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.     

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。     

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。     

气候模拟数据的订正与应用 ——以北京为例

为了消除气候模拟数据中气候平均值和气候变率的漂移,发展了一种新的数据订正方案。应用该方案对IPCC提供的B2情景下北京未来100 a气候预估值进行了订正试验,证实了方案的可用性。在此基础上分析了北京未来气候变化特征,结果表明：21世纪北京气温将继续上升,升温速率约为0.31℃/10 a,最低、最高气温的非对称变化仍将持续;未来北京年降水量呈微弱下降趋势,下降速率约为1.03 mm/10 a。     

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。     

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。     

表面气温内部变率估算方法的比较研究

本文利用37个CMIP5模式和CESM（Community Earth System Model）包含40个成员的超级集合试验的表面气温预估数据，比较了工业革命前气候参照试验、多项式拟合法和方差分析方法这三种目前在国际上运用较多的方法所估算的表面气温内部变率的异同，分析了内部变率的估算对气候预估中信号萌芽时间（TOE）的影响。结果表明:若采用CMIP5多模式集合，则工业革命前气候参照试验和多项式拟合法都是估算内部变率的合理方法，而方差分析方法则由于包含模式性能自身的影响会夸大内部变率故不推荐使用。内部变率的全球分布呈现出极向强化的现象，中高纬度地区的内部变率幅度远大于热带、副热带地区。内部变率受不同排放情景的影响较小，且随时间无显著变化，但方差分析方法估算的内部变率在热带地区容易受到排放情景的影响。若基于类似CESM这样的单个气候模式的超级集合模拟试验来估算内部变率，三种方法估算的结果相似。不同方法估算的内部变率对TOE的影响主要位于北大西洋拉布拉多海、南大洋威德尔海和罗斯海等邻近海洋深对流区。对于中国区域平均来说，基于CESM超级集合模拟试验，三种方法估算的内部变率与强迫信号之比都小于15%；对CMIP5多模式集合，采用工业革命前气候参照试验和多项式拟合法得到的结果与此接近，但若采用方差分析方法则显著高估内部变率的作用。     

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。     

IPCC AR6报告解读：极端天气气候事件变化

与IPCC第五次评估报告（AR5）相比,在第六次评估报告（AR6）评估中,观测的极端天气气候事件变化证据,特别是归因于人为影响的证据加强。人类活动造成的气候变化已影响到全球每个区域的许多极端天气气候事件。随着未来全球变暖进一步加剧,预估极端热事件、强降水、农业生态干旱的强度和频次以及强台风（飓风）比例等将增加,越罕见的极端天气气候事件,其发生频率的增长百分比越大。这些结论再次凸显了应对气候变化和极端天气气候事件的必要性和紧迫性。     

美国全球变化研究现状

美国的全球变化研究主要由美国全球变化研究计划（ＵＳＧＣＲＰ）支持,重点资助季节—年际尺度气候变率,十年—百年尺度的气候变化,臭氧、ＵＶ辐射以及大气化学的变化,土地利用以及陆地、海洋生态系统的变化等４个领域。当前,水汽与云仍是全球变化研究中不确定性较大的一个方面,因而受到关注。关于气候变化的信号检测以及成因分析也是一个研究热点。气候模拟研究是全球变化研究的一个主要方法。卫星资料在全球变化研究中的应用取得了大量成果。近期美国在全球变化研究领域的重点是气候模拟,短期气候预测,十年—百年尺度的气候变化,臭氧、ＵＶ辐射以及大气化学的变化,地表以及陆地、海洋生态系统变化,对全球变化的区域尺度估计,卫星资料的应用,气候变化影响的国家级评估等８个方面。     

