Development of a Data Assimilation System to Study Ecosystem Exchange at the National Scale Using Data from the National Ecological Observatory Network

Background/Question/Methods

Ecologists are challenged to understand the effects of a range of environmental perturbations to the Earth system from global phenomena like climatic or atmospheric change to land use transformations. While regional and global conclusions are difficult to draw from a single study site, ecologists can now observe, collect, record and store more data, more frequently and more extensively than ever before. One approach to address these problems and opportunities is to fuse observations with mathematical models to infer responses to environmental change. A key challenge in utilizing a range of observing platforms is to achieve a robust treatment of uncertainty for each system. The National Ecological Observatory Network (NEON) is a continental scale observing system which will measure a suite of ecological attributes of ecosystems across 60 sites in the continental US, Alaska, Hawaii, and Puerto Rico over 30 years.  The strengths of this observing system will lie in the uniformity of the sampling design and standardization of equipment, calibration and data processing and in the co-location of observations of multiple aspects of ecosystem structure and function across Ecoclimatic space. Ecosystem exchange of carbon and water have direct impacts on atmospheric CO₂ and land surface energy balance and influence second order ecological function. To utilize these emerging data sets, we have developed a formal data assimilation system for the Community Land Model (CLM) coupled with National Center for Atmospheric Research’s Data Assimilation Research Testbed (DART) which allows parameter optimization using the Ensemble Kalman Filter (EnKF).

Results/Conclusions

Here we present the results of a test of this system to adequately estimate ecosystem exchange across the continent, in particular (i) the sensitivity of the CLM to variations in parameters and climate drivers (ii) the representativeness of the NEON design in terms of its ability to constrain estimates of ecosystem carbon exchange across the continental US. We find that the NEON sampling design is sufficient to constrain model predictions of productivity over the majority of the continental US. However areas of the desert southwest and gulf coast may be poorly constrained.  Although model deficiency can explain part of this problem, it is clear that either strategic placement of re-locatable NEON sites or fusion of NEON data with observations from other networks will be essential to ensure robust estimation of continental scale ecosystem fluxes.