GSA Connects 2021 in Portland, Oregon

Paper No. 87-7
Presentation Time: 9:00 AM-1:00 PM


WREN, Olivia, Earth and Ocean Sciences, Unversity of Victoria, Victoria, BC V8P 5C2,, Canada and HUSSON, Jon M., School of Earth and Ocean Sciences, University of Victoria, Victoria, BC V8P5C2, Canada

The Neoproterozoic carbon isotope record is characterized by large variations in δ13C values measured from carbonate rocks (δ13Ccarb), ranging from +10‰ to -15‰. The origin of these excursions cannot be explained by the traditional steady state carbon cycle model, which is often used to interpret such records. Foundational assumptions in this carbon cycle model include (1) δ13Ccarb values accurately reflect marine dissolved inorganic carbon δ13C13CDIC), (2) the major carbon input to the ocean is delivered by rivers, and (3) the δ13C value from riverine input (δ13Criv) is equal to the average mantle δ13C value (δ13Cmantle = -5‰). The validity of ancient carbon cycling and paleoclimate interpretations depend on the accuracy of these assumptions. The third assumption - that δ13Criv is equal to δ13Cmantle - has not been tested in the modern. This study aims to quantify modern δ13CDIC variability in rivers and describe the dominant controls on the observed variability. Data compilation from 140 papers yielded 3,990 δ13CDIC values from 445 rivers around the globe. These values of δ13CDIC have a flux-weighted mean of -9.3‰ and an enormous range from -27.5‰ to +7.0‰. We consider what controls this range, including river catchment bedrock lithology (e.g., the relative abundances of carbonate, siliciclastic and igneous lithologies) inferred from geological maps compiled in the Macrostrat database. Better understanding the large-scale controls on fluvial δ13CDIC variability may be especially important for interpreting δ13Ccarb values from Neoproterozoic shallow water carbonates, as these often form in marginal marine and mixed carbonate-siliciclastic systems, where water column DIC values may be more sensitive to local river input.
  • GSA_Poster_WREN.pdf (43.1 MB)