Implications of Japan’s long term climate mitigation target and the relevance of uncertain nuclear policy

Achieving long-term climate mitigation goals in Japan faces several challenges, starting with the uncertain nuclear power policy after the 2011 earthquake, the uncertain availability and progress of energy technologies, as well as energy security concerns in light of a high dependency on fuel imports. The combined weight of these challenges needs to be clarified in terms of the energy system and macroeconomic impacts. We applied a general equilibrium energy economic model to assess these impacts on an 80% emission reduction target by 2050 considering several alternative scenarios for nuclear power deployment, technology availability, end use energy efficiency, and the price of fossil fuels. We found that achieving the mitigation target was feasible for all scenarios, with considerable reductions in total energy consumption (39%–50%), higher shares of low-carbon sources (43%–72% compared to 15%), and larger shares of electricity in the final energy supply (51%–58% compared to 42%). The economic impacts of limiting nuclear power by 2050 (3.5% GDP loss) were small compared to the lack of carbon capture and storage (CCS) (6.4% GDP loss). Mitigation scenarios led to an improvement in energy security indicators (trade dependency and diversity of primary energy sources) even in the absence of nuclear power. Moreover, preliminary analysis indicates that expanding the range of renewable energy resources can lower the macroeconomic impacts of the long term target considerably, and thus further in depth analysis is needed on this aspect.

Key policy insights

• For Japan, an emissions reduction target of 80% by 2050 is feasible without nuclear power or CCS.

• The macroeconomic impact of such a 2050 target was largest without CCS, and smallest without nuclear power.

• Energy security indicators improved in mitigation scenarios compared to the baseline.

     

模式内部变率引起的1.5℃和2℃升温阈值出现时间模拟的不确定性研究

模式内部变率是模拟结果不确定性的重要来源,然而它对于1.5℃和2℃升温阈值出现时间不确定性的影响尚不清楚。因此,基于耦合模式比较计划第五阶段(CMIP5)的多模式数据研究了模式内部变率对1.5℃和2℃升温阈值出现时间不确定性的影响以及对未来排放情景的敏感性。结果表明,模式内部变率对升温阈值出现时间模拟的影响与外强迫的影响相当,单个模式内部不同成员达到全球平均1.5℃或2℃增温的年份相差2~12年;其影响具有明显的空间差异,影响极大值出现在欧亚大陆以北洋面、白令海峡周围区域、北美东北部及其与格陵兰岛之间的海域、南半球高纬地区等;低排放情景下模式内部变率的影响大于高排放情景。     

Using Statistical Downscaling to Quantify the GCM-Related Uncertainty in Regional Climate Change Scenarios： A Case Study of Swedish Precipitation

There are a number of sources of uncertainty in regional climate change scenarios. When statistical downscaling is used to obtain regional climate change scenarios, the uncertainty may originate from the uncertainties in the global climate models used, the skill of the statistical model, and the forcing scenarios applied to the global climate model. The uncertainty associated with global climate models can be evaluated by examining the differences in the predictors and in the downscaled climate change scenarios based on a set of different global climate models. When standardized global climate model simulations such as the second phase of the Coupled Model Intercomparison Project （CMIP2） are used, the difference in the downscaled variables mainly reflects differences in the climate models and the natural variability in the simulated climates. It is proposed that the spread of the estimates can be taken as a measure of the uncertainty associated with global climate models. The proposed method is applied to the estimation of global-climate-model-related uncertainty in regional precipitation change scenarios in Sweden. Results from statistical downscaling based on 17 global climate models show that there is an overall increase in annual precipitation all over Sweden although a considerable spread of the changes in the precipitation exists. The general increase can be attributed to the increased large-scale precipitation and the enhanced westerly wind. The estimated uncertainty is nearly independent of region. However, there is a seasonal dependence. The estimates for winter show the highest level of confidence, while the estimates for summer show the least.     

