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The role of nature restoration in mitigating the impacts of climate change is receiving increasing attention, yet the mitigation potential is often assessed in terms of carbon removal rather than the ability to meet temperature goals, such as those outlined in the Paris Agreement. Here, we estimate the global removal potential from nature restoration constrained by a “responsible development” framework and the contribution this would make to a 1.5°C temperature limit. Our constrained restoration options result in a median of 103 GtC (5%–95% range of −91 to 196 GtC) in cumulative removals between 2020 and 2100. When combined with deep-decarbonization scenarios, our restoration scenario briefly exceeds 1.5°C before declining to between 1.25°C and 1.5°C by 2100 (median, 50% probability). We conclude that additional carbon sequestration via nature restoration is unlikely to be done quickly enough to notably reduce the global peak temperatures expected in the next few decades. Land restoration is an important option for tackling climate change but cannot compensate for delays in reducing fossil fuel emissions.

The remaining carbon budget (RCB), the net amount of carbon dioxide humans can still emit without exceeding a chosen global warming limit, is often used to evaluate political action against the goals of the Paris Agreement. RCB estimates for 1.5C are small, and minor changes in their calculation can therefore result in large relative shifts. Here we evaluate recent RCB assessments by the IPCC and explain differences between them. We present calculation refinements together with robustness checks that increase confidence in RCB estimates. We conclude that the RCB for a 50% chance of keeping warming to 1.5C is around 300 GtCO2 as of January 2022, less than 8 years of current emissions. This estimate changes to 530 and 110 GtCO2 for a 33% and 66% chance, respectively. Key uncertainties affecting RCB estimates are the contribution of non-CO2 emissions, which depends on socioeconomic projections as much as on geophysical uncertainty, and the potential warming after net zero is reached.

Taking stock of global progress towards achieving the Paris Agreement requires measuring aggregate national action against modelled mitigation pathways. A key gap exists, however, in how scientific studies and national inventories account for the role of anthropogenic land-based carbon fluxes, resulting in a 5.5-6.0 GtCO2yr-1 difference between the respective present-day land-use estimates. Modelled pathways mainly include direct human-induced fluxes, while inventories submitted by countries to the UNFCCC (NGHGIs) generally include a wider definition of managed land area as well as the indirect removals on that land caused by environmental changes (e.g., the CO2 fertilization effect). This difference hinders comparability between targets set by countries and scientific benchmarks. Scenarios assessed in AR6 show that a combination of deep near-term gross emissions reductions and medium-term carbon removal from the atmosphere are needed to reach net-zero and eventually net-negative CO2 emissions to limit warming in line with the Paris Agreement temperature goal. However, scenarios lacked key information needed to estimate land-based removals and to align their LULUCF projections with NGHGIs. Here, we estimate the land-based removals consistent with NGHGIs using a reduced complexity climate model with explicit treatment of the land-use sector, OSCAR, one of the models used by the Global Carbon Project. Of the 1202 pathways that passed IPCC vetting, 914 provide sufficient land-use change data to allow us to fill this information gap and enable alignment between pathways and inventories.Across both 1.5°C and 2°C scenarios, pathways aligned with NGHGIs show a strong increase in the total land sink until around mid-century. However, the ‘NGHGI alignment gap’ decreases over this period, converging in the 2050-2060s for 1.5°C scenarios and 2070s-2080s for 2°C scenarios. These dynamics lead to land-based emissions reversing their downward trend in most NGHGI-aligned scenarios by mid-century, and result in the LULUCF sector becoming a net-source of emissions by 2100 in about 25% of deep mitigation scenarios.Our results do not change any climate outcome or mitigation benchmark produced by the IPCC, but rather provide a translational lens to view those outcomes. We find that net-zero timings on average advance by around 5 years; however, this does not imply that 5 years have been lost in the race to net-zero, but rather that following the reporting conventions for natural sinks results in net-zero being reached 5 years earlier. Understanding how these different accounting frameworks can be mutually interpreted is a fundamental challenge for evaluating progress towards the Paris Agreement, given the reality that direct and indirect carbon removals cannot be estimated separately with direct observations.We propose three primary ways to address this science-policy gap. First, targets can be formulated separately for gross emission reductions, land-based removals, and technical carbon removals, allowing for nations to clearly define their expected contributions and to measure progress in each domain separately. Second, nations can clarify the nature of their deforestation pledges. Third, modelling teams can provide their assumptions for the NGHGI correction as part of their standard output which future IPCC assessments can use to vet scenarios.

