共查询到18条相似文献，搜索用时 125 毫秒
1.
2.
气候变化对重庆高温和旱涝灾害的影响 总被引：2，自引：0，他引：2
利用重庆地区1961—2006年逐日气象观测资料,研究了气候变化对重庆高温和旱涝灾害的影响。结果表明:重庆区域显著的增暖开始于20世纪90年代后期,突变检验结果显示气温距平的突变出现在1997年。增暖后,极端高温事件发生频次增加趋势明显,高温热浪风险显著上升,极端降水事件发生频次也呈现出显著增加趋势,洪涝灾害的风险不断上升。进一步分析了区域平均各级别降水日数的变化趋势,结果显示小雨和中雨日数减少趋势明显,使得干旱的风险增大。将区域平均气温距平序列分为全球气候变化对重庆区域平均气温的影响和重庆区域平均气温的自身变率两部分,发现在增暖后,全球气候变化对区域气温变化的贡献较增暖前增大。分别计算2006年全球气候变化和区域自身变率对重庆异常气温的贡献,发现2006年重庆异常高温可能是受全球气候变化和区域自身的变率共同作用的结果,但以区域自身的变率为主。 相似文献
3.
慢特征分析（SFA）方法可以从非平稳时间序列中提取出慢变的外强迫信息。近年来,SFA方法被应用于气候变化研究领域,用于探究气候变化的潜在驱动力及相关的动力学机制。本文基于SFA方法,提取全球陆地表面气温（LSAT）的慢变外强迫信息,研究全球LSAT慢变驱动力的空间结构特征及低频变率的主要驱动因子。SFA方法提取的LSAT慢变驱动力与历史时期全球辐射强迫（GRF）和全球海表温度（SST）的主模态（大西洋多年代际振荡AMO、热带太平洋ENSO变率和太平洋年代际振荡PDO）有显著的相关关系,表明全球大部分地区LSAT的变率受到GRF和三个SST模态的显著影响。GRF对LSAT变率的影响有全球一致性的特征,而三个SST模态对LSAT变率的影响则呈现出明显的区域特点。此外,由于SFA方法可以有效降低原始LSAT序列中随机噪声的干扰,GRF和SST模态对LSAT变率的解释方差显著提高,进一步表明GRF和SST模态是全球LSAT低频变率主要的驱动因子。最后,利用历史海温驱动AGCM试验（即AMIP试验）的结果,验证了三个SST模态对区域LSAT变率的显著影响。 相似文献
4.
《大气和海洋科学快报》2020,(1)
青藏高原(TP)的生态系统对气候变化,土地利用和土地覆盖变化(LULCC)以及CO_2浓度升高等极为敏感,但这些因素对于TP总初级生产力(GPP)的影响及机理仍不清晰。本研究利用12个陆地生物圈模式定量评估了气候变化, LULCC和CO_2施肥效应对TP的GPP年际变率和趋势影响。结果表明:气候变化对TP的GPP起主导作用,而LULCC和CO_2浓度升高(施肥效应)对GPP年平均值贡献分别为10%和–14%,年际变率贡献为37%和–20%,趋势贡献为52%和–24%。 相似文献
5.
A new climate scenario for assessing the climate change impacts on soil moisture over the Huang-Huai-Hai Plain region of China 
下载免费PDF全文
下载免费PDF全文 《大气和海洋科学快报》2017,(2)
本文基于CNOP-P方法、CoLM模式以及22个CMIP5模式对RCP4.5情景下未来气候变化的预估,提出了CNOP-P类型气候变化方案,以探究在我国3H地区SSM对气候变化的潜在最大响应。与传统的假定类型气候变化方案不同,CNOP-P类型气候变化方案考虑了气候变率的变化,并引起研究区域内SSM的最大变化幅度。通过对比假定类型和CNOP-P类型气候变化方案下SSM变化的差异,我们发现,仅当降水改变时,这种差异才比较明显,且该差异主要集中在3H地区北部的半干旱区域。这表明在半干旱地区SSM对降水变率更为敏感。 相似文献
6.
近50a浙江省气候变化特征分析 总被引：45，自引：2，他引：45
用1951－1999年资料详细研究了浙江省4个观测站的年，季，月降水与气温的气候变化特征，提出了用蒙特卡洛（Monte Carlo)模拟方法对气象要素的的长期变化进行统计检验，指出，气候变化也可以出现在气象要素的变率上，提出了用计算滑动均方差的方法来识别这种变率异常的方法。 相似文献
7.
