Modelled past autumn leaf phenology of deciduous trees

Autumn leaf phenology (i.e. leaf colouring or leaf senescence) marks the end of the growing season, during which trees assimilate atmospheric CO2. Since autumn leaf phenology responds to climatic conditions, climate change affects the length of the growing season. Thus, autumn phenology is often modelled to assess possible climate change effects on future CO2 mitigating capacities and species compositions of forests. Here, we give access to the entire dataset of modelled autumn phenology analyzed in Meier and Bigler (2023). The data was derived from >2.3 million model calibration runs according to 21 such models, 5 optimization algorithms, ≥7 sampling procedures, and 26 climate model chains from two representative concentration pathways. Calibration and validation were based on >45 000 observations for common beech (Fagus sylvatica L.), pedunculate oak (Quercus robur L.), and European larch (Larix decidua Mill.) from 500 Central European sites each. Cite as Meier, M., & Bigler..., Autumn leaf phenology was modelled with different process-oriented models that were calibrated as either site- or species-specific models. The calibrations differed in the choice of optimization algorithms and choice of sampling procedures to separate observations for the 5-fold cross-validation in site-specific calibration or to select the sites in species-specific calibration. Species-specific models were calibrated with 75% of the observations of the selected sites and validated with (the remaining) 25% of the observations of all 500 sites per species. The data contains files of calibrated parameter sets and files of modelled dates of autumn leaf phenology. The observed autumn leaf phenology data was downloaded from the PEP725 database on April 13, 2022 (http://www.pep725.eu/). For more detailed information consult the original publication Meier and Bigler (2023) for which full reference is given in the abstract to this dataset., The Rds files can be opened in the software R (R Core Team, 2022) with the base function readRDS(). R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.r-project.org/ , # Modelled autumn leaf phenology of deciduous trees Autumn leaf phenology was modelled with different process-oriented models that were calibrated as either site- or species-specific models. The calibrations differed in the choice of optimization algorithms and choice of sampling procedures to separate observations for the 5-fold cross-validation in site-specific calibration or to select the sites in species-specific calibration. Species-specific models were calibrated with 75% of the observations of the selected sites and validated with (the remaining) 25% of the observations of all 500 sites per species. For more detailed information consult the original publication Meier and Bigler (2023). Please cite as Meier, M., & Bigler, C. (2023). Process-oriented models of autumn leaf phenology: Ways to sound calibration and implications of uncertain projections. *Geoscientific Model Development*, *16*(23), 7171–7201. [https://doi.org/10.5194/gmd-16-7171-2023](https://doi.org/10.5194/gmd-...秋季叶片物候（即叶片变色或叶片衰老），标志着树木同化大气二氧化碳的生长季宣告结束。由于秋季叶片物候响应气候条件，气候变化会改变生长季的时长。因此，学界常通过模拟秋季物候，评估气候变化对森林未来固碳能力与物种组成的潜在影响。 在此，我们提供了Meier与Bigler（2023）中所分析的全部秋季物候模拟数据集。该数据集源自基于21种过程模型、5种优化算法、至少7种采样方案以及来自两条典型浓度路径（Representative Concentration Pathways）的26条气候模型链所开展的超230万次模型校准运行。校准与验证基于分别采自中欧500个样点的、针对欧洲山毛榉（Fagus sylvatica L.）、夏栎（Quercus robur L.）以及欧洲落叶松（Larix decidua Mill.）的超45000条观测数据。 引用格式为：Meier, M. 与Bigler, C.（2023）。秋季叶片物候采用多种过程导向模型进行模拟，这些模型被校准为样点特异性模型或物种特异性模型。在校准过程中，优化算法与采样方案的选择存在差异：样点特异性校准中，通过采样方案划分观测数据以开展5折交叉验证；物种特异性校准中，则通过采样方案选取样点。物种特异性模型的校准采用所选样点75%的观测数据，验证则采用每个物种全部500个样点中剩余25%的观测数据。 本数据集包含校准后的参数集文件以及秋季叶片物候模拟日期文件。 观测得到的秋季叶片物候数据于2022年4月13日从PEP725数据库（http://www.pep725.eu/）下载获取。 如需获取更详细的信息，请查阅本数据集摘要中收录完整引用的原始文献Meier与Bigler（2023）。Rds格式文件可通过统计软件R（R核心开发团队，2022）的基础函数readRDS()打开。 R核心开发团队（2022）。R：统计计算语言与环境。统计计算R基金会。https://www.r-project.org/ # 落叶乔木秋季叶片物候模拟数据 秋季叶片物候采用多种过程导向模型进行模拟，这些模型被校准为样点特异性模型或物种特异性模型。在校准过程中，优化算法与采样方案的选择存在差异：样点特异性校准中，通过采样方案划分观测数据以开展5折交叉验证；物种特异性校准中，则通过采样方案选取样点。物种特异性模型的校准采用所选样点75%的观测数据，验证则采用每个物种全部500个样点中剩余25%的观测数据。 如需获取更详细的信息，请查阅原始文献Meier与Bigler（2023）。 请引用为： Meier, M. 与Bigler, C.（2023）。秋季叶片物候的过程导向模型：合理校准路径与不确定投影的启示。*地球科学模型开发*，*16*(23)，7171–7201。[https://doi.org/10.5194/gmd-16-7171-2023](https://doi.org/10.5194/gmd-...)