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The eddy covariance technique (EC) is used at hundreds of field sites worldwide to measure trace gas exchange between the surface and the atmosphere. Data quality and correction methods for EC have been studied empirically and theoretically for many years. The recent development of new gas analyzers has led to an increase in technological options for users. Open-path (no inlet tube) and closed-path (long inlet tube) sensors have been used for a long time, whereas enclosed-path (short inlet tube) sensors are relatively new. We tested the comparability of fluxes calculated from five different gas analyzers including two open-path (LI-7500 A from LI-COR, IRGASON from Campbell), two enclosed-path (CPEC200 from Campbell, LI-7200 from LI-COR), and one closed-path (2311-f from Picarro) analyzers, which were all located on a single tower at an irrigated alfalfa field in Davis, CA. To effectively compare sensors with different tube characteristics we used three different spectral correction methods. We found that all sensors, regardless of type, can be used to measure fluxes if appropriate corrections are applied and quality control measures are taken. However, the comparability strongly depended on the gas (CO2 or H2O) and the correction method. Average differences were below 4% for CO2 fluxes using any spectral correction method, but for H2O average differences were between 4% and 13% for the different methods. The magnitude of corrections also varied strongly, especially for water vapor fluxes. This study does not identify the best sensor, but rather weighs the benefits and difficulties of each sensor and sensor type. Our findings show that enclosed and closed-path gas analyzers that measure water vapor with inlet tubes experience large high frequency attenuation and should be corrected with empirical correction methods. This information presented here about different the diverse sensors be considered by investigators when choosing a sensor for a site or when analyzing EC measurements from multiple sites.

In late 2015 and early 2016, work done by the AmeriFlux Management Project Technical Team (amerilfux.lbl.gov) helped to uncover an issue with Gill WindMaster and WindMaster Pro sonic anemometers used by many researchers for eddy covariance flux measurements. Gill has addressed this issue and has since sent out a notice that the vertical wind speed component (a critical piece of all eddy covariance fluxes) was being erroneously computed and reported. The problem (known as the “w-boost” bug) resulted in positive (upward) wind speeds being under-reported by 16.6% and negative (downward) wind speeds being under-reported by 28.9%. This has the potential to cause similar under estimates in fluxes derived from measurements using these instruments. Additionally, the bug affects corrections for angle of attack as derived by Nakai and Shimoyama, rendering them invalid. While the manufacturer has offered a firmware upgrade for existing instruments that will fix this issue, many existing data sets have been affected by it and are currently in use by the scientific community.To address the issue of affected data, currently in use, we analyzed multi-year and short-term data sets from a variety of ecosystems to assess methods of correcting existing flux data. We found that simple multiplicative correction factors (∼1.18) may be used to remove most of the “w-boost” bias from fluxes in existing data sets that do not include angle of attack corrections.

In late 2015 and early 2016, work done by the AmeriFlux Management Project Technical Team (amerilfux.lbl.gov) helped to uncover an issue with Gill WindMaster and WindMaster Pro sonic anemometers used by many researchers for eddy covariance flux measurements. Gill has addressed this issue and has since sent out a notice that the vertical wind speed component (a critical piece of all eddy covariance fluxes) was being erroneously computed and reported. The problem (known as the “w-boost” bug) resulted in positive (upward) wind speeds being under-reported by 16.6% and negative (downward) wind speeds being under-reported by 28.9%. This has the potential to cause similar under estimates in fluxes derived from measurements using these instruments. Additionally, the bug affects corrections for angle of attack as derived by Nakai and Shimoyama, rendering them invalid. While the manufacturer has offered a firmware upgrade for existing instruments that will fix this issue, many existing data sets have been affected by it and are currently in use by the scientific community.To address the issue of affected data, currently in use, we analyzed multi-year and short-term data sets from a variety of ecosystems to assess methods of correcting existing flux data. We found that simple multiplicative correction factors (∼1.18) may be used to remove most of the “w-boost” bias from fluxes in existing data sets that do not include angle of attack corrections.

Wetlands are the largest global natural methane (CH4/ source, and emissions between 50 and 70° N latitude contribute 10-30% to this source. Predictive capability of land models for northern wetland CH4 emissions is still low due to limited site measurements, strong spatial and temporal variability in emissions, and complex hydrological and biogeochemical dynamics. To explore this issue, we compare wetland CH4 emission predictions from the Community Land Model 4.5 (CLM4.5-BGC) with siteto regional-scale observations. A comparison of the CH4 fluxes with eddy flux data highlighted needed changes to the model's estimate of aerenchyma area, which we implemented and tested. The model modification substantially reduced biases in CH4 emissions when compared with CarbonTracker CH4 predictions. CLM4.5 CH4 emission predictions agree well with growing season (May-September) CarbonTracker Alaskan regional-level CH4 predictions and sitelevel observations. However, CLM4.5 underestimated CH4 emissions in the cold season (October-April). The monthly atmospheric CH4 mole fraction enhancements due to wetland emissions are also assessed using the Weather Research and Forecasting-Stochastic Time-Inverted Lagrangian Transport (WRF-STILT) model coupled with daily emissions from CLM4.5 and compared with aircraft CH4 mole fraction measurements from the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) campaign. Both the tower and aircraft analyses confirm the underestimate of cold-season CH4 emissions by CLM4.5. The greatest uncertainties in predicting the seasonal CH4 cycle are from the wetland extent, coldseason CH4 production and CH4 transport processes. We recommend more cold-season experimental studies in highlatitude systems, which could improve the understanding and parameterization of ecosystem structure and function during this period. Predicted CH4 emissions remain uncertain, but we show here that benchmarking against observations across spatial scales can inform model structural and parameter improvements.