Soil moisture retrieval from satellite images and its application to heavy rainfall simulation in eastern China

The soil water index (SWI) from satellite remote sensing and the observational soil moisture from agricultural meteorological stations in eastern China are used to retrieve soil moisture. The analysis of correlation coefficient (CORR), root-mean-square-error (RMSE) and bias (BIAS) shows that the retrieved soil moisture is convincible and close to the observation. The method can overcome the difficulties in soil moisture observation on a large scale and the retrieved soil moisture may reflect the distribution of the real soil moisture objectively. The retrieved soil moisture is used as an initial scheme to replace initial conditions of soil moisture (NCEP) in the model MM5V3 to simulate the heavy rainfall in 1998. Three heavy rainfall processes during 13–14 June, 18–22 June, and 21–26 July 1998 in the Yangtze River valley are analyzed. The first two processes show that the intensity and location of simulated precipitation from SWI are better than those from NCEP and closer to the observed values. The simulated heavy rainfall for 21–26 July shows that the update of soil moisture initial conditions can improve the model’s performance. The relationship between soil moisture and rainfall may explain that the stronger rainfall intensity for SWI in the Yangtze River valley is the result of the greater simulated soil moisture from SWI prior to the heavy rainfall date than that from NCEP, and leads to the decline of temperature in the corresponding area in the heavy rainfall days. Detailed analysis of the heavy rainfall on 13–14 June shows that both land-atmosphere interactions and atmospheric circulation were responsible for the heavy rainfall, and it shows how the SWI simulation improves the simulation. The development of mesoscale systems plays an important role in the simulation regarding the change of initial soil moisture for SWI.     

基于复用的软件构架评估方法及其在嫦娥工程中的应用

针对目前软件构架评估方法未考虑复用构架评估知识的局限性, 提出一种新的、基于构架评估知识复用的软件构架评估模式(包括软件构架评估方法元模型和应用框架).运用一致的评估元模型, 建立高层模型, 辅助构架评估决策; 并在应用框架支持下, 系统地复用评估历史数据, 将SAEM的活动整合为一个系统的、可复用的、可管理的过程.该方法已成功应用于中国探月工程地面应用系统的软件构架评估, 降低了深空探测航天复杂系统的研制风险.      

Future Changes in Extreme High Temperature over China at 1.5℃-5℃ Global Warming Based on CMIP6 Simulations

Extreme high temperature(EHT)events are among the most impact-related consequences related to climate change,especially for China,a nation with a large population that is vulnerable to the climate warming.Based on the latest Coupled Model Intercomparison Project Phase 6(CMIP6),this study assesses future EHT changes across China at five specific global warming thresholds(1.5℃-5℃).The results indicate that global mean temperature will increase by 1.5℃/2℃ before 2030/2050 relative to pre-industrial levels(1861-1900)under three future scenarios(SSP1-2.6,SSP2-4.5,and SSP5-8.5),and warming will occur faster under SSP5-8.5 compared to SSP1-2.6 and SSP2-4.5.Under SSP5-8.5,global warming will eventually exceed 5℃ by 2100,while under SSP1-2.6,it will stabilize around 2℃ after 2050.In China,most of the areas where warming exceeds global average levels will be located in Tibet and northern China(Northwest China,North China and Northeast China),covering 50%-70%of the country.Furthermore,about 0.19-0.44 billion people(accounting for 16%-41%of the national population)will experience warming above the global average.Compared to present-day(1995-2014),the warmest day(TXx)will increase most notably in northern China,while the number of warm days(TX90p)and warm spell duration indicator(WSDI)will increase most profoundly in southern China.For example,relative to the present-day,TXx will increase by 1℃-5℃ in northern China,and TX90p(WSDI)will increase by 25-150(10-80)days in southern China at 1.5℃-5℃ global warming.Compared to 2℃-5℃,limiting global warming to 1.5℃ will help avoid about 36%-87%of the EHT increases in China.     

Ecosystem carbon storage under different scenarios of land use change in Qihe catchment,China

Regional land use change is the main cause of the ecosystem carbon storage changes by affecting emission and sink process.However,there has been little research on the influence of land use changes for ecosystem carbon storage at both temporal and spatial scales.For this study,the Qihe catchment in the southern part of the Taihang Mountains was taken as an example;its land use change from 2005 to 2015 was analyzed,the Markov-CLUE-S composite model was used to predict land use patterns in 2025 under natural growth,cultivated land protection and ecological conservation scenario,and the land use data were used to evaluate ecosystem carbon storage under different scenarios for the recent 10-year interval and the future based on the carbon storage module of the In VEST model.The results show the following:(1) the ecosystem carbon storage and average carbon density of Qihe catchment were 3.16×107 t and 141.9 t/ha,respectively,and decreased by 0.07×107 t and 2.89 t/ha in the decade evaluated.(2) During 2005–2015,carbon density mainly decreased in low altitude areas.For high altitude area,regions with increased carbon density comprised a similar percentage to regions with decreased carbon density.The significant increase of the construction areas in the middle and lower reaches of Qihe and the degradation of upper reach woodland were core reasons for carbon density decrease.(3) For 2015–2025,under natural growth scenario,carbon storage and carbon density also significantly decrease,mainly due to the decrease of carbon sequestration capacity in low altitude areas;under cultivated land protection scenario,the decrease of carbon storage and carbon density will slow down,mainly due to the increase of carbon sequestration capacity in low altitude areas;under ecological conservation scenario,carbon storage and carbon density significantly increase and reach 3.19×107 t and 143.26 t/ha,respectively,mainly in regions above 1100 m in altitude.Ecological conservation scenario can enhance carbon sequestration capacity but cannot effectively control the reduction of cultivated land areas.Thus,land use planning of research areas should consider both ecological conservation and cultivated land protection scenarios to increase carbon sink and ensure the cultivated land quality and food safety.     

