The ability of Machine-Learning (ML) based model components to generalize to the previously unseen inputs, and the resulting stability of the models that use these components, has been receiving a lot of recent attention, especially when it comes to ML-based parameterizations. At the same time, ML-based emulators of existing parameterizations can be stable, accurate, and fast when used in the model they were specifically designed for. In this work we show that shallow-neural-network-based emulators of radiative transfer parameterizations developed almost a decade ago for a state-of-the-art GCM are robust with respect to the substantial structural and parametric change in the host model: when used in the AMIP-like experiment with the new model, they not only remain stable, but generate realistic output. Aspects of neural network architecture and training set design potentially contributing to stability of ML-based model components are discussed.
We address the question of how to use a machine learned parameterization in a general circulation model, and assess its performance both computationally and physically. We take one particular machine learned parameterization \cite{Guillaumin1&Zanna-JAMES21} and evaluate the online performance in a different model from which it was previously tested. This parameterization is a deep convolutional network that predicts parameters for a stochastic model of subgrid momentum forcing by mesoscale eddies. We treat the parameterization as we would a conventional parameterization once implemented in the…
Data Imbalance, Uncertainty Quantification, and Generalization via Transfer Learning in Data-driven Parameterizations: Lessons from the Emulation of Gravity Wave Momentum Transport in WACCM
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）
There has been a lot of recent interest in developing hybrid models that couple deterministic numerical model components to statistical model components derived using machine learning techniques. One approach that we follow in this pilot study is to replace an existing computationally expensive deterministic model component with its fast machine-learning-based emulator, leading to the model speed-up and/or improvement. We developed a shallow neural network-based emulator of a complete suite of atmospheric physics parameterizations in NCEP Global Forecast System (GFS) general circulation model …
Assessing Emulator Design and Training for Modal Aerosol Microphysics Parameterizations in E3SMv2
Advanced Computing, Mathematics, and Data Division（先进计算、数学与数据部门）；Pacific Northwest National Laboratory（太平洋西北国家实验室）；Earth and Biological Sciences Directorate（地球与生命科学 Directorate）；Lawrence Berkeley National Laboratory（伯克利国家实验室）
The impact of gravity waves (GW) on diurnal tides and the global circulation in the middle/upper atmosphere of Mars is investigated using a General Circulation Model (GCM). We have implemented a stochastic parameterization of non-orographic GW into the Laboratoire de Météorologie Dynamique (LMD) Mars GCM (LMD-MGCM) following an innovative approach. The source is assumed to be located above typical convective cells ($\sim$ 250 Pa) and the effect of GW on the circulation and predicted thermal structure above 1 Pa ($\sim$ 50 km) is analyzed. We focus on the comparison between model simulations an…
Subgrid processes in global climate models are represented by parameterizations which are a major source of uncertainties in simulations of climate. In recent years, it has been suggested that machine-learning (ML) parameterizations based on high-resolution model output data could be superior to traditional parameterizations. Currently, both traditional and ML parameterizations of subgrid processes in the atmosphere are based on a single-column approach, which only use information from single atmospheric columns. However, single-column parameterizations might not be ideal since certain atmosph…
Generating realistic global precipitation fields from modelled atmospheric circulation
Technical University of Munich（慕尼黑技术大学）；Munich Climate Center（慕尼黑气候中心）；TUM School of Engineering and Design（TUM工程与设计学院）；Department of Aerospace and Geodesy（航空航天与大地测量系）；Earth System Modelling Group（地球系统建模组）；Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）；Global Systems Institute（全球系统研究所）；Department of Mathematics（数学系）；University of Exeter（埃克塞特大学）
Adjoint-based online learning of two-layer quasi-geostrophic baroclinic turbulence
Hong Kong University of Science and Technology（香港科技大学）；Institut de Physique du Globe de Paris（巴黎地球物理研究所）；Université Grenoble-Alpes（格勒诺布尔阿尔卑斯大学）；CNRS（法国国家科学研究中心）；National Oceanography Centre, Southampton（南安普顿国家海洋中心）；Courant Institute of Mathematical Sciences, New York University（纽约大学柯朗数学科学研究所）
Cloud microphysical parameterizations in atmospheric models describe the formation and evolution of clouds and precipitation, a central weather and climate process. Cloud-associated latent heating is a primary driver of large and small-scale circulations throughout the global atmosphere, and clouds have important interactions with atmospheric radiation. Clouds are ubiquitous, diverse, and can change rapidly. In this work, we build the first emulator of an entire cloud microphysical parameterization, including fast phase changes. The emulator performs well in offline and online (i.e. when coupl…
Stable Machine-Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection-Permitting Simulations
The representations of atmospheric moist convection in general circulation models have been one of the most challenging tasks due to its complexity in physical processes, and the interaction between processes under different time/spatial scales. This study proposes a new method to predict the effects of moist convection on the environment using convolutional neural networks. With the help of considering the gradient of physical fields between adjacent grids in the grey zone resolution, the effects of moist convection predicted by the convolutional neural networks are more realistic compared to…
