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.     

Evaluation of CMP AS precipitation products over Sichuan,China

高质量和高分辨率的降水产品在天气预报,数值模式模拟和气象防灾减灾方面起着重要的作用.本文利用四川地区高密度的地面降水传感器观测数据,比较CMPAS四种不同时空尺度的降水实况分析产品,评估CMPAS的融合准确性与在四川地区的适用性.研究表明:四种CMPAS降水产品都在四川盆地内精度较高,攀西地区和川西高原次之.随着降水量...     

Comparisons in the global planetary boundary layer height obtained from COSMIC radio occultation,radiosonde, and reanalysis data 基于无线电掩星数据, 探空资料以及再分析资料估算大气边界层高度的比较

The global planetary boundary layer height (PBLH) estimated from 11 years (2007–17) of Integrated Global Radiosonde Archive (IGRA) data, Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) soundings, and European Center for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) data, are compared in this study. In general, the spatial distribution of global PBLH derived from ERA-Interim is consistent with the one from IGRA, both at 1200 UTC and 0000 UTC. High PBLH occurs at noon local time, because of strong radiation energy and convective activity. There are larger differences between the results of COSMIC and the other two datasets. PBLHs derived from COSMIC are much higher than those from radiosonde and reanalysis data. However, PBLHs derived from the three datasets all exhibit higher values in the low latitudes and lower ones in the high latitudes. The latitudinal difference between IGRA and COSMIC ranges from −1700 m to −500 m, while it ranges from −500 m to 250 m for IGRA and ERA-Interim. It is found that the differences among the three datasets are larger in winter and smaller in summer for most studied latitudes.摘要用11年的全球无线电掩星数据 (COSMIC) , 无线电探空数据 (IGRA) 以及欧洲中心再分析资料 (ERA-Interim) 对全球大气边界层高度 (PBLH) 进行估算比较. 结果表明: (1) 在1200 UTC和0000 UTC, 由ERA-Interim和IGRA数据估算得到的全球PBLH空间分布较为一致, 相关性较好, 在白天正午时候太阳辐射能力较强, 对流活动频繁, 估算得到的大气边界层高度较高. (2) 由COSMIC掩星数据估算得到的边界层高度比探空数据和再分析数据估算结果整体偏大. (3) COSMIC掩星数据, IGRA 探空数据以及 ERA-Interim 再分析资料估算结果都表明边界层高度在低纬度地区偏大, 高纬度地区偏小. (4) 分析不同数据估算边界层高度纬向季节性差异表明, IGRA探空数据和COSMIC数据间差异为-1700m至-500m, IGRA与ERA-Interim之间的差异为-500m至250m.此外, 对于大多数纬度而言, 三个数据集之间的差异在冬季较大, 在夏季较小.     

Computational uncertainty and optimal grid size and time step of the Lax–Friedrichs scheme for the 1D advection equation

This paper examines truncation and round-off errors in the numerical solution of the 1D advection equation with the Lax–Friedrichs scheme, and accumulation of the errors as they are propagated to high temporal layers. The authors obtain a new theoretical approximation formula for the upper bound of the total error of the numerical solution, as well as theoretical formulae for the optimal grid size and time step. The reliability of the obtained formulae is demonstrated with numerical experimental examples. Next, the ratio of the optimal time steps under two different machine precisions is found to satisfy a universal relation that depends only on the machine precision involved. Finally, theoretical verification suggests that this problem satisfies the computational uncertainty principle when the grid ratio is fixed, demonstrating the inevitable existence of an optimal time step size under a finite machine precision.摘要本文对于应用Lax- Friedrichs 格式数值求解一维平流方程, 研究数值求解过程中产生的截断误差与舍入误差, 以及两种误差逐层向高时间层传播的累积, 得到新的数值解总误差上界的理论近似公式, 以及最优格距和最优时间步长的理论公式. 通过数值算例验证了所得公式的可靠性. 然后, 发现了两种不同机器精度下最优时间步长之比满足的一个仅与机器精度有关的普适关系. 最后, 理论验证了在网格比固定的情况下, 此问题满足数值计算的不确定性原理, 以及在机器有限精度下最优时间步长的必然存在.     

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.     

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.     

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.     

Possible contribution of Arctic sea ice decline to intense warming over Siberia in June

西伯利亚地区异常的升温可能会给生态系统带来灾难性的影响.本文从气候角度分析西伯利亚地区初夏升温的特征以及北极海冰减小的可能贡献.观测和再分析资料表明,1979-2020年间西伯利亚地区6月地表气温有很强的升温趋势(0.9℃/10年),明显高于同纬度地区平均的升温趋势(0.46℃/10年).升温从地表延伸至300hPa左...     

