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Soil contamination by trace elements like copper (Cu) can affect soil functioning. Environmental policies with guidelines and soil survey measurements still refer to the total contents of Cu in soils. However, Cu contents in soil solution or free Cu contents have been shown to be better proxies of risks of Cu mobility or (bio)availability for soil organisms. Several empirical equations have been defined at the local scale to predict the amounts of Cu in soil solution based on both total soil Cu contents and main soil parameters involved in the soil/solution partitioning. Nevertheless, despite the relevance for risk assessment, these equations are not applied at a large spatial scale due to difficulties to perform changes from local to regional. To progress in this challenge, we collected several empirical equations from literature and selected those allowing estimation of the amount of Cu in solution, used as a proxy of available Cu, from the knowledge of both total soil Cu contents and soil parameters. We did the same for the estimation of free Cu in solution, used as a proxy of bioavailable Cu These equations were used to provide European maps of (bio-)available Cu based on the one of total soil Cu over Europe. Results allowed comparing the maps of available and bio-available Cu at the European scale. This was done with respective median values of each form of Cu to identify specific areas of risks linked to these two proxies. Higher discrepancies were highlighted between the map of bioavailable Cu and the map of soil total Cu compared to the Cu available map. Such results can be used to assess environmental-related issues for land use planning.

The factors governing stability of soil organic carbon vary from chemical characteristics to physical occlusion from either biotic (such as plant roots, soil fauna and microorganisms) or abiotic agents (such as water). By mediating the decomposition potential, microbial community diversity and structure may play an important role in the fate of soil organic carbon. In this theoretical study, we aim to understand the role of the microbial community diversity and composition in soil organic carbon storage and decomposition. We constructed a model describing a microbial process network incorporating diverse organic matter compounds and microbial groups. The microbial groups varied from each other with respect to their affinity to depolymerise, take-up, metabolise and assimilate organic compounds. We allowed for adaptation of microbial communities to available carbon, and competition among microbial groups. We tested this process network with functionally diverse microbial communities which were subjected to varying carbon availability. This framework allowed us to explore organic carbon decomposition rates and their temporal evolution under different conditions of microbial diversity and carbon availability, as well as the tendency of a soil microbial system to store carbon. We found that the microbial community functional diversity is a good predictor of organic carbon decomposition rates. This result suggests that an organic carbon decomposition rate modifier could be defined based on functional diversity and then included in soil carbon models. Furthermore, we observed that organic carbon decomposition by functionally similar communities in carbon poor conditions slowed down after approximately half of the initial carbon was consumed. In the same conditions, functionally diverse communities with a higher number of biotic agents allowed a more complete decomposition. However, with increasing initial carbon availability, the functional diversity of the microbial community ceased to play a role in soil carbon storage. These results link microbial community diversity and carbon availability to decomposition potential and thus organic carbon stability in soils.

The lack of consensus of functional dependency of soil respiration on moisture among the Earth system models (ESMs) contributes significantly to uncertainties in their projections.Based on data of soil organic C stocks and CO2 emissions from the boreal ecotone between mineral soil forests and adjacent peatlands with organic soils in Finland, we derived the field-based moisture response of respiration in a maximum range of moisture conditions (extending from xeric and mesic forests to water saturated mires). Using Bayesian data assimilation technique, we coupled Yasso07 soil carbon model with the heuristic bell shape moisture function, approximating the enzyme and oxygen limitations. As expected, the Yasso07 model fitted with the revised moisture modifier on data from catena of organic-mineral soils outperformed the previous model version in peatlands.Unlike the most found optimum of decomposition in ESM in mid- or high- moisture levels, our optimum or the highest rate of decomposition correspond to well-drained conditions of mineral soils.We speculated that the reason for the shift in the moisture optimum of the functional form was its accounting for long-term processes leading to a larger C mineralization in mineral soils related to extreme events, such as prolonged elevated moisture or rewetting after droughts, which enhance microbial access to previously protected or labile C pools and may not be detected in short-term incubation studies.Although, the moisture modifier derived here improved the match between the modelled and measured SOCs of peatlands, a shift in consensus from current decomposition rate modifiers used in ESMs requires further evaluation before it can be largely applied for the landscape level semiempirical processed-based modelling of the mineral and organic soil C stocks and CO2 emissions.

Permafrost soils located in high latitudes contain about 1500 petagrams of carbon. The strong and rapid warming of the Arctic climate threatens this important carbon stock. Permafrost thaw exposes previously frozen organic matter to decomposition by microorganisms, resulting in CO2 and CH4 emissions into the atmosphere that contribute to strengthening the initial warming. On the other hand, rising atmospheric CO2 concentration increases vegetation primary productivity in a feedback known as fertilization effect. In addition, the melting of permafrost may also likely provide more nitrogen in the soil that could stimulate plant growth. The balance between these competing processes is thought to be the primary driver of future permafrost carbon stocks. However, both the amplitude and timing of future net carbon emissions of permafrost areas remain highly uncertain. Reducing the uncertainty on net carbon balance in high latitudes would help improve the accuracy of carbon budget, and thus would impact political and social decisions towards the net zero target.Up to then, the impact of different future climate scenarios (RCP, SSP or similar) on permafrost have been largely explored with offline Land Surface Model simulations but feedbacks with the atmosphere and ocean cannot be represented in such configurations. Using the IPSL Earth System Model that couples the atmosphere, the ocean and continental surfaces (i.e., ORCHIDEE model), the future evolution of climate-carbon feedbacks in permafrost regions is assessed. In particular, the feedback between climate change and the carbon cycle and the fertilization effect are analyzed with the C4MIP formalism (γ and β parameters). A particular focus is put on the role of the nitrogen cycle through its interactions with the carbon cycle. In addition, the impact of soil insulation by soil organic carbon and surface mosses on thermal transfers is also analyzed in the context of coupled land - atmosphere simulations. The importance of surface insulation i) to maintain realistic surface air temperature, especially during spring time, avoiding large surface - atmosphere feedbacks with unrealistic cooling in the arctic and ii) to sustain large permafrost extent (close to observations) is highlighted.

