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Monitoring and estimating spatially resolved changes in soil organic carbon (SOC) stocks are necessary for supporting national and international policies aimed at assisting land degradation neutrality and climate change mitigation, improving soil fertility and food production, maintaining water quality, and enhancing renewable energy and ecosystem services. In this work, we report on the development and application of a data-driven, quantile regression machine learning model to estimate and predict annual SOC stocks at plow depth under the variability of climate. The model enables the analysis of SOC content levels and respective probabilities of their occurrence as a function of exogenous parameters such as monthly temperature and precipitation and endogenous, decision-dependent parameters, which can be altered by land use practices. The estimated quantiles and their trends indicate the uncertainty ranges and the respective likelihoods of plausible SOC content. The model can be used as a reduced-form scenario generator of stochastic SOC scenarios. It can be integrated as a submodel in Integrated Assessment models with detailed land use sectors such as GLOBIOM to analyze costs and find optimal land management practices to sequester SOC and fulfill food–water–energy–-environmental NEXUS security goals.

A fraction of CO2 uptake by terrestrial ecosystems is exported as organic carbon (C) through the terrestrial‐aquatic continuum. This translocated C plays a significant role in the terrestrial C balance; however, obtaining global assessments remains challenging due to the predominant reliance on empirical approaches. Leaching of dissolved organic C (DOC) from soils to rivers represents an important fraction of this C export and is assumed to drive a large proportion of the net‐heterotrophy of river systems and the related CO2 emissions. Using the model JULES‐DOCM, we projected DOC leaching trends over the 21st century based on three scenarios with high (RCP 2.6), intermediate (RCP 4.5), and low (RCP 8.5) climate mitigation efforts. The RCP 8.5 scenario led to the largest DOC leaching increase of +42% to 395 Tg C yr−1 by 2100. In comparison, RCP 2.6 and RCP 4.5 led to increases of 10% and 21%, respectively. Under RCP 8.5, the sub‐tropical zone showed the highest relative increase of 50% above current levels. In the boreal and tropical zones, the simulations revealed similar increases of 48% and 41%, respectively. However, given the pre‐eminence of the tropics in DOC leaching, the absolute increment is markedly substantial from this region (+59 Tg C yr−1). The temperate zone displayed the lowest relative increase with 35%. Our analysis identified the rising atmospheric CO2 concentration and its fertilizing effect on terrestrial NPP as the main reason for the future increase in DOC leaching. Plain Language Summary Terrestrial ecosystems absorb CO2 and some of this is transformed into dissolved organic carbon (DOC) that leaches from soils to inland waters, driving aquatic CO2 emissions. Using the JULES‐DOCM model, we analyzed future DOC leaching trends under different climate scenarios. The scenario of low climate change mitigation led to the most significant leaching increase by the end of the century. Among various regions, the sub‐tropical areas showed the greatest relative growth in DOC leaching, with the tropics also seeing a substantial rise. This increase in DOC leaching is mainly driven by rising atmospheric CO2 levels, which boosts plant growth and productivity on land. Key Points We predict an increase of global soil dissolved organic carbon (DOC) leaching of up to 42% or 395 Tg C yr−1 by 2100 The sub‐tropical zone experienced the greatest relative DOC leaching growth, while the tropics had a notable absolute increase The primary driver for the future surge in DOC leaching is the rising atmospheric CO2 and its fertilizing effect on terrestrial NPP

Carbon assimilation and wood production are influenced by environmental conditions and endogenous factors, such as species auto-ecology, age, and hierarchical position within the forest structure. Disentangling the intricate relationships between those factors is more pressing than ever due to climate change’s pressure. We employed the 3D-CMCC-FEM model to simulate undisturbed forests of different ages under four climate change (plus one no climate change) Representative Concentration Pathways (RCP) scenarios from five Earth system models. In this context, carbon stocks and increment were simulated via total carbon woody stocks and mean annual increment, which depends mainly on climate trends. We find greater differences among different age cohorts under the same scenario than among different climate scenarios under the same age class. Increasing temperature and changes in precipitation patterns led to a decline in above-ground biomass in spruce stands, especially in the older age classes. On the contrary, the results show that beech forests will maintain and even increase C-storage rates under most RCP scenarios. Scots pine forests show an intermediate behavior with a stable stock capacity over time and in different scenarios but with decreasing mean volume annual increment. These results confirm current observations worldwide that indicate a stronger climate-related decline in conifers forests than in broadleaves.

