研究论文 正式出版 版本 4 Vol 10 (2) : 141-148 2019
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树轮多指标研究在亚热带古气候重建中的作用—以桂林地区为例
Role of tree-ring multiproxy in palaeoclimate reconstruction in subtropical China, taking Guilin as an example
: 2018 - 09 - 13
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摘要&关键词
摘要:树轮宽度和树轮稳定同位素在古气候重建中具有不同优势。然而在我国亚热带地区,单一树轮指标中往往由于气候信号不足达不到重建要求,从而造成资源浪费,急需找到一种更好的解决办法。本文在前期独立的马尾松树轮宽度和树轮δ18O研究的基础上,采用多元线性回归方法对桂林地区树木生长季水文气候进行模拟重建。重建方程的复相关系数和方差解释量较仅利用单一树轮指标的重建显著提升,且重建值与器测时期的水文气候记录拟合度更好。空间相关分析揭示利用多指标重建的5-11月平均SPEI在空间上可以代表研究区及周边较大范围内的水文气候变化。本文研究表明联合多树轮指标在未来桂林地区水文气候重建中的光明前景,也为未来亚热带地区树轮气候重建提供了一个新思路。
关键词:马尾松;多指标;树轮宽度;树轮δ18O;水文气候重建
Abstract & Keywords
Abstract: Background, aim, and scope Due to high resolution, accurate dating and extensive geographical distribution, tree rings are playing important roles in paleoclimate and ecological studies. There are three proxies of tree ring, including tree-ring width (TRW), tree-ring stable isotopes and tree-ring density. Various climatic information can be obtained from these independent tree-ring proxies. In subtropical China (SC), TRW is generally limited by temperature and therefore used to reconstruct temperature history, while tree-ring δ18O (δ18Otree) is proved to be an ideal indicator of hydroclimatic variation. Previous study on single tree-ring proxy (TRW or δ18Otree) in Guilin, subtropical southwest China failed to be used for climate reconstruction owing to the weak climatic signals contained in tree rings, which may cause a waste of resources. Therefore, it is urgent to find a better solution. This paper attempts to explore whether combing TRW and δ18Otree will help to increase the climate signals in tree rings. Materials and Methods Based on previous work, TRW and δ18Otree records of Pinus massoniana Lamb. during 1939–2014 in Guilin are obtained. Pearson correlation analysis is utilized to calculate the relationship between tree-ring index and hydroclimatic factors, i.e. relative humidity (RH) and standardized precipitation-evapotranspiration index (SPEI). Multiple linear regression method, using TRW and δ18Otree as independent variables, is adopted to determine the most suitable climatic factor and period for future reconstruction. The stability of the regression model is evaluated by split calibration-verification test. The statistical parameters include correlation coefficient (r),explained variance (R2 ), reduction of error (RE) and coefficient of efficiency (CE). Results Simple correlation analysis revealed that TRW significantly correlated with the June-October mean SPEI (r=0.32, p<0.05), while δ18Otree indicated significantly negative relationship with the mean SPEI from current May to current October (r=-0.61, p<0.01). TRW also showed significant correlation with the previous February-November mean RH (r=0.55, p<0.01) and the current April-July mean RH (r=0.49, p<0.01). δ18Otree indicated high correlation with the August-October mean RH, with r value of -0.52 (p<0.01). Discussion April-November is the growing season of Pinus massoniana Lamb. in Guilin, the amount of effective soil moisture during these months is vital to tree growth. Therefore, it is reasonable that growing-season hydroclimatic variation directly influences the TRW and δ18Otree records. However, single tree-ring proxy is not enough to be successfully used for hydroclimatic reconstruction so far, even though the TRW and δ18Otree records are sensitive to hydroclimatic variation. It is worth noting that the results of multiple linear regression model indicated much high r values and explained variance (R2 ) when combing TRW and δ18Otree together. The r value and R2 for modeling the May-November mean SPEI was 0.667 (p<0.01) and 44.6%, respectively, and for modeling the April-October mean RH was 0.636 (p<0.01) and 40.4%, respectively. The r value and R2 of the multi linear regression models greatly enhanced compared with the simple correlation analysis that based on single proxy. Moreover, the multi-proxy-based reconstruction tracks the instrumental hydroclimatic data very well. The reconstructed May-November mean SPEI has good skill in simulating the hydrological variation in a large field around the studying site. Conclusions It shows that the multiple linear regression model based on TRW and δ18Otree is more suitable for hydroclimatic reconstruction (SPEI, RH) in Guilin than that based on single tree-ring proxy. In comparison, May-November mean SPEI is a better choice for reconstruction. Recommendations and perspectives This study provides a new way for the future dendroclimate reconstruction in the subtropical regions of China, which will be a good guidance for climate investigation and forest management.
