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Volume 18, issue 13
Atmos. Chem. Phys., 18, 10007-10023, 2018
https://doi.org/10.5194/acp-18-10007-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Chem. Phys., 18, 10007-10023, 2018
https://doi.org/10.5194/acp-18-10007-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

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 Serafimovich1, Stefan Metzger2,3, Jörg Hartmann4, Katrin Kohnert1, Donatella Zona5,6, and Torsten Sachs1,7 Andrei Serafimovich et al.
  • 1Helmholtz Centre Potsdam, GFZ German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
  • 2National Ecological Observatory Network, Fundamental Instrument Unit, 1685 38th Street, Boulder, CO 80301, USA
  • 3University of Wisconsin-Madison, Dept. of Atmospheric and Oceanic Sciences, 1225 West Dayton Street, Madison, WI 53706, USA
  • 4Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI), Am Handelshafen 12, 27570 Bremerhaven, Germany
  • 5Department of Animal and Plant Sciences, University of Sheffield, Western Bank, Sheffield S10 2TN, UK
  • 6Department of Biology, San Diego State University, 5500 Campanile Drive San Diego, CA 92182, USA
  • 7Institute of Flight Guidance, TU Braunschweig, Hermann-Blenk-Str. 27, 38108 Braunschweig, Germany

Abstract. The objective of this study was to upscale airborne flux measurements of sensible heat and latent heat and to develop high-resolution flux maps. 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.

We used airborne eddy-covariance measurements acquired by the Polar 5 research aircraft in June–July 2012 to analyze surface fluxes. Footprint-weighted surface properties were then related to 21529 sensible heat flux observations and 25608 latent heat flux observations using both remote sensing and modeled data. A boosted regression tree technique was used to estimate environmental response functions between spatially and temporally resolved flux observations and corresponding biophysical and meteorological drivers. In order to improve the spatial coverage and spatial representativeness of energy fluxes we used relationships extracted across heterogeneous Arctic landscapes to infer high-resolution surface energy flux maps, thus directly upscaling the observational data. These maps of projected sensible heat and latent heat fluxes were used to assess energy partitioning in northern ecosystems and to determine the dominant energy exchange processes in permafrost areas. This allowed us to estimate energy fluxes for specific types of land cover, taking into account meteorological conditions. Airborne and modeled fluxes were then compared with measurements from an eddy-covariance tower near Atqasuk.

Our results are an important contribution for the advanced, scale-dependent quantification of surface energy fluxes and they provide new insights into the processes affecting these fluxes for the main vegetation types in high-latitude permafrost areas.

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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.
In order to support the evaluation of coupled atmospheric–land-surface models we investigated...
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