Projects

Is More Always Better? The Future of Climate Change Science (SNSF, 2026-2029)

Social tipping points and the climate challenge: interdisciplinary perspectives (OCCR, 2023-2026)

The epistemology of climate change (SNSF, 2019-2025)

Is More Always Better? The Future of Climate Change Science (SNSF, 2026-2029)

Collaborators: Claus Beisbart, Vincent Lam, Anni Bukowski (PhD student)

Climate science and climate modelling are central to understanding and addressing the climate challenge. On a global level, they have been highly successful: attribution claims about the anthropogenic cause of global warming or claims about global long-term trends for climate variables such as temperature are extremely robust. However, essential regional projections for certain climate variables remain highly uncertain because of fundamental inadequacies and biases in the models. At the same time, in the face of the increasingly devastating impacts of climate change, there is a pressing need for climate change information at the regional and local scales, particularly in view of adaptation. Accordingly, a strong push exists to develop ever more complex, higher resolution climate models, exploiting ever-increasing computational power and ever more sophisticated data-driven machine learning tools. However, this ‘More is Better’ (MIB) strategy raises fundamental epistemic questions: How far can high-resolution modelling and data-driven techniques be pushed? How can their uncertainties be handled appropriately? And which types of information and models are most relevant for addressing the climate challenge? By addressing these issues, this project aims to provide a systematic picture of the epistemic foundations of climate science and climate modelling, including their value-laden and normative dimensions.

To achieve this purpose, the project is structured into two main parts. Part A will articulate the MIB strategy’s fundamental epistemic strengths and weaknesses in three main steps (A1-A3), taking state-of-the-art climate modelling initiatives implementing the MIB strategy as case studies. In the first step, the various purposes of the MIB strategy (such as understanding or decision-making relevance) will be disentangled, and their corresponding epistemic requirements and challenges will be identified (A1). The project will then address the extent to which these goals can be achieved, given the nature of the complex non-linear systems involved (A2) and the opacity of the deployed machine learning tools (A3).

Part B takes a more constructive stance on the MIB strategy. Its overarching question is how the MIB strategy could fruitfully be implemented in climate change science. Our fundamental premise is that every implementation of the MIB strategy is entangled with specific value-laden and normative dimensions. In a first step (B1), we will articulate these dimensions in detail and show how the planetary ambitions of the MIB strategy in climate modelling raise new challenges for the management of values in science. We’ll also assess existing proposals for value management in view of climate science and the MIB strategy. In a second step (B2), we will examine to what extent the novel approach to climate change information in terms of physical climate storylines can improve the integration of value-laden and normative dimensions while counteracting some of the epistemic issues of the MIB strategy.