KMS XINJIANG INSTITUTE OF ECOLOGY AND GEOGRAPHY，CAS
| 中亚地区气候极值时空变化特征及影响因素研究 | |
| 张曼 | |
| Subtype | 博士 |
| Thesis Advisor | 陈亚宁 |
| 2018-06-05 | |
| Degree Grantor | 中国科学院大学 |
| Place of Conferral | 新疆乌鲁木齐 |
| Degree Discipline | 理学博士 |
| Keyword | 中亚干旱区 气候极值 突变 影响因素 灾害风险 Arid Central Asia Climate extremes Abrupt change (Mutation point) Influence factors Disaster risk |
| Abstract | 全球变暖已经毋庸置疑并预期仍将持续， 气候变化直接导致全球中纬度大部分陆地区域和湿润的热带地区的极端气候事件发生不确定性的增加。 其中， 中亚地区是全球中纬度地带最干旱的地区， 对全球气候变化影响极为敏感， 导致极端气候事件发生的频率和强度的增加，区域面临的自然灾害风险上升。 由于中亚五国地区缺乏基础气象站点观测数据资料， 导致气候极值相关研究成果匮乏。 作为中国“新时期丝绸之路”建设的核心地带， 中亚地区的极端气候事件相关研究亟待加强。 本文通过综合运用站点观测数据和栅格数据，对中亚地区气候极值的时空变化趋势及其特点进行了研究；通过 9 种突变检测方法对中亚地区气候极值的突变时间进行了检测，并试图寻找最适合中亚地区的气候极值突变检测方法； 围绕大气环流因素和地理位置因素，对影响气候极值变化的主要自然因素进行了探索；探讨了中亚地区极端气候事件灾害风险管理与适应策略。1. 以 CRU 数据为基础，运用 PLS 模型对 1957-2016 年期间中亚地区的 11 个气候要素指标数据进行了时空变化趋势分析，结果表明：（1）中亚地区与气温相关的气候要素指标在时间变化上整体表现出显著的、连续的变暖趋势， 年平均气温（Tmp）以 0.32 ºC/10a 的速率显著上升， 在春季的增温速率（0.41 ºC/10a） 最快也最为显著，日最低气温的升高（0.36ºC/10a） 对全区变暖做出了主要贡献；从空间上看，全区呈现出普遍的、广泛的升温趋势，其中在北疆地区、图尔盖低地-咸海-克孜勒库姆沙漠一带地区这两个核心区域增温速率最快。（2） 与降水相关的气候要素指标在时间变化上表现出明显的湿润趋势， 连续湿润天数表现出显著的减少趋势， 年总降水量（Pre）以 2.02 mm/10a 的速率不显著的增加。 从空间上看，新疆地区整体湿润程度增加， 但在哈萨克斯坦北部地区、图尔盖高原-图尔盖低地-咸海-克孜勒库姆沙漠一带地区以及土库曼斯坦西南部有干旱趋势。2. 以站点日气象观测资料数据为基础，对 1957-2005 年期间中亚地区 17 个气候极值指标进行了时空变化趋势分析，结果显示：（1） 中亚地区气温极值时间序列和空间范围变化总体上呈现出显著的变暖趋势。年平均气温（Tav）、年平均最高气温（Txav）和年平均最低气温（Tnav） 分别以 0.032 ºC/a， 0.024 ºC/a 和 0.041 ºC/a 的速度显著（p<0.01）上升。 其中， 那些源于最低气温提取的指标比源于最高气温提取的指标变暖速率更快，在空间变化上也呈现出更显著的广泛的变暖趋势。（2） 中亚地区总降水显著增加，连续干旱天数显著减少，强降水和单日降水强度显著增加。降水极值空间分异特征呈现出了很高的变化多样性和异质性，并呈现较低的显著性水平，受地形和位置影响显著，显示出显著变化的站点多分布在天山山脉、哈萨克丘陵、里海沿岸平原、克孜勒库姆沙漠等附近地区。（3）通过分析发现 CRU 数据的五个气象指标（Tmp、Tmx、 Tmn、 Frs 和 Pre） 与站点观测数据提取的气候极值指标（Tav、 Txav、 Tnav、FD0 和 Prcptot） 变化趋势相近。3. 探讨了突变检测方法的应用和中亚地区气候极值的突变时间，结果发现：（1）气候极值指标在不同时间序列内突变时间不同，突变时间只有在确定的时间尺度才有意义，并且会随着时间序列尺度的变化，一些平常点会随着时间和数据层次的升、降进化出现漂移。（2） 根据 8 种突变检测方法对中亚地区 10 个气候极值指标的检测结果显示， Yamamoto 法最为准确，其次依次是 MTT， Cumulative Deviation， the WorsleyLikehood Ratio 和 Cusum。因此，中亚地区的气候极值突变研究建议综合使用上述 5种方法来最终确定突变时间。（3） 突变检测结果显示，在 1957-2005 年期间，中亚地区的 5 个降水极值指标的突变时间全部发生在 1986 年；而 5 个气温极值的突变时间呈现出多样性，其中年平均气温的突变时间发生在 1987 年。（4） 通过广义极值分布方法发现中亚地区气候极值的重现期值主要集中于 10 年周期及以下。4. 本文探索了大气环流因素、地理位置和地形因素对中亚地区气候极值的影响，结果发现：（1） 在 1957-2005 年期间，本文选取的 10 个大气环流因子中最影响中亚地区 17 个气候极值指标变化的是西伯利亚高压（SH） 和青藏高原指数 B（TPI-B）。（2） 1957-2016 年期间， 通过对中亚地区 5 个气候极值指标的变化率与相应高程、经度和纬度进行相关性分析发现，除了年平均气温的变化率与海拔高度没有显著的相关关系以外， 年最高和最低气温、 年霜冻天数和年总降水量这四个指标的变化率都与Ele 呈现出显著相关的关系。同期， 5 个极值指标的变化率都与经度和纬度显示出显著的相关关系。 在不同的海拔高度带上，不同的气候极值指标与纬度和经度的相关性也不同。 气候极值的影响因素分析有待于做更细致全面的深入分析。 |
| Other Abstract | Global warming is an incontestable fact and it is also expected to continue in thefuture. Under the impacts of climate change, great uncertainties have been increasing inextreme climate events in most of the global mid-latitude land area and humid tropics area.Central Asia (CA), as one of the driest regions in the world, is extremely sensitive toclimate change. All these in turn lead to the increases in the frequency and intensity ofextreme climate events and the rises in the natural disaster risks. However, the poorcontinuity of station-based daily meteorological observations in CA’s five