HR: 1340h
AN: B53B-1183    [Abstracts]
TI: Seeing the Forest Through the Trees: Investigating Signal to Noise Problems in Regional Atmospheric Inversions
AU: * Schuh, A E
EM: aschuh@atmos.colostate.edu
AF: Colorado State University Department of Atmospheric Science, 1371 Campus Delivery, Fort Collins, CO 80523-1371,
AU: Denning, A S
EM: denning@atmos.colostate.edu
AF: Colorado State University Department of Atmospheric Science, 1371 Campus Delivery, Fort Collins, CO 80523-1371,
AU: Corbin, K D
AF: Colorado State University Department of Atmospheric Science, 1371 Campus Delivery, Fort Collins, CO 80523-1371,
AU: Uliasz, M
AF: Colorado State University Department of Atmospheric Science, 1371 Campus Delivery, Fort Collins, CO 80523-1371,
AU: Zupanski, D
AF: Colorado State University Department of Atmospheric Science, 1371 Campus Delivery, Fort Collins, CO 80523-1371,
AU: Parazoo, N C
AF: Colorado State University Department of Atmospheric Science, 1371 Campus Delivery, Fort Collins, CO 80523-1371,
AB: Estimation of regional carbon fluxes from sparse atmospheric data by transport inversion is complicated by high- frequency variations in surface fluxes in both space and time. We assume that a forward coupled model of the vegetated land surface and atmosphere adequately captures most of the high-frequency variations (SiB-RAMS) as a `preprocessor` of input data from remote sensing and large-scale weather. We then use continuous CO2 observations and backward-in-time Lagrangian particle modeling to estimate persistent multiplicative biases in photosynthesis and ecosystem respiration, constraining the temporal pattern of these fluxes with the forward model. With a sparse network of continuous observing sites in North America, the inverse problem is still badly underconstrained for flux biases on the model grid scale. Previous studies have reduced the dimensionality of this problem by using large `regions` such as biomes or ecoregions, or by seeking a smooth solution in space. This could introduce substantial bias in the solution because the actual flux biases are likely to be quite heterogeneous. We have evaluated the degree to which carbon flux over large regions (500 to 1500 km) can be recovered when the true spatial pattern is not smooth. We performed ensembles of inversions for a 4-month case study in May- August, 2004 over North America with synthetic mid-day CO2 observations from a network of 8 towers. A smooth regional field of model biases was superposed with ensembles of various degrees of grid-scale `noise,` and these were then used to create synthetic concentration data. The pseudodata were then inverted to estimate gridded values of the biases, which were then combined with time-varying model fluxes to create regional maps of sources and sinks. We found that the degree to which corrections in regional fluxes are possible will depend on the relative amount of variance in the regional vs grid scales, but that the system is quite successful in estimating regional monthly fluxes even when the regional scale constitutes a smaller percentage of the overall variance.
DE: 0428 Carbon cycling (4806)
DE: 3355 Regional modeling
SC: Biogeosciences [B]
MN: 2007 Fall Meeting