| 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月平均标准降水蒸散指数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。本文研究表明了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 |
| DOI：10.7515/JEE182069 |
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| 基金项目:中国科学院“西部之光”项目；国家自然科学基金项目（41671212，41630531）；黄土与第四纪地质国家重点
实验室自主部署项目 |
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| Role of tree-ring multiproxy in palaeoclimate reconstruction in subtropical China, taking Guilin as an example |
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CAI Qiufang, LIU Yu, DUAN Bingchuang
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1. State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710061, China
2. CAS Center for Excellence in Quaternary Science and Global Change, Xi’an 710061, China
3. Open Studio for Oceanic-Continental Climate and Environment Changes, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266061, China
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| 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 (δ18O tree ) is proved to be an ideal indicator of hydroclimatic variation. Previous study on single tree-ring proxy (TRW orδ18O tree ) 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 δ18O tree will help to increase the climate signals in tree rings. Materials and Methods Based on previous work, TRW and δ18O tree 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 δ18O tree 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 (R), 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 δ18O tree 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). δ18O tree 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 δ18O tree records. However, single tree-ring proxy is not enough to be successfully used for hydroclimatic reconstruction so far, even though the TRW and δ18O tree 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 δ18O tree 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 δ18O tree 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. |
| Key words: Pinus massoniana Lamb. multiproxy tree-ring width δ18O hydroclimatic reconstruction |
