CMIP6 simulations with the compact Earth system model OSCAR v3.1

Reduced-complexity models, also called simple climate models or compact models, provide an alternative to Earth system models (ESMs) with lower computational costs, although at the expense of spatial and temporal information. It remains important to evaluate and validate these reduced-complexity models. Here, we evaluate a recent version (v3.1) of the OSCAR model using observations and results from ESMs from the current Coupled Model Intercomparison Project 6 (CMIP6). The results follow the same post-processing used for the contribution of OSCAR to the Reduced Complexity Model Intercomparison Project (RCMIP) Phase 2 regarding the identification of stable configurations and the use of observational constraints. These constraints succeed in decreasing the overestimation of global surface air temperature over 2000–2019 with reference to 1961–1900 from 0.60±0.11 to 0.55±0.04 K (the constraint being 0.54±0.05 K). The equilibrium climate sensitivity (ECS) of the unconstrained OSCAR is 3.17±0.63 K, while CMIP5 and CMIP6 models have ECSs of 3.2±0.7 and 3.7±1.1 K, respectively. Applying observational constraints to OSCAR reduces the ECS to 2.78±0.47 K. Overall, the model qualitatively reproduces the responses of complex ESMs, although some differences remain due to the impact of observational constraints on the weighting of parametrizations. Specific features of OSCAR also contribute to these differences, such as its fully interactive atmospheric chemistry and endogenous calculations of biomass burning, wetlands CH4 and permafrost CH4 and CO2 emissions. Identified main points of needed improvements of the OSCAR model include a low sensitivity of the land carbon cycle to climate change, an instability of the ocean carbon cycle, the climate module that is seemingly too simple, and the climate feedback involving short-lived species that is too strong. Beyond providing a key diagnosis of the OSCAR model in the context of the reduced-complexity models, this work is also meant to help with the upcoming calibration of OSCAR on CMIP6 results and to provide a large group of CMIP6 simulations run consistently within a probabilistic framework

Historical CO2 emissions from land-use and land-cover change and their uncertainty

Emissions from land-use and land-cover change are a key component of the global carbon cycle. Models are required to disentangle these emissions and the land carbon sink, however, because only the sum of both can be physically observed. Their assessment within the yearly community-wide effort known as the Global Carbon Budget remains a major difficulty, because it combines two lines of evidence that are inherently inconsistent: bookkeeping models and dynamic global vegetation models. Here, we propose a unifying approach relying on a bookkeeping model that embeds processes and parameters calibrated on dynamic global vegetation models, and the use of an empirical constraint. We estimate global CO2 emissions from land-use and land-cover change were 1.36 ± 0.42 Pg C yr−1 (1-σ range) on average over 2009–2018, and 206 ± 57 Pg C cumulated over 1750–2018. We also estimate that land-cover change induced a global loss of additional sink capacity – that is, a foregone carbon removal, not part of the emissions – of 0.68 ± 0.57 Pg C yr−1 and 32 ± 23 Pg C over the same periods, respectively. Additionally, we provide a breakdown of our results' uncertainty following aspects that include the land-use and land-cover change data sets used as input, and the model's biogeochemical parameters. We find the biogeochemical uncertainty dominates our global and regional estimates, with the exception of tropical regions in which the input data dominates. Our analysis further identifies key sources of uncertainty, and suggests ways to strengthen the robustness of future Global Carbon Budgets

Impact of bioenergy crop expansion on climate–carbon cycle feedbacks in overshoot scenarios

