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Carbon cycle feedbacks represent large uncertainties in climate change projections, and the response of soil carbon to climate change contributes the greatest uncertainty to this. Future changes in soil carbon depend on changes in litter and root inputs from plants and especially on reductions in the turnover time of soil carbon (τs) with warming. An approximation to the latter term for the top one metre of soil (ΔCs,τ) can be diagnosed from projections made with the CMIP6 and CMIP5 Earth System Models (ESMs), and is found to span a large range even at 2 °C of global warming (-196 ± 117 PgC). Here, we present a constraint on ΔCs,τ, which makes use of current heterotrophic respiration and the spatial variability of τs inferred from observations. This spatial emergent constraint allows us to halve the uncertainty in ΔCs,τ at 2 °C to -232 ± 52 PgC.

This study investigates and compares soil moisture and hydrology projections of broadly used land models with permafrost processes and highlights the causes and impacts of permafrost zone soil moisture projections. Climate models project warmer temperatures and increases in precipitation (P) which will intensify evapotranspiration (ET) and runoff in land models. However, this study shows that most models project a long-term drying of the surface soil (0-20 cm) for the permafrost region despite increases in the net air-surface water flux (P-ET). Drying is generally explained by infiltration of moisture to deeper soil layers as the active layer deepens or permafrost thaws completely. Although most models agree on drying, the projections vary strongly in magnitude and spatial pattern. Land models tend to agree with decadal runoff trends but underestimate runoff volume when compared to gauge data across the major Arctic river basins, potentially indicating model structural limitations. Coordinated efforts to address the ongoing challenges presented in this study will help reduce uncertainty in our capability to predict the future Arctic hydrological state and associated land-atmosphere biogeochemical processes across spatial and temporal scales.

In the version of this Article originally published, a parallelization coding problem, which meant that a subset of model grid cells were subjected to erroneous updating of atmospheric gas concentrations, resulted in incorrect calculation of atmospheric CO2 for these grid cells, and therefore underestimation of the carbon uptake by land through vegetation growth and eventual increases to soil carbon stocks. Having re-run the simulations using the corrected code, the authors found that the original estimates of the impact of the natural wetland methane feedback were overestimated. The permafrost and natural wetland methane feedback requires lower permissible emissions of 9–15% to achieve climate stabilization at 1.5 °C, compared with the original published estimate of 17–23%. The Article text, Table 1 and Fig. 3 have been updated online to reflect the revised numerical estimates. The Supplementary Information file has also been amended, with Supplementary Figs 6, 7, 8 and 9 replaced with revised versions produced using the corrected model output. As the strength of feedbacks remain significant, still require inclusion in climate policy and are nonlinear with global warming, the overall conclusions of the Article remain unchanged.

Scenarios that limit global warming to below 2 °C by 2100 assume significant land-use change to support large-scale carbon dioxide (CO2) removal from the atmosphere by afforestation/reforestation, avoided deforestation, and Biomass Energy with Carbon Capture and Storage (BECCS). The more ambitious mitigation scenarios require even greater land area for mitigation and/or earlier adoption of CO2 removal strategies. Here we show that additional land-use change to meet a 1.5 °C climate change target could result in net losses of carbon from the land. The effectiveness of BECCS strongly depends on several assumptions related to the choice of biomass, the fate of initial above ground biomass, and the fossil-fuel emissions offset in the energy system. Depending on these factors, carbon removed from the atmosphere through BECCS could easily be offset by losses due to land-use change. If BECCS involves replacing high-carbon content ecosystems with crops, then forest-based mitigation could be more efficient for atmospheric CO2 removal than BECCS.

