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.     

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.     

Short-term wind speed forecasting bias correction in the Hangzhou area of China based on a machine learning model

Accurate wind speed forecasting is of great societal importance. In this study, the short-term wind speed forecasting bias at automatic meteorological stations in Hangzhou, Zhejiang Province, China, was corrected using an XGBoost machine learning model called WSFBC-XGB. The products of the local NWP (numerical weather prediction) system were used as the inputs of WSFBC-XGB. The WSFBC-XGB-corrected results were compared with those corrected using the traditional MOS (model output statistics) method. Results showed that WSFBC-XGB performed better than MOS, with the root-mean-square errors (RMSEs)/accuracy rates of the wind speed forecasting (ACCs) of WSFBC-XGB being reduced/ promoted by 26.1% and 7.64%/35.6% and 7.02% relative to NWP and MOS, respectively. The RMSEs/ACCs of WSFBC-XGB were smaller/higher than those of MOS at 90% stations. In addition, the mean decrease in impurity method was used to analyze the interpretability of WSFBC-XGB to help users gain trust in the model. Results showed that the four most important features were the wind speed at 10 m (47.35%), meridional component of wind at 10 m (12.73%), diurnal cycle (9.97%), and meridional component of wind at 1000 hPa (7.45%). The WSFBC-XGB model will help improve the accuracy of short-term wind speed forecasting and provide support for large-scale outdoor activities.摘要准确的风速预报具有重要的社会意义. 在本研究中, 使用名为WSFBC-XGB的XGBoost机器学习模型对中国浙江省杭州市自动气象站的短期风速预报误差进行校正. WSFBC-XGB使用本地数值天气预报系统的产品作为输入. 将WSFBC-XGB校正的结果与传统MOS(模型输出统计)方法校正的结果进行了比较. 结果表明: WSFBC-XGB预报风速的均方根误差(RMSE)/准确率(ACC)分别比NWP和MOS降低/提高了26.1%和7.64%/35.6%和7.02%; 对于90%的站点WSFBC-XGB的RMSE/ACC均小于/高于MOS. 此外, 采用平均杂质减少法对WSFBC-XGB的可解释性进行分析, 以帮助用户增加对模型的信任. 结果表明: 10米风速(47.35%), 10米风的经向分量(12.73%), 日循环(9.97%)和1000百帕风的经向分量(7.45%)是前4个最重要的特征. WSFBC-XGB模型将有助于提高短期风速预报的准确性, 为大型户外活动提供支持.     

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.     

Evaluation of global high-resolution reanalysis products based on the Chinese global oceanography forecasting system: 基于中国全球海洋预报系统(CGOFS)的全球高分辨率再分析产品评估

The global high-resolution marine reanalysis products that were independently developed by the National Marine Environmental Forecasting Center based on the Chinese Global Oceanography Forecasting System (CGOFS), are evaluated by comparing their climatologies with internationally recognized data from WOA (Word Ocean Atlas), SODA (Simple Ocean Data Assimilation), AVISO (Archiving, Validation, and Interpretation of Satellite Oceanographic Data), and C-GLORS (Global Ocean Reanalysis System). The results show that the SST RMSEs of CGOFS and SODA against WOA are 0.51 °C and 0.43 °C respectively; and in the North Pacific, the SST of CGOGS is closer to that of WOA than SODA. The SSS RMSEs of CGOFS and SODA compared with WOA are 0.48 PSU and 0.40 PSU, respectively. CGOFS can reproduce the main large-scale ocean circulation globally, and obtain a similar vertical structure of the Equatorial Undercurrent as SODA. The RMSE of the CGOFS global sea-level anomaly against AVISO is 0.018 m. The monthly averaged sea-ice extents are between those of SODA and C-GLORS in each month; the growth and ablation characteristics of the ice volume are consistent with SODA and C-GLORS; but the ice volume of CGOFS is greater than that of SODA and C-GLORS. In general, the climatology of the CGOFS global high-resolution reanalysis products are basically consistent with similar international products, and can thus provide reliable data for the improvement of marine science and technology in China.摘要通过同化系统将观测资料与海洋数值模式融合得到的海洋再分析产品为海洋科学研究提供了重要的资料基础.本文采用WOA,SODA,AVISO和GLORS四种数据资料与我国自主研发的中国全球海洋预报系统(CGOFS)的气候态结果进行了对比, 结果表明:CGOFS和SODA的全球海表面温度与WOA的均方根误差分别为0.51 和 0.43°C.CGOFS和SODA的海表面盐度与WOA的均方根误差分别为0.48和0.40 PSU;海流方面, CGOFS能较好的刻画主要大洋环流分布及赤道潜流的垂向结构;CGOFS的全球海表面高度异常与AVISO的均方根误差为0.018m;多年月平均海冰外缘线覆盖面积介于SODA 和 GLORS之间, 海冰体积的生消规律与SODA 和 GLORS一致.总体来看, CGOFS全球高分辨率海洋再分析产品的气候态结果与国际同类产品基本一致, 可为提升我国海洋综合科技实力提供可靠的资料保障.     

