globalchange  > 气候变化与战略
DOI: 10.5194/hess-22-3311-2018
论文题名:
Harnessing big data to rethink land heterogeneity in Earth system models
作者: Chaney N.W.; Van Huijgevoort M.H.J.; Shevliakova E.; Malyshev S.; Milly P.C.D.; Gauthier P.P.G.; Sulman B.N.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2018
卷: 22, 期:6
起始页码: 3311
结束页码: 3330
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
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).
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/163283
Appears in Collections:气候变化与战略

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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

Recommended Citation:
Chaney N.W.,Van Huijgevoort M.H.J.,Shevliakova E.,et al. Harnessing big data to rethink land heterogeneity in Earth system models[J]. Hydrology and Earth System Sciences,2018-01-01,22(6)
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