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

Description

<p>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.</p>

<p>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 <i>in
situ</i> 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. </p>

<p>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.</p>

<p>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.</p>

<p>Finally, in order
to study a new pathway of CH₄ emissions from palm tree stem, I
develop a two-dimensional diffusion model. The model is optimized using field
data of methane emissions from palm tree stems (Chapter 5). The model is then
extrapolated to Pastaza-Marañón foreland basin (PMFB) in Peru by using a
process-based biogeochemical model. To our knowledge, this is among the first efforts
to quantify regional CH₄ emissions through this pathway. The
estimates can be improved by considering the effects of changes in temperature,
precipitation and radiation and using long-period continuous flux observations.
Regional and global estimates of CH₄ emissions through this pathway
can be further constrained using more accurate palm swamp classification and
spatial distribution data of palm trees at the global scale.</p>

Links & Downloads

1. 10.25394/pgs.12203012.v1

Tags

Atmospheric Sciences

Soil Science

Earth Sciences not elsewhere classified

Soil Biology

methane

carbon monoxide atmosphere

process-based biogeochemical model

Modeling

Wetland

Land atmosphere interaction

Additional Fields

Identifer	oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12203012
Date	02 May 2020
Creators	Licheng Liu (8771531)
Source Sets	Purdue University
Detected Language	English
Type	Text, Thesis
Rights	CC BY 4.0
Relation	https://figshare.com/articles/Quantifying_Global_Exchanges_of_Methane_and_Carbon_Monoxide_Between_Terrestrial_Ecosystems_and_The_Atmosphere_Using_Process-based_Biogeochemistry_Models/12203012