Articles | Volume 14, issue 2
https://doi.org/10.5194/esd-14-309-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-14-309-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Regime-oriented causal model evaluation of Atlantic–Pacific teleconnections in CMIP6
University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Evgenia Galytska
University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Jakob Runge
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Datenwissenschaften, Jena, Germany
Fachgebiet Klimainformatik, Technische Universität Berlin, Berlin, Germany
Gerald A. Meehl
Climate and Global Dynamics Laboratory, National Center for Atmospheric Research (NCAR), Boulder, CO, USA
Adam S. Phillips
Climate and Global Dynamics Laboratory, National Center for Atmospheric Research (NCAR), Boulder, CO, USA
Katja Weigel
University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
Veronika Eyring
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany
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Cited
10 citations as recorded by crossref.
- Causal Discovery from Temporal Data: An Overview and New Perspectives C. Gong et al. 10.1145/3705297
- Causal inference for time series J. Runge et al. 10.1038/s43017-023-00431-y
- Interdecadal tropical Pacific–Atlantic interaction simulated in CMIP6 models Y. Deng et al. 10.1007/s00382-024-07155-9
- Pushing the frontiers in climate modelling and analysis with machine learning V. Eyring et al. 10.1038/s41558-024-02095-y
- Excess topography and outburst flood: Geomorphic imprint of October 2023 extreme flood event in the Teesta catchment of Eastern Himalayas A. Kashyap & M. Behera 10.1016/j.gloplacha.2024.104540
- Changing effects of external forcing on Atlantic–Pacific interactions S. Karmouche et al. 10.5194/esd-15-689-2024
- 追索为什么? 地球系统科学中的因果推理 建. 苏 et al. 10.1360/SSTe-2023-0005
- Evaluating Causal Arctic‐Midlatitude Teleconnections in CMIP6 E. Galytska et al. 10.1029/2022JD037978
- European heatwave tracks: using causal discovery to detect recurring pathways in a single-regional climate model large ensemble A. Böhnisch et al. 10.1088/1748-9326/aca9e3
- The insight of why: Causal inference in Earth system science J. Su et al. 10.1007/s11430-023-1148-7
6 citations as recorded by crossref.
- Causal Discovery from Temporal Data: An Overview and New Perspectives C. Gong et al. 10.1145/3705297
- Causal inference for time series J. Runge et al. 10.1038/s43017-023-00431-y
- Interdecadal tropical Pacific–Atlantic interaction simulated in CMIP6 models Y. Deng et al. 10.1007/s00382-024-07155-9
- Pushing the frontiers in climate modelling and analysis with machine learning V. Eyring et al. 10.1038/s41558-024-02095-y
- Excess topography and outburst flood: Geomorphic imprint of October 2023 extreme flood event in the Teesta catchment of Eastern Himalayas A. Kashyap & M. Behera 10.1016/j.gloplacha.2024.104540
- Changing effects of external forcing on Atlantic–Pacific interactions S. Karmouche et al. 10.5194/esd-15-689-2024
4 citations as recorded by crossref.
- 追索为什么? 地球系统科学中的因果推理 建. 苏 et al. 10.1360/SSTe-2023-0005
- Evaluating Causal Arctic‐Midlatitude Teleconnections in CMIP6 E. Galytska et al. 10.1029/2022JD037978
- European heatwave tracks: using causal discovery to detect recurring pathways in a single-regional climate model large ensemble A. Böhnisch et al. 10.1088/1748-9326/aca9e3
- The insight of why: Causal inference in Earth system science J. Su et al. 10.1007/s11430-023-1148-7
Latest update: 13 Dec 2024
Short summary
This study uses a causal discovery method to evaluate the ability of climate models to represent the interactions between the Atlantic multidecadal variability (AMV) and the Pacific decadal variability (PDV). The approach and findings in this study present a powerful methodology that can be applied to a number of environment-related topics, offering tremendous insights to improve the understanding of the complex Earth system and the state of the art of climate modeling.
This study uses a causal discovery method to evaluate the ability of climate models to represent...
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