Possible impacts of climate change on extreme weather events at local scale in south–central Canada

Synoptic weather typing and regression-based downscaling approaches have become popular in evaluating the impacts of climate change on a variety of environmental problems, particularly those involving extreme impacts. One of the reasons for the popularity of these approaches is their ability to categorize a complex set of meteorological variables into a coherent index, facilitating the projection of changes in frequency and intensity of future daily extreme weather events and/or their impacts. This paper illustrated the capability of the synoptic weather typing and regression methods to analyze climatic change impacts on a number of extreme weather events and environmental problems for south–central Canada, such as freezing rain, heavy rainfall, high-/low-streamflow events, air pollution, and human health. These statistical approaches are helpful in analyzing extreme events and projecting their impacts into the future through three major steps or analysis procedures: (1) historical simulation modeling to identify extreme weather events or their impacts, (2) statistical downscaling to provide station-scale future hourly/daily climate data, and (3) projecting changes in the frequency and intensity of future extreme weather events and their impacts under a changing climate. To realize these steps, it is first necessary to conceptualize the modeling of the meteorology, hydrology and impacts model variables of significance and to apply a number of linear/nonlinear regression techniques. Because the climate/weather validation process is critical, a formal model result verification process has been built into each of these three steps. With carefully chosen physically consistent and relevant variables, the results of the verification, based on historical observations of the outcome variables simulated by the models, show a very good agreement in all applications and extremes tested to date. Overall, the modeled results from climate change studies indicate that the frequency and intensity of future extreme weather events and their impacts are generally projected to significantly increase late this century over south–central Canada under a changing climate. The implications of these increases need be taken into consideration and integrated into policies and planning for adaptation strategies, including measures to incorporate climate change into engineering infrastructure design standards and disaster risk reduction measures. This paper briefly summarized these climate change research projects, focusing on the modeling methodologies and results, and attempted to use plain language to make the results more accessible and interesting to the broader informed audience. These research projects have been used to support decision-makers in south–central Canada when dealing with future extreme weather events under climate change.     

A comparison of downscaling techniques in the projection of local climate change and wheat yields

This study aims to evaluate the performance of two mainstream downscaling techniques: statistical and dynamical downscaling and to compare the differences in their projection of future climate change and the resultant impact on wheat crop yields for three locations across New South Wales, Australia. Bureau of Meteorology statistically- and CSIRO dynamically-downscaled climate, derived or driven by the CSIRO Mk 3.5 coupled general circulation model, were firstly evaluated against observed climate data for the period 1980–1999. Future climate projections derived from the two downscaling approaches for the period centred on 2055 were then compared. A stochastic weather generator, LARS-WG, was used in this study to derive monthly climate changes and to construct climate change scenarios. The Agricultural Production System sIMulator-Wheat model was then combined with the constructed climate change scenarios to quantify the impact of climate change on wheat grain yield. Statistical results show that (1) in terms of reproducing the past climate, statistical downscaling performed better over dynamical downscaling in most of the cases including climate variables, their mean, variance and distribution, and study locations, (2) there is significant difference between the two downscaling techniques in projected future climate change except the mean value of rainfall across the three locations for most of the months; and (3) there is significant difference in projected wheat grain yields between the two downscaling techniques at two of the three locations.     

Evaluation of machine learning tools as a statistical downscaling tool: temperatures projections for multi-stations for Thames River Basin, Canada

Many impact studies require climate change information at a finer resolution than that provided by global climate models (GCMs). This paper investigates the performances of existing state-of-the-art rule induction and tree algorithms, namely single conjunctive rule learner, decision table, M5 model tree, and REPTree, and explores the impact of climate change on maximum and minimum temperatures (i.e., predictands) of 14 meteorological stations in the Upper Thames River Basin, Ontario, Canada. The data used for evaluation were large-scale predictor variables, extracted from National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis dataset and the simulations from third generation Canadian coupled global climate model. Data for four grid points covering the study region were used for developing the downscaling model. M5 model tree algorithm was found to yield better performance among all other learning techniques explored in the present study. Hence, this technique was applied to project predictands generated from GCM using three scenarios (A1B, A2, and B1) for the periods (2046–2065 and 2081–2100). A simple multiplicative shift was used for correcting predictand values. The potential of the downscaling models in simulating predictands was evaluated, and downscaling results reveal that the proposed downscaling model can reproduce local daily predictands from large-scale weather variables. Trend of projected maximum and minimum temperatures was studied for historical as well as downscaled values using GCM and scenario uncertainty. There is likely an increasing trend for T _max and T _min for A1B, A2, and B1 scenarios while decreasing trend has been observed for B1 scenarios during 2081–2100.     

