The missing risks of climate change

Here we distinguish between five categories of currently missing risks and suggest potential solutions on how to start integrating them into current and future studies. The categories below are based on the reasons behind their exclusions, and these reasons provide insight into how they can be engaged with in the near future.

Missing biophysical impacts

One group of missing risks arises from the calibration of the IAMs, which are often decades out of date²⁹. This is true of several risks now considered to have high probability at current and future levels of warming, such as the collapse of the Atlantic Meridional Overturning Circulation by 2300 (assessed as likely as not)³⁰ and abrupt permafrost melt by 2100 (assessed as high probability)³¹ (also see Supplementary Fig. 1). The pathway from improved understanding of a climate phenomenon to its valuation in economic models can be long. It often requires that the understanding of relevant climate drivers reaches a point where the science is available beyond the climate science community, for instance, through media such as IPCC reports. As part of this process. biophysical modelling is often required to translate climate risks into physical impacts; economists need to develop an understanding of the response of social systems to the physical impact, and a welfare valuation of these responses; and the risk then needs to be incorporated into IAMs, computable general equilibrium models or other comprehensive analyses. This requires close collaboration between multiple disciplines^32,33.

The physical impacts and population exposure for a large number of relevant risks have already been quantified (Supplementary Table 1). In some cases, a translation from impacts into welfare or monetary damages is readily available and these can be rapidly incorporated into evaluations. In other cases, credible valuations are unavailable (for example, biodiversity loss and natural disasters) or resilience and general equilibrium effects are first-order concerns (for example, water stress and migration). In this case, considerable work is needed to translate biophysical risks into economic ones. Examples of recent developments that are not captured in economic assessments include exposure of populations to natural disasters^34,35, the latest process-based impact-model intercomparisons across multiple sectors³⁶, and new statistical models of health, productivity, agriculture and energy³⁷. These impact estimates represent substantial developments beyond existing representations of these risks in the IAMs^38,39.

There are several possible causes for this gap, including: the disagreements within the impact community over the scale of impacts; a culture in economics that does not encourage large-team collaboration; and, to some extent, limited funding available for economic model development. The process for including these risks in the near future must confront multiple challenges. Economic damage assessments need damage functions that reflect the widest possible range of credible responses: recent advances in empirical damage estimates³⁷ go in the right direction but face the challenges of both connecting short-term weather-related impacts to long-term climate ones, and incorporating the endogeneity of adaptation. One approach to this problem is being pioneered at the Climate Impact Lab, and tries to address both problems. To account for adaptation, they use observed variation in temperature sensitivity⁴⁰. To support incorporating these results into economic models as functions of climate rather than weather, they estimate impacts under downscaled projected weather and then index these uncertain impacts to expected climate, which allows them to be emulated in models that do not have daily weather or disaggregated sectors⁴¹. Parallel work at the Potsdam Institute for Climate Impact Research develops channel-specific damage functions using process models for use in economic models (for example, ref. ⁴²). However, integration of this work into economic analyses requires that issues of valuation, equilibrium adjustments and double counting are resolved, which requires an interdisciplinary approach⁴³.

The ability to incorporate many risks into economic evaluations is being undermined by difficulties in bridging the climate science, economics and modelling cultures. Examples include climate tipping points, conflict and migration, and topics from climate justice. Natural scientists and economic modellers struggle to find a common language to discuss the possible consequences of climate change. Bridging these gaps requires the repeated, collaboration-focused convening of researchers engaged in all aspects of the problem.

Spatial and temporal extremes

The spatial and demographic variations in impacts has emerged as one of the central features of economic damages: poor and socioeconomically vulnerable groups in many regions are the most exposed to risks^5,43. IAMs often represent the world in highly aggregated terms, describing only global results (for example, the DICE model⁴⁴) or across multi-national regions (for example, PAGE¹⁴, FUND⁴⁵ and RICE⁴⁶) and for representative agents. Although these variations can be parameterized in damage functions⁴⁷ or elasticity parameters⁴⁸, doing so hides the underlying source and consequences of climate risk.

Temporal extremes are also likely to play a significant role. Although impacts of climate change result from the long-term evolution of temperature changes and sea-level rise, many will manifest as extreme shocks: heatwaves, storms and droughts. While projections of many natural disasters are available^35,49, they are not represented in IAMs and reported metrics typically hide the role of variability⁴. See examples of risks arising from spatial and temporal extremes in Supplementary Section D.

