Hybrid modeling combining data-driven techniques and numerical methods is an emerging and promising research direction for efficient climate simulation. However, previous works lack practical platforms, making developing hybrid modeling a challenging programming problem. Furthermore, the lack of standard data sets and evaluation metrics may hamper researchers from comprehensively comparing various algorithms under a uniform condition. To address these problems, we propose a framework called NeuroClim for hybrid modeling under the real-world scenario, a basic setting to simulate the real climate that we live in. NeuroClim consists of three parts: (1) Platform. We develop a user-friendly platform NeuroGCM for efficiently developing hybrid modeling in climate simulation. (2) Dataset. We provide an open-source dataset for data-driven methods in hybrid modeling. We investigate the characteristics of the data, i.e., heterogeneity and stiffness, which reveals the difficulty of regressing climate simulation data; (3) Metrics. We propose a methodology for quantitatively evaluating hybrid modeling, including the approximation ability of machine learning models and the stability during simulation. We believe that NeuroClim allows researchers to work without high level of climate-related expertise and focus only on machine learning algorithm design, which will accelerate hybrid modeling research in the AI-Climate intersection. The codes and data are released at https://github.com/x-w19/NeuroClim.
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）
In this work we present a multi-modal machine learning-based system, which we call ACORN, to analyze videos of school classrooms for the Positive Climate (PC) and Negative Climate (NC) dimensions of the CLASS observation protocol that is widely used in educational research. ACORN uses convolutional neural networks to analyze spectral audio features, the faces of teachers and students, and the pixels of each image frame, and then integrates this information over time using Temporal Convolutional Networks. The audiovisual ACORN's PC and NC predictions have Pearson correlations of $0.55$ and $0.6…
WxC-Bench: A Novel Dataset for Weather and Climate Downstream Tasks
The University of Alabama in Huntsville（阿拉巴马大学亨茨维尔分校）；Stanford University（斯坦福大学）；Colorado State University（科罗拉多州立大学）；NASA Marshall Space Flight Center（美国国家航空航天局马歇尔太空飞行中心）
Mila - Quebec AI Institute（Mila-魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）
Though machine learning has achieved notable success in modeling sequential and spatial data for speech recognition and in computer vision, applications to remote sensing and climate science problems are seldom considered. In this paper, we demonstrate techniques from unsupervised learning of future video frame prediction, to increase the accuracy of ice flow tracking in multi-spectral satellite images. As the volume of cryosphere data increases in coming years, this is an interesting and important opportunity for machine learning to address a global challenge for climate change, risk manageme…
Interpretable Machine Learning for Weather and Climate Prediction: A Survey
Public Meteorological Service Center, China Meteorological Administration（中国气象局公共气象服务中心）；University of Bristol（布里斯托大学）；Zhejiang University（浙江大学）；Institute of Atmospheric Physics, Chinese Academy of Science（中国科学院大气物理研究所）；Baidu Inc.（百度公司）；New Jersey Institute of Technology（新泽西理工学院）
Tackling extreme urban heat: a machine learning approach to assess the impacts of climate change and the efficacy of climate adaptation strategies in urban microclimates
Allen Institute for Artificial Intelligence（艾伦人工智能研究所）；Geophysical Fluid Dynamics Laboratory, NOAA（美国国家海洋和大气管理局地球物理流体动力学实验室）；NVIDIA（英伟达）
Due to computational constraints, running global climate models (GCMs) for many years requires a lower spatial grid resolution (${\gtrsim}50$ km) than is optimal for accurately resolving important physical processes. Such processes are approximated in GCMs via subgrid parameterizations, which contribute significantly to the uncertainty in GCM predictions. One approach to improving the accuracy of a coarse-grid global climate model is to add machine-learned state-dependent corrections at each simulation timestep, such that the climate model evolves more like a high-resolution global storm-resol…
Modern weather and climate models share a common heritage, and often even components, however they are used in different ways to answer fundamentally different questions. As such, attempts to emulate them using machine learning should reflect this. While the use of machine learning to emulate weather forecast models is a relatively new endeavour there is a rich history of climate model emulation. This is primarily because while weather modelling is an initial condition problem which intimately depends on the current state of the atmosphere, climate modelling is predominantly a boundary conditi…
University of Lausanne（洛桑大学）；Columbia University（哥伦比亚大学）；Massachusetts Institute of Technology（麻省理工学院）；University of California, Irvine（加利福尼亚大学尔湾分校）；Google Research（谷歌研究院）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；University of California, San Diego（加利福尼亚大学圣迭戈分校）；NVIDIA（英伟达）
The parameterization of moist convection contributes to uncertainty in climate modeling and numerical weather prediction. Machine learning (ML) can be used to learn new parameterizations directly from high-resolution model output, but it remains poorly understood how such parameterizations behave when fully coupled in a general circulation model (GCM) and whether they are useful for simulations of climate change or extreme events. Here, we focus on these issues using idealized tests in which an ML-based parameterization is trained on output from a conventional parameterization and its performa…
Latent Diffusion Model for Generating Ensembles of Climate Simulations
German Climate Computing Center (DKRZ)（德国气候计算中心（DKRZ））；CEREA Écoles des Ponts（巴黎桥路学院CEREA实验室）；EDF R&D（法国电力公司研发部）；Max-Planck Institute of Meteorology（马克斯·普朗克气象研究所）
