related precipitation responses to sea surface temperature anomalies over the tropical Pacific

SST–precipitation feedback plays an important role in ENSO evolution over the tropical Pacific and thus it is critically important to realistically represent precipitation-induced feedback for accurate simulations and predictions of ENSO. Typically, in hybrid coupled modeling for ENSO predictions, statistical atmospheric models are adopted to determine linear precipitation responses to interannual SST anomalies. However, in current coupled climate models, the observed precipitation–SST relationship is not well represented. In this study, a data-driven deep learning-based U-Net model was used to construct a nonlinear response model of interannual precipitation variability to SST anomalies. It was found that the U-Net model outperformed the traditional EOF-based method in calculating the precipitation variability. Particularly over the western-central tropical Pacific, the mean-square error (MSE) of the precipitation estimates in the U-Net model was smaller than that in the EOF model. The performance of the U-Net model was further improved when additional tendency information on SST and precipitation variability was also introduced as input variables, leading to a pronounced MSE reduction over the ITCZ.摘要SST–降水反馈过程在热带太平洋ENSO演变过程中起着重要作用, 能否真实地在数值模式中表征SST–降水年际异常之间的关系及相关反馈过程, 对于准确模拟和预测ENSO至关重要. 例如, 在一些模拟ENSO的混合型耦合模式中, 通常采用大气统计模型 (如经验正交函数; EOF) 来表征降水 (海气界面淡水通量的一个重要分量) 对SST年际异常的线性响应. 然而在当前的耦合模式中, 真实观测到的降水–SST统计关系还不能被很好地再现出来, 从而引起 ENSO模拟误差和不确定性. 在本研究中, 使用基于深度学习的U-Net模型来构建热带太平洋降水异常场对SST年际异常的非线性响应模型. 研究发现: U-Net模型的性能优于传统的基于EOF方法的模型. 特别是在热带西太平洋海区, U-Net模型估算的降水误差远小于EOF模型的模拟. 此外, 当SST和降水异常的趋势信息作为输入变量也被同时引入以进一步约束模式训练时, U-Net模型的性能可以进一步提高, 如能使热带辐合带区域的误差显著降低.     

Interannual variability of the sea surface salinity and its related freshwater flux in the tropical Pacific: A comparison of CMIP5 and CMIP6

This paper assesses the interannual variabilities of simulated sea surface salinity (SSS) and freshwater flux (FWF) in the tropical Pacific from phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). The authors focus on comparing the simulated SSS and FWF responses to El Niño–Southern Oscillation (ENSO) from two generations of models developed by the same group. The results show that CMIP5 and CMIP6 models can perform well in simulating the spatial distributions of the SSS and FWF responses associated with ENSO, as well as their relationship. It is found that most CMIP6 models have improved in simulating the geographical distribution of the SSS and FWF interannual variability in the tropical Pacific compared to CMIP5 models. In particular, CMIP6 models have corrected the underestimation of the spatial relationship of the FWF and SSS variability with ENSO in the central-western Pacific. In addition, CMIP6 models outperform CMIP5 models in simulating the FWF interannual variability (spatial distribution and intensity) in the tropical Pacific. However, as a whole, CMIP6 models do not show improved skill scores for SSS interannual variability, which is due to their overestimation of the intensity in some models. Large uncertainties exist in simulating the interannual variability of SSS among CMIP5 and CMIP6 models and some improvements with respect to physical processes are needed.摘要通过比较CMIP5和CMIP6来自同一个单位两代模式模拟, 表明CMIP5和CMIP6均能较好地模拟出热带太平洋的海表盐度 (SSS) 和淡水通量 (FWF) 对ENSO响应的分布及其响应间的关系. 与CMIP5模式相比, 大部份CMIP6模式模拟的SSS和FWF年际变化分布均呈现改进, 特别是纠正了较低的中西太平洋SSS和FWF变化的空间关系. 但是, 整体上, CMIP6模式模拟的SSS年际变化技巧没有提高, 与SSS年际变率的强度被高估有关. CMIP5和CMIP6模式模拟SSS的年际变化还存在较大的不确定性, 在物理方面需要改进.     

Strengthening the three-dimensional comprehensive observation system of multi-layer interaction on the Tibetan Plateau to cope with the warming and wetting trend

