Research Seminar Spring 2022

We are laymen on most of the knowledge we claim to possess. Today’s science is too vast for a single individual to master it by himself. We say we know about the mechanism of climate change because of what we heard from experts of those fields. On such topics, our claimed knowledge relies much more on the confidence we have in these experts, than on the evidence we can directly access.

I explore the consequences of this epistemological dependence using game-theory to study expert-based climate knowledge. I consider a game of communication between a scientific authority and multiple greenhouse gas (GHG) emitters. I derive a negative result: in the only Nash equilibrium of this game, because of the difference of interest between parties, no information transmission can take place. In other words, in this context, confidence is broken between laymen and the authority. Even if the authority reports the truth, emitters ignore her advice and act solely upon their prior belief.

The treatment of uncertainty in climate-change science is dominated by the far-reaching influence of the ‘frequentist’ tradition in statistics, which interprets uncertainty in terms of sampling statistics and emphasizes p-values and statistical significance. This is the normative standard in the journals where most climate-change science is published. Yet a sampling distribution is not always meaningful (there is only one planet Earth). Moreover, scientific statements about climate change are hypotheses, and the frequen- tist tradition has no way of expressing the uncertainty of a hypothesis. As a result, in climate-change science, there is generally a disconnect between physical reasoning and statistical practice. This paper explores how the frequentist statistical methods used in climate-change science can be embedded within the more general framework of prob- ability theory, which is based on very simple logical principles. In this way, the physical reasoning represented in scientific hypotheses, which underpins climate-change science, can be brought into statistical practice in a transparent and logically rigorous way. The principles are illustrated through three examples of controversial scientific topics: the alleged global warming hiatus, Arctic-midlatitude linkages, and extreme event attribu- tion. These examples show how the principles can be applied, in order to develop better scientific practice.

An important purpose of physical climate science research is to accommodate the needs and lessons of societal risk assessments (Sutton 2019). The Physical Climate Storyline (PCS) approach can address some of these needs and lessons, as this approach borrows key insights from scenario (see e.g. Berkhout et al 2014) and narrative (see e.g. Coulter et al 2019) thinking and develops them in the context of climate change (e.g. see Hazeleger et al 2015, Shepherd 2016, Shepherd et al. 2018, Dessai et al. 2018, Shepherd 2019, Jack et al. (2021), Sillmann et al. 2021).

The PCS approach is increasingly recognized by the research community as a tool to produce and communicate decision relevant climate risk information. For example, the IPCC AR6 relies on the definition provided in Shepherd et al. (2018) to characterize PCS as “self-consistent and possible unfolding of a physical trajectory of the climate system, or a weather or climate event, on timescales from hours to multiple decades” (WG1, 1.4.3.4). In this same report, Box 10.20 mentions PCS as tools for different purposes, e.g. for the exploration of low likelihood, high impact events (IPCC, WG1, §4.8), of cross-scale interactions for the purpose of informing adaptation (§10.3.4.2), as a particular approach including pseudo-global warming studies (§10.3.2.2), as an alternative approach to event attribution studies (§11.2.4), as information distillation exercises (§10.5.3), or as providing climate information that is integrated with socio-economic information and delivered in the form of narratives through climate services (Cross-Chapter Box 12.2).

While PCS is generally understood as a single approach, different methodologies are applied associated with the aims and purposes of the approach. To unpack this diversity of detail, this article gives an overview of the key methodological practices and assumptions of the PCS approach (the “logics”). We analyze physical climate storylines as both a product and a process (for a similar analysis of scientific practice in the context of climate research see Hulme and Dessai 2008) and recognize that (i) those who build the storylines and (ii) the purpose for which these storylines are built influence key features of the methodology (process) and hence key features of the storyline (product).

We will start our analysis from the literature in the IPCC AR6 WG1 that is classified as producing physical climate storylines (PCS). Selecting papers from IPCC assumes that the IPCC provides a comprehensive assessment of the state-of-the-art in climate change research, where climate scientists can individuate a methodology as being part of a more general approach. Moreover, inclusion in an IPCC report implies a certain level of acceptance by the scientific community, far more so than individual journal publications. The heterogeneity of the storyline approach (and the fact that many papers follow the storyline “logic” without explicitly identifying the underlying rationale as a “storyline”) motivates this choice and substantiates the assumption that the IPCC can provide a better overview than a systematic literature search. Consequently, this article provides a critical overview of key sub-methodologies associated with the concept of physical climate storylines rather than an overview of the literature at large. Compared to Shepherd et al. (2018), which was the first systematic synthesis of the PCS approach in the literature, we can provide a more extensive overview of the various PCS methodologies and their acceptance by the wider community, viewed through the lens of the IPCC AR6 WG1 report.

After a short introduction that puts PCS in a broader context of generating decision relevant information about future climate, we describe key characteristics of different approaches to developing PCS and organize the literature according to (1) key methodological choices (i.e. how the unfolding of the physical trajectory is constructed) found in the individual papers, (2) the way that the temporal element is included in the construction of PCS and (3) who builds these storylines for whom. We then discuss in more detail how value judgements (see, e.g. Intemann 2015, Pulkkinen et al. 2022) are incorporated into PCS. We conclude the article by suggesting where the PCS approach can improve and further mature.