MM5V3模式对IPCCA1B情景下中国地区极端事件的模拟和预估

利用MM5V3区域气候模式单向嵌套ECHAM5全球环流模式,对中国地区1978-2000年及IPCC A1B情景下2038-2070年气候分别进行了水平分辨率为50 km的模拟试验.文章首先检验了模式模拟的当代极端气候结果,在此基础上对6个极端温度指数和6个极端降水指数的未来变化进行了预估.检验结果表明:MM5V3模式对中国地区当代日最高、最低温度及强降水(大雨和暴雨)日数的空间分布和概率特征均具有一定的模拟能力,但模拟的日最高温度在大部分地区偏低,日最低温度在南方地区偏低、西北地区偏高.概率统计结果显示日最高温度向低值频段偏移,日最低温度在0℃的峰值附近明显偏高.模式对大雨和暴雨年平均日数的模拟在东部地区偏多,概率统计结果则为一致偏大.未来中国地区极端气候预估结果表明:极端高温、极端低温和相对高温在全国范围内都将升高,且线性趋势均为上升;霜日日数则为减少,并具有下降趋势;暖日日数和相对低温在青藏高原和新疆部分地区有所减少、其它地区均为增加,且线性趋势暖日日数为上升,相对低温不明显.极端降水指数的变化具有区域特征,其中单日最大降水、连续五日最大降水、最长无雨期、强降水日数、简单降水强度和极端降水总量均在江淮、华南及西南地区有所增多,而在东北及内蒙古地区有所减少,未来中国南方地区降水的极端化趋势将更加显著.极端降水指数的线性趋势除最长无雨期外其它均为上升.     

2020年梅雨期极端降水的归因探讨和未来风险预估研究

2020年夏季我国经历了一场非同寻常的梅雨季,其持续时间长、暴雨日数多,为近几十年罕见.梅雨期间从日尺度到月尺度的降水均显著偏多,特别是持续性强降水(如连续四周最大累积降水量, Rx28day),较气候平均态偏多94%,打破了自1961年以来的历史记录.探讨人为强迫对此次梅雨期极端降水的影响及其物理过程,有助于理解和预估极端气候的风险变化.利用第六次耦合模式比较计划(CMIP6)检测归因模式比较计划(DAMIP),从事件归因角度,研究指出,人为强迫使得2020年夏季长江中下游流域持续性强降水(Rx28day)事件的发生概率减小了46%(22～62%).其中,温室气体有利于增加类似极端事件的发生概率(44%),这由增温引起的可降水量增加导致;而人为气溶胶则减少了其发生概率(73%),这与地表降温引起的可降水量减少、东亚夏季风环流减弱有关.未来随着温室气体的排放增加和人为气溶胶的减排,类似持续性强降水事件的发生概率将持续增加.在不同的共享社会经济路径(SSP)下,未来温室排放的情景越高,这类极端降水事件的发生风险越高.在低排放情景SSP1-2.6、中等排放情景SSP2-4.5和高排放情景SSP5-8.5下,到21世纪末,其发生概率分别约为当前气候下的4.6、13.6和27.7倍.因此,采取切实有效的温室气体减排措施,将有利于减缓极端降水事件的发生风险.     

多区域模式集合的东亚陆地区域的平均和极端降水未来预估

利用东亚区域联合降尺度计划(CORDEX-EA) 15个区域模式的模拟结果,集合预估了高排放情景RCP8.5下东亚陆地区域平均和极端降水的未来时空变化,并量化未来预估的不确定性.结果 表明:区域模式基本上能够再现东亚及各个区域平均和极端降水的多年平均分布.未来多模式集合预估的平均和极端强降水在东亚各区域多表现为增加,连续无降水日数(CDD)表现为南增北减,且变幅多随时间增大.到21世纪末期,冬季和年平均降水的增幅大值都位于中国西部(WC),冬季降水的变化在WC、蒙古(MG)、中国东北(NE)和中国华北及西北地区东部(NC)的确定性都较高,年降水的变化仅在WC和MG确定性较高.夏季降水增幅大值位于朝鲜半岛和日本(KJ),且仅在这一区域确定性较高.最大5日降水量(Rx5day)和大雨日数(R20)以增加为主且变化的空间分布较为均匀,除去中国江南及华南(SC)和KJ的R20变化,其余区域两个变量的变化确定性都较高.CDD的增幅和减幅大值分别位于SC和MG,其变化在MG、NE和SC确定性较高.     

