MétaCan
Menu
← Back to cohort
Record W1974315828 · doi:10.1029/2006jc003949

Correction to “Evaluation of the simulation of the annual cycle of Arctic and Antarctic sea ice coverages by 11 major global climate models”

2006· article· en· W1974315828 on OpenAlexaboutno aff
Claire L. Parkinson, Konstantin Y. Vinnikov, Donald J. Cavalieri

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsClimatologySea iceSouthern HemisphereNorthern HemisphereArcticThe arcticClimate modelGeologyOceanographyClimate change

Abstract

fetched live from OpenAlex

[1] In the paper “Evaluation of the simulation of the annual cycle of Arctic and Antarctic sea ice coverages by 11 major global climate models” by Claire L. Parkinson, Konstantin Y. Vinnikov, and Donald J. Cavalieri (Journal of Geophysical Research, 111, C07012, doi:10.1029/2005JC003408, 2006), the ice amounts from the French Institut Pierre Simon Laplace (IPSL) CM4 model were underrepresented because of mistakenly showing the amounts for ice thicknesses of at least 60 cm instead of ice thicknesses of at least 6 cm. This affected Figures 2j, 3j, 4 and 5, and the text discussion of each of these figures. The authors apologize for the error and thank Olivier Arzel, Thierry Fichefet, and Jean-Louis Dufresne for pointing it out and for confirming the correctness of the revised figures and text. Corrected Figures 2, 3, 4, and 5 appear below, along with corrected portions of the text, all resulting exclusively from the revised IPSL results. [2] In the abstract, the statement that “in the Southern Hemisphere all except one simulate the monthly averages to within ±6.3 × 106 km2 of the observed values” should be revised to delete “except one.” [3] Paragraph [30] should read as follows: “The French IPSL CM4 model does an excellent job in simulating the annual cycle of ice extents in the Northern Hemisphere, although with a peak 1 month early, in February, and with a subsequent slight under simulation of the extent during the ice decay period (Figure 3j). However, in the winter the excellent ice extent values are deceiving, as they reflect significant but offsetting errors in different regions (Figure 2j). Specifically, the winter ice distribution has too little ice in the Bering Sea and the Sea of Okhotsk but too much ice on the Atlantic side, in the Labrador Sea, Greenland Sea, and Barents Sea. Also affecting the winter ice extent values, ice is not calculated for the Canadian Archipelago or James Bay (southeast of Hudson Bay). The simulated summer ice distributions are considerably better, being excellent in the Greenland Sea, showing slightly too much ice in the northern Barents Sea and western Kara Sea, and showing slightly too little ice in the Beaufort Sea as well as the Canadian Archipelago (Figure 2j). In the Southern Hemisphere, the timing of minimum and maximum ice extents in February and September, respectively, is correct, although the model simulates too little ice for most of the year, with too much ice in the peak winter months of August and September (Figure 3j). Spatially, the March, late summer Southern Hemisphere ice distribution correctly has the most ice in the western Weddell Sea and in the Ross Sea although has too little ice in both those seas and has none of the coastal ice that the observations show around the rest of the continent. The late winter ice distribution has close to the correct amount of ice but has it positioned with far too much ice in the Bellingshausen, Amundsen, and western Weddell seas, to the west and east of the Antarctic Peninsula, and too little ice around the rest of the continent.” [4] The third sentence of paragraph [33] should begin as follows: “In the Southern Hemisphere, 10 of the 11 models….” [5] The last sentence of paragraph [36] should read as follows: “Similarly, the much better performance of the Norwegian and UK HadGEM1 models in the Atlantic versus the Pacific portion of the sub-Arctic (Figures 2c and 2h) could result from a greater interest in the Atlantic region, even if the attention was toward the atmospheric and oceanic circulations, affecting weather conditions in Norway and the UK, rather than specifically toward the sea ice.” [6] In the next to the last sentence in paragraph [38], “all models except one have simulated” should be “all models have simulated.” [7] Paragraph [39] should read as follows: “Despite the variety of problems with the individual simulations, when the results from the 11 models are averaged, the ensemble average of the model simulations does quite well in simulating the annual cycle of sea ice extents in each hemisphere, although with the interesting contrast that the ensemble Northern Hemisphere monthly averages are all greater than the observations while the ensemble Southern Hemisphere monthly averages are mostly less than the observations (Figure 5). Percentage-wise, the Northern Hemisphere averages exceed the observations by values ranging from 2.8% in December to 14.1% in September and the Southern Hemisphere averages differ from the observations by values ranging from 2.5% too high in October to 23.1% too low in April. In terms of monthly ice extents, the Northern Hemisphere ensemble averages are greater than the observed by amounts ranging from 0.35 × 106 km2 in November to 1.3 × 106 km2 in May and the Southern Hemisphere averages differ from the observed by amounts ranging from 0.45 × 106 km2 too high in October to 1.6 × 106 km2 too low in April.” [8] The first two sentences of paragraph [40] should read as follows: “Using the root mean square (RMS) of the 12 monthly departures from the observed ice extents as one measure of how well a model is performing overall, the 11-model composite (with an RMS of 0.8 × 106 km2) comes out superior to any of the individual models in the simulation of Southern Hemisphere ice extents, followed by the ECHAM5 and CSIRO Mk3 models (RMS = 1.6 × 106 km2). In the simulation of the Northern Hemisphere ice extents, the CGCM3 and IPSL CM4 come out on top (with an RMS of 0.6 × 106 km2), followed by the 11-model composite and the MIROC3 and ECHAM5 models (RMS = 0.8 × 106 km2).”

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)0.004
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.114
Threshold uncertainty score0.381

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.051
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0040.004
Science and technology studies0.0030.001
Scholarly communication0.0040.003
Open science0.0040.003
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.1140.061

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 head0.289
Teacher spread0.270 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations3
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Geophysical Research Atmospheres→Same topicArctic and Antarctic ice dynamics→French-language works237,207→