Publications

Huo, XL; Fox, AM; Dashti, H; Devine, C; Gallery, W; Smith, WK; Raczka, B; Anderson, JL; Rogers, A; Moore, DJP (2024). Integrating State Data Assimilation and Innovative Model Parameterization Reduces Simulated Carbon Uptake in the Arctic and Boreal Region. JOURNAL OF GEOPHYSICAL RESEARCH-BIOGEOSCIENCES, 129(9), e2024JG008004.

Abstract
Model representation of carbon uptake and storage is essential for accurate projection of the response of the arctic-boreal zone to a rapidly changing climate. Land model estimates of LAI and aboveground biomass that can have a marked influence on model projections of carbon uptake and storage vary substantially in the arctic and boreal zone, making it challenging to correctly evaluate model estimates of Gross Primary Productivity (GPP). To understand and correct bias of LAI and aboveground biomass in the Community Land Model (CLM), we assimilated the 8-day Moderate Resolution Imaging Spectroradiometer (MODIS) LAI observation and a machine learning product of annual aboveground biomass into CLM using an Ensemble Adjustment Kalman Filter (EAKF) in an experimental region including Alaska and Western Canada. Assimilating LAI and aboveground biomass reduced these model estimates by 58% and 72%, respectively. The change of aboveground biomass was consistent with independent estimates of canopy top height at both regional and site levels. The International Land Model Benchmarking system assessment showed that data assimilation significantly improved CLM's performance in simulating the carbon and hydrological cycles, as well as in representing the functional relationships between LAI and other variables. To further reduce the remaining bias in GPP after LAI bias correction, we re-parameterized CLM to account for low temperature suppression of photosynthesis. The LAI bias corrected model that included the new parameterization showed the best agreement with model benchmarks. Combining data assimilation with model parameterization provides a useful framework to assess photosynthetic processes in LSMs. The arctic-boreal zone is warming rapidly, impacting regional and global carbon cycles. The Community Land Model (CLM) can be used to project future carbon uptake and storage in this region. However, CLM is biased in estimating leave area index (LAI) and aboveground biomass that can significantly affect model projections of carbon uptake and storage. We forced the model estimates of LAI and the aboveground biomass to be consistent with satellite-derived LAI observations and a high-quality machine learning product of aboveground biomass in Alaska and Western Canada using data assimilation. The change of aboveground biomass resulted in model estimates of vegetation height consistent with independent estimates at regional and site levels. The assessment using the International Land Model Benchmarking System showed that CLM's performance in simulating carbon and hydrologic cycles was improved. Fixing the model bias in LAI only removed partial bias in carbon uptake, and a new parameterization allowing two key parameters in photosynthesis to vary with leaf temperature was introduced into CLM, to further remove the remaining bias in carbon uptake. Combining data assimilation with this new parameterization yielded more accurate model estimates of carbon uptake. Assimilating leaf area index and aboveground biomass observations into CLM reduced model bias in estimating them Data assimilation significantly improved CLM's performance in carbon and hydrologic cycles, as well as the functional relationships Implementation of a new parameterization of photosynthesis in CLM further reduced model bias in estimating the gross primary productivity

DOI:
10.1029/2024JG008004

ISSN:
2169-8953