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

• Published in: Journal of climate Vol. 29; no. 6; pp. 2023 - 2040
• Main Authors: Yue, Qing, Kahn, Brian H., Fetzer, Eric J., Schreier, Mathias, Wong, Sun, Chen, Xiuhong, Huang, Xianglei
• Format: Journal Article
• Language: English
• Published: Boston American Meteorological Society 15-03-2016
• Subjects: Climate change; Climate variability; Clouds; Feedback; Meteorological satellites; Meteorology; Radiative transfer; Remote sensing; Short term; Studies
• ISSN: 0894-8755; 1520-0442
• DOI: 10.1175/JCLI-D-15-0257.1

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 clear-sky 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.