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With the advent of climate change as a major challenge of our time, Earth system modeling has become highly policy‐relevant regulatory science. In this situation, the social mechanisms that play a role in any scientific endeavor become particularly exposed. By discussing historical, philosophical, and sociological (HPS) aspects of the field's current "cultures of prediction" together with the physical science community in a physical science journal, we aim to provide an entry point into HPS reasoning for climate scientists interested in reflecting on their field and science in general. This paper, first, introduces our perspective on "science as culture" and climate modeling as "regulatory science" and, second, highlights and connects relevant ideas from the three commentaries that follow it. In so doing, we hope to give a fuller picture of climate science, the interplay it engenders between HPS and the physical sciences, the distinctions that it gives rise to as compared to some of the more traditional, exact, sciences in which it is rooted and its place in society including its role in scientific policy advice. Key Points: With the advent of climate change as a major challenge of our time, Earth system modeling has become highly policy relevantIn this situation, the social mechanisms that play a role in any scientific endeavor become particularly exposedThis paper introduces perspectives on "science as culture" and highlights relevant ideas from the three subsequent commentaries [ABSTRACT FROM AUTHOR]

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 (1850–2005) and four forcing scenarios. It is currently the only large ensemble available that includes scenario representative concentration pathway (RCP) 2.6 and a 1% CO2 scenario. These advantages make MPI‐GE a powerful tool. We present an overview of MPI‐GE, its components, and detail the experiments completed. We demonstrate how to separate the forced response from internal variability in a large ensemble. This separation allows the quantification of both the forced signal under climate change and the internal variability to unprecedented precision. We then demonstrate multiple ways to evaluate MPI‐GE and put observations in the context of a large ensemble, including a novel approach for comparing model internal variability with estimated observed variability. Finally, we present four novel analyses, which can only be completed using a large ensemble. First, we address whether temperature and precipitation have a pathway dependence using the forcing scenarios. Second, the forced signal of the highly noisy atmospheric circulation is computed, and different drivers are identified to be important for the North Pacific and North Atlantic regions. Third, we use the ensemble dimension to investigate the time dependency of Atlantic Meridional Overturning Circulation variability changes under global warming. Last, sea level pressure is used as an example to demonstrate how MPI‐GE can be utilized to estimate the ensemble size needed for a given scientific problem and provide insights for future ensemble projects. Key Points: The 100‐member MPI‐GE is currently the largest publicly available ensemble of a comprehensive climate modelMPI‐GE currently has the most forcing scenarios of all large ensemble projects: RCP2.6, RCP4.5, RCP8.5, and 1% CO2The power of MPI‐GE is to estimate the forced response and internal variability, including changing variability, to unprecedented precision [ABSTRACT FROM AUTHOR]

This study explores the extent to which convective aggregation interacts with sea surface temperature (SST) and affects climate sensitivity. For this purpose, radiative‐convective equilibrium simulations are run with a general circulation model coupled to an ocean mixed layer, and several types of perturbations are imposed to the ocean‐atmosphere system. Convective aggregation turns out to be much more sensitive to temperature in coupled experiments than in prescribed SST experiments. But changes in convective aggregation induced by a doubling of the CO2 concentration are always smaller than changes associated with the transition from a non‐aggregated to an aggregated state. If aggregation changes were acting alone, they would exert a strong negative feedback on global mean surface temperature. However, in a coupled framework, aggregation changes interact with the SST and generate SST gradients that strengthen the positive low‐cloud feedback associated with changes in SST pattern. This overcompensates the negative feedback due to aggregation changes and leads to a larger equilibrium climate sensitivity than in the absence of SST gradients. Although this effect might be model specific, interactions between convective aggregation and the spatial distribution of SST appear crucial to assess the impact of convective aggregation on climate sensitivity. Key Points: Convective aggregation interacts strongly with surface temperature gradientsAggregation can increase climate sensitivity when coupled with an interactive slab ocean, rather than decreasing itThe impact of convective aggregation on climate should also be investigated in coupled frameworks [ABSTRACT FROM AUTHOR]