DOI: 10.5061/dryad.mw6m90613
title: Projected future 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 projected autumn phenology analyzed in
 Meier and Bigler (2023). The data was derived from different combinations
 of 21 process-oriented phenology models, 5 optimization algorithms, ≥7
 sampling procedures, and 26 climate model chains from two representative
 concentration pathways. The dataset contains the average autumn phenology
 per site and for the years 2080-2099 according to each combination that
 led to a successful calibration. 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 projected to the years 2080-2099 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. The models
 werte driven with downscaled projected climate data from 16 and 10
 different climate model chains based on the representative concetration
 pathways 4.5 and 8.5, respectively, which we derived from the CMIP5-based
 CORDEX-EUR-11 datasets (Riahi et al. 2011, Thomson et al. 2011, Jacob et
 al. 2014). The CORDEX-EUR-11 data was obtained from the
 Institute for Atmospheric and Climate Science (IAC) at ETH Zurich on
 August 2021. For more detailed information
 consult the original publication Meier and Bigler (2023).
 References Jacob, D., Petersen, J., Eggert,
 B., Alias, A., Christensen, O. B., Bouwer, L. M., Braun, A., Colette, A.,
 Deque, M., Georgievski, G., Georgopoulou, E., Gobiet, A., Menut, L.,
 Nikulin, G., Haensler, A., Hempelmann, N., Jones, C., Keuler, K., Kovats,
 S., … Yiou, P. (2014). EURO-CORDEX: New high-resolution climate change
 projections for European impact research. <em>Regional Environmental
 Change</em>, <em>14</em>(2), 563–578. https://doi.org/10.1007/s10113-013-0499-2 Meier, M., & Bigler, C. (2023). Process-oriented models of autumn leaf phenology: Ways to sound calibration and implications of uncertain projections. <em>Geoscientific Model Development</em>, <em>16</em>(23), 7171–7201. https://doi.org/10.5194/gmd-16-7171-2023 Riahi, K., Rao, S., Krey, V., Cho, C., Chirkov, V., Fischer, G., Kindermann, G., Nakicenovic, N., & Rafaj, P. (2011). RCP 8.5—A scenario of comparatively high greenhouse gas emissions. <em>Climatic Change</em>, <em>109</em>(1), 33–57. https://doi.org/10.1007/s10584-011-0149-y Thomson, A. M., Calvin, K. V., Smith, S. J., Kyle, G. P., Volke, A., Patel, P., Delgado-Arias, S., Bond-Lamberty, B., Wise, M. A., Clarke, L. E., & Edmonds, J. A. (2011). RCP4.5: A pathway for stabilization of radiative forcing by 2100. <em>Climatic Change</em>, <em>109</em>(1), 77–94. https://doi.org/10.1007/s10584-011-0151-4
description: The Rds files in the zip folders 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: # Projected autumn leaf phenology of deciduous trees Autumn leaf phenology
 was projected to the years 2080-2099 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. The models werte driven with downscaled
 projected climate data from 16 and 10 different climate model chains based
 on the representative concetration pathways 4.5 and 8.5, respectively,
 which we derived from the CMIP5-based CORDEX-EUR-11 datasets (Riahi et al.
 2011, Thomson et al. 2011, Jacob et al. 2014). 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. References
 Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O. B.,
 Bouwer, L. M., Braun, A., Colette, A., Deque, M., Georgievski, G.,
 Georgopoulou, E., Gobiet, A., Menut, L., Nikulin, G., Haensler, A.,
 Hempelmann, N., Jones, C., Keuler, K., Kovats, S., … Yiou, P. (2014).
 EURO-CORDEX: New high-resolution climate change projections for European
 impact research. *Regional Environmental Change*, *14*(2), 563–578.
 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.
 Riahi, K., Rao, S., Krey,
 V., Cho, C., Chirkov, V., Fischer, G., Kindermann, G., Nakicenovic, N.,
 & Rafaj, P. (2011). RCP 8.5—A scenario of comparatively high
 greenhouse gas emissions. *Climatic Change*, *109*(1), 33–57.
 Thomson, A. M., Calvin,
 K. V., Smith, S. J., Kyle, G. P., Volke, A., Patel, P., Delgado-Arias, S.,
 Bond-Lamberty, B., Wise, M. A., Clarke, L. E., & Edmonds, J. A.
 (2011). RCP4.5: A pathway for stabilization of radiative forcing by 2100.
 *Climatic Change*, *109*(1), 77–94.
 ## Description of the
 Data and file structure Each zip folder contains corresponding Rds files
 that can be opened in R with the base function readRDS(). There is one
 file for projections based on site- or species-specific models and per
 combination of representative concentration pathway, species, and model.
 The data are... | Column name | Description | |
 :--------------------------------- |
 :------------------------------------------------------------------------------------------------------------------------------------------------------ | | Delta\_100 | The projected difference between the average autumn phenology for the years 2080-2099 (projected) and for the years 1980-1999 (observed) \[days]. | | avDOY\_projected | The the average autumn phenology for the years 2080-2099 \[day of year]. | | avYear\_projected | The the average year that corresponds to avDOY\_projected \[year]. | | avDOY\_historical | The the average autumn phenology observed for the years 1980-1999 or last 20 years of the recordings \[day of year]. | | avYear\_historical | The the average year that corresponds to the avDOY\_historical \[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). | | RepresentativeConcentrationPathway | The representative concentration pathway RCP 4.5 or RCP 8.5. | | ClimateModelChain | The climate model chain CMC1, ..., CMC16, referring to the table Appendix S1: Table S5 in Meier and Bigler (2023). | | 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. | | SiteOfCalibration\*\* | Was this site also used in the calibration of the species-specific model? (TRUE or FALSE). | | n\_Delta\_100\* | Number of projected Delta\_100 (i.e. rows) in that very file. | | n\_NA\_Delta\_100\* | Number of projected Delta\_100 that resulted in NA values in that very file. | | n\_Delta\_100\_Pop\*\* | Number of projected Delta\_100 (i.e. rows) in that very file. | | n\_NA\_Delta\_100\_Pop\*\* | Number of projected Delta\_100 that resulted in NA values in that very file. | | n\_Delta\_100\_Spl\*\* | Number of projected Delta\_100 for which SiteOfCalibration was "TRUE" in that very file. | | n\_NA\_Delta\_100\_Spl\*\* | Number of projected Delta\_100 for which SiteOfCalibration was "TRUE" and that resulted in NA values in that very file. | \* Applies only to site-specific models. \** Applies only to species-specific models. Reference 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. ## Sharing/access Information The CORDEX-EUR-11 data was obtained from the Institute for Atmospheric and Climate Science (IAC) at ETH Zurich on August 2021.