It is a conceptual challenge to integrate the small spatial and temporal scales relevant for extreme events or the effects on different income groups and related distributional effects into the IAMs operating on large world regions and long timescales. Spatially detailed research requires simulations and data often available for only a few countries. Research examining the complexity of systems and potential impacts of climate change responses at scales ranging from individual households to national policy and global governance can help in this regard.

Traditionally, the highly aggregated approach of benefit–cost IAMs has supported their use in identifying climate policies that maximize global welfare, by relying on intertemporal optimization. Economic assessments of scenarios, however, do not require optimization, and higher-resolution economic risk assessments have been produced for the United States and Europe³³, the consequences of tipping points⁵⁰ and country-level-scale information using empirical damage estimates⁵¹. Improvements in stochastic optimization techniques also provide a pathway to increasing resolution while studying optimal mitigation⁵².

A way to better engage with these features is to improve how heterogeneity, variability and uncertainty are approached generally. We propose that there is an emerging way forwards for combining parameter, model and trajectory uncertainty, while considering model inadequacy, at high spatial and temporal resolution. First, impact models should be driven by downscaled inputs available at a monthly or higher frequency, over multi-decadal periods. This captures the interaction between the dynamic uncertainty represented by both natural variability of theclimate system and climate change. Parameter uncertainty within the impact models should be represented by probability distributions over parameter values, simulated using Monte Carlos across multiple downscaled GCMs and multiple impact models, ideally drawing from initial-condition ensembles.

It is in addition important to improve how uncertainty is communicated to policymakers. When presenting model-based information, we recommend separating variability from uncertainty, that is, the 1-in-100-chance outcome for an impact conditioned on a model, alongside how that number varies between models. Finally, model inadequacy needs to be stated clearly, and unmodelled risks represented (for example, with ember plots).

Feedback risks and interactions

Feedback processes are ubiquitous within and among the climate, environment and economic systems. Critical and sometimes overlooked risks arise from the complex interplay of climate change and variability, demographic shifts, economic insecurity and political processes (Supplementary Section E). Physical risks are not independent of each other and climate change can act as a catalyst and stressor that accelerates and exacerbates conditions leading to cascading effects in the climate system and societal tipping points (Fig. 2 and Supplementary Section F). Feedback processes are often the source of heavy-tailed distributions and are therefore closely linked to black-swan events (see ‘Deep uncertainty’). However, these interactions are often missing from analyses and thus represent a source of missing risks.

The red arrows show channels of interaction. Cascading tipping points refers to the increased probability of one tipping point because of the triggering of another⁷⁵. Cascading disasters can occur as natural disasters heighten the risk of other disasters (for example, droughts causing wildfire). With multiple stressors, as climate stresses proliferate, the resilience and adaptive capacity of populations can be sapped⁵³. As with the climate system, cascading social changes can emerge, such as migration increasing the risk of conflict⁵⁴. As populations adapt and develop, this will produce simultaneous exposure/sensitivity changes, which may increase risks (for example, if populations further concentrate on coasts or along rivers).

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.

There is a need for assessment and risk management frameworks that better incorporate uncertainty and complex, cascading risks, including systems approaches built on interacting sectors, actors, geophysical hazards, scenarios and storylines. Approaches that utilize agent-based modelling and computable general equilibrium models are now being developed, but more effort is needed to understand their potential contribution in a climate change context.

An important class of feedback risks is tipping points⁵⁴. Climate, ecological and social tipping points are transitory states of a feedback process beyond which a new basin of attraction will drive further system change, resulting in a qualitatively different and self-reinforcing regime. A wide variety of tipping points have been incorporated into analyses for individual papers, but representing the full collection has been a challenge⁵⁰.

One barrier to research on tipping points and climatic extremes being incorporated into economic evaluations is that they are not well represented in GCMs, and their associated downscaled products. Social scientists look to natural scientists to provide probabilities, time evolutions and gridded projections to support their work. This is not always possible. Ensuring that climate scientists provide results in a form that is both robustly justifiable and can be readily incorporated into economic analysis requires bringing together the two disciplines.

Deep uncertainty

Deep uncertainty describes processes for which robust probability distributions do not exist. For many impacts, one or more steps in the estimation of hazards, exposure, vulnerability and welfare suffer from deep uncertainty, in terms of, for instance, the extent of their impacts and their spatiotemporal probability or frequency (Supplementary Section G). In some cases, the appropriate metrics for quantification are unclear. Yet, they can (and should) still be factored into risk assessment and planning.

