Land carbon sinks are responsible for removing about a quarter of anthropogenic CO2 emissions, and make up approximately half of total global carbon sinks. Uncertainty in the response of land carbon sinks to climate and changing atmospheric CO2 are large, and dominate the uncertainty in total carbon sinks under future climate. Understanding the carbon cycle response to net-zero and net-negative emissions has important implications for projecting future climate. Here we characterize the response of land carbon pools and fluxes from ten emissions-driven Earth system models (ESMs) under positive, net-zero, and net-negative CO2 emissions using experiments from the “flat10” model intercomparison. Although there are many differences in simulated land carbon pools and fluxes across models, we find some consistent behavior across ESMs. (1) During the positive emissions phase, carbon is gained on land primarily in vegetation pools. (2) Following net-negative emissions to the point of cumulative zero emissions, carbon is lost from land in tropical latitudes, primarily from vegetation pools, but in mid- and high-latitudes most models show net land carbon gain, primarily in soil pools. (3) Following an extended period of net-zero emissions, a majority of models again show carbon gain in mid- and high-latitudes and vegetation carbon loss in the tropics. Under net-negative emissions the timing of vegetation carbon response relative to peak emissions is relatively consistent across ESMs, but timing of soil carbon response varies widely, implying larger intermodel disagreement associated with responses of soil carbon which tends to have longer timescales relative to vegetation carbon. Our findings highlight that tropical carbon is most likely to be both gained and subsequently lost under positive, zero, declining, and negative emissions, with possible implications for carbon dioxide removal efforts.

Abstract. The Zero Emissions Commitment (ZEC), the residual warming after anthropogenic CO2 emissions cease, remains poorly constrained and directly affects remaining carbon budget calculations. We fit a simple coupled carbon-climate model to ten Earth System Models (ESMs) in the flat10 Model Intercomparison Project. Near-zero multi-model mean ZEC can be decomposed into unrealised ocean warming contributing roughly +0.2 K at 50 years post-cessation, which is almost exactly offset by ~-0.2 K of cooling from atmospheric CO2 drawdown into land and ocean carbon sinks. Yet the thermal term carries more than twice the inter-model spread of the carbon term; as such, in the context of current ESMs, reducing ZEC uncertainty depends more strongly on equilibrium climate sensitivity and the slow ocean heat uptake timescale than on carbon-cycle parameters. Individual models split into a carbon-dominated majority (eight of ten, negative ZEC) and a thermal-dominated minority (positive ZEC), distinguished primarily by their realised warming fraction and equilibrium climate sensitivity. The simple model yields a compact analytical formula for ZEC in terms of component climate and carbon-cycle parameters, reproducing ESM-simulated values to within ~0.03 K (less than 5% of the ~0.6 K inter-model spread). The decomposition also reveals that the carbon cycle buffers ZEC against uncertainty in committed warming to an existing energetic imbalance: recent downward revisions of the realised warming fraction (the ratio of transient to equilibrium warming) imply substantially more committed warming at fixed atmospheric composition, yet this additional heat is strongly attenuated before it appears in ZEC. A reduction of 10 per cent in the realised warming fraction would increase the radiatively committed warming by ~0.5 K at cessation, but this shifts the 50-year ZEC by only ~0.05 K. This allows potential for process-level constraints on climate and carbon-cycle components on ZEC to narrow carbon budgets. Out-of-sample testing on the flat10-cdr experiment shows that CO2 predictions diverge from ESMs within ~50 years of the emission trajectory departing from the training rate, so the framework does not extend to full climate reversibility under sustained negative emissions. Structural limitations, including under-sampled land biosphere diversity across ESMs, may further emerge at higher warming levels or on multi-centennial timescales.

