Exploring Randomly Wired Neural Networks for Climate Model Emulation
Harvey Mudd College（哈维穆德学院）；University of Southern California（南加州大学）；Pacific Northwest National Laboratory（太平洋西北国家实验室）；University of California San Diego（加州大学圣迭戈分校）
William Yik, Sam J. Silva, Andrew Geiss, Duncan Watson-Parris
Exploring the climate impacts of various anthropogenic emissions scenarios is key to making informed decisions for climate change mitigation and adaptation. State-of-the-art Earth system models can provide detailed insight into these impacts, but have a large associated computational cost on a per-scenario basis. This large computational burden has driven recent interest in developing cheap machine learning models for the task of climate model emulation. In this manuscript, we explore the efficacy of randomly wired neural networks for this task. We describe how they can be constructed and compare them to their standard feedforward counterparts using the ClimateBench dataset. Specifically, we replace the serially connected dense layers in multilayer perceptrons, convolutional neural networks, and convolutional long short-term memory networks with randomly wired dense layers and assess the impact on model performance for models with 1 million and 10 million parameters. We find that models with less complex architectures see the greatest performance improvement with the addition of random wiring (up to 30.4% for multilayer perceptrons). Furthermore, out of 24 different model architecture, parameter count, and prediction task combinations, only one saw a statistically significant performance deficit in randomly wired networks compared to their standard counterparts, with 14 cases showing statistically significant improvement. We also find no significant difference in prediction speed between networks with standard feedforward dense layers and those with randomly wired layers. These findings indicate that randomly wired neural networks may be suitable direct replacements for traditional dense layers in many standard models.
climt-paraformer: Stable Emulation of Convective Parameterization using a Temporal Memory-aware Transformer
Sustainability and Data Sciences Laboratory, Northeastern University（可持续性与数据科学实验室，东北大学）；Microsoft（微软）；Indian Institute of Science Education and Research Pune（印度科学教育与研究中心普纳分校）；AI for Climate and Sustainability, The Institute for Experiential AI, Northeastern University（气候与可持续性人工智能，体验式人工智能研究所，东北大学）
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 …
Department of Aeronautics and Astronautics, Massachusetts Institute of Technology（航空与航天系，麻省理工学院）；Center for Sustainability Science and Strategy, Massachusetts Institute of Technology（可持续科学与战略中心，麻省理工学院）；Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology（地球、大气与行星科学系，麻省理工学院）；Brahmal Vasudevan Institute for Sustainable Aviation, Department of Aeronautics, Imperial College London（可持续航空研究所，帝国理工学院伦敦校区）；Institute for Data, Systems, and Society, Massachusetts Institute of Technology（数据、系统与社会研究所，麻省理工学院）
MERCURY: A fast and versatile multi-resolution based global emulator of compound climate hazards
University of Oxford（牛津大学）；Montreal Polytechnique（蒙特利尔理工学院）；Columbia Climate School（哥伦比亚气候学院）；International Institute for Applied Systems Analysis（国际应用系统分析研究所）；Universität Leipzig（莱比锡大学）；IRI THESys；Humboldt-Universität zu Berlin（柏林洪堡大学）；Institute Pierre-Simone-Laplace（皮埃尔-西蒙·拉普拉斯研究所）
Western Washington University（西华盛顿大学）；Pacific Northwest National Laboratory（太平洋西北国家实验室）；Indiana University（印第安纳大学）；Joint Global Change Research Institute（联合全球变化研究所）；Computer Science Department, Western Washington University（西华盛顿大学计算机科学系）；Computing & Analytics Division, Pacific Northwest National Laboratory（太平洋西北国家实验室计算与分析部）；Earth and Atmospheric Sciences Department, Indiana University（印第安纳大学地球与大气科学系）
Climate models encapsulate our best understanding of the Earth system, allowing research to be conducted on its future under alternative assumptions of how human-driven climate forces are going to evolve. An important application of climate models is to provide metrics of mean and extreme climate changes, particularly under these alternative future scenarios, as these quantities drive the impacts of climate on society and natural systems. Because of the need to explore a wide range of alternative scenarios and other sources of uncertainties in a computationally efficient manner, climate models…
Allen Institute for Artificial Intelligence (Ai2)（人工智能艾伦研究所）；Department of Electrical and Computer Engineering, University of Washington（华盛顿大学电气与计算机工程系）
Diffusion-Based Joint Temperature and Precipitation Emulation of Earth System Models
Western Washington University（西华盛顿大学）；Utah State University（犹他州立大学）；Joint Global Change Research Institute（全球变化联合研究所）；Pacific Northwest National Laboratory（太平洋西北国家实验室）；AI and Data Analytics Division, Pacific Northwest National Laboratory（太平洋西北国家实验室数据分析与人工智能分部）
We explore the potential of feed-forward deep neural networks (DNNs) for emulating cloud superparameterization in realistic geography, using offline fits to data from the Super Parameterized Community Atmospheric Model. To identify the network architecture of greatest skill, we formally optimize hyperparameters using ~250 trials. Our DNN explains over 70 percent of the temporal variance at the 15-minute sampling scale throughout the mid-to-upper troposphere. Autocorrelation timescale analysis compared against DNN skill suggests the less good fit in the tropical, marine boundary layer is driven…
Towards Causal Representations of Climate Model Data
Mila –Quebec AI Institute（米拉-魁北克人工智能研究所）；McGill University（麦吉尔大学）；Osnabrück University（奥斯纳布吕克大学）；Université de Montréal（蒙特利尔大学）；Intel Labs（英特尔实验室）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Columbia University（哥伦比亚大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Data Imbalance, Uncertainty Quantification, and Generalization via Transfer Learning in Data-driven Parameterizations: Lessons from the Emulation of Gravity Wave Momentum Transport in WACCM
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）
University of California, Irvine（加州大学欧文分校）；Columbia University（哥伦比亚大学）；Ludwig-Maximilian-University（路德维希-马克西米利安大学）
Artificial neural-networks have the potential to emulate cloud processes with higher accuracy than the semi-empirical emulators currently used in climate models. However, neural-network models do not intrinsically conserve energy and mass, which is an obstacle to using them for long-term climate predictions. Here, we propose two methods to enforce linear conservation laws in neural-network emulators of physical models: Constraining (1) the loss function or (2) the architecture of the network itself. Applied to the emulation of explicitly-resolved cloud processes in a prototype multi-scale clim…
DiffESM: Conditional Emulation of Temperature and Precipitation in Earth System Models with 3D Diffusion Models
Utah State University（犹他州立大学）；Joint Global Change Research Institute（全球变化联合研究所）；Pacific Northwest National Laboratory（西北太平洋国家实验室）；Western Washington University（西华盛顿大学）；Indiana University（印第安纳大学）
Large-scale river models are being refined over coastal regions to improve the scientific understanding of coastal processes, hazards and responses to climate change. However, coarse mesh resolutions and approximations in physical representations of tidal rivers limit the performance of such models at resolving the complex flow dynamics especially near the river-ocean interface, resulting in inaccurate simulations of flood inundation. In this research, we propose a machine learning (ML) framework based on the state-of-the-art physics-informed neural network (PINN) to simulate the downscaled fl…