One class of impacts suffering from deep uncertainty is black-swan events, characterized by their extreme nature and long-lasting consequences⁵⁵. Statistically, black-swan events are outcomes from the tails of heavy-tailed distributions, which are common in natural and human systems^54,56,57,58. These events are difficult to predict, because they are so far outside of what we normally observe and often arise from interlinked instabilities. Because they depend on and trigger changes throughout their systems, each black-swan event can dramatically alter exposure to risks and force the need for developing new decision contexts. As advancing climate change places new stresses on climate and social systems, outcomes beyond the extremes observed within the historical record are increasingly possible. The high frequency of previously considered ‘highly improbable’ events requires their consideration in climate change evaluations. Some examples include technological breakthroughs (unforeseen dramatic efficiency gains, consequences of a new green revolution and so on); governance and geopolitical reorganization (conflict, trade blocs and so on); new climate regimes (unforeseen ocean circulation or ecosystem changes and so on); funding mechanisms (green development banks, subsidies to tip the balance towards renewables and so on); and disease outbreaks (coronavirus disease 2019, Ebola and so on).

Some of these deep uncertainties and black-swan events can be explored through scenarios. Scenarios as a combination of broad narratives and quantitative projections based on models have been employed in climate science in the past⁵⁹. It is important that climate narratives represent sequential and concurrent events across multiple regions and sectors of the global economy. The currently used Shared Socioeconomic Pathways (SSPs) cover a range of socioeconomic futures, but these scenarios do not necessarily capture disruptive deviations from the past⁶⁰. To truly assess deep uncertainty, the diversity and robustness of scenarios needs to receive more attention⁶¹. Computational techniques such as cross-impact balances can be used to systematically explore large numbers of scenarios and the coverage of scenarios space. Alternatively, the vulnerability of a (policy) strategy to disruptions can be studied. A number of projects have built on a storyline approach^{27,28,62,63,64}. Speculative storylines can begin an iterative process whereby global and regional modelling exercises and storyline refinements can offer insights.

It is noted that assessments of model uncertainty in multi-model intercomparisons and perturbed physics and parameter studies cannot provide robust probabilities owing to the shared features across models, their limited exploration of possibilities and the conceptual lack of any basis for defining the shape of ‘model space’ across which probabilities must be built⁷. Nevertheless, the uncertainty derived from such ensembles represents a starting point for consideration of deep uncertainty. Example applications include model evaluation with historical data and developing multi-sector, multi-model projections^65,66,67.

A similar process of reflection on deep uncertainties should be initiated with IAMs (and other models capturing impacts) and the economic damage integration process in general. Although IAMs have been intercompared in the past, a concerted intercomparison project would have a much broader focus on consideration of the implications of what is missing or inadequately incorporated at present.

Unidentified risks

Finally, it is appropriate to recognize a further set of risks completely unidentified in the academic literature. The coupled global environmental–human system can be disrupted in many ways that are unexpected or have not been studied. We take for granted many of the ways that the environment currently supports human needs, and not all of these functions are known, much less their sensitivity to climate change. Populations may respond to changes in their environments in unpredictable ways, driving social movements that take on a life of their own.

As these risks are fully unknown and unquantified, we cannot directly include them in valuations, but we can still factor unidentified risks into decision-making. Approaches exist for doing so. First, we could consider a precautionary principle, arguing that we might want to maintain the state with which we have long historical experience, even in the absence of clearly identified risks. The precautionary principle is already embedded in the Paris Agreement, and underlies the results of detailed-process IAMs, which identify cost-effective implementations of given mitigation scenarios⁶. We can understand the risks we face by comparing the future world to the range of conditions experienced across instrumental records (for example, see Fig. 3)⁶⁸. The precautionary principle would motivate pairing economic welfare calculations with planetary boundaries or other deviations from historical ranges⁶⁹.

a, Hazards that most exceed the distribution from recent (1980–2009) history, measured with a z-score from nine GCMs in WorldClim⁷⁶ in 2050 under SSP3-7.0, using high logged precipitation in the wettest month (labelled ‘Flood’), low logged annual precipitation (Drought), coefficient of variation of precipitation (Precipitation variation), minimum temperature of the coldest month (Chill) and maximum temperature of the warmest month (Heat). Significance is determined by bootstrapping the 95% confidence interval, and determined to be at a z-score of 0.98. b, The same as a, but showing the distribution of the z-scores across the global population.

Second, there are normative, ethical arguments to maintain the natural state of the planet, out of a rights-based demand to not subject people to undue risks, for example^70,71. The argument is that economic systems should conform to the values held by their stakeholders and that comprehensive economic evaluations should therefore account for infringements on the stated priorities of each community.

Third, there are results from complexity science that provide ways to monitor the fingerprints of risks, even if we do not know their nature⁷². These can provide early warning signals, and suggest improving resilience even without clear dangers in sight.