Preprints
Filtering by Subject: Atmospheric Sciences
Non-stationary teleconnection between the Pacific Ocean and Arctic sea ice
Published: 2019-10-08
Subjects: Atmospheric Sciences, Climate, Oceanography and Atmospheric Sciences and Meteorology, Other Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics
Over the last 40 years observations show a teleconnection between summertime Pacific Ocean sea-surface temperatures and September Arctic sea-ice extent. However, the short satellite observation record has made it difficult to further examine this relationship. Here, we use 30 fully-coupled general circulation models (GCMs) participating in Phase 5 of the Coupled Model Inter-comparison Project to [...]
Analog forecasting of extreme-causing weather patterns using deep learning
Published: 2019-07-31
Subjects: Artificial Intelligence and Robotics, Atmospheric Sciences, Computational Engineering, Computer Sciences, Engineering, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics
Numerical weather prediction (NWP) models require ever-growing computing time/resources, but still, have difficulties with predicting weather extremes. Here we introduce a data-driven framework that is based on analog forecasting (prediction using past similar patterns) and employs a novel deep learning pattern-recognition technique (capsule neural networks, CapsNets) and impact-based [...]
Evidence against a general positive eddy feedback in atmospheric blocking
Published: 2019-07-04
Subjects: Atmospheric Sciences, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics
The eddy straining mechanism of Shutts (1983; S83) has long been considered a main process for explaining the maintenance of atmospheric blocking. As hypothesized in S83, incoming synoptic eddies experience a meridional straining effect when approaching a split jetstream, and as a result, enhanced PV fluxes reinforce the block. A two-layer QG model is adopted here as a minimal model to conduct [...]
Reducing uncertainties in climate projections with emergent constraints: Concepts, Examples and Prospects
Published: 2019-06-27
Subjects: Atmospheric Sciences, Climate, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics
Models disagree on a significant number of responses to climate change, such as climate feedback, regional changes, or the strength of equilibrium climate sensitivity. Emergent constraints aim to reduce these uncertainties by finding links between the inter-model spread in an observable predictor and climate projections. In this paper, the concepts underlying this framework are recalled with an [...]
Is it always Slowdown of the Walker circulation at solar cycle maximum?
Published: 2019-06-27
Subjects: Atmospheric Sciences, Climate, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics
It is a commentary following a published paper in PNAS titled, ‘Slowdown of the Walker circulation at solar cycle maximum’, by Stergios Misios, Lesley J. Gray, Mads F. Knudsen, Christoffer Karoff, Hauke Schmidt, and Joanna D. Haigh (2019). The article of Misios et.al.(2019) claims that there is a slowdown of the Walker Circulation during maximum periods of solar cycles. In support, they provided [...]
Dynamical Systems Theory Sheds New Light on Compound Climate Extremes in Europe and Eastern North America
Published: 2019-06-27
Subjects: Applied Mathematics, Atmospheric Sciences, Climate, Dynamic Systems, Earth Sciences, Meteorology, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Physics, Statistical, Nonlinear, and Soft Matter Physics
We propose a novel approach to the study of compound extremes, grounded in dynamical systems theory. Specifically, we present the co-recurrence ratio (α), which elucidates the dependence structure between variables by quantifying their joint recurrences. This approach is applied to daily climate extremes, derived from the ERA-Interim reanalysis over the 1979-2018 period. The analysis focuses on [...]
Systems of intensive vertical vortices in turbulent atmosphere
Published: 2019-06-22
Subjects: Atmospheric Sciences, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics
At certain conditions a system of well-separated quasi-point vortices can appear in two-dimensional turbulence. Such system contains main part (almost entire) of the flow enstrophy (mean squared vorticity). Spectral properties of the two-dimensional turbulence in the presence of the system of the quasi-point vortices have been studied using notion of the distributed chaos. Results of direct [...]