The Earth's climate sensitivity remains a significant source of uncertainty in climate projections. A key metric is the Transient Climate Response (TCR), which incorporates aspects of Equilibrium Climate Sensitivity (ECS), ocean heat uptake and pattern effects, and is closely correlated with historical global warming by Earth System Models (ESMs). CMIP6 ESMs display a wider range of TCR values compared to earlier phases, with many exceeding the IPCC AR6 very likely (90 % confidence) range of 1.2-2.4 K. These high-sensitivity models also predict that warming will exceed the 2 degrees C Paris climate agreement limit, even under the relatively low emissions SSP1-2.6 scenario. Record global temperatures in 2023 and 2024 highlight how close the world already is to 1.5 degrees C of warming, raising doubts about whether the 2 degrees C limit remains within reach. Here, we use the latest observational data to update emergent constraints on TCR and projected warming. We estimate a TCR of 1.81 K with a very likely range of 1.28 to 2.33 K, which represents a small increase compared to estimates that use observational data through to 2019. Furthermore, we find that warming projections constrained by data through to 2024 fall within the low to mid-range of CMIP6 ESM projections for both the mid- and late-21st century, indicating that limiting global warming to below 2 degrees C remains feasible.

Estimates of carbon emissions budgets to limit global warming to 1.5 °C or 2 °C rely on the near-linear relationship between global temperature change and total CO2 emitted, known as the Transient Climate Response to cumulative CO2 Emissions (TCRE). The TCRE is determined from Earth System Models (ESMs) and is therefore sensitive to the physical and biogeochemical processes represented within them. Here we use an ESM (UKESM) to explore the sensitivity of TCRE to six Earth system processes in isolation. Four processes increase TCRE: fire-vegetation interactions by 14.6%; nitrogen limitation of vegetation by 9.7%; diffuse radiation effects on vegetation by 8.5%; and interactive emissions of methane from wetlands by 5.1%. Conversely, two processes marginally reduce TCRE: allowing the vegetation distribution to adapt to changing climate and CO2 lowers TCRE by 1.5%, and climate impacts from the emission of biogenic volatile organic compounds reduce it by 1.4%. We demonstrate the extent to which each process changes TCRE via its influence on the climate and on the global carbon cycle, and discuss underlying mechanisms. Our results highlight the substantial process-dependence of model-derived estimates of TCRE, with implications for remaining carbon budgets to future warming targets calculated from them.

We use a full complexity Earth System Model, UKESM1.1, to examine the impact of six individual Earth System processes on the climate-carbon cycle feedback parameters: the carbon-concentration feedback (𝛽) and the carbon-climate feedback (𝛾). The processes under consideration are i) nitrogen limitation of land carbon uptake; ii) dynamic vegetation distribution; iii) fire-vegetation interactions; iv) coupling of wetlands methane emissions and atmospheric chemistry v) emissions of biogenic volatile organic compounds; and vi) spatially varying diffuse radiation fraction. With each of these configurations we perform transient simulations in which atmospheric CO2 concentration increases at 1% per year from pre-industrial to four times the preindustrial concentration, from which we calculate 𝛽 and 𝛾, at twice and four times pre-industrial CO2 concentration (‘2xCO2’ and ‘4xCO2’ respectively). Introducing nitrogen limitation reduced 𝛽 by 23% at 2xCO2 and by 38% at 4xCO2; the magnitude of 𝛾 , |𝛾|, was reduced by just 5% at 2xCO2 but by 40% at 4xCO2. Enabling dynamic vegetation consistently increased 𝛽 by approximately 30%, and increased |𝛾| by 13% at 2xCO2, but reduced it by 8% at 4xCO2. Simulating firevegetation interactions reduced 𝛽 by around 20% at both CO2 levels, but increased |𝛾| by 51% at 2xCO2 and by 71% at 4xCO2. The other three processes changed 𝛽 by no more than 2% but each had an impact of 5% to 10% on |𝛾| at one or both CO2 levels. We discuss the complex changes in global carbon storage potential induced by the processes as climate forcing intensifies, the regional patterns underlying them, and the mechanisms responsible.

