Xueli Huo stands in front of Green Mountain.
DART_LAB is a set of instructional materials designed to teach new users the fundamentals of data assimilation. “As a user of DART, I like DART_LAB the most because it allows me to play around with different ‘knobs’ and better understand how and why each of them can remove biases in model -simulations and makes the assimilation perform better,” said Huo.
In an article that is currently in review for publication in the Journal of Geophysical Research: -Biogeosciences, Huo worked with a team that used DART to assimilate two data sources into CLM to -assess the model’s fidelity in Alaska and Western Canada. The first data source she assimilated was -Leaf Area Index (LAI), or the ratio of leaf area to ground surface area inferred from a remote -sensing instrument, in this case the Moderate Resolution Imaging Spectroradiometer. The second data -source she assimilated was a machine learning product that estimated aboveground biomass.
The experiment compared CLM’s estimates of Gross Primary Productivity (GPP) — how much carbon is +simulations and makes the assimilation perform better,” said Huo.
In an article published in the Journal of Geophysical Research: Biogeosciences, Huo worked with a +team that used DART to assimilate two data sources into CLM to assess the model’s fidelity in Alaska +and Western Canada. The first data source she assimilated was Leaf Area Index (LAI), or the ratio of +leaf area to ground surface area inferred from a remote sensing instrument, in this case the +Moderate Resolution Imaging Spectroradiometer. The second data source she assimilated was a machine +learning product that estimated aboveground biomass.
The experiment compared CLM’s estimates of Gross Primary Productivity (GPP) — how much carbon is photosynthesized per year in a given area — both with and without data assimilation. This methodology allowed Huo’s team to obtain a benchmark of the model’s default behavior and compare it against the model’s behavior when independent observational data was used as a constraint. Huo’s @@ -73,8 +73,9 @@ the net carbon fluxes from land as input to the atmosphere.”
Addressing these questions will require international collaboration among a new generation of scientists, akin to those who developed the current suite of land surface models a generation ago. Huo will surely relish the opportunity to contribute.
Huo, X., A. M. Fox, H. Dashti, C. Devine, W. Gallery, W. K. Smith, B. Raczka, J. L. Anderson, -A. Rogers, D. J. P. Moore, 2024: Assimilating leaf area index and aboveground biomass reveals new -processes in carbon update in the Arctic and Boreal Region. JGR Biosciences (in review).
Rogers, A., S. P. Serbin, K. S. Ely, and S. D. Wullschleger, 2019: Terrestrial biosphere models may +A. Rogers, D. J. P. Moore, 2024: Integrating State Data Assimilation and Innovative Model +Parameterization Reduces Simulated Carbon Uptake in the Arctic and Boreal Region. JGR Biosciences, +129, e2024JG008004, doi.org/10.1029/2024JG008004.
Rogers, A., S. P. Serbin, K. S. Ely, and S. D. Wullschleger, 2019: Terrestrial biosphere models may overestimate Arctic CO2 assimilation if they do not account for decreased quantum yield and -convexity at low temperature. New Phytologist, 223, 167–179 +convexity at low temperature. New Phytologist, 223, 167–179, doi.org/10.1111/nph.15750.
Xueli Huo stands in front of Green Mountain.