Analysis of aerosol distribution variations over China for the period 2045–2050 under different Representative Concentration Pathway scenarios: 不同代表性浓度路径情景下 2045-2050 年中国气溶胶分布变化的分析

The regional air quality modeling system RAMS-CMAQ was applied to simulate the aerosol concentration for the period 2045–2050 over China based on the downscaled meteorological field of three RCP scenarios from CESM (NCAR's Community Earth System Model) in CMIP5. The downscaling simulation of the meteorological field of the three RCP scenarios showed that, compared with that under RCP2.6, the difference in near-surface temperature between North and South China is weakened and the wind speed increases over North and South China and decreases over central China under RCP4.5 and RCP8.5. Under RCP2.6, from 2045 to 2050, the modeled average PM_2.5 concentration is highest, with a value of 40–50 µg m⁻³, over the North China Plain, part of the Yangtze River Delta, and the Sichuan Basin. Meanwhile, it is 30–40 µg m⁻³ over central China and part of the Pearl River Delta. Compared with RCP2.6, PM_2.5 increases by 4–12 µg m⁻³ under both RCP4.5 and RCP8.5, of which the SO₄²⁻ and NH₄⁺ concentration increases under both RCP4.5 and RCP8.5; the NO₃⁻ concentration decreases under RCP4.5 and increases under RCP8.5; and the black carbon concentration changes very slightly, and organic carbon concentration decreases, under RCP4.5 and RCP8.5, with some increase over part of Southwest and Southeast China under RCP8.5. The difference between RCP4.5 and RCP2.6 and the difference between RCP8.5 and RCP2.6 have similar annual variation for different aerosol species, indicating that the impact of climate change on different species tends to be consistent.摘要基于来自于 CMIP5 中 CESM 模式的三种 RCP 情景下的气象场的降尺度模拟, 应用区域空气质量模式系统 RAMS-CMAQ 模拟 2045-2050 年中国地区气溶胶浓度.三种 RCP 情景下气象场的降尺度模拟表明, 与 RCP2.6 相比, 在 RCP4.5 和 RCP8.5 下, 华北和华南的近地表温度差减小, 风速在华北和华南地区增加, 在中部地区下降. RCP2.6 情景下, 模拟的 2045 年到 2050 年平均的 PM _2.5浓度在华北平原, 长三角的部分地区和四川盆地最高, 约为 40-50 µg m^–3, 在中国中部和珠三角的部分地区约为 30-40 µg m^–3. 与 RCP2.6 相比, 在 RCP4.5 和 RCP8.5 下, PM_2.5增加了 4-12 µg m^–3, 其中在 RCP4.5 和 RCP8.5 下, SO₄^2–和 NH₄⁺的浓度增加, 在 RCP4.5 下, NO^3–浓度降低, 在 RCP8.5 下, NO^3–浓度升高, 在 RCP4.5 和 RCP8.5 下, BC 浓度变化很小, 而 OC 浓度下降, 其中在 RCP8.5 下, 西南和东南部分地区的 OC 有所增加.不同的气溶胶物种浓度在 RCP4.5 和 RCP2.6 之间的差异以及 RCP8.5 和 RCP2.6 之间的差异具有相似的年度变化, 这表明气候变化对不同物种的影响趋于一致.     

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.     

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.     

Idealized numerical simulation experiment of ice seeding in convective clouds using a bin microphysics scheme

A 2D axisymmetric bin model is used to conduct idealized numerical experiments of cloud seeding. The simulations are performed for two clouds that differ in their initial wind shear. Results show that, although cloud seeding with an ice concentration of 1000 L^?1 in a regime that has relatively high supercooled liquid water can obtain a positive effect, the rainfall enhancement seems more pronounced when the cloud develops in a wind shear environment. In no-shear environment, the change in the microphysical thermodynamic field after seeding shows that, although more graupel is produced via riming and this can increase the surface rainfall intensity, the larger drag force and cooling of melting graupel is unfavorable for the development of cloud. On the contrary, when the cloud develops in a wind shear environment, since the main downdraft is behind the direction of movement of the cloud, its negative effect on precipitation is much weaker.摘要本文采用二维轴对称分档云模式开展了人工催化数值试验, 对两种不同初始风切变的对流云进行了模拟. 结果表明, 尽管在过冷水相对较高的区域播撒 1000 L^?1 的冰晶可以增加地面降水, 但当云在风切变环境中发展时, 人工播撒对降雨增强的作用似乎更加明显. 在无切变环境下, 微物理量, 热力场的变化表明播撒后大量的霰所产生的拖曳力和融化冷却有可能切断主上升气流, 从而不利于云的发展. 相反, 当云在风切变环境中发展时, 由于播撒产生的主下沉气流位于云的运动方向之后, 其对降水的负面影响要弱得多.     

