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Climate policies such as the Paris Agreement are framed in terms of global warming levels. Based on past warming and past CO2 emissions, the amount of future cumulative CO2 emissions allowed to keep global warming at or below a global warming level can be estimated. Yet, global warming scenarios in the successive Coupled Model Intercomparison Projects are framed in terms of prescribed atmospheric CO2 concentration or emissions, yielding a wide range of warming levels per CO2 pathway in response to the different transient climate responses to cumulative emissions in the coupled climate models. Based on these scenarios and the latest model projections, the IPCC Sixth Assessment Report assessed climatic impacts of different warming levels. These impacts are thus evaluated in simulations where the warming targets are passed transiently, at different points in time, and not stabilized, as opposed to how climate agreements are framed.Here, we propose a new Model Intercomparison Project AERA-MIP building on an adaptive approach - the Adaptive Emissions Reduction Approach - that successively calculates the compatible emissions to stabilize global warming at the required temperature target. Earth System Models (ESMs) are run forward in emission-driven mode, with prescribed, model-specific emissions successively calculated every five years, so that all models reach the same warming target and thereafter stabilize at this warming level. The warming uncertainty is thus side-stepped, while different emissions pathways emerge out of the variety of participating ESMs. The approach is based on the TCRE framework and successively adapting for any changes in the Earth System that might affect global mean surface temperature, including the zero emissions commitment as emissions approach zero.Simulations of the first participating modelling centers already reveal a panel of emissions pathways that successfully stabilize global warming at 1.5ºC and 2ºC. This includes the decline rate from peak emissions, the timing of having to reach net-zero emissions, and the magnitude of negative emissions needed to stabilize the climate. These different emissions pathways result in a range of atmospheric CO2 concentration evolution (350 to 450 ppm at year 2100 in the 1.5°C stabilization scenario) and distribution of anthropogenic carbon in the Earth System components. Unlike concentration-driven projections, these AERA simulations provide an uncertainty range for impacts that are directly affected by atmospheric CO2 concentration such as ocean acidification. The project also includes temporary temperature overshoot simulations using the AERA approach.

Anthropogenic warming of the oceans and associated deoxygenation are altering marine ecosystems. Current knowledge suggests that these changes might be reversible in the centennial timescale in the ocean surface and irreversible at deeper depth if global warming were to decline. However, knowledge on the persistence of their combined effects on marine ecosystems remains limited. Here we explore to what extent global warming will drive alterations on marine habitats by following the evolution of a metabolic index that captures the ecophysiological response of marine organisms to both changes in temperature and oxygen, through an idealised ramp-up ramp-down atmospheric CO2 concentration experiment. Using a multi-model approach, we find that changes in ocean temperature and oxygen drives a centuries-long irreversible loss of ~4% in the habitable volume of the upper 1000 m of the world ocean. These results suggest the combined effect of warming and deoxygenation will diminish the capability of the ocean to hold life far after recovering from a temperature overshoot.

Future ocean acidification mainly depends on the continuous ocean uptake of CO2 from the atmosphere. The trajectory of future atmospheric CO2 is prescribed in traditional climate projections with Earth system models, leading to a small model spread and apparently low uncertainties for projected acidification, but a large spread in global warming. However, climate policies such as the Paris Agreement define climate targets in terms of global warming levels and as traditional simulations do not converge to a given warming level, they cannot be used to assess uncertainties in projected acidification. Here, we perform climate simulations that converge to given temperature levels using the Adaptive Emission Reduction Algorithm (AERA) with the Earth system model Bern3D-LPX at different setups with different Transient Climate Response to cumulative carbon Emissions (TCRE) and choices between reductions in CO2 and non-CO2 forcing agents. With these simulations, we demonstrate that uncertainties in surface ocean acidification are an order of magnitude larger than the usually reported inter-model uncertainties from simulations with prescribed atmospheric CO2. Uncertainties in acidification at a given stabilized temperature are dominated by TCRE and the choice of emission reductions of non-CO2 greenhouse gases (GHGs). High TCRE and relatively low reductions of non-CO2 GHGs, for example, necessitate relatively strong reductions in CO2 emissions and lead to relatively little ocean acidification at a given temperature level. The results suggest that choices between reducing emissions of CO2 versus non-CO2 agents should consider the economic costs and ecosystem damage of ocean acidification.

