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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 currently 30 realizations 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 reanalyzes and compare it to MPI‐GE. Then, we demonstrate with six 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 years 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. Plain Language Summary: Climate model simulations that start from different initial states and differ only due to the chaos in the climate system are used extensively to quantify the forced climate response, variability intrinsic to the climate system, and their change under global warming. Here, we present a new version of the Max Planck Institute Grand Ensemble (MPI‐GE CMIP6) that is run as part of the latest generation of climate models. This single‐model ensemble currently consists of 30 realizations for the historical period 1850–2014 and for five scenarios of possible future climates until 2100. The power of MPI‐GE CMIP6 goes beyond its predecessor by not only providing monthly mean but also 3‐hourly to daily model output, the full range of future scenarios including the two highly policy‐relevant scenarios that were designed to match the Paris Agreement global warming limits of 1.5 and 2°C, and the opportunity to compare the low‐resolution ensemble to simulations of the same model version with higher horizontal resolution. In this paper, we describe the new ensemble and demonstrate with application examples how to use its power. Key Points: The Max Planck Institute Grand Ensemble in its CMIP6 version (MPI‐GE CMIP6) is a 30‐member initial‐condition large ensemble with up to 3‐hourly model output and five emission scenariosThe ensemble is specifically suited to investigate climate extremes and Paris Agreement global warming limitsMPI‐GE CMIP6 adequately represents heat extremes, while precipitation extremes are captured by complementary high‐resolution simulations [ABSTRACT FROM AUTHOR]

The literature offers conflicting findings about which regions contribute most to increases in the global radiative feedback after a forcing increase. This paper explains the disagreement by discriminating between two common definitions of the local feedback, which use either local temperature or global temperature as their basis. Although the two definitions of feedback have been previously compared in aquaplanet models with slab oceans, here the definitions are compared for the first time in an atmosphere-ocean general circulation model (MPI-ESM1.2) integrated over four doublings of atmospheric CO2 concentrations. Large differences between the definitions can be seen in all feedbacks, but especially in the temperature and water vapor feedbacks. Differences of up to 10 W m-2 K-1 over the Southern Ocean can be explained by the pattern of surface warming, which weights the local feedbacks and reduces their contribution to the global mean. This finding is, however, dependent on the resolution of analysis, because the local-temperature definition is mathematically inconsistent across spatial scales. Furthermore, attempts to estimate the effect of "pattern weighting" by separating local feedbacks and warming patterns at the gridcell level fail, because the radiative change in key tropical regions is also determined by tropospheric stability via the global circulation. These findings indicate that studies of regional feedback change are more sensitive to methodological choices than previously thought, and that the tropics most likely dominate regional contributions to global radiative feedback change on decadal to centennial time scales. [ABSTRACT FROM AUTHOR]

Using seven single‐model ensembles and the two multimodel ensembles CMIP5 and CMIP6, we show that observed and simulated trends in sea surface temperature (SST) patterns are globally consistent when accounting for internal variability. Some individual ensemble members simulate trends in large‐scale SST patterns that closely resemble the observed ones. Observed regional trends that lie at the outer edge of the models' internal variability range allow two nonexclusive interpretations: (a) Observed trends are unusual realizations of the Earth's possible behavior and/or (b) the models are systematically biased but large internal variability leads to some good matches with the observations. The existing range of multidecadal SST trends is influenced more strongly by large internal variability than by differences in the model formulation or the observational data sets. Plain Language Summary: Climate model simulations agree well with the observed evolution of the global mean sea surface temperature, but their ability to realistically represent changes in the patterns of sea surface temperatures has been questioned. We show with an unprecedented number of simulations from different models and different initial conditions that the observed and simulated changes in SST patterns are consistent in most regions of the ocean. For each model, a few individual simulations recreate the observed patterns. In some regions, the observed changes may be an extreme realization of the Earth's possible behavior. Alternatively, structural model errors may be hidden by the large range of possible realizations. Key Points: Observed and simulated SST trends are consistent in at least 90% of the global ocean area since the midtwentieth centuryThis consistency is obtained over larger areas in CMIP6 models than in CMIP5 modelsObserved regional trends that lie at the edge of the simulated distribution of internal variability are hard to interpret [ABSTRACT FROM AUTHOR]

