Diagnosing destabilization risk in global land carbon sinks.

Global net land carbon uptake or net biome production (NBP) has increased during recent decades1. Whether its temporal variability and autocorrelation have changed during this period, however, remains elusive, even though an increase in both could indicate an increased potential for a destabilized carbon sink2,3. Here, we investigate the trends and controls of net terrestrial carbon uptake and its temporal variability and autocorrelation from 1981 to 2018 using two atmospheric-inversion models, the amplitude of the seasonal cycle of atmospheric CO2 concentration derived from nine monitoring stations distributed across the Pacific Ocean and dynamic global vegetation models. We find that annual NBP and its interdecadal variability increased globally whereas temporal autocorrelation decreased. We observe a separation of regions characterized by increasingly variable NBP, associated with warm regions and increasingly variable temperatures, lower and weaker positive trends in NBP and regions where NBP became stronger and less variable. Plant species richness presented a concave-down parabolic spatial relationship with NBP and its variability at the global scale whereas nitrogen deposition generally increased NBP. Increasing temperature and its increasing variability appear as the most important drivers of declining and increasingly variable NBP. Our results show increasing variability of NBP regionally that can be mostly attributed to climate change and that may point to destabilization of the coupled carbon-climate system.

Enhanced rock weathering increased soil phosphorus availability and altered root phosphorus-acquisition strategies.

Enhanced rock weathering (ERW) has been proposed as a measure to enhance the carbon (C)-sequestration potential and fertility of soils. The effects of this practice on the soil phosphorus (P) pools and the general mechanisms affecting microbial P cycling, as well as plant P uptake are not well understood. Here, the impact of ERW on soil P availability and microbial P cycling functional groups and root P-acquisition traits were explored through a 2-year wollastonite field addition experiment in a tropical rubber plantation. The results show that ERW significantly increased soil microbial carbon-use efficiency and total P concentrations and indirectly increased soil P availability by enhancing organic P mobilization and mineralization of rhizosheath carboxylates and phosphatase, respectively. Also, ERW stimulated the activities of P-solubilizing (gcd, ppa and ppx) and mineralizing enzymes (phoADN and phnAPHLFXIM), thus contributing to the inorganic P solubilization and organic P mineralization. Accompanying the increase in soil P availability, the P-acquisition strategy of the rubber fine roots changed from do-it-yourself acquisition by roots to dependence on mycorrhizal collaboration and the release of root exudates. In addition, the direct effects of ERW on root P-acquisition traits (such as root diameter, specific root length, and mycorrhizal colonization rate) may also be related to changes in the pattern of belowground carbon investments in plants. Our study provides a new insight that ERW increases carbon-sequestration potential and P availability in tropical forests and profoundly affects belowground plant resource-use strategies.

Atmospheric phosphorus deposition amplifies carbon sinks in simulations of a tropical forest in Central Africa.

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.

Machine learning for accelerating process-based computation of land biogeochemical cycles.

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.

Global maps and factors driving forest foliar elemental composition: the importance of evolutionary history.

Consistent information on the current elemental composition of vegetation at global scale and the variables that determine it is lacking. To fill this gap, we gathered a total of 30 912 georeferenced records on woody plants foliar concentrations of nitrogen (N), phosphorus (P) and potassium (K) from published databases, and produced global maps of foliar N, P and K concentrations for woody plants using neural networks at a resolution of 1 km2 . We used data for climate, atmospheric deposition, soil and morphoclimatic groups to train the neural networks. Foliar N, P and K do not follow clear global latitudinal patterns but are consistent with the hypothesis of soil substrate age. We additionally built generalized linear mixed models to investigate the evolutionary history effect together with the effects of environmental effects. In this comparison, evolutionary history effects explained most of the variability in all cases (mostly > 60%). These results emphasize the determinant role of evolutionary history in foliar elemental composition, which should be incorporated in upcoming dynamic global vegetation models.

Natural forests promote phosphorus retention in soil.

Soil phosphorus (P) availability often limits plant productivity. Classical theories suggest that total P content declines at the temporal scale of pedogenesis, and ecosystems develop toward the efficient use of scarce P during succession. However, the trajectory of ecosystem P within shorter time scales of succession remains unclear. We analyzed changes to P pools at the early (I), middle (II), and late (III) stages of growth of plantation forests (PFs) and the successional stages of natural forests (NFs) at 1969 sites in China. We found significantly lower P contents at later growth stages compared to earlier ones in the PF (p < .05), but higher contents at late successional stages than in earlier stages in the NF (p < .05). Our results indicate that increasing P demand of natural vegetation during succession, may raise, retain, and accumulate P from deeper soil layers. In contrast, ecosystem P in PF was depleted by the more rapidly increasing demand outpacing the development of a P-efficient system. We advocate for more studies to illuminate the mechanisms for determining the divergent changes, which would improve forest management and avoid the vast degradation of PF ecosystems suffering from the ongoing depletion of P.

