Investigating Climate Trends in 14 Years of AERI Data at the ARM SGP Site

Jonathan Gero University of Wisconsin
David Turner National Oceanic and Atmospheric Administration

Category: Radiation

Working Group: Cloud Life Cycle

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The distribution of brightness temperature at 985 cm-1 observed by the AERI at the SGP site from 1996 to 2008 (black). The clear and cloudy subsets were determined by a neural network trained using Raman lidar data and then subsequently applied to the entire data set.

The ARM Climate Research Facility has collected Atmospheric Emitted Radiance Interferometer (AERI) data at the SGP site since the mid 1990s. The AERI regularly views a high-accuracy blackbody calibration target that has been tested against NIST standards, and thus the accuracy of the AERI-observed infrared radiance is robust over the past decade. Any statistically significant trend in the AERI data over this time can be attributed to changes in the atmospheric composition, not to changes in the sensitivity or response of the instrument. We have analyzed AERI radiance data from 1996 to 2008 to see if any statistically significant climate trends could be identified over this nearly 14-year period. If the entire record of AERI observations is analyzed, then no significant climatic trends are identified in any of the spectral regions observed by the AERI (e.g., the far-infrared channels at wavelengths above 15 µm, the CO2 absorption band at 15 µm, the atmospheric window channels from 8-13 µm, etc.). However, it is possible that a significant trend in the downwelling infrared radiance may exist under certain conditions (e.g., clear-sky scenes) that is countered by a trend in other conditions. We have used a neural network, trained using Raman lidar observations over a 14-month period in 2007-2008, to identify clear vs. cloudy conditions from the AERI radiance data. This classification scheme was used to separate the AERI data set into clear-sky and cloudy conditions, where the latter category was further broken down into optically thin and thick classifications. These three subsets of data are further analyzed, as functions of the entire data set, seasonally and diurnally, for statistically significant climatic trends. Initial results demonstrate that there is a trend towards higher radiance in optically thick cloud scenes, which suggests that either the atmosphere is getting warmer in these conditions or that the clouds are becoming lower.

This poster will be displayed at ASR Science Team Meeting.

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