Changes in the water cycle on the Tibetan Plateau (TP) have a significant impact on local agricultural production and livelihoods and its downstream regions. Against the background of widely reported warming and wetting, the hydrological cycle has accelerated and the likelihood of extreme weather events and natural disasters occurring (i.e., snowstorms, floods, landslides, mudslides, and ice avalanches) has also intensified, especially in the high-elevation mountainous regions. Thus, an accurate estimation of the intensity and variation of each component of the water cycle is an urgent scientific question for the assessment of plateau environmental changes. Following the transformation and movement of water between the atmosphere, biosphere and hydrosphere, the authors highlight the urgent need to strengthen the three-dimensional comprehensive observation system (including the eddy covariance system; planetary boundary layer tower; profile measurements of temperature, humidity, and wind by microwave radiometers, wind profiler, and radiosonde system; and cloud and precipitation radars) in the TP region and propose a practical implementation plan. The construction of such a three-dimensional observation system is expected to promote the study of environmental changes and natural hazards prevention.摘要青藏高原的水循环变化对于高原及其下游区域人类的生产生活具有举足轻重的影响. 在高原暖湿化的背景下, 其水文循环加快, 极端天气和自然灾害事件概率增大, 比如, 雪灾, 洪水, 滑坡, 泥石流, 冰崩在山区频发. 因此, 如何准确的估算青藏高原水循环各分量的大小及变化幅度是评估高原环境变化影响亟需解决的科学问题. 根据水在各圈层间转换过程, 我们提出了建立第三极地区 (尤其是复杂山区) 的三维立体多圈层地气相互作用综合观测系统(包括涡动相关系统, 行星边界层塔, 微波辐射计, 风廓线仪和无线电探空系统观测的风温湿廓线及云雨雷达等)的紧迫性和具体方案, 进而为研究青藏高原环境变化和山区灾害预测服务.     

Prediction and mechanistic analysis of May precipitation in North China based on April Indian Ocean SST and the Northwest Pacific Dipole

North China May precipitation (NCMP) accounts for a relatively small percentage of annual total precipitation in North China, but its climate variability is large and it has an important impact on the regional climate and agricultural production in North China. Based on observed and reanalysis data from 1979 to 2021, a significant relationship between NCMP and both the April Indian Ocean sea surface temperature (IOSST) and Northwest Pacific Dipole (NWPD) was found, indicating that there may be a link between them. This link, and the possible physical mechanisms by which the IOSST and NWPD in April affect NCMP anomalies, are discussed. Results show that positive (negative) IOSST and NWPD anomalies in April can enhance (weaken) the water vapor transport from the Indian Ocean and Northwest Pacific to North China by influencing the related atmospheric circulation, and thus enhance (weaken) the May precipitation in North China. Accordingly, an NCMP prediction model based on April IOSST and NWPD is established. The model can predict the annual NCMP anomalies effectively, indicating it has the potential to be applied in operational climate prediction.摘要尽管华北区域五月降水 (NCMP) 占华北区域年总降水量的比率较少, 但是其气候变率较大, 对华北区域气候和农业生产等具有重要影响. 基于观测和再分析资料, 发现NCMP与前期四月的印度洋海温 (IOSST) 和西北太平洋偶极子 (NWPD) 具有显著关系, NCMP可能受到IOSST和NWPD的协同影响. 进一步分析表明, 前期四月暖 (冷) 的IOSST和正 (负) 位相的NWPD能够分别通过调节印度洋和西北太平洋区域的局地环流增强 (减弱) 从印度洋和西北太平洋向华北区域输送的水汽, 进而增强 (减弱) NCMP. 最后基于四月IOSST和NWPD构建了NCMP异常的预测模型, 后报检验显示该模型对NCMP异常具有较好的预测能力.     

Wave forecast in the Atlantic Ocean using a double-stage ConvLSTM network

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 d-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 d-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 d-ConvLSTM model is comparable to the Wave Model of European Centre for Medium-Range Weather Forecasts (ECMWF-WAM), but more economical and time-saving.摘要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.     

Observed decadal shifts and trends in global tropical cyclone activities from 1980 to 2021

This study investigates the variability of annual tropical cyclone (TC) frequency and intensity over six major ocean basins from 1980 to 2021. Statistical change-point and trend analyses were performed on the TC time series to detect significant decadal variation in TC activities. In the middle of the last decade of the 20th century, the frequency of TC genesis in the North Atlantic basin (NA) and North Indian Ocean (NIO) increased dramatically. In contrast, the frequency in the western North Pacific (WNP) decreased significantly at the end of the century. The other three basins—the East Pacific, southern Indian, and South Pacific—all experienced a declining trend in annual TC frequency. Over recent decades, the average TC intensity has decreased in the East Pacific and the NA, whereas it has risen in the other ocean basins. Specifically, from 2013 to 2021, the average peak TC intensity in the NIO has enhanced significantly. The magnitude of the Genesis Potential Index exhibits fluctuation that is consistent with large-scale parameters in the NIO, NA, and WNP, emphasizing the enhancing and declining trends in TCs. In addition, a trend and correlation analysis of the averaged large-scale characteristics with TCs revealed significant associations between the vertical wind shear and TC frequency over the NIO, NA, and WNP. Therefore, global TC trends and decadal variations associated with environmental parameters deserve further investigation in the future, mainly linked to the significant climate modes.摘要研究发现在1980–2021期间全球6个海域每年热带气旋的发生频次和强度具有显著年代际变化规律, 最近几十年, 北大西洋和北印度洋的热带气旋发生频次明显增加, 但西北太平洋的热带气旋却显著下降. 另外三个海域, 东太平洋, 南印度洋和南太平洋发现所生成的热带气旋有减少趋势. 但在过去十几年, 平均热带气旋的强度除了在东太平洋和北大西洋有所减弱但在其他几个海域有所加强, 特别是在 2013–2021期间, 北印度洋的平均热带气旋的强度增强明显. 热带气旋的潜在生成指数 (GPI) 增加或减少趋势变化与北印度洋, 北大西洋和西太平洋热带气旋变化相关的大尺度环流一致. 另外, 北印度洋, 北大西洋和西太平洋上空的垂直风切变是影响其区域热带气旋发生频次变化的主要因子, 不同的气候模态也可能对全球热带气旋的趋势变化和年代际变化有影响, 值得进一步研究.     

