The Max Planck Institute Grand Ensemble: Enabling the Exploration of Climate System Variability
2019
Nicola Maher | Sebastian Milinski | Laura Suarez‐Gutierrez | Michael Botzet | Mikhail Dobrynin | Luis Kornblueh | Jürgen Kröger | Yohei Takano | Rohit Ghosh | Christopher Hedemann | Chao Li | Hongmei Li | Elisa Manzini | Dirk Notz | Dian Putrasahan | Lena Boysen | Martin Claussen | Tatiana Ilyina | Dirk Olonscheck | Thomas Raddatz | Bjorn Stevens | Jochem Marotzke
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 (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.
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