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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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).

Název v anglickém jazyce

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

Popis výsledku anglicky

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).

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10509 - Meteorology and atmospheric sciences

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Journal of Geophysical Research-Biogeosciences

ISSN

2169-8953

e-ISSN

2169-8961

Svazek periodika

129

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

25

Strana od-do

e2023JG007839

Kód UT WoS článku

001208690000001

EID výsledku v databázi Scopus

2-s2.0-85191734712