SRES B2情景下中国区域最高、最低气温及日较差变化分布特征初步分析

本文利用Hadley气候预测与研究中心的区域气候模式系统PRECIS进行中国区域气候基准时段(1961～1990年)和SRES B2情景下2071～2100年(2080s)最高、最低气温及日较差变化响应的分析.气候基准时段的模拟结果与观测资料的对比分析表明：PRECIS具有对中国区域最高、最低气温及日较差的模拟能力，能够模拟出中国区域最高、最低气温及日较差的局地分布特征.对SRES B2情景下相对于气候基准时段的最高、最低气温及日较差变化响应分析表明：中国区域2080s时段年、冬季和夏季平均最高、最低气温变化均呈一致增加的趋势，北方地区增温幅度普遍大于南方地区.夏季东北地区极端高温事件发生的频率将会增加，而冬季华北地区极端冷害事件发生频率将会减少.未来中国区域年平均日较差将出现北方地区减小而南方地区增加的趋势.冬季长江中下游以南地区日较差呈增加趋势，而夏季华东地区、西北地区及内蒙古中部日较差将呈减小趋势，其中在青藏高原北部地区存在一个较强的低值中心.     

中国平均降水和极端降水对气候变暖的响应:CMIP5模式模拟评估和预估

基于24个CMIP5全球耦合模式模拟结果,分析了中国区域年平均降水和ETCCDI强降水量(R95p)、极端强降水量(R99p)对增暖的响应.定量分析结果显示,CMIP5集合模拟的当代中国区域平均降水对增温的响应较观测偏弱,而极端降水的响应则偏强.对各子区域气温与平均降水、极端降水的关系均有一定的模拟能力,并且极端降水的模拟好于平均降水.RCP4.5和RCP8.5情景下,随着气温的升高,中国区域平均降水和极端降水均呈现一致增加的趋势,中国区域平均气温每升高1℃,平均降水增加的百分率分别为3.5%和2.4%,R95p增加百分率为11.9%和11.0%,R99p更加敏感,分别增加21.6%和22.4%.就各分区来看,当代的区域性差异较大,未来则普遍增强,并且区域性差异减小,在持续增暖背景下,中国及各分区极端降水对增暖的响应比平均降水更强,并且越强的极端降水敏感性越大.未来北方地区平均降水对增暖的响应比南方地区的要大,青藏高原和西南地区的R95p和R99p增加最显著,表明未来这些区域发生暴雨和洪涝的风险将增大.     

气候变化对中亚五国粮食安全的影响

随着全球气候变暖趋势的逐渐加剧,气候变化对粮食安全造成的影响引发社会各界广泛关注.中亚国家深处亚洲内陆,生态环境脆弱,农业技术较低,面临着严峻的气候变化威胁.基于1990～2019年中亚五国的面板数据,构建了C-D-C模型,研究气候变化对该区粮食安全的影响,并预测未来发展趋势.研究发现,过去30年中亚五国粮食安全水平整体呈上升趋势,其中哈萨克斯坦粮食安全指数较高,而塔吉克斯坦粮食安全水平较低.年均温和年降水对五国的粮食安全影响存在倒U型关系,其中对哈萨克斯坦粮食安全的积极影响最大;极端高温和极端低温对中亚五国粮食安全具有显著的负向影响,其中对土库曼斯坦粮食安全的消极影响最大;霜冻日数对粮食安全的影响不显著.未来气候预测结果显示, 2030～2090年中亚气温和降水量有继续升高趋势,整体将对五国粮食安全具有持续抑制作用.建议各国提高气候风险认识,加强气候科学研究,提前制定多方适应策略;同时,加强国际合作,有效减少温室气体排放,强化保障粮食安全的能力.     

