A deadly cascade: A catastrophic landslide in Uttarakhand state in India on February 2021 damaged two hydropower plants, and more than 200 people were killed or are missing. Shugar et al. describe the cascade of events that led to this disaster. A massive rock and ice avalanche roared down a Himalayan valley, turning into a deadly debris flow upstream from the first of the two hydropower plants. The sequence of events highlights the increasing risk in the Himalayas caused by increased warming and development. Science , abh4455, this issue p. 300

Large Language Models have made remarkable progress in question-answering tasks, but challenges like hallucination and outdated information persist. These issues are especially critical in domains like climate change, where timely access to reliable information is vital. One solution is granting these models access to external, scientifically accurate sources to enhance their knowledge and reliability. Here, we enhance GPT-4 by providing access to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6), the most comprehensive, up-to-date, and reliable source in this domain (refer to the ’Data Availability’ section). We present our conversational AI prototype, available atwww.chatclimate.ai, and demonstrate its ability to answer challenging questions in three different setups: (1) GPT-4, (2) ChatClimate, which relies exclusively on IPCC AR6 reports, and (3) Hybrid ChatClimate, which utilizes IPCC AR6 reports with in-house GPT-4 knowledge. The evaluation of answers by experts show that the hybrid ChatClimate AI assistant provide more accurate responses, highlighting the effectiveness of our solution.

On 3 October 2023, a multihazard cascade in the Sikkim Himalaya, India, was triggered by 14.7 million cubic meters of frozen lateral moraine collapsing into South Lhonak Lake. The impact generated an ~20-meter tsunami-like impact wave, which breached the moraine and drained ~50 million cubic meters of the lake’s water. The ensuing glacial lake outburst flood (GLOF) eroded ~270 million cubic meters of sediment, which overwhelmed infrastructure, including hydropower installations along the Teesta River. The physical scale and human and economic impacts of this event prompt urgent reflection on the role of climate change and human activities in exacerbating such disasters. Insights into multihazard evolution are pivotal for informing policy development, enhancing early warning systems (EWS), and spurring paradigm shifts in GLOF risk management strategies in the Himalaya and other mountain environments.

Large Language Models (LLMs) have made significant progress in recent years, achieving remarkable results in question-answering tasks (QA). However, they still face two major challenges at inference time: hallucination and outdated information. These challenges take center stage in critical domains like climate change, where obtaining accurate and up-to-date information from reliable sources in a timely fashion is essential. To overcome these barriers, one potential solution is to provide LLMs with access to external, scientifically accurate, and robust sources (long-term memory). This access assists LLMs in continuously updating their knowledge and preventing the propagation of inaccurate, incorrect, or outdated information. In this study, we enhanced GPT-4 by providing it access to the Sixth Assessment Report of the Intergovernmental (IPCC-AR6), the most comprehensive, up-to-date, and reliable source in this domain. We present our conversational AI prototype, available at www.chatclimate.ai. The product answers challenging questions accurately in three different QA scenarios: asking from 1) GPT-4, 2) ChatClimate, and 3) hybrid ChatClimate. The answers and their sources were evaluated by a team of IPCC authors, who used their expert knowledge to score the accuracy of the answers from 1 (very-low) to 5 (very-high). The evaluation showed that the Standalone assistant provided more accurate answers.