Towards a Complex Terrain Carbon Monitoring System (CMS-Mountains): Development and Testing in the Western U.S

Despite the need to understand terrestrial biospheric carbon fluxes to account for carbon cycle feedbacks and predict future CO₂ concentrations, knowledge of these fluxes at the regional scale remains poor. This is particularly true in mountainous areas, where complex atmospheric flows and relative lack of observations lead to significant uncertainties in carbon fluxes. Yet many mountainous regions also have significant forest cover and biomass—i.e., they are areas with the potential to serve as terrestrial carbon sinks. However, these sinks are highly dynamic and vulnerable to disturbance events, such as drought, insect damage, and wildfires.

We report on progress from two years into the development and testing of a new Carbon Monitoring System over Mountains (CMS-Mountains) covering the Western U.S We make use of biospheric models along with various datastreams such as atmospheric CO₂ and Solar-Induced Fluorescence (SIF) to constrain carbon fluxes over the Western U.S.

We run the Community Land Model (CLM) at both site-level and regional scales, with an aim towards assimilating multiple observations within the Data Assimilation Research Testbed (DART) and then further constraining regional scale carbon fluxes using atmospheric CO₂. The relationship between SIF, leaf-level physiology, and gross primary productivity (GPP) is examined mechanistically at an intensive field site in Colorado.

Initial results indicate strong relationships between GPP and SIF. Site level observations revealed that sustained non-photochemical quenching influences the seasonal pattern of fluorescence. When this process was added to the CLM model, the simulated SIF was more similar to the site-level observations. At the regional scale, CLM initially simulated biospheric carbon fluxes that were highly damped in amplitude which failed to match observed CO₂ and severely underestimated above-ground biomass. A detailed investigation pointed to biases in driving meteorology that led erroneously to water stress and attenuated vegetation growth within CLM. When bias-corrected meteorology was used to drive CLM, the severe underestimation in simulated biomass was reduced.

We conclude with an outlook for the remaining steps in our project towards delivering carbon flux estimates for the mountainous regions of the Western U.S.