Evaluation of 4 years of continuous <i>δ</i>¹³C(CO₂) data using a moving Keeling plot method

Abstract

Different carbon dioxide (CO2) emitters can be distinguished by their carbon isotope ratios. Therefore measurements of atmospheric δ13C(CO2) and CO2 concentration contain information on the CO2 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 δ13C(CO2) and CO2 have become available, opening the door to the quantification of CO2 shares from different sources at high temporal resolution. Here, we present a method to compute the CO2 source signature (δS) 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 δS. 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 CO2 and δ13C(CO2) measured in Heidelberg, Germany, yields a distinct seasonal cycle of δS. 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, δbio, and the fuel mix, δF – are known. From the mean source signature record in 2012, δbio could be reliably estimated only for summer to (−25.0 ± 1.0) ‰ and δF only for winter to (−32.5 ± 2.5) ‰. As the isotopic end members δbio and δF 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.