Understanding and Assessing Climate Change

Idea & Motivation

The conference aims to explore and discuss the understanding and assessment-related issues associated with the scientific study of Earth 's climate system and will serve as the end-of-project event for the “Climate Models and Climate Scientific Understanding” project, funded by the European Commission under the Marie Skłodowska-Curie Actions.

Keynote Speakers

• Stephen John (Cambridge University)
• Vincent Lam (Universität Bern, Institut für Philosophie)
• Mathias Frisch (Leibniz Universität Hannover, Institut für Philosophie)
• Julie Jebeile (Universität Bern, Institut für Philosophie)

Contributed Speakers

• Alice Wheatley (University of East Anglia)
• Francesco Nappo (Politecnico di Milano)
• Johannes Nyström (Stockholm University)
• Micah Thomas (University of Bucharest)
• Edor J. Edor (University of Calabar)
• Futura Venuto (University of Bern)
• Laura García-Portela (Erasmus University Rotterdam)
• Elliott Woodhouse (International Institute for Applied Systems Analysis Vienna)
• Nathalie Kiepe (Università della Svizzera Italiana)

Program

Day 1 (October 28)
10:00 - 10:15	Welcome Speech
10:15 - 11:15	Gabriel Tarziu (Munich Centre for Mathematical Philosophy, LMU) – Climate Models and Climate-Scientific Understanding (presentation of CMCSU project results)
11:15 - 11:30	Coffee Break
11:30 - 12:30	Keynote: Vincent Lam (University of Bern) – Climate extremes, physical storylines and process understanding
12:30 - 12:35	Break
12:35 - 13:10	Alice Wheatley (University of East Anglia) – Understanding-Oriented Epistemology for the Environment
13:10 - 14:25	Lunch Break
14:25 - 15:25	Keynote: Julie Jebeile (University of Bern) – Model spread and progress in climate modelling
15:25 - 15:40	Coffee Break
15:40 - 16:15	Francesco Nappo (Politecnico di Milano) – Designing Climate Change Scenario Ensembles
16:15 - 16:20	Break
16:20 - 16:55	Johannes Nyström (Stockholm University) – Model-based robustness analysis as meta-empirical confirmation

Day 2 (October 29)
10:00 - 11:00	Keynote: Mathias Frisch (Universität Hannover) – Climate storylines and narrative understanding
11:00 - 11:15	Coffee Break
11:15 - 11:50	Micah Thomas & Edor J. Edor (University of Bucharest / University of Calabar, Nigeria) – Bayesian inference: A response or counter-method to traditional probabilistic reasoning in climate science?
11:50 - 11:55	Break
11:55 - 12:30	Futura Venuto (University of Bern) – Going Beyond the Epistemic Limits of Climate Models
12:30 - 12:35	Break
12:35 - 13:10	Laura García-Portela (Erasmus University Rotterdam) – An adequacy-for-purpose view on attribution studies
13:10 - 14:25	Lunch Break
14:25 - 15:25	Keynote: Stephen John (University of Cambridge) – What, if anything, do the public need to understand about climate science?
15:25 - 15:40	Coffee Break
15:40 - 16:15	Elliott Woodhouse (International Institute for Applied Systems Analysis Vienna, Austria) – Normative and non-normative approaches to justice in the development of targeted scenarios for climate modelling
16:15 - 16:20	Break
16:20 - 16:55	Nathalie Kiepe (Università della Svizzera Italiana) – Climate Science and Arts for Better Understanding

Abstracts

Vincent Lam (University of Bern): “Climate extremes, physical storylines and process understanding”

