兰州大学机构库 >大气科学学院
中国区域大气水汽变化的观测、模拟及其归因研究
Alternative TitleObservation, simulation and attribution of atmospheric water vapor change over China
张京朋
Thesis Advisor张文煜 ; 赵天保
2019-05-08
Degree Grantor兰州大学
Place of Conferral兰州
Degree Name博士
Degree Discipline大气物理学与大气环境
Keyword水汽变化 CMIP5模式 检测归因 中国区域
Abstract水汽是大气中最丰富、最重要的温室气体之一,在全球物质输送、能量传输和水循环过程中扮演了至关重要的角色,水汽的反馈作用能够放大其他温室气体的增暖效应,对天气和气候变化有着极其重要的影响。因此,对大气水汽变化的规律成因和气候反馈效应进行研究有助于我们深入认识全球变暖背景下区域气候变化及响应机理。本文首先基于最新的均一化湿度探空资料,采用多种统计分析方法就国际耦合模式比较计划第五阶段(CMIP5)多模式对中国区域大气水汽变化的模拟能力进行了系统评估,并在此基础上分析了不同排放情境下水汽的未来变化,然后进一步研究了水汽的气候反馈效应,最后利用不同外强迫下的模拟试验对中国区域水汽变化进行了检测归因。主要研究结论如下:大多数CMIP5模式能够较好地模拟出1970-2015年中国区域观测水汽的季节变化特征,也基本能够再现观测水汽的气候平均态分布,尤其是在我国东部大部分地区,多模式相对于观测值的相对偏差在30%以内,但是在我国西部,特别是在青藏高原地区,多模式要比观测结果偏低30~60%。多模式与观测值的均方根误差整体上呈现出由西北向东南递增的分布,且二者的空间相关系数较低,正相关的区域明显大于负相关的区域,大多数模式在新疆地区的相关系数一般要高于其他地区。与单个模式相比,多模式集合平均结果具有更大的模态相关系数以及更小的均方根误差和标准偏差。1970-2015年中国大部分地区水汽观测值呈现出显著的增长趋势,其中在东南沿海、东北地区东部和新疆地区增长最为显著,多模式也能够较好地模拟出中国区域水汽变化的增长趋势,同时多模式与观测水汽的时间序列具有较好的一致性。通过经验正交分解(EOF)分析发现,多模式模拟结果的EOF前两个模态及其对应的主成分时间序列与观测结果都具有较好的一致性,其中MIROC-ESM模式在模拟EOF前两个模态中表现最好。在不同的典型浓度路径(RCP)排放情境下,CMIP5多模式预估结果显示中国区域未来大气水汽含量均有显著的增长趋势,增长幅度基本呈现出由东南向西北递减的分布,中国区域未来可能会出现“干愈干、湿愈湿”的气候特征。与RCP4.5情景相比,RCP8.5情境下的预估结果显示水汽增加的范围更广,增长幅度更大;到21世纪末,RCP8.5情境下水汽的增长幅度大概是RCP4.5情境下的2倍。大多数模式在全国范围显示出未来极端偏干频率有减小的趋势,尤其在我国西北地区极端偏干频率减少的最为明显;与RCP4.5情境相比,RCP8.5情境下未来极端偏干频率减少的趋势更加显著。而极端偏湿的频率在全国范围都是显著增长的,增长幅度呈现由西南向东北递减的分布趋势,在高排放情境下(RCP8.5)极端偏湿频率增加的幅度更大。CMIP5多模式对中国大部分地区水汽反馈效应的历史模拟与观测结果基本一致;水汽距平百分率与地表气温变化具有较好的一致性,在多模式模拟结果中表现得更加明显;而水汽与相对湿度的相关性较低。在全国范围,观测和模拟结果显示的水汽距平百分率和地表气温距平拟合方程的斜率分别为6.7%/K和6.6%/K,略低于Clausius-Clapeyron(C-C)方程显示的理论结果(7%/K);在东南地区和北方地区的观测和模拟结果基本一致,均低于7%/K的理论结果,而在青藏高原地区的模拟结果明显低于观测结果。未来预估结果显示,在RCP4.5情境下,水汽距平百分率和地表温度距平在全国范围和青藏高原地区的拟合方程斜率(6.9%/K和7.1%/K)与C-C方程的理论结果基本一致;在东南地区和北方地区略低于C-C方程理论结果。与RCP4.5情景相比,RCP8.5情境下的预估结果明显偏大,且高于C-C方程的理论结果。这就意味着在高排放情景下,中国区域水汽未来变化具有更强的正反馈效应。在全强迫(ALL)下,多模式基本能够再现观测水汽的增长趋势,温室气体强迫(GHG)引起水汽的增加更加明显,而自然强迫(NAT)虽然能够再现皮那图博火山喷发的影响,但整体来看对中国区域水汽的增加贡献很小,不能够解释水汽的变化。基于最优指纹法的单信号检测结果表明,全强迫下的多模式在全国范围低估了观测结果的12%,而在中国东部高估了观测结果的7.5%。双信号的检测归因结果表明,中国区域水汽的变化主要归因于人为因素(ANT),人类活动在全国范围和中国东部对水汽增长的贡献率分别达到了90%和94%,而自然因素的贡献不足10%;经过最优缩放因子订正之后的水汽增加趋势结果表明,1973-2012年人为强迫信号在全国范围和中国东部引起水汽增加的速率分别为1.11mm/40yr和1.23mm/40yr,自然强迫分别只能引起0.12mm/40yr和0.04mm/40yr的增长速率。基于GHG、ANTnoGHG(除GHG外其他的人为强迫)和NAT三信号的检测归因结果显示,温室气体是引起中国区域水汽增加最主要的人为因素,在全国范围和中国东部对水汽增加的贡献率分别达到了133%和182%,而其他的人为活动和自然因素并没有被显著检测到。基于单信号检测归因得出的ALL强迫试验的最优缩放因子,对CMIP5未来预估结果进行订正,结果表明,在全国范围订正之后的预估结果比原始结果水汽增加幅度要大;而在中国东部,订正之后的结果比原始结果增幅要小。
Other AbstractWater vapor is one of the most abundant and important greenhouse gases in the atmosphere. It plays an important role in the process of global material transportation, energy transmission and water cycle. The feedback of water vapor can amplify the warming effect of other greenhouse gases and has a very important impact on weather and climate change. Therefore, a thorough study of the regularity and causes of atmospheric water vapor changes and regional climate effects will help us to understand the regional climate change and its response mechanism under the background of global warming. Based on the latest homogenized humidity sounding data, this paper systematically evaluates the simulation capability of Phase 5 of the Coupled Model Intercomparison