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.     

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.     

Predicting climate anomalies:A real challenge

过去几十年,气候变化和极端气候事件造成的经济损失和灾害显著增加.虽然全球的科学家在理解和预测气候变异方面做出了巨大的努力,但当前在气候预测领域仍然存在几个重大难题.2020年,依托于国家自然科学基金基础科学中心项目的气候系统预测研究中心(CCSP)成立了,该中心旨在应对和处理气候预测领域的三大科学难题:厄尔尼诺-南方涛动(ENSO)预测,延伸期天气预报,年际-年代际气候预测,并为更加准确的气候预测和更加有效的灾害防御提供科学依据.因此,本文介绍了CCSP的主要目标和面对的科学挑战,回顾了CCSP在季风动力过程,陆-气相互作用和模式开发,ENSO变率,季节内振荡,气候预测等方面已取得的重要研究成果.未来CCSP将继续致力于解决上述领域的关键科学问题.     

Effects of spring Arctic sea ice on summer drought in the middle and high latitudes of Asia

Based on data observed from 1979 to 2017, the influence of Arctic sea ice in the previous spring on the first mode of interannual variation in summer drought in the middle and high latitudes of Asia (MHA) is analyzed in this paper, and the possible associated physical mechanism is discussed. The results show that when there is more sea ice near the Svalbard Islands in spring while the sea ice in the Barents–Kara Sea decreases, the drought distribution in the MHA shows a north–south dipole pattern in late summer, and drought weakens in the northern MHA region and strengthens in the southern MHA region. By analyzing the main physical process affecting these changes, the change in sea ice in spring is found to lead to the Polar–Eurasian teleconnection pattern, resulting in more precipitation, thicker snow depths, higher temperatures, and higher soil moisture in the northern MHA region in spring and less precipitation, smaller snow depths, and lower soil moisture in the southern MHA region. Such soil conditions last until summer, affect summer precipitation and temperature conditions through soil moisture–atmosphere feedbacks, and ultimately modulate changes in summer drought in the MHA.摘要本文分析了亚洲中高纬度地区 (MHA) 年际尺度夏季干旱的主模态时空变化特征, 以及影响第一模态的主要影响因子和可能的物理过程. 结果显示该区域夏季干旱第一模态主要呈现一个南北偶极性的分布. 而影响MHA夏季干旱的主要影响因子为前春北极海冰. 当春季斯瓦尔巴群岛附近海冰偏多, 而巴伦支海-喀拉海海冰减少时, 通过冰-气相互作用, 使得MHA北部春季降水增加, 雪深加厚, 土壤湿度偏高, 而南部则相反. 然后这样的土壤湿度条件从春季持续到夏季, 通过土壤湿度-大气反馈影响夏季MHA降水和温度变化, 最终对夏季干旱主模态产生影响.     

Subseasonal reversal of haze pollution over the North China Plain

China has been frequently suffering from haze pollution in the past several decades. As one of the most emission-intensive regions, the North China Plain (NCP) features severe haze pollution with multiscale variations. Using more than 30 years of visibility measurements and PM_2.5 observations, a subseasonal seesaw phenomenon of haze in autumn and early winter over the NCP is revealed in this study. It is found that when September and October are less (more) polluted than the climatology, haze tends to be enhanced (reduced) in November and December. The abrupt turn of anomalous haze is found to be associated with the circulation reversal of regional and large-scale atmospheric circulations. Months with poor air quality exhibit higher relative humidity, lower boundary layer height, lower near-surface wind speed, and southerly anomalies of low-level winds, which are all unfavorable for the vertical and horizontal dispersion and transport of air pollutants, thus leading to enhanced haze pollution over the NCP region on the subseasonal scale. Further exploration indicates that the reversal of circulation patterns is closely connected to the propagation of midlatitude wave trains active on the subseasonal time scale, which is plausibly associated with the East Atlantic/West Russia teleconnection synchronizing with the transition of the North Atlantic SST. The seesaw relation discussed in this paper provides greater insight into the prediction of the multiscale variability of haze, as well as the possibility of efficient short-term mitigation of haze to meet annual air quality targets in North China.摘要中国近几十年来频受雾霾污染问题困扰, 其中华北平原作为排放最密集的区域之一, 常遭遇不同尺度的严重雾霾污染. 本文利用30余年的能见度和颗粒物 (PM_2.5) 观测数据, 发现了华北平原地区在秋季和早冬时雾霾污染在次季节尺度上“跷跷板式”反向变化的关系. 研究发现, 当9–10月污染较轻 (重) 时, 11–12月的污染倾向于加重 (减轻) . 这种突然的变化与局地和大尺度环流的反向变化有关. 污染较重的月份常伴随有更高的相对湿度, 更低的边界层高度和近地面风速以及低层的南风异常, 均不利于污染的垂直和水平扩散和传输, 从而导致了次季节尺度上霾污染的加重. 进一步的研究发现环流场的突然转向与在次季节尺度上活跃的中纬度波列的传播密切相关, 而此波列可能主要与大西洋海温转变及引起的EA/WR遥相关型有关. 这一次季节反向变化为霾污染多尺度变率预测提供了新的理解, 同时为华北地区年度空气质量达标的短期目标提供了具有可行性的参考方法.     