在气候影响研究中引入随机天气发生器的方法和不确定性 总被引：1，自引：0，他引：1
通过采用不同的随机天气发生器生成一定气候背景下各种气候变率情景，许多学者在最近的研究中已经认识到气候变率对农作物生长发育影响的重要性。传统的气候影响评估方法直接以大气环流模式的模拟试验结果作为未来气候情景，这样不可能理解如上的重要性。本文着重评述将随机天气发生器应用于气候变化影响研究的一般方法框架，以及作者的具体个例研究方法。文中最后分析了目前该领域研究中还存在的一些不确定性。 在当前的气候变化影响研究中，有不同的方法用来研制一种称为WGEN的典型随机天气发生器的参数化方案及其随机试验方法。不同的研究者也有不同的参数调控方法。通常的思路是通过气候控制试验和2×CO2试验之间的气候变量平均值和方差的变化来扰动随机天气发生器的参数，以生成未来逐日气候变化情景。本文作者根据短期气候预测模式的输出产品建立了一套WGEN的参数化方案及其随机试验方法，并且在时间和空间两个尺度上检验和评估了此参数化方案下WGEN的模拟能力。另外，作者由未来降水的变化，调试随机天气发生器参数，生成了气候变率变化情景。这些参数调节可以产生各种不同类型和定性大小的气候变率变化，用于气候影响评估的敏感性分析。通过如上方法，作为一个个例，文中评估了未来气候变率变化 相似文献
8.
9.
1976/1977年前后热带印度洋海表温度年际异常的变化 总被引：1，自引：0，他引：1
基于1948～2005年NCEP/NCAR（美国大气研究中心/环境预测中心）再分析资料,讨论了1976/1977年前后的年代际气候变化对热带印度洋海表温度（SST）年际变率特征的影响,结果表明:在气候变化前后,ENSO都能导致热带印度洋SSTA（海表面温度异常）出现全海盆同号的变化,这种模态在冬季最强;气候变化前与变化后相比,该模态对该地区海温年际变率的方差贡献大22.1%, 达到最强的时间早2个月。气候变化前,秋季热带印度洋SSTA的主导年际变率模态表现为全海盆同号,变化后则表现为“偶极子模态”（IODM）。导致上述SSTA特征变化的重要原因,是气候变化前后印度洋风场对ENSO的响应不同。在气候变化前,与ENSO相关联的热带印度洋东风异常首先在夏季出现,而变化后则首先在春季出现,并且有一反气旋性环流异常维持在热带东南印度洋。 相似文献
10.
北极涛动(Arctic Oscillation,AO)是北半球热带外地区大气环流变率的主导模态,对北半球以及区域尺度气温变化具有重要影响。AO可在没有外强迫条件下通过波流相互作用形成,因此它被认为是全球气候系统内部变率的重要组成部分。研究年代际尺度上AO的变化及其气候影响,可加深对当前北半球气候变化规律的物理理解,也可为预估未来年代际尺度上气候变化及其不确定性提供科学依据。本文从AO影响东亚冬季风年代际变化的物理机制、AO对北半球冬季气温长期趋势的贡献、AO年代际影响的不确定性三个方面出发,简要回顾和总结了近年来有关年代际尺度上冬季AO时空变化及其对北半球气候影响的研究成果,并初步展望一些值得继续深入研究的问题。 相似文献
11.
随机天气模型参数化方案的研究及其模拟能力评估 总被引：8，自引：2，他引：6
文中介绍了随机天气模型 WGEN的基本结构及其模拟原理 ,并针对其中随机过程的统计结构特征和 GCMs输出要素的不同时空尺度特点 ,利用动态数据的参数化分析方法等统计学技术 ,确定了该模型参数的估计方法。同时基于蒙特卡罗数值计算原理 ,给出了 WGEN的随机试验方法 ,并通过模拟基准气候 ,从时间分布和空间场两方面对模型在中国东北地区的模拟效果及其能力进行了评估。结果表明 ,模型对于最高气温、最低气温、降水和辐射等要素均具有较好的模拟效果 ,模拟序列与观测序列的取值分布有较一致的概率特性。由此可以结合 GCMs大尺度网格上输出的月和年要素值 ,通过调控随机过程的参数 ,生成具有不同气候变率的 2× CO2 逐日气候变化情景 ,实现气候预测模式与气候影响模式的嵌套 ,进一步研究气候变率变化的可能影响。 相似文献
12.
