Übel, Markus: Simulation of mesoscale patterns and diurnal variations of atmospheric CO2 mixing ratios with the model system TerrSysMP-CO2. - Bonn, 2016. - Dissertation, Rheinische Friedrich-Wilhelms-Universität Bonn.
Online-Ausgabe in bonndoc: https://nbn-resolving.org/urn:nbn:de:hbz:5n-44112
@phdthesis{handle:20.500.11811/6840,
urn: https://nbn-resolving.org/urn:nbn:de:hbz:5n-44112,
author = {{Markus Übel}},
title = {Simulation of mesoscale patterns and diurnal variations of atmospheric CO2 mixing ratios with the model system TerrSysMP-CO2},
school = {Rheinische Friedrich-Wilhelms-Universität Bonn},
year = 2016,
month = jun,

volume = 72,
note = {With the recent trend towards precise regional climate simulations information on the spatial distribution of CO2 sources and sinks at the mesoscale scale becomes more important. A common method to obtain these fluxes is inverse modeling, i.e. the variability of atmospheric CO2 observations is assimilated into atmospheric tracer transport models to estimate mesoscale and regional scale CO2 fluxes. Aircraft measurements indicate distinct mesoscale patterns in the atmospheric CO2 distribution which can only partly be resolved by climate models using a rather coarse grid resolution.
The main objectives of the present study are to understand which processes (e.g. synoptic flow, land use heterogeneity, complex terrain) generate these patterns and how the variable atmospheric CO2 contents influence the stomatal control of transpiration and photosynthesis. For that, the mesoscale terrestrial model system TerrSysMP is used that couples the atmospheric model COSMO (version 4.21) to the Community Land Model (CLM, version 3.5) which can again be coupled to the hydrological model ParFlow. TerrSysMP is extended by a fully prognostic treatment of atmospheric CO2 concentrations forming the new model version TerrSysMP-CO2. This model includes a two-way coupling of CO2 (atmosphere ↔ biosphere): the actual CO2 mixing ratios are used to calculate the biogenic CO2 fluxes with CLM and, in turn, these fluxes prognostically cause the atmospheric CO2 distribution. CLM is extended by the carbon turnover model RothC calculating heterotrophic soil respiration as well as by simple parameterizations for decomposition of organic matter and autotrophic respiration. Moreover, high-resolution anthropogenic emissions complete the CO2 budget in TerrSysMP-CO2.
High-resolution model simulations are performed using TerrSysMP-CO2 for a region in western Germany and parts of BeNeLux. The domain includes the low mountain range Eifel as well as the densely populated Rhine valley with the metropolises Cologne, Dusseldorf and Bonn. The results show a pronounced diurnal cycle of CO2 in the planetary boundary layer (PBL). The highest concentrations occur in the early morning being the result of near surface CO2 accumulation due to soil respiration. With the onset of photosynthesis a strong decrease of atmospheric CO2 concentrations is simulated followed by turbulent vertical transport within the PBL at daytime. The influence of complex terrain and anthropogenic CO2emissions on the spatio-temporal patterns of atmospheric CO2 mixing ratios is of particular interest. During night strong horizontal CO2 gradients arise between narrow valleys and mountain ridges caused by orographically induced turbulent patterns and mesoscale atmospheric flows. Moreover, downstream of densely populated regions significant higher CO2 concentrations are simulated. Additionally, the variable atmospheric CO2 mixing ratios slightly modify simulated photosynthesis and transpiration rates due to the response of the stomatal opening of leaves on available atmospheric CO2 concentrations.
The model performance of TerrSysMP-CO2 is verified with eddy-covariance measurements of CO2 and energy fluxes. Moreover, the simulated vertical distribution of atmospheric CO2 concentrations is compared with observations of CO2 made at a 124m tall tower near Jülich.
The new insights into the processes influencing mesoscale patterns of atmospheric CO2 mixing ratios can help to better integrate terrestrial and coastal CO2 observations into inverse modeling studies.},

url = {http://hdl.handle.net/20.500.11811/6840}
}

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