The Role of Type and Source of Uncertainty on the Processing of Climate Models Projections.

Daniel M Benjamin,David V Budescu

doi:10.3389/fpsyg.2018.00403

Abstract

Scientists agree that the climate is changing due to human activities, but there is less agreement about the specific consequences and their timeline. Disagreement among climate projections is attributable to the complexity of climate models that differ in their structure, parameters, initial conditions, etc. We examine how different sources of uncertainty affect people’s interpretation of, and reaction to, information about climate change by presenting participants forecasts from multiple experts. Participants viewed three types of sets of sea-level rise projections: (1) precise, but conflicting; (2) imprecise, but agreeing, and (3) hybrid that were both conflicting and imprecise. They estimated the most likely sea-level rise, provided a range of possible values and rated the sets on several features – ambiguity, credibility, completeness, etc. In Study 1, everyone saw the same hybrid set. We found that participants were sensitive to uncertainty between sources, but not to uncertainty about which model was used. The impacts of conflict and imprecision were combined for estimation tasks and compromised for feature ratings. Estimates were closer to the experts’ original projections, and sets were rated more favorably under imprecision. Estimates were least consistent with (narrower than) the experts in the hybrid condition, but participants rated the conflicting set least favorably. In Study 2, we investigated the hybrid case in more detail by creating several distinct interval sets that combine conflict and imprecision. Two factors drive perceptual differences: overlap – the structure of the forecast set (whether intersecting, nested, tangent, or disjoint) – and asymmetry – the balance of the set. Estimates were primarily driven by asymmetry, and preferences were primarily driven by overlap. Asymmetric sets were least consistent with the experts: estimated ranges were narrower, and estimates of the most likely value were shifted further below the set mean. Intersecting and nested sets were rated similarly to imprecision, and ratings of disjoint and tangent sets were rated like conflict. Our goal was to determine which underlying factors of information sets drive perceptions of uncertainty in consistent, predictable ways. The two studies lead us to conclude that perceptions of agreement require intersection and balance, and overly precise forecasts lead to greater perceptions of disagreement and a greater likelihood of the public discrediting and misinterpreting information.

Highlights

IntroductionClimate forecasts are riddled with uncertainty because climate models involve uncertainties around the model’s structure, the measurement of initial conditions, the parameters of the key variables (e.g., future radiative forcing, population growth, economic activity), and the relationship between these variables
Climate forecasts are riddled with uncertainty because climate models involve uncertainties around the model’s structure, the measurement of initial conditions, the parameters of the key variables, and the relationship between these variables
We observed the greatest reduction in range in the hybrid condition – the median range for hybrid sources is reduced by about one third – suggesting that the distinct effects of imprecision and conflict are cumulative

Summary

Introduction

Climate forecasts are riddled with uncertainty because climate models involve uncertainties around the model’s structure, the measurement of initial conditions, the parameters of the key variables (e.g., future radiative forcing, population growth, economic activity), and the relationship between these variables. Because of the interactions between these uncertainties, models are typically run multiple times with different initial conditions and parameterizations, generating a spectrum of predictions to properly capture the deep uncertainties that drive the phenomena The communication of such deep uncertainty is crucial to allow decision-makers (DMs) to make choices based on an accurate understanding of the state-of-the-art science and strength of the evidence (e.g., Drouet et al, 2015). Coastal Resources Commission’s Science Panel on Coastal Hazards, 2010) projected a 39-inch rise in sea-level (ranging from 15 to 55 inches) in the Outer Banks by 2100 In response to this overly precise, long term projection, local conservative groups, worried about the economic devastation associated with this projection, launched an effective campaign against policy initiatives. The local government subsequently banned policy addressing these sea level projections suggesting much valuable real estate would be under water (Siceloff, 2014, News and Observer)

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