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. Many mountainous regions also have significant forest cover and biomass, yet these potential carbon sinks are highly dynamic and vulnerable to disturbance events, such as drought, insect damage, and wildfires. Our new Carbon Monitoring System over Mountains (CMS-Mountains) uses a multi-scale approach by examining the site-level relationship between solar-induced fluorescence (SIF), leaf-level physiology, and gross primary productivity (GPP) at an observation intensive field site in Colorado. We incorporate this mechanistic understanding into the latest release of the Community Land Model (CLM 5) and then use this as the basis for initial simulations of biomass and carbon uptake across the Western U.S. Finally, we improve upon these regional simulations by assimilating multiple remotely-sensed observations (LAI, biomass, SIF, atmospheric CO₂) within the Data Assimilation Research Testbed (DART). At the site level, we found a strong relationship between GPP and SIF and that the GPP-SIF seasonal relationship was influenced by sustained energy dissipation. When a representation of this process was added to the CLM model, this significantly improved the seasonal representation of SIF. 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. We have improved these simulations by using a bias corrected meteorology product that more accurately represents high elevation precipitation and shortwave radiation. We further improved these regional simulations by assimilating remotely sensed LAI and biomass observations across California and Colorado. We found that this improved the simulated phenological timing, and reduced the biomass and amplitude of the carbon fluxes. We conclude with recommendations on how to best expand the data assimilation system across the entire Western U.S.