Understanding future changes in cumulative water deficit (CWD) is essential for assessing the vulnerability of Amazonian ecosystems to climate change. This study evaluates the performance of CMIP6 models in simulating CWD in the southwestern Amazon from 1985 to 2024 and projects future changes through 2100 under three emission scenarios (SSP1-2.6, SSP3-7.0, SSP5-8.5). CWD was calculated using a fixed evapotranspiration threshold of 100 mm/month, and the maximum cumulative water deficit (MCWD) was derived to quantify overall water stress over time. Model performance was assessed using statistical metrics (MAE, RMSE, bias and Pearson correlation), and the best-performing models were selected for future projections. Additionally, time series decomposition with breakpoint detection (BFAST) was applied. Results show that most models reproduce the seasonal cycle but diverge during peak dry months. Under SSP5-8.5, deficits intensify significantly, exceeding 150 mm between July and September. CAS-ESM2-0 and CESM2-WACCM exhibit large negative biases, while BCC-CSM2-MR and IPSL-CM6A-LR perform best. SSP1-2.6 shows minimal changes, whereas SSP3-7.0 and SSP5-8.5 exhibit significant negative trends, with depletion rates reaching -21 mm/month by 2100. Temporal decomposition reveals increased hydrological instability, structural breaks after 2040 (SSP3-7.0) and 2050 (SSP5-8.5), and amplified seasonality under high emissions. Residual variability also increases, indicating a higher frequency of non-seasonal anomalies. These findings highlight growing water stress under higher warming scenarios and emphasise CWD as a key indicator of ecological risk. Reliable projections depend on careful model evaluation, underscoring the need for model selection to guide climate adaptation and forest conservation strategies.

Much of the public discourse around climate changes under net zero carbon dioxide emissions has been focused on global mean temperature changes after emissions cessation, which are expected to be limited. More attention needs to be paid to the long-term impacts of climate change, such as sea level rise and subsurface ocean warming, which are likely to persist even if rapid decarbonization is achieved. Much of the public discourse around climate changes under net zero carbon dioxide emissions has been focused on global mean temperature changes after emissions cessation. More attention needs to be paid to the long-term impacts of climate change, which are likely to persist even if rapid decarbonization is achieved.

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 some can only provide global estimates of climate metrics such as mean surface temperature, CO2 concentration and effective radiative forcing. Within the Intergovernmental Panel on Climate Change (IPCC) framework, understanding of the regional impacts of scenarios that include the most recent science is needed to allow targeted policy decisions to be made quickly. 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 mechanism to run new scenarios without the significant overheads of larger, more complex Earth system models (ESMs). PRIME provides the capability to include features of the most recent ESM projections, science and scenarios to run ensemble simulations on multi-centennial timescales and include analyses of many key variables that are relevant and important for impact assessments. We use a simple climate model to provide the global temperature response to emissions scenarios. These estimated temperatures are used to scale monthly mean patterns from a large number of CMIP6 ESMs. These patterns provide the inputs to a “weather generator” algorithm and a land surface model. The PRIME system thus generates an end-to-end estimate of the land surface impacts from the emissions scenarios. We test PRIME using known scenarios in the form of the shared socioeconomic pathways (SSPs), to demonstrate that our model reproduces the ESM climate responses to these scenarios. We show results for a range of scenarios: the SSP5–8.5 high-emissions scenario was used to define the patterns, and SSP1–2.6, a mitigation scenario with low emissions, and SSP5–3.4-OS, an overshoot scenario, were used as verification data. PRIME correctly represents the climate response (and spread) for these known scenarios, which gives us confidence our simulation framework will be useful for rapidly providing probabilistic spatially resolved information for novel climate scenarios, thereby substantially reducing the time between new scenarios being released and the availability of regional impact information.

To achieve the Paris climate agreement goals, methane (CH4) emission mitigation plays a key role. Therefore, a better understanding of the global methane cycle is indispensable. Here we simulate the global methane cycle fully interactively from 1850 to 2100 with a strong mitigation action scenario (SSP1-2.6) post 2014. We show that the atmospheric methane burden largely recovers to early 20th-century levels, while wetland methane emissions follow a persistent upward trend from 166 Tg(CH4) yr-1 at pre-industrial to 221 Tg(CH4) yr-1 in 2100. The methane lifetime decreases from 9.3 to 7.3 years over the 1850-2100 period. We identify net primary productivity as the main driver behind the wetland methane trend with R2 = 0.7. This implies that important components of the methane cycle (wetland methane, methane lifetime) are subject to Earth system feedbacks, potentially impacting any prospective methane mitigation action. Therefore, methane mitigation strategies will need to consider feedbacks in the Earth system.

