Empirical assessment of uncertainties of meteorological parameters and turbulent fluxes in the AmeriFlux network

Schmidt, A.; Hanson, Chad; Sc, Chan; Law, B. E.

doi:10.1029/2012jg002100

Cited by 52 publications

(53 citation statements)

References 79 publications

(66 reference statements)

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“…We used the tower observations to calibrate the NARR PAR. It should be noted that the PAR (photoelectric) sensors at the EC flux sites gradually degrade over time and typically exhibit a drift of <2% per year (Schmidt et al, 2012) although high values (>10% per year) have been reported and thus annual calibration is needed (Fielder and Comeau, 2000). During the past 6 years, however, the AmeriFlux QA/QC reference PAR sensors have been sent to site for calibration, and the network has also encouraged annual calibrations by the manufacturer.…”

Section: Climate Datamentioning

confidence: 99%

“…During the past 6 years, however, the AmeriFlux QA/QC reference PAR sensors have been sent to site for calibration, and the network has also encouraged annual calibrations by the manufacturer. As part of the AmeriFlux QA/QC effort, the lab-calibrated roving system PAR was compared with the PAR observations from 84 sites, and the comparisons showed that the relative instrument error (RIE) was small (−3 ± 14%), although the deviation around the mean was relatively large (Schmidt et al, 2012). The relative bias of NARR PAR compared to tower PAR is often much higher than 3% ( Supplementary Fig.…”

Section: Climate Datamentioning

confidence: 99%

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Data-driven diagnostics of terrestrial carbon dynamics over North America

Xiao

Ollinger

Frolking

et al. 2014

Agricultural and Forest Meteorology

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a b s t r a c tThe exchange of carbon dioxide is a key measure of ecosystem metabolism and a critical intersection between the terrestrial biosphere and the Earth's climate. Despite the general agreement that the terrestrial ecosystems in North America provide a sizeable carbon sink, the size and distribution of the sink remain uncertain. We use a data-driven approach to upscale eddy covariance flux observations from towers to the continental scale by integrating flux observations, meteorology, stand age, aboveground biomass, and a proxy for canopy nitrogen concentrations from AmeriFlux and FluxnetCanada Research Network as well as a variety of satellite data streams from the MODIS sensors. We then use the resulting gridded flux estimates from March 2000 to December 2012 to assess the magnitude, distribution, and interannual variability of carbon fluxes for the U.S. and Canada. The mean annual gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem productivity (NEP) of the U.S. over the period 2001-2012 were 6.84, 5.31, and 1.10 Pg C yr −1 , respectively; the mean annual GPP, ER, and NEP of Canada over the same 12-year period were 3.91, 3.26, and 0.60 Pg C yr −1 , respectively. The mean nationwide annual NEP of natural ecosystems over the period 2001-2012 was 0.53 Pg C yr −1 for the U.S. and 0.49 Pg C yr −1 for the conterminous U.S. Our estimate of the carbon sink for the conterminous U.S. was almost identical with the estimate of the First State of the Carbon Cycle Report (SOCCR). The carbon fluxes exhibited relatively large interannual variability over the study period. The main sources of the interannual variability in carbon fluxes included drought and disturbance. The annual GPP and NEP were strongly related to annual evapotranspiration (ET) for both * Corresponding author. Tel.: +1 603 862 1873; fax: +1 603 862 0188. This article is a U.S. government work, and is not subject to copyright in the United States.J. Xiao et al. / Agricultural and Forest Meteorology 197 (2014) 142-157 143 the U.S. and Canada, showing that the carbon and water cycles were closely coupled. Our gridded flux estimates provided an independent, alternative perspective on ecosystem carbon exchange over North America.

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Section: Climate Datamentioning

confidence: 99%

Section: Climate Datamentioning

confidence: 99%

Data-driven diagnostics of terrestrial carbon dynamics over North America

Xiao

Ollinger

Frolking

et al. 2014

Agricultural and Forest Meteorology

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“…These sensors were shielded from the sun and aspirated, yielding an accuracy of 90.28C at 208C. To remove bias errors between two sensors, we referenced the temperature measurements to a standard that was provided by the AmeriFlux project during several site visits (Schmidt et al, 2012); these comparisons occurred at the grassland in 2004 and 2007 and at the savanna in 2005 and 2010. The standard temperature sensor was a shielded, aspirated platinum-resistance thermometer, calibrated against known standards at three points between 0 and 1008C.…”