Is the climate change mitigation effect of enhanced silicate weathering governed by biological processes?

A number of negative emission technologies (NETs) have been proposed to actively remove CO2 from the atmosphere, with enhanced silicate weathering (ESW) as a relatively new NET with considerable climate change mitigation potential. Models calibrated to ESW rates in lab experiments estimate the global potential for inorganic carbon sequestration by ESW at about 0.5-5 Gt CO2  year-1 , suggesting ESW could be an important component of the future NETs mix. In real soils, however, weathering rates may differ strongly from lab conditions. Research on natural weathering has shown that biota such as plants, microbes, and macro-invertebrates can strongly affect weathering rates, but biotic effects were excluded from most ESW lab assessments. Moreover, ESW may alter soil organic carbon sequestration and greenhouse gas emissions by influencing physicochemical and biological processes, which holds the potential to perpetuate even larger negative emissions. Here, we argue that it is likely that the climate change mitigation effect of ESW will be governed by biological processes, emphasizing the need to put these processes on the agenda of this emerging research field.

Toward a Global Model for Soil Inorganic Phosphorus Dynamics: Dependence of Exchange Kinetics and Soil Bioavailability on Soil Physicochemical Properties.

The representation of phosphorus (P) cycling in global land models remains quite simplistic, particularly on soil inorganic phosphorus. For example, sorption and desorption remain unresolved and their dependence on soil physical and chemical properties is ignored. Empirical parameter values are usually based on expert knowledge or data from few sites with debatable global representativeness in most global land models. To overcome these issues, we compiled from data of inorganic soil P fractions and calculated the fraction of added P remaining in soil solution over time of 147 soil samples to optimize three parameters in a model of soil inorganic P dynamics. The calibrated model performed well (r 2 > 0.7 for 122 soil samples). Model parameters vary by several orders of magnitude, and correlate with soil P fractions of different inorganic pools, soil organic carbon and oxalate extractable metal oxide concentrations among the soil samples. The modeled bioavailability of soil P depends on, not only, the desorption rates of labile and sorbed pool, inorganic phosphorus fractions, the slope of P sorbed against solution P concentration, but also on the ability of biological uptake to deplete solution P concentration and the time scale. The model together with the empirical relationships of model parameters on soil properties can be used to quantify bioavailability of soil inorganic P on various timescale especially when coupled within global land models.

Greening drylands despite warming consistent with carbon dioxide fertilization effect.

The rising atmospheric CO2 concentration leads to a CO2 fertilization effect on plants-that is, increased photosynthetic uptake of CO2 by leaves and enhanced water-use efficiency (WUE). Yet, the resulting net impact of CO2 fertilization on plant growth and soil moisture (SM) savings at large scale is poorly understood. Drylands provide a natural experimental setting to detect the CO2 fertilization effect on plant growth since foliage amount, plant water-use and photosynthesis are all tightly coupled in water-limited ecosystems. A long-term change in the response of leaf area index (LAI, a measure of foliage amount) to changes in SM is likely to stem from changing water demand of primary productivity in water-limited ecosystems and is a proxy for changes in WUE. Using 34-year satellite observations of LAI and SM over tropical and subtropical drylands, we identify that a 1% increment in SM leads to 0.15% (±0.008, 95% confidence interval) and 0.51% (±0.01, 95% confidence interval) increments in LAI during 1982-1998 and 1999-2015, respectively. The increasing response of LAI to SM has contributed 7.2% (±3.0%, 95% confidence interval) to total dryland greening during 1999-2015 compared to 1982-1998. The increasing response of LAI to SM is consistent with the CO2 fertilization effect on WUE in water-limited ecosystems, indicating that a given amount of SM has sustained greater amounts of photosynthetic foliage over time. The LAI responses to changes in SM from seven dynamic global vegetation models are not always consistent with observations, highlighting the need for improved process knowledge of terrestrial ecosystem responses to rising atmospheric CO2 concentration.

Bioenergy Crops for Low Warming Targets Require Half of the Present Agricultural Fertilizer Use.

