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Separating internal and externally forced contributions to global temperature variability using a Bayesian stochastic

Maybritt Schillinger1, Beatrice Ellerhoff2, Robert Scheichl3

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This study introduces ClimBayes software to separate internal and external climate variability using a Bayesian approach. The findings show a stochastic energy balance model closely approximates global mean surface temperature variability across timescales.

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Area of Science:

  • Climate science
  • Statistical modeling
  • Bayesian inference

Background:

  • Earth's temperature variability has internal and external drivers, but their separation and contributions are poorly understood, especially on decadal to centennial scales.
  • Isolating internal versus externally forced climate variability presents significant methodological challenges.

Purpose of the Study:

  • To develop a physically motivated emulation of global mean surface temperature (GMST) variability for separating internal and external components.
  • To introduce and apply the "ClimBayes" software package for climate parameter inference.

Main Methods:

  • Utilized a Bayesian approach to infer climate parameters from a stochastic energy balance model (EBM).
  • Applied the method to observed GMST data and 20 last millennium climate model simulations.
  • Performed spectral analysis to obtain timescale-dependent variance of emulated variability.

Main Results:

  • A stochastic EBM effectively emulates the power spectrum and timescale-dependent variance of GMST, closely matching climate model simulations.
  • The "ClimBayes" package successfully separates forced and internal climate variability components.
  • Small deviations at interannual timescales were noted, attributed to simplified internal variability representation in the EBM.

Conclusions:

  • Bayesian inference combined with conceptual climate models can successfully emulate climate variable statistics across various timescales.
  • The developed method provides a robust tool for understanding climate variability drivers.
  • Further refinement of EBMs could improve emulation of interannual variability.