The global climate is changing due to the accumulation of greenhouse gases (GHGs) in the atmosphere, primarily due to anthropogenic activity. The dominant GHG is CO₂ which originates from combustion of fossil fuels, land use change and management. The terrestrial biosphere is a key driver of climate and biogeochemical cycles at regional and global scales. Furthermore, the response of the Earth system to future drivers of climate change will depend on feedbacks between biogeochemistry and climate. Therefore, understanding these processes requires a mechanistic approach in any model simulation framework. However ecosystem processes are complex and nonlinear and consequently models need to be validated against observations at multiple spatial scales. In this thesis the weather research and forecasting model (WRF) has been coupled to the mechanistic terrestrial ecosystem model soil-plant-atmosphere (SPA), creating WRF-SPA. The thesis is split into three main chapters: i. WRF-SPA model development and validation at multiple spatial scales, scaling from surface fluxes of CO₂ and energy to aircraft profiles and tall tower observations of atmospheric CO₂ concentrations. ii. Investigation of ecosystem contributions to observations of atmospheric CO₂ concentrations made at tall tower Angus, Dundee, Scotland using ecosystem specific CO₂ tracers at seasonal and interannual time scales. iii. An assessment of detectability of a policy relevant national scale afforestation by observations made at a tall tower. Detectability of changes in atmospheric CO₂ concentrations was assessed through a comparison of a control simulation, using current day forest extent, and an experimentally afforested simulation using WRF-SPA. WRF-SPA performs well at both site and regional scales, accurately simulating aircraft profiles of CO₂ concentration magnitudes (error <+- 4 ppm), indicating appropriate source sink distribution and realistic atmospheric transport. Hourly observations made at tall tower Angus were also well simulated by WRF-SPA (R² = 0.67, RMSE = 3.5 ppm, bias = 0.58 ppm). Analysis of CO₂ tracers at tall tower Angus show an increase in the seasonal error between WRF-SPA simulated atmospheric CO₂ and observations, which coincides with simulated cropland harvest. WRF-SPA does not simulate uncultivated land associated with agriculture, which in Scotland represents 36 % of agricultural holdings. Therefore, uncultivated land components may provide an explanation for the increase in model-data error. Interannual variation in weather is indicated to have a greater impact on ecosystem specific contributions to atmospheric CO₂ concentrations at Angus than variation in surface activity. In a model experiment, afforestation of Scotland was simulated to test the impact on Scotland’s carbon balance. The changes were shown to be potentially detectable by observations made at tall tower Angus. Afforestation results in a reduction in atmospheric CO₂ concentrations by up to 0.6 ppm at seasonal time scales at tall tower Angus. Detection of changes in forest surface net CO₂ uptake flux due to afforestation was improved through the use of a network of tall towers (R² = 0.83) compared to tall tower Angus alone (R² = 0.75).
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:615418 |
| Date | January 2014 |
| Creators | Smallman, Thomas Luke |
| Contributors | Moncrieff, John; Grace, John |
| Publisher | University of Edinburgh |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/1842/8886 |
Page generated in 0.0017 seconds