EVALUATING ALTERNATIVE FORMULATIONS OF THE SMAP LEVEL-4 CARBON MODEL FOR THE NORTH AMERICAN ARCTIC–SUBARCTIC DURING THE GROWING SEASON

R. Madelon^1,2,3, K. A. Endsley⁴, J. S. Kimball⁴, G. J. M. De Lannoy⁵, O. Sonnentag^6,3, A. Mialon¹, A. Roy^2,3

¹Univ Toulouse, CNES/IRD/CNRS/INRAe, CESBIO, Toulouse, France, ²Université du Québec à Trois-Rivières (UQTR), Recherche en modélisation et télédétection des environnements nordiques (ReMoTE-Nord) group, Trois-Rivières, QC, Canada, 3Centre For Northern Studies (CEN), Québec, QC, Canada, ⁴Numerical Terradynamic Simulation Group (NTSG), W.A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT, USA, ⁵Katholieke Universiteit Leuven (KU Leuven), Department of Earth and Environmental Sciences, Division Soil and Water Management, Leuven, Belgium, ⁶Université de Montréal (UdeM), Département de Géographie, Montréal, QC, Canada

The Soil Moisture Active Passive (SMAP) Level-4 Terrestrial Carbon Flux (TCF) model (hereafter referred to as the L4C model) provides daily estimates of net ecosystem CO2 exchange (NEE), gross primary production (GPP), and ecosystem respiration (ER) at a global scale. Although the model aims to provide a representative estimation of the CO2 budget of Arctic and Subarctic (AS) environments, a deeper understanding of carbon cycle processes and targeted refinements are needed to improve its accuracy.

In this study, alternative model formulations are explored to identify key adjustments to implement for North American AS regions during the growing season. These formulations are calibrated and evaluated using measurements from 20 eddy covariance towers across Canada and Alaska, covering the period from 2015 to 2022.

Results show that using AS-specific ecosystem calibration, instead of a global plant functional type calibration, improves overall model performance. Incorporating growing degree days as a phenological proxy in GPP modeling enhances correlation and reduces unbiased root mean square error. In contrast to taiga forest, vapor pressure deficit is not a sensitive input variable for modeling GPP in upland tundra and northern wetlands. Finally, implementing a light response curve in GPP modeling reduces bias in GPP and NEE, which is essential for accurately estimating CO2 budget over long period of time.

This study highlights opportunities to improve the performance of the L4C model in North American AS regions during the growing season and underscores the need for additional research on ER in northern wetlands.