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Results from the fully and biogeochemically coupled simulations in which CO2 increases at a rate of 1 % yr−1 (1pctCO2) from its preindustrial value are analyzed to quantify the magnitude of carbon–concentration and carbon–climate feedback parameters which measure the response of ocean and terrestrial carbon pools to changes in atmospheric CO2 concentration and the resulting change in global climate, respectively. The results are based on 11 comprehensive Earth system models from the most recent (sixth) Coupled Model Intercomparison Project (CMIP6) and compared with eight models from the fifth CMIP (CMIP5). The strength of the carbon–concentration feedback is of comparable magnitudes over land (mean ± standard deviation = 0.97 ± 0.40 PgC ppm−1) and ocean (0.79 ± 0.07 PgC ppm−1), while the carbon–climate feedback over land (−45.1 ± 50.6 PgC ∘C−1) is about 3 times larger than over ocean (−17.2 ± 5.0 PgC ∘C−1). The strength of both feedbacks is an order of magnitude more uncertain over land than over ocean as has been seen in existing studies. These values and their spread from 11 CMIP6 models have not changed significantly compared to CMIP5 models. The absolute values of feedback parameters are lower for land with models that include a representation of nitrogen cycle. The transient climate response to cumulative emissions (TCRE) from the 11 CMIP6 models considered here is 1.77 ± 0.37 ∘C EgC−1 and is similar to that found in CMIP5 models (1.63 ± 0.48 ∘C EgC−1) but with somewhat reduced model spread. The expressions for feedback parameters based on the fully and biogeochemically coupled configurations of the 1pctCO2 simulation are simplified when the small temperature change in the biogeochemically coupled simulation is ignored. Decomposition of the terms of these simplified expressions for the feedback parameters is used to gain insight into the reasons for differing responses among ocean and land carbon cycle models.

Oceanic absorption of atmospheric carbon dioxide (CO2) is expected to slow down under increasing anthropogenic emissions; however, the driving mechanisms and rates of change remain uncertain, limiting our ability to project long‐term changes in climate. Using an Earth system simulation, we show that the uptake of anthropogenic carbon will slow in the next three centuries via reductions in surface alkalinity. Warming and associated changes in precipitation and evaporation intensify density stratification of the upper ocean, inhibiting the transport of alkaline water from the deep. The effect of these changes is amplified threefold by reduced carbonate buffering, making alkalinity a dominant control on CO2 uptake on multi‐century timescales. Our simulation reveals a previously unknown alkalinity‐climate feedback loop, amplifying multi‐century warming under high emission trajectories. Plain Language Summary Over the past century, humans have been burning fossil fuels and adding extra carbon dioxide to the atmosphere. The ocean has been doing us a big favor by absorbing some of this carbon dioxide, lowering the amount of global warming that occurs. Our study shows that the ocean will begin to lose its ability to absorb carbon dioxide beyond the year 2100, leaving more fossil‐derived carbon in the atmosphere and leading to additional global warming. Our study describes a previously undiscovered mechanism for the slowdown in ocean carbon absorption, where changes in rainfall and warming affect ocean currents that, in turn, change the chemistry of the ocean surface. Key Points Oceanic uptake of carbon could slow in upcoming centuries through previously unidentified alkalinity‐climate feedback Reduced upwelling and carbonate buffer enhance the influence of alkalinity on the increase in surface ocean carbon dioxide Reductions in surface alkalinity will reduce the rate of carbon uptake on multi‐century timescales

Global warming drives changes in Earth’s cloud cover, which, in turn, may amplify or dampen climate change. This “cloud feedback” is the single most important cause of uncertainty in Equilibrium Climate Sensitivity (ECS)—the equilibrium global warming following a doubling of atmospheric carbon dioxide. Using data from Earth observations and climate model simulations, we here develop a statistical learning analysis of how clouds respond to changes in the environment. We show that global cloud feedback is dominated by the sensitivity of clouds to surface temperature and tropospheric stability. Considering changes in just these two factors, we are able to constrain global cloud feedback to 0.43 ± 0.35 W·m−2·K−1 (90% confidence), implying a robustly amplifying effect of clouds on global warming and only a 0.5% chance of ECS below 2 K. We thus anticipate that our approach will enable tighter constraints on climate change projections, including its manifold socioeconomic and ecological impacts.

This paper describes ESM-SnowMIP, an international coordinated modelling effort to evaluate current snow schemes, including snow schemes that are included in Earth system models, in a wide variety of settings against local and global observations. The project aims to identify crucial processes and characteristics that need to be improved in snow models in the context of local- and global-scale modelling. A further objective of ESM-SnowMIP is to better quantify snow-related feedbacks in the Earth system. Although it is not part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), ESM-SnowMIP is tightly linked to the CMIP6-endorsed Land Surface, Snow and Soil Moisture Model Intercomparison (LS3MIP).

