network-based constraint to evaluate climate sensitivity

Lucile Ricard, Fabrizio Falasca, Jakob Runge and Athanasios Nenes ()
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Lucile Ricard: Ecole Polytechnique Fédérale de Lausanne (EPFL)
Fabrizio Falasca: New York University
Jakob Runge: Institute of Data Science
Athanasios Nenes: Ecole Polytechnique Fédérale de Lausanne (EPFL)

Nature Communications, 2024, vol. 15, issue 1, 1-10

Abstract: 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

Date: 2024
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DOI: 10.1038/s41467-024-50813-z

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