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Record W7019086784

Extreme precipitation and temperature relationship in current and future climate. / Relations entre précipitations extrêmes et température en climat actuel et futur.

2023· other· fr· W7019086784 on OpenAlexaboutno aff

Bibliographic record

VenueEspaceINRS Institutional Digital Repository (Institut National de la Recherche Scientifique) · 2023
Typeother
Languagefr
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Air temperatureExtratropical cyclone
DOInot available

Abstract

On s’attend à ce que la fréquence et l’intensité des précipitations extrêmes augmentent dans le futur en raison des changements climatiques, mais l’ampleur de ces variations est incertaine. Le lien entre les précipitations extrêmes et la température a été étudié afin de mieux comprendre les principaux processus impliqués dans la génération des précipitations extrêmes et d’ainsi accroître la fiabilité des projections. L’idée principale est qu’une atmosphère plus chaude peut contenir plus d’humidité et que les précipitations extrêmes augmenteront au même rythme que les tempéra tures. La capacité de stockage d’humidité de l’atmosphère est d’environ 7%/°C selon l’équation de Clausius-Clapeyron et donc, suivant cette hypothèse, l’intensité des précipitations extrêmes devrait augmenter selon ce taux si les autres processus générateurs de précipitations extrêmes restent inchangés en climat futur. Cependant, plusieurs facteurs ont été identifiés ayant un impact sur la relation entre la température et les précipitations extrêmes. La présente étude vise à mener une analyse approfondie de la relation temperature-précipitations extrêmes en climat historique et futur à partir d’un large ensemble de simulations climatiques couvrant la partir nord-est de l’Amérique du Nord. La réponse des précipitations extrêmes aux changements de température (température de l’air en surface et température du point de rosée) moyenne a d’abord été étudiée en fonction du caractère plus ou moins extrême des précipitations (périodes de retour) et de la durée (p.ex. pré cipitation maximalle annuelle sur une heure ou sur 24 heures) pour les mois de mai à septembre. Il a été constaté que la disponibilité de l’humidité changeait selon les régions et avait un impact sur les estimations des taux de variations des précipitations extrêmes en fonction de la température (TVPT), avec des estimations plus robustes obtenues lorsque les TVPT sont basées sur la température du point de rosée en surface. Il a également été constaté que les TVPT basés sur la température du point de rosée pouvaient être utilisés pour prédire les changements dans les précipitations extrêmes de courte durée dans le climat futur sur la base des TVPT estimés dans la période historique sur une grande partie du domaine d’étude. Les TVPT ont également été estimés par le biais d’une analyse basée sur les événements. Une classification des précipitations extrêmes basée sur la durée a été envisagée. Les résultats montrent que les valeurs maximales annuelles horaires seront intégrées dans des événements pluvieux plus courts et probablement plus convectifs dans un climat futur. En outre, la durée des précipitations extrêmes joue un rôle essentiel dans l’estimation des TVPT. En outre, les TVPT supérieurs aux attentes thermodynamiques dans les régions méridionales du domaine ont été expliqués par un changement dans le type de précipitations dominant à mesure que la température du point de rosée augmentait. L’étude a également été étendue pour explorer la manière dont les précipitations extrêmes réagissent au changement climatique à différentes saisons. Les résultats montrent que les TVPT dans la région étudiée ont été affectés par les variations saisonnières, qui ont également un impact sur les changements futurs des précipitations extrêmes. Bien qu’il ait été déterminé précédemment que les projections futures de précipitations extrêmes étaient réalisables pour les mois les plus chauds sur la base des estimations passées, il convient d’être prudent lorsque l’on fait des prévisions pour d’autres saisons. D’autres analyses ont été entreprises pour mieux comprendre les valeurs observées des TVPT supérieures à la relation thermodynamique dans certaines régions du domaine d’étude. Les valeurs des TVPT supérieures aux attentes théoriques ont été liées à des changements dans la nature convective des précipitations et dans les schémas de circulation. <br /><br />Extreme precipitation events are expected to increase in frequency and intensity due to climate change. The magnitude of these changes is, however, uncertain. Researchers worldwide have studied the link between extreme precipitation and temperatures to better understand the processes involved in extreme precipitation generation and try to develop more reliable projections of extreme precipitation given expected temperature changes. The main idea is that the moisture holding capacity of the atmosphere increases under warmer conditions, resulting in more intense precipitation. The rate of increase of the moisture-holding capacity is around 7%/°C as prescribed by the Clausius-Clapeyron equation (CC scaling). Extreme precipitation events are therefore expected to increase at a similar rate if the other processes involved in extreme precipitation generation remain unchanged in future climates. However, several factors impacting the temperature extreme precipitation relationship have been identified, leading to departure from the CC scaling. The current study uses a large ensemble of climate simulations to comprehensively investigate this relationship over northeastern North America in historical and future climates. The response of extreme precipitation to temperature changes (surface air temperature and surface dew point temperature) was first investigated through the estimation of the Temperature Precipitation Scaling Rates (TPSR), which