Accelerating permafrost thaw may release vast deep (>3 meters) frozen soil carbon as carbon dioxide (CO 2 ), but this magnitude remains uncertain because current Earth system models (ESMs) lack deep carbon processes. Using an updated ORCHIDEE-MICT model simulating Pleistocene Yedoma formation and Holocene peatland development, we project northern (>30°N) carbon responses under climate change. Compared to the original model, including these deep carbon pools improves agreement with observations and reduces net CO 2 uptake by 47 to 74 petagrams of carbon from 1900 to 2100 across three future scenarios because of deep carbon decomposition with accelerated active-layer deepening. Under high-emission pathways, the northern soil carbon balance shifts from a sink to a source of 32 petagrams of carbon, advancing the reversal reported in earlier studies into the 21st century. Consistent with field data, our model shows that colder soils retain more labile carbon—contrary to assumptions in many Coupled Model Intercomparison Project (CMIP) models—helping explain their persistent sink bias. Our results highlight the need to represent both the quantity and quality of permafrost carbon in ESMs.

Abstract Peatlands store about one‐third of total global soil carbon. Vegetation composition strongly regulates peatland carbon dynamics. Global warming and climate‐driven ecohydrological changes are expected to alter peatland vegetation composition, necessitating accurate simulation of vegetation dynamics to predict future fate of peatland carbon. We incorporated six plant functional types (PFTs) into the ORCHIDEE‐PEAT model to represent bryophytes (mosses), C3 graminoids (sedges and grasses), boreal broadleaf deciduous shrubs, boreal needleleaf evergreen trees, tropical evergreen and raingreen (water‐driven deciduous) trees growing in peatlands. The introduction and elimination of each PFT in response to bioclimatic conditions, as well as sapling establishment, growth, mortality, and competition among PFTs, are explicitly modeled. Simulated vegetation distributions align well with site‐level observations from West Siberian wetlands, where extensive vegetation composition measurements are available for model evaluation. The model slightly overestimated gross primary productivity (GPP) across 60 sites. Evaluation using global satellite‐derived land cover, leaf area index and GPP data was encouraging, though challenges lie in the lack of observational data specific to peatlands. From 1901 to 2020, simulated tropical peatland vegetation composition remains relatively stable. In northern peatlands, as a result of warming and declining water table, bryophyte and C3 graminoid cover decrease by 0.2 (13%) and 0.1 (13%) million km2, respectively, while shrub and tree cover increase by 0.3 (75%) and 0.03 (2%) million km2, respectively. The impacts of these vegetation shift on peatland carbon balance can be explored in future studies using the model, which integrates peatland vegetation dynamics with peatland‐specific hydrology and carbon cycling.

Peatland drying is an important process affecting greenhouse gas (GHG) emissions. Ditching of peat for drainage to plant forest has been a widespread management practice in the Nordic countries, and drying increasingly occurs also from climate change induced drought. Previously published meta-analyses from literature suggest that drainage increases soil CO2 emissions by enhancing oxic decomposition in aerated upper layers while suppressing CH4 emissions. However, these data do not elucidate short-term variations of GHG fluxes during drainage and usually only regress GHG emissions as a function of the annual mean water table. Here we developed a new parameterization of peat drainage in a land surface model that represents peat processes and fluxes of CO2 and CH4, by adding a machine-learning module to predict the daily water table depths from simulated soil moisture in the upper soil layers and a ditch that receives drainage water. Because peatland pre-drainage GHG emissions vary between sites and influence subsequent changes following drainage, idealized simulations were performed for virtual drainage applied to a collection of 10 pristine sites, where the model parameters are calibrated against observed GHG fluxes. Different drainage intensities are simulated by prescribing lower water table depths from setting the ditch depth from 5-80 cm below the initial water surface. The resulting GHG flux changes across sites are compared with meta-analysis data from northern sites and show realistic results with a reduced CO2 sink and reduced CH4 emissions. Additional comparison with continuous flux data collected in the UK for different sites associated with increasing drainage levels also shows good model performance. Overall, using GWP100 to compare the effect of CH4 vs. CO2 flux changes, our model simulations suggest only small net GHG emission changes in CO2-equivalent GHG emissions under drainage scenarios over multi-decadal timescales, yet with differences between sites. Over time, simulated emission factors of CO2 flux decrease because of exhaustion of labile soil organic substrate for decomposition, while reductions in CH4 emissions are amplified due to decreased availability of material for anoxic decomposition. The sensitivities of CO2 flux changes to increased water table depth changes are primarily controlled by initial CO2 and CH4 fluxes, initial soil carbon content, peat vegetation community, air temperature and initial water table depth. The influence of peat vegetation on the GHG flux sensitivities in the model occurs via differing lability of soil organic carbon pools, with moss-dominated sites having a lower sensitivity due to their longer peat turnover time. Nonetheless, our calibrated global model remains limited in its ability to mechanistically represent drained peatland systems, particularly regarding extrapolation and representation of dynamic soil and hydrological processes. Our model-simulated sensitivities of GHG fluxes to drainage can be approximated by linear regressions using site-level variables, which, despite the limitations, may offer a simplified, exploratory tool for estimating drainage effects.

