2 resultados para Global Estimates

em Illinois Digital Environment for Access to Learning and Scholarship Repository


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The distribution of sources and sinks of carbon over the land surface is dominated by changes in land use such as deforestation, reforestation, and agricultural management. Despite, the importance of land-use change in dominating long-term net terrestrial fluxes of carbon, estimates of the annual flux are uncertain relative to other terms in the global carbon budget. The interaction of the nitrogen cycle via atmospheric N inputs and N limitation with the carbon cycle contributes to the uncertain effect of land use change on terrestrial carbon uptake. This study uses two different land use datasets to force the geographically explicit terrestrial carbon-nitrogen coupled component of the Integrated Science Assessment Model (ISAM) to examine the response of terrestrial carbon stocks to historical LCLUC (cropland, pastureland and wood harvest) while accounting for changes in N deposition, atmospheric CO2 and climate. One of the land use datasets is based on satellite data (SAGE) while the other uses population density maps (HYDE), which allows this study to investigate how global LCLUC data construction can affect model estimated emissions. The timeline chosen for this study starts before the Industrial Revolution in 1765 to the year 2000 because of the influence of rising population and economic development on regional LCLUC. Additionally, this study evaluates the impact that resulting secondary forests may have on terrestrial carbon uptake. The ISAM model simulations indicate that uncertainties in net terrestrial carbon fluxes during the 1990s are largely due to uncertainties in regional LCLUC data. Also results show that secondary forests increase the terrestrial carbon sink but secondary tropical forests carbon uptake are constrained due to nutrient limitation.

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This thesis is actually the composition of two separate studies aimed at further understanding the role of incomplete combustion products on atmospheric chemistry. The first explores the sensitivity of black carbon (BC) forcing to aerosol vertical location since BC has an increased forcing per unit mass when it is located above reflective clouds. We used a column radiative transfer model to produce globally-averaged values of normalized direct radiative forcing (NDRF) for BC over and under different types of clouds. We developed a simple column-weighting scheme based on the mass fractions of BC that are over and under clouds in measured vertical profiles. The resulting NDRF is in good agreement with global 3-D model estimates, supporting the column-weighted model as a tool for exploring uncertainties due to diversity in vertical distribution. BC above low clouds accounts for about 20% of the global burden but 50% of the forcing. We estimate maximum-minimum spread in NDRF due to modeled profiles as about 40% and uncertainty as about 25%. Models overestimate BC in the upper troposphere compared with measurements; modeled NDRF might need to be reduced by about 15%. Redistributing BC within the lowest 4 km of the atmosphere affects modeled NDRF by only about 5% and cannot account for very high forcing estimates. The second study estimated global year 2000 carbon monoxide (CO) emissions using a traditional bottom-up inventory. We applied literature-derived emission factors to a variety of fuel and technology combinations. Combining these with regional fuel use and production data we produced CO emissions estimates that were separable by sector, fuel type, technology, and region. We estimated year 2000 stationary source emissions of 685.9 Tg/yr and 885 Tg/yr if we included adopted mobile sources from EDGAR v3.2FT2000. Open/biomass burning contributed most significantly to global CO burden, while the residential sector, primarily in Asia and Africa, were the largest contributors with respect to contained combustion sources. Industry production in Asia, including brick, cement, iron and steel-making, also contributed significantly to CO emissions. Our estimates of biofuel emissions are lower than most previously published bottom-up estimates while our other fuel emissions are generally in good agreement. Our values are also universally lower than recently estimated CO emissions from models using top-down methods.