Presents integral dynamic emergent constraints from linear response theory as a generalization of traditional ones, tested on MPI-ESM global warming simulations.
2021 Large ensemble climate model simulations: introduction, overview, and future prospects for utilising multiple types of large ensemble
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Extends linear response theory to nonautonomous systems and applies it to optimal fingerprinting for attributing changes to multiple forcings in time-dependent backgrounds, with numerical tests on a climate model.
Single-model ensemble spreads represent aleatoric uncertainty, quantified with GAMs and validated on reanalysis, showing reduced variability in Iberian drought regions that worsens under 3°C warming.
citing papers explorer
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A mathematical framework for dynamic emergent constraints in climate science
Presents integral dynamic emergent constraints from linear response theory as a generalization of traditional ones, tested on MPI-ESM global warming simulations.
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Linear Response and Optimal Fingerprinting for Nonautonomous Systems
Extends linear response theory to nonautonomous systems and applies it to optimal fingerprinting for attributing changes to multiple forcings in time-dependent backgrounds, with numerical tests on a climate model.
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Capturing Aleatoric Uncertainty in Climate Models
Single-model ensemble spreads represent aleatoric uncertainty, quantified with GAMs and validated on reanalysis, showing reduced variability in Iberian drought regions that worsens under 3°C warming.