Reliable, robust, and consistent data are essential foundations for analysis of carbon cycle feedbacks. Here, we consider the data from multiple Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We identify a mass conservation issue in the reported carbon and nitrogen data, with a few exceptions for specific models and reporting levels. The accumulated mass imbalance in the reported data can amount to hundreds of gigatons of carbon or nitrogen by the end of the simulated period, largely exceeding the total carbon–nitrogen pool size changes over the same period. Nitrogen mass imbalance is evident across all reported organic and inorganic pools, with mineral nitrogen exhibiting the most significant cumulative mass imbalance. Due to a lack of detail in the reported data, we cannot uniquely identify the cause of this imbalance. However, we postulate that the carbon mass imbalance primarily arises from missing fluxes in the reported data and inconsistencies between these data and the definitions provided by the C4MIP protocol (e.g., land-use and fire emissions), rather than from an underlying mass conservation issue in the models themselves. Our findings suggest that future CMIP reporting protocols should consider incorporating mass conservation into their data validation processes so that such issues are caught before users have to deal with them, rather than forcing all users to handle this issue in their own way. In addition, attention from model groups to the detailed diagnostic request and definitions, along with their own quality control, will also help to avoid such issues in future. Given that no additional CMIP6 data are currently being published and none are expected in the future, we recommend that data users that rely on a closed carbon–nitrogen cycle address potential flux imbalances by using the workarounds provided in this study.

Methane plays a crucial role in the Earth System as a greenhouse gas and a tropospheric ozone precursor. However, in Phase 6 of the Coupled Model Intercomparison Project (CMIP6), Earth System Models predominantly relied on prescribed surface methane concentrations derived from historical observations or predefined future pathways. This study uses novel Earth System Model capability to investigate the impact of an emissions-driven methane cycle, including interactive wetland emissions. Specifically, we explore the influence of interactive methane on the effective radiative forcing of carbon dioxide and the model’s transient and equilibrium climate responses to changes in carbon dioxide.The response of the climate to external forcings is intricately linked to climate feedbacks. With the inclusion of an interactive methane cycle in Earth System Models, understanding how changes in carbon dioxide and climate affect the methane cycle becomes imperative. This work critically re-evaluates the CMIP6 assessment of methane feedbacks and, for the first time, disentangles both the biophysical and radiative effects of carbon dioxide on wetland emissions and methane lifetime. By enabling the interaction of the biophysical and radiative effects of carbon dioxide with natural methane emissions, concentrations, and climate responses, this presentation highlights the necessity of incorporating interactive methane components in Earth System Models. Notably, this approach provides scientists with the means to assess the direct implications of methane emission reduction policies and climate feedbacks on meeting global climate and air quality targets.

The proportionality between global mean temperature and cumulative emissions of CO2 predicted in Earth system models (ESMs) is the foundation of carbon budgeting frameworks. Deviations from this behavior could impact estimates of required net-zero timings and negative emissions requirements to meet the Paris Agreement climate targets. However, existing ESM diagnostic experiments do not allow for direct estimation of these deviations as a function of defined emissions pathways. Here, we perform a set of climate model diagnostic experiments for the assessment of transient climate response to cumulative CO2 emissions (TCRE), the Zero Emissions Commitment (ZEC), and climate reversibility metrics in an emissions-driven framework. The emissions-driven experiments provide consistent independent variables simplifying simulation, analysis and interpretation, with emissions rates more comparable to recent levels than existing protocols using model-specific compatible emissions from the CMIP DECK 1pctCO2 experiment, where emissions rates tend to increase during the experiment, such that at the time of CO2 doubling in year 70, emissions are much greater than present-day values. A base experiment, "esm-flat10", has constant emissions of CO2 of 10 GtC per year (near-present-day values), and initial results show that the TCRE estimated in this experiment is about 0.1 K less than that obtained using 1pctCO2. A subset of ESMs exhibit land carbon sinks that saturate during this experiment. A branch experiment, esm-flat10-zec, illustrates that both positive and negative ZEC effects are less pronounced under esm-flat10 than under 1pctCO2 - the magnitude of ZEC50 in ESMs is, on average, reduced by 30 % compared with 1pctCO2 branch experiments. A final experiment, esm-flat10-cdr, assesses climate reversibility under negative emissions, where we find that peak warming may occur before or after net zero and that the asymmetry in temperature at a given level of cumulative emissions between the positive and negative emissions phases is well described by ZEC in most models. Further, we find that existing probabilistic simple climate model (SCM) ensembles tend to overestimate temperature reversibility compared with ESMs, highlighting the need for additional constraints. We propose a set of climate diagnostic indicators to quantify various aspects of climate reversibility. These experiments were suggested as potential candidates in CMIP7 and have since been adopted as "fast track" simulations.

