Journal: WIREs Climate Change

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Wiley

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ISSN

1757-7780
1757-7799

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2012 - 201932020 - 20212
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Publications 1 - 5 of 5
  • Building confidence in climate model projections: an analysis of inferences from fit
    Item type: Journal Article
    Baumberger, Christoph; Knutti, Reto; Hirsch Hadorn, Gertrude (2017)
    WIREs Climate Change
    Climate model projections are used to inform policy decisions and constitute a major focus of climate research. Confidence in climate projections relies on the adequacy of climate models for those projections. The question of how to argue for the adequacy of models for climate projections has not gotten sufficient attention in the climate modeling community. The most common way to evaluate a climate model is to assess in a quantitative way degrees of ‘model fit’; that is, how well model results fit observation-based data (empirical accuracy) and agree with other models or model versions (robustness). However, such assessments are largely silent about what those degrees of fit imply for a model's adequacy for projecting future climate. We provide a conceptual framework for discussing the evaluation of the adequacy of models for climate projections. Drawing on literature from philosophy of science and climate science, we discuss the potential and limits of inferences from model fit. We suggest that support of a model by background knowledge is an additional consideration that can be appealed to in arguments for a model's adequacy for long-term projections, and that this should explicitly be spelled out. Empirical accuracy, robustness and support by background knowledge neither individually nor collectively constitute sufficient conditions in a strict sense for a model's adequacy for long-term projections. However, they provide reasons that can be strengthened by additional information and thus contribute to a complex non-deductive argument for the adequacy of a climate model or a family of models for long-term climate projections.
  • The effect of carbon pricing on technological change for full energy decarbonization: A review of empirical ex‐post evidence
    Item type: Review Article
    Lilliestam, Johan; Patt, Anthony; Bersalli, Germán (2021)
    WIREs Climate Change
    In order to achieve the temperature goals of the Paris Agreement, the world must reach net‐zero carbon emissions around mid‐century, which calls for an entirely new energy system. Carbon pricing, in the shape of taxes or emissions trading schemes, is often seen as the main, or only, necessary climate policy instrument, based on theoretical expectations that this would promote innovation and diffusion of the new technologies necessary for full decarbonization. Here, we review the empirical knowledge available in academic ex‐post analyses of the effectiveness of existing, comparatively high‐price carbon pricing schemes in the European Union, New Zealand, British Columbia, and the Nordic countries. Some articles find short‐term operational effects, especially fuel switching in existing assets, but no article finds mentionable effects on technological change. Critically, all articles examining the effects on zero‐carbon investment found that existing carbon pricing scheme have had no effect at all. We conclude that the effectiveness of carbon pricing in stimulating innovation and zero‐carbon investment remains a theoretical argument. So far, there is no empirical evidence of its effectiveness in promoting the technological change necessary for full decarbonization. This article is categorized under: Climate Economics > Economics of Mitigation
  • Understanding and assessing uncertainty of observational climate datasets for model evaluation using ensembles
    Item type: Review Article
    Zumwald, Marius; Knüsel, Benedikt; Baumberger, Christoph; et al. (2020)
    WIREs Climate Change
    In climate science, observational gridded climate datasets that are based on in situ measurements serve as evidence for scientific claims and they are used to both calibrate and evaluate models. However, datasets only represent selected aspects of the real world, so when they are used for a specific purpose they can be a source of uncertainty. Here, we present a framework for understanding this uncertainty of observational datasets which distinguishes three general sources of uncertainty: (1) uncertainty that arises during the generation of the dataset; (2) uncertainty due to biased samples; and (3) uncertainty that arises due to the choice of abstract properties, such as resolution and metric. Based on this framework, we identify four different types of dataset ensembles—parametric, structural, resampling, and property ensembles—as tools to understand and assess uncertainties arising from the use of datasets for a specific purpose. We advocate for a more systematic generation of dataset ensembles by using these sorts of tools. Finally, we discuss the use of dataset ensembles in climate model evaluation. We argue that a more systematic understanding and assessment of dataset uncertainty is needed to allow for a more reliable uncertainty assessment in the context of model evaluation. The more systematic use of such a framework would be beneficial for both scientific reasoning and scientific policy advice based on climate datasets.
  • How strong is public support for unilateral climate policy and what drives it?
    Item type: Review Article
    McGrath, Liam F.; Bernauer, Thomas (2017)
    WIREs Climate Change
  • Assessment of black carbon radiative effects in climate models
    Item type: Journal Article
    Feichter, Johann; Stier, Philip (2012)
    WIREs Climate Change
Publications 1 - 5 of 5