Bioenergy with carbon capture and storage (BECCS) is a key option for removing CO2 from the atmosphere over time to achieve climate mitigation. However, an overlooked impact of BECCS is the amount of nutrients required to sustain the production. Here, we use an observation-driven approach to estimate the future bioenergy biomass production for land-use scenarios maximizing BECCS and the pertaining nutrient requirements. The projected global biomass production during the 21st century is comparable to the CO2 removal target for 2 °C warming scenarios. However, 9-19% of this future production hinges on agrotechnology improvement, which remains uncertain. Additional nutrients from fertilizers, corresponding to 56.8 ± 6.1% of the present-day agricultural fertilizer, will be needed to replenish the nutrients removed in harvested biomass at the end of the century, resulting in additional costs and greenhouse gas emissions. Our study reveals the nutrient challenges associated with BECCS and calls for additional management efforts to grow bioenergy crops in a sustainable way.

Comparison of forest above-ground biomass from dynamic global vegetation models with spatially explicit remotely sensed observation-based estimates.

Gaps in our current understanding and quantification of biomass carbon stocks, particularly in tropics, lead to large uncertainty in future projections of the terrestrial carbon balance. We use the recently published GlobBiomass data set of forest above-ground biomass (AGB) density for the year 2010, obtained from multiple remote sensing and in situ observations at 100 m spatial resolution to evaluate AGB estimated by nine dynamic global vegetation models (DGVMs). The global total forest AGB of the nine DGVMs is 365 ± 66 Pg C, the spread corresponding to the standard deviation between models, compared to 275 Pg C with an uncertainty of ~13.5% from GlobBiomass. Model-data discrepancy in total forest AGB can be attributed to their discrepancies in the AGB density and/or forest area. While DGVMs represent the global spatial gradients of AGB density reasonably well, they only have modest ability to reproduce the regional spatial gradients of AGB density at scales below 1000 km. The 95th percentile of AGB density (AGB95 ) in tropics can be considered as the potential maximum of AGB density which can be reached for a given annual precipitation. GlobBiomass data show local deficits of AGB density compared to the AGB95 , particularly in transitional and/or wet regions in tropics. We hypothesize that local human disturbances cause more AGB density deficits from GlobBiomass than from DGVMs, which rarely represent human disturbances. We then analyse empirical relationships between AGB density deficits and forest cover changes, population density, burned areas and livestock density. Regression analysis indicated that more than 40% of the spatial variance of AGB density deficits in South America and Africa can be explained; in Southeast Asia, these factors explain only ~25%. This result suggests TRENDY v6 DGVMs tend to underestimate biomass loss from diverse and widespread anthropogenic disturbances, and as a result overestimate turnover time in AGB.

Microbial dynamics and soil physicochemical properties explain large-scale variations in soil organic carbon.

First-order organic matter decomposition models are used within most Earth System Models (ESMs) to project future global carbon cycling; these models have been criticized for not accurately representing mechanisms of soil organic carbon (SOC) stabilization and SOC response to climate change. New soil biogeochemical models have been developed, but their evaluation is limited to observations from laboratory incubations or few field experiments. Given the global scope of ESMs, a comprehensive evaluation of such models is essential using in situ observations of a wide range of SOC stocks over large spatial scales before their introduction to ESMs. In this study, we collected a set of in situ observations of SOC, litterfall and soil properties from 206 sites covering different forest and soil types in Europe and China. These data were used to calibrate the model MIMICS (The MIcrobial-MIneral Carbon Stabilization model), which we compared to the widely used first-order model CENTURY. We show that, compared to CENTURY, MIMICS more accurately estimates forest SOC concentrations and the sensitivities of SOC to variation in soil temperature, clay content and litter input. The ratios of microbial biomass to total SOC predicted by MIMICS agree well with independent observations from globally distributed forest sites. By testing different hypotheses regarding (using alternative process representations) the physicochemical constraints on SOC deprotection and microbial turnover in MIMICS, the errors of simulated SOC concentrations across sites were further decreased. We show that MIMICS can resolve the dominant mechanisms of SOC decomposition and stabilization and that it can be a reliable tool for predictions of terrestrial SOC dynamics under future climate change. It also allows us to evaluate at large scale the rapidly evolving understanding of SOC formation and stabilization based on laboratory and limited filed observation.

Global vegetation biomass production efficiency constrained by models and observations.