Keywords: Pinus massoniana Lamb.; Multiproxy; Tree-ring width; δ<sup>18</sup>O; Hydroclimatic reconstruction
树木的生长除了受自身遗传因素影响外,也会受到外界气候条件变化的影响,并把这种影响记录在每年形成的年轮中(包光等,2013;蔡秋芳等,2012;蔡秋芳和刘禹,2015)。通过研究年轮特征(宽度、稳定同位素、密度)变化,在和现代气候器测记录统计相关分析的基础上,能够提取树木生长的气候限制因子,进而重建过去百–千年来的气候变化历史(刘禹等,2010,2012a,2012b, 2015; 张艳华等,2013;Lu et al., 2012),研究局地气候与海陆相互作用和大尺度环流的关系(刘禹,2010)。
作为一项最基本的树木年代学指标,树轮宽度测量相对容易、花费低,在中高纬、高海拔地区(Büntgen et al., 2011; Esper et al., 2002; Seftigen et al., 2013)和我国干旱半干旱地区(Liu et al., 2009, 2013, 2017; Cai and Liu, 2007; Cai et al., 2008, 2010, 2013, 2014, 2015)对气候要素响应敏感,因而是古气候重建中最为常用的一种指标。然而在我国热带亚热带地区,从树轮宽度中提取气候信号相对比较困难,即使能提取气候信号,也基本反映的是单一的温度信号(蔡秋芳和刘禹,2013;Cai et al., 2016, 2017a, 2018),难以提取水文气候信号(Cai et al., 2017b)。作为树轮宽度研究的补充,亚热带地区的树轮C、O同位素(δ13C、δ18O)能够记录水文气候信号,可以作为水文气候重建的一个重要指标(Xu et al., 2013; Liu et al., 2017, 2018; Cai et al., 2017c)。
树木生长过程中气候变化同时影响着树轮宽度、树轮稳定同位素和树轮密度等特征的变化,因此通过树轮多指标的最优组合共同来预测或估计气候要素的变化,可能比只用一个自变量进行预测或估计更有效、更符合实际(McCarroll et al., 2003)。近年的许多研究也表明,树轮多指标结合加强了与气候相关的程度,拓宽了可提取气候参数的范围(McCarroll et al., 2003; Li et al., 2015)。
马尾松(Pinus massoniana Lamb.)是中国南方主要材用树种,主要分布于河南—山东南部以南、四川中部—贵州以东的亚热带地区。由于马尾松年轮清晰,它也是我国亚热带地区树轮气候学研究的主要树种之一(蔡秋芳和刘禹,2013;Cai et al., 2016)。桂林位于我国西南亚热带地区,目前这一地区的树轮气候学还未有重建结果发表。段丙闯和蔡秋芳(2017)、Cai 等(2017c)在这一地区分别开展了马尾松树轮宽度和树轮δ18O记录的气候学响应分析研究,发现树轮宽度不仅受当年生长季6-9月和上年2-11月气温的影响,也受当年生长季相对湿度的影响,表明生长季温度增高导致的水分亏缺是桂林地区树轮径向生长的主要限制因子,但是不管是温度还是相对湿度均达不到重建要求。树轮δ18O与温度的关系不大,与当年生长季4-10月份的水文气候信号(降水、相对湿度和标准降水蒸散指数)呈显著负相关关系,具有重建水文气候的潜力。鉴于这两种树轮指标中都含有水文气候信号,本文尝试探讨将树轮宽度和δ18O指标结合是否有助于更好的提取水文气候信号?这一研究将为未来亚热带地区水文气候重建提供一个新思路。
1   树轮数据和气候资料
本文研究地点位于距桂林市东北方向约25公里的灵田乡(25.37oN-25.38oN; 110.52oE,海拔480米),所用马尾松树轮宽度和树轮δ18O数据来源于文献(段丙闯和蔡秋芳,2017;Cai et al.,2017c)。其中树轮宽度年表(1939-2014年)建立在成功交叉定年的63根树芯(40棵树)的基础上,并采用保守负指数拟合法去除树木的生长趋势。树轮δ18O记录来自于4个独立逐年测量年轮δ18O的序列的算术平均基础上。
