An Assessment of Climate Feedbacks in Coupled Ocean–Atmosphere Models

By Brian J. Soden, I. Held. 2006

1,091 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/9ef6ee23729b569dd3cc6c30542ec25b1a5a6f60

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Citations in the corpus

What caused Earths temperature variations during the last 800,000 years? Data-based evidences on radiative forcing and constraints on climate sensitivity (2009, result): “The strength of these feedbacks might be in a first guess estimated from recent observations and model-based studies (Bony et al., 2006; Soden and Held, 2006; Andrews and Forster, 2008; Dufresne and Bony, 2008; Soden et al., 2008).”
Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part II: Attribution to Changes in Cloud Amount, Altitude, and Optical Depth (2012, background): “(1989); Senior and 100 Mitchell (1993); Tselioudis et al. (1998); Colman et al.”
Why is longwave cloud feedback positive (2010, methodology): “From DLWCF to LW Cloud Feedback: Applying the Radiative Kernels [45] The change in LWCF is not equal to the LW cloud feedback, though they are correlated [Soden et al., 2004; Soden and Held, 2006].”
Surface and top-of-atmosphere radiative feedback kernels for CESM-CAM5 (2017, methodology): “Radiative feedback kernels for the top of the atmosphere (TOA) are useful for decomposing changes in atmospheric radiative fluxes due to feedbacks from atmosphere and surface temperature, water vapor, and surface albedo (Soden and Held, 2006).”
On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates (2013, methodology): “On average over the set of models considered282 in this study, the two feedback calculations agree to within ±0.1 Wm−2K−1, and the inter-model spread is the283 same for both models’ radiative kernels.”
How reliable are climate models? (2007, background): “A long-standing and still valid result, based on diagnostic studies of the global radiation budget in climate models, is that a majority of the variation in ECS is due to different changes in cloud amount, optical properties and altitude distribution in different models (Cess et al., 1990, 1996; Col”
GCM intercomparison of global cloud regimes: present-day evaluation and climate change response (2007, background): “Comparison studies suggest that differences in the radiative feedback from clouds account for much of the variation in climate sensitivity between current GCMs (Cess et al. 1990; Senior and Mitchell 1993; Webb et al. 2006; Soden and Held 2006; Ringer et al. 2006).”
Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High‐Resolution Simulations (2017, background): “Parameterizations of clouds dominate the uncertainties in physical processes [Cess et al., 1989, 1990; Stephens, 2005; Bony et al., 2006; Soden and Held, 2006; Vial et al., 2013; Webb et al., 2013; Brient and Schneider, 2016].”
On Constraining Estimates of Climate Sensitivity with Present-Day Observations through Model Weighting (2011, background): “Because it is not known which observable aspects, if any, of the present-day climate are connected to climate sensitivity, any aggregate metric is arbitrary; we justify the narrow focus of our choice by noting that (i) differences in cloud feedbacks drive much of the diversity in climate sensitivity”
Evaluation of cloud and water vapor simulations in CMIP5 climate models using NASA “A-Train” satellite observations (2012, background): “” Improving the accuracy of cloud and water vapor simulations by climate models is thus of critical importance [e.g., Cess et al., 1996; Soden and Held, 2006; Bony et al., 2006; Waliser et al., 2009].”
CO2 forcing induces semi‐direct effects with consequences for climate feedback interpretations (2008, background): “Previous estimates of climate feedbacks often base their calculations on model integrations forced by CO2 changes [e.g., Soden and Held, 2006; Ringer et al., 2006] and are therefore likely to include semi-direct effects in their feedback estimates.”
Evaluating Emergent Constraints on Equilibrium Climate Sensitivity (2018, neutral): “CMIP3 process-level feedback values are taken from Table 1 of Soden and Held (2006).”
The Evolution of Climate Sensitivity and Climate Feedbacks in the Community Atmosphere Model (2012, methodology): “Following Cess et al. (1990), we use a standalone atmospheric model and fix the SST to a monthly climatology based on observations (Hurrell et al.”