Local and Global Climate Feedbacks in Models with Differing Climate Sensitivities
Notice bibliographique
Résumé
Abstract The climatic response to a 5% increase in solar constant is analyzed in three coupled global ocean–atmosphere general circulation models, the NCAR Climate System Model version 1 (CSM1), the Community Climate System Model version 2 (CCSM2), and the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled General Circulation Model version 3 (CGCM3). For this simple perturbation the quantitative values of the radiative climate forcing at the top of the atmosphere can be determined very accurately simply from a knowledge of the shortwave fluxes in the control run. The climate sensitivity and the geographical pattern of climate feedbacks, and of the shortwave, longwave, clear-sky, and cloud components in each model, are diagnosed as the climate evolves. After a period of adjustment of a few years, both the magnitude and pattern of the feedbacks become reasonably stable with time, implying that they may be accurately determined from relatively short integrations. The global-mean forcing at the top of the atmosphere due to the solar constant change is almost identical in the three models. The exact value of the forcing in each case is compared with that inferred by regressing annual-mean top-of-the-atmosphere radiative imbalance against mean surface temperature change. This regression approach yields a value close to the directly diagnosed forcing for the CCCma model, but a value only within about 25% of the directly diagnosed forcing for the two NCAR models. These results indicate that this regression approach may have some practical limitation in its application, at least for some models. The global climate sensitivities differ among the models by almost a factor of 2, and, despite an overall apparent similarity, the spatial patterns of the climate feedbacks are only modestly correlated among the three models. An exception is the clear-sky shortwave feedback, which agrees well in both magnitude and spatial pattern among the models. The biggest discrepancies are in the shortwave cloud feedback, particularly in the tropical and subtropical regions where it is strongly negative in the NCAR models but weakly positive in the CCCma model. Almost all of the difference in the global-mean total feedback (and climate sensitivity) among the models is attributable to the shortwave cloud feedback component. All three models exhibit a region of positive feedback in the equatorial Pacific, which is surrounded by broad areas of negative feedback. These positive feedback regions appear to be associated with a local maximum of the surface warming. However, the models differ in the zonal structure of this surface warming, which ranges from a mean El Niño–like warming in the eastern Pacific in the CCCma model to a far-western Pacific maximum of warming in the NCAR CCSM2 model. A separate simulation with the CCSM2 model, in which these tropical Pacific zonal gradients of surface warming are artificially suppressed, shows no region of positive radiative feedback in the tropical Pacific. However, the global-mean feedback is only modestly changed in this constrained run, suggesting that the processes that produce the positive feedback in the tropical Pacific region may not contribute importantly to global-mean feedback and climate sensitivity.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».