Spatiotemporal Scales in Modeling: Identifying Target Systems

My dissertation addresses neglected roles of idealization and abstraction in scientific modeling. Current debates about epistemic issues in modeling presuppose that a model in question uncontroversially represents a particular target system. A standard line of argument is that we can gain knowledge of a target system simply by noting what aspects of the target are veridically represented in the model. But this misses epistemically important aspects of modeling. I examine how scientists identify certain phenomena as target systems in their models. Building on the distinction between data and phenomena introduced by Bogen and Woodward, I analyze how scientists target systems from data and from basic theoretical principles. I show that there are two crucial empirical assumptions that are involved in identifying phenomena. These assumptions concern the conditions under which phenomena can be indexed to a particular length or time scale and the conditions under which one can treat phenomena occurring at different length or time scales as distinct. The role of these assumptions in modeling provides the basis for a new argument that shows how, in many cases, idealizations and abstractions in models are essential for providing knowledge about the world in so far as they isolate relevant components of a phenomenon from irrelevant ones. My analysis of the identification of phenomena also shows that structural uncertainty arises in models when the scale of a phenomenon of interest is not properly identified. This clarification promises to improve the communication of the limitation of current climate models to policy makers

For a Pluralism of Climate Modelling Strategies

The continued development of General Circulation Models (GCMs) towards increasing resolution and complexity is a predominantly chosen strategy to advance climate science, resulting in channelling of research and funding to meet this aspiration. Yet many other modelling strategies have also been developed and can be used to understand past and present climates, to project future climates and ultimately to support decision-making. We argue that a plurality of climate modelling strategies and an equitable distribution of funding among them would be an improvement on the current predominant strategy for informing policy making. To support our claim, we use a philosophy of science approach to compare increasing resolution and complexity of General Circulation Models with three alternate examples: the use of machine learning techniques, the physical climate storyline approach, and Earth System Models of Intermediate Complexity. We show that each of these strategies prioritises a particular set of methodological aims, among which are empirical agreement, realism, comprehensiveness, diversity of process representations, inclusion of the human dimension, reduction of computational expense, and intelligibility. Thus, each strategy may provide adequate information to support different specific kinds of research and decision questions. We conclude that, because climate decision-making consists of different kinds of questions, many modelling strategies are all potentially useful, and can be used in a complementary way

Assessing the quality of regional climate information

There are now a plethora of data, models, and approaches available to produce regional and local climate information intended to inform adaptation to a changing climate. There is, however, no framework to assess the quality of these data, models, and approaches that takes into account the issues that arise when this information is produced. An evaluation of the quality of regional climate information is a fundamental requirement for its appropriate application in societal decision-making. Here, an analytical framework is constructed for the quality assessment of science-based statements and estimates about future climate. This framework targets statements that project local and regional climate at decadal and longer time scales. After identifying the main issues with evaluating and presenting regional climate information, it is argued that it is helpful to consider the quality of statements about future climate in terms of 1) the type of evidence and 2) the relationship between the evidence and the statement. This distinction not only provides a more targeted framework for quality, but also shows how certain evidential standards can change as a function of the statement under consideration. The key dimensions to assess regional climate information quality are diversity, completeness, theory, adequacy for purpose, and transparency. This framework is exemplified using two research papers that provide regional climate information and the implications of the framework are explored

Assessing the quality of state-of-the-art regional climate information: the case of the UK Climate Projections 2018

In this paper, we assess the quality of state-of-the-art regional climate information intended to support climate adaptation decision-making. We use the UK Climate Projections 2018 as an example of such information. Their probabilistic, global, and regional land projections exemplify some of the key methodologies that are at the forefront of constructing regional climate information for decision support in adapting to a changing climate. We assess the quality of the evidence and the methodology used to support their statements about future regional climate along six quality dimensions: transparency; theory; independence, number, and comprehensiveness of evidence; and historical empirical adequacy. The assessment produced two major insights. First, a major issue that taints the quality of UKCP18 is the lack of transparency, which is particularly problematic since the information is directed towards non-expert users who would need to develop technical skills to evaluate the quality and epistemic reliability of this information. Second, the probabilistic projections are of lower quality than the global projections because the former lack both transparency and a theory underpinning the method used to produce quantified uncertainty estimates about future climate. The assessment also shows how different dimensions are satisfied depending on the evidence used, the methodology chosen to analyze the evidence, and the type of statements that are constructed in the different strands of UKCP18. This research highlights the importance of knowledge quality assessment of regional climate information that intends to support climate change adaptation decisions

Climate Impact Storylines for Assessing Socio-Economic Responses to Remote Events