Baur et al. (2023):The Deployment Length of Solar Radiation Modification: An Interplay of Mitigation, Net-negative Emissions and Climate Uncertainty. Code corresponding to paper publicationin ESD.

Taking stock of global progress towards achieving the Paris Agreement requires measuring aggregate national action against modelled mitigation pathways. Because of differences in how land-based carbon removals are defined, scientific sources report higher global carbon emissions than national emissions inventories, a gap which will evolve in the future. We establish a first estimate aligning IPCC-assessed pathways with inventories using a climate model to explicitly include indirect carbon removal dynamics on land area reported as managed for by countries. After alignment, we find that key global mitigation benchmarks can appear more ambitious when considering this extra land sink, though changes vary amongst world regions and temperature outcomes. Our results highlight the need to enhance communication between scientific and policy communities to enable more robust alignment in the future.

Repository reproducing plots and processing used in Dooley et al., 2022 (One Earth, https://doi.org/10.1016/j.oneear.2022.06.002). For questions and comments about the paper, please contact Kate Dooley (kate.dooley@unimelb.edu.au). For questions and comments about the code, please contact Zebedee Nicholls (zebedee.nicholls@climate-energy-college.org). For full details, please see https://gitlab.com/znicholls/one-earth-2022., {"references":["Dooley et al. 2022 (One Earth, https://doi.org/10.1016/j.oneear.2022.06.002)"]}

Repository reproducing plots and processing used in Nicholls et al 2022 (under review).For questions and comments, please contact Zebedee Nicholls (zebedee.nicholls@climate-energy-college.org). For full details, please see https://gitlab.com/magicc/nicholls-et-al-2022-emulator-changes.

Scientifically rigorous guidance to policy makers on mitigation options for meeting the Paris Agreement long-term temperature goal requires an evaluation of long-term global-warming implications of greenhouse gas emissions pathways. Here, we present a uniform and transparent methodology to evaluate the climate outcome, and hence the Paris Agreement consistency of influential institutional emission scenarios from the grey literature, including those from the International Energy Agency1,2, BP3, and Shell4. We first identify challenges to performing such an assessment and then proceed to outline a sequence of steps to address these challenges by harmonizing5 all emissions to a consistent base-year (2010), extending all pathways to 2100, and filling in missing emission species6. We employ two simple climate models, MAGICC7 and FaIR8,9 to assess peak and end-of-century temperatures, and find that few published scenarios that claim to be compatible with the Paris Agreement are so. References 1. International Energy Agency. World Energy Outlook 2020. (2020).2. International Energy Agency. Net Zero by 2050 - A Roadmap for the Global Energy Sector. (2021).3. BP. Global Energy Outlook 2020. (2020).4. Shell. The Energy Transformation Scenarios. (2021) 5. Gidden, M. J. et al. A methodology and implementation of automated emissions harmonization for use in Integrated Assessment Models. Environ. Model. Softw. 105, 187–200 (2018)6. Lamboll, R. D., Nicholls, Z. R. J., Kikstra, J. S., Meinshausen, M. & Rogelj, J. Silicone v1.0.0 : an open-source Python package for inferring missing emissions data for climate change research. Geosci. Model Dev 13, 5259–5275 (2020)7. Meinshausen, M., Raper, S. C. B. & Wigley, T. M. L. Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 - Part 1: Model description and calibration. Atmos. Chem. Phys. 11, 1417–1456 (2011).8. Smith, C. J. et al. FAIR v1.3: A simple emissions-based impulse response and carbon cycle model. Geosci. Model Dev. 11, 2273–2297 (2018)9. Millar, J. R., Nicholls, Z. R., Friedlingstein, P. & Allen, M. R. A modified impulse-response representation of the global near-surface air temperature and atmospheric concentration response to carbon dioxide emissions. Atmos. Chem. Phys. 17, 7213–7228 (2017).

