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Michael A. White, Peter E. Thornton, Steven W. Running, and Ramakrishna R. Nemani

simulations. As Aber ( Aber 1997 ) stated: “ALL of the parameters used in the model should be listed, and ALL values for those parameters given, along with the references to the sources of those parameters.” Aber also argued for complete descriptions of model structure and sensitivity. To address these and related concerns, our goals in this research are to provide an account of the source (or lack thereof) for parameters in BIOME–BGC, a commonly used terrestrial ecosystem process model, for major

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J. S. Kimball, M. Zhao, A. D. McGuire, F. A. Heinsch, J. Clein, M. Calef, W. M. Jolly, S. Kang, S. E. Euskirchen, K. C. McDonald, and S. W. Running

models, BIOME–BGC (BioGeoChemical Cycles) and the Terrestrial Ecosystem Model (TEM); these model simulations are used for independent assessment of satellite remote sensing–derived results and to elucidate underlying mechanisms driving changes in vegetation productivity and the terrestrial carbon cycle. 2. Methods The WALE domain for this investigation encompasses boreal forest and tundra biomes of Alaska and northwest Canada ( Figure 1 ) and represents approximately 11% of the global aerial extent

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Lifen Jiang, Yaner Yan, Oleksandra Hararuk, Nathaniel Mikle, Jianyang Xia, Zheng Shi, Jerry Tjiputra, Tongwen Wu, and Yiqi Luo

between ESMs ( Figs. 2 and S5 ), with 48 out of 55 R 2 values being lower than 0.5. A very high R 2 was found between CESM1(BGC) and NorESM1-ME, which shared the same land carbon cycle model. b. Variability of vegetation carbon at the biome scale Most carbon-rich areas in the observations and ESMs were located in tropical and boreal regions ( Fig. S2 ). It should be noted that the boreal biome in our classification included the temperate rain forest of the North American Pacific Northwest, which

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C. L. Tague and L. E. Band

meteorological forcing ( Running et al. 1987 ) and later with hydrologic processes using the TOPMODEL ( Beven and Kirkby 1979 ) hydrologic model. The first approach to distribute ecosystem processes at the landscape level involved gridding the “topclimatic” logic of Mountain Climate Simulator (MTN-CLIM) with FOREST-BGC for a 1200 km 2 watershed in western Montana ( Running et al. 1987 ). Later versions of RHESSys followed a generalization of FOREST-BGC to multiple biomes as Biome biogeochemical cycles Model

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Shrinidhi Ambinakudige and Sami Khanal

transpiration of forests in different climates. Remote Sens. Environ. 24 : 347 – 367 . Running , S. W. and E. R. Hunt . 1993 . Generalization of a forest eco-system process model for other biomes, BIOME-BGC, and an application for global-scale models. Scaling Physiological Processes: Leaf to Globe, J. R. Ehleringer and C. Field, Eds., Academic Press, 141

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Fang Li and David M. Lawrence

.5), with its carbon–nitrogen biogeochemical module (BGC) (CLM4.5-BGC; land component) and using the fire module from CLM, version 5, with BGC (CLM5-BGC; to be released soon), and the Community Ice Code, version 4 (CICE4; sea ice component). Relative to its earlier version (CAM4), CAM5 includes new boundary layer, shallow convection, radiation, and microphysics schemes, fully interactive aerosols, and updates for the spectral element dynamical core ( Neale et al. 2012 ; Meehl et al. 2013 ). CICE4 is an

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M. Rodell, B. F. Chao, A. Y. Au, J. S. Kimball, and K. C. McDonald

). Water Resour. Res. 37 : 1327 – 1340 . Rodell , M. , J. S. Famiglietti , J. Chen , S. Seneviratne , P. Viterbo , S. Holl , and C. R. Wilson . 2004 . Basin scale estimates of evapotranspiration using GRACE and other observations. Geophys. Res. Lett. 31 . L20504, doi:10.1029/2004GL020873 . Running , S. W. and E. R. Hunt . 1993 . Generalization of a forest ecosystem process model for other biomes, BIOME-BGC, and an application for global-scale models. Scaling

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Yongwen Liu, Shilong Piao, Xu Lian, Philippe Ciais, and W. Kolby Smith

. 2014 ; Wolf et al. 2016 ), implying that the interannual sensitivity of the net CO 2 flux [net biome production (NBP)] is positively related to spring temperature variations. In summer, since warmer years are often dryer across the temperate zone, the interannual sensitivity of NBP to temperature variations generally is negative ( Angert et al. 2005 ). In autumn, years with warmer climate were analyzed by Piao et al. (2008) to be associated with an abnormal release of CO 2 (i.e., suggesting a

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Donghai Wu, Shilong Piao, Yongwen Liu, Philippe Ciais, and Yitong Yao

net primary production instead of gross primary production was used to calculate carbon turnover time. 2. Dataset and methods a. Vegetation carbon storage The spatial distribution of total biomass carbon was derived from recently published global maps of aboveground biomass carbon (ABC) over the period of 1993–2010 ( Liu et al. 2015 ) and biome-specific conversion factors between total biomass carbon (TBC) and ABC ( Flato et al. 2013 ; Liu et al. 2015 ; Robinson 2007 ). The global ABC product of

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Pierre Friedlingstein, Malte Meinshausen, Vivek K. Arora, Chris D. Jones, Alessandro Anav, Spencer K. Liddicoat, and Reto Knutti

) due to anthropogenic land use change and F Ln is the natural component, also referred to as the residual land sink (RLS) in Le Quéré et al. (2012) . The net exchange of CO 2 between the atmosphere and the land surfaces, F L , is often referred as the net biome production (NBP). Seven out of the 11 ESMs analyzed here interactively simulated LUC CO 2 emissions from the prescribed land cover change (see Table 1 ). For these models, only the field is provided and not its separate components

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