Pan-Arctic modelling of net ecosystem exchange of CO ₂

Abstract

Net ecosystem exchange (NEE) of C varies greatly among Arctic ecosystems. Here, we show that approximately 75 per cent of this variation can be accounted for in a single regression model that predicts NEE as a function of leaf area index (LAI), air temperature and photosynthetically active radiation (PAR). The model was developed in concert with a survey of the light response of NEE in Arctic and subarctic tundras in Alaska, Greenland, Svalbard and Sweden. Model parametrizations based on data collected in one part of the Arctic can be used to predict NEE in other parts of the Arctic with accuracy similar to that of predictions based on data collected in the same site where NEE is predicted. The principal requirement for the dataset is that it should contain a sufficiently wide range of measurements of NEE at both high and low values of LAI, air temperature and PAR, to properly constrain the estimates of model parameters. Canopy N content can also be substituted for leaf area in predicting NEE, with equal or greater accuracy, but substitution of soil temperature for air temperature does not improve predictions. Overall, the results suggest a remarkable convergence in regulation of NEE in diverse ecosystem types throughout the Arctic.

Estimating aboveground biomass and leaf area of low-stature Arctic shrubs with terrestrial LiDAR

“…A. Walker et al, 2003), and fire regimes (Higuera et al, 2008), as well as wildlife habitat and trophic and by definition prohibits repeat monitoring necessary to detect change (Mascaro, Asner, Davies, Dehgan, & Saatchi, 2014). Consequently, satellite-based optical remote sensing techniques that rely on spectral vegetation indices such as the normalized difference vegetation index (NDVI; Tucker, 1979) are frequently used in the Arctic tundra to estimate biomass (Boelman et al, 2003;Simms & Ward, 2013;Donald A Walker, Auerbach, & Shippert, 1995) and other plant community characteristics such as community structure (e.g., Boelman, Gough, McLaren, & Greaves, 2011) and ecosystem carbon storage and fluxes (e.g., Shaver et al, 2013;Street, Shaver, Williams, & Van Wijk, 2007). These studies have been useful for understanding multiyear trends in the greening or browning of the arctic; however, using satellite-derived passive remote sensing techniques can be problematic because such indices can be strongly affected by factors like canopy architecture, viewing geometry, and the mixing of reflectance signals from plant leaves, woody stems, background soil, and surface water (Boelman, Stieglitz, Griffin, & Shaver, 2005;Gamon, Huemmrich, Stone, & Tweedie, 2013;Jackson & Huete, 1991;Jacquemoud & Baret, 1990;Verhoef, 1984).…”

Effects of nitrogen fertilization on the forest floor carbon balance over the growing season in a boreal pine forest

“…Forest floor GPP may then be estimated for each location by adding mean R to NEE at a particular level of PAR. For each location and time period, we fit a three-parameter decay model to the relationship between PAR and measured forest floor NEE by minimizing the root mean square error between observed and predicted NEE values using Excel solver (Shaver et al, 2013):…”

The Polar Vegetation Photosynthesis and Respiration Model: a parsimonious, satellite-data-driven model of high-latitude CO<sub>2</sub> exchange

“…Simultaneously, recent successes in generating site-level, data-driven estimates of net ecosystem CO 2 exchange (NEE) at Arctic sites (e.g., Shaver et al, 2007Shaver et al, , 2013Stoy et al, 2009), with little intersite variability in parameters (Loranty et al, 2011), have indicated the tremendous potential that exists for accurate estimates of regional-scale Arctic NEE to be modeled diagnostically from satellite observations.…”