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Enregistrement W2945744074 · doi:10.5281/zenodo.1194490

Idealized Single-Forcing Gcm Simulations With Ncar Cesm

2018· dataset· en· W2945744074 sur OpenAlexaboutno aff
Michael P. Erb, C. S. Jackson, Pedro DiNezio

Notice bibliographique

RevueZenodo (CERN European Organization for Nuclear Research) · 2018
Typedataset
Langueen
DomaineEngineering
ThématiqueMetallurgy and Material Forming
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésGCM transcription factorsForcing (mathematics)ClimatologyEnvironmental scienceGeneral Circulation ModelMeteorologyGeologyPhysicsClimate change

Résumé

récupéré en direct d'OpenAlex

This repository contains a set of single-forcing general circulation model (GCM) simulations run with the National Center for Atmospheric Research (NCAR) Community Earth System Model version 1.2 (CESM1.2). In each of these equilibrium simulations, one climate forcing was altered while all others were held constant at preindustrial levels, modeling the climate response to individual climate forcings. Simulations were run for obliquity (low and high obliquity), precession (four phases of the precession cycle with high eccentricity, and one simulation with eccentricity set to zero), half CO<sub>2</sub>, and LGM-sized ice sheets. The values chosen for the orbital simulations represent the extreme values of the past 900 thousand years. Simulations were run for at least 500 years, and forcings do not change from year to year. The uploaded files are 100 year (or longer) monthly climatologies. For most simulations, variables are presented for the atmosphere (atm), sea ice (cice), land (clm2), and river runoff (rtm). For the ice sheets simulation and its corresponding preindustrial simulation (0ka), only atmospheric output is presented; to inquire about other fields for those simulations, please contact Pedro DiNezio at pdn@ig.utexas.edu. Ocean files are not currently available; please contact Michael Erb at michael.p.erb@gmail.com if you are interested in those results. === FORCINGS === Preindustrial climate forcings for the 0ka_0urb simulation were set to the following values: - Obliquity: 23.44107° - Longitude of perihelion: 102.7242° - Eccentricity: 0.01670772 - CO<sub>2</sub>: 284.7 ppm - Ice sheets: 0 ka BP The remaining simulations explore the climate response to a change in one of these forcings, with all other forcings set to preindustrial levels. Forcings are specified as follows: - lo_obliq and hi_obliq: Obliquity is set to 22.079° or 24.480°, respectively. - 0_AEQ, 90_WSOL, 180_VEQ, and 270_SSOL: Perihelion occurs at the NH autumnal equinox, winter solstice, vernal equinox, or summer solstice, respectively, with eccentricity set to 0.0493. This corresponds to a longitude of perihelion of 0°, 90°, 180°, or 270°, respectively. - ECC_0: Eccentricity is set to 0. - half_CO2: CO<sub>2</sub> is set to 142.35 ppm. - 21kaGlac: Ice sheets and sea level are set to Last Glacial Maximum (LGM) levels. Ice sheets come from the Paleoclimate Modelling Intercomparison Project Phase III (PMIP3) reconstruction, with ice shelves added in the western Labrador Sea. A note about preindustrial simulations: Some details of the model setup differ between the ice sheet simulation and the other simulations. Because of this, if a preindustrial control simulation is wanted for analysis, it is recommended that you use certain preindustrial simulations for certain comparisons, as follows: - 0ka_0urb (BG1850C5CN): Control simulation for all simulations except ice sheets. - 0ka (B1850C5): Control simulation for ice sheets (21kaGlac) simulation. === NOTES === More detailed description of these simulations, as well as results, can be found in the following papers: Erb, M. P., C. S. Jackson, A. J. Broccoli, D. W. Lea, P. J. Valdes, M. Crucifix, and P. N. DiNezio, in press: Model evidence for a seasonal bias in Antarctic ice cores. <em>Nature Communications.</em> Bosmans, J. H. C., M. P. Erb, A. M. Dolan, S. S. Drijfhout, E. Tuenter, F. J. Hilgen, D. Edge, J. O. Pope, and L. J. Lourens, in press: Response of the Asian summer monsoons to idealized precession and obliquity forcing in a set of GCMs. <em>Quat. Sci. Rev.</em> Bhattacharya, T., J. E. Tierney, and P. DiNezio, 2017: Glacial reduction of the North American Monsoon via surface cooling and atmospheric ventilation. <em>Geophys. Res. Lett.</em>, <strong>44</strong>, 5113-5122, doi:10.1002/2017GL073632. DiNezio, P. N., J. E. Tierney, B. L. Otto-Bliesner, A. Timmermann, T. Bhattacharya, N. Rosenbloom, and E. Brady, in review: Glacial changes in tropical climate amplified by the Indian Ocean. Note that the monthly data analyzed in these papers is sometimes converted to a common fixed-angular calendar in which every "month" corresponds to a 30° arc of Earth's orbit. This was done because changes in precession affect the speed at which Earth travels through different parts of its orbit according to Kepler's second law, complicating the comparison of months in different precession experiments. However, the results provided in this repository use the model's original fixed-day calendar. Computing resources (ark:/85065/d7wd3xhc) were provided by the Climate Simulation Laboratory at NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation and other agencies. If you use these simulations for research, please let the authors know. For a similar set of experiments using another model (GFDL CM2.1), see doi:10.5281/zenodo.1194480. Contact:<br> Michael Erb<br> Postdoctoral Scholar at Northern Arizona University<br> michael.p.erb@gmail.com

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Jeu de données · Signal consensuel: Jeu de données
Score de désaccord entre enseignants0,016
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0010,000
Science ouverte0,0010,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0210,006

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,030
Tête enseignante GPT0,227
Écart entre enseignants0,197 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreJeu de données

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2018
Routes d'admission1
Résumé présentoui

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