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Global terrestrial models currently predict that the Amazon rainforest will continue to act as a carbon sink in the future, primarily owing to the rising atmospheric carbon dioxide (CO2) concentration. Soil phosphorus impoverishment in parts of the Amazon basin largely controls its functioning, but the role of phosphorus availability has not been considered in global model ensembles—for example, during the Fifth Climate Model Intercomparison Project. Here we simulate the planned free-air CO2 enrichment experiment AmazonFACE with an ensemble of 14 terrestrial ecosystem models. We show that phosphorus availability reduces the projected CO2-induced biomass carbon growth by about 50% to 79 ± 63 g C m−2 yr−1 over 15 years compared to estimates from carbon and carbon–nitrogen models. Our results suggest that the resilience of the region to climate change may be much less than previously assumed. Variation in the biomass carbon response among the phosphorus-enabled models is considerable, ranging from 5 to 140 g C m−2 yr−1, owing to the contrasting plant phosphorus use and acquisition strategies considered among the models. The Amazon forest response thus depends on the interactions and relative contributions of the phosphorus acquisition and use strategies across individuals, and to what extent these processes can be upregulated under elevated CO2.

Enhanced silicate rock weathering (ERW) is an emerging strategy for carbon dioxide removal (CDR) from the atmosphere to mitigate anthropogenic climate change. ERW aims at promoting soil inorganic carbon sequestration by accelerating geochemical weathering processes. Theoretically, ERW may also impact soil organic carbon (SOC), the largest carbon pool in terrestrial ecosystems, but experimental evidence for this is largely lacking. Here, we conducted a 2‐year field experiment in tropical rubber plantations in the southeast of China to evaluate the effects of wollastonite powder additions (0, 0.25, and 0.5 kg m−2) on both soil organic and inorganic carbon at 0–10 cm depth. We found that ERW significantly increased the concentration of SOC and HCO3−, but the increases in SOC were four and eight times higher than that of HCO3− with low‐ and high‐level wollastonite applications. ERW had positive effects on the accrual of organic carbon in mineral‐associated organic matter (MAOM) and macroaggregate fractions, but not on particulate organic matter. Path analysis suggested that ERW increased MAOM mainly by increasing the release of Ca, Si, and Fe, and to a lesser extent by stimulating root growth and microbial‐derived carbon inputs. Our study indicates that ERW with wollastonite can promote SOC sequestration in stable MOAM in surface soils through both the soil mineral carbon pump and microbial carbon pump. These effects may have been larger than the inorganic CDR during our experiment. We argue it is essential to account for the responses of SOC in the assessments of CDR by ERW. [ABSTRACT FROM AUTHOR]

Biochar (BC) application to croplands aims to sequester carbon and improve soil quality, but its impact on soil organic carbon (SOC) dynamics is not represented in most land models used for assessing land-based climate change mitigation; therefore, we are unable to quantify the effects of biochar application under different climate or land management conditions. Here, to fill this gap, we implement a submodel to represent biochar in a microbial decomposition model named MIMICS (MIcrobial-MIneral Carbon Stabilization). We first calibrate and validate MIMICS with new representations of the density-dependent microbial turnover rate, adsorption of available organic carbon on mineral soil particles, and soil moisture effects on decomposition using global field-measured cropland SOC at 285 sites. We further integrate biochar in MIMICS by accounting for its effect on microbial decomposition and SOC sorption/desorption and optimize two biochar-related parameters in these processes using 134 paired SOC measurements with and without biochar addition. The MIMICS-biochar version can generally reproduce the short-term (≤ 6 years) and long-term (8 years) SOC changes after adding (mean addition rate of 25.6 t ha -1) biochar (R2= 0.79 and 0.97, respectively) with a low root-mean-square error (RMSE = 3.73 and 6.08 g kg -1 , respectively). Our study incorporates sorption and soil moisture processes into MIMICS and extends its capacity to simulate biochar decomposition, providing a useful tool to couple with dynamic land models to evaluate the effectiveness of biochar application with respect to removing CO 2 from the atmosphere. [ABSTRACT FROM AUTHOR]

