An Intercomparison of XCO2 spatio-temporal variability from AIRS, SCIAMACHY, GOSAT (ACOS) satellite datasets and TCCON and GAW ground-base measurements using spectral analysis

Thursday, 18 December 2014
Jing Li, Barbara E Carlson and Andrew A Lacis, NASA Goddard Institute for Space Studies, New York, NY, United States
With multiple sensors developed to monitor CO2 concentration from space, it is necessary to examine the coherency and discrepancy in different CO2 datasets for understanding their strengths and weaknesses and their overall spatio-temporal self-consistency. In this study, we examine column CO2 mixing ratios retrieved by three different satellite sensors, namely AIRS, SCIAMACHY and GOSAT (the ACOS algorithm). Data from two surface based observational networks, TCCON and GAW are used to validate the datasets and to test near surface sensitivity. The comparison is focused on large-scale features over land, such as trends and seasonality, which should be present in all datasets despite their different spectral sampling and altitude sensitivity. We use a newly developed spectral analysis technique – the Combined Maximum Covariance analysis (CMCA) to decompose the multi-dimensional datasets and to extract major modes of variability from different datasets combined. Results show that a global increase in XCO2 at ~2ppm/yr is found in all datasets. However, the spatial distribution of the trends are not consistent. All datasets except AIRS exhibit strong seasonal variability, especially in the Northern Hemisphere, while the AIRS seasonal cycle is much weaker. SCIAMACHY and ACOS also have good agreement with ground-based observations. ACOS is found to agree with TCCON the best, and SCIAMACHY has slightly higher near surface sensitivity. We plan to perform similar analysis and comparison using OCO-2 data and also for methane observations.

Figure caption: The first two CMCA modes for AIRS, SCIAMACHY, ACOS and TCCON data. TCCON signals are superimposed as circles. The PCs have strong seasonal cylces and the correlation between satellite data and TCCON are very high (R values). SCIAMACHY and ACOS both agree with TCCON well, while the signal for AIRS is weaker than TCCON, indicating weaker seasonality.