Assessment of a stochastic downscaling methodology in generating an ensemble of hourly future climate time series

This study extends a stochastic downscaling methodology to generation of an ensemble of hourly time series of meteorological variables that express possible future climate conditions at a point-scale. The stochastic downscaling uses general circulation model (GCM) realizations and an hourly weather generator, the Advanced WEather GENerator (AWE-GEN). Marginal distributions of factors of change are computed for several climate statistics using a Bayesian methodology that can weight GCM realizations based on the model relative performance with respect to a historical climate and a degree of disagreement in projecting future conditions. A Monte Carlo technique is used to sample the factors of change from their respective marginal distributions. As a comparison with traditional approaches, factors of change are also estimated by averaging GCM realizations. With either approach, the derived factors of change are applied to the climate statistics inferred from historical observations to re-evaluate parameters of the weather generator. The re-parameterized generator yields hourly time series of meteorological variables that can be considered to be representative of future climate conditions. In this study, the time series are generated in an ensemble mode to fully reflect the uncertainty of GCM projections, climate stochasticity, as well as uncertainties of the downscaling procedure. Applications of the methodology in reproducing future climate conditions for the periods of 2000–2009, 2046–2065 and 2081–2100, using the period of 1962–1992 as the historical baseline are discussed for the location of Firenze (Italy). The inferences of the methodology for the period of 2000–2009 are tested against observations to assess reliability of the stochastic downscaling procedure in reproducing statistics of meteorological variables at different time scales.     

Statistical downscaling of daily climate variables for climate change impact assessment over New South Wales, Australia

This paper outlines a new statistical downscaling method based on a stochastic weather generator. The monthly climate projections from global climate models (GCMs) are first downscaled to specific sites using an inverse distance-weighted interpolation method. A bias correction procedure is then applied to the monthly GCM values of each site. Daily climate projections for the site are generated by using a stochastic weather generator, WGEN. For downscaling WGEN parameters, historical climate data from 1889 to 2008 are sorted, in an ascending order, into 6 climate groups. The WGEN parameters are downscaled based on the linear and non-linear relationships derived from the 6 groups of historical climates and future GCM projections. The overall averaged confidence intervals for these significant linear relationships between parameters and climate variables are 0.08 and 0.11 (the range of these parameters are up to a value of 1.0) at the observed mean and maximum values of climate variables, revealing a high confidence in extrapolating parameters for downscaling future climate. An evaluation procedure is set up to ensure that the downscaled daily sequences are consistent with monthly GCM output in terms of monthly means or totals. The performance of this model is evaluated through the comparison between the distributions of measured and downscaled climate data. Kruskall-Wallis rank (K-W) and Siegel-Tukey rank sum dispersion (S-T) tests are used. The results show that the method can reproduce the climate statistics at annual, monthly and daily time scales for both training and validation periods. The method is applied to 1062 sites across New South Wales (NSW) for 9 GCMs and three IPCC SRES emission scenarios, B1, A1B and A2, for the period of 1900–2099. Projected climate changes by 7 GCMs are also analyzed for the A2 emission scenario based on the downscaling results.     

Development of climate change projections for small watersheds using multi-model ensemble simulation and stochastic weather generation

Regional climate models (RCMs) have been increasingly used for climate change studies at the watershed scale. However, their performance is strongly dependent upon their driving conditions, internal parameterizations and domain configurations. Also, the spatial resolution of RCMs often exceeds the scales of small watersheds. This study developed a two-step downscaling method to generate climate change projections for small watersheds through combining a weighted multi-RCM ensemble and a stochastic weather generator. The ensemble was built on a set of five model performance metrics and generated regional patterns of climate change as monthly shift terms. The stochastic weather generator then incorporated these shift terms into observed climate normals and produced synthetic future weather series at the watershed scale. This method was applied to the Assiniboia area in southern Saskatchewan, Canada. The ensemble led to reduced biases in temperature and precipitation projections through properly emphasizing models with good performance. Projection of precipitation occurrence was particularly improved through introducing a weight-based probability threshold. The ensemble-derived climate change scenario was well reproduced as local daily weather series by the stochastic weather generator. The proposed combination of dynamical downscaling and statistical downscaling can improve the reliability and resolution of future climate projection for small prairie watersheds. It is also an efficient solution to produce alternative series of daily weather conditions that are important inputs for examining watershed responses to climate change and associated uncertainties.     

