Separating internal and externally forced contributions to global temperature variability using a Bayesian stochastic energy balance framework
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 in...
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| Hauptverfasser: | , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
29 November 2022
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| In: |
Chaos
Year: 2022, Jahrgang: 32, Heft: 11, Pages: 1-17 |
| ISSN: | 1089-7682 |
| DOI: | 10.1063/5.0106123 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1063/5.0106123 Verlag, lizenzpflichtig, Volltext: https://aip.scitation.org/doi/10.1063/5.0106123 
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| Verfasserangaben: | Maybritt Schillinger, Beatrice Ellerhoff, Robert Scheichl, and Kira Rehfeld |
| Zusammenfassung: | 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. ... |
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| Beschreibung: | Gesehen am 17.01.2023 |
| Beschreibung: | Online Resource |
| ISSN: | 1089-7682 |
| DOI: | 10.1063/5.0106123 |