波段选择协同表达高光谱异常探测算法

高光谱遥感影像具有光谱分辨率极高的特点,承载了大量可区分不同类型地物的诊断性光谱信息以及区分亚类相似地物之间细微差别的光谱信息,在目标探测领域具有独特的优势。与此同时,高光谱遥感影像也带来了数据维数高、邻近波段之间存在大量冗余信息的问题,高维度的数据结构往往使得高光谱影像异常目标类和背景类之间的可分性降低。为了缓解上述问题,本文提出了一种基于波段选择的协同表达高光谱异常探测算法。首先,使用最优聚类框架对高光谱波段进行选择,获得一组波段子集来表示原有的全部波段,使得高光谱影像异常目标类与背景类之间的可分性增强。然后使用协同表达对影像上的像元进行重建,由于异常目标类和背景类之间的可分性增强,对异常目标像元进行协同表达时将会得到更大的残差,异常目标像元的输出值增大,可以更好地实现异常目标和背景类的分离。本文使用了3组高光谱影像数据进行异常目标探测实验,实验结果表明,该方法与其他现有高光谱异常目标探测算法对比,曲线下面积AUC(Area Under Curve)值更高,可以更好地实现异常目标与背景分离,能够更有效地对高光谱影像进行异常目标探测。     

基于多分辨率分析的GF-5和GF-1遥感影像空—谱融合

针对空间分辨率比率较大尺度差异下的高分五号(GF-5)与高分一号(GF-1)卫星影像的空—谱融合问题,提出多传感器影像融合策略:一方面,通过现有空—谱融合方法的分步融合得到融合影像;另一方面,在分步融合理论基础上,推导得出一体化融合基础框架,并进一步提出基于多分辨率分析的多传感器一体化融合方法,缓解现有方法因空间分辨率比率过大导致影像空、谱互补信息难以有效集成的问题。其中,提出的一体化融合方法基于调制传递函数MTF (Modulation Transfer Function)滤波对多传感器影像空间(高频)和光谱(低频)分量进行分解提取,并充分考虑多传感器高空间分辨率影像与高光谱分辨率影像之间的关系,以及高光谱分辨率影像波段间关系,设计合理的融合权重,最终可得到具有最高空间分辨率和最高光谱分辨率的融合影像。通过GF-1全色影像、GF-1多光谱影像、GF-5高光谱影像数据对提出方法进行实验验证,结果表明:本文方法可有效集成多传感器影像间的空、谱互补信息,得到较优融合结果。     

顾及水陆差异的高分五号影像条带去除

高分五号搭载的可见光短波红外高光谱相机,能获取精细的地物光谱信息,具有十分广泛的应用前景。但高光谱卫星遥感数据往往无法避免条带噪声的干扰,进行条带去除是数据预处理中不可缺少的步骤。传统方法往往对地物的异质性与丰富的细节纹理考虑不足,导致条带不能被彻底地消除。为此,本文提出了一种顾及水陆差异的影像条带去除方法,采用水体与陆地区别统计的策略,解决条带噪声在异质区域的统计特征差异问题,并结合优化统计和一维变分滤波技术实现水陆区域参考统计特征的精确估计,最终基于矩匹配方法分别实现条带去除。实验结果表明:无论是真实实验还是模拟实验,本文提出的算法相较于传统条带去除算法,能更加稳健地去除数据条带噪声,还原地表真实辐射信息;在模拟实验中,本文算法处理结果的峰值信噪比(PSNR)达到46.58,且平均绝对误差(MAE)仅有11.56,均明显优于用于比较的3种传统算法,且算法执行效率也具备优势,能够更好地适用于高分五号大数据量的处理需求。