MétaCan
Menu
← Back to cohort
Record W4411805501 · doi:10.5194/ems2025-488

Pan-Atlantic compound extremes between North America and Europe in a nested regional climate model setup

2025· preprint· en· W4411805501 on OpenAlexaffabout
Magdalena Mittermeier, Andrea Böhnisch, Martin Leduc, Ralf Ludwig

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsOuranos
Fundersnot available
KeywordsNested set modelClimatologyGeographyClimate modelClimate simulationEnvironmental scienceClimate changeGeologyOceanographyComputer science

Abstract

fetched live from OpenAlex

Teleconnections and the dynamics of extreme event co-occurrence across continents are relevant for understanding compounding risks and improving early warning systems. Pan-Atlantic compound extremes between climate extremes in North America and Europe have been receiving increasing attention lately, also due to the occurrence of recent events e.g., the February 2020 cold wave in North America that co-occurred with warm anomalies over Europe and wet and windy extremes in the UK, or the summer 2016 heat wave over Europe that was linked to upstream high-amplitude Rossby-wave packets originating from North America several days before the event. Previous studies furthermore suggest that wet extremes in Europe (e. g., 2021 floods in Germany) could be linked to particularly persistent atmospheric circulation and blocking patterns associated with upstream Rossby wave breaking over North America and the Atlantic.While recent research increasingly recognizes co-occurring extreme events over North America and Europe as interconnected phenomena—often mediated by Rossby wave breaking and atmospheric blocking—studies explicitly linking specific North American extremes (e.g., cold spells) to simultaneous European extremes (e.g., heavy precipitation) remain limited. This is particularly true when moving beyond individual historical cases to systematically analyze pan-Atlantic compound extremes using reanalysis data and climate model simulations. A key challenge lies in achieving high-resolution climate simulations that can accurately capture precipitation-related extremes, while also encompassing the broad spatial scale required to represent large-scale pan-Atlantic circulation patterns.To address these gaps, we take advantage of a large ensemble of high-resolution climate simulations over a North American and a European domain, which are nested in the atmospheric, westerly flow of a global earth system model. This dataset provides us a large data base of regional extremes in high-resolution of 0.11° while allowing us to study large-scale teleconnections of extreme events at both sides of the Atlantic. The regional nested climate simulations consist of 50 members of the Canadian Earth System Model version 2 (CanESM-2) as well as four members of the Max Planck Earth System Model (MPI-ESM), both dynamically downscaled with the Canadian Regional Climate Model version 5 (CRCM5) over the North-American and European CORDEX domain.Here, we present our research concept and preliminary results from analyzing pan-Atlantic compound extremes in reanalysis data, a reanalysis-driven CRCM5 simulation, and our ensemble of nested regional climate model simulations. This research aims to advance our understanding of the processes underlying co-occurring extreme events across the North Atlantic by identifying and investigating specific compound events in high resolution.

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)0.001
metaresearch head score (Gemma)0.002
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.175

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.052
GPT teacher head0.269
Teacher spread0.217 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same topicClimate variability and models→French-language works237,207→