Search for: All records

Abstract The evolution of the spatial pattern of ocean surface warming affects global radiative feedback, yet different climate models provide varying estimates of future patterns. Paleoclimate data, especially from past warm periods, can help constrain future equilibrium warming patterns. By analyzing marine temperature records spanning the past 10 million years with a regression‐based technique that removes temporal dimensions, we extract long‐term ocean warming patterns and quantify relative sea surface temperature changes across the global ocean. This analysis revealed a distinct pattern of amplified warming that aligns with equilibrated model simulations under high CO₂conditions, yet differs from the transient warming pattern observed over the past 160 years. This paleodata‐model comparison allows us to identify models that better capture fundamental aspects of Earth's warming response, while suggesting how ocean heat uptake and circulation changes modify the development of warming patterns over time. By combining this paleo‐ocean warming pattern with equilibrated model simulations, we characterized the likely evolution of global ocean warming as the climate system approaches equilibrium. 

Understanding howAI recommendationswork can help the younger generation become more informed and critical consumers of the vast amount of information they encounter daily. However, young learners with limited math and computing knowledge often find AI concepts too abstract. To address this, we developed Briteller, a light-based recommendation system that makes learning tangible. By exploring and manipulating light beams, Briteller enables children to understand an AI recommender system’s core algorithmic building block, the dot product, through hands-on interactions. Initial evaluations with ten middle school students demonstrated the effectiveness of this approach, using embodied metaphors, such as "merging light" to represent addition. To overcome the limitations of the physical optical setup, we further explored how AR could embody multiplication, expand data vectors with more attributes, and enhance contextual understanding. Our findings provide valuable insights for designing embodied and tangible learning experiences that make AI concepts more accessible to young learners.