The Max Planck Institute Grand Ensemble: Enabling the Exploration of Climate System Variability

Abstract

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 four forcing scenarios. It is currently the only large ensemble available that includes scenario representative concentration pathway (RCP) 2.6 and a 1% CO 2 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.Large-ensemble projects of comprehensive coupled climate models are gaining traction as methods to robustly estimate internal variability in transient simulations and to quantify the forced signal (e.g., Kay

Quantifying the role of internal variability in the temperature we expect to observe in the coming decades

“…However, they were unable to account for the fact that internal variability in all models is not the same and that this variability itself may change in the future (e.g. Sutton et al 2015,Maher et al 2019. Here, we confirm the results of Hawkins and Sutton (2009) with a more recent generation of climate models and at a higher spatial resolution, using multiple SMILEs and CMIP5 in agreement with Lehner et al (in review 2020).…”

“…• The Max Planck institute Grand Ensemble (MPI-GE) (Maher et al 2019). This model has 100 ensemble members available for RCP2.6, RCP4.5 and RCP8.5 scenarios.…”

Decadal Predictions of the Probability of Occurrence for Warm Summer Temperature Extremes

“…The linear detrending we apply to account for the global warming trend might introduce some error, because the global warming trend is likely not linear (e.g., Maher et al, 2019). For example, some of the SAT extremes identified in the early 2000s in the assimilation run might actually originate from the global warming trend and be artifacts of the linear detrending.…”

“…For example, some of the SAT extremes identified in the early 2000s in the assimilation run might actually originate from the global warming trend and be artifacts of the linear detrending. While there are more elegant approaches to subtract the warming signal from a climate data set, like using the ensemble mean of a very large ensemble (e.g., Maher et al, 2019), these techniques cannot be applied in this case because only one realization of the assimilation run is available. If the post-2010 extremes are in fact artifacts of the linear detrending, a cleaner subtraction of the warming trend might lead to a stronger connection of the probability of occurrence of summer temperature extremes to SPG temperature.…”

The Recent Decline and Recovery of Indian Summer Monsoon Rainfall: Relative Roles of External Forcing and Internal Variability

“…The relatively large ensemble size allows us to consider its ensemble mean as the externally forced signal, while the deviation of an individual member from the ensemble mean results primarily from internal variability and second from its own uncertain forced response (due to parameter uncertainty) (Murphy et al 2004). To avoid model dependence, we also analyzed the output from a 100-member ensemble of simulations generated by the Max Planck Institute Earth System Model version 1.1 (MPI-ESM) with slightly different initial conditions (Maher et al 2018(Maher et al , 2019. In this study, we therefore aim to answer the following questions: 1) What are the relative contributions of external forcing and internal variability to the recent interdecadal variations in ISM rainfall?…”