气候变化领域集成服务门户(CCPortal): Harnessing big data to rethink land heterogeneity in Earth system models

CCPortal

DOI	10.5194/hess-22-3311-2018
	Harnessing big data to rethink land heterogeneity in Earth system models
	Chaney N.W.; Van Huijgevoort M.H.J.; Shevliakova E.; Malyshev S.; Milly P.C.D.; Gauthier P.P.G.; Sulman B.N.
发表日期	2018
ISSN	1027-5606
起始页码	3311
结束页码	3330
卷号	22 期号:6
英文摘要	The continual growth in the availability, detail, and wealth of environmental data provides an invaluable asset to improve the characterization of land heterogeneity in Earth system models-a persistent challenge in macroscale models. However, due to the nature of these data (volume and complexity) and computational constraints, these data are underused for global applications. As a proof of concept, this study explores how to effectively and efficiently harness these data in Earth system models over a 1/4° (∼ 25-km) grid cell in the western foothills of the Sierra Nevada in central California. First, a novel hierarchical multivariate clustering approach (HMC) is introduced that summarizes the high-dimensional environmental data space into hydrologically interconnected representative clusters (i.e., tiles). These tiles and their associated properties are then used to parameterize the sub-grid heterogeneity of the Geophysical Fluid Dynamics Laboratory (GFDL) LM4-HB land model. To assess how this clustering approach impacts the simulated water, energy, and carbon cycles, model experiments are run using a series of different tile configurations assembled using HMC. The results over the test domain show that (1) the observed similarity over the landscape makes it possible to converge on the macroscale response of the fully distributed model with around 300 sub-grid land model tiles; (2) assembling the sub-grid tile configuration from available environmental data can have a large impact on the macroscale states and fluxes of the water, energy, and carbon cycles; for example, the defined subsurface connections between the tiles lead to a dampening of macroscale extremes; (3) connecting the fine-scale grid to the model tiles via HMC enables circumvention of the classic scale discrepancies between the macroscale and field-scale estimates; this has potentially significant implications for the evaluation and application of Earth system models. © 2018 Author(s).
语种	英语
scopus关键词	Carbon; Clustering approach; Computational constraints; Earth system model; Environmental data; Fully distributed modeling; Geophysical fluid dynamics laboratories; Global applications; Macroscale models; Big data; climate modeling; computational fluid dynamics; data set; landscape; numerical model; California; Sierra Nevada [California]; United States
来源期刊	Hydrology and Earth System Sciences
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/160000
作者单位	Chaney, N.W., Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, United States; Van Huijgevoort, M.H.J., Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, United States; Shevliakova, E., NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, NJ, United States; Malyshev, S., NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, NJ, United States; Milly, P.C.D., US Geological Survey, Princeton, NJ, United States; Gauthier, P.P.G., Department of Geosciences, Princeton University, Princeton, NJ, United States; Sulman, B.N., Sierra Nevada Research Institute, University of California, Merced, CA, United States
推荐引用方式 GB/T 7714	Chaney N.W.,Van Huijgevoort M.H.J.,Shevliakova E.,et al. Harnessing big data to rethink land heterogeneity in Earth system models[J],2018,22(6).
APA	Chaney N.W..,Van Huijgevoort M.H.J..,Shevliakova E..,Malyshev S..,Milly P.C.D..,...&Sulman B.N..(2018).Harnessing big data to rethink land heterogeneity in Earth system models.Hydrology and Earth System Sciences,22(6).
MLA	Chaney N.W.,et al."Harnessing big data to rethink land heterogeneity in Earth system models".Hydrology and Earth System Sciences 22.6(2018).