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，给社会带来前所未有的挑战，迫切需要以科学为主导的减缓和适应。地球系统模式是预测气候变化的重要工具，为决策者提供重要信息。然而，许多不确定因素削弱了对预测气候变化的信心；(i)在给定的大气二氧化碳（CO2）增加（地球平衡气候敏感性）下，esm对地球将变暖多少存在分歧；（ii）排放的二氧化碳有多少将留在大气中使地球变暖（人类排放的二氧化碳有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，延缓表面变暖（地球系统中约90%的热量进入海洋）。这些不确定性的核心是地球系统反馈，特别是云反馈、碳循环反馈和海洋热吸收，人们对这些反馈的理解和建模都很差。这些现象表现不佳会降低ESM预估的准确性，从而影响对未来极端气候和社会影响的预测。我们的目标是改善这些反馈在esm中的表现，减少全球变暖预测的不确定性。我们提出了一种多学科的方法，专注于“学习”如何准确地描述支撑这些反馈的过程，通过将观察与先进的机器学习（ML）和人工智能（AI）相融合。这些数据和方法受到物理定律的限制，将在地球系统模型的准确性方面带来一个台阶的变化。AI4PEX将使欧洲处于地球系统建模革命的最前沿，从而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。