Future heat vulnerability in California, Part I: projecting future weather types and heat events

Excessive heat significantly impacts the health of Californians during irregular but intense heat events. Through the 21st century, a significant increase in impact is likely, as the state experiences a changing climate as well as an aging population. To assess this impact, future heat-related mortality estimates were derived for nine metropolitan areas in the state for the remainder of the century. Here in Part I, changes in oppressive weather days and consecutive-day events are projected for future years by a synoptic climatological method. First, historical surface weather types are related to circulation patterns at 500mb and 700mb, and temperature patterns at 850mb. GCM output is then utilized to classify future circulation patterns via discriminant function analysis, and multinomial logistic regression is used to derive future surface weather type at each of six stations in California. Five different climate model-scenarios are examined. Results show a significant increase in heat events over the 21st century, with oppressive weather types potentially more than doubling in frequency, and with heat events of 2?weeks or longer becoming up to ten times more common at coastal locations.     

1730年夏季黄淮地区暴雨极端事件研究

1730年（清雍正八年）7—8月,我国黄淮地区连续暴雨17 d,引起黄河、大运河和淮河暴涨、决堤的大范围洪涝灾害。这是小冰期中相对温暖时段气候背景下的极端气候事件。本文依据历史文献记载复原了1730年夏季暴雨时段的天气实况,绘制了暴雨区域图显示强降雨中心移动动态和水灾地域分布图,概述当年气候特点,指出暴雨事件与台风活动的关联。1730年是太阳活动周极小年的前3年、有重大火山活动,是极强的厄尔尼诺事件后的第2年。这些天气特点和背景条件与现代罕见的1975年河南“75?8暴雨”相似。     

近二十年来暴雨和强对流可预报性研究进展

大气可预报性研究是开展天气、气候预测的基础科学问题。全球变暖背景下,近年暴雨和强对流等中小尺度灾害性天气频发,如何深入认识其可预报性问题成为了天气领域研究热点,也是制约数值天气预报模式能力提升的重要因素。本文在简要回顾国内外大气可预报性研究历程的基础上,重点对近二十年（1999～2018）国际上关于暴雨和强对流可预报性方面的最新研究进展进行了系统的综述和归纳。主要包括:中小尺度可预报性研究的主要方法和评估手段及其与传统大尺度天气可预报性研究的差异,初始误差增长机制的几种主要观点及其争论（误差升尺度、误差降尺度、升降尺度并存）,数值模式误差和对流环境误差对实际预报性的影响,以及最近的中尺度可预报性科学观测试验进展等。最后,对暴雨、强对流可预报性研究存在的问题、未来发展方向进行了简要的讨论和展望。     

Climate change and soil freezing dynamics: historical trends and projected changes

Changes to soil freezing dynamics with climate change can modify ecosystem carbon and nutrient losses. Soil freezing is influenced strongly by both air temperature and insulation by the snowpack, and it has been hypothesized that winter climate warming may lead to increased soil freezing as a result of reduced snowpack thickness. I used weather station data to explore the relationships between winter air temperature, precipitation and soil freezing for 31 sites in Canada, ranging from the temperate zone to the high Arctic. Inter-annual climate variation and associated soil temperature variation over the last 40 years were examined and used to interpolate the effects of projected climate change on soil freezing dynamics within sites using linear regression models. Annual soil freezing days declined with increasing mean winter air temperature despite decreases in snow depth and cover, and reduced precipitation only increased annual soil freezing days in the warmest sites. Annual soil freeze–thaw cycles increased in both warm and dry winters, although the effects of precipitation were strongest in sites that experience low mean winter precipitation. Overall, it was projected that by 2050, changes in winter temperature will have a much stronger effect on annual soil freezing days and freeze–thaw cycles than changes in total precipitation, with sites close to but below freezing experiencing the largest changes in soil freezing days. These results reveal that experimental data relevant to the effects of climate changes on soil freezing dynamics and changes in associated soil physical and biological processes are lacking.     

