Soil organic carbon (SOC) information is fundamental for improving global carbon cycle modeling efforts, but discrepancies exist from country‐to‐global scales. We predicted the spatial distribution of SOC stocks (topsoil; 0–30 cm) and quantified modeling uncertainty across Mexico and the conterminous United States (CONUS). We used a multisource SOC dataset (>10 000 pedons, between 1991 and 2010) coupled with a simulated annealing regression framework that accounts for variable selection. Our model explained ~50% of SOC spatial variability (across 250‐m grids). We analyzed model variance, and the residual variance of six conventional pedotransfer functions for estimating bulk density to calculate SOC stocks. Two independent datasets confirmed that the SOC stock for both countries represents between 46 and 47 Pg with a total modeling variance of ±12 Pg. We report a residual variance of 10.4 ±5.1 Pg of SOC stocks calculated from six pedotransfer functions for soil bulk density. When reducing training data to define decades with relatively higher density of observations (1991–2000 and 2001–2010, respectively), model variance for predicted SOC stocks ranged between 41 and 55 Pg. We found nearly 42% of SOC across Mexico in forests and 24% in croplands, whereas 31% was found in forests and 28% in croplands across CONUS. Grasslands and shrublands stored 29 and 35% of SOC across Mexico and CONUS, respectively. We predicted SOC stocks >30% below recent global estimates that do not account for uncertainty and are based on legacy data. Our results provide insights for interpretation of estimates based on SOC legacy data and benchmarks for improving regional‐to‐global monitoring efforts.

International audienceAbstractKey messageKnowing the uncertainty for biomass equations is critical for their use and error propagation of biomass estimates. Presented here is a method to estimate uncertainty for equations where only n and R2 values from the original equations are available.ContextTree allometric equations form the basis of research and assessments of forest biomass. Frequently, uncertainty estimations do not propagate errors from these equations since the necessary information about sampling and tree measurements is not included in the original publication. Many biomass studies were conducted decades ago and the original, raw data is unavailable.AimsBecause of this information deficiency, and to improve error estimates in applications, a system to estimate the error structures of such equations is presented.MethodsA pseudo-data approach involving the creation of possible (pseudo) data using only R2 and n with a simple Monte-Carlo process generates probable error structures that can be used to propagate errors.ResultsIn a test of five different species with varying n input data and population variability, the original error structures were successfully recreated.ConclusionThis method of creating pseudo-data is simple and extensible and requires commonly published information about the original dataset. The method can be employed to create new ecosystem-level equations from species-specific equations, implemented in systems to select allometric equations to reduce uncertainty, and aid in the design of large-scale campaigns to generate new allometric equations for improving local to national scale estimates of forest biomass. The R code will be made freely available to anyone upon request to the authors

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