2019
DOI: 10.1029/2018jd029021
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Observation‐Based Radiative Kernels From CloudSat/CALIPSO

Abstract: Radiative kernels describe the differential response of radiative fluxes to small perturbations in state variables and are widely used to quantify radiative feedbacks on the climate system. Radiative kernels have traditionally been generated using simulated data from a global climate model, typically sourced from the model's base climate. Consequently, these radiative kernels are subject to model bias from the climatological fields used to produce them. Here, we introduce the first observation‐based temperatur… Show more

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Cited by 39 publications
(56 citation statements)
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“…To convert changes in the noncloud variables to a radiative response, we multiply each time series of anomalies by radiative kernels derived from CloudSat/CALIPSO observations (Kramer et al., 2019). Following common practice, we separately diagnose radiative responses due to uniform temperature change (Planck effect) and due to departures from the uniform temperature change (lapse rate response).…”
Section: Methodsmentioning
confidence: 99%
See 2 more Smart Citations
“…To convert changes in the noncloud variables to a radiative response, we multiply each time series of anomalies by radiative kernels derived from CloudSat/CALIPSO observations (Kramer et al., 2019). Following common practice, we separately diagnose radiative responses due to uniform temperature change (Planck effect) and due to departures from the uniform temperature change (lapse rate response).…”
Section: Methodsmentioning
confidence: 99%
“…(b) Time series of the tropical‐mean Δ N observed from CERES (thick solid line) and reconstructed from kernel radiative calculations (thin solid line; Equation ). Also reported are the radiative contributions due to I o r g anomalies (in red) and to EIS anomalies (in blue) inferred from kernel calculations (Kramer et al., 2019). Note that the two reconstitutions of Δ N reported on panels (a) and (b) correspond to two distinct approximations: In (a) it is assumed that N depends only on I o r g and EIS, while in (b) it is assumed that N can be reconstructed from the variations in temperature, humidity, etc., which are congruent with I o r g and EIS variations.…”
Section: Convective Organization Versus Lower‐tropospheric Stabilitymentioning
confidence: 99%
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“…5 in ref. 27 ), atmospheric cooling is particularly sensitive to temperature change in the boundary layer. Interestingly, the rapid adjustment terms, and particularly the strong cloud adjustment, for CFCs are similar to the adjustment terms for the strongly absorbing black carbon aerosol (Fig.…”
Section: Changes In Temperature Humidity and Cloudsmentioning
confidence: 99%
“…The radiative kernel for a feedback variable x is defined as K x = ∂R/∂x, in which R is the net top-of-atmosphere (TOA) flux, and x is an individual radiative state variable (e.g., temperature, water vapor, clouds, or surface albedo). The radiative kernel is derived from CloudSat/CALIPSO measurements 37,38 .…”
Section: Methodsmentioning
confidence: 99%