Phenology of Photosynthesis in Winter-Dormant Temperate and Boreal Forests: Long-Term Observations From Flux Towers and Quantitative Evaluation of Phenology Models

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F24%3A00585733" target="_blank" >RIV/86652079:_____/24:00585733 - isvavai.cz</a>

Result on the web

<a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023JG007839" target="_blank" >https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023JG007839</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1029/2023JG007839" target="_blank" >10.1029/2023JG007839</a>

Result language

angličtina

Original language name

Phenology of Photosynthesis in Winter-Dormant Temperate and Boreal Forests: Long-Term Observations From Flux Towers and Quantitative Evaluation of Phenology Models

Original language description

We examined the seasonality of photosynthesis in 46 evergreen needleleaf (evergreen needleleaf forests (ENF)) and deciduous broadleaf (deciduous broadleaf forests (DBF)) forests across North America and Eurasia. We quantified the onset and end (StartGPP and EndGPP) of photosynthesis in spring and autumn based on the response of net ecosystem exchange of CO2 to sunlight. To test the hypothesis that snowmelt is required for photosynthesis to begin, these were compared with end of snowmelt derived from soil temperature. ENF forests achieved 10% of summer photosynthetic capacity similar to 3 weeks before end of snowmelt, while DBF forests achieved that capacity similar to 4 weeks afterward. DBF forests increased photosynthetic capacity in spring faster (1.95% d-1) than ENF (1.10% d-1), and their active season length (EndGPP-StartGPP) was similar to 50 days shorter. We hypothesized that warming has influenced timing of the photosynthesis season. We found minimal evidence for long-term change in StartGPP, EndGPP, or air temperature, but their interannual anomalies were significantly correlated. Warmer weather was associated with earlier StartGPP (1.3-2.5 days degrees C-1) or later EndGPP (1.5-1.8 days degrees C-1, depending on forest type and month). Finally, we tested whether existing phenological models could predict StartGPP and EndGPP. For ENF forests, air temperature- and daylength-based models provided best predictions for StartGPP, while a chilling-degree-day model was best for EndGPP. The root mean square errors (RMSE) between predicted and observed StartGPP and EndGPP were 11.7 and 11.3 days, respectively. For DBF forests, temperature- and daylength-based models yielded the best results (RMSE 6.3 and 10.5 days).

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10509 - Meteorology and atmospheric sciences

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Geophysical Research-Biogeosciences

ISSN

2169-8953

e-ISSN

2169-8961

Volume of the periodical

129

Issue of the periodical within the volume

5

Country of publishing house

US - UNITED STATES

Number of pages

25

Pages from-to

e2023JG007839

UT code for WoS article

001208690000001

EID of the result in the Scopus database

2-s2.0-85191734712