Reduced-order dynamical models play a central role in developing our understanding of predictability of climate irrespective of whether we are dealing with the actual climate system or surrogate climate-models. In this context, the Linear-Inverse-Modeling (LIM) approach, by capturing a few essential interactions between dynamical components of the full system, has proven valuable in providing insights into predictability of the full system. We demonstrate that Reservoir Computing (RC), a form of learning suitable for systems with chaotic dynamics, provides an alternative nonlinear approach that improves on the predictive skill of the LIM approach. We do this in the example setting of predicting sea-surface-temperature in the North Atlantic in the pre-industrial control simulation of a popular earth system model, the Community-Earth-System-Model so that we can compare the performance of the new RC based approach with the traditional LIM approach both when learning data is plentiful and when such data is more limited. The improved predictive skill of the RC approach over a wide range of conditions -- larger number of retained EOF coefficients, extending well into the limited data regime, etc. -- suggests that this machine-learning technique may have a use in climate predictability studies. While the possibility of developing a climate emulator -- the ability to continue the evolution of the system on the attractor long after failing to be able to track the reference trajectory -- is demonstrated in the Lorenz-63 system, it is suggested that further development of the RC approach may permit such uses of the new approach in more realistic predictability studies.
Università “Sapienza”（罗马第一大学）；Istituto dei Sistemi Complessi, CNR（意大利国家研究委员会复杂系统研究所）
We scrutinize the use of machine learning, based on reservoir computing, to build data-driven effective models of multiscale chaotic systems. We show that, for a wide scale separation, machine learning generates effective models akin to those obtained using multiscale asymptotic techniques and, remarkably, remains effective in predictability also when the scale separation is reduced. We also show that predictability can be improved by hybridizing the reservoir with an imperfect model.
Predicting unobserved climate time series data at distant areas via spatial correlation using reservoir computing
Toyota Central R&D Labs., Inc.（丰田中央研发实验室）；International Research Center for Neurointelligence, The University of Tokyo（东京大学国际神经智能研究中心）；Graduate School of Engineering, Nagoya Institute of Technology（名古屋工业大学工学研究科）
University of Maryland（马里兰大学）；Aunalytics；Hillsdale College（希尔斯代尔学院）；Potomac Research LLC（波托马克研究有限责任公司）
Recent work has shown that machine learning (ML) models can be trained to accurately forecast the dynamics of unknown chaotic dynamical systems. Short-term predictions of the state evolution and long-term predictions of the statistical patterns of the dynamics (``climate'') can be produced by employing a feedback loop, whereby the model is trained to predict forward one time step, then the model output is used as input for multiple time steps. In the absence of mitigating techniques, however, this technique can result in artificially rapid error growth. In this article, we systematically exami…
Long-term prediction of El Niño-Southern Oscillation using reservoir computing with data-driven realtime filter
Faculty of Sustainable Design, University of Toyama（富山大学可持续设计学部）；The Graduate School of Environment, Life, Natural Science and Technology, Okayama University（冈山大学环境、生命、自然科学及技术研究生院）；Graduate School of Business Administration, Hitotsubashi University（立正大学商学院）；Graduate School of Economics, Hitotsubashi University（立正大学经济学研究生院）
In this paper, the performance of three deep learning methods for predicting short-term evolution and for reproducing the long-term statistics of a multi-scale spatio-temporal Lorenz 96 system is examined. The methods are: echo state network (a type of reservoir computing, RC-ESN), deep feed-forward artificial neural network (ANN), and recurrent neural network with long short-term memory (RNN-LSTM). This Lorenz 96 system has three tiers of nonlinearly interacting variables representing slow/large-scale ($X$), intermediate ($Y$), and fast/small-scale ($Z$) processes. For training or testing, on…
Brookhaven National Laboratory（布鲁克海文国家实验室）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）
Since model bias and associated initialization shock are serious shortcomings that reduce prediction skills in state-of-the-art decadal climate prediction efforts, we pursue a complementary machine-learning-based approach to climate prediction. The example problem setting we consider consists of predicting natural variability of the North Atlantic sea surface temperature on the interannual timescale in the pre-industrial control simulation of the Community Earth System Model (CESM2). While previous works have considered the use of recurrent networks such as convolutional LSTMs and reservoir co…
Anticipating tipping in spatiotemporal systems with machine learning
School of Electrical, Computer, and Energy Engineering, Arizona State University（亚利桑那州立大学电气、计算机与能源工程学院）；DEVCOM Army Research Office（DEVCOM陆军研究办公室）；Department of Physics, Arizona State University（亚利桑那州立大学物理系）
Explore the Ideology of Deep Learning in ENSO Forecasts
State Key Laboratory of Physical Oceanography, Ocean University of China（中国海洋大学物理海洋学国家重点实验室）；Frontier Science Center for Deep Ocean Multispheres and Earth System（深海多球体与地球系统前沿科学中心）；Physical Oceanography Laboratory, Ocean University of China（中国海洋大学物理海洋学实验室）
Ultra-Early Prediction of Tipping Points: Integrating Dynamical Measures with Reservoir Computing
College of Science, National University of Defense Technology（国防科技大学科学学院）；Research Institute of Intelligent Complex Systems（智能复杂系统研究所）；MOE Frontiers Center for Brain Science（教育部脑科学前沿中心）；Fudan University（复旦大学）；School of Mathematical Sciences, SCMS, SCAM, and CCSB（数学科学学院、SCMS、SCAM和CCSB）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
Time-lapse seismic data acquisition is an essential tool to monitor changes in a reservoir due to fluid injection, such as CO$_2$ injection. By acquiring multiple seismic surveys in the exact location, we can identify the reservoir changes by analyzing the difference in the data. However, such analysis can be skewed by the near-surface seasonal velocity variations, inaccuracy in the acquisition parameters, and other inevitable noise. The common practice (cross-equalization) to address this problem uses the part of the data where changes are not expected to design a matching filter and then app…
The path toward realizing the potential of seasonal forecasting and its socioeconomic benefits depends heavily on improving general circulation model based dynamical forecasting systems. To improve dynamical seasonal forecast, it is crucial to set up forecast benchmarks, and clarify forecast limitations posed by model initialization errors, formulation deficiencies, and internal climate variability. With huge cost in generating large forecast ensembles, and limited observations for forecast verification, the seasonal forecast benchmarking and diagnosing task proves challenging. In this study, …
CO2 capture and storage (CCS) technology is likely to be widely deployed in coming decades in response to major climate and economics drivers: CCS is part of every clean energy pathway that limits global warming to 2C or less and receives significant CO2 tax credits in the United States. These drivers are likely to stimulate capture, transport, and storage of hundreds of millions or billions of tonnes of CO2 annually. A key part of the CCS puzzle will be identifying and characterizing suitable storage sites for vast amounts of CO2. We introduce a new software tool called SCO2T (Sequestration o…