Future climate projections from Earth system models (ESMs) typically focus on the timescale of this century. We use a set of five ESMs and one Earth system model of intermediate complexity (EMIC) to explore the dynamics of the Earth's climate and carbon cycles under contrasting emissions trajectories beyond this century to the year 2300. The trajectories include a very-high-emissions, unmitigated fossil-fuel-driven scenario, as well as a mitigation scenario that diverges from the first scenario after 2040 and features an “overshoot”, followed by a decrease in atmospheric CO2 concentrations by means of large net negative CO2 emissions. In both scenarios and for all models considered here, the terrestrial system switches from being a net sink to either a neutral state or a net source of carbon, though for different reasons and centered in different geographic regions, depending on both the model and the scenario. The ocean carbon system remains a sink, albeit weakened by carbon cycle feedbacks, in all models under the high-emissions scenario and switches from sink to source in the overshoot scenario. The global mean temperature anomaly is generally proportional to cumulative carbon emissions, with a deviation from proportionality in the overshoot scenario that is governed by the zero emissions commitment. Additionally, 23rd century warming continues after the cessation of carbon emissions in several models in the high-emissions scenario and in one model in the overshoot scenario. While ocean carbon cycle responses qualitatively agree in both globally integrated and zonal mean dynamics in both scenarios, the land models qualitatively disagree in zonal mean dynamics, in the relative roles of vegetation and soil in driving C fluxes, in the response of the sink to CO2, and in the timing of the sink–source transition, particularly in the high-emissions scenario. The lack of agreement among land models on the mechanisms and geographic patterns of carbon cycle feedbacks, alongside the potential for lagged physical climate dynamics to cause warming long after CO2 concentrations have stabilized, points to the possibility of surprises in the climate system beyond the 21st century time horizon, even under relatively mitigated global warming scenarios, which should be taken into consideration when setting global climate policy.

Over the last five years prior to the Glasgow Climate Pact

Producing targeted climate information at the local scale, including major sources of climate change projection uncertainty for diverse emissions scenarios, is essential to support climate change mitigation and adaptation efforts. Here, we present the first chain of computationally efficient Earth System Model (ESM) emulators allowing to rapidly translate greenhouse gas emission pathways into spatially resolved annual-mean temperature anomaly field time series, accounting for both forced climate response and natural variability uncertainty at the local scale. By combining the global-mean, emissions-driven emulator MAGICC with the spatially resolved emulator MESMER, ESM-specific as well as constrained probabilistic emulated ensembles can be derived. This emulation chain can hence build on and extend large multi-ESM ensembles such as the ones produced within the 6th phase of the Coupled Model Intercomparison Project (CMIP6). The main extensions are threefold. (i) A more thorough sampling of the forced climate response and the natural variability uncertainty is possible with millions of emulated realizations being readily created. (ii) The same uncertainty space can be sampled for any emission pathway, which is not the case in CMIP6, where some of the most societally relevant strong mitigation scenarios have been run by only a small number of ESMs. (iii) Other lines of evidence to constrain future projections, including observational constraints, can be introduced, which helps to refine projected future ranges beyond the multi-ESM ensemble's estimates. In addition to presenting results from the coupled MAGICC-MESMER emulator chain, we carry out an extensive validation of MESMER, which is trained on and applied to multiple emission pathways for the first time in this study. The newly developed MAGICC-MESMER coupled emulator will allow unprecedented assessments of the implications of manifold emissions pathways at regional scale.

Scientifically rigorous guidance to policy makers on mitigation options for meeting the Paris Agreement long-term temperature goal requires an evaluation of long-term global-warming implications of greenhouse gas emissions pathways. Here we employ a uniform and transparent methodology to evaluate Paris Agreement compatibility of influential “institutional” emission scenarios from the grey literature. We compare a selection of these scenarios analyzed with this methodology to the Integrated Assessment Model scenarios assessed by the Intergovernmental Panel on Climate Change. We harmonize emissions to a consistent base-year and account for all greenhouse gases and aerosol precursor emissions, ensuring a self-consistent comparison of climate variables. An evaluation of peak and end-of-century temperatures is made, with both being relevant to the Paris Agreement goal. We find that few published scenarios that claim to be compatible with the Paris Agreement are so, and investigate root causes based on the underlying energy system transformation.

This archive contains model code to accompany the paper describing version 2.0.0 of the FaIR simple climate model (https://gmd.copernicus.org/preprints/gmd-2020-390/). This version (v2.0.0-alpha1) contains a patch enabling Windows users to extract the model code (the previous version contained the reserved name "/aux/"). No other changes have been made. However, for non-Windows users, we would recommend using the previous version to retain full compatibility with the accompanying notebooks and data. The notebooks that accompany the paper are found here., {"references":["Leach, N. J., Jenkins, S., Nicholls, Z., Smith, C. J., Lynch, J., Cain, M., … Allen, M. R. (2020). FaIRv2.0.0: a generalised impulse-response model for climate uncertainty and future scenario exploration. Geosci. Model Dev. Discuss., 2020, 1–48. https://doi.org/10.5194/gmd-2020-390"]}

Data and code related toReduced Complexity Model Intercomparison Project Phase 2: Synthesising Earth system knowledge for probabilistic climate projections (updates to include full author list to come).

