TY - JOUR
T1 - Estimating surface fluxes using eddy covariance and numerical ogive optimization
AU - Sievers, J.
AU - Papakyriakou, T.
AU - Larsen, Søren Ejling
AU - Jammet, M. M.
AU - Rysgaard, Søren
AU - Sejr, M.K.
AU - Sørensen, L.L.
N1 - © Author(s) 2015. CC Attribution 3.0 License
PY - 2015
Y1 - 2015
N2 - Estimating representative surface fluxes using
eddy covariance leads invariably to questions concerning inclusion
or exclusion of low-frequency flux contributions. For
studies where fluxes are linked to local physical parameters
and up-scaled through numerical modelling efforts, low-frequency
contributions interfere with our ability to isolate
local biogeochemical processes of interest, as represented
by turbulent fluxes. No method currently exists to disentangle
low-frequency contributions on flux estimates. Here, we
present a novel comprehensive numerical scheme to identify
and separate out low-frequency contributions to vertical
turbulent surface fluxes. For high flux rates (|Sensible
heat flux| > 40Wm-2, |latent heat flux|> 20Wm-2 and |CO2
flux|> 100 mmolm-2 d-1/ we found that the average relative
difference between fluxes estimated by ogive optimization
and the conventional method was low (5–20 %) suggesting
negligible low-frequency influence and that both methods
capture the turbulent fluxes equally well. For flux rates below
these thresholds, however, the average relative difference between
flux estimates was found to be very high (23–98 %)
suggesting non-negligible low-frequency influence and that
the conventional method fails in separating low-frequency
influences from the turbulent fluxes. Hence, the ogive optimization
method is an appropriate method of flux analysis,
particularly in low-flux environments.
AB - Estimating representative surface fluxes using
eddy covariance leads invariably to questions concerning inclusion
or exclusion of low-frequency flux contributions. For
studies where fluxes are linked to local physical parameters
and up-scaled through numerical modelling efforts, low-frequency
contributions interfere with our ability to isolate
local biogeochemical processes of interest, as represented
by turbulent fluxes. No method currently exists to disentangle
low-frequency contributions on flux estimates. Here, we
present a novel comprehensive numerical scheme to identify
and separate out low-frequency contributions to vertical
turbulent surface fluxes. For high flux rates (|Sensible
heat flux| > 40Wm-2, |latent heat flux|> 20Wm-2 and |CO2
flux|> 100 mmolm-2 d-1/ we found that the average relative
difference between fluxes estimated by ogive optimization
and the conventional method was low (5–20 %) suggesting
negligible low-frequency influence and that both methods
capture the turbulent fluxes equally well. For flux rates below
these thresholds, however, the average relative difference between
flux estimates was found to be very high (23–98 %)
suggesting non-negligible low-frequency influence and that
the conventional method fails in separating low-frequency
influences from the turbulent fluxes. Hence, the ogive optimization
method is an appropriate method of flux analysis,
particularly in low-flux environments.
U2 - 10.5194/acp-15-2081-2015
DO - 10.5194/acp-15-2081-2015
M3 - Journal article
SN - 1680-7316
VL - 15
SP - 2081
EP - 2103
JO - Atmospheric Chemistry and Physics
JF - Atmospheric Chemistry and Physics
ER -