Most land surface models (LSMs), i.e. the land components of Earth system models (ESMs), include representation of nitrogen (N) limitation on ecosystem productivity. However, only a few of these models have incorporated phosphorus (P) cycling. In tropical ecosystems, this is likely to be important as N tends to be abundant, whereas the availability of rock-derived elements, such as P, can be very low. Thus, without a representation of P cycling, tropical forest response in areas such as Amazonia to rising atmospheric CO2 conditions remain highly uncertain. In this study, we introduced P dynamics and its interactions with the N and carbon (C) cycles into the Joint UK Land Environment Simulator (JULES). The new model (JULES-CNP) includes the representation of P stocks in vegetation and soil pools, as well as key processes controlling fluxes between these pools. We develop and evaluate JULES-CNP using in situ data collected at a low-fertility site in the central Amazon, with a soil P content representative of 60 % of soils across the Amazon basin, to parameterize, calibrate, and evaluate JULES-CNP. Novel soil and plant P pool observations are used for parameterization and calibration, and the model is evaluated against C fluxes and stocks and those soil P pools not used for parameterization or calibration. We then evaluate the model at additional P-limited test sites across the Amazon and in Panama and Hawaii, showing a significant improvement over the C- and CN-only versions of the model. The model is then applied under elevated CO2 (600 ppm) at our study site in the central Amazon to quantify the impact of P limitation on CO2 fertilization. We compare our results against the current state-of-the-art CNP models using the same methodology that was used in the AmazonFACE model intercomparison study. The model is able to reproduce the observed plant and soil P pools and fluxes used for evaluation under ambient CO2. We estimate P to limit net primary productivity (NPP) by 24 % under current CO2 and by 46 % under elevated CO2. Under elevated CO2, biomass in simulations accounting for CNP increase by 10 % relative to contemporary CO2 conditions, although it is 5 % lower compared to CN- and C-only simulations. Our results highlight the potential for high P limitation and therefore lower CO2 fertilization capacity in the Amazon rainforest with low-fertility soils.

The CO 2 efflux from soil (soil respiration (SR)) is one of the largest fluxes in the global carbon (C) cycle and its response to climate change could strongly influence future atmospheric CO 2 concentrations. Still, a large divergence of global SR estimates and its autotrophic (AR) and heterotrophic (HR) components exists among process based terrestrial ecosystem models. Therefore, alternatively derived global benchmark values are warranted for constraining the various ecosystem model output. In this study, we developed models based on the global soil respiration database (version 5.0), using the random forest (RF) method to generate the global benchmark distribution of total SR and its components. Benchmark values were then compared with the output of ten different global terrestrial ecosystem models. Our observationally derived global mean annual benchmark rates were 85.5 ± 40.4 (SD) Pg C yr −1 for SR, 50.3 ± 25.0 (SD) Pg C yr −1 for HR and 35.2 Pg C yr −1 for AR during 1982–2012, respectively. Evaluating against the observations, the RF models showed better performance in both of SR and HR simulations than all investigated terrestrial ecosystem models. Large divergences in simulating SR and its components were observed among the terrestrial ecosystem models. The estimated global SR and HR by the ecosystem models ranged from 61.4 to 91.7 Pg C yr −1 and 39.8 to 61.7 Pg C yr −1 , respectively. The most discrepancy lays in the estimation of AR, the difference (12.0–42.3 Pg C yr −1 ) of estimates among the ecosystem models was up to 3.5 times. The contribution of AR to SR highly varied among the ecosystem models ranging from 18% to 48%, which differed with the estimate by RF (41%). This study generated global SR and its components (HR and AR) fluxes, which are useful benchmarks to constrain the performance of terrestrial ecosystem models.

