Abstract
In deciduous forests, spring leaf development and fall leaf senescence regulate the timing and duration of photosynthesis and transpiration. Being able to model these dates is therefore critical to accurately representing ecosystem processes in biogeochemical models. Despite this, there has been relatively little effort to improve internal phenology predictions in widely used biogeochemical models. Here, we optimized the phenology algorithms in a regionally developed biogeochemical model (PnET-CN) using phenology data from eight mid-latitude PhenoCam sites in eastern North America. We then performed a sensitivity analysis to determine how the optimization affected future predictions of carbon, water, and nitrogen cycling at Bartlett Experimental Forest, New Hampshire. Compared to the original PnET-CN phenology models, our new spring and fall models resulted in shorter season lengths and more abrupt transitions that were more representative of observations. The new phenology models affected daily estimates and interannual variability of modeled carbon exchange, but they did not have a large influence on the magnitude or long-term trends of annual totals. Under future climate projections, our new phenology models predict larger shifts in season length in the fall (1.1–3.2 days decade−1) compared to the spring (0.9–1.5 days decade−1). However, for every day the season was longer, spring had twice the effect on annual carbon and water exchange totals compared to the fall. These findings highlight the importance of accurately modeling season length for future projections of carbon and water cycling.
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Data availability
Data used to train phenology models is available through the PhenoCam Network (phenocam.nau.edu). Eddy covariance data used in the analysis are available through the AmeriFlux Network (https://ameriflux.lbl.gov/sites/siteinfo/US-Bar).
Code availability
The R code used to download data, extract transition dates, then train models can be found at the repository: https://github.com/teetsaf/PhenoCam_data_download_and_modeling. The repository also contains instructions and C + + code for incorporating new phenology models into PnET.
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Acknowledgements
We thank the many collaborators in their efforts to support PhenoCam data collection, including site PIs and technicians.
Funding
The development of the PhenoCam network has been funded by the Northeastern States Research Cooperative, NSF’s Macrosystems Biology program (awards EF-1065029 and EF-1702697), and DOE’s Regional and Global Climate Modeling program (award DE-SC0016011). Research at the Bartlett Experimental Forest is supported by the USDA Forest Service’s Northern Research Station. We acknowledge funding support from the National Science Foundation for the Hubbard Brook Long Term Ecological Research (LTER) program, awards #DEB-1114804 and #DEB-1637685 and the Harvard Forest LTER award #DEB 1832210; and support from the Northeastern States Research Cooperative #12DG11242307065.
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AT and ADR designed the project, performed the analysis, and wrote the manuscript. ASB collected and curated ground-observation data. KH, CS, and BS contributed to programming and analysis. AT and SO performed the modeling. All authors reviewed and provided editorial comments.
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Communicated by Rodrigo Vargas.
We improve phenology algorithms in ecosystem-level models using empirical observations from multiple data streams. The new phenology algorithms affected daily estimates and interannual variability of modeled carbon exchange, but did not have a large influence on the long-term trends of annual totals. Using the improved phenology algorithms, we assessed the relative influence of spring and fall on future ecosystem carbon exchange.
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Teets, A., Bailey, A.S., Hufkens, K. et al. Early spring onset increases carbon uptake more than late fall senescence: modeling future phenological change in a US northern deciduous forest. Oecologia 201, 241–257 (2023). https://doi.org/10.1007/s00442-022-05296-4
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DOI: https://doi.org/10.1007/s00442-022-05296-4