USE OF A STOCHASTIC WEATHER GENERATOR IN THE DEVELOPMENT OF CLIMATE CHANGE SCENARIOS

Climate change scenarios with a high spatial and temporal resolution are required in the evaluation of the effects of climate change on agricultural potential and agricultural risk. Such scenarios should reproduce changes in mean weather characteristics as well as incorporate the changes in climate variability indicated by the global climate model (GCM) used. Recent work on the sensitivity of crop models and climatic extremes has clearly demonstrated that changes in variability can have more profound effects on crop yield and on the probability of extreme weather events than simple changes in the mean values. The construction of climate change scenarios based on spatial regression downscaling and on the use of a local stochastic weather generator is described. Regression downscaling translated the coarse resolution GCM grid-box predictions of climate change to site-specific values. These values were then used to perturb the parameters of the stochastic weather generator in order to simulate site-specific daily weather data. This approach permits the incorporation of changes in the mean and variability of climate in a consistent and computationally inexpensive way. The stochastic weather generator used in this study, LARS-WG, has been validated across Europe and has been shown to perform well in the simulation of different weather statistics, including those climatic extremes relevant to agriculture. The importance of downscaling and the incorporation of climate variability are demonstrated at two European sites where climate change scenarios were constructed using the UK Met. Office high resolution GCM equilibrium and transient experiments.     

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.     

Projections of uncertainties in climate change scenarios into expected winter wheat yields

Summary The crop model CERES-Wheat in combination with the stochastic weather generator were used to quantify the effect of uncertainties in selected climate change scenarios on the yields of winter wheat, which is the most important European cereal crop. Seven experimental sites with the high quality experimental data were selected in order to evaluate the crop model and to carry out the climate change impact analysis. The analysis was based on the multi-year crop model simulations run with the daily weather series prepared by the stochastic weather generator. Seven global circulation models (GCMs) were used to derive the climate change scenarios. In addition, seven GCM-based scenarios were averaged in order to derive the average scenario (AVG). The scenarios were constructed for three time periods (2025, 2050 and 2100) and two SRES emission scenarios (A2 and B1). The simulated results showed that: (1) Wheat yields tend to increase (40 out of 42 applied scenarios) in most locations in the range of 7.5–25.3% in all three time periods. In case of the CCSR scenario that predicts the most severe increase of air temperature, the yields would be reduced by 9.6% in 2050 and by 25.8% in 2100 if the A2 emission scenario would become reality. Differences between individual scenarios are large and statistically significant. Particularly for the time periods 2050 and 2100 there are doubts about the trend of the yield shifts. (2) The site effect was caused by the site-specific soil and climatic conditions. Importance of the site influence increases with increasing severity of imposed climatic changes and culminates for the emission scenario A2 and the time period 2100. The sustained tendency benefiting two warmest sites has been found as well as more positive response to the changed climatic conditions of the sites with deeper soil profiles. (3) Temperature variability proved to be an important factor and influenced both mean and standard deviation of the yields. Change of temperature variability by more than 25% leads to statistically significant changes in yield distribution. The effect of temperature variability decreases with increased values of mean temperature. (4) The study proved that the application of the AVG scenarios – despite possible objections of physical inconsistency – might be justifiable and convenient in some cases. It might bring results comparable to those derived from averaging outputs based on number of scenarios and provide more robust estimate than the application of only one selected GCM scenario.     

A comparison of explicit and implicit spatial downscaling of GCM output for soil erosion and crop production assessments

Spatial downscaling of climate change scenarios can be a significant source of uncertainty in simulating climatic impacts on soil erosion, hydrology, and crop production. The objective of this study is to compare responses of simulated soil erosion, surface hydrology, and wheat and maize yields to two (implicit and explicit) spatial downscaling methods used to downscale the A2a, B2a, and GGa1 climate change scenarios projected by the Hadley Centre’s global climate model (HadCM3). The explicit method, in contrast to the implicit method, explicitly considers spatial differences of climate scenarios and variability during downscaling. Monthly projections of precipitation and temperature during 1950–2039 were used in the implicit and explicit spatial downscaling. A stochastic weather generator (CLIGEN) was then used to disaggregate monthly values to daily weather series following the spatial downscaling. The Water Erosion Prediction Project (WEPP) model was run for a wheat–wheat–maize rotation under conventional tillage at the 8.7 and 17.6% slopes in southern Loess Plateau of China. Both explicit and implicit methods projected general increases in annual precipitation and temperature during 2010–2039 at the Changwu station. However, relative climate changes downscaled by the explicit method, as compared to the implicit method, appeared more dynamic or variable. Consequently, the responses to climate change, simulated with the explicit method, seemed more dynamic and sensitive. For a 1% increase in precipitation, percent increases in average annual runoff (soil loss) were 3–6 (4–10) times greater with the explicit method than those with the implicit method. Differences in grain yield were also found between the two methods. These contrasting results between the two methods indicate that spatial downscaling of climate change scenarios can be a significant source of uncertainty, and further underscore the importance of proper spatial treatments of climate change scenarios, and especially climate variability, prior to impact simulation. The implicit method, which applies aggregated climate changes at the GCM grid scale directly to a target station, is more appropriate for simulating a first-order regional response of nature resources to climate change. But for the site-specific impact assessments, especially for entities that are heavily influenced by local conditions such as soil loss and crop yield, the explicit method must be used.     

