On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates

By J. Vial, J. Dufresne, Sandrine Bony. Climate Dynamics, 2013

510 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/62aa2db83182bfbf03b231baf928ccb6615ab8e6

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Citations in the corpus

Young People's Burden: Requirement of Negative CO2 Emissions (2016, unknown): “Median sensitivity recent model inter-comparisons is 3.2 ◦ C (An-drews An-drews et al., 2012; Vial et al., 2013). clude effects of “slow” climate feedbacks such as change in ice sheet size.”
Emergent constraints on equilibrium climate sensitivity in CMIP5: do they hold for CMIP6? (2020, background): “In particular, model differences in the change in shortwave reflection of low-level clouds changes in response to climate change dominate the uncertainties in the global warming projections, particularly in the tropics but also in mid-latitudes (Brient and Schneider, 2016; Vial et al., 2013).”
Large‐eddy simulations over Germany using ICON: a comprehensive evaluation (2017, background): “This is true for both the rapid response to perturbations in carbon dioxide concentrations and aerosols on time-scales of hours to weeks (Gregory and Webb, 2008; Sherwood et al., 2015) and the feedbacks responding to seasurface temperature changes (Bony et al., 2006; Vial et al., 2013).”
Causes of Higher Climate Sensitivity in CMIP6 Models (2020, methodology): “Following Dufresne and Bony (2008) and Vial et al. (2013) as detailed in Text S4, we estimate that on average across CMIP6 models, the extratropical cloud feedback contributes 0.4 K of the 1.0 K cloud feedback-induced global temperature change (Figure S7).”
Evaluation of CNRM Earth System Model, CNRM‐ESM2‐1: Role of Earth System Processes in Present‐Day and Future Climate (2019, methodology): “The direct CO2 forcing (FCO2) induces about one third of the global warming, but it cannot explain the temperature change difference because FCO2 has been fixed to the value of Vial et al. (2013) for all models.”
Recommendations for diagnosing effective radiative forcing from climate models for CMIP6 (2016, background): “Leaving forcings undiagnosed greatly limits the understanding of why the models get the responses they do and why they might differ [Chung and Soden, 2015; Forster et al., 2013; Marvel et al., 2015; Shindell, 2014; Vial et al., 2013].”
Connections Between Clouds, Radiation, and Midlatitude Dynamics: a Review (2015, background): “The most recent generation of state-of-the-art climate models still suffers from large uncertainty in the cloud-radiative response to global warming, which affects climate sensitivity estimates [1, 3, 39].”
The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6. (2016, methodology): “, 2015) in conjunction with the CMIP5 sstClim/sstClim4xCO2 experiments which were based on climatological pre-industrial SSTs (Andrews et al., 2012; Zelinka et al., 2013; Vial et al., 2013).”
The Dependence of Global Cloud and Lapse Rate Feedbacks on the Spatial Structure of Tropical Pacific Warming (2018, result): “Note that the residual (net feedback minus sum of individual feedbacks as determined from the radiative kernels) is not negligible, as also seen in other studies that use radiative kernel techniques (e.g., Vial et al. 2013), and can be interpreted as a nonlinear term (e.g., Feldl and Roe 2013).”
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].”
Physical mechanisms controlling the initiation of convective self‐aggregation in a General Circulation Model (2015, background): “It suggests that the reason why low-level clouds do not play an important role here is that the low-cloud amount is too small at high SSTs (the strong decrease of the lowcloud amount as SST rises is a well-known feature of this model [Brient and Bony, 2012; Vial et al., 2013]) to produce a significa”
Presentation and Evaluation of the IPSL‐CM6A‐LR Climate Model (2020, methodology): “The cloud feedback is calculated as a corrected residual term, correcting for a cloud‐masking term (Vial et al., 2013), which adds a consistent offset to net cloud feedback value estimated from the cloud radiative effect method (Andrews et al.”
Anthropogenic and Natural Radiative Forcing (2014, background): “Analysis of 11 models from the current Coupled Model Intercomparison Project Phase 5 (CMIP5) generation finds that the rapid adjustments to CO2 cause fixed-SST-based ERF to be 2% less than RF, with an intermodel standard deviation of 7% (Vial et al., 2013).”
Cloud feedback mechanisms and their representation in global climate models (2017, unknown): “Feedback parameter results are from Caldwell et al. 5 , with additional cloud feedback values from Vial et al. 6 and Zelinka et al. 26 ECS values are taken from Andrews et al. 27 , Forster et al. 28 , and Flato et al. 29 Feedback parameters are calculated as in Soden et al. 30 but accounting for rap”