Separating internal and externally-forced contributions to global temperature variability using a Bayesian stochastic energy balance framework

• Verfasst von: Schillinger, Maybritt [VerfasserIn]
• Verfasst von: Ellerhoff, Beatrice [VerfasserIn]
• Verfasst von: Scheichl, Robert [VerfasserIn]
• Verfasst von: Rehfeld, Kira [VerfasserIn]
• Titel: Separating internal and externally-forced contributions to global temperature variability using a Bayesian stochastic energy balance framework
• Verf.angabe: Maybritt Schillinger, Beatrice Ellerhoff, Robert Scheichl, and Kira Rehfeld
• E-Jahr: 2022
• Jahr: 30 June 2022
• Umfang: 15 S.
• Fussnoten: Version 1 vom 27 Juni 2022, Version 2 vom 30 Juni 2022 ; Gesehen am 17.10.2022
• Titel Quelle: Enthalten in: Arxiv
• Ort Quelle: Ithaca, NY : Cornell University, 1991
• Jahr Quelle: 2022
• Band/Heft Quelle: (2022), Artikel-ID 2206.14573, Seite 1-15
• DOI: doi:10.48550/arXiv.2206.14573
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Physics - Atmospheric and Oceanic Physics
• Sach-SW: Physics - Data Analysis, Statistics and Probability
• Sach-SW: Physics - Geophysics
• K10plus-PPN: 1819004228

Abstract

Earth's temperature variability can be partitioned into internal and externally-forced components. Yet, underlying mechanisms and their relative contributions remain insufficiently understood, especially on decadal to centennial timescales. Important reasons for this are difficulties in isolating internal and externally-forced variability. Here, we provide a physically-motivated emulation of global mean surface temperature (GMST) variability, which allows for the separation of internal and external variations. To this end, we introduce the "ClimBayes" software package, which infers climate parameters from a stochastic energy balance model (EBM) with a Bayesian approach. We apply our method to GMST data from temperature observations and 20 last millennium simulations from climate models of intermediate to high complexity. This yields the best estimates of the EBM's forced and forced + internal response, which we refer to as emulated variability. The timescale-dependent variance is obtained from spectral analysis. In particular, we contrast the emulated forced and forced + internal variance on interannual to centennial timescales with that of the GMST target. Our findings show that a stochastic EBM closely approximates the power spectrum and timescale-dependent variance of GMST as simulated by modern climate models. This demonstrates the potential of combining Bayesian inference with conceptual climate models to emulate statistics of climate variables across timescales.