Our decadal climate prediction system, which is based on the Max-Planck-Institute Earth System Model, is initialized from a coupled assimilation run that utilizes nudging to selected state parameters from reanalyses. We apply full-field nudging in the atmosphere and either full-field or anomaly nudging in the ocean. Full fields from two different ocean reanalyses are considered. This comparison of initialization strategies focuses on the North Atlantic Subpolar Gyre (SPG) region, where the transition from anomaly to full-field nudging reveals large differences in prediction skill for sea surface temperature and ocean heat content (OHC). We show that nudging of temperature and salinity in the ocean modifies OHC and also induces changes in mass and heat transports associated with the ocean flow. In the SPG region, the assimilated OHC signal resembles well OHC from observations, regardless of using full fields or anomalies. The resulting ocean transport, on the other hand, reveals considerable differences between full-field and anomaly nudging. In all assimilation runs, ocean heat transport together with net heat exchange at the surface does not correspond to OHC tendencies, the SPG heat budget is not closed. Discrepancies in the budget in the cases of full-field nudging exceed those in the case of anomaly nudging by a factor of 2-3. The nudging-induced changes in ocean transport continue to be present in the free running hindcasts for up to 5 years, a clear expression of memory in our coupled system. In hindcast mode, on annual to inter-annual scales, ocean heat transport is the dominant driver of SPG OHC. Thus, we ascribe a significant reduction in OHC prediction skill when using full-field instead of anomaly initialization to an initialization shock resulting from the poor initialization of the ocean flow. [ABSTRACT FROM AUTHOR]

The article focuses on the aspects of the Mittelfristige Klimaprognose (MiKlip), the decadal climate prediction project funded by the German Federal Ministry for Education and Research. Topics discussed include the advantage of decadal prediction system on measuring climate variability caused by a chaotic process, the evolution of the international climate prediction models such as baseline and prototype, and the data- and process-oriented evaluation of the MiKlip prediction system.

We integrate the coupled climate model ECHAM5/MPIOM to equilibrium under atmospheric CO quadrupling. The equilibrium global-mean surface-temperature change is 10.8 K. The surface equilibrates within about 1,200 years, the deep ocean within 5,000 years. The impact of the deep ocean on the equilibrium surface-temperature response is illustrated by the difference between ECHAM5/MPIOM and ECHAM5 coupled with slab ocean model (ECHAM5/SOM). The equilibrium global-mean surface temperature response is 11.1 K in ECHAM5/SOM and is thus 0.3 K higher than in ECHAM5/MPIOM. ECHAM5/MPIOM shows less warming over the northern-hemisphere mid and high latitudes, but larger warming over the tropical ocean and especially over the southern-hemisphere high latitudes. ECHAM5/MPIOM shows similar polar amplification in both the Arctic and the Antarctic, in contrast to ECHAM5/SOM, which shows stronger polar amplification in the northern hemisphere. The southern polar warming in ECHAM5/MPIOM is greatly delayed by Antarctic deep-ocean warming due to convective and isopycnal mixing. The equilibrium ocean temperature warming under CO quadrupling is around 8.0 K and is near-uniform with depth. The global-mean steric sea-level rise is 5.8 m in equilibrium; of this, 2.3 m are due to the deep-ocean warming after the surface temperature has almost equilibrated. This result suggests that the surface temperature change is a poor predictor for steric sea-level change in the long term. The effective climate response method described in Gregory et al. () is evaluated with our simulation, which shows that their method to estimate the equilibrium climate response is accurate to within 10 %. [ABSTRACT FROM AUTHOR]

