The Missing Risks of Climate Change

Abstract
The risks of climate change are enormous, threatening the lives and livelihoods of millions to billions of people. The economic consequences of many of the complex risks associated with climate change cannot, however, currently be quantified. Here we argue that these unquantified, poorly understood and often deeply uncertain risks can and should be included in economic evaluations and decision-making processes. We present an overview of these unquantified risks and an ontology of them founded on the reasons behind their lack of robust evaluation. These consist of risks missing owing to delays in sharing knowledge and expertise across disciplines, spatial and temporal variations of climate impacts, feedbacks and interactions between risks, deep uncertainty in our knowledge, and currently unidentified risks. We highlight collaboration needs within and between the natural and social science communities to address these gaps. We also provide an approach for integrating assessments or speculations of these risks in a way that accounts for interdependencies, avoids double counting and makes assumptions clear. Multiple paths exist for engaging with these missing risks, with both model-based quantification and non-model-based qualitative assessments playing crucial roles. A wide range of climate impacts are understudied or challenging to quantify, and are missing from current evaluations of the climate risks to lives and livelihoods. Strong interdisciplinary collaboration and deeper engagement with uncertainty is needed to properly inform policymakers and the public about climate risks.

There is overwhelming evidence that the risks and impacts from increasing concentrations of greenhouse gases in the atmosphere are very significant, will impact nearly every aspect of human life and the environment, and could ultimately prove to be devastating. An apparent incongruity exists between the pervasiveness of anticipated physical changes and the relatively modest total losses often estimated in economic evaluations1,2. Part of the explanation for this mismatch comes from ‘missing risks’: the risks that are not currently included in economic evaluations because of their uncertainty, because of our limited understanding of them or because existing economic models do not capture them in sufficient detail.

The interplay within and between different physical and social systems plays a crucial role in defining when and where impacts will manifest themselves, and these interactions are often only poorly understood. This leads to large and growing uncertainty estimates and a wide range of incompletely understood and underestimated risks3. For example, the potential for climate change impacts to drive social discontent, dislocation and relocation, and instability and conflict, are all deeply uncertain, but potentially crippling.

Excluding these risks from economic assessments is equivalent to placing a probability of zero on their occurrence. This, clearly, is not the case. Similarly, the common practice of engaging with only the expected levels of impacts and reporting central confidence bounds can undermine the ability of decision-makers to engage with the actual range of risks. The overall consequence is an underestimation of the total risks of climate change. This Perspective aims to identify, classify and suggest ways to engage with some of the most significant risks that are not currently captured by socioeconomic evaluations of climate change, from both a natural perspective and a social perspective. As an example of how this can be achieved, we present a demonstration of how diverse impact estimates or assumptions can be coherently combined.

Background
The complexity of feedback systems has slowed the process of both understanding them and modelling them. Compound, sequential, and concurrent extremes would lead to lower thresholds (for a single driver) for substantial impacts as well as deeper impacts when two drivers align53. The overall lack of representation for this type of secondary effect leads to an underestimation of risk.