To partially compensate for CO2 emissions, the 4 per 1000 initiative proposed an annual 4‰ soil organic carbon (SOC) stock increase. Yet, the feasibility of such an ambitious target is still under debate. The most efficient way to increase the SOC stocks is to increase the C input to the soil. Yet, knowing how much of an increase of SOC should be an objective is subjected to how much carbon is already stored within soils, and the prediction of the change of carbon pool with time. The objective of this work is to predict the carbon trends in forest soils to be able to better assess the target carbon sequestration. To this end, we use the AMG SOC model to simulate the carbon increase in French forests. AMG is a simple, two-pools model that consider the influence of environmental conditions and litter inputs to simulate the dynamics of SOC. AMG has been designed for agricultural soils, and has proved able to simulate SOC dynamics in croplands but has never been tested on forest soils. The model was run over the French RENECOFOR sites network where SOC measurements have been realized 17 years apart (1994-1996 and 2008-2012) on 95 sites and over which an average increase of +0.35 tC ha-1 yr-1 has been evidenced. We have applied the RockEval method, which mixes machine learning with thermal analysis techniques, in order to initialize AMG, separating the passive from the active carbon pool. We calibrated the model with the aim of simulating the SOC dynamics observed in the RENECOFOR. The results show that even if the model can be successful in predicting the carbon trends locally, there is no general parameterization allowing to reproduce SOC stock evolution trends at the scale of the 95 sites. Our findings suggests that even with a good performance in the case of agricultural soils, there is a need to better represent the litter inputting within the AMG model in the case of forests.

State-of-the-art soil models can be used to monitor and predict the evolution of soil organic carbon (SOC) stocks and greenhouse gas (GHG) fluxes at national, sub-national, and supernational levels. This can help predict the effect of disturbances on the soil and facilitate the development of sustainable management practices to prevent further soil degradation and GHG losses under climate change.However, model simulations are still highly uncertain due to many factors. For instance, the lack of understanding of many soil processes, the way processes are represented in the models, and the parametrization, initialization, and forcing variables used to run them. One way to consider these uncertainties is to use multi-model ensembles, thus simulating the evolution of SOC stocks and GHG fluxes according to models with different structures and mechanistic assumptions.In this work, we show a webtool that enables the simulation of SOC stocks and GHG gases in European forests under different climate and land-use change scenarios, using a multi-model ensemble. In the absence of on-site measurements, the webtool directly accesses online databases of pedo-climatic data that is required to run the models.Models also need to be correctly parameterized and evaluated before their application. For that, we build a map of model parameters that can be used to force the models, and which is evaluated on the European LUCAS database. Uncertainties linked to the initialization method used are also discussed.

Soil (de)compaction is widespread and has a large impact on soil constitutive relationships including hydraulic and gas-exchange properties. Surveys estimate that about a third of EU soils are severely degraded by compaction, and lab experiments show that the effect of compaction on soil hydraulic conductivity can be as large as the differences between textural classes. Nevertheless, the effect of (de)compaction on soil properties remains absent or only provisionally represented in present-day soil-crop, ecosystem, and land-surface models. That is despite the formulation of soil structure evolution models for key land management practices, biotic factors, and wet-dry or freeze-thaw cycles. The slow maturing and uptake of these models results from observational limitations and from difficulties with upscaling and with relating their outputs to soil properties of interest. Here we address the latter by extending established models of soil hydraulic properties to represent a dynamically evolving soil structure parametrized by a discrete pore-size distribution. The extension decomposes a given water retention curve into smooth algebraic segments corresponding to predefined pore size classes. The segments are then individually scaled to represent the changing pore volumes and summed to obtain the new retention and conductivity functions. We validate this approach using lab-based compaction experiments and demonstrate its use at site scale by leveraging the soil hydrology scheme of the ORCHIDEE land surface model. Finally, we discuss new applications that our approach enables, with a focus on representing the interaction between ecosystem engineers, soil structure, and soil water.

International audience; Wetlands are the largest natural source of methane in the atmosphere. How the methane emissions from wetlands will evolve with global change is a subject of debate. In this study, we investigate the interannual variability of methane emissions from a temperate degraded peatland located in the Sologne region (French Region Centre) and test the ability of the land surface model ORCHIDEE to reproduce this variability. The site is instrumented for long term monitoring of the hydrological parameters, greenhouse gas emissions, dissolved organic content and biodiversity. The peat has undergone several perturbations due to the urbanization of the site that led to drainage and invasion by vascular plants (Molinia Caerula, Erica Tretalix). Our study focuses on the 2014-2020 period after a hydrological restoration was undertaken. The model, driven by meteorological data and constrained by in situ hydrological data, primary productivity and total soil carbon, is able to reproduce the general temporal trend in methane emissions. The model is used to investigate the effect of climatological conditions (droughts) and vegetation changes (invasion by vascular plants) on the observed trend of methane fluxes. The model is also used to study the relative contributions of different methane transport processes (by the plants, from ebullition and diffusion) to the methane flux observed in La Guette peatland.