Global methane emissions from natural wetlands and carbon release from permafrost thaw have a positive feedback on climate, yet are not represented in most state-of-the-art climate models. Furthermore, a fraction of the thawed permafrost carbon is released as methane, enhancing the combined feedback strength. We present simulations with an inverted intermediate complexity climate model, which follows prescribed global warming pathways to stabilization at 1.5 or 2.0 °C above pre-industrial levels by the year 2100, and which incorporates a state-of-the-art global land surface model with updated descriptions of wetland and permafrost carbon release. We demonstrate that the climate feedbacks from those two processes are substantial. Specifically, permissible anthropogenic fossil fuel CO2 emission budgets are reduced by 17–23% (47–56 GtC) for stabilization at 1.5 °C, and 9–13% (52–57 GtC) for 2.0 °C stabilization. In our simulations these feedback processes respond more quickly at temperatures below 1.5 °C, and the differences between the 1.5 and 2 °C targets are disproportionately small. This key finding holds for transient emission pathways to 2100 and does not account for longer-term implications of these feedback processes. We conclude that natural feedback processes from wetlands and permafrost must be considered in assessments of transient emission pathways to limit global warming.

We conducted a model-based assessment of changes in permafrost area and carbon storage for simulations driven by RCP4.5 and RCP8.5 projections between 2010 and 2299 for the northern permafrost region. All models simulating carbon represented soil with depth, a critical structural feature needed to represent the permafrost carbon-climate feedback, but that is not a universal feature of all climate models. Between 2010 and 2299, simulations indicated losses of permafrost between 3 and 5 million km2 for the RCP4.5 climate and between 6 and 16 million km2 for the RCP8.5 climate. For the RCP4.5 projection, cumulative change in soil carbon varied between 66-Pg C (1015-g carbon) loss to 70-Pg C gain. For the RCP8.5 projection, losses in soil carbon varied between 74 and 652 Pg C (mean loss, 341 Pg C). For the RCP4.5 projection, gains in vegetation carbon were largely responsible for the overall projected net gains in ecosystem carbon by 2299 (8- to 244-Pg C gains). In contrast, for the RCP8.5 projection, gains in vegetation carbon were not great enough to compensate for the losses of carbon projected by four of the five models; changes in ecosystem carbon ranged from a 641-Pg C loss to a 167-Pg C gain (mean, 208-Pg C loss). The models indicate that substantial net losses of ecosystem carbon would not occur until after 2100. This assessment suggests that effective mitigation efforts during the remainder of this century could attenuate the negative consequences of the permafrost carbon-climate feedback.

Realistic projection of future climate-carbon (C) cycle feedbacks requires better understanding and an improved representation of the C cycle in permafrost regions in the current generation of Earth system models. Here we evaluated 10 terrestrial ecosystem models for their estimates of net primary productivity (NPP) and responses to historical climate change in permafrost regions in the Northern Hemisphere. In comparison with the satellite estimate from the Moderate Resolution Imaging Spectroradiometer (MODIS; 246 ± 6 g C m−2 yr−1), most models produced higher NPP (309 ± 12 g C m−2 yr−1) over the permafrost region during 2000–2009. By comparing the simulated gross primary productivity (GPP) with a flux tower-based database, we found that although mean GPP among the models was only overestimated by 10% over 1982–2009, there was a twofold discrepancy among models (380 to 800 g C m−2 yr−1), which mainly resulted from differences in simulated maximum monthly GPP (GPPmax). Most models overestimated C use efficiency (CUE) as compared to observations at both regional and site levels. Further analysis shows that model variability of GPP and CUE are nonlinearly correlated to variability in specific leaf area and the maximum rate of carboxylation by the enzyme Rubisco at 25°C (Vcmax_25), respectively. The models also varied in their sensitivities of NPP, GPP, and CUE to historical changes in climate and atmospheric CO2 concentration. These results indicate that model predictive ability of the C cycle in permafrost regions can be improved by better representation of the processes controlling CUE and GPPmax as well as their sensitivity to climate change.

