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 >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

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. Regional Environmental Change, 14(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. Geoscientific Model Development, 16(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. Climatic Change, 109(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. Climatic Change, 109(1), 77–94. https://doi.org/10.1007/s10584-011-0151-4

The Rds files in the zip folders can be opened in the software R (R Core Team, 2022) with the base function readRDS().