Predicting the responses of terrestrial ecosystem carbon to future global change strongly relies on our ability to model accurately the underlying processes at a global scale. However, terrestrial biosphere models representing the carbon and nitrogen cycles and their interactions remain subject to large uncertainties, partly because of unknown or poorly constrained parameters. Data assimilation is a powerful tool that can be used to optimise these parameters by confronting the model with observations. In this paper, we identify sensitive model parameters from a recent version of the ORCHIDEE land surface model that includes the nitrogen cycle. These sensitive parameters include ones involved in parameterisations controlling the impact of the nitrogen cycle on the carbon cycle and, in particular, the limitation of photosynthesis due to leaf nitrogen availability. We optimise these ORCHIDEE parameters against carbon flux data collected on sites from the Fluxnet network. However, optimising against present-day observations does not automatically give us confidence in the future projections of the model, given that environmental conditions are likely to shift compared to present-day. Manipulation experiments give us a unique look into how the ecosystem may respond to future environmental changes. One such experiment, the Free Air CO2 Enrichment experiment, provides a unique opportunity to assess vegetation response to increasing CO2 by providing data at ambient and elevated CO2 conditions. Therefore, to better capture the ecosystem response to increased CO2, we add the data from two FACE sites to our optimisations, in addition to the Fluxnet data. We use data from both CO2 conditions of the Free Air CO2 Enrichment experiment, which allows us to gain extra confidence in the model simulations using this set of parameters. We find that we are able to improve the magnitude of modelled productivity. Although we are unable to correct the interannual variability fully, we start to simulate possible progressive nitrogen limitation at one of the sites. Using an idealised simulation experiment based on increasing atmospheric CO2 by 1 % per year over 100 years, we find that the rate of CO2 fertilisation is much lower when Free Air CO2 Enrichment data has been in the optimisation.

Soils store vast amounts of carbon (C) on land, and increasing soil organic carbon (SOC) stocks in already managed soils such as croplands may be one way to remove C from the atmosphere, thereby limiting subsequent warming. The main objective of this study was to estimate the amount of additional C input needed to annually increase SOC stocks by 4‰ at 16 long-term agricultural experiments in Europe, including exogenous organic matter (EOM) additions. We used an ensemble of six SOC models and ran them under two configurations: (1) with default parametrization and (2) with parameters calibrated site-by-site to fit the evolution of SOC stocks in the control treatments (without EOM). We compared model simulations and analysed the factors generating variability across models. The calibrated ensemble was able to reproduce the SOC stock evolution in the unfertilised control treatments. We found that, on average, the experimental sites needed an additional 1.5 ± 1.2 Mg C ha−1 year−1 to increase SOC stocks by 4‰ per year over 30 years, compared to the C input in the control treatments (multi-model median ± median standard deviation across sites). That is, a 119% increase compared to the control. While mean annual temperature, initial SOC stocks and initial C input had a significant effect on the variability of the predicted C input in the default configuration (i.e., the relative standard deviation of the predicted C input from the mean), only water-related variables (i.e., mean annual precipitation and potential evapotranspiration) explained the divergence between models when calibrated. Our work highlights the challenge of increasing SOC stocks in agriculture and accentuates the need to increasingly lean on multi-model ensembles when predicting SOC stock trends and related processes. To increase the reliability of SOC models under future climate change, we suggest model developers to better constrain the effect of water-related variables on SOC decomposition. This work benefited from the French state aid managed by the ANR under the “Investissements d'avenir” programme with the reference ANR-16-CONV-0003 (CLAND project). EB, RZA and BG are supported by the European Union's Horizon 2020 research and innovation program under grant agreement No 101000289 (Holisoils project). RZA was also supported by the United States Department of Energy, Office of Science, and Office of Biological and Environmental Research. Oak Ridge National Laboratory is managed by UT-Battelle, LLC, for the United States Department of Energy under contract DE-AC05-00OR22725.

Site-level observations have shown pervasive cold season CO2 release across Arctic and boreal ecosystems, impacting annual carbon budgets. Still, the seasonality of CO2 emissions are poorly quantified across much of the high latitudes due to the sparse coverage of site-level observations. Space-based observations provide the opportunity to fill some observational gaps for studying these high-latitude ecosystems, particularly across poorly sampled regions of Eurasia. Here, we show that data-driven net ecosystem exchange (NEE) from atmospheric CO2 observations implies strong summer uptake followed by strong autumn release of CO2 over the entire cold northeastern region of Eurasia during the 2015–2019 study period. Combining data-driven NEE with satellite-based estimates of gross primary production (GPP), we show that this seasonality implies less summer heterotrophic respiration (Rh) and greater autumn Rh than would be expected given an exponential relationship between respiration and surface temperature. Furthermore, we show that this seasonality of NEE and Rh over northeastern Eurasia is not captured by the TRENDY v8 ensemble of dynamic global vegetation models (DGVMs), which estimate that 47 %–57 % (interquartile range) of annual Rh occurs during August–April, while the data-driven estimates suggest 59 %–76 % of annual Rh occurs over this period. We explain this seasonal shift in Rh by respiration from soils at depth during the zero-curtain period, when sub-surface soils remain unfrozen up to several months after the surface has frozen. Additional impacts of physical processes related to freeze–thaw dynamics may contribute to the seasonality of Rh. This study confirms a significant and spatially extensive early cold season CO2 efflux in the permafrost-rich region of northeast Eurasia and suggests that autumn Rh from subsurface soils in the northern high latitudes is not well captured by current DGVMs.