Climate change is increasing the frequency and intensity of wildfires globally, with significant impacts on society and the environment. However, our understanding of the global distribution of extreme fires remains skewed, primarily influenced by media coverage and regional research concentration. This inaugural State of Wildfires report systematically analyses fire activity worldwide, identifying extreme events from the March 2023–February 2024 fire season. We assess the causes, predictability, and attribution of these events to climate change and land use, and forecast future risks under different climate scenarios. During the 2023–24 fire season, 3.9 million km2 burned globally, slightly below the average of previous seasons, but fire carbon (C) emissions were 16 % above average, totaling 2.4 Pg C. This was driven by record emissions in Canadian boreal forests (over 9 times the average) and dampened by reduced activity in African savannahs. Notable events included record-breaking wildfire extent and emissions in Canada, the largest recorded wildfire in the European Union (Greece), drought-driven fires in western Amazonia and northern parts of South America, and deadly fires in Hawai'i (100 deaths) and Chile (131 deaths). Over 232,000 people were evacuated in Canada alone, highlighting the severity of human impact. Our analyses revealed that multiple drivers were needed to cause areas of extreme fire activity. In Canada and Greece a combination of high fire weather and an abundance of dry fuels increased the probability of fires by 4.5-fold and 1.9–4.1-fold, respectively, whereas fuel load and direct human suppression often modulated areas with anomalous burned area. The fire season in Canada was predictable three months in advance based on the fire weather index, whereas events in Greece and Amazonia had shorter predictability horizons. Formal attribution analyses indicated that the probability of extreme events has increased significantly due to anthropogenic climate change, with a 2.9–3.6-fold increase in likelihood of high fire weather in Canada and a 20.0–28.5-fold increase in Amazonia. By the end of the century, events of similar magnitude are projected to occur 2.22–9.58 times more frequently in Canada under high emission scenarios. Without mitigation, regions like Western Amazonia could see up to a 2.9-fold increase in extreme fire events. For the 2024–25 fire season, seasonal forecasts highlight moderate positive anomalies in fire weather for parts of western Canada and South America, but no clear signal for extreme anomalies is present in the forecast. This report represents our first annual effort to catalogue extreme wildfire events, explain their occurrence, and predict future risks. By consolidating state-of-the-art wildfire science and delivering key insights relevant to policymakers, disaster management services, firefighting agencies, and land managers, we aim to enhance society's resilience to wildfires and promote advances in preparedness, mitigation, and adaptation. [ABSTRACT FROM AUTHOR]

Achieving climate targets requires mitigation against climate change but also understanding of the response of land and ocean carbon systems. In this context, global soil carbon stocks and their response to environmental changes are key. This paper quantifies the global soil carbon feedbacks due to changes in atmospheric CO 2 , and the associated climate changes, for Earth system models (ESMs) in CMIP6. A standard approach is used to calculate carbon cycle feedbacks, defined here as soil carbon-concentration (βs) and carbon-climate (γs) feedback parameters, which are also broken down into processes which drive soil carbon change. The sensitivity to CO 2 is shown to dominate soil carbon changes at least up to a doubling of atmospheric CO 2. However, the sensitivity of soil carbon to climate change is found to become an increasingly important source of uncertainty under higher atmospheric CO 2 concentrations. [ABSTRACT FROM AUTHOR]

Peat fires in the northern high latitudes have the potential to burn vast amounts of carbon-rich organic soil, releasing large quantities of long-term stored carbon to the atmosphere. Due to anthropogenic activities and climate change, peat fires are increasing in frequency and intensity across the high latitudes. However, at present they are not explicitly included in most fire models. Here we detail the development of INFERNO-peat, the first parameterization of peat fires in the JULES-INFERNO (Joint UK Land Environment Simulator INteractive Fire and Emission algoRithm for Natural envirOnments) fire model. INFERNO-peat utilizes knowledge from lab and field-based studies on peat fire ignition and spread to be able to model peat burnt area, burn depth, and carbon emissions, based on data of the moisture content, inorganic content, bulk density, soil temperature, and water table depth of peat. INFERNO-peat improves the representation of burnt area in the high latitudes, with peat fires simulating on average an additional 0.305×106 km 2 of burn area each year, emitting 224.10 Tg of carbon. Compared to Global Fire Emissions Database version 5 (GFED5), INFERNO-peat captures ∼ 20 % more burnt area, whereas INFERNO underestimated burning by 50 %. Additionally, INFERNO-peat substantially improves the representation of interannual variability in burnt area and subsequent carbon emissions across the high latitudes. The coefficient of variation in carbon emissions is increased from 0.071 in INFERNO to 0.127 in INFERNO-peat, an almost 80 % increase. Therefore, explicitly modelling peat fires shows a substantial improvement in the fire modelling capabilities of JULES-INFERNO, highlighting the importance of representing peatland systems in fire models. [ABSTRACT FROM AUTHOR]

