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Record W2077688253 · doi:10.3390/atmos4030272

On the Current and Future Dry Spell Characteristics over Africa

2013· article· en· W2077688253 on OpenAlexaffabout
Bessam Bouagila, Laxmi Sushama

Bibliographic record

VenueAtmosphere · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsPrecipitationEnvironmental scienceClimatologySpellClimate changeAtmospheric sciencesMeteorologyGeographyPhysics

Abstract

fetched live from OpenAlex

Changes in precipitation frequency and intensity distribution over Africa will have a direct impact on dry spells and, therefore, will affect various climate sensitive sectors. In this study, the ability of the fifth generation of the Canadian Regional Climate Model (CRCM5) in simulating annual and seasonal dry spell characteristics is assessed for four precipitation thresholds (0.5 mm, 1 mm, 2 mm and 3 mm) over Africa. The dry spell characteristics considered are the number of dry days, number of dry spells and five-year return levels of maximum dry spell durations. The performance errors are assessed by comparing ERA-Interim driven CRCM5 with the Global Precipitation Climatology Project (GPCP) dataset, for the common 1997–2008 period. Lateral boundary forcing errors, i.e., errors in the CRCM5 simulation created by errors in the driving Canadian Earth System model (CanESM2) data—as well as the added value—of CRCM5 over CanESM2 are also assessed for the current climate. This is followed by an assessment of projected changes to dry spell characteristics for two future climates (2041–2070 and 2071–2100) simulated by both CRCM5 driven by CanESM2 and CanESM2 itself, for Representative Concentration Pathway (RCP) 4.5. Results suggest that CRCM5 driven by ERA-Interim has a tendency to overestimate the annual mean number of dry days and the five-year return level of the maximum dry spell duration in a majority of the regions while it slightly underestimates the number of dry spells. In general, the CRCM5 performance errors associated with the annual and seasonal dry spell characteristics are found to be larger in magnitude compared to the lateral boundary forcing errors. Projected changes to the dry spell characteristics for the 2041–2070 and 2071–2100 periods, with respect to the 1981–2010 period suggests significant changes in the tropics, with the mean number of dry days and the five-year return levels of maximum dry spell duration increasing, while the mean number of dry spell days decreases.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.558
Threshold uncertainty score0.890

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.011
GPT teacher head0.208
Teacher spread0.196 · 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 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

Citations36
Published2013
Admission routes2
Has abstractyes

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