Using climate‐driven leaf phenology and growth to improve predictions of gross primary productivity in North American forests. (26th September 2020)

• Record Type: Journal Article
• Authors: Fang, Jing; Lutz, James A.; Wang, Leibin; Shugart, Herman H.; Yan, Xiaodong
• Is Part Of: Global change biology. Volume 26:Number 12(2020)
• Page Start: 6974
• Page End: 6988
• Publication Date: 2020-09-26
• Subjects: forest ecosystems -- non‐structural carbohydrates -- North American Carbon Program -- phenological events -- photosynthesis -- prognostic model -- terrestrial biospheric models; Climatic changes -- Environmental aspects -- Periodicals; Troposphere -- Environmental aspects -- Periodicals; Biodiversity conservation -- Periodicals; Eutrophication -- Periodicals; 551.5
• DOI: 10.1111/gcb.15349
• Languages: English
• ISSNs: 1354-1013

Abstract

Abstract: Forest ecosystems are an important sink for terrestrial carbon sequestration. Hence, accurate modeling of the intra‐ and interannual variability of forest photosynthetic productivity remains a key objective in global biology. Applying climate‐driven leaf phenology and growth in models may improve predictions of the forest gross primary productivity (GPP). We used a dynamic non‐structural carbohydrates (NSC) model (FORCCHN2) that couples leaf development and phenology to investigate the relationships among photosynthesis and environmental factors. FORCCHN2 simulates spring and autumn phenological events from heat and chilling, respectively. Leaf area index data from satellites along with climate data estimated localized phenological parameters. NSC limitation, immediate temperature, accumulated heat, and growth potential comprised a daily leaf‐growth model. Functionally, leaf growth was decoupled from photosynthesis. Leaf biomass determined overall photosynthetic production. We compared this model with outputs of the other six terrestrial biospheric models and with observations from the North American Carbon Program Site Interim Synthesis in 18 forest sites. This model improved the predicted performance of yearly GPP with a 57%–210% increase in correlation (median) and up to a 102% reduction in biases (median), compared to three prognostic models and three prescribed models. At the North America continental scale, the model predicted the average annual GPP of 7.38 Pg C/year from forest ecosystems during 1985–2016. The results showed an increasing trend of GPP in North America (1.0 Pg C/decade). The inclusion of climate‐driven phenology and growth has a significant potential for improving dynamic vegetation models, and promotes a further understanding of the complex relationship between environment and photosynthesis. Abstract : This work developed a vegetation dynamic model (FORCCHN2 model) that couples leaf development and phenology to improve predictions of the forest gross primary productivity (GPP) in the North America forests. The model predicted the average annual GPP of 7.38 Pg C/year from forest ecosystems during 1985–2016 at the North America continental scale.