A significant portion of the large amount of carbon (C) currently stored in soils of the permafrost region in the Northern Hemisphere has the potential to be emitted as the greenhouse gases CO2 and CH4 under a warmer climate. In this study we evaluated the variability in the sensitivity of permafrost and C in recent decades among land surface model simulations over the permafrost region between 1960 and 2009. The 15 model simulations all predict a loss of near-surface permafrost (within 3 m) area over the region, but there are large differences in the magnitude of the simulated rates of loss among the models (0.2 to 58.8 × 103 km2 yr−1). Sensitivity simulations indicated that changes in air temperature largely explained changes in permafrost area, although interactions among changes in other environmental variables also played a role. All of the models indicate that both vegetation and soil C storage together have increased by 156 to 954 Tg C yr−1 between 1960 and 2009 over the permafrost region even though model analyses indicate that warming alone would decrease soil C storage. Increases in gross primary production (GPP) largely explain the simulated increases in vegetation and soil C. The sensitivity of GPP to increases in atmospheric CO2 was the dominant cause of increases in GPP across the models, but comparison of simulated GPP trends across the 1982–2009 period with that of a global GPP data set indicates that all of the models overestimate the trend in GPP. Disturbance also appears to be an important factor affecting C storage, as models that consider disturbance had lower increases in C storage than models that did not consider disturbance. To improve the modeling of C in the permafrost region, there is the need for the modeling community to standardize structural representation of permafrost and carbon dynamics among models that are used to evaluate the permafrost C feedback and for the modeling and observational communities to jointly develop data sets and methodologies to more effectively benchmark models.

A realistic simulation of snow cover and its thermal properties are important for accurate modelling of permafrost. We analyse simulated relationships between air and near-surface (20 cm) soil temperatures in the Northern Hemisphere permafrost region during winter, with a particular focus on snow insulation effects in nine land surface models, and compare them with observations from 268 Russian stations. There are large cross-model differences in the simulated differences between near-surface soil and air temperatures (ΔT; 3 to 14 °C), in the sensitivity of soil-to-air temperature (0.13 to 0.96 °C °C-1), and in the relationship between ΔT and snow depth. The observed relationship between ΔT and snow depth can be used as a metric to evaluate the effects of each model's representation of snow insulation, hence guide improvements to the model's conceptual structure and process parameterisations. Models with better performance apply multilayer snow schemes and consider complex snow processes. Some models show poor performance in representing snow insulation due to underestimation of snow depth and/or overestimation of snow conductivity. Generally, models identified as most acceptable with respect to snow insulation simulate reasonable areas of near-surface permafrost (13.19 to 15.77 million km2). However, there is not a simple relationship between the sophistication of the snow insulation in the acceptable models and the simulated area of Northern Hemisphere near-surface permafrost, because several other factors, such as soil depth used in the models, the treatment of soil organic matter content, hydrology and vegetation cover, also affect the simulated permafrost distribution.