Program (CMIP5) for atmospheric water vapor change in China using various statistical analysis and dynamic diagnostic methods. On this basis, the future changes of water vapor under different emission scenarios are analyzed, and then the climate feedback of water vapor is further studied. Finally, simulated experiments under different external forcing are used to detect the water vapor changes over China. The main conclusions are as follows:Most CMIP5 models can well simulate the seasonal variation characteristics of water vapor observed in China from 1970 to 2015, and can basically reproduce the mean distribution of observed water vapor. Especially in most areas of eastern China, the relative deviation of multi-models from observed values is less than 30%. However, in Western China, especially in the Qinghai-Tibet Plateau, the multi-model is 30-60% lower than the observed results. The root mean square errors of multi-model and observation values show an increasing distribution from northwest to southeast, and their spatial correlation coefficients are low. The positive correlation regions are obviously larger than the negative correlation regions. The correlation coefficients of most models in Xinjiang are generally higher than those in other regions. Compared with single model, the multi-model mean has larger mode correlation coefficients, smaller root mean square error and standard deviation. From 1970 to 2015, the observed water vapor values in most areas of China show a significant growth trend, especially in the Southeast coast, the eastern part of northeast China and Xinjiang. The multi-model can well simulate the growth trend of water vapor change in China. At the same time, the multi-model has a good consistency with the observed water vapor in time series. Empirical orthogonal decomposition (EOF) analysis shows that the first two modes of EOF and their corresponding principal component time series are in good agreement with the observed results, and MIROC-ESM model performs best in simulating the first two modes of EOF.Under different representative concentration pathways (RCP) emission scenarios,  estimated results of CMIP5 multi-model show that the future change of atmospheric water vapor content over China has a significant growth trend, and the growth rate basically shows a decreasing distribution from southeast to northwest. In the future, dryer areas are drier and wetter areas are wetter in China. Compared with RCP4.5 scenario, the predicted results under RCP8.5 scenario show that the range of water vapor increase is wider and the growth rate is larger. By the end of the 21st century, the water vapor increase under RCP8.5 scenario is about twice as large as that under RCP4.5 scenario. Most of the models show a decreasing trend of frequency of the extreme dry over the whole China, especially in Northwest China. Compared with RCP4.5, the trend of decreasing extreme dry frequency in RCP8.5 is more significant. However, the frequency of extreme wet increased significantly throughout the country, and the growth rate showed a decreasing trend from southwest to northeast. In the high emission scenario (RCP8.5), the frequency of extreme wet increased more significantly.
