Evaluation of Land-Surface Models with GOSAT Carbon Dioxide Measurements

Monday, 15 December 2014: 8:15 AM
Christopher O'Dell1, Hannakaisa Lindqvist2, Michael Cheeseman3, Andrew E Schuh2, David F Baker2, Ian T Baker1, Katherine D Haynes2, A Scott Denning2, George James Collatz4, Frédéric Chevallier5 and Christian Frankenberg6, (1)Colorado State University, Atmospheric Sciences, Fort Collins, CO, United States, (2)Colorado State University, Fort Collins, CO, United States, (3)Appalachian State University, Boone, NC, United States, (4)NASA Goddard SFC, Greenbelt, MD, United States, (5)LSCE Laboratoire des Sciences du Climat et de l'Environnement, Gif-Sur-Yvette Cedex, France, (6)NASA Jet Propulsion Laboratory, Pasadena, CA, United States
The Greenhouse Gases Observing Satellite (GOSAT) was launched in 2009, with a primary mission to better understand net fluxes of carbon dioxide on regional scales. However, this effort has been hampered not only by uncertainties and biases in the satellite retrievals of column-mean CO2 concentrations (XCO2), but also issues on the inverse modeling side into which the measurements are fed to derive fluxes. These issues include transport model errors, incorrect or unfairly constrained prior fluxes, and issues related to the inversion scheme itself (such as temporal window length in EnKF systems).

In this work, we avoid the full global inversions and explore the idea that existing GOSAT observations can help evaluate land-surface carbon flux through direct comparison with forward model simulations. In this work, we compare forward model simulations of several biosphere models to GOSAT observations from the Atmospheric Carbon Observations from Space (ACOS) retrieval system. These include two versions of the Simple Biosphere (SiB) model, the Carnegie-Ames Stanford Approach (CASA) model, and the Organizing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) model. We provide evidence that large model-data differences tend to be driven by differences in the assumed net surface fluxes rather than from transport model errors. Tropical land fluxes, poorly sampled by the in-situ network, can exhibit significant differences when compared to the satellite observations and point to the possibility that these satellite data could help drive direct improvements to the land carbon flux models.