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.

全球变暖以惊人的速度继续，向社会提出了前所未有的挑战，这需要紧急，由科学领导的缓解和适应。地球系统模型（ESM）是投射气候变化的重要工具，向决策者提供重要信息。但是，对预测气候变化的信心受到许多不确定性的破坏。 （i）ESMS不同意地球会在大气中二氧化碳（CO2）（地球等效的气候灵敏度）的给定增加的地球变暖； （ii）将二氧化碳排放到大气中，以温暖地球（人类发出的一半的二氧化碳已被土地和海洋吸收），并且（iii）（iii）多少超过地球系统的热量将进入海洋内部，延迟地面变暖（地球系统中的热量的90％，大约90％的热量进入海洋）。这些不确定性的核心知识较低，建模较差，地球系统的反馈，特别是云反馈，碳循环反馈和海洋热吸收。这些现象的不良表示会降低ESM项目的准确性，这对预期未来的气候极端和社会影响的影响产生了影响。我们旨在提高这些反馈在ESM中的表示，从而减少全球变暖项目的不确定性。我们提出了一种多学科的方法，专注于“学习”如何通过与先进的机器学习（ML）和人工智能（AI）的观察融合来准确描述这些反馈的过程。受物理定律约束的这种数据和方法将在地球系统模型的准确性上进行步骤变化。 AI4PEX将使欧洲成为地球系统建模革命的最前沿，从而提高了气候变化项目的准确性，并为实施巴黎气候协议和欧洲绿色交易的实施提供了卓越的支持。