Earth Science Seminar
On the Observational Determination of Climate Sensitivity and Feedback: Its Limitations and New Approaches
Presented by Yong-Sang Choi
Ewha Womans University, Seoul, South Korea
Monday, January 28, 2013
Climate feedback has recently been estimated using satellite data to validate the behavior of current climate models. In order to extract the strength of climate feedback from data, previous studies have correlated time variations in sea surface temperature (SST) to outgoing energy flux anomalies. However, natural non-feedback variations (noise) in energy flux occurring independently of SST remains undistinguished from the feedback to SST in the currently known methodology, and gives rise to appreciable uncertainties in the results.
Here we show the influence of the non-feedback noise on the feedback signal. The monthly energy flux from the Clouds and the Earth's Radiant Energy System (CERES) (2000−2008) shows a distorted curve in the lagged correlation with SST. This morphology appears in both shortwave and longwave radiation (where the Planck response is removed). Based on simulations with an idealized energy balance model, the presence of this distorted curve indicates that the regression slopes at any lag are not indicative of climate feedbacks. Also, this distorted curve can readily occur even for very small non-feedback noise level (its standard deviation is ~5% of that of non-radiative forcings, e.g., those from the ocean heat transfer that are not associated with outgoing energy flux variations). Under the distorted curve, it is more likely that the non-feedback noise leads to underestimated negative feedback for the climate system where negative feedback is dominant. We found the non-feedback noise level in the present CERES data is over 16%, which is comparable to the values in current coupled climate models (11 to 28%). All these noise levels are, however, far above the critical level that begins to obscure the exact value of feedback. The result clearly suggests that current assessment of climate feedbacks requires careful isolation of the non-feedback noise in the data. Then can the non-feedback noise be effectively isolated to obtain the exact value of climate feedback? To answer this question, we further investigated variations in outgoing longwave radiation (OLR) in response to changes in sea surface temperature (SST) over the Pacific warm pool area (20°N−20°S, 130°E−170°W). OLR values were obtained from recent (January 2008−June 2010) geostationary window channel imagery at hourly resolution, which can resolve processes associated with tropical convective clouds. We used linear regression analysis with the domain-averaged OLR and SST anomalies (i.e., ΔOLR, ΔSST; deviations from their 90−day moving averages). Results show that the regression slope appears to be significant only with SST least−affected by cloud noise, for which SST needs to be obtained as daily average over cloud−free regions (ΔSSTclear). The estimated value of ΔOLR/ΔSSTclear is 15.72 W m-2 K-1, indicating the presence of strong outgoing longwave radiation in response to surface warming. This atmospheric cooling effect is found to be primarily associated with reduced areal coverage of clouds (−14.4% K-1 ).
About the Speaker
JPL Contact: Martha Farfan (4-6582)
Prof. Yong-Sang Choi is an assistant professor of atmospheric science at Ewha Womans University, and a vice-director at Center for Climate/Environment Change Prediction Research in Korea. He is a project investigator of cloud algorithm development projects for two Korean geostationary satellites, GEMS and COMS (launching in 2017). He studied cloud remote sensing and climate physics at Seoul National University, Korea. After finishing his Ph.D. in 2007, He moved to US to work with Prof. Richard S. Lindzen as a postdoctoral research associate in MIT Department of Earth, Atmospheric and Planetary Sciences. His research focuses on the roles of atmospheric aerosol and cloud processes in the climate system. To address scientific issues related with them he mainly analyzes satellite observations, and tests current climate models.