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Journal Article

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

MPS-Authors
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Maher,  Nicola
Director’s Research Group OES, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society;

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Milinski,  Sebastian
Director’s Research Group OES, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society;
IMPRS on Earth System Modelling, MPI for Meteorology, Max Planck Society;

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Suarez-Gutierrez,  Laura
Director’s Research Group OES, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society;
IMPRS on Earth System Modelling, MPI for Meteorology, Max Planck Society;

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Botzet,  Michael
Director’s Research Group OES, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society;

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Kornblueh,  Luis
Computational Infrastructure and Model Devlopment (CIMD), Scientific Computing Lab (ScLab), MPI for Meteorology, Max Planck Society;

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Takano,  Yohei
Ocean Biogeochemistry, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society;

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Kröger,  Jürgen
Decadal Climate Predictions - MiKlip, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society;

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Ghosh,  Rohit
Decadal Climate Predictions - MiKlip, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society;

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Hedemann,  Christopher
Director’s Research Group OES, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society;

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Li,  Chao
Director’s Research Group OES, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society;

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Li,  Hongmei
Ocean Biogeochemistry, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society;

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Manzini,  Elisa
Minerva Research Group Stratosphere and Climate, The Atmosphere in the Earth System, MPI for Meteorology, Max Planck Society;

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Notz,  Dirk
Max Planck Research Group The Sea Ice in the Earth System, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society;

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Putrasahan,  Dian
Ocean Statistics, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society;

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Boysen,  Lena
Climate-Biogeosphere Interaction, The Land in the Earth System, MPI for Meteorology, Max Planck Society;

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Claussen,  Martin
Director’s Research Group LES, The Land in the Earth System, MPI for Meteorology, Max Planck Society;

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Ilyina,  Tatiana
Ocean Biogeochemistry, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society;

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Olonscheck,  Dirk
Max Planck Research Group The Sea Ice in the Earth System, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society;

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Raddatz,  Thomas
Global Vegetation Modelling, The Land in the Earth System, MPI for Meteorology, Max Planck Society;

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Stevens,  Bjorn
Director’s Research Group AES, The Atmosphere in the Earth System, MPI for Meteorology, Max Planck Society;

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Marotzke,  Jochem
Director’s Research Group OES, The Ocean in the Earth System, MPI for Meteorology, Max Planck Society;

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Supplementary Material (public)

Maher_etal2019.zip
(Supplementary material), 15MB

Citation

Maher, N., Milinski, S., Suarez-Gutierrez, L., Botzet, M., Kornblueh, L., Takano, Y., et al. (2019). The Max Planck Institute Grand Ensemble - Enabling the Exploration of Climate System Variability. Journal of Advances in Modeling Earth Systems, 11, 2050-2069. doi:10.1029/2019MS001639.


Cite as: http://hdl.handle.net/21.11116/0000-0003-2113-2
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 each of 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. The ensemble also has the advantage that it is initialized by sampling the control state for all model components, meaning that for most variables the ensemble can be directly investigated from the beginning of the simulations. These advantages make MPI-GE a powerful tool. We present an overview of MPI-GE and its components and detail the experiments completed. We provide an example of how to compare multiple scenarios and discuss the value of having a large ensemble to do so. We then demonstrate multiple ways both to evaluate MPI-GE and compare observations to large ensembles, including a novel approach for comparing model internal variability with estimated observed variability. 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. Additionally we emphasize the capacity of using the ensemble dimension to quantify variability and its potential changes in a transient forcing scenario. Finally, sea level pressure is used 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.