The climate system can respond asymmetrically to warming and cooling, yet this asymmetry remains underexplored. This study uses multi‐century experiments with two coupled global climate models under idealized abrupt solar forcing changes of ±$\\pm $ 1%, 2%, 4%, and 6%. In both models, cooling has a larger impact on surface temperature than warming, driven by the ice‐albedo feedback. However, under strong cooling (−4%, −6% Solar), the models diverge significantly. One model undergoes runaway ice growth, while the other has slower ice expansion and even transient sea ice retreat in the north Pacific. The latter is linked to the development of a strong Pacific meridional overturning circulation, which transports heat northward and slows ice growth. The model with less ice growth also exhibits greater “cold uptake into” (or heat release from) the deep ocean. These findings motivate further investigation of inter‐model differences in ocean‐ice‐atmosphere interactions and their impacts on climate feedbacks.

Purpose of Review The changes or updates in ocean biogeochemistry component have been mapped between CMIP5 and CMIP6 model versions, and an assessment made of how far these have led to improvements in the simulated mean state of marine biogeochemical models within the current generation of Earth system models (ESMs). Recent Findings The representation of marine biogeochemistry has progressed within the current generation of Earth system models. However, it remains difficult to identify which model updates are responsible for a given improvement. In addition, the full potential of marine biogeochemistry in terms of Earth system interactions and climate feedback remains poorly examined in the current generation of Earth system models. Summary Increasing availability of ocean biogeochemical data, as well as an improved understanding of the underlying processes, allows advances in the marine biogeochemical components of the current generation of ESMs. The present study scrutinizes the extent to which marine biogeochemistry components of ESMs have progressed between the 5th and the 6th phases of the Coupled Model Intercomparison Project (CMIP).

Understanding the relationships between internal variability and forced climate feedbacks is key for using observations to constrain future climate change. Here we probe and interpret the differences in these relationships between the climate change projections provided by the CMIP5 and CMIP6 experiment ensembles. We find that internal variability feedbacks better predict forced feedbacks in CMIP6 relative to CMIP5 by over 50%, and that the increased predictability derives primarily from the slow (>20 years) response to climate change. A key novel result is that the increased predictability is consistent with the higher resemblance between the patterns of internal and forced temperature changes in CMIP6, which suggests temperature pattern effects play a key role in predicting forced climate feedbacks. Despite the increased predictability, emergent constraints provided by observed internal variability are weak and largely unchanged from CMIP5 to CMIP6 due to the shortness of the observational record. Plain Language Summary A key goal in climate change research is to use observed, internal climate feedbacks to constrain the forced feedbacks that govern climate change. Here the authors explore the differences in the relationships between internal and forced climate feedbacks in simulations run under the auspices of the two recent IPCC reports: The CMIP5 and CMIP6 simulations. They find notable increases in the relationships between internal and forced feedbacks between CMIP5 and CMIP6, and attribute these increases at least partially to the patterns of temperature variability associated with internal climate variability and forced climate change. However, they argue that the increases do not lead to improvements in our ability to constrain future climate change based on observations due to the uncertainty in the observed, internal climate feedbacks. Key Points Internal variability feedbacks better predict forced feedbacks in CMIP6 relative to CMIP5 by over 50% The improved prediction derives in part from the greater similarity between the patterns of internal and forced temperature changes in CMIP6 The improved prediction does not significantly improve the emergent constraint associated with internal variability feedbacks

How glaciation affects CO2${\\text{CO}}_{2}$drawdown by chemical weathering influences the weathering‐climate feedback strength, which controls the exogenic carbon cycle and planetary habitability. However, the role of glaciers remains elusive as glaciation alters multiple factors controlling weathering, the net effect of which is ambiguous even in directionality. To isolate and quantify the effect of glaciers, we developed a novel multi‐proxy system for constraining catchment‐scale weathering fluxes in the past. This approach utilizes the correlation between Ge/Si and Si isotope ratios in modern rivers and the preservation of these signals in lacustrine sediments. Reconstructed weathering fluxes in two Icelandic catchments with different glacial histories during the past 10,000 years show that chemical weathering fluxes are roughly 10 times higher when a catchment is glaciated versus ice‐free. The synchronous variations in weathering fluxes with the expansion and contraction of glaciers indicate that glaciation may rapidly amplify climatic variations via a positive feedback.

