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Winter respiratory C losses provide explanatory power for net ecosystem productivity
Type of publication
Peer-reviewed
Publikationsform
Original article (peer-reviewed)
Author
Haeni M Zweifel R Eugster W Gessler A Zielis S Carrara A Knohl A Havránková K Herbst M Ibro,
Project
Forest ecosystem responses to climatic drivers
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Original article (peer-reviewed)
Journal
Journal of Geophysical Research – Biogeosciences
Volume (Issue)
122
Page(s)
243 - 226
Title of proceedings
Journal of Geophysical Research – Biogeosciences
DOI
10.1002/2016jg003455
Abstract
Accurate predictions of net ecosystem productivity (NEPc) of forest ecosystems are essential for climate change decisions and requirements in the context of national forest growth and greenhouse gas inventories. However, drivers and underlying mechanisms determining NEPc (e.g., climate and nutrients) are not entirely understood yet, particularly when considering the influence of past periods. Here we explored the explanatory power of the compensation day (cDOY)—defined as the day of year when winter net Carbon losses are compensated by spring assimilation—for NEPc in 26 forests in Europe, North America, and Australia, using different NEPc integration methods. We found cDOY to be a particularly powerful predictor for NEPc of temperate evergreen needleleaf forests (R2 = 0.58) and deciduous broadleaf forests (R2 = 0.68). In general, the latest cDOY correlated with the lowest NEPc. The explanatory power of cDOY depended on the integration method for NEPc, forest type, and whether the site had a distinct winter net respiratory Carbon loss or not. The integration methods starting in autumn led to better predictions of NEPc from cDOY then the classical calendar method starting 1 January. Limited explanatory power of cDOY for NEPc was found for warmer sites with no distinct winter respiratory loss period. Our findings highlight the importance of the influence of winter processes and the delayed responses of previous seasons’ climatic conditions on current year’s NEPc. Such carry-over effects may contain information from climatic conditions, carbon storage levels, and hydraulic traits of several years back in time.
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