摘要:
利用历史物候记录重建的气候变化过程为人类更准确地认识过去的气候变化特征, 识别气候变化阶段提供了帮助。本文系统总结了现有用于气候定量重建的植物物候模型, 包括统计模型和过程模型两类。统计模型不考虑植物生理生态过程, 主要是利用物候期与气象因子之间的统计关系建立回归方程; 过程模型是一种半机理性模型, 通过植物每日发育进度与环境因子的关系, 利用数学模型模拟植物的生长过程, 主要包括春季积温 (spring warming, 简称SW)模型、温度变换日数(the number of days transformed to standard temperature, 简称DTS)模型和葡萄收获日期模型等。同时, 详细介绍了上述物候模型在国内外不同地区定量重建气候中的应用, 比较了重建的效果, 总结了重建过程的不确定性; 并提出了利用植物物候模型对气候定量重建中的关键问题, 即指出在未来研究中需要更多关注如何定量重建温度外的气候因子, 同时要加强对秋季物候的观测以及秋季物候生理机制的研究, 以便更清晰阐述秋季物候变化过程, 在历史气候变化定量重建中更充分利用有关秋季资料。
Abstract:
Climate of the past is an important subject in the study of global change. Reconstruction of climate change by phenological records in history provides better understanding for the climate evolution stages in the past. In this study, we systematically summarized the plant phenological models used for climate reconstruction, including statistical models and process models. Statistical models establish regression equations between phenophases and climate factors based on their statistical relationship, ignoring the physiological process of plants. Process models simulate the growth of plants through the connection between the daily growth rate of plants and environmental factors based on semi-mechanism, mainly consist of SW(Spring Warming)model, DTS(the number of days transformed to standard temperature) model and model for simulating grape harvest dates. Subsequently, we introduced the applications of the mentioned models in quantitative climate reconstruction at various regions and discussed the accuracy and uncertainties of different models. At last, we put forward some key issues in reconstruction of past climate and suggest place importance on the reconstruction of other climate factors besides temperature. In the future, it is crucial to strengthen observations and studies on autumn phenology as well as to make thorough study of physiological process of plants in autumn, so that it is easier to learn the phenological process in autumn. Under such circumstances, there will be more choices in application of phenological models in the reconstruction of past climate, and we can make better use of the material in autumn for past climate reconstructions.
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