DOI: 10.5061/dryad.dv41ns22k
title: Modelled past autumn leaf phenology of deciduous trees

description: 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 &gt;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 &gt;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, 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
description: 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.
description: 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/
description: # 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-16-7171-2023) ## Description of the Data and file structure The Rds files can be opened in the software R (R Core Team, 2022) with the base function readRDS(). ### Calibrated sets of model parameters There is one file per site- or species-specific models. The data are... | Column name | Description | | :------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Site | The ID of the site of the observation, which corresponds to the PEP\_ID of the PEP725 dataset. | | Species | The species to which the data corresponds (i.e. one of beech, oak, or larch). | | Model | The process-oriented model for autumn phenology that generated the modelled data. | | Type | The process-oriented model for autumn phenology that generated the modelled data. | | Algorithm | Which optimization algorithm was used to calibrate the model? (EGO, TREGO, GenSA, PSO, or CMA-ES executed in normal, norm., or extensive, extd., mode). | | SamplingProcedure | The procedure according to which observations or sites were selected for the site- or species-specific calibration, respectively. | | NoOfSites\*\* | The number of sites included in the calibration sample for the species-specific calibration. | | SubDraw\*\* | The draw of the sample, as each sample for species-specific calibration was drawn five times, except for the full sample. | | CV\_fold\* | The ith fold of the 5-fold cross-validation used in site-specific calibration (NA for the calibration with all observations that was used for the projections). | | OptimizationDuration | The duration of the calibration run. | | Parameter1 | The 1st calibrated parameter (Meier M., 2022). | | Parameter2 | The 2nd calibrated parameter (Meier M., 2022). | | Parameter3 | The 3rd calibrated parameter (Meier M., 2022). | | Parameter4 | The 4th calibrated parameter (Meier M., 2022). | | Parameter5 | The 5th calibrated parameter (Meier M., 2022). | | Parameter6 | The 6th calibrated parameter (Meier M., 2022). | | Parameter7 | The 7th calibrated parameter (Meier M., 2022). | *) Applies only to site-specific models. \**) Applies only to species-specific models. ### Modelled dates of autumn phenology There is one file per combination of site- or species-specific calibration, species, and optimization algorithm. The data are... | Column name | Description | | :-------------------- | :-------------------------------------------------------------------------------------------------------------------------------------------------------------- | | DOY\_observed | The observed day of year of autumn phenology, derived from the PEP725 database \[day of year]. | | DOY\_modelled | The modelled day of year \[day of year]. | | Year | The year of the observation \[year]. | | Site | The ID of the site of the observation, which corresponds to the PEP\_ID of the PEP725 dataset. | | Species | The species to which the data corresponds (i.e. one of beech, oak, or larch). | | Model | The process-oriented model for autumn phenology that generated the modelled data. | | Type | Was the data modelled with a site- or species-specific model? | | Algorithm | Which optimization algorithm was used to calibrate the model? (EGO, TREGO, GenSA, PSO, or CMA-ES executed in normal, norm., or extensive, extd., mode). | | SamplingProcedure | The procedure according to which observations or sites were selected for the site- or species-specific calibration, respectively. | | NoOfSites\*\* | The number of sites included in the calibration sample for the species-specific calibration. | | SubDraw\*\* | The draw of the sample, as each sample for species-specific calibration was drawn five times, except for the full sample. | | CV\_fold\* | The ith fold of the 5-fold cross-validation used in site-specific calibration (NA for the calibration with all observations that was used for the projections). | | ModelMode | Was the data modelled during the calibration or validation of the model? | | SiteOfCalibration\*\* | Was this site also used in the calibration of the species-specific model? (TRUE or FALSE) | | RunConverged | Did the calibration or validation run converge? (TRUE or FALSE) | **) Applies only to site-specific models.* \**) Applies only to species-specific models. ## Sharing/access Information The observed autumn leaf phenology data was downloaded from the PEP725 database on April 13, 2022 ([http://www.pep725.eu/](http://www.pep725.eu/)). ## Reference Meier, M. (2022). Process-oriented models of autumn leaf phenology (1.0). Zenodo. [https://doi.org/10.5281/zenodo.7188160](https://doi.org/10.5281/zenodo.7188160) R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing. [https://www.r-project.org/](https://www.r-project.org/)