Climate control on terrestrial biospheric carbon turnover

Abstract

Terrestrial vegetation and soils hold three times more carbon than the atmosphere. Much debate concerns how anthropogenic activity will perturb these surface reservoirs, potentially exacerbating ongoing changes to the climate system. Uncertainties specifically persist in extrapolating point-source observations to ecosystem-scale budgets and fluxes, which require consideration of vertical and lateral processes on multiple temporal and spatial scales. To explore controls on organic carbon (OC) turnover at the river basin scale, we present radiocarbon (14C) ages on two groups of molecular tracers of plant-derived carbon—leaf-wax lipids and lignin phenols—from a globally distributed suite of rivers. We find significant negative relationships between the 14C age of these biomarkers and mean annual temperature and precipitation. Moreover, riverine biospheric-carbon ages scale proportionally with basin-wide soil carbon turnover times and soil 14C ages, implicating OC cycling within soils as a primary control on exported biomarker ages and revealing a broad distribution of soil OC reactivities. The ubiquitous occurrence of a long-lived soil OC pool suggests soil OC is globally vulnerable to perturbations by future temperature and precipitation increase. Scaling of riverine biospheric-carbon ages with soil OC turnover shows the former can constrain the sensitivity of carbon dynamics to environmental controls on broad spatial scales. Extracting this information from fluvially dominated sedimentary sequences may inform past variations in soil OC turnover in response to anthropogenic and/or climate perturbations. In turn, monitoring riverine OC composition may help detect future climate-change–induced perturbations of soil OC turnover and stocks.

Soil carbon persistence governed by plant input and mineral protection at regional and global scales

“…To understand potential drivers of 14 C-based soil C persistence, previous studies have proposed a series of factors that contribute to SOM stabilisation or destabilisation (Trumbore 2009). Of them, climate is generally regarded as an important control (Davidson & Janssens 2006;Mathieu et al 2015;Eglinton et al 2021), for example, freezing temperature and flooding conditions facilitate the long-term SOM preservation (Davidson & Janssens 2006;Trumbore 2009). Besides climatic regulation, SOM quality could modulate soil C dynamics by selective preservation due to the intrinsic chemical recalcitrance of SOM (Mikutta et al 2006;Craine et al 2010), and mineral protection could inhibit SOM decomposition through forming mineral-organic associations (Torn et al 1997;Hemingway et al 2019).…”

The role of soil in regulation of climate

“…They comprise both soil organic carbon (SOC) and soil inorganic carbon (SIC), and are an important component of the global C cycle (figure 1). Estimated to 1 m depth, terrestrial soil (2500 PgC; 1 PgC = petagram of carbon = 1 billion metric tons of carbon) and vegetation (620 PgC) hold three times more C than that in the atmosphere (880 PgC) [7]. However, estimates of soil C stocks are variable, depending on the methods used [8] (table 1).…”

Recent Warming Fuels Increased Organic Carbon Export From Arctic Permafrost

“…For instance, the Kolyma River, within the continuous permafrost zone of eastern Siberia, features particulate OC (POC) with an extremely high pre‐aged/modern ratio, signaling the dominance of permafrost OC export throughout its watershed (Wild et al., 2019). For Colville River, the radiocarbon ages of bulk OC from suspended particles collected at the river mouth (6,773 years; Eglinton et al., 2021) and that from river bed sediments collected in the main channel (10,000 y BP; Zhang et al., 2017) imply mobilization of permafrost via the fluvial system. As stated before, this site is dominated by riverine inputs based on δ 13 C and biomarker results compared to other sites in the Colville delta and Simpson Lagoon region (Schreiner et al., 2013; Zhang et al., 2015).…”

“…The uncertainty ( σ Z ) associated with the pre‐depositional age ( Z ), therefore, propagates from those associated with X and Y , summing in quadrature to give: σ Z = √( σ X 2 + σ Y 2 ). For LCFAs, pre‐depositional mean age was used as the 14 C composition actually reflects the weighted mean of the mixed fatty acids inventory of a given sample (Eglinton et al., 2021).…”

“…). For LCFAs, pre-depositional mean age was used as the 14 C composition actually reflects the weighted mean of the mixed fatty acids inventory of a given sample (Eglinton et al, 2021).…”