Large uncertainty in volcanic aerosol radiative forcing derived from ice cores
Published: 2019-06-20
Subjects: Atmospheric Sciences, Climate, Earth Sciences, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Volcanology
Reconstructions of volcanic aerosol radiative forcing are required to understand past climate variability. Currently, reconstructions of pre-20th century volcanic forcing are derived from sulfate concentrations measured in polar ice cores, predominantly using a relationship between average ice sheet sulfate deposition and stratospheric sulfate aerosol based on a single explosive eruption - the [...]
Data-driven prediction of a multi-scale Lorenz 96 chaotic system using deep learning methods: Reservoir computing, ANN, and RNN-LSTM
Published: 2019-06-20
Subjects: Applied Mathematics, Artificial Intelligence and Robotics, Atmospheric Sciences, Climate, Computer Sciences, Dynamic Systems, Earth Sciences, Fluid Dynamics, Non-linear Dynamics, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Physics
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 [...]
Understanding Low Cloud Mesoscale Morphology with an Information Maximizing Generative Adversarial Network
Published: 2019-06-05
Subjects: Artificial Intelligence and Robotics, Atmospheric Sciences, Computer Sciences, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics
Generative adversarial networks (GANs) are a class of machine learning algorithms with two neural networks, one generator and one discriminator, playing adversarial games with each other. Information maximizing GANs (InfoGANs) is a particular GAN type that tries to maximize mutual information between a subset of latent variables and generated samples, thereby establishing a mapping between the [...]
Past and projected weather pattern persistence with associated multi-hazards in the British Isles
Published: 2019-05-30
Subjects: Atmospheric Sciences, Climate, Earth Sciences, Meteorology, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics
Hazards such as heatwaves, droughts and floods are often associated with persistent weather patterns. Atmosphere-Ocean General Circulation Models (AOGCMs) are important tools for evaluating projected changes in extreme weather. Here, we demonstrate that 2-day weather pattern persistence, derived from the Lamb Weather Types (LWTs) objective scheme, is a useful concept for both investigating [...]
Single-Column Emulation of Reanalysis of the Northeast Pacific Marine Boundary Layer
Published: 2019-05-30
Subjects: Atmospheric Sciences, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics
An artificial neural network is trained to reproduce thermodynamic tendencies and boundary layer properties from ERA5 HIRES reanalysis data over the summertime Northeast Pacific stratocumulus to trade cumulus transition region. The network is trained prognostically using 7-day forecasts rather than using diagnosed instantaneous tendencies alone. The resulting model, Machine Assisted Reanalysis [...]
Preprint: Dataset of global extreme climatic indices due to an acceleration of ice sheet melting during the 21st century
Published: 2019-03-25
Subjects: Atmospheric Sciences, Climate, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics, Planetary Hydrology, Planetary Sciences
This article describes extreme indices maps (Data Cube, raster X Time) for different scenarios with a more important contribution to the sea level rise from Greenland and/or Antarctica during the 21st century under the Representative Concentration Pathway (RCP) 8.5 emission scenario. The indices are produced annually and globally with a resolution of 0.5°X0.5° from 1951 to 2099. The data were [...]
Technical note on the multi-GNSS, multi-frequency and near real-time ionospheric TEC monitoring system for South America
Published: 2019-02-21
Subjects: Atmospheric Sciences, Earth Sciences, Geophysics and Seismology, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics
Development of regional services able to provide ionospheric total electron content (TEC) maps with a high spatial resolution, and in near real-time, are of high importance for applications and the research community. We provide here the methodologies, and a preliminary assessment, of such a system. The system relies on the public Global Navigational Satellite Systems (GNSS) infrastructure in [...]
Reconstruction of Cloud Vertical Structure with a Generative Adversarial Network
Published: 2019-02-19
Subjects: Artificial Intelligence and Robotics, Atmospheric Sciences, Computer Sciences, Oceanography and Atmospheric Sciences and Meteorology, Physical Sciences and Mathematics
We demonstrate the feasibility of solving atmospheric remote sensing problems with machine learning using conditional generative adversarial networks (CGANs), implemented using convolutional neural networks (CNNs). We apply the CGAN to generating two-dimensional cloud vertical structures that would be observed by the CloudSat satellite-based radar, using only the collocated Moderate-Resolution [...]