Evaluating the performance of a WRF microphysics ensemble through comparisons with aircraft observations: 与飞机观测资料对比评估WRF微物理方案集合的模拟能力

Aircraft observation data obtained in a mesoscale convective system are compared to Weather Research and Forecasting (WRF) model simulations using four microphysics schemes (Morrison, WSM6, P3, SBM) with different complexities. The main purpose of this paper is to assess the performance of the microphysics ensemble in terms of cloud microphysical properties. Results show that although the vertical distributions of liquid water content (LWC) and ice water content (IWC) simulated by the four members are quite different in the convective cloud region, they are relatively uniform in the stratiform cloud region. Overall, the results of the Morrison scheme are very similar to the ensemble average, and both of them are closer to the observations compared to the other schemes. Besides, the authors also note that all members still overpredict the LWC by a factor of 2–8 in some regions, resulting in large deviation between the observation and ensemble average.摘要使用 WRF 模式中的 Morrison,WSM6,SBM,P3 四种微物理方案的集合, 模拟中尺度对流系统降水过程.研究发现不同的微物理方案模拟的对流云区液态含水量,冰水含量的垂直分布各不相同, 而模拟的层状云区液态含水量, 冰水含量的垂直分布结果相似. 总的来说与其他方案相比, Morrison 方案和集合平均的结果最接近观测值.我们也注意到在一些区域, 所有成员均高估了液态含水量 2–8 倍, 这也导致了在这些区域集合平均值与观测相比仍然有很大的差距.     

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.     

Soil–vegetation–atmosphere interaction over a hilly forest in the southern Huaihe River Basin: Long-term field experiment and preliminary analysis of basic observations 淮河流域南部丘陵森林地区土壤-植被-大气相互作用:长期野外试验观测的初步分析

To improve the understanding of the CO₂ exchange and the cycling of energy and water between the land surface and atmosphere over a typical hilly forest in southeastern China, a long-term field experimental observatory was established in Huainan, Anhui Province. Here, the authors briefly describe the three parts of ongoing research activities: the environmental monitoring at the site, the meteorological observations on a high tower, and particularly the intensive measurement of soil–vegetation–atmosphere interaction on a lower tower. Specifically, the diurnal variation of basic meteorological variables on a typical clear day (13 July 2018), and their temporal variation in the first three months of the low tower's operation (4 June to 31 August 2018), and in combination with simultaneous data from the high tower, are analyzed. Results show that the data demonstrate reasonable variabilities, and the variables exhibit significant diurnal variation, characteristics of summer values, and considerable differences in summer months. The daily and monthly average albedos above the forest canopy were both 0.13. The daily average soil CO₂ concentration was 1726 and 4481 ppm at 2 and 10 cm, respectively. The soil CO₂ concentration changed with soil volumetric moisture contents, but showed a weak correlation with soil temperature in summer 2018. As the observatory continues to run and data continue to be collated, further investigation of the long-term variation of monsoon characteristics should be performed in the future. The experiment is useful in ecosystem and atmosphere interaction research, as well as for the development and evaluation of climate models, in the transitional climate zone of the Huaihe River basin.摘要本文简要介绍了包括三部分观测的安徽淮南长期野外试验观测站, 特别是土壤-植被-大气的集中观测, 对小塔运行前三个月 (2018年6月至8月) 的数据, 并结合同一时段大塔获得的数据, 进行了初步分析.结果表明这些资料有合理的变化特征, 日变化和夏季值特征显著, 各月份间气象变化有明显差异.土壤水分和温度受降雨影响, 在不同的下垫面条件下表现出不同的变化.土壤CO₂日平均浓度在2 cm和10 cm处分别为1726和4481 ppm.2018年夏季土壤CO₂浓度随土壤体积含水量的变化而变化, 但与土壤温度呈弱相关.     