Complex interactions involving climatic features, socio-economic vulnerability or responses, and long impact transmissions are associated with substantial uncertainty. Physical climate storylines are proposed as approach to explore complex impact transmission pathways and possible alternative unfolding of event cascades under future climate conditions. These storylines are particularly useful for climate risk assessment for complex domains, including event cascades crossing multiple disciplinary or geographical borders. For an effective role in climate risks assessments, practical guidelines are needed to consistently develop and interpret the storyline event analyses.This paper elaborates on the suitability of physical climate storyline approaches involving climate event induced shocks propagating into societal impacts. It proposes a set of common elements to construct the event storylines. In addition, criteria for their application for climate risk assessment are given, referring to the need for storylines to be physically plausible, relevant for the specific context, and risk-informative.Six examples of varying scope and complexity are presented, all involving the potential climate change impact on European socio-economic sectors induced by remote climate change features occurring far outside the geographical domain of the European mainland. The storyline examples illustrate the application of the proposed storyline components and evaluates the suitability criteria defined in this paper. It thereby contributes to the standardization of the design and application of event-based climate storyline approaches

Editorial: High-Quality Knowledge for Climate Adaptation: Revisiting Criteria of Credibility, Legitimacy, Salience, and Usability

Editorial on the Research Topic High-Quality Knowledge for Climate Adaptation: Revisiting Criteria of Credibility, Legitimacy, Salience, and Usability Climate adaptation in human systems is a process of learning and adjustment (IPCC, 2022). It involves continuously re-building a stock of knowledge, skills and foresight for anticipating, interpreting and acting relative to actual or expected climate. But what distinguishes knowledge of “high quality” for climate adaptation? This raises important ontological, epistemological and methodological questions, and at their core are the quality criteria people apply in appraising knowledge. Climate-adaptive knowledges have long been inherent to societies relationship to their environment, for example in cultural patterns of seasonal activities (Kwiecien et al., 2021). Over the past 20 years climate adaptation has become a topic of scientific enquiry across diverse disciplines, with efforts to fit that science to societal contexts and norms of quality for decision-making (see e.g., “climate services”; Hewitt et al., 2012). As such, societies have come to make sense of climatic change by juggling a repertoire of traditional, local, practical, scientific and technical knowledges—from proverbs to tailored forecasts—all assessed against different criteria of quality. Notwithstanding this plurality, certain principles have emerged in the scientific literature as fundamental to appraising knowledges' fitness for adaptive action. Specifically, the principles of credibility, legitimacy, and salience (Cash et al., 2003), as well as usability and usefulness (Lemos and Morehouse, 2005). These remain influential, but there is nuance to knowledge quality that broad principles miss. We argue for more critical studies of knowledge quality to uncover what principles mean in particular contexts, and what other criteria are appropriate. This special issue assembles nine articles from 37 authors, which take up the quality of adaptive knowledge as a topic. Three important themes emerge across these articles

Varieties of approaches to constructing Physical Climate Storylines: a review

The Physical Climate Storyline (PCS) approach is increasingly recognized by the physical climate research community as a tool to produce and communicate decision-relevant climate risk information. While PCS is generally understood as a single concept, different varieties of the approach are applied according to the aims and purposes of the PCS and the scientists that build them. To unpack this diversity of detail, this paper gives an overview of key practices and assumptions of the PCS approach as developed by physical climate scientists, as well as their ties to similar approaches developed by the broader climate risk and adaptation research community. We first examine varieties of PCS according to the length of the causal chain they explore, and the type of evidence used. We then describe how they incorporate counterfactual elements and the temporal perspective. Finally, we examine how value judgements are implicitly or explicitly included in the aims and construction of PCS. We conclude the discussion by suggesting that the PCS approach can further mature in the way it incorporates the narrative element, in the way it incorporates value judgments, and in the way that the evidence chosen to build PCS constrains what is considered plausible

Representing storylines with causal networks to support decision making: framework and example

Physical climate storylines, which are physically self-consistent unfoldings of events or pathways, have been powerful tools in understanding regional climate impacts. We show how embedding physical climate storylines into a causal network framework allows user value judgments to be incorporated into the storyline in the form of probabilistic Bayesian priors, and can support decision making through inspection of the causal network outputs. We exemplify this through a specific storyline, namely a storyline on the impacts of tropical cyclones on the European Union Solidarity Fund. We outline how the constructed causal network can incorporate value judgments, particularly the prospects on climate change and its impact on cyclone intensity increase, and on economic growth. We also explore how the causal network responds to policy options chosen by the user. The resulting output from the network leads to individualized policy recommendations, allowing the causal network to be used as a possible interface for policy exploration in stakeholder engagements

Representing storylines with causal networks to support decision making: framework and example

Physical climate storylines, which are physically self-consistent unfoldings of events or pathways, have been powerful tools in understanding regional climate impacts. We show how embedding physical climate storylines into a causal network framework allows user value judgments to be incorporated into the storyline in the form of probabilistic Bayesian priors, and can support decision making through inspection of the causal network outputs. We exemplify this through a specific storyline, namely a storyline on the impacts of tropical cyclones on the European Union Solidarity Fund. We outline how the constructed causal network can incorporate value judgments, particularly the prospects on climate change and its impact on cyclone intensity increase, and on economic growth. We also explore how the causal network responds to policy options chosen by the user. The resulting output from the network leads to individualized policy recommendations, allowing the causal network to be used as a possible interface for policy exploration in stakeholder engagements