Regional climate impacts studies are usually predicated on output from fully-coupled Earth system models, which, due to computational constraints, can only simulate a limited number of scenarios and ensemble members. Using the PRIME system, we can simulate spatially resolved impacts quickly - emulating the response of 34 CMIP6 models, and generating ensemble members that capture the IPCC assessed range of equilibrium climate sensitivity (ECS). We assess the tail risks associated with high ECS simulations on critical tropical and boreal forest ecosystems over the 21st century and beyond, using three policy-relevant strong-mitigation IPCC WG3 emissions scenarios with different relationships to 1.5°C global warming. We quantify the future resilience and risk of dieback across these ecosystems, focus on the reversibility of loss using a temperature overshoot-and-return scenario and delineate hazardous climatic space for the Amazon basin, with climate-boundaries consistent with the literature. We show that despite using emissions scenarios which achieve 1.5 and 2 degrees Paris Agreement targets, uncertainty in ECS exhibits unavoidable risk of Amazon forest health decline and dieback, further highlighting the requirement for urgent, focused, global mitigative actions.

Current frameworks for evaluating biogeochemical climate change feedbacks in Earth System Models lack an explicit consideration of nitrogen cycling in the land and ocean spheres despite its vital role in limiting primary productivity. As coupled carbon-nitrogen cycling becomes the norm, a better understanding of the role of nitrogen cycling is needed. Here we develop a new framework for quantifying carbon-nitrogen feedbacks in Earth System Models and show that rising nitrogen deposition acts as a negative feedback over both land and ocean, enhancing carbon dioxide (CO2) fertilisation in a model ensemble. However, increased CO2 uptake due to rising nitrogen deposition is small relative to the large reduction in CO2 uptake when coupled carbon-nitrogen cycling is implemented in Earth System Models. Altogether, rising nitrogen deposition leads to only a minor increase in CO2 uptake but also enhances nitrous oxide (N2O) emissions over land and ocean, contributing only marginally to mitigating climate change.

The Amazon Rainforest, crucial for climate regulation, carbon and water cycles, and biodiversity preservation, faces escalating threats from heightened forest degradation, including disturbances from fire and logging. In 2020, Brazil was responsible for a concerning 70% of the active fire hotspots detected in the Amazon, signaling a notable 60% increase compared to 2019. This surge has pushed the region into an extreme fire situation. Urgent and effective interventions are imperative to mitigate these extremes, ensuring the preservation of the Amazon and global climate stability. The study focuses on the Boca do Acre region in the southwest Amazon, one of the most recent hotspots of deforestation and forest degradation in the Amazon. We project the suitability of fire for 2030, following the timeframe set by the United Nations for the implementation of actions aimed at creating a better world for all peoples and nations through the Agenda 2030. Using the MAXENT algorithm within the R software, we conducted a detailed analysis exclusively within the non-forest land-use class on a 5 km x 5 km grid. Burned area data from products Fire CCI (250m), MapBiomas Fire (30m), and MODIS MCD64 (500m) were used to study fire occurrence across the study area. The chosen baseline year is 2014, representing the last year of historical data before the influence of different Shared Socioeconomic Pathways (SSPs) on IPCC models (1-2.6 and 3-7.0). The statistic involves the use of specifically selected variables, determined by their performance in correlation tests and principal component analysis. These variables encompass the percentage of forested areas, agriculture, pasture, and a 1000 m buffer along the region's roads. Additionally, factors such as the percentage of conservation unit occupancy, indigenous lands, and medium-sized properties (400-1000 ha) in the Rural Environmental Registry (CAR), along with precipitation values during dry months, are taken into account. Model validation incorporates AUC analysis, where the model must exhibit performance greater than 0.7, background analysis with the same curve behavior, false positive rate (FPR), accuracy evaluation, and sensitivity analysis. Following this process, we project the feasibility of fire for 2030. Results consistently demonstrate high performance, with AUC values surpassing 0.7 and pixel-to-pixel accuracy ranging from 60% to 90%, lower FRP values, and higher sensitivity values. Projected results indicate an increased susceptibility to fires that spread in the region, especially under less sustainable scenarios, emphasizing the urgency of preventive measures before 2030. Projections reveal an advancement in fire suitability, particularly in the SSP 3-7.0 scenario, with a significant increase in non-forest areas. However, as the scenario worsens, areas prone to fires that spread decrease due to the advancement of agricultural and pasture areas, underscoring the need for more sustainable practices. In conclusion, this study holds promise as a management tool for decision-makers, offering valuable insights for the development of mitigation and adaptation measures to climate change in the Boca do Acre region. These contributions are essential for preserving this vital ecosystem, highlighting the importance of implementing effective strategies.

