Grene-Terrestrial Model Intercomparison Project in Arctic (GTMIP)
Abstract
The GTMIP, a part of the terrestrial branch on Japan-funded Arctic Climate Change Research (GRENE-TEA), aims to 1) enhance communications and understanding of the "mind and hands" between the modeling and field scientists, and 2) assess the uncertainty and variations stemmed from the model implementation/designation, and the variability due to climatic and historical conditions among the Arctic terrestrial regions. The target metrics cover both physics and biogeochemistry such as snow, permafrost, hydrology, and carbon budget. The MIP consists of two stages: one-dimensional, historical GRENE-TEA site evaluations (stage 1) and circumpolar evaluations using projected climate change data from GCM outputs (stage 2). At the current stage 1, forcing and validation data are prepared, taking maximum advantage of the observation data taken at GRENE-TEA sites (e.g., Fairbanks in Alaska, Yakutsk and Tiksi in Russia, and Kevo in Finland), to evaluate the inter-model and inter-site variations. Since the observation data are prone to missing or lack of the consistency, and not ready to drive the numerical model directly, we create continuous forcing data (called version 0) derived from the reanalysis product (i.e. ERA-interim) with monthly bias corrections using the CRU (for temperature) and GPCP (for precipitation) datasets taken from the respectively nearest grid to the GRENE-TEA sites. Then, it is modified to reflect the local characteristics (version 1), and, in addition, replaced with the observed data (version 1 with obs). These data are partly open at Arctic Data Archive System. The project is open to any modelers who are interested, and welcomes participation of wide range of the terrestrial models possibly with different levels of complexity and philosophy.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2014
- Bibcode:
- 2014AGUFM.B31G0117S
- Keywords:
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- 0428 Carbon cycling;
- 0486 Soils/pedology;
- 0702 Permafrost;
- 1615 Biogeochemical cycles;
- processes;
- and modeling