Evaluating consistencies between total columnCO2 measurements from OCO-2 and the in-situ network
Abstract
Satellite retrievals of total column CO2 such as those from NASA's Orbiting Carbon Observatory-2 (OCO-2), have the potential to provide unprecedented global information about CO2 spatiotemporal variability and help constrain surface fluxes of carbon. However, these data need to be highly precise and unbiased for use in distinguishing concentration changes emanating from surface fluxes from those due to synoptic variability in the atmosphere. Systematic errors in satellite have been identified and, while bias correction algorithms are applied globally, inconsistencies persist at regional and smaller scales that may complicate or confound flux estimation. To evaluate the satellite and assess potential biases, we compare OCO-2 with in situ data-constrained simulated total columns over North America, estimated using surface fluxes optimized with in situ data from the NOAA's Global Greenhouse Gas Reference Network and from Environment and Climate Change Canada, using the CarbonTracker-Lagrange inverse model. Systematic errors in atmospheric transport are independently evaluated using unassimilated aircraft data. We find that the OCO-2 bias correction shifts the distribution of retrievals closer to the data-constrained simulated columns. Bias corrected OCO-2 retrievals agree well with simulated columns, particularly in the summertime. We are able to generate seasonally dependent bias correction parameters over North America, which vary slightly from the global, static parameter set that is currently used in the OCO-2 bias correction. Lastly, this exercise lays the groundwork for creating a combined dataset of and in situ observations that is optimally consistent for use in a regional inversion, as well as demonstrate a method to dynamically evaluate OCO-2 against in situ observations that are calibrated on WMO scales. This work represents a significant step towards assimilating OCO-2 retrievals alongside in-situ data in regional inversions to fully leverage their complementarity and provide robust estimates of surface flux variability with rigorously quantified uncertainties.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2020
- Bibcode:
- 2020AGUFMA221.0011R
- Keywords:
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- 3305 Climate change and variability;
- ATMOSPHERIC PROCESSES;
- 0428 Carbon cycling;
- BIOGEOSCIENCES;
- 0480 Remote sensing;
- BIOGEOSCIENCES;
- 1615 Biogeochemical cycles;
- processes;
- and modeling;
- GLOBAL CHANGE