Observation-Based Longwave Cloud Radiative Kernels Derived from the A-Train

Abstract

The authors present a new method to derive both the broadband and spectral longwave observation-based cloud radiative kernels (CRKs) using cloud radiative forcing (CRF) and cloud fraction (CF) for different cloud types using multisensor A-Train observations and MERRA data collocated on the pixel scale. Both observation-based CRKs and model-based CRKs derived from the Fu-Liou radiative transfer model are shown. Good agreement between observation-and model-derived CRKs is found for optically thick clouds. For optically thin clouds, the observation-based CRKs show a larger radiative sensitivity at TOA to cloud-cover change than model-derived CRKs. Four types of possible uncertainties in the observed CRKs are investigated: 1) uncertainties in Moderate Resolution Imaging Spectroradiometer cloud properties, 2) the contributions of clearsky changes to the CRF, 3) the assumptions regarding clear-sky thresholds in the observations, and 4) the assumption of a single-layer cloud. The observation-based CRKs show the TOA radiative sensitivity of cloud types to unit cloud fraction change as observed by the A-Train. Therefore, a combination of observation-based CRKs with cloud changes observed by these instruments over time will provide an estimate of the short-term cloud feedback by maintaining consistency between CRKs and cloud responses to climate variability.

Band‐by‐Band Contributions to the Longwave Cloud Radiative Feedbacks

“…The contrasts between the two methods shown in Figure 3, therefore, indicate that the differences between the adjustment method and the kernel method are, to a large extent, due to the uncertainties in estimating band-by-band features of all-sky and clear-sky differences for other radiative feedbacks and forcings shown at the right sides of equation (2). The kernel is derived from 3-hourly Community Earth System Model simulation output using the method described in Yue et al (2016). Thus, for the following analysis, we use the CRK method to obtain the band-by-band decomposition of CRFs.…”

“…Unlike Yue et al (2016) in which the synergy of the TOA CRE, cloud fraction, CTP, and τ vis was obtained from a variety of pixel-scale collocated observations, here all relevant variables are from the same set of instantaneous CESM output. Overbar denotes a monthly average of all 3-hourly data for a given grid centered at latitude y, longitude x, CTP, and τ vis .…”

“…Top panels in Figure 1 show January global mean LW broadband CRK from Zelinka et al (2012), from Yue et al (2016), and as derived in this study. The methodology can be used to derive the CRK for each climate model tailored to its own cloud simulation statistics and cloud-radiation parameterizations.…”

“…The methodology can be used to derive the CRK for each climate model tailored to its own cloud simulation statistics and cloud-radiation parameterizations. The observation-based spectral LW CRK for the RRTMG_LW spectral bands is shown in Figure 1d, which is based on Yue et al (2016) and utilized the spectral fluxes derived from collocated Atmospheric Infrared Sounder (AIRS) and Clouds and the Earth's Radiant Energy System (CERES) observations (Chen, Huang, Loeb, et al, 2013;Huang et al, 2008Huang et al, , 2010Huang, Chen, Potter, et al, 2014). The CRK derived in this study resembles the one from Zelinka et al (2012) to a large extent.…”

“…Using the spectral CRK developed by Yue et al (2016) and cloud observations from the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) instrument (Yue et al, 2017), the LW band-by-band cloud feedback to the interannual climate variability then can be computed in a way similar to Zhou et al (2013), Dessler (2010) and Dessler and Loeb (2013). Short-term here refers to variations over the decade, in contrast to secular change.…”