Scale-dependency of surface fluxes in an atmospheric mesoscale model: Effect of spatial heterogeneity in atmospheric conditions

Jinkyu Hong, Joon Kim

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

We examined the nonlinear effect of spatial heterogeneity in atmospheric conditions on the simulation of surface fluxes in the mesoscale model, MM5 by testing their scale-invariance from a tower footprint to regional scales. The test domain was a homogeneous shortgrass prairie in the central part of the Tibetan Plateau with an eddy-covariance flux tower at the center. We found that the spatial variability resulting from changing distribution of clouds and precipitation in the model domain affected radiative forcing at the ground surface, thereby altering the partitioning of surface fluxes. Consequently, due to increasing spatial variability in atmospheric conditions, the results of MM5 did not produce convergent estimates of surface fluxes with increasing grid sizes. Our finding demonstrates that an atmospheric model can underestimate surface fluxes in regional scale not necessarily due to intrinsic model inaccuracy (e.g., inaccurate parameterization) but due to scale-dependent nonlinear effect of spatial variability in atmospheric conditions.

Original languageEnglish
Pages (from-to)965-975
Number of pages11
JournalNonlinear Processes in Geophysics
Volume15
Issue number6
DOIs
Publication statusPublished - 2008 Nov 3

Fingerprint

surface flux
meteorology
Fluxes
towers
Towers
eddy covariance
radiative forcing
grasslands
footprint
atmospheric models
prairie
footprints
parameterization
Parameterization
Invariance
partitioning
plateau
invariance
plateaus
grids

All Science Journal Classification (ASJC) codes

  • Statistical and Nonlinear Physics
  • Geophysics
  • Geochemistry and Petrology

Cite this

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abstract = "We examined the nonlinear effect of spatial heterogeneity in atmospheric conditions on the simulation of surface fluxes in the mesoscale model, MM5 by testing their scale-invariance from a tower footprint to regional scales. The test domain was a homogeneous shortgrass prairie in the central part of the Tibetan Plateau with an eddy-covariance flux tower at the center. We found that the spatial variability resulting from changing distribution of clouds and precipitation in the model domain affected radiative forcing at the ground surface, thereby altering the partitioning of surface fluxes. Consequently, due to increasing spatial variability in atmospheric conditions, the results of MM5 did not produce convergent estimates of surface fluxes with increasing grid sizes. Our finding demonstrates that an atmospheric model can underestimate surface fluxes in regional scale not necessarily due to intrinsic model inaccuracy (e.g., inaccurate parameterization) but due to scale-dependent nonlinear effect of spatial variability in atmospheric conditions.",
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