Lateral movement of soil organic carbon (SOC) induced by soil erosion and runoff changes spatial distributions of SOC, and further changes the land‐atmosphere CO2 exchange and terrestrial carbon budget. However, current ecosystem models do not or only poorly integrate the process of SOC lateral movement and cannot simulate the impacts of soil erosion on the carbon cycle. This study integrated SOC erosion and deposition processes into a process‐based ecosystem model (i.e., Integrated BIosphere Simulator (IBIS)), and separately simulated the lateral movements of dissolved organic carbon (DOC) and particulate organic carbon (POC). The model was evaluated in three river basins in Northeast China that are dominated by cropland, forest, and grassland. The results showed that the model reproduced well the production, erosion, and deposition of DOC and POC. The annual SOC lateral movement (1.34–7.22 g C m−2 yr−1) induced by erosion in the three tested basins was 0.27%–1.45% of the annual net primary production. The model developed in this study has great implications for simulating the lateral movements of SOC in terrestrial ecosystems, which can improve model performance in projecting the terrestrial carbon budget. Plain Language Summary Lateral movement of soil organic carbon (SOC) with soil erosion and runoff is an important process in estimating land carbon budget. However, the current ecosystem models are not or poorly integrated this process, and cannot simulate the impacts of lateral movement of SOC on carbon cycle. This study integrates SOC erosion and deposition processes into a process‐based ecosystem model (Integrated BIosphere Simulator (IBIS)), and separately simulates the lateral movements of dissolved organic carbon (DOC) and particulate organic carbon (POC). The model was evaluated at three river basins in Northeast China dominated by cropland, forest and grassland, respectively. The results showed the model can reproduce well the production, erosion, deposition of DOC and POC. The model developed in this study has great implications for simulating the lateral movements of SOC in terrestrial ecosystem, which can improve model performance on projecting terrestrial carbon budget. Key Points Current ecosystem models inadequately depict the lateral movement of SOC, causing uncertainties in terrestrial carbon cycle modeling We integrated SOC erosion and deposition into a process‐based ecosystem model and evaluated it in three Chinese river basins Model simulations well represented observed data for the production, erosion, and deposition of organic carbon

The lateral transfer of organic carbon along the terrestrial-aquatic continuum is an important link in the global carbon (C) cycle and an important process which should not be ignored when assessing or modelling changes in terrestrial and aquatic C budgets. The amounts of C exported from terrestrial ecosystem into the inland water network have so far only coarsely been estimated by closing a budget based on observed fluvial C exports to the coast and the still poorly constrained estimates of inland water CO2 evasion and C burial in aquatic sediments. The representation of lateral C transfers in Earth System models (ESMs) will arguably help to improve the representation of soil C cycling and its response to climate change and atmospheric CO2 increase. A first and critical step in that direction is to include processes of production and export of dissolved organic carbon (DOC) in soils. Hence, in the first part of my thesis I developed an extension of the Joint UK Land Environment Simulator (JULES-DOCM) that integrates a representation of DOC production in terrestrial ecosystems based on incomplete decomposition of organic matter, DOC decomposition within the soil column, and DOC export to the river network via leaching. Our results showed that the model is able to reproduce the DOC concentration and controlling processes including leaching to the riverine system which is fundamental for integrating terrestrial and aquatic ecosystems.In the second part of my thesis, I optimized JULES-DOCM for global scale application by recalibrating two key processes controlling soil DOC concentrations: the rate of DOC production associated with soil organic carbon decomposition and the rate of DOC decomposition for the locations where observations were available. Then I used JULESDOCM with these optimised parameters to simulate the global distribution of soil DOC concentrations and DOC leaching fluxes from soils to rivers.For the third part of my thesis, I used JULES-DOCM to simulate spatial-temporal trends in DOC inputs from soil to the river system from 1860 to 2010 at global scale, quantifying the impacts of major environmental drivers such as CO2 fertilization, climate and land use change. At the global scale, CO2 fertilization was identified as the main controller, followed by climate and land use change. Contrary to general assumptions, we find land use changes to only play a minor role in driving the changes in DOC leaching.In the last of my work I used JULES-DOCM and three representative concentration pathways (RCPs), RCP 2.6, RCP 4.5 and RCP 8.5 in order to estimate the future of terrestrial transported DOC flux to the river system. We find the increase of the atmospheric CO2 concentration as the main reason of the future increase of transported terrestrial DOC. In this thesis, I focussed on the detailed representation of soil DOC cycling and leaching, and simulated the historical and future trend of it. However, future work should include the fate of exported DOC in the river system as well as the exports of dissolved inorganic C and particulate organic C from soils to complete the representation of lateral C exports through the terrestrialaquatic continuum.

