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Record W4413214919 · doi:10.1175/jcli-d-25-0114.1

Future Changes in North American Summer Heatwave Variability and Associated Dynamic and Thermodynamic Processes

2025· article· en· W4413214919 on OpenAlexaff
Dae Il Jeong, Bin Yu, Alex J. Cannon

Bibliographic record

VenueJournal of Climate · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsGeopotential heightOutgoing longwave radiationClimatologyEmpirical orthogonal functionsLongwaveEnvironmental scienceCloud coverShortwaveShortwave radiationTroposphereAtmospheric sciencesClimate modelGeopotentialClimate changePrecipitationGeologyMeteorologyGeographyRadiationRadiative transfer

Abstract

fetched live from OpenAlex

Abstract Projected changes in summer heatwave variability over North America (NA) are examined using a multimodel ensemble of 32 global climate models (GCMs), including three models with 30-member initial-condition ensembles, under the shared socioeconomic pathway 5-8.5 (SSP5-8.5) scenario. Applying the common empirical orthogonal function (EOF) approach, two distinct modes of heatwave variability are identified for a historical (1961–2010) and future (2051–2100) period, collectively explaining ∼30% of variability in each period. Spatial patterns of these modes differ between the periods, with significant regional changes detected regarding the variability across GCMs. Prominent North Pacific-to-North America (NA) wave trains, linked to the distinct modes, are evident in tropospheric geopotential and mean sea level pressure anomalies. These wave trains significantly weaken in future projections, reflecting a decline in large-scale dynamic processes. However, enhanced thermodynamic influences on these modes become apparent over the North Pacific and NA in the future. At the regional scale over NA, dynamically driven changes in cloud cover and shortwave downward radiation weaken, while thermodynamically driven changes in moisture availability and longwave downward radiation strengthen, reshaping energy balance and heat redistribution associated with heatwave variability. Consequently, surface radiation heating, the sum of shortwave and longwave radiation, emerges as the most influential variable for explaining changes in the distinct modes of heatwave variability between the periods. Internal variability, assessed using initial-condition ensembles, is comparable to model structure uncertainty in both large-scale and regional dynamic and thermodynamic processes that drive heatwave variability across the periods. Significance Statement Understanding how heatwaves and their associated dynamic and thermodynamic processes will evolve is essential for climate impact assessments. This study identifies distinct modes of summer heatwave variability over North America and reveals shifts in their drivers by 2100. While large-scale and regional dynamic processes that shape heatwaves weaken, thermodynamic influences—particularly changes in moisture availability and longwave radiation—become more dominant in the future. These findings highlight a fundamental shift in the physical mechanisms controlling heatwave variability, emphasizing the increasing role of surface radiation heating. By evaluating multiple climate models and internal variability, this study provides robust evidence of future changes in heatwave variability and its drivers, offering insights to improve projections of extreme events.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.251
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

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