Analysis of currently-available results from the collection of OCO-2 inverse models of CO2 suggests that they suffer from significant biases in atmospheric transport. This is revealed through…

The Regents of the University of Colorado, doing business as the University of Colorado Office of Contracts and Grants Division, was awarded a $349,856.32 grant from the National Aeronautics and Space Administration Shared Services Center to conduct research related to atmospheric transport biases in inverse models of carbon dioxide from the Orbiting Carbon Observatory-2 (OCO-2) satellite. The proposed research aims to improve CO2 flux estimates by addressing transport errors, developing bias correction methods, and assessing tropical variability and observability of flux anomalies. This work supports NASA's goals of analyzing satellite-based XCO2 data, refining bias corrections, and validating CO2 distributions in the undersampled tropics. The University of Colorado, as a public research institution, has a history of executing fundamental research and technology development contracts across various science and engineering disciplines for federal agencies including NASA, the Department of Defense, and NOAA. This award is not associated with any set-aside designation.

Analysis of currently-available results from the collection of OCO-2 inverse models of CO2 suggests that they suffer from significant biases in atmospheric transport. This is revealed through forward simulations of SF6 and CO2, and supported by evidence from optimized fluxes considered as an ensemble. We propose continued meta-analysis of the inversion ensemble to target and remove some of this transport error by comparing the meridional transport of CO2 in models. We will do this by decomposing that transport into components associated with the mean meridional circulation, stationary eddies, and transient eddies. We will develop a method for tuning inverse models to maximize information extraction from observations while avoiding overfitting, by introducing the novel "l-curve" diagnostic. Expected tropical variability will be assessed using the multi-scale terrestrial model intercomparison project (mstmip) ensemble. An empirical orthogonal function decomposition of that variability will allow US to summarize flux anomalies and to rank them by their importance and spatial scale. These modes will then be propagated as pulsed emissions through atmospheric transport models to assess the observability of flux anomalies as a function of observing system and of atmospheric transit time between flux and observation. Interannually-varying flux priors from a near-real time terrestrial biosphere model will be developed and applied to atmospheric inversions, with the specific goal of better representing the 2015-16 EL nino. Atmospheric CO2 simulations will be evaluated against emerging in situ data constraints from sites in the amazon basin, including new light aircraft profile time series. Online bias correction will be implemented within carbontracker, both in order to refine standard bias correction parameterizations, and also to look for systematic variability in residuals from OCO-2 retrievals. Such patterns in the residuals may be due to heretofore-unidentified retrieval biases. The proposed research will address three stated goals of the solicitation. First, IT involves performing and substantially improving inverse model analyses of satellite XCO2 data. Second, IT includes work to improve existing bias corrections and to identify additional systematic features of the retrievals that cannot be explained by inverse models. These features may be caused by previously unidentified biases. Third, IT concentrates on cross-validation in the undersampled tropics of south america, where refined flux uncertainties and the introduction of new datasets will help to constrain CO2 distributions and highlight unexplained features in OCO datasets.