Reduced-complexity climate models form part of the climate model hierarchy and are increasingly relied upon at the science-policy interface. Historically, evaluation of reduced-complexity climate models has been limited to a number of independent studies. Here we present the reduced-complexity model intercomparison project (RCMIP), the first systematic, community-organised evaluation of reduced-complexity climate models. We introduce the motivation behind RCMIP, where to find information about it and key insights arising from its first two scientific outputs. Future phases of RCMIP will examine specific behaviour of reduced-complexity climate models in more detail, for example their carbon cycle response. We are particulalry keen to hear from users of reduced-complexity models to discuss their use cases, how we can evaluate our models in the way most relevant to them and where key model improvements can be made.

The remaining carbon budget represents the total amount of CO2 that can still be emitted in the future while limiting global warming to a given temperature target. Remaining carbon budget estimates range widely, however, and this uncertainty can be used to either trivialize the most ambitious mitigation targets by characterizing them as impossible, or to argue that there is ample time to allow for a gradual transition to a low-carbon economy. Neither of these extremes is consistent with our best understanding of the policy implications of remaining carbon budgets. Understanding the scientific and socio-economic uncertainties affecting the size of the remaining carbon budgets, as well as the methodological choices and assumptions that underlie their calculation, is essential before applying them as a policy tool. Here we provide recommendations on how to calculate remaining carbon budgets in a traceable and transparent way, and discuss their uncertainties and implications for both international and national climate policies. ISSN:1752-0908 ISSN:1752-0894

Here we present an update to the FaIR model for use in probabilistic future climate and scenario exploration, integrated assessment, policy analysis and education. In this update we have focussed on identifying a minimum level of structural complexity in the model. The result is a set of six equations, five of which correspond to the standard Impulse Response model used for greenhouse gas (GHG) metric calculations in the IPCC's fifth assessment report, plus one additional physically-motivated additional equation to represent state-dependent feedbacks on the response timescales of each greenhouse gas cycle. This additional equation is necessary to reproduce non-linearities in the carbon cycle apparent in both Earth System Models and observations. These six equations are transparent and sufficiently simple that the model is able to be written in standard tabular data analysis packages, such as Excel; increasing the potential user base considerably. However, we demonstrate that the equations are flexible enough to be tuned to emulate the behaviour of several key processes within more complex models from CMIP6. The model is exceptionally quick to run, making it ideal for integrating large probabilistic ensembles. We apply a constraint based on the current estimates of the global warming trend to a one million member ensemble, using the constrained ensemble to make scenario dependent projections and infer ranges for properties of the climate system. Through these analyses, we reaffirm that simple climate models (unlike more complex models) are not themselves intrinsically biased hot or cold: it is the choice of parameters and how those are selected that determines the model response, something that appears to have been misunderstood in the past. This updated FaIR model is able to reproduce the global climate system response to GHG and aerosol emissions with sufficient accuracy to be useful in a wide range of applications; and therefore could be used as a lowest common denominator model to provide consistency in different contexts. The fact that FaIR can be written down in just six equations greatly aids transparency in such contexts.

The assessment of long-term greenhouse gas emissions scenarios and societal transformation pathways is a key component of the IPCC Working Group 3 (WG3) on the Mitigation of Climate Change. A large scientific community, typically using integrated assessment models and econometric frameworks, supports this assessment in understanding both near-term actions and long-term policy responses and goals related to mitigating global warming. WG3 must systematically assess hundreds of scenarios from the literature to gain an in-depth understanding of long-term emissions pathways, across all sectors, leading to various levels of global warming. Systematic assessment and understanding the climate outcomes of each emissions scenario, requires coordinated processes which have developed over consecutive IPCC assessments. Here, we give an overview of the processes involved in the systematic assessment of long-term mitigation pathways as used in recent IPCC Assessments1 and being further developed for the IPCC 6th Assessment Report (AR6). The presentation will explain how modelling teams can submit scenarios to AR6 and invite feedback to the process.Following discussions amongst IPCC Lead Authors to define the scope of scenarios desired and variables requested, a call for scenarios to support AR6 was launched in September 2019. Modelling teams have registered and submitted scenarios through Autumn 2019 using a new and secure online submission portal, from which authorised Lead Authors can interrogate the scenarios interactively.This analysis is underpinned by the open-source software pyam, a Python package specifically designed for analysis and visualisation of integrated assessment scenarios2. Submitted scenarios are automatically checked for errors and processed using a new climate assessment pipeline. The climate assessment involves infilling and harmonization3 of emissions data, then the scenarios are processed through Simple Climate Models, using the OpenSCM framework4, to give probabilistic climate implications for each scenario – atmospheric concentrations, radiative forcing and global mean temperature. The climate assessment accounts for updated climate sensitivity estimates from CMIP6 and WG1,s scenarios are categorized according to climate outcomes and distinguish between timing and levels of net-negative emissions, emissions peak and temperature overshoot. Scenarios are also categorized by other indicators, for consistent use across WG3 chapters, such as: population and GDP; Primary and Final energy use; and shares of renewables, bioenergy and fossil fuels.The automated framework also facilitates bolt-on analyses, such as estimating the population impacted by biophysical climate impacts5, and estimates of avoided damages with the social cost of carbon6.Upon publication of the WG3 AR6 report, all scenario data used in the WG3 Assessment will be publicly available on a Scenario Explorer, an online tool for interrogating and visualizing the data that supports the report. In combination, this framework brings new levels of consistency, transparency and reproducibility to the assessment of scenarios in IPCC WG3 and will be a key resource for the climate community in understanding the main drivers of different transformation pathways.Huppmman et al 2018, Nature Climate Change Gidden and Huppmann, 2019, Journal of Open Source Software Gidden et al 2018 Environ. Model. Softw Nicholls et al 2020 Byers et al 2018 Environmental Research Letters Ricke et al 2018 Nature Climate Change

