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An analysis of regional climate model performance over the tropical Americas. Part II: simulating subseasonal variability of precipitation associated with ENSO forcing

2009· article· en· W4239612110 on OpenAlexafffund
Étienne Tourigny, Colin Jones

Bibliographic record

VenueTellus A Dynamic Meteorology and Oceanography · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversité du Québec à Montréal
FundersNational Supercomputer Centre, Linköpings UniversitetNatural Sciences and Engineering Research Council of CanadaCanadian Foundation for Climate and Atmospheric Sciences
KeywordsClimatologyPrecipitationForcing (mathematics)PredictabilityEnvironmental scienceEl Niño Southern OscillationMadden–Julian oscillationClimate modelWet seasonMultivariate ENSO indexTropicsAtmospheric sciencesLa NiñaClimate changeMeteorologyGeographyGeologyConvectionMathematicsOceanography

Abstract

fetched live from OpenAlex

The El Niño/Southern Oscillation (ENSO) constitutes a major source of potential predictability in the tropics. The majority of past seasonal prediction studies have concentrated on precipitation anomalies at the seasonal mean timescale. However, fields such as agriculture and water resource management require higher time frequency forecasts of precipitation variability. Regional climate models (RCMs), with their increased resolution, may offer one means of improving general circulation model forecasts of higher time frequency precipitation variability.Part I of this study evaluated the ability of the Rossby Centre regional atmospheric model (RCA), forced by analysed boundary conditions, to simulate seasonal mean precipitation anomalies over the tropical Americas associated with ENSO variability. In this paper the same integrations are analysed, with the focus now on precipitation anomalies at subseasonal (pentad) timescales.RCA simulates the climatological annual cycle of pentad-mean precipitation intensity quite accurately. The timing of the rainy season (onset, demise and length) is well simulated, with biases generally of less than 2 weeks. Changes in the timing and duration of the rainy season, associated with ENSO forcing, are also well captured. Finally, pentad-mean rainfall intensity distributions are simulated quite accurately, as are shifts in these distributions associated with ENSO forcing.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.114
Threshold uncertainty score0.453

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.248
Teacher spread0.235 · 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.

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

Citations9
Published2009
Admission routes2
Has abstractyes

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