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We explore the response of the Earth’s coupled climate and carbon system to an idealized sequential addition and removal of CO2 to the atmosphere, following a symmetric and continuous emissions pathway, in contrast to the discontinuous emissions pathways that have largely informed our understanding of the climate response to net zero and net negative emissions to date. We find, using both an Earth system model and an ensemble of simple climate model realizations, that warming during the emissions reduction and negative emissions phases is defined by a combination of a proportionality of warming to cumulative emissions characterized by the transient climate response to emissions (TCRE), and a deviation from that proportionality that is governed by the zero emissions commitment (ZEC). About half of the ZEC is realized before reaching zero emissions, and the ZEC thus also controls the timing between peak cumulative CO2 emissions and peak temperature, such that peak temperature may occur before peak cumulative emissions if ZEC is negative, underscoring the importance of ZEC in climate policies aimed to limit peak warming. Thus we argue that ZEC is better defined as the committed warming relative to the expected TCRE proportionality, rather than as the additional committed warming that will occur after reaching net zero CO2 emissions. Once established, the combined TCRE and ZEC relationship holds almost to complete removal of prior cumulative CO2 emissions. As cumulative CO2 emissions approach zero through negative CO2 emissions, CO2 concentrations drop below preindustrial values, while residual long-term climate change continues, governed by multicentennial dynamical processes.

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. Copyright:

The Community Land Model (CLM) is the land component of the Community Earth System Model (CESM) and is used in several global and regional modeling systems. In this paper, we introduce model developments included in CLM version 5 (CLM5), which is the default land component for CESM2. We assess an ensemble of simulations, including prescribed and prognostic vegetation state, multiple forcing data sets, and CLM4, CLM4.5, and CLM5, against a range of metrics including from the International Land Model Benchmarking (ILAMBv2) package. CLM5 includes new and updated processes and parameterizations: (1) dynamic land units, (2) updated parameterizations and structure for hydrology and snow (spatially explicit soil depth, dry surface layer, revised groundwater scheme, revised canopy interception and canopy snow processes, updated fresh snow density, simple firn model, and Model for Scale Adaptive River Transport), (3) plant hydraulics and hydraulic redistribution, (4) revised nitrogen cycling (flexible leaf stoichiometry, leaf N optimization for photosynthesis, and carbon costs for plant nitrogen uptake), (5) global crop model with six crop types and time-evolving irrigated areas and fertilization rates, (6) updated urban building energy, (7) carbon isotopes, and (8) updated stomatal physiology. New optional features include demographically structured dynamic vegetation model (Functionally Assembled Terrestrial Ecosystem Simulator), ozone damage to plants, and fire trace gas emissions coupling to the atmosphere. Conclusive establishment of improvement or degradation of individual variables or metrics is challenged by forcing uncertainty, parametric uncertainty, and model structural complexity, but the multivariate metrics presented here suggest a general broad improvement from CLM4 to CLM5.

Earth system models are complex and represent a large number of processes, resulting in a persistent spread across climate projections for a given future scenario. Owing to different model performances against observations and the lack of independence among models, there is now evidence that giving equal weight to each available model projection is suboptimal. This Perspective discusses newly developed tools that facilitate a more rapid and comprehensive evaluation of model simulations with observations, process-based emergent constraints that are a promising way to focus evaluation on the observations most relevant to climate projections, and advanced methods for model weighting. These approaches are needed to distil the most credible information on regional climate changes, impacts, and risks for stakeholders and policy-makers.

We present a weighting strategy for use with the CMIP5 multi-model archive in the fourth National Climate Assessment, which considers both skill in the climatological performance of models over North America as well as the inter-dependency of models arising from common parameterizations or tuning practices. The method exploits information relating to the climatological mean state of a number of projection-relevant variables as well as metrics representing long-term statistics of weather extremes. The weights, once computed can be used to simply compute weighted means and significance information from an ensemble containing multiple initial condition members from potentially co-dependent models of varying skill. Two parameters in the algorithm determine the degree to which model climatological skill and model uniqueness are rewarded; these parameters are explored and final values are defended for the assessment. The influence of model weighting on projected temperature and precipitation changes is found to be moderate, partly due to a compensating effect between model skill and uniqueness. However, more aggressive skill weighting and weighting by targeted metrics is found to have a more significant effect on inferred ensemble confidence in future patterns of change for a given projection.