The parties of the Paris Agreement agreed to keep global warming well below 2°C and assess the necessary greenhouse gas emissions reductions every five years during the global stocktake. Globally, the necessary reductions in greenhouse gases are often derived using the remaining emissions budget concept. However, estimations of this budget vary by a factor of two to three and may hamper efforts to establish ambitions emissions reductions. Here, we propose an adaptive approach that side-step these uncertainties to quantify these global emissions reductions during the successive global stocktake solely based on regularly updated observations of past temperatures, radiative forcing, and emissions statistics. The approach consists of three main steps repeated every five years: (1) determining the anthropogenic warming to date and hence the remaining warming allowed, (2) estimating the remaining CO2 forcing equivalent (CO2-fe) emission budget, and (3) proposing a CO2-fe or CO2 emission trajectory for the next 5 years. We test this approach using the Bern3D-LPX Earth System Model of Intermediate Complexity and demonstrate that the temperature targets 1.5°C and 2°C can be reached following a smooth emissions pathway. The adaptive nature makes the approach robust against inherent uncertainties in the observational records, climate sensitivity to emissions, and effectiveness of emissions reduction implementations. The approach thus allows developing an emissions trajectory that would iteratively adapt to ultimately meet the agreed temperature goal. The approach also provides a strong alternative to the often-used pre-defined emissions or concentration pathways (such as SSPs), which can result in very different end-of-century temperatures for the same emission or concentration trajectories. Some of these pathways are developed to be consistent with a given warming level (e.g., SSP1-1.9 for 1.5°C), not knowing the actual response of the Earth system to emissions. As opposed to these simulations, simulations from different models using the adaptive approach we propose here would be directly comparable in terms of warming and broader climate impacts but would differ in terms of required emissions. Our approach would hence guide a valuable and highly policy-relevant complementary set of simulations for the next generation of CMIP models resulting in a range of future emission trajectories compatible with a given global warming target.

Predicting carbon fluxes and atmospheric CO2 can constrain the expected next-year atmospheric CO2 growth rate and thereby allow to independently monitor total anthropogenic CO2 emission rates. Several studies have established predictive skill in retrospective forecasts of carbon fluxes. These studies are usually backed by perfect-model simulations of single models showing the origins of predictive skill in carbon fluxes and atmospheric CO2 concentration. Yet, a comprehensive multi-model comparison of perfect-model predictions, which can be valuable in explaining differences in retrospective predictions, is still lacking. Moreover, as of now, we don't have sufficient understanding of how well do the models predict their own integrated carbon cycles and how congruent this predictability is across models.Here, we show the predictive skill of land and ocean carbon fluxes as well as atmospheric CO2 concentration in seven Earth-System-Models. Our first results indicate predictive skill of globally aggregated carbon fluxes of 2±1 years and atmospheric CO2 of 3±2 years. However, the regional patterns, hotspots and origins of predictive skill diverge among models. This heterogeneity explains the regional differences found in existing retrospective forecasts and backs the overall consistent predictability time-scales at global scale.

Current and projected emissions trends suggest a likely reliance on carbon dioxide removal (CDR) to achieve the long-term temperature goal of the Paris Agreement. Studies using Integrated Assessment Models have considered scenarios limiting global warming to 1.5°C in 2100 that include cumulative net removal of up to ~1000 GtCO2 over the 21st century, although also scenarios deploying substantially less CDR have been considered. Large CDR deployment beyond achieving net zero emissions is often assumed to achieve a Global Mean Temperature (GMT) decline after peak warming of several tenths of a degree by the end of the century. The feasibility of achieving CDR on such a large scale has been strongly contested for environmental, technological, geophysical, economic, institutional or socio-cultural reasons. Beyond these considerations, it is important to assess the possible realisation of a GMT decline in the context of uncertainties in the Earth System response.Here we attempt to review and compare these various sources of uncertainty. Some relevant Earth System feedbacks are not represented in climate models or only in a limited manner, such as carbon release from permafrost melting, wetlands and wildfires, which could release ~100 GtCO2 over the 21st century even in stringent mitigation scenarios, but there is low confidence around this number. Another source of uncertainty is the Zero Emissions Commitment, which quantifies how much global warming would occur after global CO2 emissions reach net zero. Its best estimate over 50 years is close to zero, but uncertainties were quantified to be as high as +/-0.3°C for cumulative emissions of 1000 PgC and uncertainties remain on the potential for warming on multi century timescales. Given this, additional post net-zero warming countering CDR induced cooling cannot be ruled out. Furthermore, it is not clear whether the TCRE assessed for increasing CO2 emissions would remain the same once we reach net-negative emissions. In light of these various sources of uncertainty we argue that a broader perspective on CDR and future GMT evolution beyond net zero is advisable. Acknowledging the uncertain efficacy of CDR-induced GMT decrease is important for comprehensive risk assessments and reflections on scenario design.

