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An Assessment of Climate Feedbacks in Coupled Ocean–Atmosphere Models
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Citations
17
References
2006
Year
EngineeringClimate ModelingOceanographyWater VaporEarth ScienceClimate ProjectionHydroclimate ModelingClimate ChangeClimate VariabilityGlobal Warming ModellingGeographyClimate FeedbacksOceanic ForcingClimate SystemCloud FeedbackEarth's ClimateClimate DynamicsClimatologyClimate Modelling
The study compares climate feedbacks in coupled ocean–atmosphere models by conducting a coordinated set of twenty‑first‑century climate change experiments. The authors performed these comparisons using a coordinated suite of experiments across multiple models. The experiments show that water vapor provides the strongest positive feedback, with clouds and surface albedo also positive, while temperature response is the sole negative feedback; intermodel variability is greatest in cloud feedback, whereas lapse‑rate feedback differences are linked to regional warming patterns and the combined lapse‑rate and water‑vapor feedbacks vary little across models.
Abstract The climate feedbacks in coupled ocean–atmosphere models are compared using a coordinated set of twenty-first-century climate change experiments. Water vapor is found to provide the largest positive feedback in all models and its strength is consistent with that expected from constant relative humidity changes in the water vapor mixing ratio. The feedbacks from clouds and surface albedo are also found to be positive in all models, while the only stabilizing (negative) feedback comes from the temperature response. Large intermodel differences in the lapse rate feedback are observed and shown to be associated with differing regional patterns of surface warming. Consistent with previous studies, it is found that the vertical changes in temperature and water vapor are tightly coupled in all models and, importantly, demonstrate that intermodel differences in the sum of lapse rate and water vapor feedbacks are small. In contrast, intermodel differences in cloud feedback are found to provide the largest source of uncertainty in current predictions of climate sensitivity.
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