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.     

Decadal–centennial hydroclimate variability over eastern China during the last millennium: Results from the product of Paleo Hydrodynamics Data Assimilation: 过去千年中国东部年代际-百年尺度干湿变化特征:基于古水文动力同化数据

Decadal–centennial hydroclimate variability over eastern China during the last millennium is investigated using the product of Paleo Hydrodynamics Data Assimilation (PHYDA). Results reveal that the PHYDA depicts a more homogeneous temporal pattern during the early part of the Little Ice Age with other reconstructions than those during the other periods, and could also identify the droughts of 1352–90 AD, 1445–98 AD, 1580–94 AD, and 1626–65 AD during this period. On centennial time scales, the PHYDA shows that the linkage between the Palmer drought severity index over eastern China and the Atlantic Multidecadal Oscillation (AMO) index is more marked than that with the El Niño–Southern Oscillation and the location of the intertropical convergence zone over the Asian–Australian monsoon area during the period after the 1350s. For the decadal droughts, the PHYDA suggests most of the drought events during the last millennium were linked to the El Niño–like mean states and the negative AMO states.摘要利用古水文动力同化数据 (PHYDA) 研究了过去千年中国东部年代际-百年尺度干湿变化特征.结果表明, 对比其它重建数据PHYDA在百年尺度上对小冰期前期中国东部干湿变化的再现能力最好, 其对这一时期发生的年代际干旱事件包括1352–90年,1445–98年,1580–94年和1626–65年干旱事件的再现能力也最强.通过与强迫因子的对比和回归分析, 发现1350年后中国东部百年尺度干事变化主要受北大西洋年代际振荡影响, 而年代际干旱事件的主导因子则是厄尔尼诺和负位相的北大西洋年代际振荡.     

Key regions where land surface processes shape the East Asian climate

Land–atmosphere interaction, as one of the key processes affecting the atmosphere and climate over East Asia, has drawn increasing attention during the past few decades. However, the current level of understanding regarding the mechanisms through which land surface processes impact the East Asian climate needs to be improved. Based on existing studies, six key regions where land surface processes affect the East Asian climate are proposed in this study, which can provide a valuable reference for future research into land–atmosphere interaction in East Asia.摘要陆气相互作用是影响东亚大气环流和气候的一个关键过程, 受到了越来越多的关注. 然而, 关于陆面过程影响东亚气候的相关机理的理解还有待提升. 在已有研究基础上, 提出了陆面过程影响东亚气候研究值得关注的青藏高原, 欧亚中高纬地区, 中国东部季风区, 中南半岛, 中亚中纬度区域, 西亚等6个关键区, 期待为加强陆面过程与东亚气候研究提供一定参考.     

Assessment of CMIP6 model performance for temperature and precipitation in Xinjiang,China

作者使用国际耦合模式比较计划第六阶段(CMIP6)的历史模拟试验数据,评估了42个全球气候模式对1995-2014年新疆温度和降水气候态的模拟能力.结果表明,CMIP6模式能够合理模拟新疆年和季节的温度和降水气候态的空间分布.相较于观测,多模式中位数的年均,春季,夏季,秋季和冬季区域平均温度偏差分别为0.1℃,-1.6...     

3D DBSCAN detection and parameter sensitivity of the 2022 Yangtze river summertime heatwave and drought

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.摘要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.     

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.     

Evaluating the effects of topographical factors on the precipitation simulated by kilometer-scale versus quarter-degree dynamical downscaling models in eastern China

Topography as well as its attributes are fundamental factors during precipitation generation. Various models with different complexity have been established to interpret the topography–precipitation relationship. In this study, the topography–precipitation relationships simulated by two dynamical downscaling models (DDMs) at the kilometer-scale and traditional quarter-degree resolution in eastern China are evaluated by utilizing multi-scale geographically weighted regression with station precipitation observations as reference. The precipitation simulated by the kilometer-scale DDM had a higher agreement with observations than the quarter-degree simulation. For the effects of topography on precipitation, observations revealed a dominant role played by the topographical relief in the precipitation distribution at most stations in the study region. The kilometer-scale DDM generally reflected this dominant role of topographical relief. However, the quarter-degree DDM showed an excessive dependency of the precipitation distribution on the topographical elevation. This research highlights the key role of underground sub-grid variations on the precipitation in eastern China, which implies a potential way forward for precipitation simulation improvements.摘要与传统的1/4度 (≈25-30 km) 动力降尺度模拟相比, 公里尺度模拟的降水空间分布与观测结果更为接近. 为了研究这一差异原因, 本研究以华东地区为例, 探究了地形因子在观测和模拟的降水中的作用. 为了更好地体现地形因子对降水分布非均匀性的影响, 以及不同地形因子作用的尺度差异, 本研究采用多尺度地理加权回归模型, 对五个主要地形因子与公里尺度和1/4度分辨率模拟的降水的关系进行了评估. 基于观测数据的研究结果显示地形起伏度, 地形高程和离海岸线距离对华东地区降水分布的非均匀性都有重要影响, 其中地形起伏度在研究区大部分站点降水分布中起主导作用; 公里尺度模拟结果基本反映了地形起伏度的主导作用; 而1 / 4度模拟结果表现出降水对地形高程的过度依赖. 本研究揭示了公里尺度地形分布对中国东部降水的非均匀分布的关键作用, 研究结果可以为改进降水模拟提供新的思路.     

