The application of phenological methods for reconstructing past climate change
Climate variations inferred from historical phenological records enhance our understanding of the characteristics of the past climate changes.However,the extraction and processing of historical phenological records,the selection of the reconstruction method and the assessment of reconstruction results need to be further summarized.This study first analyzed the data sources and spatial distribution of historical phenological records.Then we discussed pre-processing issue in phenological reconstruction studies such as identification of plant species and specific phenological phases.With regards to the reconstruction methods of past climate changes,we found that earlier studies used the simple comparative method,i.e.directly comparing phenological timing in ancient vs.modem times to reflect climate change.Recent studies often used more complex methods,such as regression and process-based models.Regression models combine the timing of phenological events with climatic factors without considering specific biological process,while process-based models formally describe known or assumed cause-effect relationships between biological processes and some driving factors,e.g.certain environmental variables for plants.The reconstruction results derived from these methods have been conductive to recognize historical climate changes in typical periods of the past 2000 years such as the Medieval Warm Period,the Little Ice Age and quick warming during the last 100 years.In addition,reconstruction results derived from the phenological evidences are consistent with the results derived from other paleoclimatic proxy data,such as tree rings and lake sediments.In the future,in order to obtain more solid and pronounced conclusions in this field,it is necessary to investigate the extraction techniques of historical phenology records,to improve the reconstruction methods and to comprehensively compare the previous reconstruction results,as well as to improve problematic approaches.
NETL
NSTL
万方数据
维普
