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首页> 外文期刊>Journal of Geophysical Research. Biogeosciences >Time series modeling and central European temperature impact assessment of phenological records over the last 250 years
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Time series modeling and central European temperature impact assessment of phenological records over the last 250 years

机译：最近250年间物候记录的时间序列建模和中欧温度影响评估

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摘要

Long-term spring and autumn phenological observations from Switzerland and Burgundy (eastern France) as well as long-term Swiss monthly and seasonal temperature measurements offer a unique possibility to evaluate plant phenological variability and temperature impacts over the last 250 years. We compare Pearson correlation coefficients and linear moving window trends of two different lengths with a Bayesian correlation and model comparison approach. The latter is applied to calculate model probabilities, change-point probabilities, functional descriptions, and rates of change of three selected models with increasing complexity and temperature weights of single months. Both approaches, the moving window trends as well as the Bayesian analysis, detect major changes in long-term phenological and temperature time series at the end of the 20th century. Especially for summer temperatures since the 1980s, Bayesian model-averaged trends reveal a warming rate that increased from an almost zero rate of change to an unprecedented rate of change of 0.08°C/a in 2006. After 1900, temperature series of all seasons show positive model-averaged trends. In response to this temperature increase, the onset of phenology advanced significantly. We assess the linear dependence of phenological variability by a linear Pearson correlation approach. In addition we apply the Bayesian correlation to account for nonlinearities within the time series. Grape harvest dates show the highest Bayesian correlations with June temperatures of the current year. Spring phenological phases are influenced by May temperatures of the current year and summer temperatures of the preceding growing season. For future work we suggest testing increasingly complex time series models such as multiple change-point models.
机译：来自瑞士和勃艮第（法国东部）的长期春季和秋季物候观测以及瑞士的长期月度和季节温度测量值为评估过去250年植物物候变异性和温度影响提供了独特的可能性。我们使用贝叶斯相关和模型比较方法比较了两个不同长度的皮尔逊相关系数和线性移动窗口趋势。后者用于计算三个所选模型的模型概率，更改点概率，功能描述和变化率，随着单个月的复杂性和温度权重的增加。两种方法（移动窗口趋势以及贝叶斯分析）都可以检测出20世纪末长期物候和温度时间序列的重大变化。特别是对于1980年代以来的夏季温度，贝叶斯模型平均趋势显示，升温速度从几乎为零的变化率增加到2006年的前所未有的0.08°C / a的变化率。1900年之后，所有季节的温度序列显示出模型平均趋势为正。响应于该温度升高，物候学的发作显着发展。我们通过线性皮尔逊相关方法评估物候变异性的线性依赖性。此外，我们应用贝叶斯相关性来说明时间序列内的非线性。葡萄收获日期与当年6月温度之间的贝叶斯相关性最高。春季物候期受当年五月温度和前一个生长季节的夏季温度影响。对于以后的工作，我们建议测试越来越复杂的时间序列模型，例如多个变更点模型。

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