Differential Absorption LiDAR for the Total Column Measurement of Atmospheric CO2 from Space

Since the beginning of the industrial revolution (1750 to 1800) the Earth’s atmospheric composition has undergone significant change as a result of human activities, in particular the burning of fossil fuels. As a consequence the atmospheric concentrations of a number of gases known to be influential to the Earth’s climate have increased far beyond natural levels. Atmospheric gases such as carbon dioxide which naturally exist in the Earth system have increased in correlation with anthropogenic emissions. The effect of this perturbation on the Earth system has been predicted through computer simulations to have undesirable consequences on the Earth’s future climate. The present measurement systems for atmospheric carbon dioxide have limited spatial coverage and temporal resolution which restricts their ability to accurately attribute observations of atmospheric composition to particular terrestrial sources and sinks. This inability to accurately locate and quantify the key carbon dioxide sources and sinks in the terrestrial and marine biospheres is hindering the understanding of the processes that are driving the Earth’s natural uptake of approximately half of the anthropogenic carbon dioxide emissions. With such uncertainty it is currently unknown precisely how the Earth’s climate will respond to global warming in the future. Through computer simulation it has been demonstrated that improving the spatial distribution of global measurements of atmospheric carbon dioxide is likely to advance the present understanding of the Earth’s terrestrial sources and sinks. Regions that require particular improvement in measurement coverage are the southern oceans owing to a lack of landmass on which to site instruments, and much of the tropics because of difficulties in locating instruments in some of the worlds more politically unstable regions. Satellite remote sensing instruments which measure atmospheric carbon dioxide from low Earth orbit provide some coverage of these sparsely sampled locations, however cloud cover often prevents measurements being made (particularly in the tropics), and limited latitudinal coverage caused by current instruments using passive remote sensing techniques prevents measurements at very high and low latitudes (including much of the southern ocean during local winter). An alternative remote sensing technique has been proposed in the scientific literature for measuring atmospheric carbon dioxide concentrations using laser emissions from a satellite platform known as total column differential absorption LiDAR (TC-DIAL). The TC-DIAL technique has been identified as having the theoretical potential to meet the coverage and precision requirements to greatly aid in identifying and quantifying terrestrial carbon dioxide sources and sinks. The TC-DIAL technique has the potential to achieve these goals largely owing to its unique capabilities of being able to make measurements during both the day and night and at all latitudes with a footprint which may be small enough to see between patchy cloud cover in the tropics. This thesis builds on previous studies of the TC-DIAL measurement technique from a satellite platform to assess its current and future capabilities to meet the observation requirements defined by the atmospheric carbon and modeling scientific communities. Particular investigations are carried out to assess the optimum system configuration in the context of global carbon modeling using up-to-date spectroscopy and instrument parameters for the latest technology. Optimum systems for both direct and heterodyne detection TC-DIAL instruments are defined, and it is found that direct detection provides the lowest retrieval errors under clear sky conditions. For a system based on current technology TC-DIAL retrievals are expected to have errors of approximately 0.68 ppm for direct detection and 1.01 ppm for heterodyne detection over a 50 km surface track. Using global cloud statistics two suitable pulse repetition frequencies (PRF) for a heterodyne detection system have been identified as 5 and 15 kHz. These PRF’s provide the minimum probability of an effect known as cross signal contamination occurring when measurements are made in the presence of cloud. In this thesis it is shown that the retrieval error incurred by cross signal contamination is > 16 ppm for a heterodyne detection TC-DIAL system measuring through cloud with optical depth > 2. The most important retrieval error component in TC-DIAL retrievals has been found to be the uncertainties introduced by the use of numerical weather prediction data for the ancillary atmospheric profiles. The limited spatial resolution of current NWP models (> 20 km) implies the uncertainties associated with the ancillary data are required to be treated as systematic, and as a consequence their errors dominate over other TC-DIAL retrieval errors following multiple pulse integration.