Journal of Geophysical Research Atmospheres · 2007 · 88 citations · 64 references
Natural SciencesPlant BiologyPlant SciencesClimate ScienceClimatologyPlant EcologyCrop PhenologyPhenological AssessmentVegetation HistoryChronology Of Vegetation ChangeClimate Impact On Crop PhenologyClimate ImpactsInterannual Phenological VariationPhenological Data AnalysisPhenological RecordsSeasonality ReconstructionStatistical Reconstruction
Plant phenological observations are accurately dated information of seasonal vegetation variability in midlatitude climates. In order to extend phenological records into the past and assess climate impacts on vegetation on long timescales, there is a need to make use of historical observations of plant phenology. Here we present a continuous, annually resolved reconstruction of a statistical ‘Spring plant’ defined as the weighted mean for the flowering of cherry and apple tree and budburst beech from plant phenological observations across a range of sites in Switzerland from 1702 to 2005. The reconstruction indicates a statistical reconstruction uncertainty (±3.4 days) at interannual timescale. The earliest and the latest year were observed in 1961 (14 April) and 1879 (13 May), respectively. In the context of the last 300 years, the recent three decades do not show a preponderance of very early years as expected from increased spring temperatures. Most of the years in the period after 1990, however, are earlier than the reconstruction mean (27 April). The 1940s, 1910s, 1890s and the early 18th century are periods with similarly early starts of spring season in comparison with the recent decades. Moving linear trend analysis shows unprecedented agreement towards earlier spring onsets in observed and temperature‐based, reconstructed plant phenological records in the late 20th/early 21st century. Our reconstructed ‘Spring plant’ provides long‐term evidence of vegetation variability for comparisons with temperature measurement and other spring onset indicators such as snow melt. The multicentennial long record offers a high potential for applications in long‐term climate impact studies and vegetation model validations.
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