There are two main ways of obtaining top-down global estimates of the costs of climate change. The first relies on integrated assessment models (IAMs) that incorporate a simplified model of the macroeconomy together with a climate model and a hypothesized link between the two (typically based only on the impact of temperature on economic production). The three most notable IAMs are DICE, FUND, and PAGE. The other alternative is to rely on macroeconometric estimates that are based on the past, typically annual, national level data on production (GDP) and average temperature (e.g., (Burke et al., 2015)).

For example, the DICE model (Nordhaus and Boyer, 1999) estimates a climate change damage function based on the equation D = φ∆T2, where D is climate change-induced damages to the global economy in percent and ∆T is the change in global mean surface temperature (we assume ∆T 2019 = 1.0 K). The coefficient φ was calculated based on the previous estimates from other studies and equals 0.00267. (The 2016 update has an even lower value for φ.) If the global GDP for 2017 equals US$79,845bn (from the World Bank’s World Development Indicators), then the DICE equation, which calculates the cost as a percent of global GDP, suggests that the annual global climate change damages ought to be of the order of US$213bn. Furthermore, a recent study of the domestic social cost of carbon in the USA estimated that roughly 10% of global damages occur within the USA (Office of Air Quality Planning and Standards, 2017) (EPA document, p162), implying that the total US damages from climate change should currently be around US$21.3bn. This DICE-based estimate is less than our likely lower bound of US$30bn for the direct climate change-attributable costs of rainfall from Harvey alone, and it is much lower than our main estimate of US$67bn. The study (Office of Air Quality Planning and Standards, 2017) of the domestic social cost of carbon also used two other IAMs, PAGE and FUND. The central estimates of climate damages of both of these models are lower than that of DICE, so the comparison we make between our bottom-up estimate of attributable direct costs compared with the estimated damages apply just as much to these other models, too.

The estimates for current climate change impacts for the USA from a recent macroeconometric study (Burke et al., 2015) are similarly low. While the paper does not provide country-specific estimates, they describe a general non-linear effect of temperature on the output. They find that the output peaks at about 13 °C and declines thereafter. Since the US annual average temperature is still below that threshold, and was below it in 2017, their modeling estimates a current benign impact of climate change on the US economy. More recent unpublished work, using regional rather than national-level data, suggests current adverse impacts for the USA, but as yet quite small (Kalkuhl and Wenz, 2018). It is noteworthy, however, to point out that their macroeconometric estimates ultimately predict future losses, for the year 2100, that are much larger than the future losses predicted by the leading IAMs.

In any case, our “bottom-up” estimate of the anthropogenic component of this very damaging single event, the precipitation associated with Hurricane Harvey, is far in excess of the “top-down” macro estimate arrived at through the application of a simple damage function in integrated assessment models (IAMs) such as DICE or those obtained from macroeconometric estimates of historical data. That a single event can do direct damages in excess of three times the modeled value of all annual loss in the USA as predicted by DICE is striking. There may be several elements driving this result.

The best estimate of FAR (0.75) implies that an event similar to the rainfall associated with Harvey has become far more likely than an event of the same magnitude would have been in the preindustrial world. This kind of event is still very rare, so it may be possible that the average annual losses for the USA are closer to the DICE-based estimate than to the figure obtained in this study for one rare event. However, Hurricane Harvey was the only one event in 2017. The inclusion of the two other destructive hurricanes that made landfall (Irma and Maria; together, they caused more damage and took far more lives than Harvey) and the numerous other weather events that impacted the USA that year raise the NOAA estimate of US weather-related losses to over $300Bn, a new record. EMDAT lists 24 weather-related disasters in 2017 in the USA, and each of them may be associated with a FAR > 0, but it is important to note that climate change could have also made some catastrophic events less likely (FAR < 0). Ultimately, to obtain a full bottom-up estimate of the impact of climate change, one that could be directly comparable with the top-down estimates from the IAMs or the macroeconometric estimates, one would have to obtain economic damage assessments of all types of possible extreme weather events.

Damages functions are of course very difficult to estimate–they have been called “a notoriously weak link in the economics of climate change” (Weitzman, 2012) on the basis of deep structural uncertainty in their underlying functional form. It is quite likely that the simple approach used to characterize damages in IAMs ignores important dimensions of climate change. For instance, the functional form of climate damages is usually taken to be some function of the temperature anomalies, as is the case in the DICE example above, while losses from Harvey had very little to do with the direct effect of temperature anomalies and more to do with increased available moisture and storm structural changes leading to super Clausius-Clapeyron scaling of extreme precipitation (Patricola and Wehner, 2018). In other words, some aspects of climate change that are highly relevant to economic losses from climate change may be changing faster, and with a different pattern, than IAM-based damage functions suggest or can account for.

In summary, this study reveals that a bottom-up approach to a single event based on the FAR method and a top-down model-based approach such as DICE, PAGE, or FUND do not produce consistent estimates of the cost of climate change. There are important senses in which the two are not directly comparable: the IAM and macro-based approaches are aggregative, and in this study, we have focused on a single event. The FAR-based estimate is not a substitute or alternative for the total cost estimates. Nevertheless, the scale of the FAR-based estimate is striking and indicates that considerably more research is needed to reconcile top-down and bottom-up estimates of the costs of climate change. This need to quantify the costs has a particular urgency in light of recent decisions taken at the UN Climate Negotiations in Katowice, which opened a path for Parties to “provide, as appropriate, information related to enhancing understanding, action and support, on a cooperative and facilitative basis, to avert, minimize and address loss and damage associated with climate change impacts, taking into account projected changes in climate-related risks, vulnerabilities, adaptive capacities and exposure, including, as appropriate, on: (a) Observed and potential climate change impacts, including those related to extreme weather events and slow onset events, drawing upon the best available science.”(UNFCCC, 2018). We have shown that there is a gap between the tools we use to inform climate policy and the actual damages being done by climate change. Research to understand the nature of this gap, and then to close it, is urgently needed.