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RCP8.5气候变化情景下21世纪印度粮食单产变化的多模式集合模拟 总被引：1，自引：0，他引：1
基于跨部门影响模型比较计划（ISI-MIP）中20种气候模式与作物模型组合的模拟结果,预估了RCP 8.5排放情景下21世纪印度小麦和水稻单产变化。研究发现：① 多模式集合模拟结果基本再现了印度小麦和水稻单产的空间差异;同时,再现了小麦和水稻单产对温度和降水变化的响应特征：与温度呈负相关,与降水呈正相关。② RCP 8.5情景下,水稻和小麦生长季温度和降水均呈增加趋势,小麦生长季的温度、降水增加幅度大于水稻。空间上,温度增加幅度自北向南逐渐减小,降水增幅则逐渐增加,并且小麦种植区升温幅度大于非种植区,降水增幅则少于非种植区,水稻种植区升温幅度小于非种植区,降水增幅则多于非种植区。③ RCP 8.5情景下,小麦和水稻单产均呈下降趋势,21世纪后半叶尤为明显。小麦单产的下降速度明显大于水稻,其中21世纪前半叶小麦和水稻单产下降速度约分别为1.3%/10a （P < 0.001）和0.7%/10a （P < 0.05）,后半叶分别增至4.9%/10a （P < 0.001）和4.4%/10a （P < 0.001）。小麦和水稻单产变化存在明显的空间异质性,小麦单产的最大下降幅度出现在德干高原西南部,降幅约60%,水稻单产最大下降幅度出现在印度河平原北部,降幅约50%。这意味着未来气候变化情景下印度粮食供给将面临较大的挑战。 相似文献
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采用应用于跨行业影响模式比较计划(ISIMIP)的5个CMIP5全球气候模式模拟的历史和未来RCP排放情景下的逐日降水数据,在评估模式对汉江流域1961—2005年极端降水变化特征模拟能力的基础上,进一步计算了RCP2.6、RCP4.5和RCP8.5排放情景下汉江流域未来2016—2060年极端降水总量(R95p)、极端降水贡献率(PEP)、连续5 d最大降水(RX5d)和降水强度(SDII),结果表明:RCP4.5情景下的极端降水指数上升最明显,R95p和RX5d分别较基准期增加12.5%和8.2%,PEP增加3.2个百分点,SDII微弱上升。在不同排放情景下,PEP均有一定的增幅,以流域西北和东南部增幅较大;R95p在流域绝大部分区域表现出一定的增加,且流域东南部和北部是增幅高值区;RX5d在RCP2.6和RCP4.5情景下整体表现为增加的特征,但在RCP8.5情景下整体表现为减少的特征。对极端降水预估的不确定性中,SDII的不确定性最小,RX5d的不确定性最大;不确定性大值区主要位于流域东部、东南部和西北部部分区域。 相似文献
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利用首都圈地区2017~2019年流动重力观测资料,分析2019-12-05河北唐山丰南4.5级地震前不同时间尺度的重力场变化。结果表明：1）0.5 a尺度重力场变化图像可较好地反映此次地震“震前1 a重力场持续出现上升（正）变化-震前局部反向（负）变化-在重力变化梯度带零等值线附近发震”的演化过程;2）震前0.5 a、1 a和2 a尺度重力场均显示,地震发生在重力变化零等值线附近;3）地震前观测到的重力变化可能由深部热物质上涌引起。 相似文献
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Projected changes in population exposure to extreme heat in China under a RCP8.5 scenario 总被引：1，自引：0，他引：1
Overall population exposure is measured by multiplying the annual average number of extremely hot days by the number of people exposed to the resultant heat. Extreme heat is also subdivided into high temperature (HT) and extremely high temperature (EHT) in cases where daily maximum temperature exceeds 35°C and 40°C, respectively. Chinese population exposure to HT and EHT over four periods in the future (i.e., 2021–2040, 2041–2060, 2060–2081 and 2081–2100) were projected at the grid cell level in this study using daily maximum temperature based on an ensemble mean of 21 global climate models under the RCP8.5 scenario and with a population projection based on the A2r socio-economic scenario. The relative importance of population and climate as drivers of population exposure was evaluated at different spatial scales including national and meteorological geographical divisions. Results show that, compared with population exposure seen during 1981–2010, the base period, exposure to HT in China is likely to increase by 1.3, 2.0, 3.6, and 5.9 times, respectively, over the four periods, while concomitant exposure to EHT is likely to increase by 2.0, 8.3, 24.2, and 82.7 times, respectively. Data show that population exposure to HT is likely to increase significantly in Jianghuai region, Southwest China and Jianghan region, in particular in North China, Huanghuai region, South China and Jiangnan region. Population exposure to EHT is also likely to increase significantly in Southwest China and Jianghan region, especially in North China, Huanghuai, Jiangnan, and Jianghuai regions. Results reveal that climate is the most important factor driving the level of population exposure in Huanghuai, Jianghuai, Jianghan, and Jiangnan regions, as well as in South and Southwest China, followed by the interactive effect between population and climate. Data show that the climatic factor is also most significant at the national level, followed by the interactive effect between population and climate. The rate of contribution of climate to national-level projected changes in exposure is likely to decrease gradually from ca. 70% to ca. 60%, while the rate of contribution of concurrent changes in both population and climate is likely to increase gradually from ca. 20% to ca. 40% over the four future periods in this analysis. 相似文献