During land model development, simulated carbon dynamics are often benchmarked against observational data sets to evaluate model performance. Functional relationship benchmarks are the relationship between a driving variable (e.g., temperature) and a response variable (e.g., ecosystem respiration) and are a promising tool for assessing model performance by evaluating modeled sensitivities to changing environmental conditions. However, observed functional relationships can be influenced by choices made during data collection and throughout the benchmarking process, impacting the inferred skill of land models. To avoid misrepresenting a model's true performance, it is necessary to systematically evaluate best practices when constructing functional relationship benchmarks. We developed a set of guidelines for constructing functional relationship benchmarks, considering the choice of data set, number of daily observations, temporal extent, and temporal resolution across Alaska and Canada over a 20-year period from 2001 to 2020. The temperature sensitivity of ecosystem respiration from observations, evaluated through an apparent Q 10, is highly variable both spatially and as a result of the data processing approach applied in the benchmark formation. When benchmarking 13 models from the Warming Permafrost Model Intercomparison Project (WrPMIP), the range in inferred model skill is substantially impacted by the choices applied in constructing functional relationship benchmarks. The inferred performance of a given model is most sensitive to the number of daily observations and temporal extent, followed by choice of benchmark data set and temporal averaging. Results from this analysis can guide the development of consistent and robust functional relationships for future model evaluation studies.

Accelerating permafrost thaw may mobilize vast stores of deep and frozen soil carbon (>3 m), releasing CO2 into the atmosphere. Yet, the magnitude of this release remains uncertain due to the absent deep carbon processes in current Earth system models (ESMs). Here, we use an updated ORCHIDEE-MICT model that explicitly simulates Yedoma formation during the Pleistocene and the transient development of northern peatlands during the Holocene to project northern (>30°N) carbon responses under climate change. Incorporating these deep, frozen carbon pools improves agreement with carbon cycle observations and reduces previously projected net CO2 uptake by 47–74 Pg C between 1900 and 2100 across three future scenarios. Under high-emission pathways, the northern soil carbon balance shifts from a net sink to a net source of up to 32 Pg C, advancing the reversal predicted by the original model earlier in the 21st century. This earlier reversal is primarily driven by accelerated deepening of the active layer after mid-century, exposing more previously frozen carbon, particularly from Yedoma. Consistent with field data, our model shows that colder soils retain more labile carbon—contrary to assumptions in many IPCC models, which helps explain their prediction of a continuous carbon sink. Our results highlight the need to represent both the quantity and quality of permafrost carbon in ESMs to improve projections of permafrost–climate feedbacks.

Arctic and boreal fires are critical threats to terrestrial carbon reservoirs, particularly peat fires that trigger long-term irrecoverable carbon losses and permafrost thaw. However, the occurrence of peat fires and their associated carbon emissions remain highly uncertain. 30-meter satellite-derived maps of burned area and peatland coverage reveal that Siberian fires burned over 107 million hectares during the 2001 to 2023 period, with peat fires accounting for up to one-third of this area. These peat fires emitted 1.24 ± 0.06 petagram of carbon, largely exceeding conventional datasets' estimates. We found that anomalous dry and warm climatic conditions represent the primary driver of extreme peat fire seasons and that overwintering of 2020's late-season peat fires substantially contributed to extensive fires of 2021. Peat fires, especially those in Arctic regions, exhibit a pronounced sensitivity to extreme weather, posing a critical threat to the stability of permafrost peatlands and their large carbon stocks.

