Model Studies

  1. Winkler et al., 2024. Carbon system state determines warming potential of emissions, PLoS ONE 19(8): e0306128. doi: 10.1371/journal.pone.0306128
  2. 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
  3. Chi et al., 2020. Biophysical impacts of Earth greening largely controlled by aerodynamic resistance. Sci. Adv., 6 : eabb1981
  4. 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
  5. Piao et al., 2019. Characteristics, drivers and feedbacks of global greening. Nature Reviews Earth and Environment, doi: 10.1038/s43017-019-0001-x
  6. 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
  7. Winkler et al., 2019. Investigating the applicability of emergent constraints. Earth System Dynamics, doi:10.5194/esd-10-501-2019
  8. 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
  9. Zeng et al., 2018. Impact of Earth Greening on the Terrestrial Water Cycle. J. Climate, doi: 10.1175/JCLI-D-17-0236.1
  10. Zhu et al., 2016. Greening of the Earth and its Drivers. Nature Climate Change, doi:10.1038/nclimate3004
  11. Mao et al., 2016. Human-induced Greening of the Northern Extratropical Land Surface. Nature Climate Change, doi: 10.1038/nclimate3056
  12. 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
  13. Reid et al., 2015. Global impacts of the 1980s regime shift. Global Change Biology, 2015 (doi: 10.1111/gcb.13106)
  14. 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)
  15. 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.
  16. 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
  17. Xu et al., 2013. Temperature and vegetation seasonality diminishment over northern lands. Nature Climate Change, doi: 10.1038/NCLIMATE1836
    Supplementary Information
  18. Ichii et al., 2013 Recent changes in terrestrial gross primary productivity in Asia from 1982 to 2011, Remote Sens., doi: 10.3390/rs5116043
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. Mao et al., 2013. Global Latitudinal-Asymmetric Vegetation Growth Trends and Their Driving Mechanisms: 1982-2009, Remote Sens. doi:10.3390/rs5031484
  25. 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
  26. Samanta et al., 2010. Physical climate response to a reduction of anthropogenic climate forcing, Earth Interactions, doi: 10.1175/2010EI325.1
  27. Robinson et al., 2008. An empirical approach to retrieve monthly evapotranspiration over Amazonia, Int. J. Remote Sens., Vol. 29:7045–7063.
  28. 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
  29. 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
  30. 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
  31. 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
  32. Zeng et al., 2002. Coupling of the common land model to the NCAR community climate model. J. Clim., 15: 1832-1854.
  33. Dickinson et al., 2002. Nitrogen Controls on Climate Model Evapotranspiration. J. Clim., 15(3): 278-295.
  34. 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.
  35. 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.