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Winter respiratory C losses provide explanatory power for net ecosystem productivity

Haeni, M.; Zweifel, R.; Eugster, W.; Gessler, A; Zielis, S.; Bernhofer, C; Carrara, A.; Grünwald, T.; Havránková, K. and Heinesch, B., et al. (2017) In Journal of Geophysical Research - Biogeosciences 122(1). p.243-260
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... (More)

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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publication status
published
subject
keywords
Carbon sink, Carbon source, CO exchange, Eddy covariance, Growing season length, Winter respiration
in
Journal of Geophysical Research - Biogeosciences
volume
122
issue
1
pages
243 - 260
publisher
American Geophysical Union
external identifiers
  • scopus:85011317447
  • wos:000394944400016
ISSN
2169-8953
DOI
10.1002/2016JG003455
language
English
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yes
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25a56b88-9782-4d4d-9f95-5d04bb17596e
date added to LUP
2017-02-16 09:11:57
date last changed
2018-01-15 10:48:35
@article{25a56b88-9782-4d4d-9f95-5d04bb17596e,
  abstract     = {<p>Accurate predictions of net ecosystem productivity (NEP<sub>c</sub>) 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 NEP<sub>c</sub> (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 NEP<sub>c</sub> in 26 forests in Europe, North America, and Australia, using different NEP<sub>c</sub> integration methods. We found cDOY to be a particularly powerful predictor for NEP<sub>c</sub> of temperate evergreen needleleaf forests (R<sup>2</sup>=0.58) and deciduous broadleaf forests (R<sup>2</sup>=0.68). In general, the latest cDOY correlated with the lowest NEP<sub>c</sub>. The explanatory power of cDOY depended on the integration method for NEP<sub>c</sub>, 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 NEP<sub>c</sub> from cDOY then the classical calendar method starting 1 January. Limited explanatory power of cDOY for NEP<sub>c</sub> 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 NEP<sub>c</sub>. Such carry-over effects may contain information from climatic conditions, carbon storage levels, and hydraulic traits of several years back in time.</p>},
  author       = {Haeni, M. and Zweifel, R. and Eugster, W. and Gessler, A and Zielis, S. and Bernhofer, C and Carrara, A. and Grünwald, T. and Havránková, K. and Heinesch, B. and Herbst, M. and Ibrom, A and Knohl, A and Lagergren, F. and Law, B. E. and Marek, M and Matteucci, G and Mccaughey, J. H. and Minerbi, Stefano and Montagnani, L and Moors, E and Olejnik, Janusz and Pavelka, Marian and Pilegaard, K and Pita, G and Rodrigues, A A and Sanz Sánchez, M. J. and Schelhaas, Mart-Jan and Urbaniak, M. and Valentini, R and Varlagin, Andrej and Vesala, T and Vincke, Cécile and Wu, J. and Buchmann, N.},
  issn         = {2169-8953},
  keyword      = {Carbon sink,Carbon source,CO exchange,Eddy covariance,Growing season length,Winter respiration},
  language     = {eng},
  number       = {1},
  pages        = {243--260},
  publisher    = {American Geophysical Union},
  series       = {Journal of Geophysical Research - Biogeosciences},
  title        = {Winter respiratory C losses provide explanatory power for net ecosystem productivity},
  url          = {http://dx.doi.org/10.1002/2016JG003455},
  volume       = {122},
  year         = {2017},
}