Skillful Seasonal Prediction of North American Summertime Heat Extremes

Jia, Liwei; Delworth, T. L.; Kapnick, S. B.; Yang, Xiaosong; Johnson, Nathaniel C.; Cooke, William; Lu, Feiyu; Harrison, Matthew; Zeng, Fanrong; McHugh, Colleen; Wittenberg, Andrew T.; Zhang, Liping; Murakami, Hiroyuki; Tseng, Kuo-Hung

doi:10.1175/jcli-d-21-0364.1

Cited by 14 publications

(10 citation statements)

References 52 publications

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“…The focus of this study is to demonstrate the distinction of TSI skill from SAT and the process level understanding of the achieved skill, so we only examined the predictions of boreal winter season initialized on 1 st December. The skill of the primary seasonal predictability drivers (e.g., ENSO) tends to decline with forecast lead times (Yang et al, 2015b;Lu et al, 2020;Jia et al, 2022), so the skill of predicting TSI drops with lead times consequently (Supplementary Figure 9). A detailed analysis of the skill evolution with lead time will be left for future research.…”

Section: Discussionmentioning

confidence: 99%

“…The focus of this study is on the December-January-February (DJF) season from SRF initialized on 1 st December. SPEAR's SRF has shown significant seasonal forecast skill in predicting a wide range of essential indicators of climate variability, including but not limited to the SST, SAT over land, mid-latitude baroclinic waves, Antarctic/Arctic sea ice, Kuroshio extension, North American summertime heat extremes, and atmospheric rivers over Western North America (Lu et al, 2020;Bushuk et al, 2021Bushuk et al, , 2022Tseng et al, 2021;Zhang et al, 2021;Jia et al, 2022;Joh et al, 2022).…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

et al. 2022

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“…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).…”

Section: Datamentioning

confidence: 99%

Changes in United States Summer Temperatures Revealed by Explainable Neural Networks

Labe,

Johnson,

Delworth

2024

Earth's Future

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To better understand the regional changes in summertime temperatures across the conterminous United States (CONUS), we adopt a recently developed machine learning framework that can be used to reveal the timing of emergence of forced climate signals from the noise of internal climate variability. Specifically, we train an artificial neural network (ANN) on seasonally averaged temperatures across the CONUS and then task the ANN to output the year associated with an individual map. In order to correctly identify the year, the ANN must therefore learn time‐evolving patterns of climate change amidst the noise of internal climate variability. The ANNs are first trained and tested on data from large ensembles and then evaluated using observations from a station‐based data set. To understand how the ANN is making its predictions, we leverage a collection of ad hoc feature attribution methods from explainable artificial intelligence (XAI). We find that anthropogenic signals in seasonal mean minimum temperature have emerged by the early 2000s for the CONUS, which occurred earliest in the Eastern United States. While our observational timing of emergence estimates are not as sensitive to the spatial resolution of the training data, we find a notable improvement in ANN skill using a higher resolution climate model, especially for its early twentieth century predictions. Composites of XAI maps reveal that this improvement is linked to temperatures around higher topography. We find that increases in spatial resolution of the ANN training data may yield benefits for machine learning applications in climate science.

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“…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).…”

Section: Datamentioning

confidence: 99%

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

McHugh,

Delworth,

Cooke

et al. 2023

Earth's Future

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The frequency and intensity of heat extremes over the United States have increased since the mid‐20th century and are projected to increase with additional anthropogenic greenhouse gas forcing. We define heat extremes as summertime (June–August) daily maximum 2m temperatures that exceed historical records. We examine characteristics of historical and near‐future heat extremes using observations and past and future projections using 100 ensemble members from three coupled global climate models large ensemble simulations. We find that the large ensembles capture the trend and variability of heat extremes over the period 2006–2020 relative to the 1991–2005 climatology but overestimate the frequency at which the heat extremes occur. In future warming scenarios, heat extremes continue to increase over the next 30 years, with high amplitude records in the Northwest and Central US. After 2050, we find there is a spread in the frequency of heat extremes that is dependent on the emissions scenario, with a high emissions until mid‐century followed by a high mitigation scenario showing a decrease in heat extremes by the end of the century. Although the frequency of future heat extremes is likely overestimated in the large ensembles, they are still a powerful tool for researching extreme temperatures in the climate system.

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Skillful Seasonal Prediction of North American Summertime Heat Extremes

Cited by 14 publications

References 52 publications

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

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

Changes in United States Summer Temperatures Revealed by Explainable Neural Networks

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

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