countries hasresulted in a lack of researches on extreme climate events or climate extremes. Meanwhile,as the core area of China's new “Silk Road economic belt”, CA should strengthenresearches on the extreme climate events or climate extremes. In this paper, the spatial andtemporal variation trends and characteristics of climate extremes in CA were analyzedthrough the comprehensive application of station-based meteorological observation dataand raster data; The research detected the mutation points of climate extremes through 9mutation detection methods, and also attempted to find the most suitable mutationdetecting method for region of CA; The main natural factors that influence climateextremes are explored in terms of atmospheric circulations and geographical location;Finally, the risk management and adaptation strategies of extreme climate events in CAwere discussed.1. This research analyzed the spatial and temporal variation trends of 11 climateelement indices in CA during 1957-2016 based on CRU data and the PLS model. Theresults showed that: (1) On the whole, the temperature element indices showed aremarkable and continuous warming trend. The annual mean temperature increased at arate of 0.32 ºC/10a, and the fastest warming rates occurred in spring (0.41 ºC/10a). The risein minimum temperature (0.36ºC/10a) was the main driving force behind climate warmingin this region. Spatially, it showed a widespread significant warming trend in the wholeregion, especially in the areas of the Northern Xinjiang and the Turgay Valley – Aral Sea –Kyzylkum Desert. (2) The precipitation element indices showed an obvious wetting trend,except for the Wet day frequency (Wet), which showed a significant decreasing trend.. The annual total precipitation (Pre) insignificantly increased at a rate of 2.02 mm/10a. Spatially,there were wetting trends in the overall Xinjiang region, but there were drying trends innorthern Kazakhstan, the Turgayskoye Plato-Turgay Valley-Aral Sea-Kyzylkum Desert andthe southwestern of Turkmenistan.2. The research investigated the spatial-temporal variation trends of 17 climateextreme indices in CA during 1957-2005 based on daily climate observations from 55meteorological stations. The results showed that: (1) The spatial and temporal variationtrends of the temperature extremes all had widespread significant warming trends. Theannual mean temperature (Tav), the annual average daily maximum temperature (Txav)and the annual average daily minimum temperature (Txav) significantly increased at a rateof 0.032 ºC/a， 0.024 ºC/a and 0.041 ºC/a, respectively. Of which, the indices whichderived from daily minimum temperature showed higher warming rates than the indiceswhich derived from daily maximum temperature. (2) The annual total precipitation,number of heavy precipitation days and maximum 1-day precipitation all increasedsignificantly, but the consecutive dry days decreased significantly. Compared totemperature changes, precipitation extremes were generally less consistent and significant,and showed higher spatial diversity and heterogeneity across the entire study area.Influenced