温室效应对未来长江中下游地区温度和降水变化的影响

使用全球海气耦合模式和区域气候模式,对人类活动影响导致的温室气体和气溶胶增加引起的长江中下游地区的气候变化进行了分析研究.全球模式部分使用的是IPCC数据分发中心提供的5个模式模拟结果,包括IS92a中未来温室气体增加(GG)以及温室气体和硫化物气溶胶共同增加(GS)和A2、B2共4种排放情景.分析表明在温室气体增加的情况下,这里未来的地面气温变化与全球和全国一样,都呈增加趋势.以GG和GS为例,GG情景下,这一地区的变暖幅度在21世纪末期达到4.2℃, GS情景下达到3.1℃.但总体来说这里的变暖幅度较全球和中国其它大部分地区小.各个季节中,冬春季的增温幅度大于夏秋季.对降水的分析表明,GG情景下长江中下游地区是中国降水增加较少的地区之一,而在GS情景下,降水将出现微弱的减少.区域气候模式的模拟,在气温变化方面得到的结果和全球模式类似.但降水与全球模式的结果有所差别,主要表现在降水增加的季节分布不同上,模拟结果中降水增加最多的是冬季和夏季(增加值分别为44%和23%),而春秋季的降水将减少.     

城市化对北京气象站极端气温指数趋势变化的影响

利用5个乡村气象站和北京气象站(简称北京站)1960～2008年日最高、最低气温资料,比较分析了北京地区城市和乡村极端气温指数年、季节的时间变化以及城市化对北京站各极端气温指数趋势变化的影响.结果表明:1960～2008年北京站霜冻日数、冷夜日数、冷昼日数和平均日较差均显著减少,暖夜日数、暖昼日数、平均最高气温和平均最...     

用全球大气环流模式嵌套区域气候模式模拟东亚现代气候

将区域气候模式RegCM2与中国科学院大气物理研究所的9层全球格点大气环流模式IAP AGCM单向嵌套,对东亚现代气候进行数值模拟研究,同时检验和分析该嵌套模式的性能.已完成的10年积分结果表明,单向嵌套RegCM2由于具有较高分辨率和较完善的物理过程,因此对地面气温和降水的空间分布形势和季节变化趋势都有较好的模拟能力,且较与之嵌套的IAP AGCM的模拟效果有较大改善,如在中国区域,它模拟的年均地面气温与实况的空间相关系数由全球环流模式的092提高到094,模拟的年均降水由05提高到07. 这与嵌套RegCM2能模拟出IAP AGCM所不能分辨的中尺度信号有很大关系.     

极端气候:影响人类生存的最大风险

正极端天气气候事件是指一定地区在一定时间内出现的历史上罕见的气象事件,其发生概率通常小于5%或10%。极端天气气候事件总体可以分为极端高温、极端低温、极端干旱、极端降水等几类,一般特点是发生概率小、社会影响大。随着全球气候变暖,极端天气气候事件的出现频率发生变化,呈现出增多增强的趋势。世界气象组织指出,2007年1月和4月的全球地表气温分别比历史同期平均值高出1.89℃和1.37℃,都超过了1998年     

Climate change effects on hydrological system conditions influencing generation of storm runoff in small Alpine catchments