University of Bristol（布里斯托尔大学）；Met Office Hadley Centre（英国气象局哈德利中心）；DeepMind（深度思维）
Climate change is causing the intensification of rainfall extremes. Precipitation projections with high spatial resolution are important for society to prepare for these changes, e.g. to model flooding impacts. Physics-based simulations for creating such projections are very computationally expensive. This work demonstrates the effectiveness of diffusion models, a form of deep generative models, for generating much more cheaply realistic high resolution rainfall samples for the UK conditioned on data from a low resolution simulation. We show for the first time a machine learning model that is …
Global climate models represent small-scale processes such as clouds and convection using quasi-empirical models known as parameterizations, and these parameterizations are a leading cause of uncertainty in climate projections. A promising alternative approach is to use machine learning to build new parameterizations directly from high-resolution model output. However, parameterizations learned from three-dimensional model output have not yet been successfully used for simulations of climate. Here we use a random forest to learn a parameterization of subgrid processes from output of a three-di…
Machine Learning Workflows in Climate Modeling: Design Patterns and Insights from Case Studies
Department of Statistics, Columbia University（统计学系，哥伦比亚大学）；NSF STC Learning the Earth with AI and Physics (LEAP)（NSF STC 学习地球与人工智能和物理（LEAP））；Data Science Institute, Columbia University（数据科学研究所，哥伦比亚大学）；Jet Propulsion Laboratory, California Institute of Technology（喷气推进实验室，加州理工学院）；Department of Applied Physics and Applied Mathematics, Columbia University（应用物理与应用数学系，哥伦比亚大学）
Climate models are complicated software systems that approximate atmospheric and oceanic fluid mechanics at a coarse spatial resolution. Typical climate forecasts only explicitly resolve processes larger than 100 km and approximate any process occurring below this scale (e.g. thunderstorms) using so-called parametrizations. Machine learning could improve upon the accuracy of some traditional physical parametrizations by learning from so-called global cloud-resolving models. We compare the performance of two machine learning models, random forests (RF) and neural networks (NNs), at parametrizin…
Hewlett Packard Enterprise（慧与公司）；National Center for Atmospheric Research（美国国家大气研究中心）；University of Victoria（维多利亚大学）
We demonstrate the first climate-scale, numerical ocean simulations improved through distributed, online inference of Deep Neural Networks (DNN) using SmartSim. SmartSim is a library dedicated to enabling online analysis and Machine Learning (ML) for traditional HPC simulations. In this paper, we detail the SmartSim architecture and provide benchmarks including online inference with a shared ML model on heterogeneous HPC systems. We demonstrate the capability of SmartSim by using it to run a 12-member ensemble of global-scale, high-resolution ocean simulations, each spanning 19 compute nodes, …
CondensNet: Enabling stable long-term climate simulations via hybrid deep learning models with adaptive physical constraints
Department of Mechanical Engineering, National University of Singapore（新加坡国立大学机械工程系）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；NVIDIA AI Technology Centre（NVIDIA人工智能技术中心）；Centre for Climate Research Singapore（新加坡气候研究中心）；Environmental Science Division, Argonne National Laboratory（阿贡国家实验室环境科学部）；Information Sciences and Technology Department, The Pennsylvania State University（宾夕法尼亚州立大学信息科学与技术系）
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…
Climate-Adaptive and Cascade-Constrained Machine Learning Prediction for Sea Surface Height under Greenhouse Warming
Tianjin University（天津大学）；The Hong Kong University of Science and Technology（香港科技大学）；Institute of Atmospheric Physics, Chinese Academy of Sciences（中国科学院大气物理研究所）；University of Chinese Academy of Sciences（中国科学院大学）
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.
There is a growing interest in the climate community to improve the prediction of high impact climate events, for instance ENSO (El-Ni{ñ}o-Southern Oscillation) or extreme events, using a combination of model and observation data. In this note we explain that, in a dynamical context, the relevant quantity for predicting a future event is a committor function. We explain the main mathematical properties of this probabilistic concept. We compute and discuss the committor function of the Jin and Timmerman model of El-Ni{ñ}o. Our first conclusion is that one should generically distinguish between …
Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；University of California, Davis（加州大学戴维斯分校）
Almonds are one of the most lucrative products of California, but are also among the most sensitive to climate change. In order to better understand the relationship between climatic factors and almond yield, an automated machine learning framework is used to build a collection of machine learning models. The prediction skill is assessed using historical records. Future projections are derived using 17 downscaled climate outputs. The ensemble mean projection displays almond yield changes under two different climate scenarios, along with two technology development scenarios, where the role of t…
While previous works have shown that machine learning (ML) can improve the prediction accuracy of coarse-grid climate models, these ML-augmented methods are more vulnerable to irregular inputs than the traditional physics-based models they rely on. Because ML-predicted corrections feed back into the climate model's base physics, the ML-corrected model regularly produces out of sample data, which can cause model instability and frequent crashes. This work shows that adding semi-supervised novelty detection to identify out-of-sample data and disable the ML-correction accordingly stabilizes simul…
