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Climate Model Confirmation: From Philosophy to Predicting Climate in the Real World

In Elisabeth A. Lloyd & Eric Winsberg (eds.), Climate Modelling: Philosophical and Conceptual Issues. Springer Verlag. pp. 325-359 (2018)

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Diagnosing errors in climate model intercomparisons.Ryan O’Loughlin - 2023 - European Journal for Philosophy of Science 13 (2):1-29.details I examine error diagnosis (model-model disagreement) in climate model intercomparisons including its difficulties, fruitful examples, and prospects for streamlining error diagnosis. I suggest that features of climate model intercomparisons pose a more significant challenge for error diagnosis than do features of individual model construction and complexity. Such features of intercomparisons include, e.g., the number of models involved, how models from different institutions interrelate, and what scientists know about each model. By considering numerous examples in the climate modeling literature, I distill (...) Download Export citation Bookmark 1 citation
Climate modelling and structural stability.Vincent Lam - 2021 - European Journal for Philosophy of Science 11 (4):1-14.details Climate modelling plays a crucial role for understanding and addressing the climate challenge, in terms of both mitigation and adaptation. It is therefore of central importance to understand to what extent climate models are adequate for relevant purposes, such as providing certain kinds of climate change projections in view of decision-making. In this perspective, the issue of the stability of climate models under small relevant perturbations in their structure seems particularly important. Within this framework, a debate has emerged in the (...) Download Export citation Bookmark 1 citation
Confirming (climate) change: a dynamical account of model evaluation.Suzanne Kawamleh - 2022 - Synthese 200 (2):1-26.details Philosophers of science have offered various accounts of climate model evaluation which have largely centered on model-fit assessment. However, despite the wide-spread prevalence of process-based evaluation in climate science practice, this sort of model evaluation has been undertheorized by philosophers of science. In this paper, I aim to expand this narrow philosophical view of climate model evaluation by providing a philosophical account of process evaluation that is rooted in a close examination of scientific practice. I propose dynamical adequacy as a (...) Download Export citation Bookmark 2 citations
Understanding climate change with statistical downscaling and machine learning.Julie Jebeile, Vincent Lam & Tim Räz - 2020 - Synthese (1-2):1-21.details Machine learning methods have recently created high expectations in the climate modelling context in view of addressing climate change, but they are often considered as non-physics-based ‘black boxes’ that may not provide any understanding. However, in many ways, understanding seems indispensable to appropriately evaluate climate models and to build confidence in climate projections. Relying on two case studies, we compare how machine learning and standard statistical techniques affect our ability to understand the climate system. For that purpose, we put five (...) Download Export citation Bookmark 6 citations
Conceptualizing uncertainty: the IPCC, model robustness and the weight of evidence.Margherita Harris - 2021 - Dissertation, London School of Economicsdetails The aim of this thesis is to improve our understanding of how to assess and communicate uncertainty in areas of research deeply afflicted by it, the assessment and communication of which are made more fraught still by the studies’ immediate policy implications. The IPCC is my case study throughout the thesis, which consists of three parts. In Part 1, I offer a thorough diagnosis of conceptual problems faced by the IPCC uncertainty framework. The main problem I discuss is the persistent (...) Download Export citation Bookmark

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