Floods and debris flows in small Alpine torrent catchments (<10 km²) arise from a combination of critical antecedent system state conditions and mostly convective precipitation events with high precipitation intensities. Thus, climate change may influence the magnitude–frequency relationship of extreme events twofold: by a modification of the occurrence probabilities of critical hydrological system conditions and by a change of event precipitation characteristics. Three small Alpine catchments in different altitudes in Western Austria (Ruggbach, Brixenbach and Längentalbach catchment) were investigated by both field experiments and process‐based simulation. Rainfall–runoff model (HQsim) runs driven by localized climate scenarios (CNRM‐RM4.5/ARPEGE, MPI‐REMO/ECHAM5 and ICTP‐RegCM3/ECHAM5) were used in order to estimate future frequencies of stormflow triggering system state conditions. According to the differing altitudes of the study catchments, two effects of climate change on the hydrological systems can be observed. On one hand, the seasonal system state conditions of medium altitude catchments are most strongly affected by air temperature‐controlled processes such as the development of the winter snow cover as well as evapotranspiration. On the other hand, the unglaciated high‐altitude catchment is less sensitive to climate change‐induced shifts regarding days with critical antecedent soil moisture and desiccated litter layer due to its elevation‐related small proportion of sensitive areas. For the period 2071–2100, the number of days with critical antecedent soil moisture content will be significantly reduced to about 60% or even less in summer in all catchments. In contrast, the number of days with dried‐out litter layers causing hydrophobic effects will increase by up to 8%–11% of the days in the two lower altitude catchments. The intensity analyses of heavy precipitation events indicate a clear increase in rain intensities of up to 10%.     

Climate change impacts on extreme precipitation of water supply area in Istanbul: use of ensemble climate modelling and geo-statistical downscaling

ABSTRACT

Numerous statistical downscaling models have been applied to impact studies, but none clearly recommended the most appropriate one for a particular application. This study uses the geographically weighted regression (GWR) method, based on local implications from physical geographical variables, to downscale climate change impacts to a small-scale catchment. The ensembles of daily precipitation time series from 15 different regional climate models (RCMs) driven by five different general circulation models (GCMs), obtained through the European Union (EU)-ENSEMBLES project for reference (1960–1990) and future (2071–2100) scenarios are generated for the Omerli catchment, in the east of Istanbul city, Turkey, under scenario A1B climate change projections. Special focus is given to changes in extreme precipitation, since such information is needed to assess the changes in the frequency and intensity of flooding for future climate. The mean daily precipitation from all RCMs is under-represented in the summer, autumn and early winter, but it is overestimated in late winter and spring. The results point to an increase in extreme precipitation in winter, spring and summer, and a decrease in autumn in the future, compared to the current period. The GWR method provides significant modifications (up to 35%) to these changes and agrees on the direction of change from RCMs. The GWR method improves the representation of mean and extreme precipitation compared to RCM outputs and this is more significant, particularly for extreme cases of each season. The return period of extreme events decreases in the future, resulting in higher precipitation depths for a given return period from most of the RCMs. This feature is more significant with downscaling. According to the analysis presented, a new adaption for regulating excessive water under climate change in the Omerli basin may be recommended.     

Climate change and uncertainty assessment over a hydroclimatic transect of Michigan

Predictions of a warmer climate over the Great Lakes region due to global change generally agree on the magnitude of temperature changes, but precipitation projections exhibit dependence on which General Circulation Models and emission scenarios are chosen. To minimize model- and scenario-specific biases, we combined information provided by the 3rd phase of the Coupled Model Intercomparison Project database. Specifically, the results of 12 GCMs for three emission scenarios B1, A1B, and A2 were analyzed for mid- (2046–2065) and end-century (2081–2100) intervals, for six locations of a hydroclimatic transect of Michigan. As a result of Bayesian Weighted Averaging, total annual precipitation averaged over all locations and the three emission scenarios increases by 7 % (mid-)–10 % (end-century), as compared to the control period (1961–1990). The projected changes across seasons are non-uniform and precipitation decreases by 3 % (mid-)–5 % (end-) for the months of August and September are likely. Further, average temperature is very likely to increase by 2.02–2.85 °C by the mid-century and 2.58–4.73 °C by the end-century. Three types of non-additive uncertainty sources due to climate models, anthropogenic forcings, and climate internal variability are addressed. When compared to the emission uncertainty, the relative magnitudes of the uncertainty types for climate model ensemble and internal variability are 149 and 225 % for mean monthly precipitation, and they are respectively 127 and 123 % for mean monthly temperature. A decreasing trend of the frost days and an increasing trend of the growing season length are identified. Also, a significant increase in the magnitude and frequency of heavy rainfall events is projected, with relatively more pronounced changes for heavy hourly rainfall as compared to daily events. Quantifying the inherent natural uncertainty and projecting hourly-based extremes, the study results deliver useful information for water resource stakeholders interested in impacts of climate change on hydro-morphological processes.     

