''This page is about the intersection of climate change adaptation and machine learning. For an overview of climate change adaptation as a whole, please see the [https://en.wikipedia.org/wiki/Climate_change_adaptation Wikipedia page] on this topic.''[[File:Societal-adaptation.png|thumb|A summary of the different domains within which machine learning can support climate adaptation.]]
Changes in the atmosphere have impacts on the ground. The expected societal impacts of climate change include prolonged ecological and socioeconomic stresses as well as brief, but severe, societal disruptions. For example, impacts could include both gradual decreases in crop yield and localized food shortages. If we can anticipate climate impacts well enough, then we can prepare for them by asking:
* How do we reduce vulnerability to climate impacts?
* How do we support rapid recovery from climate-induced disruptions?
A wide variety of strategies have been put forward, from robust power grids to food shortage prediction, and while this is good news for society, it can be overwhelming for an ML practitioner hoping to contribute. Fortunately, a few critical needs tend to recur across strategies – it is by meeting these needs that machine learning has the greatest potential to support societal adaptation. From a high level, these involve
* Sounding alarms: Identifying and prioritizing the areas of highest risk, by using evidence of risk from historical data.
* Providing annotation: Extracting actionable information or labels from unstructured raw data.
* Promoting exchange: Making it easier to share resources and information to pool and reduce risk.
== Machine Learning Application Areas ==