Journal articles

M. Schillinger, B. Ellerhoff, R. Scheichl, K. Rehfeld: Separating internal and externally-forced contributions to global temperature variability using a Bayesian stochastic energy balance framework. Chaos. 2022. Full paper

Conference Contributions

M. Schillinger, X. Shen, M. Samarin, N. Meinshausen: Generative Modelling for Multivariate Downscaling via Proper Scoring Rules. IMSC Toulouse. 2024. Slides
M. Schillinger, X. Shen, M. Samarin, N. Meinshausen: Machine Learning for Multivariate Downscaling: A Generative Model Inspired by Forecast Evaluation. EGU General Assembly. 2024. Abstract
M. Schillinger, X. Shen, M. Samarin, N. Meinshausen: Machine Learning for High-Resolution Climate Projections: Generative Models Meet Proper Scoring Rules. MathSEE Symposium Karlsruhe. 2023.
M. Schillinger, B. Ellerhoff, K. Rehfeld, R. Scheichl: Emulating internal and external components of global temperature variability with a stochastic energy balance model and Bayesian approach. EGU General Assembly. 2023. Abstract
M. Schillinger, B. Ellerhoff, K. Rehfeld, R. Scheichl: Bayesian Inference of Climate Parameters Using Multibox EBMs. EGU General Assembly. 2022. Abstract
M. Schillinger, B. Ellerhoff, K. Rehfeld, R. Scheichl: Bayesian parameter estimation for EBMs: What can we learn about climate variability? DPG Meeting of the Matter and Cosmos Section (SMuK). 2021.

M. Schillinger and B. Ellerhoff. ClimBayes, Bayesian inference of climate parameters using multi-box energy balance models (EBMs). 2022. Link to Github