Deep learning to represent sub-grid processes in climate models

The representation of nonlinear sub-grid processes, especially clouds, has been a major source of uncertainty in climate models for decades. Cloud-resolving models better represent many of these processes and can now be run globally but only for short-term simulations of at most a few years because of computational limitations. Here we demonstrate that deep learning can be used to capture many advantages of cloud-resolving modeling at a fraction of the computational cost. We train a deep neural network to represent all atmospheric sub-grid processes in a climate model by learning from a multi-scale model in which convection is treated explicitly. The trained neural network then replaces the traditional sub-grid parameterizations in a global general circulation model in which it freely interacts with the resolved dynamics and the surface-flux scheme. The prognostic multi-year simulations are stable and closely reproduce not only the mean climate of the cloud-resolving simulation but also key aspects of variability, including precipitation extremes and the equatorial wave spectrum. Furthermore, the neural network approximately conserves energy despite not being explicitly instructed to. Finally, we show that the neural network parameterization generalizes to new surface forcing patterns but struggles to cope with temperatures far outside its training manifold. Our results show the feasibility of using deep learning for climate model parameterization. In a broader context, we anticipate that data-driven Earth System Model development could play a key role in reducing climate prediction uncertainty in the coming decade.Comment: View official PNAS version at https://doi.org/10.1073/pnas.181028611

Using neural networks to improve simulations in the gray zone

Machine learning represents a potential method to cope with the gray zone problem of representing motions in dynamical systems on scales comparable to the model resolution. Here we explore the possibility of using a neural network to directly learn the error caused by unresolved scales. We use a modified shallow water model which includes highly nonlinear processes mimicking atmospheric convection. To create the training dataset, we run the model in a high- and a low-resolution setup and compare the difference after one low-resolution time step, starting from the same initial conditions, thereby obtaining an exact target. The neural network is able to learn a large portion of the difference when evaluated on single time step predictions on a validation dataset. When coupled to the low-resolution model, we find large forecast improvements up to 1 d on average. After this, the accumulated error due to the mass conservation violation of the neural network starts to dominate and deteriorates the forecast. This deterioration can effectively be delayed by adding a penalty term to the loss function used to train the ANN to conserve mass in a weak sense. This study reinforces the need to include physical constraints in neural network parameterizations

Climate-Invariant Machine Learning

Data-driven algorithms, in particular neural networks, can emulate the effects of unresolved processes in coarse-resolution climate models when trained on high-resolution simulation data; however, they often make large generalization errors when evaluated in conditions they were not trained on. Here, we propose to physically rescale the inputs and outputs of machine learning algorithms to help them generalize to unseen climates. Applied to offline parameterizations of subgrid-scale thermodynamics in three distinct climate models, we show that rescaled or "climate-invariant" neural networks make accurate predictions in test climates that are 4K and 8K warmer than their training climates. Additionally, "climate-invariant" neural nets facilitate generalization between Aquaplanet and Earth-like simulations. Through visualization and attribution methods, we show that compared to standard machine learning models, "climate-invariant" algorithms learn more local and robust relations between storm-scale convection, radiation, and their synoptic thermodynamic environment. Overall, these results suggest that explicitly incorporating physical knowledge into data-driven models of Earth system processes can improve their consistency and ability to generalize across climate regimes.Comment: 12+18 pages, 8+12 figures, 2+2 tables in the main text + supplementary information. Submitted to PNAS on December 14th, 202

Электроснабжение установки перекачки нефти п. Пионерный ОАО «Томскнефть»

РЕФЕРАТ Выпускная квалификационная работа 149 с., 23 рис., 32 табл., 29 источников, 6 прил. Ключевые слова: нефтепровод, насос, электрооборудование, схема электроснабжения, линия, сеть, электроприемник, нагрузка, оборудование, защита, ток, напряжение, мощность. Объектом исследования является электрическая часть УПН п. Пионерный ОАО «Томскенефть». Цель работы – проектирование схемы электроснабжения предприятия, выбор оборудования. В процессе исследования проводился сбор исходных данных в ходе производственной практики на объекте исследования. В результате была спроектирована схема электроснабжения от подстанции энергосистемы, до конечного электроприемника. Были выбраны кабели и провода, коммутационное оборудование, были сделаны необходимые проверки. Также результатом работы сталESSAY Final qualifying work 149 p., 23 fig., 32 tab., 29 sources, 6 adj. Keywords: oil, pump, electrical equipment, power supply circuit, line, network, power-consuming equipment, load equipment, protection, current, voltage, power. The object of research is the electrical part of UPN claim. Pionerny of "Tomskeneft". The purpose of work - designing enterprise power scheme, the choice of equipment. The study was conducted to collect baseline data in the course of practical training on the subject of the study. As a result, power supply circuit has been designed from the substation grid, appliance, to the end. Were selected cables and wires, switching equipment, the necessary checks have been made. It is also the result of the work became an economic calculation of capital costs for the con