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.     

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.     

Aerosol characteristics over the Tibetan Plateau simulated with a coupled aerosol–climate model (FGOALS-f3-L) 耦合气溶胶气候模式FGOALS-f3-L模拟青藏高原地区气溶胶特性

This study presents the simulated aerosol spatiotemporal characteristics over the Tibetan Plateau (TP) with a newly developed coupled aerosol–climate model (FGOALS-f3-L). The aerosol properties are simulated over the TP for the period 2002–11. The results indicate that soil dust, sulfate, and carbonaceous aerosols (black carbon (BC), organic carbon (OC) and BC/OC) account for 53.6%, 32.2%, and 14.2% of the total aerosol mass over the TP, respectively. The simulated aerosol surface mass concentrations and aerosol optical depths (AODs) are evaluated with ground-based and satellite observations, respectively. Underestimations of the aerosol surface mass concentration are found at the Lhasa site, especially for BC and OC. The spatial distribution and interannual variation of AOD are consistent with MODIS observations, with the RMSE of 0.081 and bias of 0.036. Due to the uncertainty of the parameterization of dust emissions, the model's performance in summer and autumn is much better than that in spring.摘要基于新耦合气溶胶气候模式FGOALS-f3-L模拟分析了2002–2011年青藏高原地区气溶胶时空分布特征.结果表明:青藏高原地区, 沙尘,硫酸盐,碳质气溶胶 (包括黑碳,有机碳和混合碳) 地表质量浓度分别占比为53.6%, 32.2%, 14.2%;在拉萨站点, 模拟的气溶胶地表质量浓度被低估, 尤其是黑碳和有机碳气溶胶;模拟的气溶胶光学厚度 (AOD) 时空分布与卫星观测结果较为一致, 均方根误差和偏差分别为0.081和0.036;由于模式中沙尘排放参数化的不确定性, 模式对AOD的模拟效果在夏季和秋季优于春季     

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.     

Response of a westerly-trough rainfall episode to multi-scale topographic control in southwestern China

This study aims to quantify the response of a westerly-trough rainfall episode that occurred in summer 2020 to multi-scale topographic control in southwestern China, based on observations and numerical simulations. The multi-scale topography is composed of the Tibetan Plateau, Hengduan Cordillera (HC), and Sichuan Basin (SB). The westerly trough was characterized by southeastward deepening together with an in-phase propagating rainfall episode. By utilizing the results of numerical experiments, how the multi-scale topography impacted this westerly trough rainfall episode is explored. It is found that HC was the pivotal topographic factor affecting the southeastward extension of the trough and related rainfall, while SB accerelated the eastward movement of the westerly trough and changed the tilting direction of the trough line, thus further changing the location and orientation of precipitation. For extreme rainfall with intensity exceeding 10 mm h^?1, a roughly threefold rise in the cover ratio (from 1.8% to 7.2%) and fourfold increase in the areal rainfall amount per hour occurred by removing the HC barrier, due to the strongest vorticity and long-distance transport capacity to potential vorticy mass accompanying the southeast-stretching trough. Our results quantitatively reveal a strong response of westerly trough rainfall to multi-scale topographic control in southwestern China, therefore serving as an important reference for future decision making and effective model improvement.摘要中国西南部地形复杂, 降水频发, 地形对降水的影响至关重要. 本文基于观测和数值模拟, 定量揭示了青藏高原, 横断山脉和四川盆地多尺度地形对该地区西风槽降水的影响. 发现横断山脉是影响槽东南伸展, 降水传播的关键地形要素, 而四川盆地可加速西风槽东移, 改变槽线倾斜方向, 进而改变降水的位置和方向. 对于极端降水事件, 移除横断山脉屏障后, 降水覆盖率约增加3倍 (从1.8%增至7.2%), 小时面雨量增强4倍. 这些研究, 可为地形复杂地区降水的未来预报决策和有效模式改进提供参考.     

