Prediction of ENSO using multivariable deep learning

A novel multivariable prediction system based on a deep learning (DL) algorithm, i.e., the residual neural network and pure observations, was developed to improve the prediction of the El Niño–Southern Oscillation (ENSO). Optimal predictors are automatically determined using the maximal information for spatial filtering and the Taylor diagram criteria, enabling the best prediction skills at lead times of eight months compared with most operational prediction models. The hindcast skill for the most challenging decade (2011–18) outperforms the multi-model ensemble operational forecasts. At the six-month lead, the correlation (COEF) skill of the DL model reaches 0.82 with a normalized root-mean-square error (RMSE) of 0.58 °C, which is significantly better than the average multi-model performance (COEF = 0.70 and RMSE = 0.73°C). DL prediction can effectively alleviate the long-standing spring predictability barrier problem. The automatically selected optimal precursors can explain well the typical ENSO evolution driven by both tropical dynamics and extratropical impacts.摘要本文基于残差神经网络和观测数据构建了一套深度学习多因子预报测模型, 以改进厄尔尼诺-南方涛动(ENSO)的预报. 该模型基于最大信息系数进行因子时空特征提取, 并根据泰勒图的评估标准可自动确定关键预报因子进行预报. 该模型在超前8个月以内的预报性能要优于当前传统的业务预报模式. 2011–2018年间, 该模型的预报性能优于多模式集成预报的结果. 在超前6个月预报时效上, 模型预报相关性可达0.82, 标准化后的均方根误差仅为0.58°C, 多模式集成预报的相关性和标准化后的均方根误差分别为0.70和0.73°C. 该模型春季预报障碍问题有所缓解, 并且自动选取的关键预报因子可用于解释热带和副热带热动力过程对于ENSO变化的影响.     

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.     

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.     

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.     

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.     

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.     

A quality control method for high-resolution radiosonde temperature and wind data 高频探空温度和风速的质量控制方法研究

This study proposes a method to derive the climatological limit thresholds that can be used in an operational/historical quality control procedure for Chinese high vertical resolution (5–10 m) radiosonde temperature and wind speed data. The whole atmosphere is divided into 64 vertical bins, and the profiles are constructed by the percentiles of the values in each vertical bin. Based on the percentile profiles (PPs), some objective criteria are developed to obtain the thresholds. Tibetan Plateau field data are used to validate the effectiveness of the method in the application of experimental data. The results show that the derived thresholds for 120 operational stations and 3 experimental stations are effective in detecting the gross errors, and those PPs can clearly and instantly illustrate the characteristics of a radiosonde variable and reveal the distribution of errors.摘要针对中国高分辨率探空资料, 本文提出了一种计算气候学界限值的方法以满足业务中对资料进行质量控制的需求.首先在垂直方向上将整个大气划分为64层, 将落在每层范围内的观测数据都收集到一起进行排序并计算百分位, 在此基础上通过比较不同百分位廓线值来获得气候学界限值.除了业务台站, 本文还使用了TIPEX-III的探空数据来验证本方法在科学试验数据中的应用效果.评估表明, 应用气候学界限值可以有效检测到业务站和试验站观测数据中的粗大误差;百分位廓线则可以清晰的体现出探空观测的整体变化特征并揭示出误差的整体分布范围.     

Ensemble-based diurnally varying background error covariances and their impact on short-term weather forecasting

Background error covariance (BEC) plays an essential role in variational data assimilation. Most variational data assimilation systems still use static BEC. Actually, the characteristics of BEC vary with season, day, and even hour of the background. National Meteorological Center–based diurnally varying BECs had been proposed, but the diurnal variation characteristics were gained by climatic samples. Ensemble methods can obtain the background error characteristics that suit the samples in the current moment. Therefore, to gain more reasonable diurnally varying BECs, in this study, ensemble-based diurnally varying BECs are generated and the diurnal variation characteristics are discussed. Their impacts are then evaluated by cycling data assimilation and forecasting experiments for a week based on the operational China Meteorological Administration-Beijing system. Clear diurnal variation in the standard deviation of ensemble forecasts and ensemble-based BECs can be identified, consistent with the diurnal variation characteristics of the atmosphere. The results of one-week cycling data assimilation and forecasting show that the application of diurnally varying BECs reduces the RMSEs in the analysis and 6-h forecast. Detailed analysis of a convective rainfall case shows that the distribution of the accumulated precipitation forecast using the diurnally varying BECs is closer to the observation than using the static BEC. Besides, the cycle-averaged precipitation scores in all magnitudes are improved, especially for the heavy precipitation, indicating the potential of using diurnally varying BEC in operational applications.摘要背景场误差协方差在资料同化系统中具有非常重要的作用, 目前业务变分同化系统中常采用静态背景场误差协方差, 未考虑其具体的日变化特征. 为构建更为合理且便于业务系统应用的日变化背景误差协方差, 本文构建了高分辨率集合预报样本的日变化背景场误差协方差, 揭示了其日变化特征, 并应用到了CMA-BJ业务系统中, 开展了基于业务框架的批量循环同化预报试验. 结果表明, 背景场误差存在明显的日变化特征, 采用集合日变化背景场误差协方差能够改进模式的预报效果.     