Stringent mitigation pathways frame the deployment of second-generation bioenergy crops combined with carbon capture and storage (CCS) to generate negative CO2 emissions. This bioenergy with CCS (BECCS) technology facilitates the achievement of the long-term temperature goal of the Paris Agreement. Here, we use five state-of-the-art Earth system models (ESMs) to explore the consequences of large-scale BECCS deployment on the climate–carbon cycle feedbacks under the CMIP6 SSP5-3.4-OS overshoot scenario keeping in mind that all these models use generic crop vegetation to simulate BECCS. First, we evaluate the land cover representation by ESMs and highlight the inconsistencies that emerge during translation of the data from integrated assessment models (IAMs) that are used to develop the scenario. Second, we evaluate the land-use change (LUC) emissions of ESMs against bookkeeping models. Finally, we show that an extensive cropland expansion for BECCS causes ecosystem carbon loss that drives the acceleration of carbon turnover and affects the CO2 fertilization effect- and climate-change-driven land carbon uptake. Over the 2000–2100 period, the LUC for BECCS leads to an offset of the CO2 fertilization effect-driven carbon uptake by 12.2 % and amplifies the climate-change-driven carbon loss by 14.6 %. A human choice on land area allocation for energy crops should take into account not only the potential amount of the bioenergy yield but also the LUC emissions, and the associated loss of future potential change in the carbon uptake. The dependency of the land carbon uptake on LUC is strong in the SSP5-3.4-OS scenario, but it also affects other Shared Socioeconomic Pathway (SSP) scenarios and should be taken into account by the IAM teams. Future studies should further investigate the trade-offs between the carbon gains from the bioenergy yield and losses from the reduced CO2 fertilization effect-driven carbon uptake where BECCS is applied

Reduced Complexity Model Intercomparison Project Phase 1: introduction and evaluation of global-mean temperature response

Reduced-complexity climate models (RCMs) are critical in the policy and decision making space, and are directly used within multiple Intergovernmental Panel on Climate Change (IPCC) reports to complement the results of more comprehensive Earth system models. To date, evaluation of RCMs has been limited to a few independent studies. Here we introduce a systematic evaluation of RCMs in the form of the Reduced Complexity Model Intercomparison Project (RCMIP). We expect RCMIP will extend over multiple phases, with Phase 1 being the first. In Phase 1, we focus on the RCMs' global-mean temperature responses, comparing them to observations, exploring the extent to which they emulate more complex models and considering how the relationship between temperature and cumulative emissions of CO2 varies across the RCMs. Our work uses experiments which mirror those found in the Coupled Model Intercomparison Project (CMIP), which focuses on complex Earth system and atmosphere–ocean general circulation models. Using both scenario-based and idealised experiments, we examine RCMs' global-mean temperature response under a range of forcings. We find that the RCMs can all reproduce the approximately 1 ∘C of warming since pre-industrial times, with varying representations of natural variability, volcanic eruptions and aerosols. We also find that RCMs can emulate the global-mean temperature response of CMIP models to within a root-mean-square error of 0.2 ∘C over a range of experiments. Furthermore, we find that, for the Representative Concentration Pathway (RCP) and Shared Socioeconomic Pathway (SSP)-based scenario pairs that share the same IPCC Fifth Assessment Report (AR5)-consistent stratospheric-adjusted radiative forcing, the RCMs indicate higher effective radiative forcings for the SSP-based scenarios and correspondingly higher temperatures when run with the same climate settings. In our idealised setup of RCMs with a climate sensitivity of 3 ∘C, the difference for the ssp585–rcp85 pair by 2100 is around 0.23∘C(±0.12 ∘C) due to a difference in effective radiative forcings between the two scenarios. Phase 1 demonstrates the utility of RCMIP's open-source infrastructure, paving the way for further phases of RCMIP to build on the research presented here and deepen our understanding of RCMs

Reduced Complexity Model Intercomparison Project Phase 2: Synthesizing Earth System Knowledge for Probabilistic Climate Projections

