What Can We Infer About the Atmospheric Composition Within the South Coast Air Basin from Remote Sensing?

Author: Hedelius, Jacob K.

Year: 2017

Degree: Dissertation (Ph.D.)

Advisor: Wennberg, Paul O.

Committee Members: Seinfeld, John H.; Wennberg, Paul O.; Beauchamp, Jesse L.; Okumura, Mitchio

Option: Chemistry

DOI: 10.7907/Z9862DGR

Abstract

To observe a change in a gas (e.g., CO2) flux from an area, the change must exceed the error of the flux estimate. Changing bias could be misinterpreted as a change in flux, and should be avoided. Errors can arise in column CO2 (XCO2) retrievals, in mis-interpreting XCO2 variations, or in the models to estimate fluxes. My thesis work has focused on recognizing and quantifying these errors and biases.

The most widely-used ground-based observations of XCO2 are from the Total Carbon Column Observing Network (TCCON), which uses observations from similar spectrometers at high (0.02 cm-1) resolution. Within the past 5 years there has been increased use of portable, lower resolution (0.5 cm-1) spectrometers for focused, short-term campaigns. This thesis discusses sources of errors and biases in retrievals from these lower resolution spectrometers.

Previous error estimates for the TCCON were made by propagating various perturbations through the retrieval. These uncertainty estimates were about 0.2 % for CO2 and 0.4 % for CH4. A pair of portable 0.5 cm-1 resolution spectrometers were used to empirically diagnose the magnitude of bias among TCCON sites. Median estimates were about 0.1 %.

Column measurements have increased in popularity within the last 15 years because of their reduced sensitivity to the dry mole fractions (DMF) of gases near the surface. However, in the presence of a sharp gradient between the atmospheric mixed layer (ML) and free troposphere rapid changes in terrain may cause the ML height above ground level and XCO2 to vary significantly over a small area. This explains ~20-36 % of the difference in XCO2 between 2 sites (Caltech and JPL) within 10 km of each other in the South Coast Air Basin (SoCAB).

Dynamical models may have biases (e.g., in wind speed) compared to true atmospheric behavior. This may cause biases in flux estimates. An estimate of the SoCAB CO2 flux using readily available model data is higher than those reported by bottom-up methods, perhaps due to a high wind speed bias. The flux is also sensitive to sub-sampling, which highlights the need to filter out biased data and the benefits additional observations could provide.

Carbon dioxide is not the only radiative forcer---aerosols are the largest source of uncertainty on the global radiative forcing budget, and additional measurements may better constrain their impacts. Estimate of changes in aerosol optical depth (AOD) can be made using portable spectrometers. While these estimates are not highly accurate, they are a value-added product and may increase the understanding of atmospheric behavior.

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