    The historical simulation of water vapor feedback effect of CMIP5 models over most areas of China is basically consistent with the observed results. The percentage of water vapor anomaly is in good agreement with the change of surface temperature, which is more obvious in the multi-model simulation results, and the correlation between water vapor content and relative humidity is lower. Over the whole country,  the slopes of fitting equation between the percentage of water vapor anomaly and surface temperature anomaly are 6.7%/K and 6.6%/K, respectively, slightly lower than the theoretical results (7%/K) of Clausius-Clapeyron (C-C) equation. The results of observation and simulation in southeastern and northern regions are basically the same, both lower than the theoretical results of 7%/K, while over the Qinghai Tibet Plateau, the simulation results are basically the same. The results were significantly lower than those observed. The future prediction results show that, under the RCP4.5 scenario, the fitting equation slopes (6.9%/K and 7.1%/K) of water vapor anomaly and surface temperature anomaly over the whole country and the Qinghai Tibet Plateau are basically consistent with the theoretical results of C-C equation, and slightly lower than those of C-C equation in the southeast and North regions. Compared with RCP4.5 scenario, the predicted results under RCP8.5 scenario are significantly larger and higher than the theoretical results of C-C equation. This means that in the high emission scenario, the future change of water vapor in China has a stronger positive feedback effect.Under total forcing (ALL), multi-model can basically reproduce the growth trend of observed water vapor. The increase of water vapor caused by greenhouse gas forcing (GHG) is more obvious, while natural forcing (NAT) can reproduce the impact of Pinatubo volcano eruption, but overall, it contributes little to the increase of water vapor in China and can not explain the change of water vapor. The single-signal detection results based on the optimal fingerprint method show that the multi-model simulations under total forcing underestimates 12% of the observed results over the whole China, while overestimates 7.5% of the observed results in eastern China. The results of two-signal detection and attribution show that the change of water vapor in China is mainly attributed to human factors (ANT). The contribution of human activities to water vapor changes over the whole country and eastern China is 90% and 94%, respectively, while that of natural factors is less than 10%. The trends of water vapor increase after the correction of the optimal scaling factor show that the rate of water vapor increase caused by human-induced forcing is 1.11mm/40yr in the whole China and 1.23mm/40yr in eastern China, respectively. Natural forcing can only cause 0.12mm/40yr and 0.04mm/40yr growth rates, respectively. Based on the detection and attribution results of GHG, ANTnoGHG (human factors except GHG) and NAT signals, greenhouse gases are the most important anthropogenic factors causing water vapor increase in China. The contribution rates of GHG to water vapor increase are 133% and 182% in the whole country and eastern China, respectively, while other human activities and natural factors have not been significantly detected. Based on the optimal scaling factor of ALL forcing obtained from single signal detection attribution, the future prediction results of CMIP5 are adjusted The results show that the predicted results after nationwide adjusting are larger than the raw simulations, while in eastern China, the adjusted results are smaller than the raw simulations.
Pages111
URL查看原文
Language中文
Document Type学位论文
Identifierhttp://ir.lzu.edu.cn/handle/262010/338280
Collection大气科学学院
Affiliation大气科学学院
First Author AffilicationCollege of Atmospheric Sciences
Recommended Citation
GB/T 7714
张京朋. 中国区域大气水汽变化的观测、模拟及其归因研究[D]. 兰州. 兰州大学,2019.
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