Consistent and comparable climate assessments of scenarios are critical within the context of IPCC assessment reports. Given the number of scenarios assessed by WG3, the assessment “pipeline” must be almost completely automated. Here, we present the application of a new assessment pipeline which combines state-of-the-art components into a single workflow in order to derive climate outcomes for integrated assessment model (IAM) scenarios assessed by WG3 of the IPCC. A consistent analysis ensures that WG3’s conclusions about the socioeconomic transformations required to maintain a safe climate are based on the best understanding of our planetary boundaries from WG1. For example, if WG1 determines that climate sensitivity is higher than previously considered, then WG3 could incorporate this insight by e.g. considering much smaller remaining carbon budgets for any given temperature target. The scenario-climate assessment pipeline is comprised of three primary components. First, a consistent harmonization algorithm which maintains critical model characteristics between harmonized and unharmonized scenarios [1] is employed to harmonize emissions trajectories to a common and consistent historical dataset as used in CMIP6 [2]. Next, a scenario’s reported emissions trajectories are analyzed as to the completeness of its species and sectoral coverage. A consistent set of 14 emissions species are expected, aligning with published work within ScenarioMIP and CMIP6 (see ref [2], Table 2). Should any component of this full set of emissions trajectories be absent for a given scenario, an algorithm (e.g., generalised quantile walk [3]) is employed in order to “back-fill” missing species at the native model regional resolution. Finally, full emissions scenarios are analyzed by an Earth System Model emulator, e.g., MAGICC [4]. In this presentation, we explore differences in climate assessments and estimated remaining carbon budgets across various components of the pipeline for available scenarios in the literature. We consider the impact of alternative choices, especially those made in prior assessments by the IPCC (AR5, SR15), including, for example, the historical emissions database used, the effect of harmonization and back-filling, as well as the version and setup of MAGICC used. References [1] Gidden, M.J., Fujimori, S., van den Berg, M., Klein, D., Smith, S.J., van Vuuren, D.P. and Riahi, K., 2018. A methodology and implementation of automated emissions harmonization for use in Integrated Assessment Models. Environmental Modelling & Software, 105, pp.187-200. [2] Gidden, M. J., Riahi, K., Smith, S. J., Fujimori, S., Luderer, G., Kriegler, E., van Vuuren, D. P., van den Berg, M., Feng, L., Klein, D., Calvin, K., Doelman, J. C., Frank, S., Fricko, O., Harmsen, M., Hasegawa, T., Havlik, P., Hilaire, J., Hoesly, R., Horing, J., Popp, A., Stehfest, E., and Takahashi, K.: Global emissions pathways under different socioeconomic scenarios for use in CMIP6: a dataset of harmonized emissions trajectories through the end of the century, Geosci. Model Dev., 12, 1443-1475, https://doi.org/10.5194/gmd-12-1443-2019, 2019. [3] Teske, S. et al., Achieving the Paris Climate Agreement Goals. Springer, 2019. [4] Meinshausen, M., Raper, S.C. and Wigley, T.M., 2011. Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6–Part 1: Model description and calibration. Atmospheric Chemistry and Physics, 11(4), pp.1417-1456.

