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| Titel |
Biases in atmospheric CO2 estimates from correlated meteorology modeling errors |
| VerfasserIn |
S. M. Miller, M. N. Hayek, A. E. Andrews, I. Fung, J. Liu |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 15, no. 5 ; Nr. 15, no. 5 (2015-03-13), S.2903-2914 |
| Datensatznummer |
250119522
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| Publikation (Nr.) |
 copernicus.org/acp-15-2903-2015.pdf |
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| Zusammenfassung |
Estimates of CO2 fluxes that are based on atmospheric measurements rely
upon a meteorology model to simulate atmospheric transport. These models
provide a quantitative link between the surface fluxes and CO2
measurements taken downwind. Errors in the meteorology can therefore cause
errors in the estimated CO2 fluxes. Meteorology errors that correlate or
covary across time and/or space are particularly worrisome; they can cause
biases in modeled atmospheric CO2 that are easily confused with the CO2
signal from surface fluxes, and they are difficult to characterize. In this
paper, we leverage an ensemble of global meteorology model outputs combined
with a data assimilation system to estimate these biases in modeled
atmospheric CO2. In one case study, we estimate the magnitude of
month-long CO2 biases relative to CO2 boundary layer enhancements and
quantify how that answer changes if we either include or remove error
correlations or covariances. In a second case study, we investigate which
meteorological conditions are associated with these CO2 biases.
In the first case study, we estimate uncertainties of 0.5–7 ppm in
monthly-averaged CO2 concentrations, depending upon location (95%
confidence interval). These uncertainties correspond to 13–150% of the
mean afternoon CO2 boundary layer enhancement at individual observation
sites. When we remove error covariances, however, this range drops to
2–22%. Top-down studies that ignore these covariances could therefore
underestimate the uncertainties and/or propagate transport errors into the
flux estimate.
In the second case study, we find that these month-long errors in atmospheric transport
are anti-correlated with temperature and planetary boundary layer
(PBL) height over terrestrial regions. In marine environments, by contrast,
these errors are more strongly associated with weak zonal winds. Many errors,
however, are not correlated with a single meteorological parameter,
suggesting that a single meteorological proxy is not sufficient to
characterize uncertainties in atmospheric CO2. Together, these two case
studies provide information to improve the setup of future top-down inverse
modeling studies, preventing unforeseen biases in estimated CO2 fluxes. |
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