Future increases in stratospheric water vapour risk amplifying climate change and slowing down the recovery of the ozone layer. However, state-of-the-art climate models strongly disagree on the magnitude of these increases under global warming. Uncertainty primarily arises from the complex processes leading to dehydration of air during its tropical ascent into the stratosphere. Here we derive an observational constraint on this longstanding uncertainty. We use a statistical-learning approach to infer historical co-variations between the atmospheric temperature structure and tropical lower stratospheric water vapour concentrations. For climate models, we demonstrate that these historically constrained relationships are highly predictive of the water vapour response to increased atmospheric carbon dioxide. We obtain an observationally constrained range for stratospheric water vapour changes per degree of global warming of 0.31 ± 0.39 ppmv K−1. Across 61 climate models, we find that a large fraction of future model projections are inconsistent with observational evidence. In particular, frequently projected strong increases (>1 ppmv K−1) are highly unlikely. Our constraint represents a 50% decrease in the 95th percentile of the climate model uncertainty distribution, which has implications for surface warming, ozone recovery and the tropospheric circulation response under climate change.@en

In the age of machine learning, deep learning and artificial intelligence (AI) are expected to improve our lives. Particularly in the field of medicine and medical imaging, AI can make sense of tens if not hundreds of different parameters and find patterns and correlations that are difficult for humans to process. AI is expected to assist doctors in improving patient care and reducing burden. Despite many papers showing how AI algorithms can match or outperform humans in different domains of medicine, not many have been adopted into practice (Kelly et al., 2019). One of the major challenges is trust and acceptance of AI results. These are important issues that are complex. Confidence, trust, and uncertainty influence the way humans make decisions using AI. AI (deep learning algorithms in particular) is a “black box” to users and even the creators of these algorithms, making it very difficult to adopt. Should humans trust AI? Do humans overly trust AI? This chapter explores the human–AI relationship. It starts with a discussion on trust and human interactions. The expert–apprentice model is described to inform how AI could interact with clinicians. Recent technological developments and experience design aspects are detailed, giving an outline of recommendations for designing explainable AI, or XAI.@en