The Paris Agreement of December 2015 stated a goal to pursue efforts to keep global temperatures below 1.5°C above preindustrial levels and well below 2°C. The IPCC was charged with assessing climate impacts at these temperature levels, but fully coupled equilibrium climate simulations do not currently exist to inform such assessments. In this study, we produce a set of scenarios using a simple model designed to achieve long-term 1.5 and 2°C temperatures in a stable climate. These scenarios are then used to produce century-scale ensemble simulations using the Community Earth System Model, providing impact-relevant long-term climate data for stabilization pathways at 1.5 and 2°C levels and an overshoot 1.5°C case, which are realized (for the 21st century) in the coupled model and are freely available to the community. Here we describe the design of the simulations and a brief overview of their impact-relevant climate response. Exceedance of historical record temperature occurs with 60% greater frequency in the 2°C climate than in a 1.5°C climate aggregated globally, and with twice the frequency in equatorial and arid regions. Extreme precipitation intensity is statistically significantly higher in a 2.0°C climate than a 1.5°C climate in some specific regions (but not all). The model exhibits large differences in the Arctic, which is ice-free with a frequency of 1 in 3 years in the 2.0°C scenario, and 1 in 40 years in the 1.5°C scenario. Significance of impact differences with respect to multi-model variability is not assessed.

The calibration of Earth system model parameters is subject to data, time, and computational constraints. The high dimensionality of this calibration problem, combined with errors arising from model structural assumptions, makes it impossible to find model versions fully consistent with historical observations. Therefore, the potential for multiple plausible configurations presenting different trade-offs between skills in various variables and spatial regions remains usually untested. In this study, we lay out a formalism for making different assumptions about how ensemble variability in a perturbed physics ensemble relates to model error, proposing an empirical but practical solution for finding diverse near-optimal solutions. A meta-model is used to predict the outputs of a climate model reduced through principal component analysis. Then, a subset of input parameter values yielding results similar to a reference simulation is identified. We argue that the effective degrees of freedom in the model performance response to parameter input (the "parametric component") are, in fact, relatively small, illustrating why manual calibration is often able to find near-optimal solutions. The results explore the potential for comparably performing parameter configurations that have different trade-offs in model errors. These model candidates can inform model development and could potentially lead to significantly different future climate evolution. [ABSTRACT FROM AUTHOR]

The CICERO Simple Climate Model (CICERO-SCM) is a lightweight, semi-empirical model of global climate. Here we present a new open-source Python port of the model for use in climate assessment and research. The new version of CICERO-SCM has the same scientific logic and functionality as the original FORTRAN version but it is considerably more flexible and open source via Github. We describe the basic structure, improvements compared to the previous FORTRAN version, together with technical descriptions of the global thermal dynamics and carbon cycle components and the emissions module, before presenting a range of standard figures demonstrating its application. A new parameter calibration tool is demonstrated to make an example calibrated parameter set to span and fit a simple target specification. CICERO-SCM is fully open source and available through GitHub (https://github.com/ciceroOslo/ciceroscm). [ABSTRACT FROM AUTHOR]