Ice Storm ’98 in Southcentral Canada and Northeastern United States: A Climatological Perspective

Summary Dubbed Ice Storm ’98, an extreme weather event characterized by two synoptic systems in succession dropped about 70–100 mm (in terms of water equivalent) of freezing precipitation over southeastern Ontario, southwestern Quebec and northeastern New York during a 6-day period from January 5 to 10 in 1998. Individually, the two synoptic systems were not dramatically more extreme in freezing precipitation than other major freezing rain events (4 since 1961) which occurred in the past over the affected area. Some regions in the target area, however, were impacted more by the second system. Based on an analysis of the 500 hPa vorticity field during the ’98 event, we suggest that the 1997/98 El Ni?o had a role in creating a flow environment conducive to the rapid formation of the second synoptic system. In contrast, other major freezing rain events in the last 30 years involved only one synoptic system per event lasting no more than 3 days, and producing 20–50 mm of precipitation. We have also found that, 3 out of 4 past major freezing rain events since 1958 were associated with the positive phase of the North Atlantic Oscillation (NAO). Consistent with this usual past association between the NAO and a major freezing rain event, Ice Storm ’98 also occurred when the phase of the NAO was positive. Analysis of these 3 past and the ’98 events also indicates an apparent connection between the positive phase of the NAO and the northern Quebec high pressure system, which is an essential synoptic feature of a major freezing rain occurrence over the southcentral region of Canada. As measured by their respective indices, the maximum positive NAO state leads the maximum northern Quebec high by about 2 days (5 days in the ’98 event). There is some suggestive evidence to indicate that the persistence of the northern Quebec high pressure system is connected to the persistence of the positive phase of the NAO. Received January 17, 2000     

Potential impact of climate change on carbon in agricultural soils in Canada 2000–2099

Under the threat of global warming it is important to determine the impact that future changes in climate may have on the environment and to what extent any adverse effects can be mitigated. In this study we assessed the impact that climate change scenarios may have on soil carbon stocks in Canada and examined the potential for agricultural management practices to improve or maintain soil quality. Historical weather data from 1951 to 2001 indicated that semi-arid soils in western Canada have become warmer and dryer and air temperatures have increased during the spring and winter months. Results from the Canadian Center for Climate Modelling and Analysis (CCCma) Coupled Global Climate Model (CGCM1,2) under two climate change forcing scenarios also indicated that future temperatures would increase more in the spring and winter. Precipitation increased significantly under the IPCC IS92a scenario and agreed with historical trends in eastern Canada whereas the IPCC SRES B2 scenario indicated very little change in precipitation and better matched historical trends in western Canada. The Century model was used to examine the influence of climate change on agricultural soil carbon (C) stocks in Canada. Relative to simulations using historical weather data, model results under the SRES B2 climate scenario indicated that agricultural soils would lose 160 Tg of carbon by 2099 and under the IS92a scenario would lose 53 Tg C. Carbon was still lost from soils in humid climatic regions even though C inputs from crops increased by 10–13%. Carbon factors associated with changes in management practices were also estimated under both climate change scenarios. There was little difference in factors associated with conversion from conventional to no-till agriculture, while carbon factors associated with the conversion of annual crops to perennial grass were lower than for historical data in semi-arid soils because water stress hampered crop production but were higher in humid soils.     

西藏色齐拉山地区立体气候特征初步分析

利用西藏色齐拉山地区不同海拔高度的8个自动站和3个实测气象站1年的近地面观测资料,分析了该地区气温、地温、降水量、湿度和风速等气象要素的季节变化特征,探讨了东、西坡局地气候特征差异形成的原因。结果表明：色齐拉山地区1月为最冷月、7月为最暖月;月平均最高气温、最低气温与平均气温的季节变化一致。气温日较差大年较差小。年平均气温直减率东、西坡分别为0．54℃／100m和0．73℃／100m,西坡大于东坡。地气温差冬季西坡大于东坡,夏季东坡大于西坡。年、月平均地温直减率西坡仍大于东坡;东坡除夏季7、8月份外,地温直减率小于气温直减率;西坡除冬季（12月和1月）,地温直减率大于气温直减率。降水量东坡比西坡多,海拔2500m以上地区4～10月降水总量随着海拔高度的升高呈增加趋势,增加率为20．9mm／100m。空气相对湿度冬季低夏季高,年变化呈单峰型。东、西坡冬季风速较强夏季相对较弱,初春风速最大。东、西坡气候差异与海拔高度、坡向、下垫面性质有关。     

