04 Oct 2022
04 Oct 2022

Regime-oriented causal model evaluation of Atlantic-Pacific teleconnections in CMIP6

Soufiane Karmouche1,2, Evgenia Galytska1,2, Jakob Runge3,4, Gerald A. Meehl5, Adam S. Phillips5, Katja Weigel1,2, and Veronika Eyring2,1 Soufiane Karmouche et al.
  • 1University of Bremen, Institute of Environmental Physics (IUP), Bremen, Germany
  • 2Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
  • 3Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Datenwissenschaften, Jena, Germany
  • 4Technische Universität Berlin, Berlin, Germany
  • 5National Center for Atmospheric Research (NCAR), Boulder, CO, USA

Abstract. The climate system and its spatio-temporal changes are strongly affected by modes of long-term internal variability, like the Pacific Decadal Varibility (PDV) and the Atlantic Multidecadal Variability (AMV). As they alternate between warm and cold phases, the interplay between PDV and AMV varies over decadal to multidecadal timescales. Here, we use a causal discovery method to derive fingerprints in the Atlantic-Pacific interactions and investigate their phase-dependent changes. Dependent on the phases of PDV and AMV, different regimes with characteristic causal fingerprints are identified in reanalyses in a first step. In a second step, a regime-oriented causal model evaluation is performed to evaluate the ability of models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) in representing the observed changing interactions between PDV, AMV and their extra-tropical teleconnections. The causal graphs obtained from reanalyses detect a direct opposite-sign response from AMV on PDV when analysing the complete 1900–2014 period, and during several defined regimes within that period, for example, when AMV is going through its negative (cold) phase. Reanalyses also demonstrate a same-sign response from PDV on AMV during the cold phase of PDV. Historical CMIP6 simulations exhibit varying skill in simulating the observed causal patterns. Generally, Large Ensemble (LE) simulations showed better network similarity when PDV and AMV are out of phase compared to other regimes. Also, the two largest ensembles (in terms of number of members) were found to contain realizations with similar causal fingerprints to observations. For most regimes, these same models showed higher network similarity when compared to each other. This work shows how causal discovery on LEs complements the available diagnostics and statistics metrics of climate variability to provide a powerful tool for climate model evaluation.

Soufiane Karmouche et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-1013', Anonymous Referee #1, 04 Dec 2022
  • RC2: 'Comment on egusphere-2022-1013', Anonymous Referee #2, 04 Dec 2022

Soufiane Karmouche et al.

Soufiane Karmouche et al.


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Short summary
Causal model evaluation is helping to better understand remote contributions to internal variability over specific regions. The approach and findings presented in this study have the potential of bringing up a powerful methodology that can be applied in a number of environment-related topics, offering tremendous insight to improve the understanding of the complex earth system and the state-of-the-art of climate modeling.