The readings of environmentally-exposed IRGAs located in-situ outside climate-control enclosure and positioned next to the sonic anemometer can be affected by several types of drift. Diurnal variations of incoming radiation and air temperature can cause small short-term temperature drifts on the order of hours. Cell contamination in cases of an unfiltered sampling flow can lead to longer-term drifts between cell cleaning events, on the order of weeks to months. Aging of electronic components in uncalibrated instrument could cause long-term drifts on the order of years. Left unmitigated, these drifts can potentially combine and become large enough to cause serious errors in the resulting eddy-covariance fluxes.
Here, we present a spatio-temporal hierarchy of validation measurements and a corresponding algorithmic process to consistently and continuously correct drifts in NEON's IRGA measurements. Specifically, every 23 hours the fast-response IRGA with high temporal resolution positioned in-situ near sonic anemometer is referenced to traceable gas standards of known concentrations. This addresses any potential consistent drifts, on the order of days to years.
At the same time, mean concentration data are also collected from a heavily filtered slow-response analyzer with strict pressure- and temperature-controls, located in a climate enclosure. The data from the second analyzer are used for bridging between subsequent validation events of the first analyzer. This results in a better accuracy and helps address any drifts on the order of hours.
To our knowledge, this is the first time that IRGA drift with temperature is addressed during flux computation. We also quantify that the temperature, cell contamination, and long-term electronic drifts, if not appropriately resolved, can lead to hourly flux errors on the order of several percent and can accumulate to a significant amount overtime, especially in case of CO2 exchange and for yearly carbon budget.
The presented hierarchy and the entire validation procedure compensate the potential errors at the level of absorptance, and as a result, mitigate zero drifts, related non-linear effects on the gains, and eddy-covariance fluxes. The procedure is done before calculating higher level data products, which lead to a more accurate and precise determination of atmospheric CO2 and H2O fluxes and related long-term carbon and water budgets.
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