MEAN AND VARIANCE CHANGE IN CLIMATE SCENARIOS: METHODS, AGRICULTURAL APPLICATIONS, AND MEASURES OF UNCERTAINTY

Our central goal is to determine the importance of including both mean and variability changes in climate change scenarios in an agricultural context. By adapting and applying a stochastic weather generator, we first tested the sensitivity of the CERES-Wheat model to combinations of mean and variability changes of temperature and precipitation for two locations in Kansas. With a 2°C increase in temperature with daily (and interannual) variance doubled, yields were further reduced compared to the mean only change. In contrast, the negative effects of the mean temperature increase were greatly ameliorated by variance decreased by one-half. Changes for precipitation are more complex, since change in variability naturally attends change in mean, and constraining the stochastic generator to mean change only is highly artificial. The crop model is sensitive to precipitation variance increases with increased mean and variance decreases with decreased mean. With increased mean precipitation and a further increase in variability Topeka (where wheat cropping is not very moisture limited) experiences decrease in yield after an initial increase from the 'mean change only case. At Goodland Kansas, a moisture-limited site where summer fallowing is practiced, yields are decreased with decreased precipitation, but are further decreased when variability is further reduced. The range of mean and variability changes to which the crop model is sensitive are within the range of changes found in regional climate modeling (RegCM) experiments for a CO₂ doubling (compared to a control run experiment). We then formed two types of climate change scenarios based on the changes in climate found in the control and doubled CO₂ experiments over the conterminous U. S. of RegCM: (1) one using only mean monthly changes in temperature, precipitation, and solar radiation; and (2) another that included these mean changes plus changes in daily (and interannual) variability. The scenarios were then applied to the CERES-Wheat model at four locations (Goodland, Topeka, Des Moines, Spokane) in the United States. Contrasting model responses to the two scenarios were found at three of the four sites. At Goodland, and Des Moines mean climate change increased mean yields and decreased yield variability, but the mean plus variance climate change reduced yields to levels closer to their base (unchanged) condition. At Spokane mean climate change increased yields, which were somewhat further increased with climate variability change. Three key aspects that contribute to crop response are identified: the marginality of the current climate for crop growth, the relative size of the mean and variance changes, and timing of these changes. Indices for quantifying uncertainty in the impact assessment were developed based on the nature of the climate scenario formed, and the magnitude of difference between model and observed values of relevant climate variables.     

Agricultural risk management policies under climate uncertainty

Climate change is forecasted to increase the variability of weather conditions and the frequency of extreme events. Due to potential adverse impacts on crop yields it will have implications for demand of agricultural risk management instruments and farmers’ adaptation strategies. Evidence on climate change impacts on crop yield variability and estimates of production risk from farm surveys in Australia, Canada and Spain, are used to analyse the policy choice between three different types of insurance (individual, area-yield and weather index) and ex post payments. The results are found to be subject to strong uncertainties and depend on the risk profile of different farmers and locations; the paper provides several insights on how to analyse these complexities. In general, area yield performs best more often across our countries and scenarios, in particular for the baseline and marginal climate change (without increases in extreme events). However, area yield can be very expensive if farmers have limited information on how climate change affects yields (misalignment in expectations), and particularly so under extreme climate change scenarios. In these more challenging cases, ex post payments perform well to increase low incomes when the risk is systemic like in Australia; Weather index performs well to reduce the welfare costs of risks when the correlation between yields and index is increased by the extreme events. The paper also analyses the robustness of different instruments in the face of limited knowledge of the probabilities of different climate change scenarios; highlighting that this added layer of uncertainty could be overcome to provide sound policy advice under uncertainties introduced by climate change. The role of providing information to farmers on impacts of climate change emerges as a crucial result of this paper as indicated by the significantly higher budgetary expenditures occurring across all instruments when farmers’ expectations are misaligned relative to actual impacts of climate change.