Here we present a Generalised Impulse Response (GIR) model for use in probabilistic future climate and scenario exploration, integrated assessment, policy analysis and teaching. This model is based on a set of only six equations, which correspond to the standard Impulse Response model used for greenhouse gas metric calculations by the IPCC, plus one physically-motivated additional equation to represent state-dependent feedbacks on the response timescales of each greenhouse gas cycle. These six equations are simple and transparent enough to be easily understood and implemented in other models without reliance on the original source code, but flexible enough to reproduce observed well-mixed greenhouse gas (GHG) concentrations and atmospheric lifetimes, best-estimate effective radiative forcing, and temperature response. We describe the assumptions and methods used in selecting the default parameters, but emphasize that other methods would be equally valid: our focus here is on identifying a minimum level of structural complexity. The tunable nature of the model lends it to use as a fully transparent emulator of complex Earth System Models, such as those participating in CMIP6, while also reproducing the behaviour of other simple climate models. We argue that this GIR model is adequate to reproduce the global temperature response to global emissions and effective radiative forcing, and that it should be used as a lowest-common denominator to provide consistency and continuity between different climate assessments. The model design is such that it can be written in tabular data analysis software, such as Excel, increasing the potential user base considerably.

Here we present results from the first phase of the Reduced Complexity Model Intercomparison Project (RCMIP). RCMIP is a systematic examination of reduced complexity climate models (RCMs), which are used to complement and extend the insights from more complex Earth System Models (ESMs), in particular those participating in the Sixth Coupled Model Intercomparison Project (CMIP6). In Phase 1 of RCMIP, with 14 participating models namely ACC2, AR5IR (2 and 3 box versions), CICERO-SCM, ESCIMO, FaIR, GIR, GREB, Hector, Held et al. two layer model, MAGICC, MCE, OSCAR and WASP, we highlight the structural differences across various RCMs and show that RCMs are capable of reproducing global-mean surface air temperature (GSAT) changes of ESMs and historical observations. We find that some RCMs are capable of emulating the GSAT response of CMIP6 models to within a root-mean square error of 0.2 °C (of the same order of magnitude as ESM internal variability) over a range of scenarios. Running the same model configurations for both RCP and SSP scenarios, we see that the SSPs exhibit higher effective radiative forcing throughout the second half of the 21st Century. Comparing our results to the difference between CMIP5 and CMIP6 output, we find that the change in scenario explains approximately 46 % of the increase in higher end projected warming between CMIP5 and CMIP6. This suggests that changes in ESMs from CMIP5 to CMIP6 explain the rest of the increase, hence the higher climate sensitivities of available CMIP6 models may not be having as large an impact on GSAT projections as first anticipated. A second phase of RCMIP will complement RCMIP Phase 1 by exploring probabilistic results and emulation in more depth to provide results available for the IPCC's Sixth Assessment Report author teams.

Limiting global warming to a 1.5°C temperature rise requires drastic emissions reductions and removal of carbon-dioxide from the atmosphere. Most modelled pathways for 1.5°C assume substantial removals in the form of biomass energy with carbon capture and storage, which brings with it increasing risks to biodiversity and food security via extensive land-use change. Recently, multiple efforts to describe and quantify potential removals via ecosystem-based approaches have gained traction in the climate policy discourse. However, these options have yet to be evaluated in a systematic and scientifically robust way. We provide spatially explicit estimates of ecosystem restoration potential quantified with a Dynamic Global Vegetation Model. Simulations covering forest restoration, reforestation, reduced harvest, agroforestry and silvopasture were combined and found to sequester an additional 93 Gt C by 2100, reducing mean global temperature increase by ∼0.12°C (5%–95% range 0.06°C–0.21°C) relative to a baseline mitigation pathway. Ultimately, pathways to achieving the 1.5°C goal garner broader public support when they include land management options that can bring about multiple benefits, including ecosystem restoration, biodiversity protection, and resilient agricultural practices.

The open-source Python package pyam provides a suite of features and methods for the analysis, validation and visualization of reference data and scenario results generated by integrated assessment models, macro-energy tools and other frameworks in the domain of energy transition, climate change mitigation and sustainable development. It bridges the gap between scenario processing and visualisation solutions that are "hard-wired" to specific modelling frameworks and generic data analysis or plotting packages. The package aims to facilitate reproducibility and reliability of scenario processing, validation and analysis by providing well-tested and documented methods for working with timeseries data in the context of climate policy and energy systems. It supports various data formats, including sub-annual resolution using continuous time representation and "representative timeslices". The pyam package can be useful for modelers generating scenario results using their own tools as well as researchers and analysts working with existing scenario ensembles such as those supporting the IPCC reports or produced in research projects. It is structured in a way that it can be applied irrespective of a user's domain expertise or level of Python knowledge, supporting experts as well as novice users. The code base is implemented following best practices of collaborative scientific-software development. This manuscript describes the design principles of the package and the types of data which can be handled. The usefulness of pyam is illustrated by highlighting several recent applications.