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...     

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 impacts of the East Asian subtropical westerly jet on weather extremes over China in early and late summer

Summer weather extremes (e.g., heavy rainfall, heat waves) in China have been linked to anomalies of summer monsoon circulations. The East Asian subtropical westerly jet (EASWJ), an important component of the summer monsoon circulations, was investigated to elucidate the dynamical linkages between its intraseasonal variations and local weather extremes. Based on EOF analysis, the dominant mode of the EASWJ in early summer is characterized by anomalous westerlies centered over North China and anomalous easterlies centered over the south of Japan. This mode is conducive to the occurrence of precipitation extremes over Central and North China and humid heat extremes over most areas of China except Northwest and Northeast China. The centers of the dominant mode of the EASWJ in late summer extend more to the west and north than in early summer, and induce anomalous weather extremes in the corresponding areas. The dominant mode of the EASWJ in late summer is characterized by anomalous westerlies centered over the south of Lake Baikal and anomalous easterlies centered over Central China, which is favorable for the occurrence of precipitation extremes over northern and southern China and humid heat extremes over most areas of China except parts of southern China and northern Xinjiang Province. The variability of the EASWJ can influence precipitation and humid heat extremes by driving anomalous vertical motion and water vapor transport over the corresponding areas in early and late summer.摘要东亚副热带西风急流是影响中国极端天气的重要原因之一, 然而之前的研究主要关注整个夏季急流的变率, 对其早夏和晚夏变率的区别及其对极端天气的影响关注较少. 本文研究了早夏和晚夏东亚副热带西风急流季节内变化特征的区别, 以及这种区别带来的极端天气的差异及其可能的动力学机制. 研究结果表明, 相比于早夏, 晚夏急流季节内变化中心位置偏西偏北, 通过改变垂直运动和水汽输送可以影响极端降水和湿热浪在相应区域的发生概率.     

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.     

On the long-term memory characteristic in land surface air temperatures: How well do CMIP6 models perform?

Stemming from the multi-scale interactions of various processes, long-term memory (LTM) has become a well-recognized property in the climate system. Whether a dynamic model can reproduce the observed LTM is a widely used criterion for model evaluation, especially regarding its ability in simulating natural variabilities. While many works have shown poor model skill in simulating the LTM of land surface air temperature (LSAT), it is not yet known whether CMIP6 models offer any improvement. In this study, the performances of 60 CMIP6 models in simulating the LTM characteristics in LSAT were evaluated. Results showed that most models reproduced the LTM in the global-mean LSAT, among which AWI-ESM-1-1-LR and E3SM-1-0 performed best. All 60 models reproduced the variation in LTM with latitude. CNRM-CM6-1 and HadGEM3-GC31-LL performed best in simulating the LTM of LSAT at the global scale. The multi-model mean (MMM) performed better than any single model. The biases of the MMM and CRUTEM5, and among the 60 models, were significant in the equatorial and coastal regions, which may be attributable to the simulation differences of the models in terms of their ocean–atmosphere coupling processes.摘要利用去趋势涨落分析 (DFA) 方法计算序列的长程记忆性 (LTM) , 以CRUTEM5数据集的结果作为观测参照, 评估了60个参与第六次国际耦合模式比较计划 (CMIP6) 的气候模式对地表气温LTM的再现能力. 结果表明: 大部分模式可以再现全球平均地表气温序列的LTM特征, 其中AWI-ESM-1-1-LR和E3SM-1-0的模拟效果最好; 60个模式均能模拟LTM随纬度带的变化; 综合来说, 全球水平上CNRM-CM6-1和HadGEM3-GC31-LL对地表气温LTM的模拟性能最好; 多模式平均相比单一模式模拟性能更好; 多模式平均与观测结果的偏差以及模式之间的模拟差异显著体现在赤道和沿海区域, 这种偏差可能源于模式对海气耦合过程的模拟差异.     

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.     

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.     

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.