Comparisons between SCIAMACHY atmospheric CO sub(2) retrieved using (FSI) WFM- DOAS to ground based FTIR data and the TM3 chemistry transport model

• Published in: Atmospheric chemistry and physics discussions Vol. 6; no. 3; pp. 5387 - 5425
• Main Authors: Barkley, M P, Monks, P S, Frie beta, U, Mittermeier, R L, Fast, H, Koerner, S, Heimann, M
• Format: Journal Article
• Language: English
• Published: 01.01.2006
• Subjects: Canada
• ISSN: 1680-7367; 1680-7375

Atmospheric CO sub(2) concentrations, retrieved from spectral measurements made in the near infrared (NIR) by the SCIAMACHY instrument, using Full Spectral Initiation Weighting Function Modified Differential Optical Absorption Spectroscopy (FSI WFM-DOAS), are compared to ground based Fourier Transform Infrared (FTIR) data and to the output from a global chemistry-transport model. Analysis of the FSI WFM-DOAS retrievals with respect to the ground based FTIR instrument, located at Egbert, Canada, show good agreement with an average negative bias of approximately -4.0% with a standard deviation of similar to 3.0%. This bias which exhibits an apparent seasonal trend, is of unknown origin, though slight differences between the averaging kernels of the instruments and the limited temporal coverage of the FTIR data may be the cause. The relative scatter of the retrieved vertical column densities is comparable to the spread of the FTIR measurements themselves. Normalizing the CO sub(2) columns using the surface pressure does not affect the magnitude of this bias although it slightly increases the scatter of the FSI data. Comparisons of the FSI retrievals to the TM3 global chemistry-transport model, performed over four selected Northern Hemisphere scenes show good agreement. The correlation, between the time series of the SCIAMACHY and model monthly scene averages, are similar to 0.7 or greater, demonstrating the ability of SCIAMACHY to detect seasonal changes in the CO sub(2) distribution. The amplitude of the seasonal cycle, peak to peak, observed by SCIAMACHY however, is overestimated by a factor of 2-3, which cannot be explained. The yearly means detected by SCIAMACHY are within 2% of those of the model with the mean difference between the CO sub(2) distributions also approximately 2.0%. Additionally, analysis of the retrieved CO sub(2) distributions reveals structure not evident in the model fields which correlates well with land classification type. From these comparisons, the overall precision and bias of the CO sub(2) columns retrieved by the FSI algorithm are estimated to be close to 1.0% and <4.0% respectively.