Articles | Volume 18, issue 13
Atmos. Chem. Phys., 18, 10007–10023, 2018
https://doi.org/10.5194/acp-18-10007-2018
Atmos. Chem. Phys., 18, 10007–10023, 2018
https://doi.org/10.5194/acp-18-10007-2018
Research article
13 Jul 2018
Research article | 13 Jul 2018

Upscaling surface energy fluxes over the North Slope of Alaska using airborne eddy-covariance measurements and environmental response functions

Andrei Serafimovich et al.

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Cited articles

Beljaars, A. C. M.: The parameterization of surface fluxes in large-scale models under free convection, Q. J. R. Meteorol. Soc., 121, 255–270, 1994. a
Beringer, J., Chapin III, F. S., Thompson, C. C., and McGuire, A. D.: Surface energy exchanges along a tundra-forest transition and feedbacks to climate, Agric. For. Meteorol., 131, 143–161, 2005. a
Bonan, G. B., Pollard, D., and Thompson, S. L.: Effects of boreal forest vegetation on global climate, Nature, 359, 716–718, 1992. a
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In order to support the evaluation of coupled atmospheric–land-surface models we investigated spatial patterns of energy fluxes in relation to land-surface properties and upscaled airborne flux measurements to high resolution flux maps. A machine learning technique allows us to estimate environmental response functions between spatially and temporally resolved flux observations and corresponding biophysical and meteorological drivers.
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