本文所用的两个水文气候指标为相对湿度(RH)和标准降水蒸散指数(standardized precipitation-evapotranspiration index,SPEI)。为了既与以前研究(段丙闯和蔡秋芳,2017;Cai et al., 2017c)所采用气象资料保持一致,又能看出研究结果的差别, RH数据仍来自马尾松采样点附近的桂林(25.19°N, 110.18°E, 海拔164.4米)和道县(25.32°N, 111.36°E, 海拔192.2米)两个气象站点的平均值,数据长度为1951-2014年。SPEI月值数据来自http://climexp.knmi.nl网站位于采样点附近的两个格点(25.25°N, 110.75°E;25.75°N, 110.75°E)的平均值,数据长度为1901-2013年。本文仅选用和气象记录相同的时段(1951-2013)进行分析。
2   研究方法
首先采用皮尔逊相关分析法计算不同树轮指标与各气候要素的关系,确定影响不同树轮指标的主要气候因子及时段,再采用多元线性回归法分析气候要素和树轮多指标的关系,确定最适合重建的气候因子及时段。其中树轮宽度和树轮δ18O为自变量,气候要素为因变量。回归方程的统计参数有复相关系数(r),方差解释量(R2 )、调整自由度后的方差解释量(R2adj )和F检验值。为了确定究竟哪种水文气候信号更适合重建,除了考虑重建方程的复相关系数和方差解释量外,还采用分段检验法(Meko and Graybill, 1995)、重建值与器测时段记录的高(一阶差)频、低频(原始序列)对比的方法。空间场分析(http://climexp.knmi.nl/)被用来探究重建的空间代表性。
3   结果
如图1a所示,树轮宽度仅与当年7月的SPEI显著相关(r=0.25, p<0.05),而树轮δ18O与当年5月至10月各月SPEI都显著相关,相关系数在-0.32(p<0.05)到-0.51(p<0.01)之间。进行不同月份SPEI组合后,树轮宽度与6-10月SPEI相关系数最高(r=0.32, p<0.05),而树轮δ18O与5-11月平均SPEI相关系数达到-0.61(p<0.01)。


图1   树轮宽度(蓝色)和δ18O记录(红色)与月平均(a)SPEI和(b)相对湿度的相关分析结果。*和**分别代表95%和99%置信度
Fig. 1 Results of Pearson correlation analysis between tree-ring proxies (blue: tree-ring width; red: tree-ring δ18O) and monthly hydroclimatic parameters. (a) standardized precipitation-evapotranspiration index (SPEI), (b) relative humidity. * and **represent the 95% and 99% confidence level, respectively
相对湿度的相关分析结果稍有不同(图1b)。树轮宽度不仅与当年4月、6月、7月、8月和10月的相对湿度相关系数达到95%的置信度,还与上年2月至11月(5月、6月和9月除外)树轮宽度的相关系数达到95%的置信度。组合不同月份相对湿度后,树轮宽度与上年2-11月平均相对湿度相关系数最高,达到0.55(p<0.01),与当年相关最高的月份组合为4-7月(r=0.49, p<0.01)。树轮δ18O依然与当年8、9、10三个月的月平均相对湿度显著相关(p<0.01),且与当年8-10月平均相对湿度相关最高,达到-0.52(p<0.01)。
