2018
DOI: 10.1002/2017jg003988
|View full text |Cite
|
Sign up to set email alerts
|

Impact of Canopy Decoupling and Subcanopy Advection on the Annual Carbon Balance of a Boreal Scots Pine Forest as Derived From Eddy Covariance

Abstract: Apparent net uptake of carbon dioxide (CO2) during wintertime by an ∼ 90 year old Scots pine stand in northern Sweden led us to conduct canopy decoupling and subcanopy advection investigations over an entire year. Eddy covariance (EC) measurements ran simultaneously above and within the forest canopy for that purpose. We used the correlation of above‐ and below‐canopy standard deviation of vertical wind speed (σw) as decoupling indicator. We identified 0.33 m s−1 and 0.06 m s−1 as site‐specific σw thresholds f… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

0
33
1

Year Published

2018
2018
2024
2024

Publication Types

Select...
5
4

Relationship

0
9

Authors

Journals

citations
Cited by 30 publications
(34 citation statements)
references
References 84 publications
0
33
1
Order By: Relevance
“…are not yet widely adopted for ET partitioning studies due to a limited understanding of their performance (Perez-Priego et al, 10 2017); most work to date has used below-canopy eddy covariance to partition canopy GPP and soil respiration (Misson et al, 2007). Several recent studies demonstrated the additional value of concurrent below-canopy measurements for quantifying the coupling and decoupling of below-and above-canopy airspace to accurately apply the eddy covariance technique in forested ecosystems (Jocher et al, 2017(Jocher et al, , 2018Paul-Limoges et al, 2017;Thomas et al, 2013), arguing that below-canopy eddy covariance measurements should be more widely adopted. Other eddy covariance-based partitioning methods take a different 15 approach and use the relationship between T and GPP to partition ecosystem-scale E and T. Scott and Biederman (2017) assumed that T is linearly related to GPP at monthly time scales over many years such that:…”
Section: Partitioning Et Using Half-hourly Eddy Covariance Observationsmentioning
confidence: 99%
“…are not yet widely adopted for ET partitioning studies due to a limited understanding of their performance (Perez-Priego et al, 10 2017); most work to date has used below-canopy eddy covariance to partition canopy GPP and soil respiration (Misson et al, 2007). Several recent studies demonstrated the additional value of concurrent below-canopy measurements for quantifying the coupling and decoupling of below-and above-canopy airspace to accurately apply the eddy covariance technique in forested ecosystems (Jocher et al, 2017(Jocher et al, , 2018Paul-Limoges et al, 2017;Thomas et al, 2013), arguing that below-canopy eddy covariance measurements should be more widely adopted. Other eddy covariance-based partitioning methods take a different 15 approach and use the relationship between T and GPP to partition ecosystem-scale E and T. Scott and Biederman (2017) assumed that T is linearly related to GPP at monthly time scales over many years such that:…”
Section: Partitioning Et Using Half-hourly Eddy Covariance Observationsmentioning
confidence: 99%
“…where F OCS , F H 2 0 , OCS , and H 2O are the fluxes and gradients of OCS and H 2 O, respectively, and S OCS is the change in storage flux of OCS. A change in storage flux is subject to large uncertainties, and estimates have been shown to vary depending on the averaging time and vertical resolution of the storage profile (Yang et al, 2007), horizontal resolution, and site heterogeneity (de Araújo et al, 2010;Nicolini et al, 2018) as well as canopy decoupling (Jocher et al, 2018). Since large parts of the canopy at the site are decoupled from the bulk air at all times (Pyles et al, 2004), we inferred change in storage as the height-integrated change in the time derivative of mixing ratios between the canopy top and above the canopy.…”
Section: Ocs Flux Estimationmentioning
confidence: 99%
“…Carbonyl sulfide (OCS) is the most abundant sulfur gas in the atmosphere, with a mean atmospheric concentration of ∼ 500 ppt (parts per trillion), and therefore a significant part of the tropospheric and stratospheric sulfur cycles, with implications for the global radiation budget and ozone depletion (Johnson et al,1993;Notholt et al, 2003). The dominant sink of atmospheric OCS is vegetation (Kesselmeier and Merk, 1993;Kettle et al, 2002;Montzka et al, 2007, and references therein), through rapid and irreversible hydrolysis by the ubiquitous enzyme carbonic anhydrase (Protoschill-Krebs et al, 1996;Protoschill-Krebs and Kesselmeier, 1992).…”
Section: Introductionmentioning
confidence: 99%
“…where F OCS , F H 2 0 , OCS , and H 2O are the fluxes and gradients of OCS and H 2 O, respectively, and S OCS is the change in storage flux of OCS. A change in storage flux is subject to large uncertainties, and estimates have been shown to vary depending on the averaging time and vertical resolution of the storage profile (Yang et al, 2007), horizontal resolution, and site heterogeneity (de Araújo et al, 2010;Nicolini et al, 2018) as well as canopy decoupling (Jocher et al, 2018). Since large parts of the canopy at the site are decoupled from the bulk air at all times , we inferred change in storage as the height-integrated change in the time derivative of mixing ratios between the canopy top and above the canopy.…”
Section: Ocs Flux Estimationmentioning
confidence: 99%