Evaluation of 4 years of continuous <i>δ</i><sup>13</sup>C(CO<sub>2</sub>) data using a moving Keeling plot method

Abstract

Abstract. Different carbon dioxide (CO 2 ) emitters can be distinguished by their carbon isotope ratios. Therefore measurements of atmospheric δ 13 C(CO 2 ) and CO 2 concentration contain information on the CO 2 source mix in the catchment area of an atmospheric measurement site. This information may be illustratively presented as the mean isotopic source signature. Recently an increasing number of continuous measurements of δ 13 C(CO 2 ) and CO 2 have become available, opening the door to the quantification of CO 2 shares from different sources at high temporal resolution. Here, we present a method to compute the CO 2 source signature (δ S ) continuously and evaluate our result using model data from the Stochastic Time-Inverted Lagrangian Transport model. Only when we restrict the analysis to situations which fulfill the basic assumptions of the Keeling plot method does our approach provide correct results with minimal biases in δ S . On average, this bias is 0.2 ‰ with an interquartile range of about 1.2 ‰ for hourly model data. As a consequence of applying the required strict filter criteria, 85 % of the data points -mainly daytime values -need to be discarded. Applying the method to a 4-year dataset of CO 2 and δ 13 C(CO 2 ) measured in Heidelberg, Germany, yields a distinct seasonal cycle of δ S . Disentangling this seasonal source signature into shares of source components is, however, only possible if the isotopic end members of these sources -i.e., the biosphere, δ bio , and the fuel mix, δ F -are known. From the mean source signature record in 2012, δ bio could be reliably estimated only for summer to (−25.0 ± 1.0) ‰ and δ F only for winter to (−32.5 ± 2.5) ‰. As the isotopic end members δ bio and δ F were shown to change over the season, no year-round estimation of the fossil fuel or biosphere share is possible from the measured mean source signature record without additional information from emission inventories or other tracer measurements.

Does vapor pressure deficit drive the seasonality of δ13C of the net land‐atmosphere CO2exchange across the United States?

“…The similarity of the δ source seasonal cycle between 2002 and 2005–2012 suggests that the timing and contribution of emissions in Salt Lake City were similar with a higher proportion of natural gas emissions during the winter for home heating (−37.7‰) and a higher proportion of gasoline combustion (−28.3‰) during the summer [ Bush et al , ]. These seasonal ranges of values found for Salt Lake City were similar but more depleted than those calculated in the urban area of Heidelberg, Germany (summer: −25.0‰, winter: −32.5‰) [ Vardag et al , ]. In general, the seasonal pattern and amplitude of δ source at Salt Lake City reflect the dominance of natural gas combustion for heating in winter and transportation in summer [ Pataki et al , ].…”

Interpreting the <sup>13</sup>C ∕ <sup>12</sup>C ratio of carbon dioxide in an urban airshed in the Yangtze River Delta, China

“…S2). To determine whether a shorter timescale would improve the result, we applied the 5 h moving window technique described by Vardag et al (2016) to the observations made in January and July 2014. Only 4 % of the data, all obtained at nighttime, satisfy their data screening criteria.…”

“…The uncertainty in δ P was assumed to be ±1.00 ‰ (Verdag et al, 2016). The mass flux terms F F and F C were assumed to have a 10 % uncertainty, which is typical of fossil fuel consumption data (Vardag et al, 2016).…”

“…The cement isotopic ratio is based on Andres et al (1994). The approximate range in the biological isotope ratio is based on Vardag et al (2016).…”

20th century changes in carbon isotopes and water-use efficiency: tree-ring-based evaluation of the CLM4.5 and LPX-Bern models

“…The δ 13 C data representing atmospheric air are used to quantify the global ocean and land carbon sources and sinks (Keeling et al, 1989;Joos and Bruno, 1998;Trudinger et al, 2002;Bauska et al, 2015). Furthermore, δ 13 C observations allow identification of the imprint of fossil-fuel carbon in atmospheric air to quantify regional-tolocal-scale land carbon sources and sinks (Torn et al, 2011;Vardag et al, 2016), or to evaluate air-sea transfer velocity parameterizations (Krakauer et al, 2006). The δ 13 C data from the modern ocean are applied to infer the oceanic uptake of anthropogenic carbon (Heimann and Maier-Reimer, 1996;Gruber et al, 1999;Sonnerup and Quay, 2012;Becker et al, 2016), while paleoproxy δ 13 C data from ocean sediments and ice cores permit inference of land carbon changes between the last glacial maximum and the current warm period (Shackleton, 1977;Ciais et al, 2012;Peterson et al, 2014).…”