Anthropogenic disturbances stand as the primary driver of degradation in the remaining Amazon forests, posing a significant threat to their future. Notable among these disturbances are edge effects, timber extraction, fire, extreme droughts and temperatures, which have been intensified by human-induced climate change. A pilot study aiming to integrate forest fire occurrence, timber extraction and climate change scenarios was developed for a new deforestation frontier in southwestern Amazonia. We integrated a series of remote sensing fire products, spatialized land tenure information, selective logging mapping techniques and Global Climate Models (GCMs) simulated projections of three SSPs (SSP climate forcing scenarios) for 2015–2100 period. The results showed that the increased deforestation trend occurred between 2003 and 2019 predominantly on public lands, following the implementation of the new forest code. This surge contributed to a spike in fires, escalating from 66% to 84% in 2019. Over the period from 2007 and 2019, 2.4% of the primary forest was logged. By 2022, precipitation values aligned closely with SSP 5-8.5, and temperature values neared SSP 3-7.0. Projections for 2100 indicated an alarming increase of 5.19 ºC in overall temperature and a reduction of 55 mm in annual precipitation compared to 2003 baseline. The results indicate that the study region is already heading towards a less sustainable future. Logging activities, as well as agricultural production, are threatened by both increase in economic losses by fires and temperatures, and rainfall reduction. Implementing mitigation measures, such as fire-free land management, traceability controls for all wood production from logged forests, and addressing issues of land tenure and regulation are pivotal in steering the current development pathway towards a more sustainable pathway.