Data related to ourpublication about remaining carbon budget assessment frameworks.

Anthropogenic increases of atmospheric greenhouse gas concentrations are the main driver of current and future climate change. The Integrated Assessment community quantified anthropogenic emissions for the Shared Socioeconomic Pathways (SSP) scenarios, each of which represents a different future socio-economic projection and political environment. Here, we provide the greenhouse gas concentration for these SSP scenarios – using the reduced complexity climate-carbon cycle model MAGICC7.0. We extend historical, observationally-based concentration data with SSP concentration projections from 2015 to 2500 for 43 greenhouse gases with monthly and latitudinal resolution. CO2 concentrations by 2100 range from 393 to 1135 ppm for the lowest (SSP1-1.9) and highest (SSP5-8.5) emission scenarios respectively. We also provide the concentration extensions beyond 2100 based on assumptions in the trajectories of fossil fuels and land use change emissions, net negative emissions, and the fraction of non-CO2 emissions. By 2150, CO2 concentrations in the lowest emission scenario are approximately 350 ppm and approximately plateau at that level until 2500, whereas the highest fossil-fuel driven scenario projects CO2 concentrations of 1737 ppm and reaches concentrations beyond 2000 ppm by 2250. We estimate that the share of CO2 in the total radiative forcing contribution of all considered 43 long-lived greenhouse gases increases from today 66 % to roughly 68 % to 85 % by the time of maximum forcing in the 21st century. For this estimation, we updated simple radiative forcing parameterisations that reflect the Oslo Line by Line model results. In comparison to the RCPs, the five main SSPs (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) are more evenly spaced in terms of their expected global-mean temperatures, extend to lower 2100 temperatures and sea level rise than the RCP set. Performing 2 pairs of 6-member historical ensembles with CESM1.2.2, we estimate the effect on surface air temperatures of applying latitudinally and seasonally resolved GHG concentrations. We find that the ensemble differences in the MAM season provide a regional warming in higher northern latitudes of up to 0.4 K over the historical period, latitudinally averaged of about 0.1 K, which we estimate to be comparable to the upper bound (∼ 5 % level) of natural variability. In comparison to the comparatively straight line of the last 2000 years, the greenhouse gas concentrations since the onset of the industrial period and this studies’ projections over the next 100 to 500 years unequivocally depict a ‘hockey-stick’ upwards shape – it is a collective choice whether the hothouse pathway is pursued or whether we manage climate damages to the SSP1-1.9 equivalent of around 1.5 °C warming.

To understand how global warming can be kept well-below 2°C and even 1.5°C, climate policy uses scenarios that describe how society could transform in order to reduce its greenhouse gas emissions. Such scenario are typically created with integrated assessment models that include a representation of the economy, and the energy, land-use, and industrial system. However, current climate change scenarios have a key weakness in that they typically focus on reaching specific climate goals in 2100 only. This choice results in risky pathways that delay action and seemingly inevitably rely on large quantities of carbon-dioxide removal after mid-century. Here we propose a framework that more closely reflects the intentions of the UN Paris Agreement. It focusses on reaching a peak in global warming with either stabilisation or reversal thereafter. This approach provides a critical extension of the widely used Shared Socioecononomic Pathways (SSP) framework and reveals a more diverse picture: an inevitable transition period of aggressive near-term climate action to reach carbon neutrality can be followed by a variety of long-term states. It allows policymakers to explicitly consider near-term climate strategies in the context of intergenerational equity and long-term sustainability.

To determine the remaining carbon budget, a new framework was introduced in the Intergovernmental Panel on Climate Change’s Special Report on Global Warming of 1.5 °C (SR1.5). We refer to this as a ‘segmented’ framework because it considers the various components of the carbon budget derivation independently from one another. Whilst implementing this segmented framework, in SR1.5 the assumption was that there is a strictly linear relationship between cumulative CO2 emissions and CO2-induced warming i.e. the TCRE is constant and can be applied to a range of emissions scenarios. Here we test whether such an approach is able to replicate results from model simulations that take the climate system’s internal feedbacks and non-linearities into account. Within our modelling framework, following the SR1.5’s choices leads to smaller carbon budgets than using simulations with interacting climate components. For 1.5 °C and 2 °C warming targets, the differences are 50 GtCO2 (or 10%) and 260 GtCO2 (or 17%), respectively. However, by relaxing the assumption of strict linearity, we find that this difference can be reduced to around 0 GtCO2 for 1.5 °C of warming and 80 GtCO2 (or 5%) for 2.0 °C of warming (for middle of the range estimates of the carbon cycle and warming response to anthropogenic emissions). We propose an updated implementation of the segmented framework that allows for the consideration of non-linearities between cumulative CO2 emissions and CO2-induced warming.

