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This digital collection includes publications by faculty, researchers, and/or students in the Department of Atmospheric Science.

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Browsing Publications by Subject "carbon cycle"

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    Simple Biosphere Model version 4.2 (SiB4) technical description
    (Colorado State University. Libraries, 2020-02) Haynes, Katherine, author; Baker, Ian, author; Denning, Scott, author
    The Simple Biosphere Model (SiB4) is a mechanistic, prognostic land surface model that integrates heterogeneous land cover, environmentally responsive prognostic phenology, dynamic carbon allocation, and cascading carbon pools from live biomass to surface litter to soil organic matter. By combining biogeochemical, biophysical, and phenological processes, SiB4 predicts vegetation and soil moisture states, land surface energy and water budgets, and the terrestrial carbon cycle. Rather than relying on satellite data, SiB4 fully simulates the terrestrial carbon cycle by using the carbon fluxes to determine the above and belowground biomass, which in turn feeds back to impact carbon assimilation and respiration. Every 10-minute time-step, SiB4 computes the albedo, radiation budget, hydrological cycle, layered temperatures, and soil moisture, as well as the resulting energy exchanges, moisture fluxes, carbon fluxes, and carbon pool transfers. Photosynthesis depends directly on environmental factors (humidity, moisture, and temperature) and aboveground biomass; and carbon uptake is determined using enzyme kinetics and stomatal physiology. Carbon release and pool transfers depend on assimilation rate, day length, moisture, phenology, temperature, and pool size. Once daily the net assimilated carbon is allocated to the live pools depending on phenology, soil moisture, and temperature; all live and dead pools are updated, including any necessary carbon transfers between pools; and the land surface state and related properties are revised. The new LAI and pools are then used for sub-hourly assimilation and respiration, completing the carbon cycle and providing self-consistent predicted vegetation states, soil hydrology, carbon pools, and land-atmosphere exchanges.