Global Atmospheric CO2 Distributions from Satellite Observations.

High Resolution Mapping of Atmospheric Carbon Dioxide;Satellite Observations of Carbon Dioxide;Civil and Environmental Engineering;Engineering;Environmental Engineering

Hammerling, Dorit M.Thelen, Brian J. ;

University of Michigan

关键词: High Resolution Mapping of Atmospheric Carbon Dioxide;    Satellite Observations of Carbon Dioxide;    Civil and Environmental Engineering;    Engineering;    Environmental Engineering;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/96085/doritmh_1.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Carbon dioxide (CO2) is the most important anthropogenic greenhouse gas contributing to climate change.The advent of satellite observations of CO2 offers exciting opportunities to address some of the open questions in carbon cycle science, but also poses challenges such as large gaps and high measurement errors in CO2 satellite observations. Mapping is one way to extract valuable information from these observations, by creating observation-based global CO2 concentration products suitable both for direct interpretation and comparisons with model predictions.In this dissertation, a geostatistical mapping method for CO2 satellite observations is developed.Results from a simulation study for the Orbiting Carbon Observatory 2 (OCO-2) show that maps of atmospheric CO2 concentrations can be generated at high spatial and temporal resolution.These maps represent the atmospheric CO2 concentrations accurately at synoptic time scales, and the uncertainty estimates correctly describe the true uncertainty of the mapped concentrations.This represents a significant improvement over existing approaches, which typically have monthly or lower temporal resolutions and lack quantitative estimates of uncertainties.In an application to observations from the Japanese Greenhouse Gases Observing Satellite (GOSAT), CO2 concentration maps are shown to capture much of the synoptic scale and regional variability of CO2, in addition to its overall seasonality.Uncertainties are generally highest in the Northern Hemisphere during the height of the growing season, and lowest in areas with good data coverage and low CO2 variability in the Southern Hemisphere.A probabilistic comparison to a state-of-the-art model reveals that the most significant discrepancies captured by the GOSAT maps occur in South America in July and August, and central Asia in September to December.A signal detection study employing the developed mapping methodologyis used to assess the capability of the future Active Sensing of CO2 Emissions over Nights, Days and Seasons (ASCENDS) satellite mission to detect changes in atmospheric CO2 concentrations resulting from carbon flux perturbations of high relevance: carbon release from the melting of Arctic permafrost, the shifting of fossil fuel emissions from Europe to China, and changing source/sink characteristics in the Southern Ocean.