Peatlands at high latitudes have accumulated >400 Pg carbon (C) because saturated soil and cold temperatures suppress C decomposition. This substantial amount of C in Arctic and Boreal peatlands is potentially subject to increased decomposition if the water table (WT) decreases due to climate change, including permafrost thaw-related drying. Here, we optimize a version of the Organizing Carbon and Hydrology In Dynamic Ecosystems model (ORCHIDEE-PCH4) using site-specific observations to investigate changes in CO2 and CH4 fluxes as well as C stock responses to an experimentally manipulated decrease of WT at six northern peatlands. The unmanipulated control peatlands, with the WT 2 kg C m−2 over 100 years when WT is lowered by 50 cm, while permafrost peatlands temporally switched from C sinks to sources. These results highlight that reductions in C storage capacity in response to drying of northern peatlands are offset in part by reduced CH4 emissions, thus slightly reducing the positive carbon climate feedbacks of peatlands under a warmer and drier future climate scenario.

Leaching of dissolved organic carbon (DOC) from soils into the river network is an important component of the land carbon (C) budget. At regional to global scales, its significance has been estimated through simple mass budgets, often using multi-year averages of observed fluvial DOC fluxes as a proxy of DOC leaching due to the limited availability of observations of the leaching flux itself. This procedure leads to a systematic underestimation of the leaching flux because of the decay of DOC during fluvial transport. Moreover, this procedure does not allow for revealing spatiotemporal variability in DOC leaching from soils, which is vital to better understand the drivers of DOC leaching and its impact on the local soil C budget. In this study, we use the land surface model (LSM) ORCHILEAK to simulate the terrestrial C budget, including leaching of DOC from the soil and its subsequent reactive transport through the river network of Europe. The model performance is evaluated not only against the sparse observations of the soil DOC leaching rate, but also against the more abundant observations of fluxes and reactivity of DOC in rivers, providing further evidence that our simulated DOC fluxes are realistic. The model is then used to simulate the spatiotemporal patterns of DOC leaching across Europe over the period 1972–2012, quantifying both the environmental drivers of these patterns and the impact of DOC leaching on the land C budget. Over the simulation period, we find that, on average, 14.3 Tg C yr−1 of DOC is leached from land into European rivers, which is about 0.6 % of the terrestrial net primary production (NPP), a fraction significantly lower than that reported for tropical river networks. On average, 12.3 Tg C yr−1 of the leached DOC is finally exported to the coast via the river network, and the rest is respired during transit. DOC leaching presents a large seasonal variability, with the maximum occurring in winter and the minimum in summer, except for most parts of northern Europe, where the maximum occurs in spring due to snowmelt. The DOC leaching rate is generally low in warm and dry regions, and high in the cold and wet regions of Europe. Furthermore, runoff and the ratio between runoff from shallower flow paths on one hand and deep drainage and groundwater flow on the other hand are the main drivers of the spatiotemporal variation of DOC leaching. Temperature, as a major control of DOC production and decomposition rates in the soils, plays only a secondary role.

International audience; Given the prospects of low short- term emissions reduction, carbon removals (CDRs) are expected to play an important role in achieving ambitious mitigation targets in future scenarios of integrated assessment models (IAMs), particularly Bioenergy with Carbon Capture and Storage (BECCS). In this paper, we explore the IAMC 1.5 data-base to depict the characteristics of the two main CDR options present in mitigation scenarios: BECCS and afforestation/reforestation. We apply a linear mixed- effect model to capture the specific regional and cross- IAM effects. Results reveal that the distribution of BECCS and afforestation deployment differs across IAMs and regions and, to a second extent, time. BECCS is preferred in the scenarios not for its ability to expand energy use but actually because it appears as an alternative to afforestation, which is associated with a decrease in energy use. However, the regional distribution of CDR deployment does not show a common pattern across scenarios and IAMs. Therefore, a more comprehensive investigation is needed before it can support policy proposals.

Promoting sustainable soil management is a possible option for achieving net-zero greenhouse gas emissions in the future. Several efforts in this area exist, and the application of spatially explicit models to anticipate the effect of possible actions on soils at a regional scale is widespread. Currently, models can simulate the impacts of changes on land cover, land management, and the climate on the soil carbon stocks. However, existing modeling tools do not incorporate the lateral transport and deposition of soil material, carbon, and nutrients caused by soil erosion. The absence of these fluxes may lead to an oversimplified representation of the processes, which hinders, for example, a further understanding of how erosion has been affecting the soil carbon pools and nutrients through time. The sediment transport during deposition and the sediment loss to rivers create dependence among the simulation units, forming a cumulative effect through the territory. If, on the one hand, such a characteristic implies that calculations must be made for large geographic areas corresponding to hydrological units, on the other hand, it also can make models computationally expensive, given that erosion and redeposition processes must be modeled at high resolution and over long timescales. In this sense, the present work has a three-fold objective. First, we provide the development details to represent in matrix form a spatially explicit process-based model coupling sediment, carbon, and erosion, transport, and deposition (ETD) processes of soil material in hillslopes and valley bottoms (i.e., the CE-DYNAM model). Second, we illustrate how the model can be calibrated and validated for Europe, where high-resolution datasets of the factors affecting erosion are available. Third, we presented the results for a depositional site, which is highly affected by incoming lateral fluxes from upstream lands. Our results showed that the benefits brought by the matrix approach to CE-DYNAM enabled the before-precluded possibility of applying it on a continental scale. The calibration and validation procedures indicated (i) a close match between the erosion rates calculated and previous works in the literature at local and national scales, (ii) the physical consistency of the parameters obtained from the data, and (iii) the capacity of the model in predicting sediment discharge to rivers in locations observed and unobserved during its calibration (model efficiency (ME) =0.603, R2=0.666; and ME =0.152, R2=0.438, respectively). The prediction of the carbon dynamics on a depositional site illustrated the model's ability to simulate the nonlinear impact of ETD fluxes on the carbon cycle. We expect that our work advances ETD models' description and facilitates their reproduction and incorporation in land surface models such as ORCHIDEE. We also hope that the patterns obtained in this work can guide future ETD models at a European scale.

