Quantifying Global Exchanges of Methane and Carbon Monoxide Between Terrestrial Ecosystems and the Atmosphere Using Process-based Biogeochemistry Models

Licheng Liu, Purdue University

Abstract

Methane (CH4) is the second most powerful greenhouse gas (GHG) behind carbon dioxide (CO2), and is able to trap a large amount of long-wave radiation, leading to surface warming. Carbon monoxide (CO) plays an important role in controlling the oxidizing capacity of the atmosphere by reacting with OH radicals that affect atmospheric CH4 dynamics. Terrestrial ecosystems play an important role in determining the amount of these gases into the atmosphere. However, global quantifications of CH4 emissions from wetlands and its sinks from uplands, and CO exchanges between land and the atmosphere are still fraught with large uncertainties, presenting a big challenge to interpret complex atmospheric CH4 dynamics in recent decades. In this dissertation, I apply modeling approaches to estimate the global CH4 and CO exchanges between land ecosystems and the atmosphere and analyze how they respond to contemporary and future climate change.Firstly, I develop a process-based biogeochemistry model embedded in Terrestrial Ecosystem Model (TEM) to quantify the CO exchange between soils and the atmosphere at the global scale (Chapter 2). Parameterizations were conducted by using the CO in situ data for eleven representative ecosystem types. The model is then extrapolated to global terrestrial ecosystems. Globally soils act as a sink of atmospheric CO. Areas near the equator, Eastern US, Europe and eastern Asia will be the largest sink regions due to their optimum soil moisture and high temperature. The annual global soil net flux of atmospheric CO is primarily controlled by air temperature, soil temperature, SOC and atmospheric CO concentrations, while its monthly variation is mainly determined by air temperature, precipitation, soil temperature and soil moisture.Secondly, to better quantify the global CH4 emissions from wetlands and their uncertainties, I revise, parameterize and verify a process-based biogeochemical model for methane for various wetland ecosystems (Chapter 3). The model is then extrapolated to the global scale to quantify the uncertainty induced from four different types of uncertainty sources including parameterization, wetland type distribution, wetland area distribution and meteorological input. Spatially, the northeast US and Amazon are two hotspots of CH4 emissions, while consumption hotspots are in the eastern US and eastern China. The relationships between both wetland emissions and upland consumption and El Niño and La Niña events are analyzed. This study highlights the need for more in situ methane flux data, more accurate wetland type and area distribution information to better constrain the model uncertainty.Thirdly, to further constrain the global wetland CH4 emissions, I develop a predictive model of CH4 emissions using an artificial neural network (ANN) approach and available field observations of CH4 fluxes (Chapter 4). Eleven explanatory variables including three transient climate variables (precipitation, air temperature and solar radiation) and eight static soil property variables are considered in developing the ANN models. The models are then extrapolated to the global scale to estimate monthly CH4 emissions from 1979 to 2099. Significant interannual and seasonal variations of wetland CH4 emissions exist in the past four decades, and the emissions in this period are most sensitive to variations in solar radiation and air temperature. This study reduced the uncertainty in global CH4 emissions from wetlands and called for better characterizing variations of wetland areas and water table position and more long-term observations of CH4 fluxes in tropical regions

Degree

Ph.D.

Advisors

Zhuang, Purdue University.

Subject Area

Climate Change|Biogeochemistry|Artificial intelligence|Ecology|Forestry|Hydrologic sciences|Microbiology|Soil sciences

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