气候变化领域集成服务门户(CCPortal): Representing Grasslands Using Dynamic Prognostic Phenology Based on Biological Growth Stages: Part 2. Carbon Cycling

CCPortal

DOI	10.1029/2018MS001541
	Representing Grasslands Using Dynamic Prognostic Phenology Based on Biological Growth Stages: Part 2. Carbon Cycling
	Haynes K.D.; Baker I.T.; Denning A.S.; Wolf S.; Wohlfahrt G.; Kiely G.; Minaya R.C.; Haynes J.M.
发表日期	2019
ISSN	19422466
起始页码	4440
结束页码	4465
卷号	11 期号:12
英文摘要	Grasslands 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. ©2019. The Authors.
英文关键词	Grassland Ecology; Land Surface Model; Prognostic Phenology; Terrestrial Carbon Cycle
语种	英语
scopus关键词	Biology; Carbon dioxide; Climate change; Climate models; Drought; Ecosystems; Mean square error; Radiometers; Surface measurement; Gross primary production; Land surface modeling; Mean annual precipitation; Moderate resolution imaging spectroradiometer; Net primary productivity; Prognostic Phenology; Seasonal and interannual variability; Terrestrial carbon cycle; Silicon compounds; accuracy assessment; annual variation; carbon cycle; drought; grassland; growing season; leaf area index; MODIS; phenology; primary production; respiration; vegetation type
来源期刊	Journal of Advances in Modeling Earth Systems
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156797
作者单位	Department of Atmospheric Science, Colorado State University, Fort Collins, CO, United States; Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland; Institute for Ecology, University of Innsbruck, Innsbruck, Austria; Environmental Research Institute, University College Cork, Cork, Ireland; Center for Science Education, University Corporation for Atmospheric Research, Boulder, CO, United States; Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, United States
推荐引用方式 GB/T 7714	Haynes K.D.,Baker I.T.,Denning A.S.,et al. Representing Grasslands Using Dynamic Prognostic Phenology Based on Biological Growth Stages: Part 2. Carbon Cycling[J],2019,11(12).
APA	Haynes K.D..,Baker I.T..,Denning A.S..,Wolf S..,Wohlfahrt G..,...&Haynes J.M..(2019).Representing Grasslands Using Dynamic Prognostic Phenology Based on Biological Growth Stages: Part 2. Carbon Cycling.Journal of Advances in Modeling Earth Systems,11(12).
MLA	Haynes K.D.,et al."Representing Grasslands Using Dynamic Prognostic Phenology Based on Biological Growth Stages: Part 2. Carbon Cycling".Journal of Advances in Modeling Earth Systems 11.12(2019).