Partitioning uncertainty in projections of future climate change into contributions from internal variability, model response uncertainty, and emissions scenarios has historically relied on making assumptions about forced changes in the mean and variability. With the advent of multiple Single-Model Initial-Condition Large Ensembles (SMILEs), these assumptions can be scrutinized, as they allow a more robust separation between sources of uncertainty. Here, the iconic framework from Hawkins and Sutton (2009) for uncertainty partitioning is revisited for temperature and precipitation projections using seven SMILEs and the Climate Model Intercomparison Projects CMIP5 and CMIP6 archives. The original approach is shown to work well at global scales (potential method error < 20 %), while at local to regional scales such as British Isles temperature or Sahel precipitation, there is a notable potential method error (up to 50 %) and more accurate partitioning of uncertainty is achieved through the use of SMILEs. Whenever internal variability and forced changes therein are important, the need to evaluate and improve the representation of variability in models is evident. The available SMILEs are shown to be a good representation of the CMIP5 model diversity in many situations, making them a useful tool for interpreting CMIP5. CMIP6 often shows larger absolute and relative model uncertainty than CMIP5, although part of this difference can be reconciled with the higher average transient climate response in CMIP6. This study demonstrates the added value of a collection of SMILEs for quantifying and diagnosing uncertainty in climate projections. [ABSTRACT FROM AUTHOR]

If the Paris agreement at the Conference of Parties 21 is implemented very effectively, greenhouse‐gas emissions might decrease after year 2020. Whether this would lead to identifiable near‐term responses in "iconic" climate quantities of wide scientific and public interest is unclear, because the climate response would be obscured by quasi‐random internal variability. I define the climate response as an increase or decrease in a linear climate trend over the period 2021–2035, compared to 2006–2020, and establish the probability of such a trend change being caused by an assumed policy shift toward emissions reductions after 2020. I quantify the irreducible uncertainty in projecting such a trend change through very large (100‐member) ensembles of the state‐of‐the‐art climate model MPI‐ESM‐LR. Trends in global‐mean surface temperature (GMST) are higher over the period 2021–2035 than over 2006–2020 in one‐third of all realizations in the mitigation scenario RCP2.6, interpreted as implementing the Paris agreement, compared to around one‐half in the no‐mitigation scenario RCP4.5. Mitigation is sufficient to cause a GMST trend reduction with a probability of 0.40 and necessary with a probability of 0.33. Trend increases in Arctic September sea‐ice area and the Atlantic meridional overturning circulation are caused by the emissions reductions with a probability of only around 0.1. By contrast, emissions reductions are necessary for a trend decrease in upper‐ocean heat content with a probability of over one‐half. Some iconic climate quantities might thus by year 2035 exhibit an identifiable response to a successful Paris agreement but sometimes with low probability, creating a substantial communication challenge. This article is categorized under: Climate Models and Modeling > Knowledge Generation with Models Even if CO2 emissions fall after 2020, there is a one‐in‐three chance that global warming will speed up until 2035 instead of slowing down—and we cannot tell right now. [ABSTRACT FROM AUTHOR]

A preface for the special section, in 19th volume of the "Journal of Climate," published in August 2006 is presented.

The Max Planck Institute Grand Ensemble (MPI-GE) is the largest ensemble of a single comprehensive climate model currently available, with 100 members for the historical simulations and each of four future forcing scenarios. It is currently the only large ensemble available that includes scenario RCP2.6, which has become vital for projections given the targets set by the Paris agreement. The ensemble also has the advantage that it is initialized by sampling the control state both for the ocean and atmosphere, which means that, except for long-timescale deep-ocean variables, the ensemble can be directly investigated from the beginning of the model runs. Given this initialization, deep ocean drift can also be efficiently removed using the parallel control simulation. These advantages make MPI-GE the most powerful large ensemble available today.We present an overview of MPI-GE and its components. A novel approach for comparing model internal variability with observed variability is demonstrated, with six interesting regions highlighted. The power of MPI-GE to accurately estimate both the forced signal and internal variability is also shown, with the Atlantic Meridional Overturning Circulation (AMOC) used as an example. In this example, we quantify the forced signal as the ensemble mean and the internal variability as the standard deviation across the ensemble dimension. Finally, MPI-GE can be used to determine the ensemble size needed to investigate a specific application. Here, we find that 30-40 members are sufficient to quantify the projected sea level pressure trend and its variability in RCP4.5 over the period 2007-2099. We suggest that MPI-GE can be used to estimate the ensemble size needed for many future studies. [ABSTRACT FROM AUTHOR]