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network-based constraint to evaluate climate sensitivity

Author

Listed:
  • Lucile Ricard

    (Ecole Polytechnique Fédérale de Lausanne (EPFL))

  • Fabrizio Falasca

    (New York University)

  • Jakob Runge

    (Institute of Data Science
    Technische Universität Berlin
    TU Dresden)

  • Athanasios Nenes

    (Ecole Polytechnique Fédérale de Lausanne (EPFL)
    Institute of Chemical Engineering Sciences, Foundation for Research and Technology Hellas (FORTH))

Abstract

The 2015 Paris agreement was established to limit Greenhouse gas (GHG) global warming below 1.5°C above preindustrial era values. Knowledge of climate sensitivity to GHG levels is central for formulating effective climate policies, yet its exact value is shroud in uncertainty. Climate sensitivity is quantitatively expressed in terms of Equilibrium Climate Sensitivity (ECS) and Transient Climate Response (TCR), estimating global temperature responses after an abrupt or transient doubling of CO2. Here, we represent the complex and highly-dimensional behavior of modelled climate via low-dimensional emergent networks to evaluate Climate Sensitivity (netCS), by first reconstructing meaningful components describing regional subprocesses, and secondly inferring the causal links between these to construct causal networks. We apply this methodology to Sea Surface Temperature (SST) simulations and investigate two different metrics in order to derive weighted estimates that yield likely ranges of ECS (2.35–4.81°C) and TCR (1.53-2.60°C). These ranges are narrower than the unconstrained distributions and consistent with the ranges of the IPCC AR6 estimates. More importantly, netCS demonstrates that SST patterns (at “fast” timescales) are linked to climate sensitivity; SST patterns over the historical period exclude median sensitivity but not low-sensitivity (ECS

Suggested Citation

  • Lucile Ricard & Fabrizio Falasca & Jakob Runge & Athanasios Nenes, 2024. "network-based constraint to evaluate climate sensitivity," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50813-z
    DOI: 10.1038/s41467-024-50813-z
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    References listed on IDEAS

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    1. Steven C. Sherwood & Sandrine Bony & Jean-Louis Dufresne, 2014. "Spread in model climate sensitivity traced to atmospheric convective mixing," Nature, Nature, vol. 505(7481), pages 37-42, January.
    2. Peer Nowack & Jakob Runge & Veronika Eyring & Joanna D. Haigh, 2020. "Causal networks for climate model evaluation and constrained projections," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    3. Jakob Runge & Vladimir Petoukhov & Jonathan F. Donges & Jaroslav Hlinka & Nikola Jajcay & Martin Vejmelka & David Hartman & Norbert Marwan & Milan Paluš & Jürgen Kurths, 2015. "Identifying causal gateways and mediators in complex spatio-temporal systems," Nature Communications, Nature, vol. 6(1), pages 1-10, December.
    4. Peter M. Cox & Chris Huntingford & Mark S. Williamson, 2018. "Emergent constraint on equilibrium climate sensitivity from global temperature variability," Nature, Nature, vol. 553(7688), pages 319-322, January.
    5. Jakob Runge & Sebastian Bathiany & Erik Bollt & Gustau Camps-Valls & Dim Coumou & Ethan Deyle & Clark Glymour & Marlene Kretschmer & Miguel D. Mahecha & Jordi Muñoz-Marí & Egbert H. Nes & Jonas Peters, 2019. "Inferring causation from time series in Earth system sciences," Nature Communications, Nature, vol. 10(1), pages 1-13, December.
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