Significant progress in permafrost carbon science made over the past decades include the identification of vast permafrost carbon stocks, the development of new pan‐Arctic permafrost maps, an increase in terrestrial measurement sites for CO2 and methane fluxes, and important factors affecting carbon cycling, including vegetation changes, periods of soil freezing and thawing, wildfire, and other disturbance events. Process‐based modeling studies now include key elements of permafrost carbon cycling and advances in statistical modeling and inverse modeling enhance understanding of permafrost region C budgets. By combining existing data syntheses and model outputs, the permafrost region is likely a wetland methane source and small terrestrial ecosystem CO2 sink with lower net CO2 uptake toward higher latitudes, excluding wildfire emissions. For 2002–2014, the strongest CO2 sink was located in western Canada (median: −52 g C m−2 y−1) and smallest sinks in Alaska, Canadian tundra, and Siberian tundra (medians: −5 to −9 g C m−2 y−1). Eurasian regions had the largest median wetland methane fluxes (16–18 g CH4 m−2 y−1). Quantifying the regional scale carbon balance remains challenging because of high spatial and temporal variability and relatively low density of observations. More accurate permafrost region carbon fluxes require: (a) the development of better maps characterizing wetlands and dynamics of vegetation and disturbances, including abrupt permafrost thaw; (b) the establishment of new year‐round CO2 and methane flux sites in underrepresented areas; and (c) improved models that better represent important permafrost carbon cycle dynamics, including non‐growing season emissions and disturbance effects. Plain Language Summary: Climate change and the consequent thawing of permafrost threatens to transform the permafrost region from a carbon sink into a carbon source, posing a challenge to global climate goals. Numerous studies over the past decades have identified important factors affecting carbon cycling, including vegetation changes, periods of soil freezing and thawing, wildfire, and other disturbance events. Overall, studies show high wetland methane emissions and a small net carbon dioxide sink strength over the terrestrial permafrost region but results differ among modeling and upscaling approaches. Continued and coordinated efforts among field, modeling, and remote sensing communities are needed to integrate new knowledge from observations to modeling and predictions and finally to policy. Key Points: Rapid warming of northern permafrost region threatens ecosystems, soil carbon stocks, and global climate targetsLong‐term observations show importance of disturbance and cold season periods but are unable to detect spatiotemporal trends in C fluxCombined modeling and syntheses show the permafrost region is a small terrestrial CO2 sink with large spatial variability and net CH4 source [ABSTRACT FROM AUTHOR]

Climate policies evolve quickly, and new scenarios designed around these policies are used to illustrate how they impact global mean temperatures using simple climate models (or climate emulators). Simple climate models are extremely efficient although limited to showing only the global picture. Within the Intergovernmental Panel on Climate Change (IPCC) framework, there is a need to understand the regional impacts of scenarios that include the most recent science and policy decisions quickly to support government in negotiations. To address this, we present PRIME (Probabilistic Regional Impacts from Model patterns and Emissions), a new flexible probabilistic framework which aims to provide an efficient means to run new scenarios without the significant overheads of larger more complex Earth system models (ESMs). PRIME provides the capability to include the most recent models, science and scenarios to run ensemble simulations on multi-centennial timescales and include analysis of many variables that are relevant and important for impacts assessments. We use a simple climate model to provide the global temperatures to scale the patterns from a large number of the CMIP6 ESMs. These provide the inputs to a weather generator and a land-surface model, which generates an estimate of the land-surface impacts from the emissions scenarios. Here we test PRIME using known scenarios in the form of the Shared Socioeconomic Pathways (SSPs) to demonstrate that PRIME reproduces the climate response to a range of emissions scenarios, as shown in the IPCC reports. We show results for a range of scenarios including the SSP5-8.5 high emissions scenario, which was used to define the patterns; SSP1-2.6, a mitigation scenario with low emissions and SSP5-3.4-OS, an overshoot scenario. PRIME correctly represents the climate response for these known scenarios, which gives us confidence that PRIME will be useful for rapidly providing probabilistic spatially resolved information for novel climate scenarios; substantially reducing the time between the scenarios being released and being used in impacts assessments. [ABSTRACT FROM AUTHOR]

Wetland methane responses to temperature and precipitation were studied in a boreal wetland-rich region in Northern Europe using ecosystem process models. Six ecosystem models (JSBACH-HIMMELI, LPX-Bern, LPJ-GUESS, JULES, CLM4.5 and CLM5) were compared to multi-model mean of ecosystem models and atmospheric inversions from the Global Carbon Project and up-scaled eddy covariance flux results for their temperature and precipitation responses and seasonal cycles of the regional fluxes. Two models with contrasting response patterns, LPX-Bern and JSBACH-HIMMELI, were used as priors in atmospheric inversions with Carbon Tracker Europe – CH4 in order to find out how the inversion attempts to change the prior fluxes in the posterior and how this alters the interpretation of the flux responses to temperature and precipitation. The inversion attempted to move emissions of both models in posterior towards co-limitation by temperature and precipitation. In general high temperature and/or high precipitation periods often resulted in high posterior emissions. This was not the case for the warm and dry period of summer 2018. The process models showed strong temperature as well as strong precipitation responses for the region (51–91 % of the variance explained by both), and the month of maximum emissions varied from May to September. However, multi-model means, inversions and up-scaled eddy covariance flux observations agreed on the month of maximum emissions, and had rather balanced temperature and precipitation responses. The set-up of different emission components (peatland emissions, mineral land fluxes) had a significant role in building up the response patterns. Considering the significant differences among the models, it is essential to pay more attention to the magnitude, composition, annual cycle and climate driver responses of wetland emissions in different regions. [ABSTRACT FROM AUTHOR]

