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

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     

利用随机天气模式及多种插值方法生成逐日气候变化情景的研究

针对目前大气环流模式在用于气候变化影响评估研究中时间分辨率较低的局恨性, 以及气候情景的要求和气候变化影响研究的需要, 结合GCM的模拟试验结果, 利用随机天气模式WGEN生成了中国东北地区未来气候变化的逐日情景, 其中包含了可能的气候变率信息, 可与作物动力模式等气候影响模式嵌套, 研究作物生长发育及其产量的可能变化, 及气候变率变化的可能影响等.     

年际气候变率的数值模拟

本文利用IAP GCM 20年的模式输出结果，计算了海平面气压、表面气温和降水的年际气候变率，并与观测资料作了对比分析，以考察模式模拟年际变率的能力。结果表明，模式成功地再现了观测变率地理分布的基本特征，这说明大气内部动力-物理相互作用过程对年际变率有重要影响，而模拟值的偏低则显示了模式中未包括的某些外界强迫因子如海温和海冰年际变化的潜在作用。     

气候标准序列变率

首先,综论已有气候序列变率的各种量数,提出气候标准序列变率和推导其与他种序列变率间的关系,以资比较。然后,推导了气候标准序列变率抽样分布的参数(平均数与方差),并证明了在正态假设下这些参数的简化结果和气候标准序列变率抽样分布函数中的参数是一致的。这些是本文的重点。最后,应用气候标准序列变率的一些统计理论说明其在资料统计方面的实用价值,指出其优越性。气候标准序列变率不仅可以分析气候特征,它可以用于研究气候变迁与旱涝规律,以及用于气候统计予报。     

百分位统计降尺度方法及在GCMs日降水订正中的应用

在格点观测的逐日降水量数据基础上,采用百分位统计降尺度方法对全球气候模式(GCM)输出的日降水量进行了订正处理。5种订正方案的比较结果表明,取12个百分位数进行日降水量订正是合理的。观测资料与3个GCMs订正前后全国平均年、季降水量空间分布以及主要流域平均年、月和日降水序列多年平均、变化趋势及概率密度的对比分析表明:(1)统计降尺度处理可在一定程度上降低GCMs模拟的降水量偏差,特别是中国中部、长江以南和东北部分地区,对德国马普研究所的海气耦合模式(MPI/ECHAM5)模拟的降水量订正效果最显著;(2)GCMs统计降尺度处理的降水量季节分布特征与观测更为接近,所有流域MPI/ECHAM5订正的降水量优于或接近直接输出结果;(3)与GCM直接输出的降水相比,部分流域经统计降尺度处理后降水量变化趋势与观测的一致性有所增加,但不明显;(4)当日降水量<30mm时,订正的降水量与观测的偏差明显减小;当日降水量>30mm时,部分流域由负偏差转为正偏差。由于GCMs结构和降尺度方法的局限性,在用于具体流域未来气候变化预估及气候变化影响评估时,应选择尽可能多的、模拟能力强的GCMs数据,以包含尽可能多的模拟气候情景。     

降水气候特征的随机模拟试验

本文利用多状态一阶Markov链,根据随机模拟理伦建立一种用以产生单站逐日降水量模拟记录的随机模式。由该模拟模式可推求降水的各种长年气候统计特征,因而这种模拟模式可为缺少逐日资料的地区提供获取足够长年代资料的有效途径。利用该模式对我国上海、北京、广州、沈阳、荆州等5站不同月份逐日降水的试验给果表明,模拟记录与历史资料所统计的气候特征如累年平均月降水量及其方差、累年平均日降水量及其方差、累年平均各月雨日数、最大日降水量等气候统计参数十分吻合,且具有相当的稳定性和可靠性。由于试验站点分属不同气候区域,因而该模拟方法具有普适性。     

