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     

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。     

A new climate scenario for assessing the climate change impacts on soil moisture over the Huang-Huai-Hai Plain region of China

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。     

气候变化对雨养冬小麦水分利用效率的影响估算

研究气候变化对雨养冬小麦水分利用效率的影响规律,可为农业适应气候变化提供科学依据。通过构建代表站雨养冬小麦产量和土壤水分变化量的模拟方程,分析水分利用效率的历史变化,并结合两种区域气候模式PRECIS和REGCM4.0输出的4种不同气候变化情景资料,估算未来2021—2050年雨养冬小麦水分利用效率的可能变化。结果表明:1981—2010年甘肃、山西和河南代表站的雨养冬小麦水分利用效率呈二次曲线变化趋势,最大值出现在2003年前后。4种气候变化情景的模拟结果均显示:2021—2050年冬小麦全生育期耗水量明显增加,各代表站不同情景平均增加6.2%;产量有增有减,平均产量变化率为1.4%;水分利用效率平均减小3.8%,且变率减小。区域气候模式PRECIS估算的水分利用效率的减小量A2情景大于B2情景,REGCM4.0模式估算的水分利用效率的减小量RCP8.5情景大于RCP4.5情景。整体来看,RCP气候情景对雨养冬小麦水分利用效率的负面影响更大。     

全球变暖中的科学问题

2013年各国政府间气候变化专门委员会（IPCC）第一工作组发布了第五次气候变化科学评估报告,以大量的观测分析和气候模式模拟证据,继续强调由于人类排放增加,全球正在变暖,未来将继续变暖的观点。本文综述研究全球变暖的几个深层次的科学问题,即多套全球气温观测资料的差异、不同标准气候态时段的作用、20世纪全球变暖的检测和归因及未来全球气温变化的走向,以此提出需进一步研究的科学问题。结果表明;需要进一步提高观测资料的质量;注意不同标准气候态时段对应的数值的不同;应进一步改善气候模式模拟年代际变率的能力及研究近15 a全球变暖减缓和停滞的原因,从而改善气候模式的模拟效果;造成预估未来全球气候变化的不确定性主要来自气候模式的差异、未来排放情景的差异及气候系统内部变率影响和自然外强迫的作用。     

气候变化对我国南方双季稻发育和产量的影响

基于WOFOST作物模式,结合气候模式输出的气候情景资料,模拟研究了未来100a(2001-2100年)气候变化对我国南方双季稻发育和产量的影响。结果表明：未来气候情景下,我国南方大部分地区双季稻(早稻、晚稻)的生长期会有所缩短;产量可能会有所下降,但下降的幅度不是很大,其中早稻受气候变化的影响较大。     

随机天气模型参数化方案的研究及其模拟能力评估

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。     

Impacts of internal climate variability on meteorological drought changes in China

干旱受气候内部变率和外部强迫共同影响。本文利用地球系统模式CESM对历史时期和RCP8.5下的40个集合模拟的降水资料,并结合实际观测,研究了上述两因子对气象学干旱–标准化降水指数变化的贡献。通过对干旱频率、强度、持续时间、及最长持续时间的变化分析发现:在历史时期,气候内部变率对干旱变化起主要影响,而在未来(RCP8.5)情景下,外部强迫变得更为重要。本文建议,在利用模式模拟结果研究干旱变化时应考虑气候内部变率的影响。     

江苏区域性天气气候变化对常见疾病发生的可能影响北大核心

基于2010—2019年江苏省两所具有区域代表性的医院较为完整的10 a常见疾病住院病例数据、区域同期气象资料,从不同时间尺度分析了气象要素与疾病发病的可能关系,探讨了气象条件和气候变化背景下主要疾病病例数的变化特征。结果表明:儿科肺炎、慢性阻塞性肺病等呼吸系统疾病具有明显的季节变化特征,其对气温等气象要素有较强的敏感性,而寒潮等天气过程对此类疾病有诱发作用。作为年际气候变化关键信号的ENSO模态,可以通过调节不同地区的天气频率,间接对疾病发生产生影响。根据天气变化与疾病发病机理的关系和气候模式模拟预估的未来气候变化情景,到本世纪末的气候背景可能有利于减少肺炎和慢性阻塞性肺病的发病率,但也需注意天气尺度气温变率加剧造成相关疾病的多发。     

IPCC AR6报告解读：未来的全球气候——基于情景的预估和近期信息

依据IPCC第六次评估报告（AR6）第一工作组报告第四章的内容,对未来全球气候的预估结果进行解读。报告对21世纪全球表面气温、降水、大尺度环流和变率模态、冰冻圈和海洋圈的可能变化进行了系统评估,并对2100年以后的气候变化做了合理估计。评估指出全球平均表面气温将在未来20年内达到或超过1.5℃,平均降水也将增加,但随季节和区域而异,同时变率将增大。大尺度环流和变率模态受内部变率影响较大。到21世纪末,北冰洋可能出现无冰期;全球海洋会继续酸化,平均海平面将持续上升,百年内上升幅度依赖不同排放情景,都在2100年后继续升高。在最新的评估中采用多种约束方法,减小了预估不确定性的范围。AR6对于低排放情景以及“小概率高增暖情节”的关注为应对气候变化提供了更多、更完整的信息。综合报告的评估结果指出,未来需要进一步减小区域,特别是季风区气候预估的不确定性,并从科学研究和模式发展两方面加强我国气候预估能力的建设。     