4   讨论
4-11月是研究区马尾松的生长季(何月等,2012;张雨等,2016),这一时期土壤水分供给的充足或匮乏对树木生长意义非凡。结合以前研究(段丙闯和蔡秋芳,2017;Cai et al.,2017c)和上文分析结果可知,桂林地区生长季水文气候变化是影响当地马尾松树轮宽度和树轮δ18O记录的直接因素。此外,上年生长季相对湿度变化对树轮宽度生长存在显著滞后影响。尽管当年及上年生长季的温度变化对树轮宽度生长的影响也不可忽视(段丙闯和蔡秋芳,2017),但是还不足以利用树轮宽度来对温度进行重建。
图1和表1中的数据表明,尽管水文气候是影响桂林地区各树轮指标的直接限制因素,但是就目前任何一种树轮指标与气候要素的相关系数来说进行重建都不十分理想。即使利用树轮δ18O指标对5-11月的平均SPEI进行重建,方差解释量也仅为37.6%(表1),差强人意。
鉴于树轮宽度和树轮δ18O记录中都包含有水文气候信号,且二者之间无显著相关(r=-0.04)(Cai et al., 2017c),本文以这两个树轮指标为自变量,以不同时段的水文气候记录为因变量进行多元线性回归分析。结果发现,在带入多个树轮指标后,重建的水文气候信号得到极大提升(表1,表2)。就SPEI而言,对5-11月平均SPEI(SPEI5-11)重建的方程的复相关系数最高,达到0.667(p<0.01),方差解释量达到44.6%(调整自由度后为42.7%),较原来的最高方差解释量(表1)提升7%。就相对湿度而言,对4-10月平均相对湿度(RH4-10)重建的方程的复相关系数最高,达到0.636,方差解释量可达40.4%,较原来当年相对湿度最高方差解释量提升13.8%。由此可见,将桂林地区树轮宽度和树轮δ18O记录联合确实能够提高重建对器测记录的方差解释量,适合对生长季的水文气候进行重建。
表1   树轮宽度、树轮δ18O与水文气候要素的最高相关时段和系数
树轮宽度树轮δ18O
月份组合相关系数方差解释量月份组合相关系数方差解释量
RHL2-L110.5530.4%RH8-10-0.5226.6%
RH4-70.4924.1%
SPEI6-100.3210.3%SPEI5-11-0.6137.6%
RH为相对湿度,SPEI为标准降水蒸散指数,L代表上年,5-11代表5月到11月的平均值,其他类同
表2   重建多元线性回归方程及统计特征
重建指标回归方程rR2R2adjF
SPEI5-11SPEI5-11=0.583×RW-0.385×δ18Otree +10.24 (1)0.66744.6%42.7%24.105
RH4-10RH4-10=3.88×RW-1.152×δ18Otree +105.51 (2)0.63640.4%38.4%20.668
RH:相对湿度;SPEI5-11:5-11月平均标准降水蒸散指数;RH4-10:4-10月平均相对湿度;RW:树轮宽度;δ18Otree:树轮δ18O
图2为基于表2中回归方程重建的桂林地区水文气候要素,重建和观测记录不管是在低频还是高频(一阶差)变化上步调基本一致,其中对SPEI的重建在高频信号上较相对湿度高。尽管就相关系数来说,相对湿度和SPEI都可以作为最终重建的目标,但是如果同时考虑高、低频气候信号的话,5-11月平均SPEI是一个更好的选择。


图2   重建(点线)和观测(实线)水文气候对比。(a)、(c)为原始序列;(b)和(d)为一阶差序列
Fig. 2 Comparisons between the reconstructed (dotted line) and instrumental (solid line) hydroclimatic variations. (a) and (c) are raw data sequence, (b) and (d) are first-order difference series of the raw data.