Analysis of lower-boundary climate factors contributing to the summer heatwave frequency over eastern Europe using a machine-learning model

A machine-learning (ML) model, the light gradient boosting machine (LightGBM), was constructed to simulate the variation in the summer (June–July–August) heatwave frequency (HWF) over eastern Europe (HWF_EUR) and to analyze the contributions of various lower-boundary climate factors to the HWF_EUR variation. The examined lower-boundary climate factors were those that may contribute to the HWF_EUR variation—namely, the sea surface temperature, soil moisture, snow-cover extent, and sea-ice concentration from the simultaneous summer, preceding spring, and winter. These selected climate factors were significantly correlated to the summer HWF_EUR variation and were used to construct the ML model. Both the hindcast simulation of HWF_EUR for the period 1981–2020 and its real-time simulation for the period 2011–2020, which used the constructed ML model, were investigated. To evaluate the contributions of the climate factors, various model experiments using different combinations of the climate factors were examined and compared. The results indicated that the LightGBM model had comparatively good performance in simulating the HWF_EUR variation. The sea surface temperature made more contributions to the ML model simulation than the other climate factors. Further examination showed that the best ML simulation was that which used the climate factors in the preceding winter, suggesting that the lower-boundary conditions in the preceding winter may be critical in forecasting the summer HWF_EUR variation.摘要本文使用LightGBM机器学习模型模拟了欧洲东部夏季热浪频率的变化, 并分析了多个底边界层气候因子的贡献. 所选取的气候因子包括前期冬季, 前期春季以及同期夏季的下垫面海温, 土壤湿度, 积雪以及海冰. 分析结果说明LightGBM模型能够较好的模拟出欧洲东部夏季热浪频率的变化, 其中海温因子对模拟的贡献最大. 进一步的分析研究显示, 使用前期冬季的气候因子进行的模拟可以获得最佳模拟结果, 意味着前期冬季的下垫面气候因子可能对夏季欧洲东部热浪频率变化的预报能起到关键作用.     

Inversion method for multi-point source pollution identification: Sensitivity analysis and application to European Tracer Experiment data

Fast and accurate identification of unknown pollution sources plays a crucial role in the emergency response and source control of air pollution. In this work, the applicability of a previously proposed two-step inversion method is investigated with sensitivity experiments and real data from the first release of the European Tracer Experiment (ETEX-1). The two-step inversion method is based on the principle of least squares and carries out additional model correction through the residual iterative process. To evaluate its performance, its retrieval results are compared with those of two other existing algorithms. It is shown that for those cases with richer measurements, all three methods are less sensitive to errors, while for cases where measurements are sparse, their retrieval accuracy will rapidly decrease as errors increase. From the results of sensitivity experiments, the new method provides higher estimation accuracy and a more stable performance than the other two methods. The new method presents the smallest maximum location error of 18.20 km when the amplitude of the measurement error increases to 100%, and 22.67 km when errors in the wind fields increase to 200%. Moreover, when applied to ETEX-1 data, the new method also exhibits good performance, with a location error of 4.71 km, which is the best estimation with respect to source location.摘要快速并且准确地识别未知污染源, 在大气污染应急响应和源头控制过程中起着至关重要的作用. 本文利用敏感性试验及欧洲示踪物测场试验(ETEX-1)数据研究了新提出的两步反演算法的实用性, 并将其反演结果与现有的两种算法进行了对比分析. 敏感试验表明, 在观测数据较为丰富的情况下, 三种算法对观测误差和风场误差的敏感性均较低; 而当观测数据较为稀疏时, 所有算法的估计精度都将随着误差的增加而下降, 但与其他两种算法相比, 两步反演算法具有更高的估计精度以及更稳定的估计性能. 此外, 欧洲示踪物测场试验的源项估计结果也表明, 在三个算法中, 两步反演算法具有最小的位置估计误差.     

