On the seasonal prediction and predictability of winter surface Temperature Swing Index over North America

Abstract

The rapid day-to-day temperature swings associated with extratropical storm tracks can cause cascading infrastructure failure and impact human outdoor activities, thus research on seasonal prediction and predictability of extreme temperature swings is of huge societal importance. To measure the extreme surface air temperature (SAT) variations associated with the winter extratropical storm tracks, a Temperature Swing Index (TSI) is formulated as the standard deviation of 24-h-difference-filtered data of the 6-hourly SAT. The dominant term governing the TSI variability is shown to be proportional to the product of eddy heat flux and mean temperature gradient. The seasonal prediction skill of the winter TSI over North America was assessed using Geophysical Fluid Dynamics Laboratory's new seasonal prediction system. The locations with skillful TSI prediction show a geographic pattern that is distinct from the pattern of skillful seasonal mean SAT prediction. The prediction of TSI provides additional predictable climate information beyond the traditional seasonal mean temperature prediction. The source of the seasonal TSI prediction can be attributed to year-to-year variations of the El Niño-Southern Oscillation (ENSO), North Pacific Oscillation (NPO), and Pacific/North American (PNA) teleconnection. Over the central United States, the correlation skill of TSI prediction reaches 0.75 with strong links to observed ENSO, NPO, and PNA, while the skill of seasonal SAT prediction is relatively low with a correlation of 0.36. As a first attempt of diagnosing the combined predictability of the first-order (the seasonal mean) and second-order (TSI) statistics for SAT, this study highlights the importance of the eddy-mean flow interaction perspective for understanding the seasonal climate predictability in the extra tropics. These results point toward providing skillful prediction of higher-order statistical information related to winter temperature extremes, thus enriching the seasonal forecast products for the research community and decision makers.

Changes in United States Summer Temperatures Revealed by Explainable Neural Networks

“…All model versions include a common ocean grid of approximately 1.0° spacing (though refined to 0.33° around the equator). The SPEAR system has already been successfully used for several studies in evaluating the predictability of temperature variability and heat extremes across North America (e.g., Jia et al., 2022; Yang et al., 2022).…”

Using Large Ensembles to Examine Historical and Projected Changes in Record‐Breaking Summertime Temperatures Over the Contiguous United States

“…SPEAR shares many components with GFDL CM4 (Held et al., 2019) but with configuration and physical parameterization choices geared toward climate prediction and projection on seasonal to decadal time scales for use in real‐time seasonal (Kirtman et al., 2014) and decadal predictions (Yang et al., 2021). SPEAR has shown demonstrated skill in seasonal prediction of North American temperature including summertime heat extremes (Jia et al., 2022), wintertime cold extremes (Jia et al., 2023), and wintertime temperature swings (Yang et al., 2022), and has been used in climate change studies of various systems (Delworth et al., 2022; Murakami et al., 2020; Pascale et al., 2020). We use the 30‐member large ensemble output from the medium resolution (SPEAR_MED) model with 50 km horizontal global atmosphere/land resolution (AM4‐LM4, Zhao et al., 2018a, 2018b) and an approximate 1° horizonal resolution for ocean and ice components (OM4, Adcroft et al., 2019).…”

Boreal Winter Extratropical Weather Regime Changes During 1979–2019 and Their Weather Impacts and Possible Linkages to Sea‐Ice in the Nordic Seas

“…According to previous studies the most significant weather regimes that affect temperature and precipitation in North America include both the positive and negative phases of the NAO, Pacific‐North America (PNA), and Western Pacific (WP) patterns (e.g., Leathers et al., 1991; Robertson & Ghil, 1999; Thompson & Wallace, 2001; Wise et al., 2015; Yang et al., 2022). It is well known that these circulation patterns are influenced by tropical oscillations, such as the Madden‐Julian Oscillation and El Nino Southern Oscillation (e.g., Jenney et al., 2019; Riddle et al., 2013; Roundy et al., 2010; Xiang et al., 2021).…”