Achieving climate targets requires mitigation against climate change, but also understanding of the response of land and ocean carbon systems. In this context, global soil carbon stocks and its response to environmental changes is key. This paper quantifies the global soil carbon feedback to changes in atmospheric CO2, and associated climate changes, for Earth system models (ESMs) in CMIP6. A standard approach is used to calculate carbon cycle feedbacks, defined here as soil specific carbon-concentration (βs) and carbon-climate (γs) feedback parameters. The sensitivity to CO2 is shown to dominate soil carbon changes at least up to a doubling of atmospheric CO2. However, the sensitivity of soil carbon to climate change is found to become an increasingly important source of uncertainty under higher atmospheric CO2 concentrations. [ABSTRACT FROM AUTHOR]

Peat fires in the Northern high latitudes have the potential to burn vast amounts of carbon rich organic soil, releasing large quantities of long-term stored carbon to the atmosphere. Due to anthropogenic activities and climate change, peat fires are increasing in frequency and intensity across the high latitudes. However, at present they are not explicitly included in most fire models. Here we detail the development of INFERNO-peat, the first parameterisation of peat fires in the JULES-INFERNO fire model. INFERNO-peat utilises knowledge from lab and field-based studies on peat fire ignition and spread to be able to model peat burnt area, burn depth and carbon emissions, based on data of the moisture content, inorganic content, bulk density, soil temperature and water table depth of peat. INFERNO-peat improves the representation of burnt area in the high latitudes, with peat fires simulating on average an additional 0.305 M km2 of burn area each year, emitting 224.10 Tg of carbon. Compared to GFED5, INFERNO-peat captures ~20 % more burnt area, whereas INFERNO underestimated burning by 50 %. Additionally, INFERNO-peat substantially improves the representation of interannual variability in burnt area and subsequent carbon emissions across the high latitudes. The coefficient of variation in carbon emissions is increased from 0.071 in INFERNO to 0.127 in INFERNO-peat, an almost 80 % increase. Therefore, explicitly modelling peat fires shows a substantial improvement in the fire modelling capabilities of JULES-INFERNO, highlighting the importance of representing peatland systems in fire models. [ABSTRACT FROM AUTHOR]

Reliable estimates of soil carbon change are required to determine the carbon budgets consistent with the Paris Agreement climate targets. This study evaluates projections of soil carbon during the 21st century in Coupled Model Intercomparison Project Phase 6 (CMIP6) Earth system models (ESMs) under a range of atmospheric composition scenarios. In general, we find a reduced spread of changes in global soil carbon (ΔCs) in CMIP6 compared to the previous CMIP5 model generation. However, similar reductions were not seen in the derived contributions to ΔCs due to both increases in plant net primary productivity (NPP, named ΔCs,NPP) and reductions in the effective soil carbon turnover time (τs , named ΔCs,τ). Instead, we find a strong relationship across the CMIP6 models between these NPP and τs components of ΔCs , with more positive values of ΔCs,NPP being correlated with more negative values of ΔCs,τ. We show that the concept of "false priming" is likely to be contributing to this emergent relationship, which leads to a decrease in the effective soil carbon turnover time as a direct result of NPP increase and occurs when the rate of increase in NPP is relatively fast compared to the slower timescales of a multi-pool soil carbon model. This finding suggests that the structure of soil carbon models within ESMs in CMIP6 has likely contributed towards the reduction in the overall model spread in future soil carbon projections since CMIP5. [ABSTRACT FROM AUTHOR]