Regional Inverse Modeling in North and South America for the NASA Carbon Monitoring System

Arlyn Andrews, NOAA Earth System Research Laboratory, arlyn.andrews@noaa.gov (Presenter)
John Miller, NOAA Earth System Research Laboratory, john.b.miller@noaa.gov
Kirk Thoning, NOAA Earth System Research Laboratory, kirk.w.thoning@noaa.gov
Marikate Mountain, AER, Inc., mmountai@aer.com
Thomas Nehrkorn, AER, Inc., tnehrkor@aer.com
Anna Michalak, Carnegie Institution for Science and Stanford University, michalak@carnegiescience.edu
Vineet Yadav, Stanford University, vineety@stanford.edu
Christopher O'Dell, Colorado State University, odell@atmos.colostate.edu
Chris Sloop, Earth Networks, Inc., csloop@earthnetworks.com

Two CMS-2012 projects, “North American Regional-Scale Flux Estimation and Observing System Design for the NASA Carbon Monitoring System” (A. Andrews, PI) and “In situ CO2-based evaluation of the Carbon Monitoring System flux product” (J. Miller, PI), have been combined under CMS-2014. Both CMS-2012 projects leveraged available in situ measurements of CO2 and used high-resolution regional inverse modeling tools to quantify CO2 fluxes on regional scales and to investigate consistency among in situ and remote sensing datasets and flux products. We have incorporated remote sensing measurements of CO2 into CarbonTracker-Lagrange (CT-L), a NOAA-led effort to implement a regional inverse modeling framework that uses footprints from a suite of Lagrangian transport models and a flexible inversion scheme with geostatistical and Bayesian capability. Under CMS-2014, CT-L inversions will be further developed for North America and Amazonia. The CT-L inversions complement the CMS Flux Pilot estimates, because they are obtained for a regional domain and at higher resolution (1o), using different transport models (i.e. Lagrangian vs. Eulerian), augmented CO2 data sets (in situ and remote sensing), and using explicit matrix inversions rather than a data assimilation approach. The North American inversions leverage dense datasets and models that were developed for the North American Carbon Program, while the Amazon component emphasizes use of vertical profiles from aircraft above the Brazilian Amazon, a critically important yet under-sampled region where extensive cloud and aerosol contamination limit the usefulness of satellite data. Efficient inversion algorithms enable ensemble calculations to test the sensitivity of inferred fluxes to uncertainties caused by possible satellite retrieval errors and model inadequacies, such as errors in simulated atmospheric transport and assumed prior flux error.

This work will further develop strategies for incorporating diverse CO2 observations in CMS data assimilation efforts and for quantifying fluxes at scales relevant for Monitoring, Reporting and Verification (MRV) and quantifying uncertainties of CMS products.

Associated Project(s): 

• Andrews (CMS 2011)
• Andrews (CMS 2014)
• Miller (CMS 2011)