Resolving temperature limitation on spring productivity in an evergreen conifer forest using a model–data fusion framework

Abstract. The flow of carbon through terrestrial ecosystems and the response to climate are critical but highly uncertain processes in global cycle. However, with a rapidly expanding array situ satellite data, there is an opportunity improve our mechanistic understanding (C) cycle's land use change. Uncertainty temperature limitation on productivity poses significant challenge predicting ecosystem fluxes changing climate. Here we diagnose quantitatively resolve environmental limitations growing-season onset gross primary production (GPP) using nearly 2 decades meteorological C flux data (2000–2018) at subalpine evergreen forest Colorado, USA. We implement CARbon DAta-MOdel fraMework (CARDAMOM) model–data fusion network sensitivity spring GPP. To capture GPP – component integrated introduced cold-temperature scaling function CARDAMOM regulate photosynthetic productivity. found that was gradually inhibited temperatures below 6.0 ∘C (±2.6 ∘C) completely −7.1 (±1.1 ∘C). addition this factor improved model's ability replicate interannual decadal timescales (r=0.88), relative nominal configuration (r=0.47), model predictability outside assimilation training period (r=0.88). While has important influence GPP, it does not have impact revealing other controls, such as precipitation, play more role annual This study highlights key limiting for growth winter-dormant forests, which future responses