Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part I: Cloud Radiative Kernels

By M. Zelinka, Stephen A. Klein, D. Hartmann. 2012

295 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/2ff22fb2df8880a7936dc52126499b76651d72ce

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Citations in the corpus

Causes of Higher Climate Sensitivity in CMIP6 Models (2020, background): “…models becomes available, more detailed analysis of the cloud feedback will be possible (Cesana et al., 2019; Gordon & Klein, 2014; Terai et al., 2016; Tsushima et al., 2016; Zelinka et al., 2012a, 2012b, 2013, 2016), permitting better understanding of why the low cloud feedback has strengthened.”
Insights from a refined decomposition of cloud feedbacks (2016, methodology): “Because midlevel and high cloud feedbacks are considered together rather than distinctly in the refined decomposition, we recommend using it in concert with the standard breakdown of high, midlevel, and low cloud feedbacks [Zelinka et al., 2012a].”
Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part II: Attribution to Changes in Cloud Amount, Altitude, and Optical Depth (2012, background): “As we have noted in Zelinka et al. 2012 (hereafter Part I), however, this should not be taken as evidence that other cloud responses are consistently modeled or that they make a narrow range of contributions to the feedback.”
Clouds, circulation and climate sensitivity (2015, background): “This riddle is manifest in the tendency of models to exhibit a large degree of freedom in their prediction of upper-level cloud cover responses 18 , and in their representation of shallow convective mixing, which appears to determine the strength of their low-cloud feedbacks 2 .”
Analyzing the dependence of global cloud feedback on the spatial pattern of sea surface temperature change with a Green's function approach (2017, background): “…radiative effect of high and thin cirrus clouds is larger than their solar radiative effect, but the solar radiative effect of thick high clouds is as least as strong as their thermal radiative effect, so high clouds could either warm or cool the Earth’s climate system [Zelinka et al., 2012].”
Exposing Global Cloud Biases in the Community Atmosphere Model (CAM) Using Satellite Observations and Their Corresponding Instrument Simulators (2012, background): “Research also is needed to connect COSP diagnostics to cloud feedbacks (e.g., Zelinka et al. 2012).”
The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6. (2016, methodology): “…of clouds using A-train instantaneous observations (Konsta et al., 2012), quality control and low-cloud diagnostics (Nam et al., 2012; Nam and Quaas, 2012), sensitivity of low cloud cover to estimated inversion strength and SST (Qu et al., 2014) and cloud-radiative kernels (Zelinka et al., 2012b).”
The Radiative Forcing Model Intercomparison Project (RFMIP): Experimental Protocol for CMIP6 (in open review: 10.5194/gmd-2016-88) (2016, methodology): “These will be used to estimate rapid adjustments by clouds using radiative kernels (Zelinka et al., 2012, 2014) that map changes in cloud properties into top-of-atmosphere radiative flux perturbations.”
Cloud feedback mechanisms and their representation in global climate models (2017, unknown): “The values presented in this paper are either based on CRE changes corrected for noncloud effects,(30) or estimated directly from changes in cloud properties, for those GCMs providing appropriate cloud output.(31) The most accurate procedure involves running the GCM radiation code offline—replacing ”
Contributions of Different Cloud Types to Feedbacks and Rapid Adjustments in CMIP5 (2012, methodology): “As found in Zelinka et al. (2012b), the large negative feedback over the Southern Ocean comes from the shift towards thicker clouds and the increase in total cloud amount, but the former is roughly three times stronger in the zonal mean at 60◦S.”
Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator (2012, methodology): “[26] We explore these issues by using cloud radiative kernels [Zelinka et al., 2012] to compute the radiative effects of errors in cloud properties.”