HR: 11:50h
AN: A12B-07    [Abstracts]
TI: Assessing sampling and representation errors in assimilation of satellite CO₂ retrievals
AU: * Denning, S
EM: denning@atmos.colostate.edu
AF: Colorado State University, Department of Atmospheric Science, Fort Collins, CO 80523- 1371, United States
AU: Corbin, K D
EM: kdcorbin@atmos.colostate.edu
AF: Colorado State University, Department of Atmospheric Science, Fort Collins, CO 80523- 1371, United States
AU: Parazoo, N
EM: nparazoo@atmos.colostate.edu
AF: Colorado State University, Department of Atmospheric Science, Fort Collins, CO 80523- 1371, United States
AU: Kawa, S R
AF: NASA Goddard Space Flight Center, Mail Code 916, Greenbelt, MD 20771, United States
AB: Retrieval of the mean dry mole faction of atmospheric CO₂ (X_CO2) from spectra measured by dedicated spaceborne instruments will be made a few times each month over a given location and will only represent clear atmosphere columns. Variations in atmospheric CO₂ on synoptic time scales may lead to temporal sampling errors in transport inversions, especially if they covary strongly with cloud cover. Note that these are modeling errors, and that they arise if the model used for the inversion is sampled differently than the real atmosphere is sampled by the satellite. To assess the potential magnitude of these errors, we investigated sources of synoptic variability and its relationship to clouds using continuous tower observations, a coupled cloud-resolving model, and a global chemical transport model. An observational assessment of systematic differences between mid-day CO₂ on clearsky vs all days used multiyear timeseries of continuous data from the WLEF-TV tower and Harvard Forest in the USA, and Flona Tapajos, Brazil. The WLEF site is a temperate forest in a remote rural area, and Harvard Forest is located in a region with strong anthropogenic emissions. Flona Tapajos is a very remote equatorial forest reserve. We found systematic differences of 1 to 3 ppm in measured mid-day CO₂ , with lower values on sunny days than average. At the temperate sites, the differences are greatest in winter and are not attributable to anomalous surface fluxes. The tropical site showed the biggest differences in the rainy season, with very little sampling error in the dry season. We performed cloud-resolving simulations of two cases using the SiB-RAMS coupled ecosystem-atmosphere model, one during summer at the temperate WLEF site and one during the dry season at the tropical Tapajos site. Simulated X_CO2 was sampled on 1-km-wide swaths in clear columns only as if nadir sampling from space, and compared to a (100-km) mean which represents the grid column of a global CTM such as might be used in a transport inversion. In both cases, this "local" clearsky sampling error was found to be much smaller than the likely instrument retrieval error. At the temperate site, temporal sampling errors in representing time means from individual swaths were comparable to likely retrieval errors because of systematic X_CO2 anomalies associated with fronts that were masked by clouds. In the tropics, these temporal sampling errors were much smaller. The magnitude and seasonal variation of clearsky sampling errors at the three towers were reproduced well using the global PCTM. Global simulations of monthly mean X_CO2 were compared to the means of samples taken only under clearsky conditions to assess spatial and seasonal patterns of these errors. Clearsky temporal sampling errors were found to be greatest over land, and were dominated by positive errors over the midlatitudes (especially Asia) in summer and negative errors over the tropics (especially South America) during the rainy season. These errors often exceeded 1 ppm, and must be addressed in a data assimilation system by correct simulation of synoptic variations in X_CO2 associated with cloud systems.
UR: http://biocycle.atmos.colostate.edu
DE: 0315 Biosphere/atmosphere interactions (0426, 1610)
DE: 0321 Cloud/radiation interaction
DE: 0414 Biogeochemical cycles, processes, and modeling (0412, 0793, 1615, 4805, 4912)
DE: 0428 Carbon cycling (4806)
DE: 0480 Remote sensing
SC: Atmospheric Sciences [A]
MN: 2007 Fall Meeting