Artificial Intelligence and Machine Learning for Enhanced Representation of Processes and Extremes in Earth System Models (AI4PEX)

人工智能和机器学习增强地球系统模型中过程和极值的表示（AI4PEX）

• 批准号: 10093450
• 金额: $ 63.22万
• 依托单位: UNIVERSITY OF READING
• 依托单位国家: 英国
• 项目类别: EU-Funded
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10093450
• 关键词: Artificial; Intelligence; Machine; Learning; Enhanced

Global warming continues at an alarming rate, presenting unprecedented challenges to society that require urgent, science-led mitigation and adaptation. Earth system models (ESMs) are essential tools for projecting climate change, providing important information to decision makers. However, confidence in predicted climate change is undermined by a number of uncertainties; (i) ESMs disagree on how much the Earth will warm for a given increase in atmospheric carbon dioxide (CO2) (Earth’s equilibrium climate sensitivity); (ii) how much emitted CO2 will stay in the atmosphere to warm the planet (half the CO2 emitted by humans has been absorbed by the land and ocean) and (iii) how much excess heat in the Earth system will enter the ocean interior, delaying surface warming (~90 % of the heat in the Earth system goes into the ocean). Central to these uncertainties are poorly understood, and poorly modelled, Earth system feedbacks, in particular cloud feedbacks, carbon cycle feedbacks and ocean heat uptake. Poor representation of these phenomena degrades the accuracy of ESM projections, with implications for anticipating future climate extremes and societal impacts. We aim to improve the representation of these feedbacks in ESMs, reducing uncertainty in global warming projections. We propose a multidisciplinary approach, focused on “learning” how to accurately describe processes underpinning these feedbacks, through a fusion of observations with advanced machine learning (ML) and artificial intelligence (AI). Such data and approaches, constrained by the laws of physics, will deliver a step change in the accuracy of Earth system models. AI4PEX will place Europe at the forefront of a revolution in Earth system modelling, leading to increased accuracy of climate change projections and superior support for implementation of the Paris Climate Agreement and the European Green Deal.

全球变暖继续以惊人的速度发展，给社会带来了前所未有的挑战，需要以科学为主导的紧急缓解和适应地球系统模型（ESM）作为预测气候变化的重要工具，为决策者提供重要的信息。气候变化受到许多不确定性的影响；(i) ESM 对于大气二氧化碳 (CO2) 增加一定程度后地球变暖的程度（地球的平衡气候敏感性）存在分歧；气氛使地球变暖（人类排放的二氧化碳的一半已被陆地和海洋吸收）以及（iii）地球系统中有多少多余的热量将进入海洋内部，从而延缓地表变暖（约 90% 的热量来自海洋）这些不确定性的核心是对地球系统反馈的了解和建模不当，特别是云反馈、碳循环反馈和海洋吸热，这些现象的不良表征降低了 ESM 预测的准确性，并对环境造成影响。我们的目标是改善这些反馈在 ESM 中的代表性，减少全球变暖预测的不确定性，重点是“学习”如何通过融合准确描述支撑这些反馈的过程。利用先进的机器学习 (ML) 和人工智能 (AI) 进行观测，这些受物理定律约束的数据和方法将大幅提高地球系统模型的准确性，从而使欧洲处于领先地位。革命地球系统建模，提高气候变化预测的准确性，并为《巴黎气候协定》和《欧洲绿色协议》的实施提供更好的支持。