Abstract. Peatlands cover only ~3 % of Earth’s land surface yet store ~30 % of global soil carbon (C), making them critical components of the terrestrial C cycle and influential regulators of C-climate feedbacks. However, their responses to climate warming and elevated CO₂ remain highly uncertain, in part because Earth system models represent peatland processes with varying levels of complexity and realism, and because peat C pools turn over on centennial to millennial timescales that challenge model evaluation. Here, we present results from the SPRUCE (Spruce and Peatland Responses Under Changing Environments) Model Intercomparison Project (SPRUCE-MIP), which evaluates 15 terrestrial ecosystem models against observations from a long-term whole-ecosystem warming and CO₂-enrichment experiment at an ombrotrophic bog in northern Minnesota, USA. Models were driven by observed meteorology from in situ warming treatments spanning +0 to +9 °C, at either ambient or elevated CO₂ (+500 ppm). Simulated net ecosystem exchange (NEE), net primary productivity (NPP), heterotrophic respiration (HR), and methane (CH₄) fluxes were benchmarked against multi-year observations. Model predictions exhibit a large spread in both baseline C balance, ranging from strong sinks to strong sources under +0 °C warming, and temperature sensitivity, indicating substantial uncertainty in predicting peatland C responses to environmental forcing. Across the ensemble, models fall into four distinct functional response types: (i) models that simulate peatlands as net C sources across all warming and CO₂ conditions; (ii) models that transition from C sinks to sources under warming irrespective of CO₂ level; (iii) models that transition only under ambient CO₂ but remain C sinks under elevated CO₂, reflecting strong CO₂ fertilization effects; and (iv) models that maintain persistent sinks or near-neutral C balance even under extreme warming. While most models predict enhanced NPP under elevated CO₂ across all warming treatments, SPRUCE observations show little or no NPP enhancement under elevated CO₂ at the +0 and +2.25 °C warming levels, with a positive CO₂ fertilization effect emerging only under stronger warming. This discrepancy highlights persistent model biases in representing interactions between warming and CO₂ responses. Process-based analysis further indicates that divergence in modeled responses is associated with differences in vegetation structure (light competition and moss representation), nutrient cycling (nitrogen and phosphorus), and dynamic peat representation. These structural differences systematically influence ecosystem productivity, heterotrophic respiration, and net C balance across the model ensemble. An illustrative parameter sensitivity analysis using the SPRUCE-specific Energy Exascale Earth System Model (E3SM) Land Model (ELM-SPRUCE) further shows that parameter choices can also modulate the magnitude of simulated responses. Together, these results demonstrate that peatland responses to warming and elevated CO2 are highly sensitive to model structure, parameterization, and process coupling, highlighting the need for improved representation of key peatland processes to reduce uncertainty in Earth system projections. The SPRUCE experimental framework provides a unique benchmark for evaluation and improving process representation and constraining near-term peatland response to environmental change, thereby strengthening confidence in longer-term C-climate projections.

To meet the Paris Agreement temperature goal, allowable carbon emissions in the future are tightly limited. It is very likely that the 1.5°C temperature limit will be at least temporarily exceeded (overshoot) under an emission pathway following current climate policies and actions. Peatlands store large amounts of soil carbon, the destabilization of which could potentially cause large amplifying feedback on global warming. Using the reduced-complexity Earth system model OSCAR v3.1.2 and a new peat carbon module, we assessed whether carbon emissions from northern peatlands triggered by climate change will increase the chance and intensity of temperature overshoot. We found that, although northern peatlands continue to accumulate carbon, they represent positive feedback under climate change through their high CH4 emissions. For a 1°C increase in peak temperature anomaly, emissions from peatlands further contribute to the peak temperature by 0.02 (0.01-0.02) °C. Considering northern peatlands would lead to a reduction in the carbon budget by about 40 (16-60) GtCO2, or 8.6% for 1.5°C, and a reduction of about 105 (45-166) GtCO2 reduction (or 4.2% relative decrease) for 2.5°C. Our findings highlight the importance of properly accounting for northern peatland emissions for estimating climate feedbacks, especially under overshoot scenarios.