农业气象学在作物生产模拟及其应用中的作用

就作物模拟模型及其应用,以及气象因子在这些模拟应用中的重要性作了综合论述.为了说明气象和气候变率对作物生产的影响,农业气象要素(包括最高和最低气温、太阳辐射及降水量)被当作作物模拟模型的基本输入因子.多数作物模型使用的气象资料为逐日值,因为很多地区没有更小时间尺度的观测资料.气象及其它输入数据均采用标准文件格式,这有利于数据在不同模型中使用.如果输入因子缺少或数据丢失、无效,将会影响到作物模型模拟的准确性.利用天气发生器随机生成的逐日气象数据可以解决数据丢失或序列较短的问题.当然,模拟时要尽可能采用气象观测资料,以确保作物产量模拟值的准确性,这在热带地区显得尤其重要.许多作物模型用来进行产量预报和决策管理,作物产量及其相关变量的预测变率以及自然资源的利用主要由天气气候条件的短期与长期变化所致.模型的产品可用来制定相应的管理决策,提供给农户和其他关注农业生产的人们.作物模型也广泛应用于气候变化对农业生产和粮食安全性的影响研究.最近,作物模型又用于气候变率和厄尔尼诺与南方涛动(ENSO)的影响研究.随着计算机的普及,农场主、农业咨询人员以及政策制定、决策者等模型的使用者也将越来越多.历史资料和作物生长期间的气象观测资料以及短期、中期、长期天气预报将在这些应用中发挥重要作用.     

CDF-T方法在站点尺度日降水预估中的应用

基于1961-2015年上海降水观测数据和8个全球气候模式GCMs模拟的日降水量数据,采用累计概率分布函数构建转换模型CDF-T建立了站点尺度日降水量的统计降尺度模型。结果表明,降尺度模型显著改善了GCMs对降水日数偏多、降水强度偏低和降水量偏少的模拟结果。与利用全年日降水序列建模结果相比,利用汛期日降水序列建模更好地刻画了汛期降水的累计概率分布曲线,同时提高了汛期总降水量、降水强度和年平均暴雨日数、暴雨量、暴雨强度的均值和变化趋势的降尺度效果。模型对较长年份的暴雨重现期订正效果更佳。与当代(2006-2015年)气候相比,2016-2095年上海降水呈现以下特征:全年和汛期总降水量和降水强度增加,降水日数减少,未来可能出现更多的旱涝年;汛期降水极端性增强,暴雨降水均值和极端值均增加;50年以上重现期的年最大日降水量未来呈前40年减少后40年增加的变化。CDF-T模型为站点尺度气候变化影响评估和未来预估提供降尺度技术和基础气候数据。     

气候变化对我国江南双季稻生产可能影响的数值模拟研究

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     

基于弱耦合资料同化的冬季北大西洋涛动年际变率预测

北大西洋涛动作为冬季北大西洋地区大气环流的主模态之一,其年际变率对全球许多地区气候变率具有重要影响,但目前其预测技巧并不高。采用降维投影四维变分同化方法,在耦合模式中建立了基于全球大气资料的弱耦合资料同化系统,直接同化月平均再分析资料,并进行了年代际后报试验。结果表明,通过耦合资料同化的手段,可以显著提升耦合模式对冬季北大西洋涛动年际变率及其相关的欧洲北部、美国东部、欧亚大陆北部的冬季近地面温度年际变率的后报效果,相关系数均超过0.05显著水平t检验。该后报效果的改进主要与在耦合同化过程中通过耦合模式中自由发展的海-气相互作用将大气的观测信息储存在耦合模式的海洋分量中,改进了冬季北大西洋地区海表温度“三极”型分布的时空变率及其时间序列的后报效果有关。该研究强调了耦合模式初始状态的准确度对提升冬季北大西洋涛动年际变率的后报技巧具有重要作用。     

气候变化情景生成技术研究综述

简单回顾了气候变化对农业生产影响研究的进展,分析了气候变化情景生成技术研究的必要性,即影响模式与ＧＣＭｓ的嵌套困难及对气候变率和产量变率的认识。指出该技术是目前这一领域研究的关键所在。     

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.     

