Abstract
AbstractGrasslands are one of the most widely distributed and abundant vegetation types globally, and land surface models struggle to accurately simulate grassland carbon dioxide, energy, and water fluxes. Here we hypothesize that this is due to land surface models having difficulties in reproducing grassland phenology, in particular in response to the seasonal and interannual variability of precipitation. Using leaf area index (LAI), net primary productivity, and flux data at 55 sites spanning climate zones, the aim of this study is to evaluate a novel prognostic phenology model (Simple Biosphere Model, SiB4) while simultaneously illustrating grassland relationships across precipitation gradients. Evaluating from 2000 to 2014, SiB4 predicts daily LAI, carbon, and energy fluxes with root‐mean‐square errors < 15% and individual biases <10%; however, not including management likely reduces its performance. Grassland mean annual LAI increases linearly with mean annual precipitation, with both SiB4 and the Moderate Resolution Imaging Spectroradiometer (MODIS) showing a 0.13 increase in LAI per 100‐mm increase in precipitation. Both gross primary production and ecosystem respiration increase with growing season length by ∼8.5 g C m−2 per day, with SiB4 and Fluxnet estimates within 18%. Despite differences in mean annual precipitation and growing season length, all grassland sites shift to seasonal carbon sinks one month prior to peak uptake. During a U.S. drought, MODIS and SiB4 had nearly identical LAI responses, and the LAI change due to drought was less than the LAI change across the precipitation gradient, indicating that grassland drought response is not as strong as the overlying climate response.
Highlights
Grasslands are one of the most widely distributed and abundant types of vegetation globally
Interannual variability in leaf area index (LAI) across sites, because large differences for any year would increase the root-mean-square errors (RMSEs) to error values closer to observed values and consistent early or late phenology would lead to a larger mean bias errors (MBEs)
Seasonal Patterns The length of the growing season for grasslands ranges from a few months to year-round, and land surface models (LSMs) have a difficult time accurately simulating monthly carbon fluxes for these complex ecosystems; we analyzed the mean seasonal cycle across all grassland sites to see if any patterns emerge across climate zones
Summary
Grasslands are one of the most widely distributed and abundant types of vegetation globally. While changes in grassland productivity do not coincide with precipitation changes in humid environments (Jaksic et al, 2006), grasslands are highly responsive to precipitation in drier environments (Moran et al, 2014) For these ecosystems, variability in grassland growth and productivity varies spatially across sites, and the aboveground net primary production (ANPP) generally increases linearly with rainfall amount (Jia et al, 2015; Sala et al, 1988; Shi et al, 2014; Zhu & Southworth, 2013). Variability in grassland growth and productivity varies spatially across sites, and the aboveground net primary production (ANPP) generally increases linearly with rainfall amount (Jia et al, 2015; Sala et al, 1988; Shi et al, 2014; Zhu & Southworth, 2013) In addition to this large-scale tendency across precipitation gradients, for any given location grasslands are highly responsive to temporal variations in precipitation (Hsu et al, 2012; Knapp et al, 2015). Geography, light availability, nutrient limitations, current year precipitation, previous year precipitation, and previous year ANPP all impact current year production, the relative importance of these factors is still uncertain (Moran et al, 2014; Oesterheld et al, 2001; Sala et al, 2012)
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