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Regrowing natural forests is a prominent natural climate solution, but accurate assessments of its potential are limited by uncertainty and variability around carbon accumulation rates. To assess why and where rates differ, we compiled 13,112 georeferenced measurements of carbon accumulation. Climate explained variation in rates better than land use history, so we combined field data with 66 environmental covariate layers to create a global, 1-km resolution map of potential aboveground carbon accumulation rates for the first 30 years of forest regrowth. Our results indicate that on average default forest regrowth rates from the Intergovernmental Panel on Climate Change are underestimated by 32% and miss 8-fold variation within ecozones.Conversely, we conclude that previously reported maximum climate mitigation potential from natural forest regrowth is overestimated by 11% due to the use of overly high rates. Our results therefore provide a much needed and globally consistent method for assessing natural forest regrowth as a climate mitigation strategy. BackgroundTo constrain global warming, we must reduce emissions and capture excess carbon dioxide (CO2) in the atmosphere 1,2 . Restoring forest cover, defined here as the transition from < 25% tree cover to > 25% tree cover where forests historically occurred, is a promising option for additional carbon capture 3 and has been prioritized in many national and international goals 4,5 . It is deployable, scalable, and provides important biodiversity and ecosystem services 6 . Yet the magnitude and distribution of climate mitigation opportunity from restoring forest cover is poorly described, with large confidence intervals around estimates 2,3 . To evaluate the appropriateness of forest cover restoration for climate mitigation, compared to the multitude of other potential climate mitigation actions, countries, corporations, and multilateral entities need more accurate assessments of its potential 7 .Mitigation potential from restoring forest cover (reported here in terms of MgCO2 yr -1 ) is determined by the potential extent and location of new forest ("area of opportunity") and the rate at which those forests remove atmospheric CO2 (reported here in terms of MgC ha -1 yr -1 ). While there are now multiple estimates of area of opportunity based on diverse and often heavily debated criteria (e.g., references 3,8-11 ), we lack spatially explicit and globally comprehensive estimates of accumulation rates. This is especially true for natural forest regrowth, defined here as the recovery of forest cover on deforested lands through spontaneous regrowth after cessation of prior disturbance or land use. Many countries do not have nationally specific forest carbon accumulation rates and instead rely on default rates from the Intergovernmental Panel on Climate Change (IPCC) 12,13 . Although these rates were recently updated 8,12 , they nonetheless represent coarse estimates based on continent and ecological zone, and do not account for finer scale variation in rates due to mor...

Summary Simulation models of net mineralization of nitrogen (N) in soil need to be able to incorporate the effect of soil water. Our objective was to identify and define the best way of expressing soil water and its effect on net mineralization across a range of soil types. We collated data from 12 laboratory incubation studies, including a total of 33 different soils, where rates of net mineralization of N were determined from the net accumulation of mineral N under a range of water contents at near‐optimal temperatures. Measurements of water potential and limits of water content observed in the field were available for most of these soils. The percentage of pore space filled with water was estimated from measurements of soil bulk density. We found that relative water content, particularly when expressed relative to an upper and lower limit of water content observed in the field, was the best descriptor for net mineralization. The next best descriptions were soil water potential, water content relative to the optimal water content for mineralization, and percentage of pore space filled with water, with water content alone being poor. Although various functions may be used to describe the relation between relative water content and net mineralization of N, an equation for a sigmoidal curve provided the best fit, and explained 78% of the variation.

The nature of the climate–carbon cycle feedback depends critically on the response of soil carbon to climate, including changes in moisture. However, soil moisture–carbon feedback responses have not been investigated thoroughly. Uncertainty in the response of soil carbon to soil moisture changes could arise from uncertainty in the relationship between soil moisture and heterotrophic respiration. We used twelve soil moisture–respiration functions (SMRFs) with a soil carbon model (RothC) and data from a coupled climate–carbon cycle general circulation model to investigate the impact of direct heterotrophic respiration dependence on soil moisture on the climate–carbon cycle feedback. Global changes in soil moisture acted to oppose temperature‐driven decreases in soil carbon and hence tended to increase soil carbon storage. We found considerable uncertainty in soil carbon changes due to the response of soil respiration to soil moisture. The use of different SMRFs resulted in both large losses and small gains in future global soil carbon stocks, whether considering all climate forcings or only moisture changes. Regionally, the greatest range in soil carbon changes across SMRFs was found where the largest soil carbon changes occurred. Further research is needed to constrain the soil moisture–respiration relationship and thus reduce uncertainty in climate–carbon cycle feedbacks. There may also be considerable uncertainty in the regional responses of soil carbon to soil moisture changes since climate model predictions of regional soil moisture changes are less coherent than temperature changes.