Research Seminar Autumn 2022

2022-10-11 Machine Learning and the Quest for Objectivity in Climate Model Parameterization

Unitobler, F004, 16:15-18:00

Discussion of a draft by Julie Jebeile, Vincent Lam, Mason Majszak and Tim Räz (University of Bern).

Abstract Parameterization and parameter tuning are central aspects of climate modeling, and there is widespread consensus that these procedures involve certain subjective elements. Even if the use of these subjective elements is not necessarily epistemically problematic, there is an intuitive appeal for replacing them with more objective (automated) methods, such as machine learning. Relying on several case studies, we argue that, while machine learning techniques may help improving climate model parameterization in several ways, they still require expert judgment that involves subjective elements not so different from the ones arising in standard parameterization and tuning. The use of machine learning in parameterizations is an art as well as a science and requires careful supervision.

2022-11-03 A possibilistic epistemology of climate modelling and its application to the cases of sea level rise and climate sensitivity

Joel Katzav, University of Queensland.

Unitobler, F014, 14:15-16:00

Abstract It has been argued that possibilitic assessment of climate model output is preferable to probabilistic assessment (Stainforth et al. 2007; Betz 2010, 2015; Katzav 2014; Katzav et al. 2012 and 2021). I aim to articulate a variant of a possibilistic approach to such assessment. On my variant, the output of climate models should typically be assessed in light of two questions: is it fully epistemically possible? If the output is (fully) epistemically possible, how remote a possibility does it represent? Further, on my variant, if the output is judged to be epistemically possible, it should be taken to represent objective possibilities, specifically potentialities of the actual climate system. Having articulated my possibilistic approach, I apply it to two key issues in climate science, namely the potential contribution of marine ice cliff instability to sea level rise over the rest of the twenty-first century and climate sensitivity. Marine ice cliff instability (MICI) has been posited as a mechanism that might lead to substantially more sea level rise than had previously been projected (DeConto and Pollard 2016). I will suggest that existing assessment of the contribution of MICI to future sea level rise illustrates the strengths of my possibilistic approach and weaknesses of probabilistic approaches to assessing the output of climate models. I will also argue that the most recent Intergovernmental Panel on Climate Change assessment of climate sensitivity, especially its reliance on variety of evidence considerations to address the challenges of unexpectedly high climate sensitivity projections by state-of-the-art climate models, illustrate the strength of my possibilistic approach and the weakness of probabilistic approaches.

2022-12-14 Evidence in Classical Statistics

Sam Fletcher, University of Minnesota.

Uni Mittelstrasse 43, room 216, 12:00-13:30

Abstract On one hand, science is a paradigmatic source of good evidence, with quantitative experimental science often described in classical statistical terms. On the other hand, many philosophers, statisticians, and scientists who study the foundations of classical statistics have rejected its techniques as unfounded. How to resolve this tension? I suggest that one can better understand the evidential import of classical statistics in terms of probabilistic versions of conditions of adherence, safety, and sensitivity. Although these conditions have been employed with debatable success in the service of definitions of knowledge, I suggest that they are fruitful in understanding evidence in classical statistics. I also sketch how they lead to improvements in statistical methodology, along some lines that Deborah Mayo has suggested.