Contemporary general circulation models (GCMs) and Earth system models (ESMs) are developed by a large number of modeling groups globally. They use a wide range of representations of physical processes, allowing for structural (code) uncertainty to be partially quantified with multi‐model ensembles (MMEs). Many models in the MMEs of the Coupled Model Intercomparison Project (CMIP) have a common development history due to sharing of code and schemes. This makes their projections statistically dependent and introduces biases in MME statistics. Previous research has focused on model output and code dependence, and model code genealogy of CMIP models has not been fully analyzed. We present a full reconstruction of CMIP3, CMIP5, and CMIP6 code genealogy of 167 atmospheric models, GCMs, and ESMs (of which 114 participated in CMIP) based on the available literature, with a focus on the atmospheric component and atmospheric physics. We identify 12 main model families. We propose family and ancestry weighting methods designed to reduce the effect of model structural dependence in MMEs. We analyze weighted effective climate sensitivity (ECS), climate feedbacks, forcing, and global mean near‐surface air temperature, and how they differ by model family. Models in the same family often have similar climate properties. We show that weighting can partially reconcile differences in ECS and cloud feedbacks between CMIP5 and CMIP6. The results can help in understanding structural dependence between CMIP models, and the proposed ancestry and family weighting methods can be used in MME assessments to ameliorate model structural sampling biases. Plain Language Summary Contemporary global climate models are developed by a large number of modeling groups internationally. Commonly, projections from multiple models are used together to calculate multi‐model means and quantify uncertainty. Because many of the models share parts of their computer code, algorithms and parametrization schemes, they are not independent. Overrepresented models can cause biases in multi‐model means, and uncertainty may be underestimated if model dependence is not taken into account. We document a full code genealogy of 167 models, of which 114 participated in the Coupled Model Intercomparison Project (CMIP) phases 3, 5, and 6, with a focus on the atmospheric component. We identify 12 main model families. We show that models in the same family often have similar estimates of key climate properties. We propose statistical weighting methods based on the model family and code relationship, and show that they can reconcile some of the difference in results between the two most recent CMIP phases. The weighting methods or a selection of independent models based on the genealogy can be used in model assessment studies to reduce the effects of model dependence. Key Points We reconstruct a code genealogy of 167 climate models with a focus on the atmospheric component and atmospheric physics All models originate from 12 main model families, and models in the same family often have similar climate feedbacks and sensitivity Proposed ancestry and family weighting can partly reconcile differences in means between the Coupled Model Intercomparison Project phases

This paper documents the biogeochemistry configuration of the Energy Exascale Earth System Model (E3SM), E3SMv1.1‐BGC. The model simulates historical carbon cycle dynamics, including carbon losses predicted in response to land use and land cover change, and the responses of the carbon cycle to changes in climate. In addition, we introduce several innovations in the treatment of soil nutrient limitation mechanisms, including explicit dependence on phosphorus availability. The suite of simulations described here includes E3SM contributions to the Coupled Climate‐Carbon Cycle Model Intercomparison Project and other projects, as well as simulations to explore the impacts of structural uncertainty in representations of nitrogen and phosphorus limitation. We describe the model spin‐up and evaluation procedures, provide an overview of results from the simulation campaign, and highlight key features of the simulations. Cumulative warming over the twentieth century is similar to observations, with a midcentury cold bias offset by stronger warming in recent decades. Ocean biomass production and carbon uptake are underpredicted, likely due to biases in ocean transport leading to widespread anoxia and undersupply of nutrients to surface waters. The inclusion of nutrient limitations in the land biogeochemistry results in weaker carbon fertilization and carbon‐climate feedbacks than exhibited by other Earth System Models that exclude those limitations. Finally, we compare with an alternative representation of terrestrial biogeochemistry, which differs in structure and in initialization of soil phosphorus. While both configurations agree well with observational benchmarks, they differ significantly in their distribution of carbon among different pools and in the strength of nutrient limitations. Plain Language Summary A new state‐of‐the‐art Earth System Model has been funded by the United States Department of Energy (DOE) to explore questions relevant to DOE's mission. The Energy Exascale Earth System Model version 1.1 (E3SMv1.1) represents nitrogen and phosphorous controls on the carbon cycle and extends the recently released E3SMv1 to include active biogeochemistry in the land, ocean, and ice components. E3SMv1.1 also includes an alternative representation of terrestrial carbon and nutrient cycles that is used to explore model structural uncertainties. E3SMv1.1's capabilities are demonstrated through a set of experiments described by the Coupled Climate‐Carbon Cycle Model Intercomparison Project, aimed at understanding the influence of changes in climate and CO2 on the carbon cycle. Simulations of the land surface properties and terrestrial carbon cycle compare well with observations, as does the simulated global and regional climate. Nutrient limitations result in less land carbon uptake compared to models that exclude these limitations. However, variations in model structure and initialization influence the magnitude of those limitations and carbon cycle dynamics. The ocean biogeochemistry in E3SMv1.1 simulates less biomass and slightly lower anthropogenic carbon uptake than is observed. Future efforts will aim to reduce model biases as well as to include additional aspects of global carbon cycle dynamics. Key Points Introduces the U.S. DOE's Energy Exascale Earth System Model‐Biogeochemistry version, E3SMv1.1‐BGC, is introduced Ecosystem‐climate responses are characterized in a standard set of C4MIP‐type simulations The impacts of terrestrial nitrogen and phosphorus limitations and their structural uncertainties are explored