Super-ensemble of three RCMs for climate projection over East Asia and Taiwan

Runs of three regional climate models (RCMs) dynamically downscaling the outputs of atmosphere?Cocean coupling general circulation models (AOGCMs) are studied. These RCMs are NCAR-MM5, NCEP-RSM (Regional Spectral Model), and Purdue-PRM (Purdue Regional Model). A useful approach is developed to compare the variability, error, and spatial distribution of model-simulated results with respect to the Climatic Research Unit (CRU) datasets over East Asia and seven sub-regions during the 1990s. The results show that NCEP-RSM outperforms the other two in meeting criteria selected on evaluating the model performance. Furthermore, three super-ensemble approaches are tested on merging RCMs?? outputs. The inverse of the square error summation (ISES) method is selected as a suitable method with a generally good performance during the verification period. The projected future climate changes by ISES indicate larger temperature increases over high-latitude continent and smaller over low-latitude maritime areas. Rainfall will increase in summer over the central simulation domain, i.e. the eastern China, but decrease in winter, which are clearly linked to the variation in the synoptic airflows. Also, a more frequent occurrence of extreme rainfall events than what happened in the 1990s is projected. The projection over Taiwan suggests strong warming in summer, followed by autumn, winter, and spring. The interaction between the synoptic flow and the local terrain affects significantly the changes in precipitation. In general, larger change of the variability of rainfall will be over areas with lesser rainfall in the future, while lesser change will be over areas with more projected rainfall.     

Assessment of future climate change over East Asia due to the RCP scenarios downscaled by GRIMs-RMP

This study assesses future climate change over East Asia using the Global/Regional Integrated Model system—Regional Model Program (RMP). The RMP is forced by two types of future climate scenarios produced by the Hadley Center Global Environmental Model version 2 (HG2); the representative concentration pathways (RCP) 4.5 and 8.5 scenarios for the intergovernmental panel on climate change fifth assessment report (AR5). Analyses for the current (1980–2005) climate are performed to evaluate the RMP’s ability to reproduce precipitation and temperature. Two different future (2006–2050) simulations are compared with the current climatology to investigate the climatic change over East Asia centered in Korea. The RMP satisfactorily reproduces the observed seasonal mean and variation of precipitation and temperature. The spatial distribution of the simulated large-scale features and precipitation by the RMP is generally less reflective of current climatic conditions than that is given by the HG2, but their inter-annual variations in East Asia are better captured by the RMP. Furthermore, the RMP shows higher reproducibility of climate extremes including excessive heat wave and precipitation events over South Korea. In the future, strong warming is distinctly coupled with intensified monsoonal precipitation over East Asia. In particular, extreme weather conditions are increased and intensified over South Korea as follows: (1) The frequency of heat wave events with temperature greater than 30 °C is projected to increase by 131 and 111 % in the RCP 8.5 and 4.5 downscaling, relative to the current climate. (2) The RCP 8.5 downscaling shows the frequency and variability of heavy rainfall to increase by 24 and 31.5 %, respectively, while the statistics given by the RCP 4.5 downscaling are similar to those of the current climate.     

Improved atmospheric circulation over Europe by the new generation of CMIP6 earth system models

Global Climate Models (GCMs) generally exhibit significant biases in the representation of large-scale atmospheric circulation. Even after a sensible bias adjustment these errors remain and are inherited to some extent by the derived downscaling products, impairing the credibility of future regional projections. In this study we perform a process-based evaluation of state-of-the-art GCMs from CMIP5 and CMIP6, with a focus on the simulation of the synoptic climatological patterns having a most prominent effect on the European climate. To this aim, we use the Lamb Weather Type Classification (LWT, Lamb British isles weather types and a register of the daily sequence 736 of circulation patterns 1861-1971. METEOROL OFF, GEOPHYS MEM; 737 GB; DA 1972; NO 116; PP 1-85; BIBL 2P1/2, 1972), a subjective classification of circulation weather types constructed upon historical simulations of daily mean sea level pressure. Observational uncertainty has been taken into account by considering four different reanalysis products of varying characteristics. Our evaluation unveils an overall improvement of salient atmospheric circulation features consistent across observational references, although this is uneven across models and large frequency biases still remain for the main LWTs. Some CMIP6 models attain similar or even worse results than their CMIP5 counterparts, although in most cases consistent improvements have been found, demonstrating the ability of the new models to better capture key synoptic conditions. In light of the large differences found across models, we advocate for a careful selection of driving GCMs in downscaling experiments with a special focus on large-scale atmospheric circulation aspects.

