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Atmospheric models used for weather and climate prediction are traditionally formulated in a deterministic manner. In other words, given a particular state of the resolved scale variables, the most likely forcing from the sub-grid scale…

Machine Learning · Computer Science 2024-02-16 Hannah M. Christensen , Salah Kouhen , Greta Miller , Raghul Parthipan

It is largely agreed that subseasonal-to-seasonal (S2S) predictability arises from the atmospheric initial state during early lead times and from the land and ocean during intermediate and late lead times. We test this hypothesis for the…

Atmospheric and Oceanic Physics · Physics 2025-01-14 Jhayron S. Pérez-Carrasquilla , Maria J. Molina

The goal of this paper is to explore the potential multistability of the climate of a planet around the habitable zone. A thorough investigation of the thermodynamics of the climate system is performed for very diverse conditions of energy…

Atmospheric and Oceanic Physics · Physics 2015-06-05 Robert Boschi , Valerio Lucarini , Salvatore Pascale

Polar vortices are common planetary flows that encircle the pole in the middle or high latitudes, and are observed on most of the solar systems' planetary atmospheres. The polar vortices on Earth, Mars, and Titan are dynamically related to…

Earth and Planetary Astrophysics · Physics 2022-02-28 Ilai Guendelman , Darryn W. Waugh , Yohai Kaspi

Most operational climate services providers base their seasonal predictions on initialised general circulation models (GCMs) or statistical techniques that fit past observations. GCMs require substantial computational resources, which…

Complex numerical weather prediction models incorporate a variety of physical processes, each described by multiple alternative physical schemes with specific parameters. The selection of the physical schemes and the choice of the…

Numerical Analysis · Computer Science 2018-02-23 Azam Moosavi , Vishwas Rao , Adrian Sandu

Internal climate variability arises from the climate system's inherently chaotic dynamics. Quantifying it is essential for climate science, as it enables risk-based decision-making and differentiates between externally forced change and…

Trends in terrestrial temperature variability are perhaps more relevant for species viability than trends in mean temperature. In this paper, we develop methodology for estimating such trends using multi-resolution climate data from polar…

Machine Learning · Statistics 2019-01-23 Arash Khodadadi , Daniel J McDonald

We write a nonlinear model that predicts the climate (temperature and humidity) on the surface of a small region on Earth, perform numerical investigations using the model, and compare the results to real climate on a variety of regions on…

Atmospheric and Oceanic Physics · Physics 2020-01-24 Gabriele Di Bona , Andrea Giacobbe

The regimes of possible global atmospheric circulation patterns in an Earth-like atmosphere are explored using a simplified GCM based on the University of Hamburg's Portable University Model for the Atmosphere with simplified (linear)…

Earth and Planetary Astrophysics · Physics 2019-06-19 Yixiong Wang , Peter Read , Fachreddin Tabataba-Vakili , Roland Young

Images from outdoor scenes may be taken under various weather conditions. It is well studied that weather impacts the performance of computer vision algorithms and needs to be handled properly. However, existing algorithms model weather…

Computer Vision and Pattern Recognition · Computer Science 2024-04-01 Qi Bi , Shaodi You , Theo Gevers

This article investigates penetrative turbulence in the atmospheric boundary layer. Using a large eddy simulation approach, we study characteristics of the mixed layer with respect to surface heat flux variations in the range from 231.48…

Fluid Dynamics · Physics 2015-05-05 Jahrul M. Alam , M. Alamgir Hossain

Classifying the state of the atmosphere into a finite number of large-scale circulation regimes is a popular way of investigating teleconnections, the predictability of severe weather events, and climate change. Here, we investigate a…

Machine Learning · Computer Science 2022-05-02 Andreas Holm Nielsen , Alexandros Iosifidis , Henrik Karstoft

The last decade has seen the success of stochastic parameterizations in short-term, medium-range and seasonal forecasts: operational weather centers now routinely use stochastic parameterization schemes to better represent model inadequacy…

The annual temperature cycle of the earth closely follows the annual cycle of solar flux. At temperate latitudes, both driving and response cycles are well described by a strong annual sinusoidal component and a non-vanishing semiannual…

Atmospheric and Oceanic Physics · Physics 2020-11-13 Yunxiang Song , Kyle B. Lawlor , Thomas A. Witten

Modern weather and climate models share a common heritage, and often even components, however they are used in different ways to answer fundamentally different questions. As such, attempts to emulate them using machine learning should…

Atmospheric and Oceanic Physics · Physics 2022-03-21 Duncan Watson-Parris

Motivated by the pursuit of safe, reliable, and weather-tolerant urban air mobility (UAM) solutions, this work proposes a generative modeling approach for characterizing microweather wind velocities. Microweather, or the weather conditions…

Computational Engineering, Finance, and Science · Computer Science 2025-03-05 Tristan A. Shah , Michael C. Stanley , James E. Warner

The authors argue for the hypothesis that interactive feedbacks involving surface enthalpy fluxes are important to the dynamics of tropical intraseasonal variability. These include cloud-radiative feedbacks as well as surface turbulent flux…

Atmospheric and Oceanic Physics · Physics 2008-05-13 Adam H. Sobel , Eric D. Maloney , Gilles Bellon , Dargan M. Frierson

Clouds' efficiency at reflecting solar radiation and trapping the terrestrial one is strongly modulated by their diurnal cycle. Much attention has been paid to mean cloud properties due to their critical role in climate projections;…

Atmospheric and Oceanic Physics · Physics 2018-03-06 Jun Yin , Amilcare Porporato

A key challenge for computationally intensive state-of-the-art Earth System models is to distinguish global warming signals from interannual variability. Here we introduce DLESyM, a parsimonious deep learning model that accurately simulates…

Atmospheric and Oceanic Physics · Physics 2025-10-21 Nathaniel Cresswell-Clay , Bowen Liu , Dale Durran , Zihui Liu , Zachary I. Espinosa , Raul Moreno , Matthias Karlbauer