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. Plain Language Summary Our best tools to capture state-of-the-art knowledge are complex, fully coupled Earth System Models (ESMs). However, ESMs are expensive to run and no single ESM can easily produce responses which represent the full range of uncertainties. Instead, for some applications, computationally efficient reduced complexity climate models (RCMs) are used in a probabilistic setup. An example of these applications is estimating the likelihood that an emissions scenario will stay below a certain global-mean temperature change. Here we present a study (referred to as the Reduced Complexity Model Intercomparison Project (RCMIP) Phase 2) which investigates the extent to which different RCMs can be probabilistically calibrated to reproduce knowledge from specialized research communities. We find that the agreement between each RCM and the benchmarks varies, although the best performing models show good agreement across the majority of benchmarks. Under a very-low-emissions scenario median peak warming projections range from 1.3 to 1.7°C (relative to 1850–1900, assuming historical warming of 0.8°C between 1850–1900 and 1995–2014). Investigating new ways to reduce these projection uncertainties seems paramount given the international community's goal to limit warming to below 1.5°C above preindustrial in the long-term

Extending MESMER-X: a spatially resolved Earth system model emulator for fire weather and soil moisture

Climate emulators are models calibrated on Earth system models (ESMs) to replicate their behavior. Thanks to their low computational cost, these tools are becoming increasingly important to accelerate the exploration of emission scenarios and the coupling of climate information to other models. However, the emulation of regional climate extremes and water cycle variables has remained challenging. The MESMER emulator was recently expanded to represent regional temperature extremes in the new “MESMER-X” version, which is targeted at impact-related variables, including extremes. This paper presents a further expansion of MESMER-X to represent indices related to fire weather and soil moisture. Given a trajectory of global mean temperature, the extended emulator generates spatially resolved realizations for the seasonal average of the Canadian Fire Weather Index (FWI), the number of days with extreme fire weather, the annual average of the soil moisture, and the annual minimum of the monthly average soil moisture. For each ESM, the emulations mimic the statistical distributions and the spatial patterns of these indicators. For each of the four variables considered, we evaluate the performances of the emulations by calculating how much their quantiles deviate from those of the ESMs. Given how it performs over a large range of annual indicators, we argue that this framework can be expanded to further variables. Overall, the now expanded MESMER-X emulator can emulate several climate variables, including climate extremes and soil moisture availability, and is a useful tool for the exploration of regional climate changes and their impacts.</p

Uncertainty in projected climate change arising from uncertain fossil-fuel emission factors

International audienceEmission inventories are widely used by the climate community, but their uncertainties are rarely accounted for. In this study, we evaluate the uncertainty in projected climate change induced by uncertainties in fossil-fuel emissions, accounting for non-CO2 species co-emitted with the combustion of fossil-fuels and their use in industrial processes. Using consistent historical reconstructions and three contrasted future projections of fossil-fuel extraction from Mohr et al we calculate CO2 emissions and their uncertainties stemming from estimates of fuel carbon content, net calorific value and oxidation fraction. Our historical reconstructions of fossil-fuel CO2 emissions are consistent with other inventories in terms of average and range. The uncertainties sum up to a ±15% relative uncertainty in cumulative CO2 emissions by 2300. Uncertainties in the emissions of non-CO2 species associated with the use of fossil fuels are estimated using co-emission ratios varying with time. Using these inputs, we use the compact Earth system model OSCAR v2.2 and a Monte Carlo setup, in order to attribute the uncertainty in projected global surface temperature change (∆T) to three sources of uncertainty, namely on the Earth system's response, on fossil-fuel CO2 emission and on non-CO2 co-emissions. Under the three future fuel extraction scenarios, we simulate the median ∆T to be 1.9, 2.7 or 4.0 °C in 2300, with an associated 90% confidence interval of about 65%, 52% and 42%. We show that virtually all of the total uncertainty is attributable to the uncertainty in the future Earth system's response to the anthropogenic perturbation. We conclude that the uncertainty in emission estimates can be neglected for global temperature projections in the face of the large uncertainty in the Earth system response to the forcing of emissions. We show that this result does not hold for all variables of the climate system, such as the atmospheric partial pressure of CO2 and the radiative forcing of tropospheric ozone, that have an emissions-induced uncertainty representing more than 40% of the uncertainty in the Earth system's response