Emulating internal and external components of global temperature variability with a stochastic energy balance model and Bayesian approach
- 1ETH Zurich, Seminar for Statistics, Department of Mathematics, Zürich, Switzerland (maybritt.schillinger@stat.math.ethz.ch)
- 2Department of Physics and Department of Geosciences, Tübingen University, Schnarrenbergstr. 94-96, 72076 Tübingen, Germany
- 3nstitute of Applied Mathematics and Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Im Neuenheimer Feld 205, 69120 Heidelberg, Germany
To characterize Earth’s temperature variability, it is necessary to better understand underlying mechanisms and contributions from internal and externally forced components. Here, we utilize a stochastic two-box energy balance model to emulate internal and forced global mean surface temperature (GMST) variability [1]. As target data for the emulation, we employ observations and 20 last millennium simulations from climate models of intermediate to high complexity. We infer the parameters of the stochastic EBM using the target data and a Bayesian approach, as implemented with a Markov Chain Monte Carlo algorithm in our “ClimBayes” software package [2]. This yields the best estimates of the EBM’s forced and forced + internal response. Applying spectral analysis, we contrast timescale-dependent variances of the EBM’s forced and forced + internal variance with that of the GMST target. Our findings show that the simple two-box stochastic EBM reproduces the characteristics of simulated global temperature fluctuations, even from comprehensive climate models. Minor deviations occur mainly at interannual timescales and are related to the simplistic representation of internal variability in the EBM. Furthermore, the relative contribution of internal dynamics increases with model complexity and decreases with timescale. Altogether, we demonstrate that the combined use of simple stochastic climate models and Bayesian inference provides a valuable tool to emulate climate variability across timescales.
[1] M. Schillinger, B. Ellerhoff, R. Scheichl, and K. Rehfeld: “Separating internal and externally forced contributions to global temperature variability using a Bayesian stochastic energy balance framework,” Chaos, https://doi.org/10.1063/5.0106123 (2022).
[2] M. Schillinger, B. Ellerhoff, R. Scheichl, and K. Rehfeld, “The ClimBayes package in R,” Zenodo, V. 0.1.1, https://doi.org/10.5281/zenodo.7317984 (2022).
How to cite: Schillinger, M., Ellerhoff, B., Scheichl, R., and Rehfeld, K.: Emulating internal and external components of global temperature variability with a stochastic energy balance model and Bayesian approach, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-9335, https://doi.org/10.5194/egusphere-egu23-9335, 2023.