Climate warming with permafrost thaw will modify lateral carbon export, from terrestrial to aquatic ecosystems with a potential huge impact on the Arctic rivers, draining organic-rich soils and in fine into the Arctic Ocean. The majority of annual DOC fluxes by Arctic rivers are transported during the snowmelt break-up period, which makes field measurements of DOC difficult. Passive spatial remote sensing is a very relevant tool to increase the spatial and temporal coverage of these observed values.In the framework of the French CNES DOC-Rivers project we proposed to apply the approach consisting in analyzing satellite imageries to evaluate DOC concentrations in the 6 great Arctic Rivers: Lena, Ob’, Yenisey, Yukon, MacKenzie, Kolyma. The algorithm, first, establishes a multi-linear relationship between ground-based chromatic dissolved organic matter (CDOM) observations and specific satellite color bands to construct a complete satellite CDOM database. Another linear regression is used afterward with in-situ data from the Arctic Great Rivers Observatory (ArcticGRO) initiative to correlate CDOM and DOC observations. Using this second linear regression, we can predict the DOC content from the previous construct satellite CDOM database. River discharge measurements from the ArcticGRO database also enable to estimate the evolution of DOC export to the Arctic Ocean from satellite data.We applied this approach to high-resolution satellite data issued from Sentinel 2 (A 2015-2022, B 2017-2022) and Landsat 8 (2013-2022) to create a multi-instrumental synergy. This new database provides an unprecedented source of information for understanding DOC dynamics of in Arctic rivers and assessing its transfer from large catchments to the Arctic Ocean. This database provides information on the variability of DOC during the whole ice-free season and serve to locate areas with higher concentrations and fluxes during the 2013-2021 period. We plan to complement our database on future period with data from new satellite missions (Landsat 9, Sentinel 2C), on the present time with data from on-going missions (Sentinel 3, MODIS) and on past period with data from low resolution observations as Landsat 5 and Landsat 7. This extension of the database over a longer period of time will furnish insight in response to climate warming.

The importance of carbon fluxes between soils and atmosphere and their storage capacities have made soils a key component of nature-based solutions to mitigate climate change. Consequently, the need to forecast soil carbon dynamics at the decadal time scales is becoming a key research avenue for soil scientists and biogeochemists. One of the most important tools to fill this objective is the use of models, which have been developed and implemented since the mid- 20th century. Presuming that integrating more mechanisms would improve these models, some models of increased complexity were recently developed. Indeed, since roughly two decades, several approaches have been proposed to better represent the effect of some key mechanisms in particular related to soil biology and soil physics. For instance, few models are now able to describe explicitly and reliably the importance of soil microorganisms on the soil carbon dynamics from plot to global scale. Here, we will discuss what is the rationale behind model complexity increase, what are the limitations associated and discuss the status of evaluation for model prediction. In particular, we will show that despite model of increased complexity may provide accurate predictions in some conditions, those complex models also came with their own implicit assumptions and limitations that must be well understood before using complex models to forecast soil carbon dynamics and feed policy decisions. We also consider that models of lower complexity, which have been generally developed earlier, have also their own assets and have often been better evaluated. As a consequence, models of lower complexity may be considered as more robust and more adapted to forecast soil carbon dynamic and the improvement of their parameterization should also be considered as a valuable alternative. We will also present why multi model approach is important to reduce uncertainties and explain why using model ensemble, when implemented a diversity of carefully evaluated models, is a key method to forecast soil carbon dynamics. Finally, we will show that the fabulous growth of model complexity and the societal needs associated to soil is an incredible opportunity for soil scientists to increase the understanding of soil carbon cycle, in particular in the context of global changes, and to improve our future predictions on soil carbon.

Recently identified post-fire carbon fluxes indicate that in order to understand if global fires represent a net carbon source or sink, one must consider both terrestrial carbon retention through pyrogenic carbon (PyC) production, and carbon losses via multiple pathways. Here, these legacy source and sink pathways are quantified using a CMIP6 land surface model to estimate Earth's fire carbon budget. Over 1901-2010, global PyC drives annual soil carbon accumulation of 337 TgCyr-1, offset by legacy carbon losses totalling -248 TgCyr-1. The residual of these values constrains maximum annual pyrogenic carbon mineralisation to 89 TgCyr-1, and PyC mean residence time to 5387 years, assuming steady state. However, paucity of observational constraints for representing PyC mineralisation mean that without assuming steady state, we are unable to determine the sign of the overall fire carbon balance. The residual is negative over forests and positive over grassland-savannahs (implying a potential sink), suggesting contrasting roles of vegetation in the fire carbon cycle. Without widespread tropical grassland-savannah coverage, the legacy effects of fires could not feasibly enhance terrestrial C storage -a result afforded by grasses’ capacity for fire recovery. The dependency of the fire C residual on vegetation composition suggests that the preservation/restoration of native grasslands may be an important vector for decreasing C losses from future fire activity. We call for significant investments in understanding of PyC degradation and its drivers, in addition to improved estimates of legacy fire C fluxes. Reliable quantification of PyC mineralisation and erosion, particularly over grasslands, remains the principal missing link in a holistic understanding of fire’s role in the Earth system.