在利用田间试验资料对双季稻生长动力（态）模拟模型进行验证的基础上,将基于GCMs的输出和历史气候资料相结合的气候变化情景与双季稻模式相连接,就气候变暖对我国江南双季稻主产区水稻生产的可能影响进行网格化定量模拟和客观评估,并就调整对策（改变播种日期和种植品种）在减缓气候变暖对双季稻生产影响中的作用作了初步的探讨。结果表明,在未来可能的气候变化情景下,若维持目前的品种和生产技术措施,双季稻产量将有不同程度的下降。产量变化的地域分布既有一定的规律性,又体现出气候变化影响的复杂性。适应对策分析表明,改种长生育期的 相似文献
13.
Statistical downscaling of daily climate variables for climate change impact assessment over New South Wales, Australia 总被引：1，自引：0，他引：1
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. 相似文献
14.
Use of general circulation model output in the creation of climate change scenarios for impact analysis 总被引：1，自引：0，他引：1
Alan Robock Richard P. Turco Mark A. Harwell Thomas P. Ackerman Rigoberto Andressen Hsin-Shih Chang M. V. K. Sivakumar 《Climatic change》1993,23(4):293-335
Many scientific studies warn of a rapid global climate change during the next century. These changes are understood with much less certainty on a regional scale than on a global scale, but effects on ecosystems and society will occur at local and regional scales. Consequently, in order to study the true impacts of climate change, regional scenarios of future climate are needed. One of the most important sources of information for creating scenarios is the output from general circulation models (GCMs) of the climate system. However, current state-of-the-art GCMs are unable to simulate accurately even the current seasonal cycle of climate on a regional basis. Thus the simple technique of adding the difference between 2 × CO2 and 1 × CO2 GCM simulations to current climatic time series cannot produce scenarios with appropriate spatial and temporal details without corrections for model deficiencies. In this study a technique is developed to allow the information from GCM simulations to be used, while accommodating for the deficiencies. GCM output is combined with knowledge of the regional climate to produce scenarios of the equilibrium climate response to a doubling of the atmospheric CO2 concentration for three case study regions, China, Sub-Saharan Africa and Venezuela, for use in biological effects models. By combining the general climate change calculated with several GCMs with the observed patterns of interannual climate variability, reasonable scenarios of temperature and precipitation variations can be created. Generalizations of this procedure to other regions of the world are discussed. 相似文献
15.
Assessing uncertainties in climate change impact analyses on the river flow regimes in the UK. Part 2: future climate 总被引：3，自引：0，他引：3
The first part of this paper demonstrated the existence of bias in GCM-derived precipitation series, downscaled using either a statistical technique (here the Statistical Downscaling Model) or dynamical method (here high resolution Regional Climate Model HadRM3) propagating to river flow estimated by a lumped hydrological model. This paper uses the same models and methods for a future time horizon (2080s) and analyses how significant these projected changes are compared to baseline natural variability in four British catchments. The UKCIP02 scenarios, which are widely used in the UK for climate change impact, are also considered. Results show that GCMs are the largest source of uncertainty in future flows. Uncertainties from downscaling techniques and emission scenarios are of similar magnitude, and generally smaller than GCM uncertainty. For catchments where hydrological modelling uncertainty is smaller than GCM variability for baseline flow, this uncertainty can be ignored for future projections, but might be significant otherwise. Predicted changes are not always significant compared to baseline variability, less than 50% of projections suggesting a significant change in monthly flow. Insignificant changes could occur due to climate variability alone and thus cannot be attributed to climate change, but are often ignored in climate change studies and could lead to misleading conclusions. Existing systematic bias in reproducing current climate does impact future projections and must, therefore, be considered when interpreting results. Changes in river flow variability, important for water management planning, can be easily assessed from simple resampling techniques applied to both baseline and future time horizons. Assessing future climate and its potential implication for river flows is a key challenge facing water resource planners. This two-part paper demonstrates that uncertainty due to hydrological and climate modelling must and can be accounted for to provide sound, scientifically-based advice to decision makers. 相似文献
16.
