Autumn temperature deduced from historical records of autumn tints phenology of maple tree in Kyoto, Japan

Abstract

We investigated historical records (e.g. old diaries, historiography, and daily newspapers) and acquired phenological data series for autumn-tinted maple (Acer spp.) leaves in Kyoto, Japan. From the 10th to 21st centuries, we gathered 504 years of phenological data for maple leaves' autumn tints. A preliminary analysis suggested that the dates of the maple autumn tints were affected by the October mean temperature. Therefore, an attempt was made to reconstruct the October mean temperature in Kyoto by applying the phenological data for autumn tints of maple leaves. Autumn temperature series partially showed a pattern similar to springtime temperature series. A cooling trend over the 15th -16th centuries and cool conditions in the late 17th and the early 19th centuries were detected in both the October and the March temperature series; however, October temperature change preceded March temperature change by about 10-20 years. This suggests that the time-lag of response of October temperatures in Kyoto to the solar variation may be smaller than that of March temperatures.

An updated global atmospheric paleo‐reanalysis covering the last 400 years

“…The five documentary data from Central Europe provide monthly information about the climate including the winter (or non‐growing) season, an advantage compared to proxy records. In the input file of EKF400v2, two further documentary data series from Europe (Fernandez‐Fernandez et al, 2014 ; Loader et al, 2011 ) and five new data series from Japan (Aono, 2015 ; Aono & Kazui, 2008 ; Aono & Tani, 2014 ; Mikami, 1996 ; Ohba et al, 2013 ) are included, leading to a total number of 12 documentary data series.…”

Review: Monitoring of land cover changes and plant phenology by remote‐sensing in East Asia

“…The data, including information on both plants and animals, have now been archived for 69 years. These data let us predict the flowering, leaf‐flushing, leaf‐coloration, and leaf‐fall dates by developing statistical phenology models based on the relationship between each phenological phenomenon and meteorological data (Aono, 2015; Aono & Kazui, 2008; Aono & Tani, 2014; Archetti et al, 2013; Xu & Chen, 2013; Yu et al, 2016), although prediction of how these dates will change in the future requires more sophisticated statistical phenology models (Allen et al, 2014; Hadano et al, 2013; Maruoka & Itoh, 2009; Nagai et al, 2019).…”