Abstract
Markov Chain Monte Carlo (MCMC) is a powerful and promising tool for assessing the uncertainties in the Orbiting Carbon Observatory 2 (OCO-2) satellite’s carbon dioxide measurements. Previous research in comparing MCMC and Optimal Estimation (OE) for the OCO-2 retrieval has highlighted the issues of slow convergence of MCMC, and furthermore OE and MCMC not necessarily agreeing with the simulated ground truth. In this work, we exploit the inherent low information content of the OCO-2 measurement and use the Likelihood-Informed Subspace (LIS) dimension reduction to significantly speed up the convergence of MCMC. We demonstrate the strength of this analysis method by assessing the non-Gaussian shape of the retrieval’s posterior distribution, and the effect of operational OCO-2 prior covariance’s aerosol parameters on the retrieval. We further show that in our test cases we can use this analysis to improve the retrieval to retrieve the simulated true state significantly more accurately and to characterize the non-Gaussian form of the posterior distribution of the retrieval problem.
Highlights
One of the greatest challenges to the future of planet Earth is climate change arising from global warming, which in turn is accelerated by human-originated emissions of greenhouse gases
With the Markov chain Monte Carlo (MCMC) approach we can compute the full posterior distribution, and a natural question is whether the Optimal Estimation (OE) optimization algorithm finds the mode of the posterior distribution and how well this corresponds to the expected value obtained from MCMC sampling
In the studied case the initial termination criteria for OE suggested that the cost function did not have a unique minimum, but our tests concluded that by utilizing the information of the MCMC run, the OE algorithm could be tuned so that differing starting values did converge to same minimum given enough computation time
Summary
One of the greatest challenges to the future of planet Earth is climate change arising from global warming, which in turn is accelerated by human-originated emissions of greenhouse gases. The major warming effects are due to atmospheric carbon dioxide (CO2), which is emitted in increasing amounts since industrialization. Fluxes are estimated by a procedure called inverse modeling, which in addition to computational models needs observations of atmospheric greenhouse gas concentrations. While notable ground based measurement networks exist for this purpose (e.g., GLOBALVIEW-CO2: Cooperative Atmospheric Data Integration Project—Carbon Dioxide for in-situ, flask, and aircraft measurements [1] used in flux inversions, and TCCON [2,3] for total atmospheric column measurements), their coverage is not extensive enough to account for all of the global phenomena relating to the carbon cycle and in particular CO2 emissions
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