     

广东省暴雨强对流历史信息查询系统的设计

利用Visual Basic5.0设计了广东省暴雨强对流历史信息查询系统，该系统不仅能查询暴雨和强对流天气的历史资料，而且能查询发生某类天气的天气形势。此外，系统还提供实时天气图与历史天气图之间的比较，对预报非常有帮助。     

2008年初粤北罕见的低温雨雪冰冻天气过程分析

利用NCEP地面、探空、海温等分析资料,以及本地自动站和灾情资料,对2008年1月中旬-2月中旬粤北一场罕见的低温雨雪冰冻天气过程的特点、气候背景、地面和高空形势进行研究分析。结果发现：长时间的冻雨和雨夹雪是造成粤北损失巨大的主要原因,冷暖空气长时间在粤北上空交汇是造成雨雪天气的直接原因,拉尼娜事件引起大气环流异常是这次过程的气候背景,异常偏强的副热带高压和孟加拉湾到广西一带低槽的共同作用,为粤北上空输送了强盛的西南暖湿气流,北方长时间稳定的强冷空气从边界层侵入粤北后的冷舌,与对流层中低层的暖舌构成的逆温层,为冻雨的出现提供了有利的大气层结,极涡异常偏心对这次过程有指示意义。     

Winter weather regimes over the Mediterranean region: their role for the regional climate and projected changes in the twenty-first century

The winter time weather variability over the Mediterranean is studied in relation to the prevailing weather regimes (WRs) over the region. Using daily geopotential heights at 700 hPa from the ECMWF ERA40 Reanalysis Project and Cluster Analysis, four WRs are identified, in increasing order of frequency of occurrence, as cyclonic (22.0 %), zonal (24.8 %), meridional (25.2 %) and anticyclonic (28.0 %). The surface climate, cloud distribution and radiation patterns associated with these winter WRs are deduced from satellite (ISCCP) and other observational (E-OBS, ERA40) datasets. The LMDz atmosphere–ocean regional climate model is able to simulate successfully the same four Mediterranean weather regimes and reproduce the associated surface and atmospheric conditions for the present climate (1961–1990). Both observational- and LMDz-based computations show that the four Mediterranean weather regimes control the region’s weather and climate conditions during winter, exhibiting significant differences between them as for temperature, precipitation, cloudiness and radiation distributions within the region. Projections (2021–2050) of the winter Mediterranean weather and climate are obtained using the LMDz model and analysed in relation to the simulated changes in the four WRs. According to the SRES A1B emission scenario, a significant warming (between 2 and 4 °C) is projected to occur in the region, along with a precipitation decrease by 10–20 % in southern Europe, Mediterranean Sea and North Africa, against a 10 % precipitation increase in northern European areas. The projected changes in temperature and precipitation in the Mediterranean are explained by the model-predicted changes in the frequency of occurrence as well as in the intra-seasonal variability of the regional weather regimes. The anticyclonic configuration is projected to become more recurrent, contributing to the decreased precipitation over most of the basin, while the cyclonic and zonal ones become more sporadic, resulting in more days with below normal precipitation over most of the basin, and on the eastern part of the region, respectively. The changes in frequency and intra-seasonal variability highlights the usefulness of dynamics versus statistical downscaling techniques for climate change studies.     

1997年全球重大气候事件概述

近年来,全球气候持续偏暖,１９９７年又成为一个多世纪以来最暖的一年。年内赤道中、东太平洋发生了一次本世纪最强的厄尔尼诺事件,全球气候受到重大影响,特别是热带地区出现了大范围的气候异常,高温干旱、暴雨洪水事件此起彼伏,连续不断,造成严重灾害。欧洲、北美前冬和春季严寒多雪;欧洲夏季暴雨频繁,中欧发生了百年不遇的特大洪水;中国北部和朝鲜出现罕见的持续高温干旱天气;美国和加拿大边境地区发生了一百多年来最严