Recently identified post-fire carbon fluxes indicate that, to understand whether global fires represent a net carbon source or sink, one must consider both terrestrial carbon retention through pyrogenic carbon production and carbon losses via multiple pathways. Here these legacy source and sink pathways are quantified using a CMIP6 land surface model to estimate Earth’s fire carbon budget. Over the period 1901–2010, global pyrogenic carbon has driven an annual soil carbon accumulation of 337 TgC yr−1, offset by legacy carbon losses totalling −248 TgC yr−1. The residual of these values constrains the maximum annual pyrogenic carbon mineralization to 89 TgC yr−1 and the pyrogenic carbon mean residence time to 5,387 years, assuming a steady state. The residual is negative over forests and positive over grassland-savannahs (implying a potential sink), suggesting contrasting roles of vegetation in the fire carbon cycle. The paucity of observational constraints for representing pyrogenic carbon mineralization means that, without assuming a steady state, we are unable to determine the sign of the overall fire carbon balance. Constraining pyrogenic carbon mineralization rates, particularly over grassland-savannahs, is a critical research frontier that would enable a fuller understanding of fire’s role in the Earth system and inform attendant land use and conservation policy.

Global warming may lead to carbon transfers from soils to the atmosphere, yet this positive feedback to the climate system remains highly uncertain, especially in subsoils (Ilyina and Friedlingstein, 2016; Shi et al., 2018). Using natural geothermal soil warming gradients of up to +6.4 ∘C in subarctic grasslands (Sigurdsson et al., 2016), we show that soil organic carbon (SOC) stocks decline strongly and linearly with warming (−2.8 t ha−1 ∘C−1). Comparison of SOC stock changes following medium-term (5 and 10 years) and long-term (>50 years) warming revealed that all SOC stock reduction occurred within the first 5 years of warming, after which continued warming no longer reduced SOC stocks. This rapid equilibration of SOC observed in Andosol suggests a critical role for ecosystem adaptations to warming and could imply short-lived soil carbon–climate feedbacks. Our data further revealed that the soil C loss occurred in all aggregate size fractions and that SOC stock reduction was only visible in topsoil (0–10 cm). SOC stocks in subsoil (10–30 cm), where plant roots were absent, showed apparent conservation after >50 years of warming. The observed depth-dependent warming responses indicate that explicit vertical resolution is a prerequisite for global models to accurately project future SOC stocks for this soil type and should be investigated for soils with other mineralogies.

International audience; The priming effect (PE) is a key mechanism contributing to the carbon balance of the soil ecosystem. Almost 100 years of research since its discovery in 1926 have led to a rich body of scientific publications to identify the drivers and mechanisms involved. A few review articles have summarised the acquired knowledge; the last major one was published in 2010. Since then, knowledge on the soil microbial communities involved in PE and in PE + C sequestration mechanisms has been considerably renewed. This article reviews current knowledge on soil PE to state to what extent new insights may improve our ability to understand and predict the evolution of soil C stocks. We propose a framework to unify the different concepts and terms that have emerged from the international scientific community on this topic, report recent discoveries and identify key research needs. Seventy per cent of the studies on the soil PE were published in the last 10 years, illustrating a renewed interest for PE, probably linked to the increased concern about the importance of soil carbon for climate change and food security issues. Among all the drivers and mechanisms proposed along with the different studies to explain PE, some are named differently but actually refer to the same object. This overall introduces 'artificial' complexity for the mechanistic understanding of PE, and we propose a common, shared terminology. Despite the remaining knowledge gaps, consistent progress has been achieved to decipher the abiotic mechanisms underlying PE, together with the role of enzymes and the identity of the microbial actors involved. However, including PE into mechanistic models of SOM dynamics remains challenging as long as the mechanisms are not fully understood. In the meantime, empirical alternatives are available that reproduce observations accurately when calibration is robust. Based on the current state of knowledge, we propose different scenarios depicting to what extent PE may impact ecosystem services under climate change conditions.

Continental biogeochemical models are commonly used to predict the effect of land use, exogenous organic matter input or climate change on soil greenhouse gas emission. However, they cannot be used for this purpose to investigate the effect of soil contamination, while contamination affects several soil processes and concerns a large fraction of land surface. For that, in this study we implemented a commonly used model estimating soil nitrogen (N) emissions, the DeNitrification DeCompostion (DNDC) model, with a function taking into account soil copper (Cu) contamination in nitrate production control. Then, we aimed at using this model to predict N2O-N, NO2-N, NO-N and NH4-N emissions in the presence of contamination and in the context of changes in precipitations. Initial incubations of soils were performed at different soil moisture levels in order to mimic expected rainfall patterns during the next decades and in particular drought and excess of water. Then, a bioassay was used in the absence or presence of Cu to assess the effect of the single (moisture) or double stress (moisture and Cu) on soil nitrate production. Data of nitrate production obtained through a gradient of Cu under each initial moisture incubation were used to parameterise the DNDC model and to estimate soil N emission considering the various effects of Cu. Whatever the initial moisture incubation, experimental results showed a NO3-N decreasing production when Cu was added but depending on soil moisture. The DNDC-Cu version we proposed was able to reproduce these observed Cu effects on soil nitrate concentration with r2 > 0.99 and RMSE < 10 % for all treatments in the DNDC-Cu calibration range (> 40 % of the water holding capacity) but showed poor performances for the dry treatments. We modelled a Cu effect inducing an increase in NH4-N soil concentration and emissions due to a reduced nitrification activity and therefore a decrease in NO3-N, N2O-N and NOx-N concentrations and emissions. The effect of added Cu predicted by the model was larger on N2-N and N2O-N emissions than on the other N species and larger for the soils incubated under constant than variable moisture. Our work shows that soil contamination can be considered in continental biogeochemical models to better predict soil greenhouse gas emissions.

