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Record W4407977765 · doi:10.3390/atmos16030273

Spatio-Temporal Analysis of Changes in the Iranian Summer Subtropical High-Pressure System from a Climate Change Perspective

2025· article· en· W4407977765 on OpenAlexaff
Mokhtar Fatahian, Zahra Hejazizadeh, Ali Reza Karbalaee, Hamed Shahidinia, Junye Wang

Bibliographic record

VenueAtmosphere · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsAthabasca University
FundersIran National Science Foundation
KeywordsPerspective (graphical)ClimatologySubtropicsClimate changeSubtropical ridgeEnvironmental scienceMeteorologyGeographyOceanographyGeologyPrecipitationMathematicsEcology

Abstract

fetched live from OpenAlex

Climate change plays a significant role in altering the behavior of large-scale atmospheric systems, particularly the subtropical high-pressure systems relevant to the climate of Iran. This study investigates the impact of climate change on the subtropical high-pressure system over Iran by utilizing ERA5 reanalysis data and CORDEX projections. Focusing on future projections (2022–2063) under RCP4.5 and RCP8.5 scenarios, the analysis reveals substantial shifts in the position and intensity of the subtropical high when comparing the high-pressure center between currently observed data and the projected scenarios. The center of the high-pressure system exhibits a northward migration, particularly pronounced in August; a consistent upward trend in geopotential height, analyzed using the Kendall trend method, is observed, indicating a strengthening of the high-pressure system. This intensification leads to a westward and northward expansion of the summer high-pressure cell. Consequently, this study anticipates the emergence of more pronounced cyclonic circulations at higher latitudes (>38° N) in the future. These findings suggest that climate change will substantially alter the behavior of the subtropical high over Iran, impacting regional weather patterns and potentially leading to climate anomalies.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.020
GPT teacher head0.253
Teacher spread0.234 · 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 teacher head, not a consensus.

Study designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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