Related papers: El Nino and the Delayed Action Oscillator
To answer the questions of whether global warming is accelerating and when the 1.5{\deg}C Paris Agreement target will be exceeded, the global mean surface temperature from 1880 to 2025 is first examined using a purely graphical approach and…
This paper considers the problem of data-driven prediction of partially observed systems using a recurrent neural network. While neural network based dynamic predictors perform well with full-state training data, prediction with partial…
As artificial intelligence (AI) continues to rapidly evolve, the realm of Earth and atmospheric sciences is increasingly adopting data-driven models, powered by progressive developments in deep learning (DL). Specifically, DL techniques are…
We build a conceptual coupled model of the climate and tidal evolution of the Earth-Moon system to find the influence of the former on the latter. An energy balance model is applied to calculate steady-state temperature field from the mean…
Energetic electrons from the magnetosphere deposit their energy in the atmosphere and lead to production of nitric oxide (NO) in the mesosphere and lower thermosphere. We study the atmospheric NO response to a geomagnetic storm in April…
``Online" data assimilation (DA) is used to generate a new seasonal-resolution reanalysis dataset over the last millennium by combining forecasts from an ocean--atmosphere--sea-ice coupled linear inverse model with climate proxy records.…
Paleoclimate proxies reveal abrupt transitions of the North Atlantic climate during past glacial intervals known as Dansgaard--Oeschger (DO) events. A central feature of DO events is a sudden warming of about 10$^{\circ}$C in Greenland…
Models with three active neutrinos and one sterile neutrino can naturally account for maximal oscillations of atmospheric neutrinos, explain the solar neutrino deficit, and accommodate the results of the LSND experiment. The models predict…
Global deep-learning weather prediction models have recently been shown to produce forecasts that rival those from physics-based models run at operational centers. It is unclear whether these models have encoded atmospheric dynamics, or…
Different to classical theories which emphasize east--west feedback along the equator, data analyses via the extreme-point symmetric mode decomposition (ESMD) method support a new viewpoint that the El Ni\~{n}o--Southern Oscillation (ENSO)…
A discrete delay is included to model the time between the capture of the prey and its conversion to viable biomass in the simplest classical Gause type predator-prey model that has equilibrium dynamics without delay. As the delay increases…
During the last ice age there were several quasi-periodic abrupt warming events. The climatic effects of the so-called Dansgaard-Oeschger (DO) events were felt globally, although the North Atlantic experienced the largest and most abrupt…
When the agent's observations or interactions are delayed, classic reinforcement learning tools usually fail. In this paper, we propose a simple yet new and efficient solution to this problem. We assume that, in the undelayed environment,…
We describe a flexible and modular delayed-feedback nonlinear oscillator that is capable of generating a wide range of dynamical behaviours, from periodic oscillations to high-dimensional chaos. The oscillator uses electrooptic modulation…
Some of the hot topics in neutrino physics are discussed, with particular emphasis on neutrino oscillations. After proposing credibility criteria for assessing various claimed effects, particular stress is laid on the solar neutrino…
Learning dynamics governed by differential equations is crucial for predicting and controlling the systems in science and engineering. Neural Ordinary Differential Equation (NODE), a deep learning model integrated with differential…
Time--delayed feedback is exploited for controlling noise--induced motion in coherence resonance oscillators. Namely, under the proper choice of time delay, one can either increase or decrease the regularity of motion. It is shown that in…
Learning how complex dynamical systems evolve over time is a key challenge in system identification. For safety critical systems, it is often crucial that the learned model is guaranteed to converge to some equilibrium point. To this end,…
Learning to perform tasks by leveraging a dataset of expert observations, also known as imitation learning from observations (ILO), is an important paradigm for learning skills without access to the expert reward function or the expert…
This study investigates temporal variability in U.S. climate using harmonic decomposition techniques, specifically Fourier and wavelet transforms. Monthly temperature, precipitation, and drought index data from the National Oceanic and…