Physical climate storylines provide a conceptual framework and a set of tools to address different issues in climate science, such as in the context of low-likelihood high impact outcomes, regional climate change information or extreme event attribution. Exploiting causal modelling methods, the storyline approach allows in particular for a better control of the different types of uncertainties involved in these contexts, as well as, crucially, a greater sensibility to the contextual aspects of the vulnerabilities in place. Much of the epistemic relevance and flexibility of the storyline approach relies on its explicitly causal perspective, and more specifically on what is generically called ‘process understanding’ in the climate science literature. However, depending on the situation, the notion of process understanding refers to different causal features. This talk aims to disambiguate the different causal meanings and roles at work in the physical climate storyline approach. We highlight three interrelated elements at the heart of the causal reasoning underlying the epistemic foundations of physical climate storylines. First, theoretical knowledge about the nature of the various causal factors (‘drivers’) plays a central role in the elaboration of the relevant causal models––in particular the distinction between dynamical and thermodynamical factors. Second, the conditional claims characteristic of the storyline approach crucially exploit various types of counterfactual dependencies. Third, physical climate storylines build on the capacity of certain (high-resolution) climate models to adequately represent the quantitative relationships among the relevant variables (that is, ‘resolving’ the relevant physical processes). Taken together, these three elements characterize the storyline approach in climate science, in particular at the regional and local scales (with specific salience for extreme events). Indeed, we argue that it is the adequate articulation of these causal elements that provides physical climate storylines the epistemic ground to “explore the boundaries of plausibility”, which is at the heart of the storyline focus on (fair and robust) climate change risk assessment and decision-making.

Stephen John (University of Cambridge): “What, if anything, do the public need to understand about climate science?”

One key question in philosophy of climate science concerns the epistemic relevance (if any) of climate scientists' understanding of the climate to their inferential and predictive practices. This paper takes up a related question about the public. Educating the public about climate change seems a necessary step for preventing or mitigating climate catastrophe. What, precisely, though, is it that we need the public to know or to understand? This paper addresses various possible answers to this question. The first part argues that our focus should be less on ensuring that the public understands the phenomenon of climate change and more on ensuring that they understand the social and epistemic processes which warrant climate scientists' claims and predictions. The second part sets out some reasons to think that, given the differences between climate science and more familiar epistemic practices, generating such understanding may be a complex and difficult task. In conclusion, I suggest some general lessons for communicating climate science, arguing that this is a case where inculcating true belief may be far more sensible than aiming at understanding.

Julie Jebeile (University of Bern): “Model spread and progress in climate modelling”

Model spread, which is the spread of ensemble-based models projections, is used as a quantification of model uncertainty in climate science. Reduction of model spread is often used as an indication for progress. In this talk, I will offer an interpretation of model spread and will argue that reduction of model spread is of lower priority than model independence (following Jebeile and Barberousse 2021).

Alice Wheatley (University of East Anglia): “Understanding-Oriented Epistemology for the Environment”

We face significant epistemic difficulties in our attempts to understand the natural environment and current environmental challenges (e.g. the changing climate) and, as some have already pointed out, epistemology should have something to say about this (Kawall, 2009; Levy and Brownstein, 2021). Although epistemology has traditionally focused on knowledge and true belief, I provide two reasons that an adequate epistemology for the environment should instead turn its attention to understanding as a distinct epistemic state.

Francesco Nappo (Politecnico di Milano): “Designing Climate Change Scenario Ensembles”

In this paper, we first review the procedure for design and assessment of emission scenarios in the latest IPCC report and the current uses of the ensemble results in WGIII. We stress that philosophical coverage so far has been limited to scenario ensembles in the physical climate science within WGI, which are different in both scope and number (Parker 2010; Jebeile and Crucifix 2020). Having noted the distinct features of the emission scenario ensemble, we then argue that WGIII should move past the unstructured ensemble approach currently in use and towards a structured ensemble of emission scenarios for the next cycle of assessments.

Johannes Nyström (Stockholm University): “Model-based robustness analysis as meta-empirical confirmation”

Model-based robustness analysis is the practice of assessing the degree to which a set of different scientific models of the same target system congregate at a common conclusion. In climate science, it is one of several strategies employed to increase trust in the core conclusions of climate models. However, the epistemic basis of the strategy is contested in philosophical literature. The situation underwrites what Harris and Frigg (2023) call the ‘justificatory challenge’ for model-based robustness analysis. This paper describes a new conceptual and formal framework for modelbased
robustness analysis, based on meta-empirical confirmation (Dawid 2013, 2015), which effectively responds to the challenge.

