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A. Anav, P. Friedlingstein, M. Kidston, L. Bopp, P. Ciais, P. Cox, C. Jones, M. Jung, R. Myneni, and Z. Zhu

would highlight that there are several limitations in the satellite observations that could explain the mismatch between the LAI dataset and CMIP5 results. Fig . 10. Mean annual LAI as simulated by CMIP5 models and the reference LAI3g data (black triangle) over the land subdomains. The remote sensing LAI products are estimates derived from top-of-the-atmosphere reflectances and use different sensors and algorithms ( Los et al. 2000 ; Myneni et al. 2002 ). Therefore, the quality of LAI retrievals is

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

annual NPP from the year 2000 as a reference NPP. The data were produced by the National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) at 1-km spatial resolution ( Heinsch et al. 2003 ). MOD17A3 did not include an uncertainty analysis, but uncertainties could be large because of possible errors related to inputs of the algorithm for MOD17A3, including land cover, fraction of photosynthetically active radiation/leaf area index (FPAR/LAI), and other meteorological data ( Zhao

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ChuanLi Jiang, Sarah T. Gille, Janet Sprintall, and Colm Sweeney

with in situ values is not straightforward. In addition, its estimation through the bulk algorithm described above ( Sweeney et al. 2007 ; Takahashi et al. 2009 ), using the measured p CO 2 and the other available data products, results in large variations and uncertainties. This further complicates the evaluation of the CMIP5 model performance. Hence, in the following we present only an intermodel comparison of the sea-to-air CO 2 flux from the eight CMIP5 ESMS ( Fig. 3b ) with no comparison

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V. Brovkin, L. Boysen, V. K. Arora, J. P. Boisier, P. Cadule, L. Chini, M. Claussen, P. Friedlingstein, V. Gayler, B. J. J. M. van den Hurk, G. C. Hurtt, C. D. Jones, E. Kato, N. de Noblet-Ducoudré, F. Pacifico, J. Pongratz, and M. Weiss

distribution at a specific location (i.e., 100% anthropogenic types on the original land-cover map used), then the model algorithm searches for the nearest point that has natural vegetation and introduces those vegetation types. The desert extent is kept unchanged from preindustrial times until the end of the twenty-first century, with one exception: desert is reduced if the anthropogenic area is larger than the natural vegetation part of the grid cell. After this first step where the change in crop area

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Pu Shao, Xubin Zeng, Koichi Sakaguchi, Russell K. Monson, and Xiaodong Zeng

disturbance and lag. Jung et al. (2011) estimated the uncertainty of the globally averaged GPP to be ±6 kgC m −2 yr −1 . The NPP data ( Zhao et al. 2005 ) are derived from MODIS products, and the data quality is affected by the uncertainties in the descriptions of biome type and meteorological input data as well as in the algorithm that translates measured parameters into inferred process rates. Zhao et al. (2005) indicated that these uncertainties may be large in some regions or during some seasons

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