对回归方程(1)的分段法检验(表3)结果表明,不管是校准时段还是验证时段,方程的复相关系数都达到99%的置信度。误差缩减值(RE—reduction of error)和效率系数(CE—coefficient of efficiency)(Cook et al., 1999)这两个统计硬指标都大于0,表明基于回归方程(1)的重建结果可以很好的模拟原始器测数据,该回归方程可以用来进行SPEI重建。
表3   5-11月SPEI重建方程稳定性校验
校准时段
1951-1985
验证时段
1986-2013
校准时段
1979-2013
验证时段
1951-1978
全时段
1951-2013
r0.73**0.57**0.53**0.80**0.67**
R20.530.330.280.640.40
RE0.290.56
CE0.280.55
** 代表99% 置信度
表4中相对湿度的回归方程虽然复相关系数在所有时段都达到99%的置信度,但是1986-2012年这一验证时段的RE和CE值都小于但是接近0,说明基于回归方程(2)的重建结果对相对湿度的模拟结果尚可,但不如SPEI。
表4   4-10月相对湿度重建方程稳定性校验
校准时段
1951-1985
验证时段
1986-2013
校准时段
1979-2013
验证时段
1951-1978
全时段
1951-2013
r0.72**0.57**0.58**0.74**0.636**
R20.520.330.330.55
RE-0.030.41
CE-0.130.40
** 代表99% 置信度
结合图2和表3、表4的分析结果可知,选择利用回归方程(1)对5-11月平均SPEI进行的重建结果质量更高,更可靠。如果未来能在这一区域获得更长的树木年轮年表,则可重建过去数百年的生长季(5-11月)SPEI变化历史。
空间分析结果(图3a)显示,桂林地区5-11月平均SPEI能够代表包括云贵高原东北部和华南地区在内的较大区域内的同期SPEI变化,而重建结果 (图3b)也较好的模拟了器测阶段的SPEI空间代表范围。


图3   器测(a)和重建(b)5-11月SPEI的空间场分析(http://climexp.knmi.nl/)。黑色方块为采样点位置。
Fig. 3 Spatial analysis maps of the May-November SPEI (http://climexp.knmi.nl/). (a) instrumental record, (b) reconstruction. Black square indicates the position of the sampling site.
5   结论
在前期树轮宽度和树轮δ18O研究的基础上,采用多元线性回归方法对树木生长季水文气候进行模拟重建。结果表明,采用树轮多指标进行重建的方差解释量远大于采用单一树轮指标进行的重建,且重建值与观测时期的水文气候记录拟合度更好。在桂林地区,树轮多指标可用于5-11月的SPEI重建。本文研究为未来亚热带地区树轮气候重建提供了一个新思路。
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稿件与作者信息
蔡秋芳1*, 2, 3
CAI Qiufang1*, 2, 3
caiqf@ieecas.cn
刘禹1, 2, 3
LIU Yu1, 2, 3
段丙闯1
DUAN Bingchuang1
中国科学院“西部之光”项目;国家自然科学基金项目(41671212,41630531);中国科学院黄土与第四纪地质国家重点实验室开放基金项目
CAS “Light of West China” Program, National Natural Science Foundation of China (41671212, 41630531), State Key Laboratory of Loess and Quaternary Geology foundation (SKLLQG)
出版历史
出版时间: 2018年9月13日 (版本4
参考文献列表中查看
地球环境学报
Journal of Earth Environment