Peatlands have often been neglected in Earth system models (ESMs). Where they are included, they are usually represented via a separate, prescribed grid cell fraction that is given the physical characteristics of a peat (highly organic) soil. However, in reality soils vary on a spectrum between purely mineral soil (no organic material) and purely organic soil, typically with an organic layer of variable thickness overlying mineral soil below. They are also dynamic, with organic layer thickness and its properties changing over time. Neither the spectrum of soil types nor their dynamic nature can be captured by current ESMs.Here we present a new version of an ESM land surface scheme (Joint UK Land Environment Simulator, JULES) where soil organic matter accumulation – and thus peatland formation, degradation and stability – is integrated in the vertically resolved soil carbon scheme. We also introduce the capacity to track soil carbon age as a function of depth in JULES and compare this to measured peat age–depth profiles. The new scheme is tested and evaluated at northern and temperate sites.This scheme simulates dynamic feedbacks between the soil organic material and its thermal and hydraulic characteristics. We show that draining the peatlands can lead to significant carbon loss, soil compaction and changes in peat properties. However, negative feedbacks can lead to the potential for peatlands to rewet themselves following drainage. These ecohydrological feedbacks can also lead to peatlands maintaining themselves in climates where peat formation would not otherwise initiate in the model, i.e. displaying some degree of resilience.The new model produces similar results to the original model for mineral soils and realistic profiles of soil organic carbon for peatlands. We evaluate the model against typical peat profiles based on 216 northern and temperate sites from a global dataset of peat cores. The root-mean-squared error (RMSE) in the soil carbon profile is reduced by 35 %–80 % in the best-performing JULES-Peat simulations compared with the standard JULES configuration. The RMSE in these JULES-Peat simulations is 7.7–16.7 kg C m-3 depending on climate zone, which is considerably smaller than the soil carbon itself (around 30–60 kg C m-3). The RMSE at mineral soil sites is also reduced in JULES-Peat compared with the original JULES configuration (reduced by ∼ 30 %–50 %). Thus, JULES-Peat can be used as a complete scheme that simulates both organic and mineral soils. It does not require any additional input data and introduces minimal additional variables to the model. This provides a new approach for improving the simulation of organic and peatland soils and associated carbon-cycle feedbacks in ESMs.

Current global models of the carbon (C) cycle consider only vertical gas exchanges between terrestrial or oceanic reservoirs and the atmosphere, thus not considering the lateral transport of carbon from the continents to the oceans. Therefore, those models implicitly consider all of the C which is not respired to the atmosphere to be stored on land and hence overestimate the land C sink capability. A model that represents the whole continuum from atmosphere to land and into the ocean would provide a better understanding of the Earth's C cycle and hence more reliable historical or future projections. A first and critical step in that direction is to include processes representing the production and export of dissolved organic carbon in soils. Here we present an original representation of dissolved organic C (DOC) processes in the Joint UK Land Environment Simulator (JULES-DOCM) that integrates a representation of DOC production in terrestrial ecosystems based on the incomplete decomposition of organic matter, DOC decomposition within the soil column, and DOC export to the river network via leaching. The model performance is evaluated in five specific sites for which observations of soil DOC concentration are available. Results show that the model is able to reproduce the DOC concentration and controlling processes, including leaching to the riverine system, which is fundamental for integrating terrestrial and aquatic ecosystems. Future work should include the fate of exported DOC in the river system as well as DIC and POC export from soil.