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| Titel |
The GRENE-TEA model intercomparison project (GTMIP): overview and experiment protocol for Stage 1 |
| VerfasserIn |
S. Miyazaki, K. Saito, J. Mori, T. Yamazaki, T. Ise, H. Arakida, T. Hajima, Y. Iijima, H. Machiya, T. Sueyoshi, H. Yabuki, E. J. Burke, M. Hosaka, K. Ichii, H. Ikawa, A. Ito, A. Kotani, Y. Matsuura, M. Niwano, T. Nitta, R. O'ishi, T. Ohta, H. Park, T. Sasai, A. Sato, H. Sato, A. Sugimoto, R. Suzuki, K. Tanaka, S. Yamaguchi, K. Yoshimura |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 8, no. 9 ; Nr. 8, no. 9 (2015-09-09), S.2841-2856 |
| Datensatznummer |
250116556
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| Publikation (Nr.) |
 copernicus.org/gmd-8-2841-2015.pdf |
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| Zusammenfassung |
| As part of the terrestrial branch of the Japan-funded Arctic Climate Change
Research Project (GRENE-TEA), which aims to clarify the role and function of
the terrestrial Arctic in the climate system and assess the influence of its
changes on a global scale, this model intercomparison project (GTMIP) is
designed to (1) enhance communication and understanding between the modelling
and field scientists and (2) assess the uncertainty and variations stemming
from variability in model implementation/design and in model outputs using
climatic and historical conditions in the Arctic terrestrial regions. This
paper provides an overview of all GTMIP activity, and the experiment
protocol of Stage 1, which is site simulations driven by statistically
fitted data created using the GRENE-TEA site observations for the last 3
decades. The target metrics for the model evaluation cover key processes in
both physics and biogeochemistry, including energy budgets, snow,
permafrost, phenology, and carbon budgets. Exemplary results for
distributions of four metrics (annual mean latent heat flux, annual maximum
snow depth, gross primary production, and net ecosystem production) and for
seasonal transitions are provided to give an outlook of the planned analysis
that will delineate the inter-dependence among the key processes and
provide clues for improving model performance. |
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