Downscaling daily precipitation over the Yellow River source region in China: a comparison of three statistical downscaling methods 总被引：2，自引：2，他引：0
Yurong Hu Shreedhar Maskey Stefan Uhlenbrook 《Theoretical and Applied Climatology》2013,112(3-4):447-460
Three statistical downscaling methods are compared with regard to their ability to downscale summer (June–September) daily precipitation at a network of 14 stations over the Yellow River source region from the NCEP/NCAR reanalysis data with the aim of constructing high-resolution regional precipitation scenarios for impact studies. The methods used are the Statistical Downscaling Model (SDSM), the Generalized LInear Model for daily CLIMate (GLIMCLIM), and the non-homogeneous Hidden Markov Model (NHMM). The methods are compared in terms of several statistics including spatial dependence, wet- and dry spell length distributions and inter-annual variability. In comparison with other two models, NHMM shows better performance in reproducing the spatial correlation structure, inter-annual variability and magnitude of the observed precipitation. However, it shows difficulty in reproducing observed wet- and dry spell length distributions at some stations. SDSM and GLIMCLIM showed better performance in reproducing the temporal dependence than NHMM. These models are also applied to derive future scenarios for six precipitation indices for the period 2046–2065 using the predictors from two global climate models (GCMs; CGCM3 and ECHAM5) under the IPCC SRES A2, A1B and B1scenarios. There is a strong consensus among two GCMs, three downscaling methods and three emission scenarios in the precipitation change signal. Under the future climate scenarios considered, all parts of the study region would experience increases in rainfall totals and extremes that are statistically significant at most stations. The magnitude of the projected changes is more intense for the SDSM than for other two models, which indicates that climate projection based on results from only one downscaling method should be interpreted with caution. The increase in the magnitude of rainfall totals and extremes is also accompanied by an increase in their inter-annual variability. 相似文献
17.
Influence of seasonality on glacier mass balance, and implications for palaeoclimate reconstructions
Climates inferred from former glacier geometries in some areas exhibit discrepancies with regional palaeoclimates predicted
by General Circulation Models (GCMs) and modelling of palaeoecological data, possibly as a consequence of their differing
treatments of climatic seasonality. Since glacier-based climate reconstructions potentially offer an important tool in the
calibration of GCMs, which themselves need validation if used to predict future climate scenarios, we attempt to resolve mismatches
between these techniques by (1) investigating the influence of seasonality on glacier mass balance, and (2) refining the methodology
used for the derivation of glacier-based palaeoclimates. Focussing on the Younger Dryas stadial glaciation of Scotland, northeast
Atlantic, we show that sea-ice amplified seasonality led to a significantly drier climate than has been suggested by glacier-based
interpretations. This was characterised by a relatively short ablation season and the survival of a more substantial winter
snowpack. We suggest that if palaeoglaciological studies were to account for changes in seasonal temperature and precipitation
variability, their results would agree more closely with the cold, arid, northeast Atlantic palaeoenvironment predicted by
atmospheric modelling and northwest European pollen studies, and would therefore provide more accurate constraints for GCM
calibration. 相似文献
18.
This paper describes the regional climate change scenarios that are recommended for use in the U.S. Country Studies Program (CSP) and evaluates how well four general circulation models (GCMs) simulate current climate over Europe. Under the umbrella of the CSP, 50 countries with varying skills and experience in developing climate change scenarios are assessing vulnerability and adaptation. We considered the use of general circulation models, analogue warm periods, and incremental scenarios as the basis for creating climate change scenarios. We recommended that participants in the CSP use a combination of GCM based scenarios and incremental scenarios. The GCMs, in spite of their many deficiencies, are the best source of information about regional climate change. Incremental scenarios help identify sensitivities to changes in a particular meteorological variable and ensure that a wide range of regional climate change scenarios are considered. We recommend using the period 1951–1980 as baseline climate because it was a relatively stable climate period globally. Average monthly changes from the GCMs and the incremental changes in climate variables are combined with the historical record to produce scenarios. The scenarios do not consider changes in interannual, daily, or subgrid scale variability. Countries participating in the Country Studies Program were encouraged to compare the GCMs' estimates of current climate with actual long-term climate means. In this paper, we compare output of four GCMs (CCCM, GFDL, UKMO, and GISS) with observed climate over Europe by performing a spatial correlation analysis for temperature and precipitation, by statistically comparing spatial patterns averaged climate estimates from the GCMs with observed climate, and by examining how well the models estimate seasonal patterns of temperature and precipitation. In Europe, the GISS and CCCM models best simulate current temperature, whereas the GISS and UK89 models, and the CCCM model, best simulate precipitation in defined northern and southern regions, respectively. 相似文献