Impact of global sea surface temperature on the recent early winter Arctic tropospheric warming in coordinated large ensemble simulations 协调大集合模拟下全球海表面温度对北极对流层早冬增暖的影响

Coordinated numerical ensemble experiments with six different state-of-the-art atmosphere models were used to evaluate and quantify the impact of global SST (from reanalysis data) on the early winter Arctic warming during 1982–2014. Two sets of experiments were designed: in the first set (EXP1), OISSTv2 daily sea-ice concentration and SST variations were used as the lower boundary forcing, while in the second set (EXP2) the SST data were replaced by the daily SST climatology. In the results, the multi-model ensemble mean of EXP1 showed a near-surface (~850 hPa) warming trend of 0.4 °C/10 yr, which was 80% of the warming trend in the reanalysis. The simulated warming trend was robust across the six models, with a magnitude of 0.36–0.50 °C/10 yr. The global SST could explain most of the simulated warming trend in EXP1 in the mid and low troposphere over the Arctic, and accounted for 58% of the simulated near-surface warming. The results also suggest that the upper-tropospheric warming (~200 hPa) over the Arctic in the reanalysis is likely not a forced signal; rather, it is caused by natural climate variability. The source regions that can potentially impact the early winter Arctic warming are explored and the limitations of the study are discussed.摘要本文使用六个不同的最新大气模式进行了协调数值集合实验, 评估和量化了全球海表面温度 (SST) 对1982–2014年冬季早期北极变暖的影响.本研究设计了两组实验:在第一组 (EXP1) 中, 将OISSTv2逐日变化的海冰密集度和SST数据作为下边界强迫场;在第二组 (EXP2) 中, 将逐日变化的SST数据替换为逐日气候态.结果表明: (1) EXP1的多模式集合总体平均值显示0.4 °C/10年的近地表 (约850 hPa) 升温趋势, 为再分析数据结果中升温趋势的80%. (2) 在这六个模式中, 模拟的变暖趋势均很强, 幅度为0.36–0.50 °C/10年. (3) 全球海表温度可以解释北极对流层中低层EXP1的大部分模拟的变暖趋势, 占再分析数据结果的58%. (4) 再分析数据结果中, 北极上空的对流层上层变暖 (约200 hPa) 不是由强迫信号而可能是由自然气候变率引起的.本文还探索了影响北极初冬变暖的可能源区, 并讨论了该研究的局限性.     

The ocean response to climate change guides both adaptation and mitigation efforts

The ocean's thermal inertia is a major contributor to irreversible ocean changes exceeding time scales that matter to human society. This fact is a challenge to societies as they prepare for the consequences of climate change, especially with respect to the ocean. Here the authors review the requirements for human actions from the ocean's perspective. In the near term (～2030), goals such as the United Nations Sustainable Development Goals (SDGs) will be critical. Over longer times (～2050–2060 and beyond), global carbon neutrality targets may be met as countries continue to work toward reducing emissions. Both adaptation and mitigation plans need to be fully implemented in the interim, and the Global Ocean Observation System should be sustained so that changes can be continuously monitored. In the longer-term (after ～2060), slow emerging changes such as deep ocean warming and sea level rise are committed to continue even in the scenario where net zero emissions are reached. Thus, climate actions have to extend to time scales of hundreds of years. At these time scales, preparation for “high impact, low probability” risks — such as an abrupt showdown of Atlantic Meridional Overturning Circulation, ecosystem change, or irreversible ice sheet loss — should be fully integrated into long-term planning.摘要在全球变化背景下, 海洋的很多变化在人类社会发展的时间尺度上 (百年至千年) 具有不可逆转性, 海洋巨大的热惯性是造成该不可逆性的主要原因. 这个特征为人类和生态系统应对海洋变化提出一系列挑战. 本文从海洋变化的角度总结了人类应对气候变化的要求, 提出需要进行多时间尺度的规划和统筹. 在近期 (到2030年) , 实现联合国可持续发展目标至关重要. 在中期 (2050–2060年前后) , 全球需要逐步减排并实现碳中和目标. 同时, 适应和减缓气候变化的行动和措施必须同步施行; 全球海洋观测系统需要得以维持并完善以持续监测海洋变化. 在远期 (在2060年之后) , 即使全球达到净零排放, 包括深海变暖和海平面上升在内的海洋变化都将持续, 因此应对全球变化的行动需持续数百年之久. 在该时间尺度, 应对“低概率, 高影响”气候风险 (即发生的可能性较低, 但一旦发生影响极大的事件带来的风险, 例如: 大西洋经圈反转环流突然减弱, 海洋生态系统跨过临界点, 无可挽回的冰盖质量损失等) 的准备应充分纳入长期规划.