Natural aerosol emission represents one of the largest uncertainties in our understanding of the climate system. Sulfur emitted by marine organisms, as dimethyl sulfide (DMS), constitutes one fifth of the global sulfur budget and yet the distribution, fluxes and fate of DMS remain poorly constrained. In this study we quantify the role of DMS in the chemistry-climate system and determine the climate's response to large DMS perturbations. By removing all marine DMS in the Australian Community Climate and Earth System Simulator (ACCESS) – United Kingdom Chemistry and Aerosol (UKCA), we find a top of atmosphere radiative effect of 1.7 W m−2. The largest responses to removing marine DMS are in stratiform cloud decks in the Southern Hemisphere's eastern ocean basins. These regions show significant differences in low-cloud (−9 %), radiation (+7 W m−2 in short wave incoming surface radiation) and large-scale rainfall (+15 %) when all DMS is removed. We demonstrate a precipitation suppression effect of DMS-derived aerosol in stratiform cloud deck regions, coupled with an increase in low cloud fraction. The increase in low cloud fraction is an example of the aerosol lifetime effect. Other areas of low cloud formation, such as the Southern Ocean and stratiform cloud decks in the Northern Hemisphere, have a relatively weak response to DMS perturbations. Our study highlights the need for further modelling and field studies of natural aerosols and their impact on cloud and precipitation, in particular in Southern Hemisphere stratiform cloud regions.

Projections of the response to anthropogenic emission scenarios, evaluation of some greenhouse gas metrics and estimates of the social cost of carbon, often require a simple model that links emissions of carbon dioxide CO2 to atmospheric concentrations and global temperature changes. An essential requirement of such a model is to reproduce the behaviour of more complex models as well as an ability to sample their range of response in a transparent, accessible and reproducible form. Here we adapt the simple model of the Intergovernmental Panel on Climate Change 5th Assessment Report (IPCC-AR5) to explicitly represent the state-dependence of the CO2 airborne fraction and reproduce several idealised experiments performed with more complex models. We find that a simple linear increase in 100-year integrated airborne fraction with cumulative carbon uptake and global temperature change is both necessary and sufficient to represent the response of the climate system to CO2 on a range of timescales and under a range of experimental designs. Quantified ranges of uncertainty (analogous to current assessed ranges in Equilibrium Climate Sensitivity and Transient Climate Response) in integrated airborne fraction over the 21st century under a representative mitigation scenario, and an assessed range in how much this quantity may have changed relative to pre-industrial conditions, would be valuable in future scientific assessments.

Over the last decades, climate science has evolved rapidly across multiple expert domains. Our best tools to capture state‐of‐the‐art knowledge in an internally self‐consistent modeling framework are the increasingly complex fully coupled Earth System Models (ESMs). However, computational limitations and the structural rigidity of ESMs mean that the full range of uncertainties across multiple domains are difficult to capture with ESMs alone. The tools of choice are instead more computationally efficient reduced complexity models (RCMs), which are structurally flexible and can span the response dynamics across a range of domain‐specific models and ESM experiments. Here we present Phase 2 of the Reduced Complexity Model Intercomparison Project (RCMIP Phase 2), the first comprehensive intercomparison of RCMs that are probabilistically calibrated with key benchmark ranges from specialized research communities. Unsurprisingly, but crucially, we find that models which have been constrained to reflect the key benchmarks better reflect the key benchmarks. Under the low‐emissions SSP1‐1.9 scenario, across the RCMs, median peak warming projections range from 1.3 to 1.7°C (relative to 1850–1900, using an observationally based historical warming estimate of 0.8°C between 1850–1900 and 1995–2014). Further developing methodologies to constrain these projection uncertainties seems paramount given the international community's goal to contain warming to below 1.5°C above preindustrial in the long‐term. Our findings suggest that users of RCMs should carefully evaluate their RCM, specifically its skill against key benchmarks and consider the need to include projections benchmarks either from ESM results or other assessments to reduce divergence in future projections., Key Points Probabilistic global‐mean temperature projections often use reduced complexity climate models (RCMs) because of their low computational costWe evaluate how well RCMs’ probabilistic setups can synthesize knowledge from multiple research domains for policy relevant projectionsNo RCM is able to capture all forcing, warming, heat uptake and carbon cycle metrics we evaluate, however some come close across a range

Increased ambition and implementation are essential