Section: Meteorological and Soil Measurementsmentioning

confidence: 99%

How will land use affect air temperature in the surface boundary layer? Lessons learned from a comparative study on the energy balance of an oak savanna and annual grassland in California, USA

Baldocchi¹,

Ma²

2013

Tellus B: Chemical and Physical Meteorology

104

117

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A B S T R A C TWe investigated the effect of land use on differences in air temperature. We based our analysis on a decade of weather and energy flux measurements, collected over two contrasting landscapes, an oak savanna and an annual grassland, growing under the same climate conditions. Over the decade, the daily-averaged, potential air temperature above the aerodynamically rougher and optically darker oak savanna was 0.58C warmer than that above the aerodynamically smoother and optically brighter annual grassland. However, air temperature differences were seasonal. Smallest differences in potential air temperature occurred towards the end of spring, when much of the soil moisture reservoir was depleted. Largest differences in potential air temperature occurred during the winter rain season when the grass was green and transpiring and when the trees were senescent or deciduous. To understand the effect of land use on the local climate, we examined the concomitant changes in net radiation, sensible and latent heat exchange, the aerodynamic roughness (R a ), the surface resistance to water transfer (R s ), aerodynamic surface temperature and the growth of the planetary boundary layer, with measurements and model computations. Overall, these biophysical variables provide us with mechanistic information to diagnose and predict how changes in air temperature will follow changes in land use or management. In conclusion, land use change is responsible for having a marked impact on the local climate of a region. At the local level, the change in the surface energy balance, towards a darker and rougher surface, will produce an additive increment to climate warming induced by a greater greenhouse gas burden in the atmosphere.

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“…For the uncertainty due to instrumental noise, the method proposed by Lenschow et al (2000) has been recently applied to EC measurements not only for energy and CO 2 (Mauder et al, 2013;Mammarella et al, 2015) but also for CH 4 (Peltola et al, 2014) and N 2 O fluxes . Few authors (Detto et al, 2011;Schmidt et al, 2012;Sturm et al, 2012;Peltola et al, 2013;Deventer et al, 2015) have used the method proposed by Billesbach (2011) as a means of estimating the random instrumental noise. This approach, also called "random shuffle method", consists of randomly shuffling one of the data records in time and then estimating the error as covariance between the two decorrelated time series.…”

Section: Introductionmentioning

confidence: 99%

Random uncertainties of flux measurements by the eddy covariance technique

2016

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Abstract. Large variability is inherent to turbulent flux observations. We review different methods used to estimate the flux random errors. Flux errors are calculated using measured turbulent and simulated artificial records. We recommend two flux errors with clear physical meaning: the flux error of the covariance, defining the error of the measured flux as 1 standard deviation of the random uncertainty of turbulent flux observed over an averaging period of typically 30 min to 1 h duration; and the error of the flux due to the instrumental noise. We suggest that the numerical approximation by Finkelstein and Sims (2001) is a robust and accurate method for calculation of the first error estimate. The method appeared insensitive to the integration period and the value 200 s sufficient to obtain the estimate without significant bias for variety of sites and wide range of observation conditions. The filtering method proposed by Salesky et al. (2012) is an alternative to the method by Finkelstein and Sims (2001) producing consistent, but somewhat lower, estimates. The method proposed by Wienhold et al. (1995) provides a good approximation to the total flux random uncertainty provided that independent cross-covariance values far from the maximum are used in estimation as suggested in this study. For the error due to instrumental noise the method by Lenschow et al. (2000) is useful in evaluation of the respective uncertainty. The method was found to be reliable for signal-to-noise ratio, defined by the ratio of the standard deviation of the signal to that of the noise in this study, less than three. Finally, the random uncertainty of the error estimates was determined to be in the order of 10 to 30 % for the total flux error, depending on the conditions and method of estimation.

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Empirical assessment of uncertainties of meteorological parameters and turbulent fluxes in the AmeriFlux network

Cited by 52 publications

References 79 publications

Data-driven diagnostics of terrestrial carbon dynamics over North America

Data-driven diagnostics of terrestrial carbon dynamics over North America

How will land use affect air temperature in the surface boundary layer? Lessons learned from a comparative study on the energy balance of an oak savanna and annual grassland in California, USA

Random uncertainties of flux measurements by the eddy covariance technique

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