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Volume 8, issue 1
Earth Syst. Sci. Data, 8, 1-14, 2016
https://doi.org/10.5194/essd-8-1-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Earth Syst. Sci. Data, 8, 1-14, 2016
https://doi.org/10.5194/essd-8-1-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  15 Jan 2016

15 Jan 2016

The GRENE-TEA model intercomparison project (GTMIP) Stage 1 forcing data set

T. Sueyoshi1,2, K. Saito2, S. Miyazaki1,2,a, J. Mori1,2, T. Ise3, H. Arakida4, R. Suzuki2, A. Sato5, Y. Iijima2, H. Yabuki1,2, H. Ikawa6, T. Ohta7, A. Kotani7, T. Hajima2, H. Sato2, T. Yamazaki8, and A. Sugimoto9 T. Sueyoshi et al.
  • 1National Institute of Polar Research, Tachikawa, Japan
  • 2Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan
  • 3Kyoto University, Field Science Education and Research Center, Kyoto, Japan
  • 4RIKEN Advanced Institute for Computational Science, Kobe, Japan
  • 5National Research Institute for Earth Science and Disaster Prevention, Snow and Ice Research Center, Nagaoka, Japan
  • 6National Institute for Agro-Environmental Sciences, Tsukuba, Japan
  • 7Nagoya University, Graduate School of Bioagricultural Sciences, Nagoya, Japan
  • 8Tohoku University, Graduate School of Science, Sendai, Japan
  • 9Hokkaido University, Faculty of Environmental Earth Science, Sapporo, Japan
  • anow at: Sonic Corporation, Tachikawa, Japan

Abstract. Here, the authors describe the construction of a forcing data set for land surface models (including both physical and biogeochemical models; LSMs) with eight meteorological variables for the 35-year period from 1979 to 2013. The data set is intended for use in a model intercomparison study, called GTMIP, which is a part of the Japanese-funded Arctic Climate Change Research Project. In order to prepare a set of site-fitted forcing data for LSMs with realistic yet continuous entries (i.e. without missing data), four observational sites across the pan-Arctic region (Fairbanks, Tiksi, Yakutsk, and Kevo) were selected to construct a blended data set using both global reanalysis and observational data. Marked improvements were found in the diurnal cycles of surface air temperature and humidity, wind speed, and precipitation. The data sets and participation in GTMIP are open to the scientific community (doi:10.17592/001.2015093001).

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This paper describes the construction of a forcing data set for land surface models (LSMs) with eight meteorological variables for the 35-year period from 1979 to 2013. The data set is intended for use in a model intercomparison (MIP) study, called GTMIP. In order to prepare a set of site-fitted forcing data for LSMs with realistic yet continuous entries, four observational sites were selected to construct a blended data set using both global reanalysis and observational data.
This paper describes the construction of a forcing data set for land surface models (LSMs) with...
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