Model Studies

  1. Pu et al., 2025. Large gains in leaf scale photosynthetic rates of sparsely vegetated arid and semi-arid lands, Commun Earth Environ (2025).
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  2. Pu et al., 2025. MCI GPP: ensembling a global model- and climate-independent gross primary productivity for 2001–2023, Scientific Data, (2025) 12:1965 | https://doi.org/10.1038/s41597-025-06218-8
  3. Duanmu et al., 2025. Changes in leaf and root carbon allocation of global vegetation simulated by the optimally integrated ecosystem models, Agric. For. Meteorol., 362 (2025) 110366
  4. Winkler et al., 2024. Carbon system state determines warming potential of emissions, PLoS ONE 19(8): e0306128. doi: 10.1371/journal.pone.0306128
  5. Zuo et al., 2023. Simulating Potential Tree Height for Beech–Maple–Birch Forests in Northeastern United States on Google Earth Engine, J. Remote Sens., 2023;3:Article 0084. https://doi.org/10.34133/remotesensing.0084
  6. Chi et al., 2020. Biophysical impacts of Earth greening largely controlled by aerodynamic resistance. Sci. Adv., 6 : eabb1981
  7. Zhao et al., 2020. Future greening of the Earth may not be as large as previously predicted. Agric. For. Meteorol., doi: 10.1016/j.agrformet.2020.108111
  8. Piao et al., 2019. Characteristics, drivers and feedbacks of global greening. Nature Reviews Earth and Environment, doi: 10.1038/s43017-019-0001-x
  9. Winkler et al., 2019. Earth system models underestimate carbon fixation by plants in the high latitudes. Nature Communications, doi:10.1038/s41467-019-08633-z
  10. Winkler et al., 2019. Investigating the applicability of emergent constraints. Earth System Dynamics, doi:10.5194/esd-10-501-2019
  11. Zhang et al., 2019. Mapping Maximum Tree Height of the Great Khingan Mountain, Inner Mongolia Using the Allometric Scaling and Resource Limitations Model. Forests, doi:10.3390/f10050380
  12. Zeng et al., 2018. Impact of Earth Greening on the Terrestrial Water Cycle. J. Climate, doi: 10.1175/JCLI-D-17-0236.1
  13. Zhu et al., 2016. Greening of the Earth and its Drivers. Nature Climate Change, doi:10.1038/nclimate3004
  14. Mao et al., 2016. Human-induced Greening of the Northern Extratropical Land Surface. Nature Climate Change, doi: 10.1038/nclimate3056
  15. Choi et al., 2016. Application of the metabolic scaling theory and water–energy balance equation to model large-scale patterns of maximum forest canopy height. Global Ecol. Biogeography, doi:10.1111/geb.12503
  16. Reid et al., 2015. Global impacts of the 1980s regime shift. Global Change Biology, 2015 (doi: 10.1111/gcb.13106)
  17. Piao et al., 2015. Detection and attribution of vegetation greening trend in China over the last 30 years, Global Change Biology, 2015 (doi: 10.1111/gcb.12795)
  18. Traore et al., 2014. Evaluation of the ORCHIDEE ecosystem model over Africa against 25 years of satellite-based water and carbon measurements, J. Geophys. Res. Biogeosci., 119, 1554–1575, doi:10.1002/2014JG002638.
  19. Sitch et al., 2013 Trends and drivers of regional sources and sinks of carbon dioxide over the past two decades, Biogeosciences Discuss., doi:10.5194/bgd-10-20113-2013, 10:20113–20177, 2013
  20. Xu et al., 2013. Temperature and vegetation seasonality diminishment over northern lands. Nature Climate Change, doi: 10.1038/NCLIMATE1836
    Supplementary Information
  21. Ichii et al., 2013 Recent changes in terrestrial gross primary productivity in Asia from 1982 to 2011, Remote Sens., doi: 10.3390/rs5116043
  22. Xin et al., 2013 A production efficiency model-based method for satellite estimates of corn and soybean yields in the midwestern US, Remote Sens., doi: 10.3390/rs5115926
  23. Barichivich et al., 2013. Large-scale variations in the vegetation growing season and annual cycle of atmospheric CO2 at high northern latitudes from 1950 to 2011, Global Change Biol., 2013, doi: 10.1111/gcb.12283
  24. Wang et al., 2013. Evaluation of CLM4 Solar Radiation Partitioning Scheme Using Remote Sensing and Site Level FPAR Datasets, Remote Sens. 2013, 5, 2857-2882; doi: 10.3390/rs5062857
  25. Piao et al., 2013. Evaluation of Terrestrial Carbon Cycle Models for their Response to Climate Variability and to CO2 Trends, Global Change Biology, doi: 10.1111/gcb.12187
  26. Anav et al., 2013. Evaluating the Land and Ocean Components of the Global Carbon Cycle in the CMIP5 Earth System Models, J. Climate, doi:10.1175/JCLI-D-12-00417.1
  27. Mao et al., 2013. Global Latitudinal-Asymmetric Vegetation Growth Trends and Their Driving Mechanisms: 1982-2009, Remote Sens. doi:10.3390/rs5031484
  28. Zeng et al., 2012. Global evapotranspiration over the past three decades: estimation based on the water balance equation combined with empirical models, Environ. Res. Lett., doi: 10.1088/1748-9326/7/1/014026
  29. Samanta et al., 2010. Physical climate response to a reduction of anthropogenic climate forcing, Earth Interactions, doi: 10.1175/2010EI325.1
  30. Robinson et al., 2008. An empirical approach to retrieve monthly evapotranspiration over Amazonia, Int. J. Remote Sens., Vol. 29:7045–7063.
  31. Ichii et al., 2007. Constraining rooting depths in tropical rainforests using satellite data and ecosystem modeling for accurate simulation of gross primary production seasonality, Global Change Biology, doi: 10.1111/j.1365-2486.2006.01277.x
  32. Tian et al., 2004. Land boundary conditions from MODIS data and consequences for the albedo of a climate model. Geophys. Res. Lett., doi: 10.1029/2003GL019104
  33. Tian et al., 2004. Comparison of seasonal and spatial variations of LAI/FPAR from MODIS and Common Land Model. J. Geophys. Res., doi: 10.1029/2003JD003777
  34. Zhou et al., 2003. Comparison of seasonal and spatial variations of albedos from MODIS and the Common Land Model. J. Geophys. Res., doi: 10.1029/2002JD003326
  35. Zeng et al., 2002. Coupling of the common land model to the NCAR community climate model. J. Clim., 15: 1832-1854.
  36. Dickinson et al., 2002. Nitrogen Controls on Climate Model Evapotranspiration. J. Clim., 15(3): 278-295.
  37. Buermann et al., 2001. Evaluation of the utility of satellite-based vegetation leaf area index data for climate simulations. J. Climate, 14(17): 3536-3550.
  38. Dong et al., 2001. Improving numerical precision of simulated soil water fluxes in land surface models. J. Geophys. Res., 106(D13): 14,357-14,368.