K. L. Mueller

The utility of continuous atmospheric measurements for identifying biospheric CO2 flux variability

[1] Motivated by the need to improve the modeling of land‐atmosphere carbon exchange, this study examines the extent to which continuous atmospheric carbon dioxide (CO2) observations can be used to evaluate flux variability at regional scales. The net ecosystem exchange estimates of four terrestrial biospheric models (TBMs) are used to represent plausible scenarios of surface flux distributions, which are compared in terms of their resulting atmospheric signals. The analysis focuses on North America using the nine towers of the continuous observation network that were operational in 2004. Four test cases are designed to isolate the influence on the atmospheric observations of (1) overall flux differences, (2) magnitude differences in flux across large regions, (3) differences in the flux patterns within ecoregions, and (4) flux variability in the near and far field of observation locations. The CO2 signals generated from the different representations of surface flux distribution are compared using a Chi‐square test of variance. Differences found to be significant are driven primarily by differences in flux magnitude over large scales, and the fine‐scale (primarily temporal) variability of fluxes within the near field of observation locations. Differences in the spatial distribution of fluxes within individual ecoregions, on the other hand, do not translate into significant differences in the observed signals at the towers. Thus, given the types of variation in flux represented by the four TBMs, the atmospheric data may be most informative in the evaluation of aggregated fluxes over large spatial scales (e.g., ecoregions), as well as in the improvement of how the diurnal cycle of fluxes is represented in TBMs, particularly in areas close to tower locations.

Regional CO2 fluxes estimated over North America for 2004 using a geostatistical inverse model

Regional CO2 fluxes estimated over North America for 2004 using a geostatistical inverse model Sharon Gourdji(1) , K Mueller(1), D Huntzinger(1), V Yadav(1), A Hirsch(2,3), A Andrews(3), A Michalak(1,4) (1) Department of Civil & Environmental Engineering, University of Michigan, Ann Arbor, MI, USA (2) Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA (3) Global Monitoring Division, Earth System Research Laboratory, National Oceanic and Atmospheric Administration, Boulder, CO, USA (4) Department of Atmospheric, Oceanic & Space Sciences, University of Michigan, Ann Arbor, MI, USA