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<strong class="journal-contentHeaderColor">Abstract.</strong> The Soil Moisture Active Passive Level-4 Terrestrial Carbon Flux model (hereafter referred to as the L4C model) provides daily estimates of net ecosystem CO<sub>2</sub> exchange (NEE), gross primary production (GPP), and ecosystem respiration (ER) at a global scale. The model is based on direct mechanistic forcing–response relationships between CO<sub>2</sub> fluxes and energy proxies (absorbed photosynthetically active radiation and temperature) and moisture proxies (soil moisture and vapor pressure deficit). Although the L4C model aims to provide a representative estimation of the CO<sub>2</sub> 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 proposed for the North American AS regions during the growing season. These formulations are calibrated and evaluated using NEE-derived GPP and ER from 20 eddy covariance towers across western Canada and Alaska, covering the period from 2015 to 2022. Refinements in the representation of energy proxies resulted in greater improvements in model performance than adjustments to moisture proxies. Specifically, implementing a light-response curve in GPP estimation reduced unbiased root mean squared error and bias, while incorporating growing degree days improved correlation. Adjustments to rootzone and surface soil moisture in GPP and ER estimation, respectively, did not yield conclusive performance improvements. Vapor pressure deficit showed limited importance as a driver of GPP in upland tundra and wetlands, whereas it had a stronger impact in taiga forests. Finally, the litterfall scheme used to represent SOC dynamics in the L4C ER model formulation in version 8 demonstrated improved performance relative to version 7. These results highlight opportunities to enhance the accuracy of the L4C model for the North American AS growing season but also underscores the need for further research on ER modeling.