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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.
Geoengineering describes a range of technologies that attempt to mitigate the effects of global warming caused by increasing greenhouse gas concentrations. Some geoengineering approaches remove carbon dioxide from the atmosphere. These are not controversial, but they are currently too expensive to serve as a viable option. The most cost-effective technique, called solar radiation management, aims to reflect sunlight by continuously dumping large quantities of sulfate aerosols into the stratosphere, much as a volcanic eruption would. But geoengineering attempts to address the symptoms of the disease of global warming rather than the disease itself, which will persist as long as carbon emissions continue. Computer models of climate are essential to assess the efficacy of any geoengineering approach, because large-scale physical experimentation would be dangerous. However, the information that is most crucial for us to know – the impact geoengineering would have on regional climates – is something models have trouble predicting.
Global warming became a growing public concern following Jim Hansen’s US Senate testimony in 1988 asserting that the warming was happening. The Intergovernmental Panel on Climate Change (IPCC) was formed in response to this concern. The IPCC issues periodic assessments summarizing recent scientific developments relating to climate change. Climate models were used to attribute global warming to increasing concentrations of carbon dioxide and other greenhouse gases. Certain types of extreme weather can also be probabilistically attributed to these causes. The effect of aerosols and stochastic variability on the past global warming signal is described. The IPCC projects the global warming signal into the future using a range of carbon dioxide emission scenarios, resulting in different degrees of predicted warming. The importance of regional climate change and the difficulty of predicting it are discussed.
Downscaling is a widely used technique for translating information from large-scale climate models to the spatial and temporal scales needed to assess local and regional climate impacts, vulnerability, risk and resilience. This book is a comprehensive guide to the downscaling techniques used for climate data. A general introduction of the science of climate modeling is followed by a discussion of techniques, models and methodologies used for producing downscaled projections, and the advantages, disadvantages and uncertainties of each. The book provides detailed information on dynamic and statistical downscaling techniques in non-technical language, as well as recommendations for selecting suitable downscaled datasets for different applications. The use of downscaled climate data in national and international assessments is also discussed using global examples. This is a practical guide for graduate students and researchers working on climate impacts and adaptation, as well as for policy makers and practitioners interested in climate risk and resilience.
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