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Damage functions in integrated assessment models (IAMs) map changes inclimate to economic impacts and form the basis for most of estimates of thesocial cost of carbon. Implicit in these functions lies an unwarrantedassumption that restricts the spatial variation (Svar) and temporal variability(Tvar) of changes in climate to be null. This could bias damage estimates andthe climate policy advice from IAMs. While the effects of Tvar have beenstudied in the literature, those of Svar and their interactions with Tvar havenot. Here we present estimates of the economic costs of climate change thataccount for both Tvar and Svar, as well as for the seasonality of damagesacross sectors. Contrary to the results of recent studies which show littleeffect that of Tvar on expected losses, we reveal that ignoring Svar produceslarge downward biases, as warming is highly heterogeneous over space. Using aconservative calibration for the damage function, we show that previousestimates are biased downwards by about 23-36%, which represents additionallosses of about US$1,400-US$2,300 billion by 2050 and US$17-US$28 trillion bythe end of the century, under a high emissions scenario. The present value oflosses during the period 2020-2100 would be larger than reported in previousstudies by $47-$66 trillion or about 1/2 to 3/4 of annual global GDP in 2020.Our results imply that using global mean temperature change in IAMs as asummary measure of warming is not adequate for estimating the costs of climatechange. Instead, IAMs should include a more complete description of climateconditions.

Economic consequences of the spatial and temporal variability of climate change