Solar radiation modification (SRM) is increasingly being discussed as a potential tool to reduce global and regional temperatures to buy time for conventional carbon mitigation measures to take effect. However, most simulations to date assume SRM to be an additive component to the climate change toolbox, without any physical coupling between mitigation and SRM. In this study we analyze one aspect of this coupling: how renewable energy (RE) capacity, and therefore decarbonization rates, may be affected under SRM deployment by modification of photovoltaic (PV) and concentrated solar power (CSP) production potential. Simulated 1 h output from the Earth system model CNRM-ESM2-1 for scenario-based experiments is used for the assessment. The SRM scenario uses stratospheric aerosol injections (SAIs) to approximately lower global mean temperature from the high-emission scenario SSP585 baseline to the moderate-emission scenario SSP245. We find that by the end of the century, most regions experience an increased number of low PV and CSP energy weeks per year under SAI compared to SSP245. Compared to SSP585, while the increase in low energy weeks under SAI is still dominant on a global scale, certain areas may benefit from SAI and see fewer low PV or CSP energy weeks. A substantial part of the decrease in potential with SAI compared to the SSP scenarios is compensated for by optically thinner upper-tropospheric clouds under SAI, which allow more radiation to penetrate towards the surface. The largest relative reductions in PV potential are seen in the Northern and Southern Hemisphere midlatitudes. Our study suggests that using SAI to reduce high-end global warming to moderate global warming could pose increased challenges for meeting energy demand with solar renewable resources. [ABSTRACT FROM AUTHOR]

Solar Radiation Modification (SRM) is increasingly being discussed as a potential tool to reduce global and regional temperatures to buy time for conventional carbon mitigation measures to take effect. However, most simulations to date assume SRM as an additive component to the climate change toolbox, without any physical coupling between mitigation and SRM. In this study we analyse one aspect of this coupling: How renewable energy (RE) capacity, and therefore decarbonization rates, may be affected under SRM deployment by modification of photovoltaic (PV) and concentrated solar power (CSP) production potential. Simulated 1-hour output from the Earth System Model CNRM-ESM2-1 for scenario-based experiments are used for the assessment. We find that by the end of the century, most regions experience an increased number of low PV and CSP energy weeks per year under SAI (Stratospheric Aerosol Injections) compared to the moderately ambitiously mitigated scenario SSP245. Compared to the unmitigated SSP585 scenario, while the increase in low energy weeks is still dominant, some areas see fewer low PV or CSP energy weeks under SAI. A substantial part of the decrease in potential with SAI compared to the SSP-scenarios is compensated by optically thinner upper tropospheric clouds under SAI. Our study suggests that using SAI to reduce high-end global warming to moderate global warming could pose increased challenges for meeting energy demand with solar renewable resources. [ABSTRACT FROM AUTHOR]

The calibration of Earth System Model parameters is subject to both data, time and computational constraints. The high dimensionality of this calibration problem, combined with errors which arise from model structural assumptions makes it impossible to find model versions fully consistent with historical observations, with the potential for multiple plausible configurations which make different tradeoffs between skill in different variables or spatial regions. In this study, we lay out a formalism for making different assumptions about how ensemble variability in a Perturbed Physics Ensemble (PPE) relates to model error, proposing an empirical but practical solution for finding diverse near-optimal solutions. We argue that the effective degrees of freedom in model performance response to parameter input (the 'parametric component') is, in fact, relatively small, illustrating why manual calibration is often able to find near-optimal solutions. Comparison with a perturbed initial condition ensemble reveals that internal variability associated with this parametric component of model error is negligible. Finally, there is a potential for comparably performing parameter configurations making different trade-offs in model errors. These alternative configurations can inform model development and could potentially lead to significantly different future climate evolution. [ABSTRACT FROM AUTHOR]

To estimate equilibrium climate sensitivity, a common approach is to linearly extrapolate temperatures as a function of top of atmosphere energetic imbalance ("Effective Climate Sensitivity"). In this study, we consider an alternative approach for estimating equilibrium climate sensitivity through Bayesian calibration of a multiple timescale simple climate model. Results suggest potential biases in effective sensitivity estimates in 10 the case of particular models where radiative tendencies imply energetic imbalances which differ between preindustrial and quadrupled CO2 states. These biases imply the need for reconsideration of some model published values of climate sensitivity, and the presence of radiative imbalances in a number of CMIP5 and CMIP6 models underlines the urgent requirement for operational climate sensitivity experiments on millennial timescales to assess if such biases exist in estimates of climate sensitivity in the wider CMIP ensembles. [ABSTRACT FROM AUTHOR]