Extreme events, such as marine heatwaves (MHWs), severely impact marine ecosystems. Of particular concern are compound events, i.e. situations when conditions are extreme for multiple ecosystem stressors, such as temperature and net primary productivity (NPP). In 2013-2015 for example, an extensive MHW, known as the Blob, cooccurred with low NPP and severely impacted marine life in the northeast Pacific, with cascading impacts on fisheries. Yet, little is known about the distribution and drivers of compound MHW and low NPP extreme events. We use satellite-based sea surface temperature and NPP estimates to provide a first assessment of these compound events. We reveal hotspots of compound MHW and low NPP events in the equatorial Pacific, along the boundaries of the subtropical gyres, and in the northern Indian Ocean. In these regions, compound events that typically last one week occur three to seven times more often than expected under the assumption of independence between MHWs and low NPP events. At the seasonal timescale, most compound events occur in summer in both hemispheres. At the interannual time-scale, their frequency is strongly modulated by large-scale modes of climate variability such as the El Niño-Southern Oscillation, whose positive phase is associated with increased compound event occurrence in the eastern equatorial Pacific by a factor of up to four. Using large ensemble simulations of two Earth system models, we then investigate the exact physical and biological drivers of these compound events. We find that both models suggest that MHWs in the low latitudes are often associated with low surface ocean nutrient concentrations due to enhance stratification and/or reduced upwelling, which limits the growth of phytoplankton resulting in extremely low NPP. However, the models show large disparities in simulated compound events and its drivers in the high latitudes. This identifies an important need for improved process understanding for high latitude compound MHW and low NPP events.

Marine heatwaves (MHWs) are periods of extreme warm ocean temperatures that can have devastating impacts on marineorganisms and socio-economic systems. Despite recent advances in understanding the underlying processes of individual events, aglobal view of the local oceanic and atmospheric drivers of MHWs is currently missing. Here, we use daily-mean output oftemperature tendency terms from a comprehensive fully coupled Earth system model to quantify the main local processes leadingto the buildup and decay of MHWs in the surface ocean. Our analysis reveals that net ocean heat uptake associated with moreshortwave heat absorption and less latent heat loss is the primary driver of the buildup of MHWs in the subtropics and mid-to-highlatitudes. Reduced vertical mixing from the nonlocal portion of the KPP boundary layer scheme partially dampens the temperatureincrease. In contrast, ocean heat uptake is reduced during the MHW build-up in the tropics, where reduced vertical local mixingand diffusion cause the warming. In the subsequent decay phase, ocean heat loss to the atmosphere dominates the temperaturedecrease globally. The processes leading to the buildup and decay of MHWs are similar for short and long MHWs. Different types ofMHWs with distinct driver combinations are identified within the large variability among events. Our analysis contributes to abetter understanding of MHW drivers and processes and may therefore help to improve the prediction of high-impact marineheatwaves.

Multiple lines of evidence from observation- and model-based studies show that anthropogenic greenhouse gas emissions cause ocean warming and oxygen depletion, with adverse impacts on marine organisms and ecosystems.Temperatures increase is a primary indicator for climate change. However, in the thermocline, changes in oxygen and other biogeochemical tracers might be detectable before warming (Hameau et al., 2019a).Here, we compare the local time of emergence (ToE) of anthropogenic temperature and oxygen changes in the thermocline within an ensemble of Earth system model simulations from the CMIP5 dataset (Hameau et al., 2019b).Generally, warming emerges from internal variability prior to changes in oxygen.Yet, in 35$\pm$11\% of the global thermocline, anthropogenic deoxygenation is detectable before warming.Earlier emergence of oxygen changes is typically related to decreasing trends in ventilation, which reduce the supply of oxygen-rich surface waters to the thermocline.In addition, reduced ventilation slows the propagation of anthropogenic warming from the surface into the ocean interior, further contributing to the delayed emergence of warming compared to deoxygenation.As the magnitude of simulated interval variability and of simulated anthropogenic changes vary considerably across models, we introduce the relative ToE metric. This reduces the inter-model spread, allowing for a better comparison among models.Our results underline the importance of an ocean biogeochemical observing system and that the detection of anthropogenic impacts becomes more likely when using multi-tracer observations.