Land use effects on climate in China as simulated by a regional climate model

A regional climate model (RegCM3) nested within ERA40 re-analyzed data is used to investigate the climate effects of land use change over China. Two 15-year simulations (1987―2001), one with current land use and the other with potential vegetation cover without human intervention, are conducted for a domain encompassing China. The climate impacts of land use change are assessed from the difference between the two simulations. Results show that the current land use (modified by anthropogenic ac- tivities) influences local climate as simulated by the model through the reinforcement of the monsoon circulation in both the winter and summer seasons and through changes of the surface energy budget. In winter, land use change leads to reduced precipitation and decreased surface air temperature south of the Yangtze River, and increased precipitation north of the Yangtze River. Land use change signifi- cantly affects summer climate in southern China, yielding increased precipitation over the region, de- creased temperature along the Yangtze River and increased temperature in the South China area (south-end of China). In summer, a reduction of precipitation over northern China and a temperature rise over Northwest China are also simulated. Both daily maximum and minimum temperatures are affected in the simulations. In general, the current land use in China leads to enhanced mean annual precipitation and decreased annual temperature over south China along with decreased precipitation over North China.     

Changes in daily temperature and precipitation extremes in the Yellow River Basin, China

Spatiotemporal changes in climatic extremes in the Yellow River Basin from 1959 to 2008 were investigated on the basis of a suite of 27 climatic indices derived from daily temperature and precipitation data from 75 meteorological stations with the help of the Mann–Kendall test, linear regression method and GIS technique. Furthermore, the changes in the probability distribution of the extreme indices were examined. The results indicate: (1) The whole basin is dominated by significant increase in the frequency of warm days and warm nights, and dominated by significant decrease in the frequency of cold days and cold nights. Although trends in absolute temperature indices show less spatial coherence compared with that in the percentile-based temperature indices, overall increasing trends can be found in Max Tmax (TXx), Min Tmax (TXn), Max Tmin (TNx) and Min Tmin (TNn). (2) Although the spatial patterns and the number of stations with significant changes for threshold and duration temperature indices are also not identical, general positive trends in warm indices (i.e., summer days (SU25), tropical nights (TR20), warm spell duration indicator and growing season length) and negative trends in cold indices (i.e., frost days, ice days and cold spell duration indicator) can be found in the basin. Annual nighttime temperature has increased at a faster rate than that in daytime temperature, leading to obvious decrease in diurnal temperature range. (3) The changes in precipitation indices are much weaker and less spatially coherent compared with these of temperature indices. For all precipitation indices, only few stations are characterized by significantly change in extreme precipitation, and their spatial patterns are always characterized by irregular and insignificant positive and negative changes. However, generally, changes in precipitation extremes present drying trends, although most of the changes are insignificant. (4) Results at seasonal scale show that warming trends occur for all seasons, particularly in winter. Different from that in other three seasons, general positive trends in max 1-day precipitation (Rx1DAY) and max 5-day precipitation (Rx5DAY) are found in winter. Analysis of changes in probability distributions of indices for 1959–1983 and 1984–2008 indicate a remarkable shift toward warmer condition and a less pronounced tendency toward drier condition during the past decades. The results can provide beneficial reference to water resource and eco-environment management strategies in the Yellow River Basin for associated policymakers and stakeholders.     