Global change ecology nowadays embraces ever‐growing large observational datasets (big‐data) and complex mathematical models that track hundreds of ecological processes (big‐model). The rapid advancement of the big‐data‐big‐model has reached its bottleneck: high computational requirements prevent further development of models that need to be integrated over long time‐scales to simulate the distribution of ecosystems carbon and nutrient pools and fluxes. Here, we introduce a machine‐learning acceleration (MLA) tool to tackle this grand challenge. We focus on the most resource‐consuming step in terrestrial biosphere models (TBMs): the equilibration of biogeochemical cycles (spin‐up), a prerequisite that can take up to 98% of the computational time. Through three members of the ORCHIDEE TBM family part of the IPSL Earth System Model, including versions that describe the complex interactions between nitrogen, phosphorus and carbon that do not have any analytical solution for the spin‐up, we show that an unoptimized MLA reduced the computation demand by 77%–80% for global studies via interpolating the equilibrated state of biogeochemical variables for a subset of model pixels. Despite small biases in the MLA‐derived equilibrium, the resulting impact on the predicted regional carbon balance over recent decades is minor. We expect a one‐order of magnitude lower computation demand by optimizing the choices of machine learning algorithms, their settings, and balancing the trade‐off between quality of MLA predictions and need for TBM simulations for training data generation and bias reduction. Our tool is agnostic to gridded models (beyond TBMs), compatible with existing spin‐up acceleration procedures, and opens the door to a wide variety of future applications, with complex non‐linear models benefit most from the computational efficiency. [ABSTRACT FROM AUTHOR]

Summary: Spatial redistribution of nutrients by atmospheric transport and deposition could theoretically act as a continental‐scale mechanism which counteracts declines in soil fertility caused by nutrient lock‐up in accumulating biomass in tropical forests in Central Africa. However, to what extent it affects carbon sinks in forests remains elusive.Here we use a terrestrial biosphere model to quantify the impact of changes in atmospheric nitrogen and phosphorus deposition on plant nutrition and biomass carbon sink at a typical lowland forest site in Central Africa.We find that the increase in nutrient deposition since the 1980s could have contributed to the carbon sink over the past four decades up to an extent which is similar to that from the combined effects of increasing atmospheric carbon dioxide and climate change. Furthermore, we find that the modelled carbon sink responds to changes in phosphorus deposition, but less so to nitrogen deposition.The pronounced response of ecosystem productivity to changes in nutrient deposition illustrates a potential mechanism that could control carbon sinks in Central Africa. Monitoring the quantity and quality of nutrient deposition is needed in this region, given the changes in nutrient deposition due to human land use. [ABSTRACT FROM AUTHOR]

Most phosphorus (P) in soils is unavailable for direct biological uptake as it is locked within primary or secondary mineral particles, adsorbed to mineral surfaces, or immobilized inside of organic material. Deciphering the composition of different P pools in soil is critical for understanding P bioavailability and its underlying dynamics. However, widely used global estimates of different soil P pools are based on a dataset containing few measurements in which many regions or soil types are unrepresented. This poses a major source of uncertainty in assessments that rely on these estimates to quantify soil P constraints on biological activity controlling global food production and terrestrial carbon balance. To address this issue, we consolidated a database of six major soil P pools containing 1857 entries from globally distributed (semi-)natural soils and 11 related environmental variables. The P pools (labile inorganic P (Pi), labile organic P (Po), moderately labile Pi, moderately labile Po, primary mineral P, and occluded P) were measured using a sequential P fractionation method. Using the database, we trained random forest regression models for each of the P pools and captured observed variation with R2 higher than 60%. We identified total soil P concentration as the most important predictor of all soil P pool concentrations, except for primary mineral P concentration, which is primarily controlled by soil pH. When expressed in relative concentrations (i.e., as a proportion of total P), the model showed that soil pH is the most important predictor for proportions of all soil P pools, except for labile Pi proportion, which is primarily controlled by soil depth. Using the trained random forest models, we predicted soil P pools’ distributions in natural systems at a resolution of 0.5° × 0.5°. Our global maps of different P pools in soils as well as the pools’ underlying drivers can inform assessments of the role of natural P availability for ecosystem productivity, climate change mitigation, and the functioning of the Earth system. [ABSTRACT FROM AUTHOR]