The surface energy budget plays a critical role in terrestrial hydrologic and biogeochemical cycles. Nevertheless, its highly spatial heterogeneity across different vegetation types is still missing in the land surface model, ORCHIDEE-MICT (ORganizing Carbon and Hydrology in Dynamic EcosystEms–aMeliorated Interactions between Carbon and Temperature). In this study, we describe the representation of a multi-tiling energy budget in ORCHIDEE-MICT and assess its short and long-term impacts on energy, hydrology, and carbon processes. We found that: 1) With the specific values of surface properties for each vegetation type, the new version presents warmer surface and soil temperatures, wetter soil moisture, and increased soil organic carbon storage across the Northern Hemisphere. 2) Despite reproducing the absolute values and spatial gradients of surface and soil temperatures from satellite and in-situ observations, the considerable uncertainties in simulated soil organic carbon and hydrologic processes prevent an obvious improvement of temperature bias existing in the original ORCHIDEE-MICT. 3) The simulated continuous permafrost area (15.2 Mkm2) and non-continuous permafrost area (3.1 Mkm2) are comparative to observation-based datasets from Brown et al. (2002) (10.8 Mkm2 for continuous and 4.6 Mkm2 for non-continuous) and Obu et al. (2019) (11.5 Mkm2 for continuous and 5.3 Mkm2 for non-continuous). Consequently, the new version will facilitate various model-based permafrost studies in the future.

Peatlands cover only 3% of Earth’s land surface but contain about 30% of the global soil carbon pool. The strong sensitivity of C cycle to environmental factors such as soil temperature and moisture has let to concerns about potential positive feedbacks to climate change. However, global models disagree as to the magnitude and spatial distribution of emissions, partially due to missing representations of peatland relevant processes and a scarcity of in situ observations. The Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment is a large‐scale climate change manipulation that focuses on the combined response of multiple levels of warming at both ambient and elevated CO2 concentration (eCO2), making it a valuable testbed for the broader modeling community to improve the diagnosis and attribution of C fluxes in peatland ecosystems. Currently, there are 11 models participating in the SPRUCE Model Intercomparison Project (SPRUCE-MIP). In the first stage, all model groups used observed ambient plot atmospheric forcing data to drive a model spin-up simulation with pre-industrial conditions, and a transient simulation with transient atmospheric CO2 concentrations and nitrogen deposition from 1850 to 2014. Then, measured plot-level meteorological forcing and CO2 concentrations from the 10 treatment enclosures and the ambient plot drove 11 transient simulations from 2015 to 2021. The total of 11 simulations represents five levels of temperature treatment with two CO2 levels, and ambient control plot with no enclosure. The five treatment temperatures are +0, 2.25, 4.5, 6.75, 9oC, and the two CO2 levels are ambient and +500 ppm. We evaluated the performance of multiple models against SPRUCE observations, such as the net ecosystem exchange (NEE) and CH4 fluxes warming responses under ambient and eCO2 conditions and found that there were wide spreads for warming responses among different models. We will further evaluate the model performances and quantify the associated uncertainties, which may have helpful implications for our understanding of the peatland C cycle and for future projections of Earth system models.