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.     

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.     

Discrepancies in surface temperature between NCEP reanalysis data and station observations over China and their implications: 中国地区NCEP再分析资料与台站观测的地表温度差异及其影响

Previous studies show that temporal irreversibility (TI), as an important indicator of the nonlinearity of time series, is almost uniformly overestimated in the daily air temperature anomaly series over China in NCEP reanalysis data, as compared with station observations. Apart from this highly overestimated TI in the NCEP reanalysis, some other important atmospheric metrics, such as predictability and extreme events, might also be overestimated since there are close relations between nonlinearity and predictability/extreme events. In this study, these issues are fully addressed, i.e., intrinsic predictability, prediction skill, and the number of extreme events. The results show that intrinsic predictability, prediction skill, and the occurrence number of extreme events are also almost uniformly overestimated in the NCEP reanalysis daily minimum and maximum air temperature anomaly series over China. Furthermore, these overestimations of intrinsic predictability, prediction skill, and the number of extreme events are only weakly correlated with the overestimated TI, which indicates that the quality of the NCEP reanalysis should be carefully considered when conclusions on both predictability and extreme events are derived.摘要作为时间序列非线性的一个重要指标, 从NCEP再分析得到日气温异常的时间不可逆性 (TI) 与观测站的相比几乎一致地被高估了.因为非线性与可预报性/极端事件之间有着密切的关系, 除了高估的TI外, 这些大气测度也可能被高估.本文结果表明:NCEP再分析的日最低和最高气温异常序列的内在可预报性,预报技巧和极端事件发生次数也几乎一致被高估.而且, 这些高估的测度与高估的TI只存在微弱的相关性, 这表明利用NCEP再分析研究可预测性和极端事件时, 需要仔细考虑其质量对结论的可能影响.     

Effect of vertical overturning circulation scale and moist static energy tendency on MJO phase speed

本文通过对1979-2019年ERA-I再分析资料进行诊断分析,研究了MJO垂直环流(VOC)纬向尺度和湿静力能(MSE)趋势纬向不对称性对MJO传播速度的综合影响.研究结果表明,MJO传播速度与VOC的纬向尺度和MSE趋势纬向梯度之间存在显著的正相关关系.基于上述两个参数,本文建立了线性回归模型,该模型可以较好的估计...     

Performance of CMIP6 models in simulating the dynamic sea level: Mean and interannual variance

Observational data from satellite altimetry were used to quantify the performance of CMIP6 models in simulating the climatological mean and interannual variance of the dynamic sea level (DSL) over 40°S–40°N. In terms of the mean state, the models generally agree well with observations, and high consistency is apparent across different models. The largest bias and model discrepancy is located in the subtropical North Atlantic. As for simulation of the interannual variance, good agreement can be seen across different models, yet the models present a relatively low agreement with observations. The simulations show much weaker variance than observed, and bias is apparent over the subtropics in association with strong western boundary currents. This nearshore bias is reduced considerably in HighResMIP models. The underestimation of DSL interannual variance is at least partially due to the misrepresentation of ocean processes in the CMIP6 historical simulation with its relatively low resolution. The results identify directions for future model development towards a better understanding of the mean and interannual variability of DSL.摘要本研究采用卫星测高数据与第六次国际耦合模式比较计划 (CMIP6) 海平面动力进行对比, 重点针对40°S–40°N地区的动力海平面 (DSL) , 评估了模式对其平均态与年际变率的综合模拟能力. 结果表明, 对于DSL平均态的模拟, 模式与观测结果非常吻合, 模式之间的差异较小. 其中, 副热带北大西洋是模拟偏差和模式间差异较为显著的区域. 对于DSL年际变率的模拟, 模式之间保持较高的一致性, 但是, 模式与观测结果存在明显差异, 模式普遍低估了DSL的年际方差; 其中, 误差大值区域出现在副热带西边界流附近. 模式分辨率会影响CMIP6对中小尺度海洋过程的重现能力, 这可能是导致CMIP6历史模拟出现误差的原因之一.     