Detection of the anthropogenic signal and urbanization effects in extreme temperature changes in eastern China

With its rapid rise in temperatures and accelerated urbanization in recent decades, eastern China may be affected by both global warming and the urban heat island effect. To investigate the influence of anthropogenic forcing and urbanization on extreme temperature, the authors conducted detection and attribution analyses on 16 extreme indices using extended observational data during 1958–2020 and the models that participated in CMIP5 and CMIP6. The extended observational data till 2020 show continued warming in extreme temperatures in recent years. Most of the indices display an increase in warm extremes and decrease in cold extremes. Both CMIP5 and CMIP6 models are able to reflect these warming features, albeit the models can over- or underestimate some extreme indices. The two-signal detection with anthropogenic and urbanization effects jointly considered showed that the anthropogenic and urban signals can be simultaneously detected and separated only in two frequency indices, i.e., the frequency of warm and cold nights. The anthropogenic forcing explains about two-thirds of the warming, while URB contributes about one-third for these two indices. For most of the other indices, only the anthropogenic signal can be detected. This indicates that the urban signal is distinct from the natural variability mainly for the nighttime frequency indices but not for the other extreme temperature indies. Given the important influence of nighttime extremes on human health, this suggests an urgent need for cities to adapt to both global warming and urbanization.摘要作为中国经济最发达的地区, 中国东部受到城市热岛效应和温室气体排放等人类活动的明显影响. 本文利用最新的观测和全球气候模式资料, 对极端温度强度, 频率和持续时间等16个极端温度指数进行了检测归因分析, 研究了人为强迫和城市化效应对中国东部极端温度变化的影响. 结果表明, 近年来极端温度持续增暖, 极端暖事件增加, 极端冷事件减少. 新一代全球气候模式能够合理地反映这些变暖特征, 但是部分模式可能高估或低估了观测到的变化. 基于最优指纹方法的双信号检测表明, 人为信号和城市化效应只能在暖夜和冷夜两个频率指标上同时被检测并分离, 其变化约三分之二可归因于人类活动, 剩余的三分之一可归因于城市化效应. 而在极端温度其他指数的变化中, 只有人类活动的影响能够被检测到.     

Different response of North Pacific storm tracks in the upper and lower troposphere to jet strength

The midwinter suppression of North Pacific storm tracks (NPSTs) reflects that the linear relationship between the NPST and baroclinicity breaks in winter. Based on the reanalysis data during the cold seasons of 1979–2019 and a tracking algorithm, this study analyzes the eddy growth process and shows that an enhanced upper-tropospheric jet favors the generation of upper-level eddies on the northeast side of the Pacific jet, but increasingly suppresses the generation of those in the Northwest Pacific. The upper-level eddies generated upstream of the jet core are unable to grow sufficiently throughout the whole cold season, and only those generated downstream of the jet core can grow normally and constitute the main body of the upper-level NPST. By contrast, the main lower-level eddy genesis area and growth area coincide with the baroclinic zone, with the genesis number and local growth rate increasing with the baroclinicity.摘要北太平洋风暴轴的深冬抑制表明风暴轴强度与斜压性之间的线性关系在冬季破裂. 本研究基于1979–2019年冷季的再分析数据和拉格朗日跟踪算法, 对比分析了高低层扰动的具体生长过程. 结果表明太平洋急流的增强有利于高层扰动在急流核东北侧产生, 但却抑制其在西北太平洋的生成. 在急流核上游产生的高层扰动在整个冷季都无法充分发展, 只有在急流核下游产生的高层扰动才能正常生长且它们是构成高层太平洋风暴轴的主体. 相比之下, 低层扰动的生成区和生长区都与斜压区重合, 并且它们的生成数量和局部增长率随着斜压性的增强而增强.     

A machine learning approach to quality-control Argo temperature data

A machine learning approach is proposed to identify temperature outliers from Argo float profiles as a complementary procedure to current Argo quality control. A machine learning unsupervised classification (i.e., the Gaussian mixture model, GMM) is applied to cluster the Argo data into classes to construct convex hulls with the smallest polygons encompassing all the data points. Good or bad temperature data are identified as within or outside the polygons based on point-in-polygon analysis implemented by the ray casting algorithm. The South China Sea was selected as an example and results showed that the proposed approach could identify more than 70% of the profiles containing the outliers and mark the outliers automatically at the same time. This highlights the potential of the proposed methodology to be a good complementary quality control method.摘要本文提出了一种基于机器学习的Argo浮标温度异常值检测方法. 该方法采用机器学习无监督算法高斯混合模型对Argo浮标数据进行聚类分析, 并构建包围所有数据点的最小多边形的凸包. 基于射线投影算法实现点在多边形内分析, 通过自动识别数据点位于凸包内外来判断该数据点数据质量的好坏. 本文采用南海区域Argo浮标数据对该方法进行测试, 结果表明该方法可以识别70%以上的包含异常值的温度剖面, 同时自动标记出各异常值点.     

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.     

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.