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首页> 外文期刊>Journal of Geophysical Research. Biogeosciences >A novel analysis of spring phenological patterns over Europe based on co-clustering
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A novel analysis of spring phenological patterns over Europe based on co-clustering

机译：基于共聚的欧洲春季物候模式的新颖分析

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The study of phenological patterns and their dynamics provides insights into the impacts of climate change on terrestrial ecosystems. Herewe present a novel analyticalworkflow, based on co-clustering, that enables the concurrent study of spatio-temporal patterns in spring phenology. The workflow is illustrated with a long-term time series of first leaf dates (FLD) over Europe, northern Africa, and Turkey calculated using the extended spring index models and the European E-OBS daily maximum and minimum temperatures (1950 to 2011 with a spatial resolution of 0.25°). This FLD dataset was co-clustered using the Bregman block average co-clustering with I-divergence (BBAC_I), and the results were refined using k-means. These refined co-clusters were mapped to provide a first spatially-continuous delineation of phenoregions in Europe. Our results show that the study area exhibits four main spatial phenological patterns of spring onset. The temporal dynamics of these phenological patterns indicate that the first years of the study period tend to have late spring onsets and the recent years have early spring onsets. Our results also show that the study period exhibits 12 main temporal phenological patterns of spring onset. The spatial distributions of these temporal phenological patterns show that western Turkey tends to have the most variable spring onsets. Changes in the boundaries of other phenoregions can also be observed. These results indicate that this co-clustering based analytical workflow effectively enables the simultaneous study of both spatial patterns and their temporal dynamics and of temporal patterns and their spatial dynamics in spring phenology.
机译：物候模式及其动态的研究提供了有关气候变化对陆地生态系统影响的见解。在此，我们提出了一种基于共同聚类的新颖分析工作流程，该工作流程可同时研究春季物候时空模式。通过使用扩展的春季指数模型和欧洲E-OBS每日最高和最低温度（1950年至2011年，使用0.25°的空间分辨率）。该FLD数据集使用Bregman块均值与I-散度（BBAC_I）进行联合聚类，并使用k均值进行了精炼。这些精炼的共同集群被映射以提供欧洲表象区域的首次空间连续描绘。我们的结果表明，研究区域表现出春季发作的四个主要空间物候模式。这些物候模式的时间动态表明，研究期的头几年往往有春末发作，而近年来则有早春发作。我们的结果还表明，研究期表现出12种主要的春季发作时态物候模式。这些时间物候模式的空间分布表明，土耳其西部的春季发病趋势最易变。也可以观察到其他表型区域边界的变化。这些结果表明，这种基于共同聚类的分析工作流程有效地使得能够同时研究春季物候学中的空间格局及其时空动态和时间格局及其时空动态。

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