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Record W2896974429 · doi:10.5194/nhess-19-421-2019

Projected intensification of sub-daily and daily rainfall extremes in convection-permitting climate model simulations over North America: implications for future intensity–duration–frequency curves

2019· article· en· W2896974429 on OpenAlexaff
Alex J. Cannon, Silvia Innocenti

Bibliographic record

VenueNatural hazards and earth system sciences · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsInstitut National de la Recherche ScientifiqueEnvironment and Climate Change Canada
Fundersnot available
KeywordsClimatologyClimate modelEnvironmental scienceDuration (music)Climate changeScalingScale (ratio)MeteorologyMagnitude (astronomy)Return periodMathematicsGeographyGeologyCartographyPhysics

Abstract

fetched live from OpenAlex

Convection-permitting climate models have been recommended for use in projecting future changes in local-scale, short-duration rainfall extremes that are of the greatest relevance to engineering and infrastructure design, e.g., as commonly summarized in intensity–duration–frequency (IDF) curves. Based on thermodynamic arguments, it is expected that rainfall extremes will become more intense in the future. Recent evidence also suggests that shorter-duration extremes may intensify more than longer durations and that changes may depend on event rarity. Based on these general trends, will IDF curves shift upward and steepen under global warming? Will long-return-period extremes experience greater intensification than more common events? Projected changes in IDF curve characteristics are assessed based on sub-daily and daily outputs from historical and late 21st century pseudo-global-warming convection-permitting climate model simulations over North America. To make more efficient use of the short model integrations, a parsimonious generalized extreme value simple scaling (GEVSS) model is used to estimate historical and future IDF curves (1 to 24 h durations). Simulated historical sub-daily rainfall extremes are first evaluated against in situ observations and compared with two high-resolution observationally constrained gridded products. The climate model performs well, matching or exceeding performance of the gridded datasets. Next, inferences about future changes in GEVSS parameters are made using a Bayesian false discovery rate approach. Large portions of the domain experience significant increases in GEVSS location (>99 % of grid points), scale (>88 %), and scaling exponent (>39 %) parameters, whereas almost no significant decreases are projected to occur (<1 %, <5 %, and <5 % respectively). The result is that IDF curves tend to shift upward (increases in location and scale), and, with the exception of the eastern US, steepen (increases in scaling exponent), which leads to the largest increases in return levels for short-duration extremes. The projected increase in the GEVSS scaling exponent calls into question stationarity assumptions that form the basis for existing IDF curve projections that rely exclusively on simulations at the daily timescale. When changes in return levels are scaled according to local temperature change, median scaling rates, e.g., for the 10-year return level, are consistent with the Clausius–Clapeyron (CC) relation at 1 to 6 h durations, with sub-CC scaling at longer durations and modest super-CC scaling at sub-hourly durations. Further, spatially coherent but small increases in dispersion – the ratio of scale and location parameters – of the GEVSS distribution are found over more than half of the domain, providing some evidence for return period dependence of future changes in extreme rainfall.

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 score0.972
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.019
GPT teacher head0.258
Teacher spread0.239 · 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

Citations62
Published2019
Admission routes1
Has abstractyes

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