Modeling the diurnal variability of respiratory fluxes in the Canadian Terrestrial Ecosystem Model (CTEM). (1st May 2016) - Badawy, Bakr; Arora, Vivek K.; Melton, Joe R.; Nassar, Ray
Abstract: The Canadian Terrestrial Ecosystem Model (CTEM) coupled to the Canadian Land Surface Scheme (CLASS) is a dynamic vegetation model that incorporates photosynthesis and respiration submodules among many other physiological processes of the terrestrial biosphere. While the photosynthesis and leaf respiration submodules of CTEM operate at a time step of 30 min, other respiratory fluxes are estimated at a daily time step using the daily‐averaged values of canopy and soil temperature, and soil moisture content. Here we modify CTEM to be able to simulate the diurnal variation of ecosystem respiratory fluxes caused by the diurnal variation in the driving climate data. Simulating respiration at a 30 min time step changed the equilibrium states of primary carbon pools and fluxes. This required changes to some of the model's parameters in order to realistically simulate the equilibrium values of carbon pools and fluxes. The resulting estimated daily and annual carbon fluxes from the modified and original CTEM are similar with small relative differences. The similar annual cycles of daily values of primary CO2 fluxes confirm that modeling the respiratory fluxes at a subdaily time step does not affect the annual cycles of these fluxes. We also demonstrate that the modified version of the model is able to simulate the diurnal cycle of net ecosystem exchange of CO2 fluxes that are broadly comparable to observation‐based estimates at two flux tower sites and exhibit realistic seasonal patterns. Limitations remain in the modeled diurnal patterns of primary land‐atmosphere CO2 fluxes which will form the basis of further model improvements. Key Points: A terrestrial ecosystem model was modified to simulate subdaily ecosystem respiratory fluxes Modeling respiratory fluxes at half‐hourly time step reduces the amplitude of the diurnal NEE cycle Comparison with observation‐based estimates provides insight into the model performance