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.     

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.     

Distinct patterns of monthly Southern Annular Mode events

The Southern Annular Mode (SAM) is the leading mode of atmospheric variability in the mid–high latitudes of the Southern Hemisphere, representing large-scale variations in pressure and the polar front jet (PFJ). In SAM events, the combination of the SAM and other modes may result in different atmospheric patterns. In this study, a neural-network-based cluster technique, the self-organizing map, was applied to extract the distinct patterns of SAM events on the monthly time scale based on geopotential height anomalies at 500 hPa. Four pairs of distinguishable patterns of positive and negative SAM events were identified, representing the diversity in spatial distribution, especially the zonal symmetry of the center of action at high latitudes—that is, symmetric patterns, split-center patterns, West Antarctica patterns, and a tripole pattern. Although the SAM is well known to be belt-shaped, within the selected SAM events, the occurrence frequency of symmetric patterns is only 23.8%—less than that of West Antarctica patterns. Diverse PFJ variations were found in the symmetric and asymmetric patterns of SAM events. The more asymmetric the spatial distribution of the pressure anomaly, the more localized the adjusted zonal wind anomaly. The adjusted PFJ varied in meridional displacement and strength in different patterns of SAM events. In addition, the entrance and exit of the jet changed in most of the patterns, especially in the asymmetric patterns, which might result in different climate impacts of the SAM.摘要南半球环状模 (SAM) 是南半球中–高纬度地区大气变化的主导模态, 表现为气压和极锋急流 (PFJ) 的大尺度变动, 形成强烈的气候影响. 当SAM事件发生时, 气压场异常可呈现出不同的空间结构. 本文利用自组织映射网络方法对月尺度的SAM事件进行分类, 可识别出四对具有显著差异的正, 负SAM事件类型, 包括对称型, 中心分裂型, 西南极洲型和一种三极型分布. 气压异常的空间分布越不对称, 调整后的纬向风异常越局地化. PFJ的经向位移和强度变化入口和出口的变化, 可能导致了SAM的不同气候影响.     

Land–sea breeze circulation structure on the west coast of the Yellow Sea,China: 中国黄海西岸海陆风循环结构研究

Land–sea breeze (LSB) is an atmospheric mesoscale circulation that occurs in the vicinity of the coast and is caused by uneven heating resulting from the difference in specific heat capacity between the sea and land surfaces. The circulation structure of LSB was quantitatively investigated with a Doppler wind lidar Windcube100s on the west coast of the Yellow Sea for the first time. The time of observation was 31 August to 28 September 2018. It was found that the height of LSB development was 700 m to 1300 m. The duration of conversion of LSB was between 6 h and 8 h. The biggest average horizontal sea-breeze wind speed at 425 m was 5.6 m s⁻¹, and at 375 m it was 4.5 m s⁻¹. During the conversion process from sea breeze to land breeze, the maximum wind shear exponent was 2.84 at 1300 m altitude. During the conversion process from land breeze to sea breeze, the maximum wind shear exponent was 1.28 at 700 m altitude. The differences in wind shear exponents between sea-breeze and land-breeze systems were between 0.2 and 3.6 at the same altitude. The maximum value of the wind shear exponent can reflect the height of LSB development.摘要陆海风是由于海陆表面之间的比热容不同而导致的昼夜热量分布差异, 从而在海岸附近引发的大气中尺度循环系统.本文利用多普勒风激光雷达Windcube100s首次对黄海西海岸的海陆风的循环结构进行了观测研究.在2018年8月31日至9月28日观测期间发现, 海陆风发展高度一般在700 m至1300 m.海陆风转化持续的时间为6小时至8小时.在425m高度, 海风水平风速出现最大值, 平均为 5.6 m s⁻¹.陆风最大水平风速出现在370 m, 约为4.5 m s⁻¹.最大风切变指数在1300m处, 为2.84;在陆风向海风转换过程中, 最大风切变指数在700m处, 为1.28.在同一高度上, 风切变指数在海风盛行和陆风盛行时的差值范围为0.2–3.6, 风切变能反映出海陆风的发展高度.