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Record W1975977118 · doi:10.3137/ao1008.2010

The first coupled historical forecasting project (CHFP1)

2010· article· en· W1975977118 on OpenAlexafffundvenueabout
William J. Merryfield, Woosung Lee, G. J. Boer, Viatcheslav Kharin, Badal K. Pal, John Scinocca, Gregory M. Flato

Bibliographic record

VenueATMOSPHERE-OCEAN · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsUniversity of Victoria
FundersCanadian Foundation for Climate and Atmospheric Sciences
KeywordsInitializationClimatologyThermoclineEnvironmental scienceMeteorologyClimate modelWind stressForecast skillSea surface temperatureClimate changeComputer scienceGeologyOceanographyGeography

Abstract

fetched live from OpenAlex

Abstract A set of retrospective multi‐seasonal ensemble predictions based on a coupled global atmosphere‐ocean model is described. These predictions, designated as the first coupled Historical Forecasting Project or CHFP1, are produced with the climate model CGCM3.1 of the Canadian Centre for Climate Modelling and Analysis using a very simple initialization procedure in which model sea surface temperatures (SSTs) are nudged toward the observed values during a multi‐year period preceding the beginning of a forecast. This procedure, in addition to constraining initial SSTs to be close to observations, initializes equatorial Pacific zonal wind stress and thermocline depth with some skill. The ability of the subsequent forecasts to predict the evolution of SSTs, particularly in the equatorial Pacific, and surface air temperatures globally and in Canada is assessed. The results are compared with those of the second Historical Forecasting Project or HFP2, which uses a two‐tier strategy in which model SSTs are externally specified. Skill of CHFP1 forecasts, though generally modest, exceeds those of HFP2 in some cases, despite the larger multi‐model ensemble used in HFP2. CHFP1 represents an initial step in development directed toward a coupled operational seasonal prediction system for Canada.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.726
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.023
GPT teacher head0.228
Teacher spread0.205 · 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.

Study designNot applicable
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

Citations7
Published2010
Admission routes4
Has abstractyes

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