Peatlands are significant carbon reservoirs vulnerable to climate change and land use change such as drainage for cultivation or forestry. We modified the ORCHIDEE-PEAT global land surface model, which has a detailed description of peat processes, by incorporating three new peatland-specific plant functional types (PFTs), namely deciduous broadleaf shrub, moss and lichen, as well as evergreen needleleaf tree in addition to previously peatland graminoid PFT to simulate peatland vegetation dynamic and soil CO2 fluxes. Model parameters controlling photosynthesis, autotrophic respiration, and carbon decomposition have been optimized using eddy-covariance observations from 14 European peatlands and a Bayesian optimization approach. Optimization was conducted for each individual site (single-site calibration) or all sites simultaneously (multi-site calibration). Single-site calibration performed better, particularly for gross primary production (GPP), with root mean square deviation (RMSD) reduced by 53%. While multi-site calibration showed limited improvement (e.g., RMSD of GPP reduced by 22%) due to the model's inability to account for spatial parameter variations under different climatic contexts (trait-climate correlations). Site-optimized parameters, such as Q10, the temperature sensitivity of heterotrophic respiration, revealed strong empirical relationships with environmental factors, such as air temperature. For instance, Q10 decreased significantly at warmer sites, consistent with independent field data. To improve the model by using the lessons from single-site optimization, we incorporated two key trait-climate relationships for Q10 and Vcmax (maximum carboxylation rate) into a new version of the ORCHIDEE-PEAT models. Using this description of spatial variability of parameters holds significant promise for improving the accuracy of carbon cycle simulations in peatlands.

Peatlands store about one-third of global soil organic carbon. The carbon dynamics and storage of peatlands depend on the balance between plants’ carbon uptake and microbial carbon decomposition. As a result of global warming and climate-driven ecohydrological changes, the plant community composition of peatlands is projected to change, affecting the carbon sequestration and storage capacity of these ecosystems both directly and indirectly by modulating water flows. However, while there has been a notable focus on studying the variation in the water table position of peatlands and its consequential influence on the dynamics of peatland soil carbon, the impacts of peatland plant community composition have been largely overlooked. To accurately predict peatland carbon dynamics, land surface models need to account for the diversity of peatlands plant types and the competitive interactions among them. We incorporated six plant functional types (PFT) into the ORCHIDEE-PEAT model to represent mosses, grasses, shrubs, and trees growing in peatlands. Areas covered by each PFT are functions of the bioclimatic limitations, mortality, and establishment of each PFT, as well as competitions among PFTs. The model will be employed to assess the effect of climate change on peatland vegetation dynamics and carbon fluxes.

Organic carbon burial (OCB) in lakes, a critical component of the global carbon cycle, surpasses that in oceans, yet its response to global warming and associated feedbacks remains poorly understood. Using a well-dated biomarker sequence from the southern Tibetan Plateau and a comprehensive analysis of Holocene total organic carbon variations in lakes across the region, here we demonstrate that lake OCB significantly declined throughout the Holocene, closely linked to changes in temperature seasonality. Process-based land surface model simulations clarified the key impact of temperature seasonality on OCB in lakes: increased seasonality in the early Holocene saw warmer summers enhancing ecosystem productivity and organic matter deposition, while cooler winters improved organic matter preservation. The Tibetan Plateau's heightened sensitivity to climate and ecosystem dynamics amplifies these effects. With declining temperature seasonality, we predict a significant slowdown or reduction in OCB across these lake sediments, leading to carbon emissions and amplified global warming.

Field measurements, after extrapolation, suggest that deep Yedoma deposits (ice-rich, organic-rich permafrost, formed during the late Pleistocene) and peatlands (formed mostly during the Holocene) account for about 600 Pg C of soil carbon storage. Incorporating this old, deep, cold carbon into land surface models (LSMs) is crucial for accurately quantifying soil carbon responses to future warming. However, it remains underrepresented or absent in current LSMs, which typically include a passive soil carbon pool (a conceptual soil carbon pool with the longest turnover time) to represent all "old carbon" and lack the vertical accumulation processes that deposited deep carbon in the layers of peatlands and Yedoma deposits. In this study, we propose a new, more realistic protocol for simulating deep and cold carbon accumulation in the northern high latitudes (30-90 degrees N), using the ORCHIDEE-MICT (ORganizing Carbon and Hydrology in Dynamic EcosystEms-aMeliorated Interactions between Carbon and Temperature) model. This is achieved by (1) integrating deep carbon from Yedoma deposits whose formation is calculated using Last Glacial Maximum climate conditions and (2) prescribing the inception time and location of northern peatlands during the Holocene using spatially explicit data on peat age. Our results show an additional 157 Pg C in present-day Yedoma deposits, as well as a shallower peat carbon depth (by 1-5 m) and a smaller passive soil carbon pool (by 35 Pg C, 43 %) in northern peatlands, compared to the old protocol that ignored Yedoma deposits and applied a uniform, long-duration (13 500 years) peat carbon accumulation across all peatlands. As a result, the total organic carbon stock across the Northern Hemisphere (30-90 degrees N) simulated by the new protocol is 2028 Pg C, which is 226 Pg C higher than the previous estimate. Despite the significant challenge of simulating deep carbon with ORCHIDEE-MICT, the improvements in the representation of carbon accumulation from this study provide a model version to predict deep carbon evolution during the last glacial-deglacial transition and its response to future warming. The methodology implemented for deep carbon initialization in permafrost and cold regions in ORCHIDEE-MICT is also applicable to other LSMs.