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Based on the tropical cyclone (TC) observations in the western North Pacific from 2000 to 2008, this paper adopts the particle swarm optimization (PSO) algorithm of evolutionary computation to optimize one comprehensive classification rule, and apply the optimized classification rule to the forecasting of TC intensity change. In the process of the optimization, the strategy of hierarchical pruning has been adopted in the PSO algorithm to narrow the search area, and thus to enhance the local search ability, i.e. hierarchical PSO algorithm. The TC intensity classification rule involves core attributes including 12-HMWS, MPI, and Rainrate which play vital roles in TC intensity change. The testing accuracy using the new mined rule by hierarchical PSO algorithm reaches 89.6%. The current study shows that the novel classification method for TC intensity change analysis based on hierarchic PSO algorithm is not only easy to explain the source of rule core attributes, but also has great potential to improve the forecasting of TC intensity change. 相似文献
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利用国际协同强化观测期(CEOP)在中国半干旱区退化草地站——通榆站的观测资料,对一个较为完善的陆面过程模式NCAR_CLM4.5(Community Land Model 4.5)的模拟性能进行检验。模拟结果与观测资料的对比表明,CLM4.5能很好地模拟出观测站点的辐射通量、水热交换、土壤温湿的空间分布和时间变化特征。但地表吸收的辐射模拟值略低,土壤湿度偏低,地表吸收的辐射及土壤温度等日变化略大;大气强迫变量处于某些特定的形势下时,模拟存在较大误差,如8月底的模拟。此外,冬季辐射通量、水热交换以及土壤温湿的模拟均存在较大误差,说明CLM4.5模式在冬季地表物理过程的参数化方案上需要进一步改进。 相似文献
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CMIP5模式对西太平洋副热带高压的模拟和预估 总被引：4，自引：0，他引：4
利用国际耦合模式比较计划第五阶段（CMIP5）26个模式的模拟结果,从空间分布和振幅变化、年际周期及年代际趋势等方面,初步评估了CMIP5模式对西太平洋副热带高压（副高）的模拟能力。在此基础上,还对未来不同典型浓度路径（RCPs）情景下副高的可能变化给出了定性的预估。CMIP5模式历史试验结果显示,大多数模式对500 hPa位势高度气候平均值的模拟有明显误差,这主要是由于模式对热带印度洋和西太平洋地区海表温度（SST）的模拟普遍较观测值低,从而导致模式对副高的模拟能力有限。但大多数模式对高度场和纬向风场变化的空间形态与振幅都有较强的模拟能力。因此,通过用NCEP/NCAR再分析资料的气候平均值替代CMIP5模式气候平均值的简单方法,对CMIP5模拟结果进行了订正。经订正后的模式结果均有能力刻画副高指数的历史时间序列,且能够反映出20世纪70年代末期之后,副高面积增大、强度增强和显著西伸的变化趋势。此外,通过对副高指数的长期趋势、年际周期及标准差等的定量评估,注意到CNRM-CM5、FGOALS-g2、FIO-ESM、MIROC-ESM和MPI-ESM-P这5个模式对副高的模拟能力较强。未来气候预估试验中,副高面积和强度均增大,且显著西伸;其线性增长趋势在RCP8.5情景下最高,RCP4.5情景下次之,RCP2.6情景下最弱。有趣的是副高脊线指数在3种排放情景下都没有明显的长期变化趋势。这些结果为选取和利用CMIP5模式进行东亚地区气候变化的归因分析和未来预估提供了一定的科学依据。 相似文献
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This study investigated the changes of high temperature events during important growing period of rice (graining filling to maturity) of 2021-2050 due to climate change. Future climate scenarios were HadGEM2-ES simulation with RCP2.6 and RCP8.5 emission pathways. Relationship between high temperature and yield change was established from historical weather and field observations during 1981-2009 period. The impacts of high temperatures on China’s rice production up to 2050 were assessed by applying deduced regression models to climate scenarios. Key messages drawn from this exercise include: ①High temperature event exhibited gradual increase from 2021 to 2050 under both RCP2.6 and RCP8.5 scenarios, characterized by increased number of high temperature days (HSD), rising accumulated temperature with Tmax greater than 35 ℃ (HDD), and increased lasting days of high temperature (CHD). The HSD and HDD increased substantially in double rice cropping system of South China, single rice cropping system of Yangtze River Basin and rice area of Northeast China. ②High temperature hotspot was located near the border between Hunan and Hubei during 1961-2000, and might move towards northeast in the period of 2021-2050. ③Except the Northeast, China’s rice production suffered most from increased HDD during grain filling to maturity, indicated by significant negative and linear relationship between yield and HDD, whereas rice in Northeast China was subject to the increase of SDD during grain filling to maturity, with a significant and quadratic relationship between the yield and SDD. ④Compared to the high temperature risks during 1961-1990, climate change would increase the risks in majority of the rice area, especially in Hubei and Anhui-the central portion of Yangtze River Basin rice area, Guangdong, Guangxi and Hainan-south China double rice area, and south part of Northeast China single rice area. 相似文献