Quantifying the lapse rate feedback using GNSS radio occultation
Presenter:
Panagiotis Vergados
Jet Propulsion Laboratory
Jet Propulsion Laboratory
Talk
The lapse rate defines the rate of change of temperature with increasing altitude and regulates the amount of outgoing longwave radiation. Thus, precise and accurate knowledge of the vertical variability of temperature is key to accurately quantifying the top-of-the-atmosphere (TOA) radiative budget, which is central to climate change research. To date, climate models predict a decrease in the lapse rate as green-house gases increase, which suggests larger temperature variations in the upper troposphere (UT). However, measurements of the rate of temperature change with altitude are very sensitive to small observational errors, making it difficult to establish whether the models agree with observations. Current research shows that models and reanalyses disagree both on the magnitude and the sign of the lapse rate feedback, which could introduce errors in the estimation of climate sensitivity. Global Positioning System Radio Occultation (GPS-RO) measurements provide high quality temperature observations with an accuracy of < 0.5 K and a vertical resolution of ~ 100–200 m. We will exploit these measurements to observationally constrain results from models and reanalyses. We will present the time series of tropospheric temperature using GPS-RO data sets from 2006 onwards, and will compare our results with different sources such as, the European Center for Medium Range Weather Forecasts (ECMWF), the Modern-Era Retrospective Analysis for Research and Applications (MERRA), and the Atmospheric Infrared Sounder (AIRS). We will carefully quantify the statistical differences among the series to identify and document biases among the data sets. Finally, we will correlate the aforementioned temperature series with surface temperature climatologies in order to estimate the variability of the UT temperature in response to surface warming, dT/dTs, which is directly related to the lapse rate feedback. Inter–comparing the dT/dTs among the different data sets will provide us with an additional constraint on the lapse rate feedback.
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