Heat released from soil organic carbon (SOC) decomposition (referred to as microbial heat hereafter) could alter the soil's thermal and hydrological conditions, subsequently modulate SOC decomposition and its feedback with climate. While understanding this feedback is crucial for shaping policy to achieve specific climate goal, it has not been comprehensively assessed. This study employs the ORCHIDEE-MICT model to investigate the effects of microbial heat, referred to as heating effect, focusing on their impacts on SOC accumulation, soil temperature and net primary productivity (NPP), as well as implication on land-climate feedback under two CO2 emissions scenarios (RCP2.6 and RCP8.5). The findings reveal that the microbial heat decreases soil carbon stock, predominantly in upper layers, and elevates soil temperatures, especially in deeper layers. This results in a marginal reduction in global SOC stocks due to accelerated SOC decomposition. Altered seasonal cycles of SOC decomposition and soil temperature are simulated, with the most significant temperature increase per unit of microbial heat (0.31 K J-1) occurring at around 273.15 K (median value of all grid cells where air temperature is around 273.15 K). The heating effect leads to the earlier loss of permafrost area under RCP8.5 and hinders its restoration under RCP2.6 after peak warming. Although elevated soil temperature under climate warming aligns with expectation, the anticipated accelerated SOC decomposition and large amplifying feedback on climate warming were not observed, mainly because of reduced modeled initial SOC stock and limited NPP with heating effect. These underscores the multifaceted impacts of microbial heat. Comprehensive understanding of these effects would be vital for devising effective climate change mitigation strategies in a warming world.

AbstractMicrobial carbon use efficiency (CUE) affects the fate and storage of carbon in terrestrial ecosystems, but its global importance remains uncertain. Accurately modeling and predicting CUE on a global scale is challenging due to inconsistencies in measurement techniques and the complex interactions of climatic, edaphic, and biological factors across scales. The link between microbial CUE and soil organic carbon relies on the stabilization of microbial necromass within soil aggregates or its association with minerals, necessitating an integration of microbial and stabilization processes in modeling approaches. In this perspective, we propose a comprehensive framework that integrates diverse data sources, ranging from genomic information to traditional soil carbon assessments, to refine carbon cycle models by incorporating variations in CUE, thereby enhancing our understanding of the microbial contribution to carbon cycling.

The surface energy budget plays a critical role in terrestrial hydrological and biogeochemical cycles. Nevertheless, its highly spatial heterogeneity across different vegetation types is still missing in the ORCHIDEE-MICT (ORganizing Carbon and Hydrology in Dynamic EcosystEms–aMeliorated Interactions between Carbon and Temperature) land surface model. In this study, we describe the representation of a tiling energy budget in ORCHIDEE-MICT and assess its short-term and long-term impacts on energy, hydrology, and carbon processes. With the specific values of surface properties for each vegetation type, the new version presents warmer surface and soil temperatures (∼ 0.5 °C, +3 %), wetter soil moisture (∼ 10 kg m−2, +2 %), and increased soil organic carbon storage (∼ 170 Pg C, +9 %) across the Northern Hemisphere. Despite reproducing the absolute values and spatial gradients of surface and soil temperatures from satellite and in situ observations, the considerable uncertainties in simulated soil organic carbon and hydrological processes prevent an obvious improvement in the temperature bias existing in the original ORCHIDEE-MICT model. However, the separation of sub-grid energy budgets in the new version improves permafrost simulation greatly by accounting for the presence of discontinuous permafrost types (∼ 3×106 km2), which will facilitate various permafrost-related studies in the future.