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Attribution of the record-shattering global annual heat in 2023 to human and/or natural factors is fundamentally required for reliable predictions of upcoming global warming and its impacts. An observation-model comparison of global hot areas supports a key role for human-induced climate change, with a small contribution from El Niño.
In 2021, one in five people in Africa was affected by hunger, and the continent had the highest prevalence of undernourished people globally. We argue that food systems in Africa can be more resilient if their development includes climate adaptation.
Large language models can summarize, aggregate, and convey localized climate-related data to people in a cost-effective and expeditious manner. This Comment introduces a simple, proof-of-concept prototype and argues that the approach holds the potential to truly democratize climate information.