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.     

Changes in the seasonal amplitude of northern ecosystem productivity under future global warming

Observations have shown a largely enhanced seasonal amplitude of northern atmospheric CO₂ in the past several decades, and this enhancement is attributable to the increased seasonal amplitude of northern net ecosystem productivity (NEP amplitude). In the future, however, the changes in NEP amplitude are not clear, because of the uncertainties in climate change and vegetation dynamics. This study investigated the changes in NEP amplitude north of 45°N under future global warming by using a dynamic global vegetation model (DGVM). The authors conducted two sets of simulations: a present-day simulation (1981–2000) and future simulations (2081–2100) forced by RCP8.5 outputs from CMIP5. The results showed an overall enhanced northern NEP amplitude under the RCP8.5 scenario because of the increased maximum NEP and the decreased minimum NEP. The increases (decreases) in the maximum (minimum) NEP resulted from stronger (weaker) positive changes in gross primary production (GPP) than ecosystem respiration (ER). Changes in GPP and ER are both dominantly driven by surface air temperature and vegetation dynamics. This work highlights the key role of vegetation dynamics in regulating the northern terrestrial carbon cycle and the importance of including a DGVM in Earth system models.摘要观测显示过去几十年北半球大气二氧化碳季节幅度大幅增加, 这主要是由北半球陆地净生态系统生产力季节幅度的增加所致. 但是, 因为气候变化和植被动态的不确定性, 未来陆地净生态系统生产力季节幅度的变化还很不清楚. 本工作利用全球植被动力学模式研究了全球变暖背景下北纬45°以北陆地净生态系统生产力季节幅度的变化. 作者做了两大类试验: 当代试验 (1981−2000) 和CMIP5 RCP8.5 变暖情景驱动的未来试验 (2081−2100) . 结果显示, 在RCP8.5变暖情景下北半球中高纬陆地净生态系统生产力季节幅度整体增加, 这是因为陆地净生态系统生产力的月最大值增加且月最小值减小. 最大 (最小) 陆地净生态系统生产力的增加 (减小) 是由于总初级生产力的增加强 (弱) 于生态系统总呼吸. 总初级生产力和生态系统总呼吸的变化都主要受地表气温和植被动态的驱动. 本工作强调了植被动态对北半球中高纬陆地生态系统碳循环的关键调制作用, 也强调了在地球系统模式中包含全球植被动力学模式的重要性.     

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.     

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.     

China Ocean ReAnalysis (CORA) version 1.0 products and validation for 2009–18: 2009–18年西北太平洋区域海洋再分析CORA1.0产品研制及检验