Micah Thomas & Edor J. Edor (University of Bucharest / University of Calabar, Nigeria): “Bayesian inference: A response or counter-method to traditional probabilistic reasoning in climate science?”

The complexity and uncertainty inherent in climate science often challenge the efficacy of traditional probabilistic reasoning methods. In this talk, we particularly analyse how probabilistic reasoning has historically relied on frequentist approaches, which assume a fixed parameter structure and struggle with the dynamic nature of climate systems. We maintain that Bayesian inference, in contrast, offers a framework that incorporates prior knowledge and updates predictions as new data becomes available, potentially addressing some of the limitations of traditional methods. Specifically, Bayesian inference allows for the integration of prior distributions with observed data to continually refine predictions and uncertainty estimates. We argue that this approach is particularly useful in climate science, where data is often incomplete and models must adapt to evolving understandings of climate processes.

Futura Venuto (University of Bern): “Going Beyond the Epistemic Limits of Climate Models”

Climate models are proven to be inaccurate: not only are the variables involved usually non-linearly interdependent, but also many processes and factors are underdetermined or even unknown. Such limits lead to an epistemic discrepancy between the model output and the real-world observation. My interest lies in the tension between the accuracy and the usability of a climate model, aiming to answer the question of their value for policy.

Laura García-Portela (Erasmus University Rotterdam): “An adequacy-for-purpose view on attribution studies”

We know that anthropogenic greenhouse gas emissions and other human activities are a major forcing of recent climatic changes. We are less certain about the link between particular extreme weather events (EWEs) and anthropogenic forcing since EWEs would occur even in a preindustrial climate (IPCC 2021). Scientists have contributed to these pressing social demands by proposing different attribution methodologies to establish a link between EWEs and ACC. The first methodology to emerge was the probabilistic approach – also known as risk-based approach, probabilistic event attribution or just PEA (Allen 2003; Stott et al. 2013; Stott et al. 2016; Otto et al. 2017). Later, another group of scientists offered an alternative based on conditional attribution, which has become known as the storyline approach (Shepherd 2016; Shepherd et al. 2018). The presence of these two approaches has raised the question of which of them should be used for legal and policy purposes, including compensation for the negative effects of climate change. In this paper, I argue, using an adequacy-for-purpose view, that PEA seems to be more adequate for the purpose of compensating for the negative effects of climate change.

Elliott Woodhouse (International Institute for Applied Systems Analysis Vienna, Austria): “Normative and non-normative approaches to justice in the development of targeted scenarios for climate modelling”

Climate change and the process of decarbonization raise a number of important and well discussed justice questions, that it is necessary for researchers, modelers, and policy makers to address. Moreover, it is increasingly recognized that a failure to address questions of justice is an impediment to creating effective climate policy (Martin et al., 2020; Thaller et al., 2023). Within this context, it is imperative that climate and ecosystem services modelers find ways to incorporate justice considerations into their work.

Nathalie Kiepe (Università della Svizzera Italiana): “Climate Science and Arts for Better Understanding”

Understanding and assessing climate change requires extensive training as it requires dealing with several complex ideas, such as the notions of uncertainty and of the climate system, that are not available and – not yet – part of common pop culture like other areas might have permeated through their representation in media, such as The Big Bang Theory and its impact on a pop culture understanding of physics. I would argue that, here, what is required to bridge that gap between the scientist and the layperson is extensive vulgarization work that makes the climate findings accessible and understandable to the broadest audience possible, as well as cooperation between two areas that traditionally have been seen as distinct – arts and science.

Organizer

Gabriel Tarziu (MCMP, LMU)

Registration

Please register by sending an email to Gabriel.Tarziu@lmu.de by 07.10.2024.

Venue

Carl Friedrich von Siemens Stiftung, Südliches Schlossrondell 23, 80638 Munich

Acknowledgement

This workshop has been supported by funding from the European Union's Horizon Europe research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 101067782. Additionally, we gratefully acknowledge the generous funding provided by the Carl Friedrich von Siemens Foundation.