Carbon cycle confidence and uncertainty: Exploring variation among soil biogeochemical models

Abstract

Emerging insights into factors responsible for soil organic matter stabilization and decomposition are being applied in a variety of contexts, but new tools are needed to facilitate the understanding, evaluation, and improvement of soil biogeochemical theory and models at regional to global scales. To isolate the effects of model structural uncertainty on the global distribution of soil carbon stocks and turnover times we developed a soil biogeochemical testbed that forces three different soil models with consistent climate and plant productivity inputs. The models tested here include a first-order, microbial implicit approach (CASA-CNP), and two recently developed microbially explicit models that can be run at global scales (MIMICS and CORPSE). When forced with common environmental drivers, the soil models generated similar estimates of initial soil carbon stocks (roughly 1,400 Pg C globally, 0-100 cm), but each model shows a different functional relationship between mean annual temperature and inferred turnover times. Subsequently, the models made divergent projections about the fate of these soil carbon stocks over the 20 century, with models either gaining or losing over 20 Pg C globally between 1901 and 2010. Single-forcing experiments with changed inputs, temperature, and moisture suggest that uncertainty associated with freeze-thaw processes as well as soil textural effects on soil carbon stabilization were larger than direct temperature uncertainties among models. Finally, the models generated distinct projections about the timing and magnitude of seasonal heterotrophic respiration rates, again reflecting structural uncertainties that were related to environmental sensitivities and assumptions about physicochemical stabilization of soil organic matter. By providing a computationally tractable and numerically consistent framework to evaluate models we aim to better understand uncertainties among models and generate insights about factors regulating the turnover of soil organic matter.

Spatial and temporal turnover of soil microbial communities is not linked to function in a primary tropical forest

“…Earth system and ecosystem models that incorporate microbial composition (Sulman et al , ), dormancy (Wang et al ), functional genes and traits (Yao et al ), and physiology (Wang et al , Wieder et al ) more accurately predict soil C storage and fluxes than those that do not (Wieder et al ). Although overall model accuracy is still low, incorporating spatial variation in microbial functional groups (Sulman et al ) improves model fit.…”

Model Structure and Climate Data Uncertainty in Historical Simulations of the Terrestrial Carbon Cycle (1850–2014)

“…Structural differences among the terrestrial biosphere models used in Earth system models produce divergent carbon cycle simulations (Huntzinger et al, 2013(Huntzinger et al, , 2017, and many studies have focused on structural error resulting from incomplete understanding of how to appropriately represent ecosystem processes and their responses to environmental change. For example, studies have examined uncertainty in terms of particular process parameterizations such as ecosystem nitrogen losses (Meyerholt & Zaehle, 2018; Thomas et al, 2013), biological nitrogen fixation (Wieder et al, 2015), soil organic matter decomposition (Koven et al, 2015;Wieder et al, 2018), photosynthetic temperature acclimation (Lombardozzi et al, 2015), and photosynthetic triose phosphate utilization (Lombardozzi et al, 2018). Structural uncertainties are particularly evident in determining simulated responses to elevated concentrations of atmosphere CO 2 (Huntzinger et al, 2017;Wieder et al, 2019;Zaehle et al, 2014).…”

Leveraging Environmental Research and Observation Networks to Advance Soil Carbon Science

“…Moreover, because all of these networks include a long‐term monitoring component, they have the potential to shed light on changing SOM dynamics over time with variations in climate, disturbance, and other ecosystem changes (Melillo et al, ). This remains a key uncertainty in biogeochemical model projections (Sulman et al, ; Tian et al, ; Todd‐Brown et al, ; Wieder et al, ). The networks can also facilitate an improved understanding and predictive capacity of SOM heterogeneity within sites or watersheds, where broad‐scale state factor variation intersects with local‐scale variation in soil, organisms, and land surface properties (Bradford et al, ).…”

“…For instance, where available, radiocarbon data can be used to constrain the age of pools and fluxes of SOM (He et al, ), while isotope tracers can shed light on the partitioning of new C inputs into free versus protected fractions (Cotrufo et al, ). Such data sets would be very useful in evaluating structural uncertainty among different SOM models (Sierra et al, ; Sulman et al, ; Wieder et al, ). As such, network SOM data must be discoverable in order to maximize utility beyond the initial data collection effort.…”