by topography and location, stations which showed significant changes weremainly distributed around the Tien Shan Mountains, Kazakhskiy Melkosopochnik, CaspianDepression and the Kyzylkum Desert. (3) The 5 climate element indices from CRUmeteorological data (Tmp, Tmx, Tmn, Frs and Pre) have the similar changing trends withthe 5 extreme climate indices (Tav, Txav, Tnav, FD0 and Prcptot) from 55 meteorologicalstation data.3. This paper also detected the application of mutation detection method and themutation time of climate extremes in CA during 1957-2005. The results showed that: (1)Climatic extremes have different mutation points in different time series, and the mutationpoint can only make sense in a definite timescale. Meanwhile, some common points willdrift (i.e., increasing and decreasing) in accordance with changes in the time scale. (2)According to the results of 9 mutation detecting methods, we found that Yamamoto is the most accurate method, which followed by Moving t-test, Cumulative Deviation, theWorsley Likehood Ratio and Cusum. Therefore, these five methods are recommended foran integrated use when we want to conduct climate mutation detection in CA. (3) Themutation detection results show that the abrupt changes of all five precipitation extremesoccurred around 1986. But the mutation time of all five temperature extremes showeddiversity. The mutation point of the annual mean temperature occurred around 1987. (4)According to the method of Generalized Extreme Value, we found that return values forclimate extremes in the history of Central Asia primarily focused on 10-year cycles.4. This paper explored the influence of atmospheric circulations and geographicallocation on climate extremes in CA. (1) According to the results of correlation analysisbetween 17 climate extremes and 10 atmospheric circulations, we found that Siberian Highand Tibetan Plateau Index_B are likely the most important atmospheric circulation factorsaffecting the climate extremes during 1957-2005. (2) We also conducted correlationanalysis between 5 climate extremes and elevation, longitude and latitude of CA during1957-2016, respectively. Except for the insignificance between annual mean temperatureand elevation, the other four indices (the annual average daily maximum and minimumtemperature, frost day frequency and the annual total Precipitation) all showed significantcorrelations. Meanwhile, all 5 climate indices showed significant correlation withlongitude and latitude. At different elevation, the correlations between climate extremesand latitude/longitude are also different. Above these, the possible factors that influencingclimate extremes in CA still needs more detailed and comprehensive analysis in the future. |
| Subject Area | 自然地理学 |
| Language | 中文 |
| Document Type | 学位论文 |
| Identifier | http://ir.xjlas.org/handle/365004/14947 |
| Collection | 研究系统_荒漠环境研究室 |
| Affiliation | 中国科学院新疆生态与地理研究所 |
| First Author Affilication | 中国科学院新疆生态与地理研究所 |
| Recommended Citation GB/T 7714 | 张曼. 中亚地区气候极值时空变化特征及影响因素研究[D]. 新疆乌鲁木齐. 中国科学院大学,2018. |
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