Estimates of the economic costs of climate change rely on guesswork in the face of huge uncertainties, and arbitrary judgements about what is important. The models can produce any number their creators want them to; and typically, they trivialise the risks. Despite being described as ‘worse than useless’ by leading academics, economic analysis of this kind has been credited with a Nobel Prize, and it continues to inform government policy.

Estimates of the economic costs of climate change rely on guesswork in the face of huge uncertainties, and arbitrary judgements about what is important. The models can produce any number their creators want them to; and typically, they trivialise the risks. Despite being described as ‘worse than useless’ by leading academics, economic analysis of this kind has been credited with a Nobel Prize, and it continues to inform government policy.

To confront the climate crisis, we need to hedge our bets against the risk of climate change. We must be willing to spend some money now in order reap a larger benefit that will take many decades to deliver. This is similar to the philosophical concept of Pascal’s Wager, where one bets a finite resource for a potentially infinite reward. But estimating how much we should spend now is not easy; attempts to estimate this amount – the “social cost of carbon” – produce a wide range of numbers. We may need to accept that there is going to be radical uncertainty in any numerical estimate. Nevertheless, we can use climate models to quantify the risk of climate change as best we can, while taking into account the different types of uncertainties. Often, risk assessment requires numerical probabilities, but these are not always available for uncertainties associated with climate prediction.

Although climate change is a global issue, its impacts are experienced primarily at the local to regional scale. This chapter describes important aspects of regional climate and how climate projections can be used to assess climate impacts at the regional to local scale. It summarizes projections and sources of information on changes in continental-scale annual and seasonal temperature and precipitation, climate and weather extremes, and sea-level rise projections.

Dynamical downscaling uses high-resolution regional climate models (RCMs) to bias-correct and downscale global climate model output.This chapter discusses the models and methods used in dynamical downscaling. It provides an overview of the basic physics used in RCMs, and how this is similar to and differs from that used in global models. It also discusses the methods and metrics used to evaluate RCMs, and how projections from RCMs can be used to assess climate impacts at the regional scale