The Potential Impact of Global Warming on Hail Losses to Winter Cereal Crops in New South Wales

This study was undertaken to determine the impact of potential global warming on the magnitude of hail losses to winter cereal crops within two areas situated on the western slopes of New South Wales, Australia. A model relating historical crop hail losses to climatic variables was developed for each area. These models included seasonal measures of vertical instability, low-level moisture and the height of the freezing level. In both areas, windshear was not found to be an important factor influencing seasonal crop hail losses. The two crop hail loss models were then used in conjunction with upper-air climatic data from three single mixed-layer global climate models (GCMs). Each GCM was run for 1 × CO₂ conditions and for 2 × CO₂ conditions. The enhanced greenhouse effect on climatic variables was taken to be the difference between their values for these two runs. Changes to climatic variables were then translated directly into changes in the percentage value of the winter cereal crop lost due to hail. In both areas, the three GCMs agreed concerning the direction of change in each of the variables used in the crop hail loss model. GCM simulations of the greenhouse effect resulted in a decline in winter cereal crop hail losses, with the exception of one GCM simulation at one location where losses increased slightly. None of the changes due to the enhanced greenhouse effect, however, were significant owing to a large observed seasonal variability of crop hail losses. Also, the simulated seasonal variability of crop hail losses did not change significantly due to the enhanced greenhouse effect. These results depended on two important assumptions. Firstly, it was assumed that the dominant relationships between climatic variables and crop hail losses in the past would remain the same in a future climate. Secondly, it was assumed that the single mixed-layer GCMs used in the study were correctly predicting climate change under enhanced greenhouse conditions.     

Multi-criteria evaluation of CMIP5 GCMs for climate change impact analysis

Climate change is expected to have severe impacts on global hydrological cycle along with food-water-energy nexus. Currently, there are many climate models used in predicting important climatic variables. Though there have been advances in the field, there are still many problems to be resolved related to reliability, uncertainty, and computing needs, among many others. In the present work, we have analyzed performance of 20 different global climate models (GCMs) from Climate Model Intercomparison Project Phase 5 (CMIP5) dataset over the Columbia River Basin (CRB) in the Pacific Northwest USA. We demonstrate a statistical multicriteria approach, using univariate and multivariate techniques, for selecting suitable GCMs to be used for climate change impact analysis in the region. Univariate methods includes mean, standard deviation, coefficient of variation, relative change (variability), Mann-Kendall test, and Kolmogorov-Smirnov test (KS-test); whereas multivariate methods used were principal component analysis (PCA), singular value decomposition (SVD), canonical correlation analysis (CCA), and cluster analysis. The analysis is performed on raw GCM data, i.e., before bias correction, for precipitation and temperature climatic variables for all the 20 models to capture the reliability and nature of the particular model at regional scale. The analysis is based on spatially averaged datasets of GCMs and observation for the period of 1970 to 2000. Ranking is provided to each of the GCMs based on the performance evaluated against gridded observational data on various temporal scales (daily, monthly, and seasonal). Results have provided insight into each of the methods and various statistical properties addressed by them employed in ranking GCMs. Further; evaluation was also performed for raw GCM simulations against different sets of gridded observational dataset in the area.     

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.     

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.     

Climate Change Effects on Plant Growth, Crop Yield and Livestock

A review is given of the state of knowledge in the field of assessing climate change impacts on agricultural crops and livestock. Starting from the basic processes controlling plant growth and development, the possible impacts and interactions of climatic and other biophysical variables in different agro-environments are highlighted. Qualitative and quantitative estimations of shifts in biomass production and water relations, inter-plant competition and crop species adaptability are discussed. Special attention is given to the problems encountered when scaling up physiological responses at the leaf- and plant level to yield estimates at regional to global levels by using crop simulation models in combination with geo-referenced, agro-ecological databases. Some non-linear crop responses to environmental changes and their relations to adaptability and vulnerability of agro-ecosystems are discussed.     

Estimating the consequences of CO2-induced climatic change on north american grain agriculture using general circulation model information

A procedure to estimate the potential climatic effects of a doubling of atmospheric carbon dioxide concentration on agricultural production is illustrated. The method combines use of atmospheric general circulation models (GCMs) and process-oriented crop models. Wheat and corn (maize) yields in three important North American grain cropping regions are treated. Combined use of these two types of models can provide insights into the impacts of climate changes at the level of plant physiology, and potential means by which agricultural production practices may adapt to these changes.Specific agronomic predictions are found to depend critically on the details of the projected climate change. Uncertainties in the specification of the doubled-CO₂ climate by the GCM, particularly with respect to precipitation, dictate that agricultural predictions derived from them at this time must be regarded only as illustrative of the impact assessment method.     

ON THE WEATHER-YIELD SIMULATION AND MODELS IN CHINA

In this paper,the development of the studies on the weather-yield simulation and forecasting model inChina is briefly reviewed,and the main features of the current development stage are presented.Moreover,through examples the technical characters,approaches and experimental results are detailedly described anddiscussed of several major statistical forecasting models,dynamic crop growth simulation and the satelliteremote sensing methods to estimate crop yield.Finally,the line of further development and the applied fieldare pointed out.At the present time,in particular,using the above-mentioned modelling techniques to simu-late and evaluate the possible impact of climatic variation on agricultural production and further on man'ssurvival and activities are of a very practical significance as well as socioeconomic benefits.