Soil carbon (C) models are used to predict C sequestration responses to climate and land use change. Yet, the soil models embedded in Earth system models typically do not represent processes that reflect our current understanding of soil C cycling, such as microbial decomposition, mineral association, and aggregation. Rather, they rely on conceptual pools with turnover times that are fit to bulk C stocks and/or fluxes. As measurements of soil fractions become increasingly available, it is necessary for soil C models to represent these measurable quantities so that model processes can be evaluated more accurately. Here we present Version 2 (V2) of the Millennial model, a soil model developed to simulate C pools that can be measured by extraction or fractionation, including particulate organic C, mineral-associated organic C, aggregate C, microbial biomass, and low molecular weight C. Model processes have been updated to reflect the current understanding of mineral-association, temperature sensitivity and reaction kinetics, and different model structures were tested within an open-source framework. We evaluated the ability of Millennial V2 to simulate total soil organic C (SOC), as well as the mineral-associated and particulate fractions, using three independent data sets of soil fractionation measurements spanning a range of climate and geochemistry in Australia (N = 495), Europe (N = 175), and across the globe (N = 659). When using all the data together (N = 1329), the Millennial V2 model predicted SOC (RMSE = 3.3 kg C m−2, AIC = 675, R i n 2 = 0.31, R o u t 2 = 0.26) better than the widely-used first-order decomposition model Century (RMSE = 3.4 kg C m−2, AIC = 696, R i n 2 = 0.21, R o u t 2 = 0.18) across sites, despite the fact that Millennial V2 has an increase in process complexity and number of parameters compared to Century. Millennial V2 also reproduced the observed fraction of C in MAOM and larger particle size fractions for most latitudes and biomes, and allows for a more detailed understanding of the pools and processes that affect model performance. It is important to note that this study evaluates the spatial variation in C stock only, and that the temporal dynamics of Millennial V2 remain to be tested. The Millennial V2 model updates the conceptual Century model pools and processes and represents our current understanding of the roles that microbial activity, mineral association and aggregation play in soil C sequestration.

Grasslands absorb and release carbon dioxide (CO2), emit methane (CH4) from grazing livestock, and emit nitrous oxide (N2O) from soils. Little is known about how the fluxes of these three greenhouse gases, from managed and natural grasslands worldwide, have contributed to past climate change, or the roles of managed pastures versus natural grasslands. Here, global trends and regional patterns of the full greenhouse gas balance of grasslands are estimated for the period 1750 to 2012. A new spatially explicit land surface model is applied, to separate the direct effects of human activities from land management and the indirect effects from climate change, increasing CO2 and regional changes in nitrogen deposition. Direct human management activities are simulated to have caused grasslands to switch from a sink to a source of greenhouse gas, because of increased livestock numbers and accelerated conversion of natural lands to pasture. However, climate change drivers contributed a net carbon sink in soil organic matter, mainly from the increased productivity of grasslands due to increased CO2 and nitrogen deposition. The net radiative forcing of all grasslands is currently close to neutral, but has been increasing since the 1960s. Here, we show that the net global climate warming caused by managed grassland cancels the net climate cooling from carbon sinks in sparsely grazed and natural grasslands. In the face of future climate change and increased demand for livestock products, these findings highlight the need to use sustainable management to preserve and enhance soil carbon storage in grasslands and to reduce greenhouse gas emissions from managed grasslands., Grasslands, and the livestock that live there, are dynamic sources and sinks of greenhouse gases, but what controls these fluxes remains poorly characterized. Here the authors show that on the global level, grasslands are climate neutral owing to the cancelling effects of managed vs. natural systems.

Continental biogeochemical models are commonly used to prospect the effect of land use, exogenous organic matter input or climate change on soil greenhouse gas emission. However, they can still not be used to investigate the effect of soil contamination while it is known to affect several soil processes and to concern a large fraction of land surface. We implemented a commonly used model estimating soil nitrogen (N) emission, the DeNitrification DeCompostion (DNDC) model, with a function taking into account soil copper (Cu) contamination in nitrate production modulation. Then, we aimed at using this model to predict N-N2O, N-NO2 NOx and N-NH4 emissions in the presence of contamination and in the context of changes in precipitations. For that, incubations of soils were performed at different soil moistures in order to mimic expected rainfall patterns during the next decades and in particular drought and excess of water. The effect of this double stress on soil nitrate production was studied using a bio-assay. Then, data of nitrate production obtained under each moisture treatment were used to parameterize the DNDC model and estimate soil N emission considering the various effect of Cu. Whatever the moisture preincubation, experimental results showed a N-NO3 decreasing production when Cu was added but with different sharpness depending on soil moisture. The DNDC-Cu version we proposed was able to reproduce these observed Cu effects on soil nitrate concentration with r2 > 0.99 and RMSE < 10 % for all treatments in the DNDC-Cu calibration range (> 40 % of the water holding capacity) but showed poor performances for the dry treatments. We modelled a Cu-effect inducing an increase in N-NH4 soil concentration and emissions due to a reduced nitrification activity, and therefore a decrease in N-NO3, N-N2O and N-NOx concentrations and emissions. The effect of added Cu was larger on N-N2 and N-N2O emissions than on the other N species and larger for the soils incubated under constant than variable moisture.

