摘 要(英文)：Information about the uncertainties associated
with eddy covariance observations of surface-atmosphere
CO2 exchange is of importance for model-data fusion in
carbon cycling studies and the accurate evaluation of ecosystem
carbon budgeting. In this paper, a comprehensive
analysis was conducted to investigate the influence of data
processing procedures, focusing especially on the nocturnal
data correction and three procedures in nonlinear regression
method of gap filling [i.e., the selection of respiration model
(REM), light-response model (LRM) and parameter optimization
criteria (POC)], on the annual net ecosystem CO2
exchange estimation at three forest ecosystems in China-
FLUX with three yearly datasets for each site. The results
showed that uncertainties caused from four methodological
uncertainties were between 61 and 108 g C m-2 year-1,
with 61–93 g C m-2 year-1 (21–30%) in a temperate mixed
forest, 80–107 g C m-2 year-1 (19–21%) in a subtropical
evergreen coniferous plantation and 77–108 g C m-2year-1 (16–19%) in a subtropical evergreen broad-leaved
forest. Factorial analysis indicated that the largest uncertainty
was associated with the choice of POC in the regression
method across all sites in all years, while the influences
of the choice of models (i.e., REM and LRM) varied with
climate conditions at the measurement station. Furthermore,
the uncertainty caused by data processing procedures was of
approximately the same magnitude as the interannual variability
in the three sites. This result stressed the importance to
understand the uncertainty caused by data processing to
avoid the introduction of artificial between-year and
between-site variability that hampers comparative analysis.
Uncertainty analysis in data processing on the estimation of net carbon exchanges at different forest ecosystems in China