Water level variability and trends in Lake Constance in the light of the 1999 centennial flood

The extreme flood of Lake Constance in 1999 focused attention on the variability of annual lake levels. The year 1999 not only brought one of the highest floods of the last 180 years but also one of the earliest in the season. The 1999 extreme event was caused by heavy rainfall in the alpine and pre-alpine regions. The influence of precipitation in the two distinct regional catchments on lake level variations can be quantified by correlation analysis. The long-term variations in lake level and precipitation show similar patterns. This is seen through the use of spectral analysis, which gives similar bands of spectral densities for precipitation and lake level time series. It can be concluded from the comparison of these results with the analysis of climate change patterns in northern Europe, i.e. the index of the North Atlantic Oscillation, that the regional effects on lake level variations are more pronounced than those of global climate change.     

Spatial and Temporal Variations in Indian Summer Monsoon Rainfall and Temperature: An Analysis Based on RegCM3 Simulations

Regional climate models are important tools to examine the spatial and temporal characteristics of rainfall and temperature at high resolutions. Such information has potential applications in sectors like agriculture and health. In this study, the Regional Climate Model Version 3 (RegCM3) has been integrated in the ensemble mode at 55 km resolution over India for the summer monsoon season during the years 1982–2009. Emphasis has been given on the validation of the model simulation at the regional level. In Central India, both rainfall and temperature show the best correlations with respective observed values. The model gives rise to large wet biases over Northwest and Peninsular India. RegCM3 slightly underestimates the summer monsoon precipitation over the Central and Northeast India. Nevertheless, over these regions, RegCM3 simulated rainfall is closer to the observations when compared to the other regions where rainfall is overestimated. The position of the monsoon trough simulated by the model lies to the north of its original observed position. This is similar to the usual monsoon break conditions leading to less rainfall over Central India. RegCM3 simulated surface maximum temperature shows a large negative bias over the country while the surface minimum temperature is close to the observation. Nevertheless, there is a strong correlation between the all India weighted average surface temperature simulated by RegCM3 and IMD observed values. While examining the extreme weather conditions in Central India, it is found that RegCM3 simulated frequencies of occurrence of very wet days, extremely wet days, warm days and warm nights more often as compared to those in IMD observed values. However, these are systematic biases. The model biases in the frequencies of distribution of rainfall extremes explain the wet and dry biases in different regions in the country. Overall, the inter-annual characteristics of both the rainfall and temperature extremes simulated by RegCM3 in Central India are well in phase with those found in the observed data.     

The impact of climate change on groundwater recharge and runoff in a humid,equatorial catchment: sensitivity of projections to rainfall intensity

Abstract

Projected warming in equatorial Africa, accompanied by greater evaporation and more frequent heavy precipitation events, may have substantial but uncertain impacts on terrestrial hydrology. Quantitative analyses of climate change impacts on catchment hydrology require high-resolution (<50 km) climate data provided by regional climate models (RCMs). We apply validated precipitation and temperature data from the RCM PRECIS (Providing Regional Climates for Impact Studies) to a semi-distributed soil moisture balance model (SMBM) in order to quantify the impacts of climate change on groundwater recharge and runoff in a medium-sized catchment (2098 km²) in the humid tropics of southwestern Uganda. The SMBM explicitly accounts for changes in soil moisture, and partitions effective precipitation into groundwater recharge and runoff. Under the A2 emissions scenario (2070–2100), climate projections from PRECIS feature not only rises in catchment precipitation and modelled potential evapotranspiration by 14% and 53%, respectively, but also increases in rainfall intensity. We show that the common application of the historical rainfall distribution using delta factors to the SMBM grossly underestimates groundwater recharge (i.e. 55% decrease relative to the baseline period of 1961–1990). By transforming the rainfall distribution to account for changes in rainfall intensity, we project increases in recharge and runoff of 53% and 137%, respectively, relative to the baseline period.