China Ocean ReAnalysis (CORA) version 1.0 products for the period 2009–18 have been developed and validated. The model configuration and assimilation algorithm have both been updated compared to those of the 51-year (1958–2008) products. The assimilated observations include temperature and salinity field data, satellite remote sensing sea surface temperature, and merged sea surface height (SSH) anomaly data. The validation includes the following three aspects: (1) Temperature, salinity, and SSH anomaly root-mean-square errors (RMSEs) are computed as a primary evaluation of the reanalysis quality. The 0–2000 m domain-averaged RMSEs of temperature and salinity are 0.61°C and 0.08 psu, respectively. The SSH anomaly RMSE is less than 0.2 m in most regions. (2) The 35°N temperature section is used to evaluate the ability to reproduce the thermocline, mixing layer, and Yellow Sea cold water mass. In summer, the thermocline is reinforced, with the gradient changing from 3°C in May to 10°C in August. The mixing-layer depth reproduced by CORA is consistent with that computed from the observed climatology. The Yellow Sea cold water mass forms at a depth of 50 m. (3) The reanalysis current is examined against the tracks of some drifting buoys. The results show that the reanalysis current can capture the mesoscale eddies near the Kuroshio, which are similar to those described by the drifting buoys. Overall, the 2009–18 CORA reanalysis products are capable of reproducing major oceanic phenomena and processes in the coastal waters of China and adjacent seas.摘要在51年 (1958–2008) 西北太平洋区域海洋再分析CORA1.0产品的基础上, 改进了模式配置和同化方法, 研制了2009-18年的CORA产品并对其进行以下检验: (1) 温盐和海表高度异常均方根误差分布检验; (2) 35°N处温度断面分布检验; (3) 再分析流场和表漂浮标轨迹对比检验.结果显示, 2009–18年的CORA产品可以再现海洋要素长时间序列,时空多尺度的变化特征, 为研究特征海洋现象和过程提供背景信息.     

Influence of the North American Dipole on ENSO onset as simulated by a coupled ocean-Atmosphere model

北美偶极子(NAD)是热带北大西洋西部和北美东北部的南北向海平面气压异常偶极型模态.以往的观测研究表明,NAD可以有效地影响ENSO事件的爆发.本文利用全球耦合模式FGOALS-g2,评估了NAD与ENSO的关系.结果表明,该模式能较好地重现NAD模态.进一步的分析验证了冬季NAD可以通过强迫冬末春初副热带东北太平洋上空的反气旋和暖海温的出现,在随后的冬季触发El Ni?o事件.此外,在同化NAD实验中,发生El Ni?o事件的概率增加了将近一倍.相比之下,NAO未能在副热带东北太平洋上空引起表面风和海温的异常,因而不能有效地激发次年冬季ENSO事件.     

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...     

CWRF-based ensemble simulation of tropical cyclone activity near China and its sensitivity to the model physical parameterization schemes: 基于CWRF的中国近海热带气旋集合模拟及其对物理参数化方案的敏感性研究

To evaluate the downscaling ability with respect to tropical cyclones (TCs) near China and its sensitivity to the model physics representation, the authors performed a multi-physics ensemble simulation with the regional Climate–Weather Research and Forecasting (CWRF) model at a 30 km resolution driven by ERA-Interim reanalysis data. The ensemble consisted of 28 integrations during 1979–2016 with varying CWRF physics configurations. Both CWRF and ERA-Interim can generally capture the seasonal cycle and interannual variation of the TC number near China, but evidently underestimate them. The CWRF downscaling and its multi-physics ensemble can notably reduce the underestimation and significantly improve the simulation of the TC occurrences. The skill enhancement is especially large in terms of the interannual variation, which is most sensitive to the cumulus scheme, followed by the boundary layer, surface and radiation schemes, but weakly sensitive to the cloud and microphysics schemes. Generally, the Noah surface scheme, CAML(CAM radiation scheme as implemented by Liang together with the diagnostic cloud cover scheme of Xu and Randall(1996)) radiation scheme, prognostic cloud scheme, and Thompson microphysics scheme stand out for their better performance in simulating the interannual variation of TC number. However, the Emanuel cumulus and MYNN boundary layer schemes produce severe interannual biases. Our study provides a valuable reference for CWRF application to improve the understanding and prediction of TC activity.摘要为评估CWRF模式的降尺度能力和其热带气旋模拟对物理参数化方案的敏感性, 本文利用ERI再分析资料驱动CWRF在30km网格上对1982-2016年中国近海热带气旋开展了一次集合模拟.结果表明:CWRF与ERI均能模拟出热带气旋的季节变化和年际变化形势且均存在低估, 但相较ERI, CWRF的降尺度技术和集合模拟可以再现更多的热带气旋, 显著减少低估.年际变化结果提升最为明显, 它对积云方案最为敏感, 其次是边界层, 陆面和辐射方案, 对云和微物理方案较弱.该研究为应用CWRF理解和预报热带气旋提供了参考.     

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.     

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.