Adapting stochastic weather generation algorithms for climate change studies

While large-scale climate models (GCMs) are in principle the most appropriate tools for predicting climate changes, at present little confidence can be placed in the details of their projections. Use of tools such as crop simulation models for investigation of potential impacts of climatic change requires daily data pertaining to small spatial scales, not the monthly-averaged and large-scale information typically available from the GCMs. A method is presented to adapt stochastic weather generation models, describing daily weather variations in the present-day climate at particular locations, to generate synthetic daily time series consistent with assumed future climates. These assumed climates are specified in terms of the commonly available monthly means and variances of temperature and precipitation, including time-dependent (so-called transient) climate changes. Unlike the usual practice of applying assumed changes in mean values to historically observed data, simulation of meteorological time series also exhibiting changes in variability is possible. Considerable freedom in climate change scenario construction is allowed. The results are suitable for investigating agricultural and other impacts of a variety of hypothetical climate changes specified in terms of monthly-averaged statistics.     

REGIONAL CLIMATE CHANGE SCENARIOS FOR VULNERABILITY AND ADAPTATION ASSESSMENTS

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.     

Use of general circulation model output in the creation of climate change scenarios for impact analysis

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.     

High resolution modeling of the regional impacts of climate change on irrigation water demand

In the Arkansas River Basin in southeastern Colorado, surface irrigation provides most of the water required for agriculture. Consequently, the region’s future could be significantly affected if climate change impacts the amount of water available for irrigation. A methodology to model the expected impacts of climate change on irrigation water demand in the region is described. The Integrated Decision Support Consumptive Use model, which accounts for spatial and temporal variability in evapotranspiration and precipitation, is used in conjunction with two climate scenarios from the Vegetation-Ecosystem Modeling and Analysis Project. The two scenarios were extracted and scaled down from two general circulation models (GCMs), the HAD from the Hadley Centre for Climate Prediction and Research and the CCC from the Canadian Climate Centre. The results show significant changes in the water demands of crops due to climate change. The HAD and CCC climate change scenarios both predict an increase in water demand. However, the projections of the two GCMs concerning the water available for irrigation differ significantly, reflecting the large degree of uncertainty concerning what the future impacts of climate change might be in the study region. As new or updated predictions become available, the methodology described here can be used to estimate the impacts of climate change.     

Uncertainties in projected impacts of climate change on European agriculture and terrestrial ecosystems based on scenarios from regional climate models

The uncertainties and sources of variation in projected impacts of climate change on agriculture and terrestrial ecosystems depend not only on the emission scenarios and climate models used for projecting future climates, but also on the impact models used, and the local soil and climatic conditions of the managed or unmanaged ecosystems under study. We addressed these uncertainties by applying different impact models at site, regional and continental scales, and by separating the variation in simulated relative changes in ecosystem performance into the different sources of uncertainty and variation using analyses of variance. The crop and ecosystem models used output from a range of global and regional climate models (GCMs and RCMs) projecting climate change over Europe between 1961–1990 and 2071–2100 under the IPCC SRES scenarios. The projected impacts on productivity of crops and ecosystems included the direct effects of increased CO₂ concentration on photosynthesis. The variation in simulated results attributed to differences between the climate models were, in all cases, smaller than the variation attributed to either emission scenarios or local conditions. The methods used for applying the climate model outputs played a larger role than the choice of the GCM or RCM. The thermal suitability for grain maize cultivation in Europe was estimated to expand by 30–50% across all SRES emissions scenarios. Strong increases in net primary productivity (NPP) (35–54%) were projected in northern European ecosystems as a result of a longer growing season and higher CO₂ concentrations. Changing water balance dominated the projected responses of southern European ecosystems, with NPP declining or increasing only slightly relative to present-day conditions. Both site and continental scale models showed large increases in yield of rain-fed winter wheat for northern Europe, with smaller increases or even decreases in southern Europe. Site-based, regional and continental scale models showed large spatial variations in the response of nitrate leaching from winter wheat cultivation to projected climate change due to strong interactions with soils and climate. The variation in simulated impacts was smaller between scenarios based on RCMs nested within the same GCM than between scenarios based on different GCMs or between emission scenarios.