International audience

Some steps of the soil nitrogen (N) cycle are sensitive to environmental pressures like soil moisture or contamination, which are expected to evolve during the next decades. Individual stresses have been well studied, but their combination is not yet documented. In this work, we aimed at assessing the importance of the soil moisture on the impact of copper (Cu) contaminations on the N cycling soil function using the potential nitrification activities (PNA) as bioindicator. A two-step experiment was performed. First, a loamy soil was incubated 5 weeks in either 30, 60, or 90% of its water holding capacity (WHC) or alternating drought and rewetting periods. Thereafter, soil samples were exposed to a gradient of Cu concentrations through a bioassay involving nitrification. The dose-response curves of PNA in function of added Cu were modeled to calculate the effective Cu concentrations, namely ECx with x being the percentage of PNA inhibition. These values were then compared between experimental conditions to highlight differences in threshold values. The preincubation moisture treatments significantly affected the PNA responses to the secondary Cu stress with, for instance, hormetic responses in all cases except for the dry-rewetting treatment. Small PNA inhibitions were estimated for high Cu doses in the soils with low water contents (30% WHC) or submitted to dry-rewetting cycles, contrarily to the patterns observed for the soils with high water contents (90% WHC) or submitted to a single period of drought. Overall, significant differences were found in estimated ECx values between moisture treatments.

In the global methane budget, the largest natural source is attributed to wetlands, which encompass all ecosystems composed of waterlogged or inundated ground, capable of methane production. Among them, northern peatlands that store large amounts of soil organic carbon have been functioning, since the end of the last glaciation period, as long-term sources of methane (CH4) and are one of the most significant methane sources among wetlands. To reduce uncertainty of quantifying methane flux in the global methane budget, it is of significance to understand the underlying processes for methane production and fluxes in northern peatlands. A methane model that features methane production and transport by plants, ebullition process and diffusion in soil, oxidation to CO2, and CH4 fluxes to the atmosphere has been embedded in the ORCHIDEE-PEAT land surface model that includes an explicit representation of northern peatlands. ORCHIDEE-PCH4 was calibrated and evaluated on 14 peatland sites distributed on both the Eurasian and American continents in the northern boreal and temperate regions. Data assimilation approaches were employed to optimized parameters at each site and at all sites simultaneously. Results show that methanogenesis is sensitive to temperature and substrate availability over the top 75 cm of soil depth. Methane emissions estimated using single site optimization (SSO) of model parameters are underestimated by 9 g CH4 m−2 yr−1 on average (i.e., 50 % higher than the site average of yearly methane emissions). While using the multi-site optimization (MSO), methane emissions are overestimated by 5 g CH4 m−2 yr−1 on average across all investigated sites (i.e., 37 % lower than the site average of yearly methane emissions).

International audience; • A meta-analysis was performed to test whether organic matter "priming" occurs in aquatic ecosystems • We show that despite large variability between observations, priming is a generalizable feature of aquatic ecosystems • This variability can be explained by two main factors: How priming is measured and field/laboratory differences

Leaching of dissolved organic carbon (DOC) from soils to the river network is an important component of the land carbon (C) budget. At regional to global scales, its significance has been estimated through simple mass budgets, often using multi-year averages of observed fluvial DOC fluxes as proxy of DOC leaching due to the limited availability of observations of the leaching flux itself. This procedure leads to a systematic underestimation of the leaching flux because of the reactivity of DOC during fluvial transport. Moreover, this procedure does not allow to reveal spatio-temporal variability in DOC leaching from soils, which is needed to better understand the drivers of DOC leaching and its impact on the local soil C budget. In this study, we use the land surface model ORCHILEAK to simulate the terrestrial C budget including leaching of DOC from the soil and its subsequent reactive transport through the river network of Europe. The model performance is not only evaluated against the sparse observations of DOC leaching, but also against the more abundant observations of fluxes and reactivity of DOC in rivers, providing further evidence that our simulated DOC leaching fluxes are realistic. The model is then used to simulate the spatio-temporal patterns of DOC leaching across Europe over the period 1972 to 2012, quantifying both the environmental drivers of these patterns as well as the impact of DOC leaching on the land C budget. Over the simulation period, we find that on average 14.3 TgC yr−1 of DOC is leached from land to European rivers, which is only about 0.6 % of the terrestrial net primary production, a fraction about one order of magnitude lower than reported for tropical river networks. Of the DOC leaching, on average 12.3 TgC yr−1 is exported to the coast via the river network, the rest being respired in transit. DOC leaching presents a large seasonal variability, with a maximum occurring in winter and a minimum in summer, except for the Northern most part of Europe where the maximum occurs in spring due to the snow melt. DOC leaching rate is generally lower in warm and dry regions, and higher in cold and wet regions of Europe. Furthermore, runoff, and the ratio between runoff from shallower flow paths vs. deep drainage and groundwater flow, is the main driver of the spatial variation of DOC leaching. Temperature, as a major control of DOC production and decomposition rates in the soils, plays only a secondary role.