There is a perception that climate science can only be approached with complex computer simulations. But working climate scientists often use simple models to understand their simulations and make order-of-magnitude estimates. This article presents some of these simple models with the goal of making climate science more accessible and comprehensible.
Using observational data and an elementary rigorous statistical fact it is easily shown that the distribution of Earth's climate is non-stationary. Examination of records of hundreds of local Industrial Era temperature histories in the Northern Hemisphere were used to show this fact. Statistically, the mean of the ensemble has been rising during the Industrial Era. All of this confirms what climate scientists already know. The issue of predictions under uncertainties was tackled as well: a simple balance model was tuned to track an ensemble of climate records. Stochastic parametrizations were …
The Centre for Climate Change Research (CCCR;http://cccr.tropmet.res.in) at the Indian Institute of Tropical Meteorology (IITM; http://www.tropmet.res.in), Pune, launched in 2009 with the support of the Ministry of Earth Sciences (MoES), Government of India, focuses on the development of new climate modelling capabilities in India and South Asia to address issues concerning the science of climate change. CCCR-IITM has the mandate of developing an Earth System Model and to make the regional climate projections. An important achievement was made by developing an Earth System Model at IITM, which…
The Nobel Prize in Physics 2021 was awarded to Syukuro Manabe, Klaus Hasselmann, and Giorgio Parisi for their 'groundbreaking contributions to our understanding of complex systems' including major advances in the understanding of our climate and climate change. In this perspective article, we review their key contributions and discuss their relevance in relation to the present understanding of our climate. We conclude by outlining some promising research directions and open questions in climate science.
University of Texas at Austin（德克萨斯大学奥斯汀分校）；Indian Institute of Tropical Meteorology, Ministry of Earth Sciences（印度热带气象研究所）；Krea University（克雷亚大学）；University of Petroleum and Energy Studies（石油与能源研究大学）；Queen Mary University of London（伦敦玛丽女王大学）
Climate change has become one of the biggest global problems increasingly compromising the Earth's habitability. Recent developments such as the extraordinary heat waves in California & Canada, and the devastating floods in Germany point to the role of climate change in the ever-increasing frequency of extreme weather. Numerical modelling of the weather and climate have seen tremendous improvements in the last five decades, yet stringent limitations remain to be overcome. Spatially and temporally localized forecasting is the need of the hour for effective adaptation measures towards minimizing…
Understanding the causes and consequences of, and devising countermeasures to, global warming is a profoundly complex problem. Network representations are sometimes the only way forward, and sometimes able to reduce the complexity of the original problem. Networks are both necessary and natural elements of climate science. Furthermore, networks form a mathematical foundation for a multitude of computational and analytical techniques. We are only beginning to see the benefits of this connection between the sciences of climate change and network science. In this review, we cover the wide spectru…
The climate system is a complex, chaotic system with many degrees of freedom and variability on a vast range of temporal and spatial scales. Attaining a deeper level of understanding of its dynamical processes is a scientific challenge of great urgency, especially given the ongoing climate change and the evolving climate crisis. In statistical physics, complex, many-particle systems are studied successfully using the mathematical framework of Large Deviation Theory (LDT). A great potential exists for applying LDT to problems relevant for geophysical fluid dynamics and climate science. In parti…
A climate network represents the global climate system by the interactions of a set of anomaly time-series. Network science has been applied on climate data to study the dynamics of a climate network. The core task and first step to enable interactive network science on climate data is the efficient construction and update of a climate network on user-defined time-windows. We present TSUBASA, an algorithm for the efficient construction of climate networks based on the exact calculation of Pearsons correlation of large time-series. By pre-computing simple and low-overhead statistics, TSUBASA ca…
Klaus Hasselmann's revolutionary intuition in climate science was to take advantage of the stochasticity associated with fast weather processes to probe the slow dynamics of the climate system. This has led to fundamentally new ways to study the response of climate models to perturbations, and to perform detection and attribution for climate change signals. Hasselmann's program has been extremely influential in climate science and beyond. We first summarise the main aspects of such a program using modern concepts and tools of statistical physics and applied mathematics. We then provide an over…
The advent of digital computing in the 1950s sparked a revolution in the science of weather and climate. Meteorology, long based on extrapolating patterns in space and time, gave way to computational methods in a decade of advances in numerical weather forecasting. Those same methods also gave rise to computational climate science, studying the behaviour of those same numerical equations over intervals much longer than weather events, and changes in external boundary conditions. Several subsequent decades of exponential growth in computational power have brought us to the present day, where mo…
A Self-Evolving AI Agent System for Climate Science
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Fudan University（复旦大学）；Institute of Atmospheric Physics, Chinese Academy of Sciences（中国科学院大气物理研究所）；The University of Sydney（悉尼大学）；Shanghai Jiao Tong University（上海交通大学）；Nanjing University of Information Science and Technology（南京信息工程大学）；Seoul National University（首尔大学）；Ocean University of China（中国海洋大学）；Laoshan Laboratory（崂山实验室）；Columbia University（哥伦比亚大学）；Japan Agency for Marine-Earth Science and Technology（日本海洋研究开发机构）；The Chinese University of Hong Kong（香港中文大学）；East China Normal University（华东师范大学）；Chinese Academy of Meteorological Sciences（中国气象科学研究院）；Tsinghua University（清华大学）
We propose a comprehensive framework able to address both the predictability of the first and of the second kind for high-dimensional chaotic models. For this purpose, we analyse the properties of a newly introduced multistable climate toy model constructed by coupling the Lorenz '96 model with a zero-dimensional energy balance model. First, the attractors of the system are identified with Monte Carlo Basin Bifurcation Analysis. Additionally, we are able to detect the Melancholia state separating the two attractors. Then, Neural Ordinary Differential Equations are applied in order to predict t…
University of California, Irvine（加州大学尔湾分校）；Learning the Earth with Artificial Intelligence and Physics (LEAP)（人工智能与物理学习地球中心）；CMCC Foundation — Euro-Mediterranean Center on Climate Change（CMCC基金会——欧洲-地中海气候变化中心）；Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR)（德国航空航天中心）；University of Bremen（不来梅大学）；Columbia University（哥伦比亚大学）；Ecole Polytechnique（巴黎综合理工学院）；University of Salento（萨伦托大学）
ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning
Mila Quebec AI Institute（魁北克人工智能研究所）；McGill University（麦吉尔大学）；University of Osnabrück（奥斯纳布吕克大学）；University of Montreal（蒙特利尔大学）；Intel Labs（英特尔实验室）；German Aerospace Center（德国航空航天中心）；Technische Universität Berlin（柏林工业大学）