Anthropogenically emitted CO2 from fossil fuel use and land use change is partly absorbed by terrestrial ecosystems and the ocean, while the remainder retained in the atmosphere adds to the ongoing increase in atmospheric CO2 concentration. Earth system models (ESMs) can simulate such dynamics of the global carbon cycle and consider its interaction with the physical climate system. The ESMs that participated in the Coupled Model Intercomparison Project phase 6 (CMIP6) performed historical simulations to reproduce past climate–carbon cycle dynamics. This study investigated the cause of CO2 concentration biases in ESMs and identified how they might be reduced. First, we compared simulated historical carbon budgets in two types of experiments: one with prescribed CO2 emissions (the emission-driven experiment, “E-HIST”) and the other with a prescribed CO2 concentration (the concentration-driven experiment, “C-HIST”). Because the design of CMIP7 is being considered, it is important to explore any differences or implications associated with such variations. The findings of this confirmed that the multi-model means of the carbon budgets simulated by one type of experiment generally showed good agreement with those simulated by the other. However, the multi-model average of cumulative compatible fossil fuel emission diagnosed from the C-HIST experiment was lower by 35 PgC than that used as the prescribed input data to drive the E-HIST experiment; the multi-model average of the simulated CO2 concentration for 2014 in E-HIST was higher by 7 ppmv than that used to drive C-HIST. Regarding individual models, some showed a distinctly different magnitude of ocean carbon uptake from C-HIST because the E-HIST setting allows ocean carbon fluxes to be dependent on land carbon fluxes via CO2 concentration. Second, we investigated the potential linkages of two types of carbon cycle indices: simulated CO2 concentration in E-HIST and compatible fossil fuel emission in C-HIST. It was confirmed quantitatively that the two indices are reasonable indicators of overall model performance in the context of carbon cycle feedbacks, although most models cannot accurately reproduce the cumulative compatible fossil fuel emission and thus cannot reproduce the CO2 concentration precisely. Third, analysis of the atmospheric CO2 concentration in five historical eras enabled the identification of periods that caused the concentration bias in individual models. Fourth, it is suggested that this non-CO2 effect is likely to be the reason why the magnitude of the natural land carbon sink in historical simulations is difficult to explain based on analysis of idealized experiments. Finally, accurate reproduction of land use change emission is critical for better reproduction of the global carbon budget and CO2 concentration. The magnitude of simulated land use change emission not only affects the level of net land carbon uptake but also determines the magnitude of the ocean carbon sink in the emission-driven experiment. This study confirmed that E-HIST enables an evaluation of the full span of the uncertainty range covering the entire carbon–climate system and allows for an explicit simulation of the interlinking process of the carbon cycle between land and ocean. By isolating the forced responses and feedback processes of the carbon cycle processes, the usefulness of C-HIST in elucidating climate–carbon cycle systems and in identifying the cause of CO2 biases was confirmed.

It will be important to know when global warming has reached 1.5°C, as this will be a key marker in global policy given the ambition to pursue efforts to limit warming to this level. But how should the temperature increase be defined in this context? The Global Stocktake agreed at COP28 in Dubai noted “global warming of about 1.1 °C” based on the IPCC 6th Assessment Report, but this number applies to the average of 2011-2020 and hence is already out of date. We propose that the metric for current global warming should allow immediate of identification of passing particular levels of global warming, such as 1.5°C, to avoid unnecessary delays in responding to the exceedance. We also propose that the metric should be consistent with the definition of future Global Warming Levels in the IPCC 6th Assessment Report, which uses 20-year means of projected temperature anomalies with an exceedance year defined as the mid-point of the 20-year period. Without this consistency, the apparent time of reaching 1.5°C could differ from the time previously projected by the IPCC merely because of differences in the definition, which could be misinterpreted as indicting that global warming had reached 1.5°C either earlier or later than projected. This could either undermine confidence in projections or misinform discussions on action to address climate change.While various indicators are already in use that provide a more instantaneous measure of global warming, none are consistent with the IPCC definition of future GWLs nor are suitable for use as a baseline for impacts assessments. To address this, we propose a new metric, the Current Global Warming Level (CGWL), which uses a 20-year average over the previous 10 years from observations and the next 10 years from a forecast or projections. Here we compare the CGWL with the various indicators currently in use for quantifying the current level of global mean temperature change, and compare their indications of global temperature change over recent decades and of the current level of global warming. We also compare the year of exceeding past global warming levels of 0.5°C, 1.0°C and 1.2°C for each indicator. We use a combined observational dataset following IPCC methods and process the indicators from this. For each indicator, we explain potential difficulties that could arise from its use to assess when global warming reaches 1.5°C relative to pre-industrial, and explain the rationale for our proposed indicator, the Current Global Warming Level.

We present PRIME, a framework for analysis of scenarios of regional impacts for user-prescribed future emissions. PRIME combines global mean temperature and CO2 concentrations from the emissions driven FaIR simple climate model, as used in the IPCC Sixth Assessment Report, with patterns of climate change from CMIP6 Earth System models to drive the JULES land model. This simulation system projects regional changes to the land surface and carbon cycle. We evaluate PRIME by running it with Shared Socioeconomic Pathways and illustrate its robustness by comparing these known scenarios with ESMs that have also been run for the same scenarios. 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 impact assessments. Therefore PRIME fulfills an important need, providing 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 impact assessments.