Plants use only a fraction of their photosynthetically derived carbon for biomass production (BP). The biomass production efficiency (BPE), defined as the ratio of BP to photosynthesis, and its variation across and within vegetation types is poorly understood, which hinders our capacity to accurately estimate carbon turnover times and carbon sinks. Here, we present a new global estimation of BPE obtained by combining field measurements from 113 sites with 14 carbon cycle models. Our best estimate of global BPE is 0.41 ± 0.05, excluding cropland. The largest BPE is found in boreal forests (0.48 ± 0.06) and the lowest in tropical forests (0.40 ± 0.04). Carbon cycle models overestimate BPE, although models with carbon-nitrogen interactions tend to be more realistic. Using observation-based estimates of global photosynthesis, we quantify the global BP of non-cropland ecosystems of 41 ± 6 Pg C/year. This flux is less than net primary production as it does not contain carbon allocated to symbionts, used for exudates or volatile carbon compound emissions to the atmosphere. Our study reveals a positive bias of 24 ± 11% in the model-estimated BP (10 of 14 models). When correcting models for this bias while leaving modeled carbon turnover times unchanged, we found that the global ecosystem carbon storage change during the last century is decreased by 67% (or 58 Pg C).

Rainfall manipulation experiments as simulated by terrestrial biosphere models: Where do we stand?

Changes in rainfall amounts and patterns have been observed and are expected to continue in the near future with potentially significant ecological and societal consequences. Modelling vegetation responses to changes in rainfall is thus crucial to project water and carbon cycles in the future. In this study, we present the results of a new model-data intercomparison project, where we tested the ability of 10 terrestrial biosphere models to reproduce the observed sensitivity of ecosystem productivity to rainfall changes at 10 sites across the globe, in nine of which, rainfall exclusion and/or irrigation experiments had been performed. The key results are as follows: (a) Inter-model variation is generally large and model agreement varies with timescales. In severely water-limited sites, models only agree on the interannual variability of evapotranspiration and to a smaller extent on gross primary productivity. In more mesic sites, model agreement for both water and carbon fluxes is typically higher on fine (daily-monthly) timescales and reduces on longer (seasonal-annual) scales. (b) Models on average overestimate the relationship between ecosystem productivity and mean rainfall amounts across sites (in space) and have a low capacity in reproducing the temporal (interannual) sensitivity of vegetation productivity to annual rainfall at a given site, even though observation uncertainty is comparable to inter-model variability. (c) Most models reproduced the sign of the observed patterns in productivity changes in rainfall manipulation experiments but had a low capacity in reproducing the observed magnitude of productivity changes. Models better reproduced the observed productivity responses due to rainfall exclusion than addition. (d) All models attribute ecosystem productivity changes to the intensity of vegetation stress and peak leaf area, whereas the impact of the change in growing season length is negligible. The relative contribution of the peak leaf area and vegetation stress intensity was highly variable among models.

Global forest carbon uptake due to nitrogen and phosphorus deposition from 1850 to 2100.

Spatial patterns and temporal trends of nitrogen (N) and phosphorus (P) deposition are important for quantifying their impact on forest carbon (C) uptake. In a first step, we modeled historical and future change in the global distributions of the atmospheric deposition of N and P from the dry and wet deposition of aerosols and gases containing N and P. Future projections were compared between two scenarios with contrasting aerosol emissions. Modeled fields of N and P deposition and P concentration were evaluated using globally distributed in situ measurements. N deposition peaked around 1990 in European forests and around 2010 in East Asian forests, and both increased sevenfold relative to 1850. P deposition peaked around 2010 in South Asian forests and increased 3.5-fold relative to 1850. In a second step, we estimated the change in C storage in forests due to the fertilization by deposited N and P (∆Cν dep ), based on the retention of deposited nutrients, their allocation within plants, and C:N and C:P stoichiometry. ∆Cν dep for 1997-2013 was estimated to be 0.27 ± 0.13 Pg C year-1 from N and 0.054 ± 0.10 Pg C year-1 from P, contributing 9% and 2% of the terrestrial C sink, respectively. Sensitivity tests show that uncertainty of ∆Cν dep was larger from P than from N, mainly due to uncertainty in the fraction of deposited P that is fixed by soil. ∆CPdep was exceeded by ∆CNdep over 1960-2007 in a large area of East Asian and West European forests due to a faster growth in N deposition than P. Our results suggest a significant contribution of anthropogenic P deposition to C storage, and additional sources of N are needed to support C storage by P in some Asian tropical forests where the deposition rate increased even faster for P than for N.

