Single-model initial-condition large ensembles are powerful tools to quantify the forced response, internal climate variability, and their evolution under global warming. Here, we present the CMIP6 version of the Max Planck Institute Grand Ensemble (MPI-GE CMIP6) with 30 realisations for the historical period and five emission scenarios. The power of MPI-GE CMIP6 goes beyond its predecessor ensemble MPI-GE by providing high-frequency output, the full range of emission scenarios including the highly policy-relevant low emission scenarios SSP1-1.9 and SSP1-2.6, and the opportunity to compare the ensemble to complementary high-resolution simulations. First, we describe MPI-GE CMIP6, evaluate it with observations and reanalyses and compare it to MPI-GE. Then, we demonstrate with six novel application examples how to use the power of the ensemble to better quantify and understand present and future climate extremes, to inform about uncertainty in approaching Paris Agreement global warming limits, and to combine large ensembles and artificial intelligence. For instance, MPI-GE CMIP6 allows us to show that the recently observed Siberian and Pacific North American heatwaves would only avoid reaching 1-2 year return periods in 2071-2100 with low emission scenarios, that recently observed European precipitation extremes are captured only by complementary high-resolution simulations, and that 3-hourly output projects a decreasing activity of storms in mid-latitude oceans. Further, the ensemble is ideal for estimates of probabilities of crossing global warming limits and the irreducible uncertainty introduced by internal variability, and is sufficiently large to be used for infilling surface temperature observations with artificial intelligence.

Quantifying the anthropogenic fluxes of CO2 is important to understand the evolution of carbon sink capacities, on which the required strength of our mitigation efforts directly depends. For the historical period, the global carbon budget (GCB) can be compiled from observations and model simulations as is done annually in the Global Carbon Project’s (GCP) carbon budgets. However, the historical budget only considers a single realization of the Earth system and cannot account for internal climate variability. Understanding the distribution of internal climate variability is critical for predicting the future carbon budget terms and uncertainties. We present here a decomposition of the GCB for the historical period and the RCP4.5 scenario using single model large ensemble simulations from the Max Planck Institute Grand Ensemble (MPI-GE) to capture internal variability. We calculate uncertainty ranges for the natural sinks and anthropogenic emissions that arise from internal climate variability, and by using this distribution, we investigate the likelihood of historical fluxes with respect to plausible climate states. Our results show these likelihoods have substantial fluctuations due to internal variability, which are partially related to ENSO. We find that the largest internal variability in the MPI-GE stems from the natural land sink and its increasing carbon stocks over time. The allowable fossil fuel emissions consistent with 3°C warming may be between 9–18 PgCyr-1. The MPI-GE is generally consistent with GCP’s global budgets with the notable exception of land-use change emissions in recent decades, highlighting that human action is inconsistent with climate mitigation goals.

Inter-annual to decadal variability in the strength of the land and ocean carbon sinks impede accurate predictions of year-to-year atmospheric carbon dioxide (CO2) growth rate. Such information is crucial to verify the effectiveness of fossil fuel emissions reduction measures. Using a multi-model framework comprising prediction systems based on Earth system models, we find a predictive skill for the global ocean carbon sink of up to 6 years. Longer regional predictability horizons and robust spatial patterns are found across single models. On land, a predictive skill of up to 2 years is primarily maintained in the tropics and extra-tropics enabled by the initialization of the physical climate variables towards observations. We further show that anomalies of atmospheric CO2 growth rate inferred from natural variations of the land and ocean carbon sinks are predictable at lead time of 2 years and the skill is limited by the land carbon sink predictability horizon.

The global carbon budget including fluxes of CO2 between atmosphere, land and ocean, and its atmospheric growth rate show large interannual to decadal variations. Yet, these variations are poorly represented in uninitialized simulations. In a novel approach we reconstruct and predict the global carbon cycle with the decadal prediction system based on the Max Planck Institute Earth system model (MPI-ESM) extended with an interactive carbon cycle. By assimilating atmospheric and oceanic data products into the MPI-ESM, we can well reproduce historical global carbon budget variations with high correlations relative to the assessments from the global carbon project of 0.75, 0.75 and 0.97 for atmospheric CO2 growth, air-land CO2 fluxes and air-sea CO2 fluxes, respectively. Retrospective predictions initializing from the assimilation simulation show the predictive skill of the air-sea CO2 fluxes up to 5 years, and the air-land CO2 fluxes and atmospheric carbon growth rate of 2 years.