Evaluating Indices of Blocking Anticyclones in Terms of Their Linear Relations With Surface Hot Extremes

Abstract

Changes in frequencies of blocking anticyclones are sometimes used to explain changes in surface hot extremes. However, there is no consensus on the definition of blocking anticyclones, and several indices have been developed to measure them. Here we linearly regress interannual variations of hemispheric continental summer surface hot extreme area on the corresponding variations of blocking anticyclones in the ERA‐Interim reanalysis data and use cross‐validation test error to measure the blocking‐extreme link. Relative mean square validation error is at least 0.91 (relative to no knowledge of blocking) for existing indices defined on 500‐hPa geopotential height, when summed over land and ocean. This can be reduced to 0.55, largely by excluding blocks over ocean and optimizing parameters/thresholds and partly by using anomaly‐based or anomaly‐reversal hybrid indices. This framework helps to quantify the association of hot extremes and blocks with an uncertainty estimate and can be used for other extremes as well.

Analog Forecasting of Extreme‐Causing Weather Patterns Using Deep Learning

“…However, given the incomplete understanding of extreme‐causing weather patterns and their complexities, expert‐labeled data are not useful for our objective. For example, indices designed to find blocking patterns in the Z500 field based on their presumed properties are known to perform poorly, for example, in identifying extreme‐causing patterns even on the same day as the heat or cold extreme events (Chan et al, ). Expert‐labeling becomes even less effective for the purpose of prediction, which requires accounting for the nonlinear spatiotemporal evolution of the atmospheric circulation over several days or longer.…”

“…We focus on extreme temperature events over the North American continent in the subtropical and midlatitude regions between 30 • N and 60 • N. For a given calendar date in a given ensemble member, the T2m anomalies are computed by removing the climatological mean, defined as the 15-day running mean of T2m centered around that date and averaged over all ensemble members. Following Chan et al (2019), heat waves (cold spells) are defined as land grid points over North America in summer (winter) with daily T2m anomaly in the 99th (1st) percentile and larger (smaller) than 3 K (−1 K) for a sequence of at least five consecutive days. We then identify the onsets of these extreme temperature events as the first day of each sequence.…”

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.…”

“…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. Each approach suffers from some major shortcomings (see the reviews by Grotjahn et al 6 and Monahan et al 30 ); for example, there are dozens of blocking indices which frequently disagree and produce conflicting statistics on how these high-impact extreme-causing weather patterns will change with climate change 10,14,31 .…”

The 3D Structure of Northern Hemisphere Blocking Events: Climatology, Role of Moisture, and Response to Climate Change

“…Blocking events are large-scale, quasi-stationary, highpressure (anticyclonic) anomalous systems that last beyond the synoptic time scales (sometimes for weeks) and block or divert the midlatitude westerlies (Rex 1950;Green 1977;Hoskins and James 2014;Woollings et al 2018). Due to their persistence and size, depending on the season and the region, blocking events can cause, or contribute to, various types of extreme events such as heat waves, cold spells, droughts, and heavy rainfall episodes (e.g., Barriopedro et al 2011;Dole et al 2011;Pfahl and Wernli 2012;Brunner et al , 2018Schaller et al 2018;Zschenderlein et al 2019;Röthlisberger and Martius 2019;Wehrli et al 2019;Lenggenhager et al 2019;Chan et al 2019). Despite much effort, the dynamical mechanisms responsible for the generation and maintenance of the blocking events are still not well understood (Hoskins and James 2014;Hassanzadeh and Kuang 2015;Nakamura and Huang 2018;Woollings et al 2018).…”