Size of the Atmospheric Blocking Events: Scaling Law and Response to Climate Change

Abstract

Understanding the response of atmospheric blocking events to climate change has been of great interest in recent years. However, potential changes in the blocking area (size), which can affect the spatiotemporal characteristics of the resulting extreme events, have not received much attention. Using two large‐ensemble, fully coupled general circulation model (GCM) simulations, we show that the size of blocking events increases with climate change, particularly in the Northern Hemisphere (by as much as 17%). Using a two‐layer quasi‐geostrophic model and a dimensional analysis technique, we derive a scaling law for the size of blocking events, which shows that area mostly scales with width of the jet times the Kuo scale (i.e., the length of stationary Rossby waves). The scaling law is validated in a range of idealized GCM simulations. Predictions of this scaling law agree well with changes in blocking events' size under climate change in fully coupled GCMs in winters but not in summers.

Analog Forecasting of Extreme‐Causing Weather Patterns Using Deep Learning

“…Yet the state-of-the-art NWP models have difficulties with accurately predicting the formation and persistence of blocking events (Ferranti et al, 2015;Matsueda, 2011;Pelly & Hoskins, 2003). Overall, the key characteristics of extreme-causing weather patterns, their dynamics, and conditions that favor their formation (i.e., precursors) are not well understood (Coumou et al, 2015;Hassanzadeh et al, 2014;Horton et al, 2015;Hassanzadeh & Kuang, 2015;McKinnon et al, 2016;Nakamura & Huang, 2018;Nabizadeh et al, 2019;Teng et al, 2013;Woollings et al, 2018).…”

Predicting clustered weather patterns: A test case for applications of convolutional neural networks to spatio-temporal climate data

“…Classifying, identifying, and predicting specific patterns or key features in spatio-temporal climate and environmental data are of great interest for various purposes such as finding circulation regimes and teleconnection patterns [1][2][3][4][5] , identifying extreme-causing weather patterns [6][7][8][9][10][11][12] , studying the effects of climate change [13][14][15][16] , understanding ocean-atmosphere interaction 8,17,18 , weather forecasting 8,12,19,20 , and investigating air pollution transport 21,22 , just to name a few. Such classifications/identifications and predictions are often performed by employing empirical orthogonal function (EOF) analysis, clustering algorithms (e.g., K-means, hierarchical, self-organizing maps 1,3,[23][24][25][26][27][28][29] ), linear regression, or specifically designed indices, such as those used to identify atmospheric blocking events.…”

Deep spatial transformers for autoregressive data-driven forecasting of geophysical turbulence

“…In this paper, we build on the problem set-up similar to Chattopadhyay et al [8], where only part of the system's dynamics is available for training. Instead of the Lorenz 96 system, as used in Chattopadhyay et al [8] we consider a fully turbulent flow model represented by the two-layered quasi-geostrophic equations (QG) with a baroclinically unstable jet [10,11]. The model complexity of this QG system based on the instantaneous attractor dimension [12] of the upper layer's stream function (Ψ 1 ) is about 20.9 and comparable to the instantaneous attractor dimension of Z500 in the observed atmosphere [6].…”

“…The non-dimensional dynamical equations of QG has been developed following Lutsko et al [10], and Nabizadeh et al [11].…”

“…We have induced a baroclinically unstable jet at the center of a horizontally (zonally) periodic channel by setting Ψ 1 − Ψ 2 to be equal to a hyperbolic secant centered at y = 0. When eddy fluxes are absent, Ψ 2 is identically zero, making zonal velocity in the upper layer U 1 (y) = − ∂Ψ1 ∂y = − ∂Ψ R ∂y where [11]. Eq.…”