Model-data synthesis for the next generation of forest free-air CO2 enrichment (FACE) experiments.

The first generation of forest free-air CO2 enrichment (FACE) experiments has successfully provided deeper understanding about how forests respond to an increasing CO2 concentration in the atmosphere. Located in aggrading stands in the temperate zone, they have provided a strong foundation for testing critical assumptions in terrestrial biosphere models that are being used to project future interactions between forest productivity and the atmosphere, despite the limited inference space of these experiments with regards to the range of global ecosystems. Now, a new generation of FACE experiments in mature forests in different biomes and over a wide range of climate space and biodiversity will significantly expand the inference space. These new experiments are: EucFACE in a mature Eucalyptus stand on highly weathered soil in subtropical Australia; AmazonFACE in a highly diverse, primary rainforest in Brazil; BIFoR-FACE in a 150-yr-old deciduous woodland stand in central England; and SwedFACE proposed in a hemiboreal, Pinus sylvestris stand in Sweden. We now have a unique opportunity to initiate a model-data interaction as an integral part of experimental design and to address a set of cross-site science questions on topics including responses of mature forests; interactions with temperature, water stress, and phosphorus limitation; and the influence of biodiversity.

A global dataset on phosphorus in agricultural soils.

Numerous drivers such as farming practices, erosion, land-use change, and soil biogeochemical background, determine the global spatial distribution of phosphorus (P) in agricultural soils. Here, we revised an approach published earlier (called here GPASOIL-v0), in which several global datasets describing these drivers were combined with a process model for soil P dynamics to reconstruct the past and current distribution of P in cropland and grassland soils. The objective of the present update, called GPASOIL-v1, is to incorporate recent advances in process understanding about soil inorganic P dynamics, in datasets to describe the different drivers, and in regional soil P measurements for benchmarking. We trace the impact of the update on the reconstructed soil P. After the update we estimate a global averaged inorganic labile P of 187 kgP ha-1 for cropland and 91 kgP ha-1 for grassland in 2018 for the top 0-0.3 m soil layer, but these values are sensitive to the mineralization rates chosen for the organic P pools. Uncertainty in the driver estimates lead to coefficients of variation of 0.22 and 0.54 for cropland and grassland, respectively. This work makes the methods for simulating the agricultural soil P maps more transparent and reproducible than previous estimates, and increases the confidence in the new estimates, while the evaluation against regional dataset still suggests rooms for further improvement.

Global critical soil moisture thresholds of plant water stress.

During extensive periods without rain, known as dry-downs, decreasing soil moisture (SM) induces plant water stress at the point when it limits evapotranspiration, defining a critical SM threshold (θcrit). Better quantification of θcrit is needed for improving future projections of climate and water resources, food production, and ecosystem vulnerability. Here, we combine systematic satellite observations of the diurnal amplitude of land surface temperature (dLST) and SM during dry-downs, corroborated by in-situ data from flux towers, to generate the observation-based global map of θcrit. We find an average global θcrit of 0.19 m3/m3, varying from 0.12 m3/m3 in arid ecosystems to 0.26 m3/m3 in humid ecosystems. θcrit simulated by Earth System Models is overestimated in dry areas and underestimated in wet areas. The global observed pattern of θcrit reflects plant adaptation to soil available water and atmospheric demand. Using explainable machine learning, we show that aridity index, leaf area and soil texture are the most influential drivers. Moreover, we show that the annual fraction of days with water stress, when SM stays below θcrit, has increased in the past four decades. Our results have important implications for understanding the inception of water stress in models and identifying SM tipping points.

Size, distribution, and vulnerability of the global soil inorganic carbon.

Global estimates of the size, distribution, and vulnerability of soil inorganic carbon (SIC) remain largely unquantified. By compiling 223,593 field-based measurements and developing machine-learning models, we report that global soils store 2305 ± 636 (±1 SD) billion tonnes of carbon as SIC over the top 2-meter depth. Under future scenarios, soil acidification associated with nitrogen additions to terrestrial ecosystems will reduce global SIC (0.3 meters) up to 23 billion tonnes of carbon over the next 30 years, with India and China being the most affected. Our synthesis of present-day land-water carbon inventories and inland-water carbonate chemistry reveals that at least 1.13 ± 0.33 billion tonnes of inorganic carbon is lost to inland-waters through soils annually, resulting in large but overlooked impacts on atmospheric and hydrospheric carbon dynamics.