defines the incremental changes in extreme precipitation intensity with increasing temperature. Annual maximum precipitations over the May to September period for a given duration (e.g., hourly or daily) were first considered. Specific percentiles (e.g., 50th and 99th percentile) of the annual maximum precipitation distribution for various durations were estimated. It was found that moisture availability differs between regions and impacts TPSR estimations. Using surface dew point temperature, which considers moisture availability through relative humidity, led to more con sistent estimates. It was also found that TPSRs based on surface dew point temperature could be used to estimate changes for short-duration extreme precipitation in future climates for the May September period based on TPSRs estimated in the historical period over a large part of the study domain. In order to better characterize the rainfall type associated with 1-hr annual maximum precipitation, an event-based analysis was performed. Rainfall events, defined by a 3-hr inter-event dry period, in which 1-hr annual maximum precipitations are embedded, were extracted from simulated series. Rainfall event duration was classified into the groups: 1) short-duration event (less than 5- hr event); 2) mid-duration event (between 5-hr and 10-hr event); 3) long-duration event (more than 10-hr event). Rainfall event duration was used to indicate the more or less convective or stratiform character of the 1-hr annual maximum precipitations, with short-duration events being associated with more likely convective and long-duration events with more likely stratiform events. Results show that 1-hr annual maximum precipitations will be embedded in shorter and likely more convective dominant rainfall events in a future climate. Moreover, rainfall event duration was found to play an essential role in TPSR estimations since it is related to the more or less convective character of the rainfall events in which 1-hr annual maximum precipitations are embedded. In addition, it was found that for moderate events (50th percentile), rainfall events generate more precipitation with the highest contribution from the 1-hour peak values. However, for more extreme (99th percentile) rainfall events, the 1-hour annual maximum precipitations are more sensitive to surface dew point temperature changes in the central and northern regions than the total rainfall dept associated with such events. In contrast, in the southern regions, the convective nature of the rainfall events leads to higher scaling values for the total rainfall depth. Furthermore, TPSRs above CC scaling observed in the southern part of the simulation domain were attributed to a change in dominant rainfall type as surface dew point temperature increases. Seasonal temperature-precipitation relationships were also investigated. Seasonal (December-January-February or DJF, March-April-May or MAM, June-July-August or JJA, September-October November or SON) TPSR values were estimated using the 1-hr seasonal maximum precipitation series. Large variations in seasonal TPSR values were obtained. Super CC scaling was observed in the south and northeast parts of the domain in JJA. The links between the occurrence of super CC, seasons, and the duration of rainfall events in which 1-hr seasonal maximum precipitations are embedded, were investigated. Two types of super CC scaling were observed. The first one corresponds to super CC with no concurrent changes in rainfall type. Therefore, this type of super CC is explained by the intensification or changes in circulation patterns under warming conditions. It is mainly observed in the northeast region of the domain (Labrador, Newfoundland, and northern Quebec). The second one corresponds to super CC going along with large changes in rainfall type (more 1-hr seasonal maximum precipitation embedded in short-duration events as surface dew point temperature increases). This super CC is explained by a shift in rainfall types with temperature, a hypothesis already put forward in the literature. It is observed in the southwest of the domain in US states south of the Great Lakes. These findings have important consequences for assessing projected extreme precipitation changes based on temperature-precipitation relationships. Departure from the CC scaling, notably the super CC observed in some regions and seasons, may lead to underestimating the projected changes in extreme precipitation for these regions. Considering the importance of applying the most reliable estimates of projected changes

Stored with the screening record, where it is evidence for the labels above.

How this classification was reachedexpand

The three-model screen

all 5,600 screened works →

All three models called this out of scope.

stratum: french · design weight: 1554.47 (the sample is stratified; any rate computed without the weight is wrong)
Claude Opus 4.8OUT
genre: empirical
about Canada: no
confidence: high

Analysis of the extreme-precipitation and temperature relationship under climate change; a climate-science question.

GPT-5.6 (high)OUT
genre: empirical
about Canada: no
confidence: high

This studies extreme precipitation and climate relationships, not research itself.

Grok 4.5OUT
genre: empirical
about Canada: no
confidence: high

Climate-science thesis on extreme precipitation–temperature relationships; domain geosciences.

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)0.001
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 score0.004
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0040.000

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 head0.068
GPT teacher head0.339
Teacher spread0.271 · 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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Same venueEspaceINRS Institutional Digital Repository (Institut National de la Recherche Scientifique)French-language works237,207