As part of the terrestrial branch of the Japanfunded 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. Published by CopernicusPublications on behalf of the European Geosciences Union. 2842 S. Miyazaki et al.: GTMIP: overview and experiment protocol for Stage 1 Geosci. Model Dev., 8, 2841-2856, 2015 www.geosci-model-dev.net/8/2841/2015/

ABSTRACT1. Several recent studies have predicted potential habitats along coastal areas using large-scale physical environmental variables to identify target areas for conservation. However, no indices or methodologies for predicting tidal-flat habitats at a large spatial scale have been developed. Tidal flats supporting large populations of shorebirds have been identified in semi-enclosed seas. Thus, bays are probably important topographic units for evaluating the locations of shorebirds' non-breeding habitats.2. A GIS-based methodology was developed to extract 'bay units' at any scale from coastline data. Using three environment variables (the area of the bay units at three spatial scales, the percentage of shallow water area in each bay unit, and the spring-tide range), it was possible to predict tidal-flat habitats for six shorebird species with high accuracy (AUC > 0.95, sensitivity >90%).3. Results showed that the percentage of shallow water area in small-and medium-scale bays was the best predictor of tidal-flat habitats, followed by the area of bays at a large spatial scale. This indicates that the size (scale) of a bay and the percentage of shallow water present are highly related to the presence of tidal-flat habitats.4. The prediction maps for individual species of shorebirds clearly showed differences in the distribution patterns of species. These maps were overlaid to identify potentially species-rich areas and thus where conservation and restoration of the tidal flats in these bays would be important. 5. The model, which uses simple coastal data, is a useful, resource-efficient method for identifying target conservation and restoration areas across broad scales.

Abstract. We developed a data assimilation system based on a particle filter approach with the spatially explicit individual-based dynamic global vegetation model (SEIB-DGVM). We first performed an idealized observing system simulation experiment to evaluate the impact of assimilating the leaf area index (LAI) data every 4 days, simulating the satellite-based LAI. Although we assimilated only LAI as a whole, the tree and grass LAIs were estimated separately with high accuracy. Uncertain model parameters and other state variables were also estimated accurately. Therefore, we extended the experiment to the real world using the real Moderate Resolution Imaging Spectroradiometer (MODIS) LAI data and obtained promising results.

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

Abstract. Here, the authors describe the construction of a forcing dataset 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 dataset is intended for use in a model intercomparison (MIP) 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 dataset 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 datasets and participation in GTMIP are open to the scientific community (https://ads.nipr.ac.jp/gtmip/gtmip.html).

This study examined the regional performance of a data assimilation (DA) system that couples the particle filter and the Spatially Explicit Individual-based Dynamic Global Vegetation Model (SEIB-DGVM). This DA system optimizes model parameters of defoliation and photosynthetic rate, which are sensitive to phenology in the SEIB-DGVM, by assimilating satellite-observed leaf area index (LAI). The experiments without DA overestimated LAIs over Siberia relative to the satellite-observed LAI, whereas the DA system successfully reduced the error. DA provided improved analyses for the LAI and other model variables consistently, with better match with satellite observed LAI and with previous studies for spatial distributions of the estimated overstory LAI, gross primary production (GPP), and aboveground biomass. However, three main issues still exist: (1) the estimated start date of defoliation for overstory was about 40 days earlier than the in situ observation, (2) the estimated LAI for understory was about half of the in situ observation, and (3) the estimated overstory LAI and the total GPP were overestimated compared to the previous studies. Further DA and modeling studies are needed to address these issues.

Abstract. 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 Arctic terrestrial system in the climate system, and assess the influence of its changes on a global scale, this model intercomparison project (GTMIP) is planned and being conducted to (1) enhance communication and understanding between the "minds and hands" (i.e., between the modelling and field scientists) and (2) assess the uncertainty and variations stemming from variability in model implementation/design and in model outputs due to climatic and historical conditions in the Arctic terrestrial regions. This paper provides an overview and the experiment protocol of Stage 1 of the project, site simulations driven by statistically fitted data created using the GRENE-TEA site observations for the last three 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. The preliminary results on four metrics (annual mean latent heat flux, annual maximum snow depth, gross primary production, and net ecosystem production) already demonstrate the range of variations in reproducibility among existing models and sites. Full analysis on annual as well as seasonal time scales, to be conducted upon completion of model outputs submission, will delineate inter-dependence among the key processes, and provide the clue for improving the model performance.

This study examined the regional performance of a data assimilation (DA) system that couples the particle filter and the Spatially Explicit Individual-based Dynamic Global Vegetation Model (SEIB-DGVM). This DA system optimizes model parameters of dormancy and photosynthetic rate, which are sensitive to phenology in the SEIB-DGVM, by assimilating satellite-observed leaf area index (LAI). The experiments without DA overestimated LAIs over Siberia relative to the satellite-observed LAI, whereas the DA system successfully reduced the error. DA provided improved analyses for the LAI and other model variables consistently, with better match with satellite observed LAI and with previous studies for spatial distributions of the estimated tree LAI, gross primary production (GPP), and above ground biomass. Most remarkably, the spatial distribution of tree LAI was estimated separately from undergrowth LAI because the SEIB-DGVM simulated the vertical structure of forest explicitly, and because satellite-observed LAI provided information on the onset and the end of the leaf season of tree and undergrowth, respectively. The DA system also provided the spatial distribution of the model parameters for tree separately from those of undergrowth. DA experiments started dormancy of trees more than a month earlier than the default phenology model and resulted in a decrease of the GPP.