Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/hess-2017-603
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
01 Nov 2017
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).
Harnessing Big Data to Rethink Land Heterogeneity in Earth System Models
Nathaniel W. Chaney1, Marjolein H. J. Van Huijgevoort1, Elena Shevliakova2, Sergey Malyshev3, Paul C. D. Milly4, Paul P. G. Gauthier5, and Benjamin N. Sulman1 1Program in Atmospheric and Oceanic Sciences,Princeton University, Princeton, New Jersey
2NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey
3Cooperative Institute for Climate Science, Princeton University, Princeton, New Jersey
4U.S. Geological Survey, Princeton, New Jersey
5Department of Geosciences, Princeton University, Princeton, New Jersey
Abstract. 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 the computational constraints of macroscale models, until now these data have been underutilized for global applications. As a proof of concept, this study explores over a 1/4 degree (~ 25 km) grid cell in southeastern California how to effectively and efficiently harness these data in Earth System models. First, a novel hierarchical multivariate clustering approach (HMC) is used to summarize 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 data-driven approach to assemble the model tiles impacts the simulated water, energy, and carbon cycles, model experiments are run using a series of different tile configurations assembled by HMC. The results over the 1/4 degree macroscale grid cell and the underlying 30-meter fine-scale grid in southeastern California show that: 1) the observed similarity over the landscape makes it possible to robustly account for the role of multi-scale heterogeneity in the macroscale states and fluxes with around 300 sub-grid land model tiles; 2) assembling the sub-grid tiles from observed data, at times, leads to noticeable differences in the macroscale water, energy, and carbon cycles; for example, explicit subsurface interactions between the tiles leads to a dampening of macroscale extremes; 3) connecting the fine-scale grid to the model tiles via HMC enables circumventing 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.

Citation: Chaney, N. W., Van Huijgevoort, M. H. J., Shevliakova, E., Malyshev, S., Milly, P. C. D., Gauthier, P. P. G., and Sulman, B. N.: Harnessing Big Data to Rethink Land Heterogeneity in Earth System Models, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-603, in review, 2017.
Nathaniel W. Chaney et al.
Nathaniel W. Chaney et al.
Nathaniel W. Chaney et al.

Viewed

Total article views: 333 (including HTML, PDF, and XML)

HTML PDF XML Total BibTeX EndNote
266 64 3 333 3 4

Views and downloads (calculated since 01 Nov 2017)

Cumulative views and downloads (calculated since 01 Nov 2017)

Viewed (geographical distribution)

Total article views: 333 (including HTML, PDF, and XML)

Thereof 328 with geography defined and 5 with unknown origin.

Country # Views %
  • 1

Saved

Discussed

Latest update: 19 Nov 2017
Publications Copernicus
Download
Short summary
The petabytes of existing global environmental data provide an invaluable asset to improve the characterization of land heterogeneity in Earth system models. This study introduces a clustering algorithm that summarizes a domain's heterogeneity through spatially interconnected clusters. A series of land model simulations over southeastern California using this approach illustrate the critical role that multi-scale heterogeneity can have on the macroscale water, energy, and carbon cycles.
The petabytes of existing global environmental data provide an invaluable asset to improve the...
Share