Terrestrial Carbon Cycle

The Greening Earth

  1. Zhu et al., 2016. Greening of the Earth and its Drivers. Nature Climate Change, doi:10.1038/nclimate3004
  2. Piao et al., 2017. Weakening temperature control on the interannual variations of spring carbon uptake across northern lands. Nature Climate Change, doi: 10.1038/NCLIMATE3277
  3. Zhu et al., 2017. Attribution of seasonal leaf area index trends in the northern latitudes with “optimally” integrated ecosystem models. Global Change Biol., doi: 10.1111/gcb.13723
  4. Li et al., 2016. Reducing uncertainties in decadal variability of the global carbon budget with multiple datasets. PNAS, doi: 10.1073/pnas.1603956113
  5. 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
  6. Ukkola et al., 2015. Reduced streamflow in water-stressed climates consistent with CO2 effects on vegetation. Nature Climate Change, 2015 (DOI: 10.1038/NCLIMATE2831)
  7. Anderegg et al., 2015. Tropical nighttime warming as a dominant driver of variability in the terrestrial carbon sink. Proc. Natl. Acad. Sci. USA, 2015 (www.pnas.org/cgi/doi/10.1073/pnas.1521479112)
  8. Xu et al., 2015. Satellite observation of tropical forest seasonality: spatial patterns of carbon exchange in Amazonia. Environ. Res. Lett., 2015 (doi: 10.1088/1748-9326/10/8/084005)
  9. Sitch et al., 2015. Recent trends and drivers of regional sources and sinks of carbon dioxide, Biogeosciences, 2015 (doi: 10.5194/bg-12-653-2015)
  10. 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)
  11. 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.
  12. Tan et al., 2014. Seasonally different response of photosynthetic activity to daytime and night-time warming in the Northern Hemisphere, Global Change Biology, (doi:
  13. Traore et al., 2014. 1982-2010 trends of light use efficiency and inherent water use efficiency in African vegetation: Sensitivity to climate and atmospheric CO2 concentrations, Remote Sensing, 6, 8923-8944; doi:10.3390/rs6098923
  14. Ciais et al., 2014. Current systematic carbon-cycle observations and the need for implementing a policy-relevant carbon observing system, Biogeosciences, 11: 3547-3602 (doi:10.5194/bg-11-3547-2014).
  15. Van Oijen et al., 2014. Impact of droughts on the C-cycle in European Vegetation: a probabilistic risk analysis using six vegetation models, Biogeosciences Discussion, doi:10.5194/bgd-11-8325-2014.
  16. Poulter et al., 2014. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle, Nature, 2014 (doi:10.1038/nature13376)
  17. Zhou et al., 2014. Widespread decline of Congo rainforest greenness in the past decade, Nature, 2014 (doi: 10.1038/nature13265)
  18. Zhang et al., 2014. Estimation of forest aboveground biomass in California using canopy height and leaf area index estimated from satellite data, Remote Sens. Environ, 2014 (http://dx.doi.org/10.1016/j.rse.2014.01.025)
  19. Ni and Park et al., 2014. Allometric Scaling and Resource Limitations Model of Tree Heights: Part 3. Model Optimization and Testing over Continental China, Remote Sens. 2014 (doi: 10.3390/rs6053533)
  20. Wang et al., 2014. A two-fold increase of carbon cycle sensitivity to tropical temperature variations, Nature, 2014 (doi: 10.1038/nature12915)
  21. Ciais et al., 2013. Carbon and Other Biogeochemical Cycles, IPCC AR5 Chapter 6, 2013.
  22. Xu et al., 2013. Temperature and vegetation seasonality diminishment over northern lands. Nature Climate Change, doi: 10.1038/NCLIMATE1836
    Supplementary Information
  23. Peng et al., 2013. Asymmetric effects of daytime and night-time warming on Northern Hemisphere vegetation, Nature, doi: 10.1038/nature12434
  24. Wang et al., 2013.Variations in atmospheric CO2 growth rates coupled with tropical temperature, Proc. Natl. Acad. Sci. USA,  doi: 10.1073/pnas.1219683110
  25. Ichii et al., 2013 Recent changes in terrestrial gross primary productivity in Asia from 1982 to 2011, Remote Sens., doi: 10.3390/rs5116043
  26. 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., doi: 10.1111/gcb.12283
  27. 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
  28. 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
  29. Mao et al., 2013. Global Latitudinal-Asymmetric Vegetation Growth Trends and Their Driving Mechanisms: 1982-2009, Remote Sens., doi:10.3390/rs5031484
  30. Hashimoto et al., 2012. Exploring Simple Algorithms for Estimating Gross Primary Production in Forested Areas from Satellite Data, Remote Sens., doi:10.3390/rs4010303
  31. Samanta et al., 2011. Comment on “Drought-Induced Reduction in Global Terrestrial Net Primary Production from 2000 Through 2009”, Science, doi: 10.1126/science.1199048
    Supplementary Online Material
  32. Zhousen et al., 2011. Retrieval of canopy height using moderate-resolution imaging spectroradiometer (MODIS) data, Remote Sens. Environ., doi:10.1016/j.rse.2011.02.010
  33. Yu et al., 2010. Regional distribution of forest height and biomass from multisensor data fusion, J. Geophys. Res.,  doi:10.1029/2009JG000995
  34. Kaufmann et al., 2008. The Power of Monitoring Stations and a CO2 Fertilization Effect: Evidence from Causal Relationships Between NDVI and Carbon Dioxide. Earth Interactions, doi: 10.1175/2007EI240.1
  35. Hashimoto et al., 2004. El Nin˜o–Southern Oscillation–induced variability in terrestrial carbon cycling. J. Geophys. Res., doi:10.1029/2004JD004959
  36. Kotchenova et al., 2004. Lidar remote sensing for modelling net primary productivity of deciduous forests. Remote Sens. Environ., 92: 158-172.
  37. Potter et el., 2003. Satellite data help predict terrestrial carbon sinks. EOS, 84(46): pages 502 & 508.
  38. Potter et al., 2003. Continental scale comparisons of terrestrial carbon sinks estimated from satellite data and ecosystem modeling 1982-98. Global and Planetary Change, 39:201-213.
  39. Potter et al., 2003. Global teleconnections of climate to terrestrial carbon flux. J. Geophys. Res., doi: 10.1029/2002JD002979
  40. Kotchenova et al., 2003. Modeling lidar waveforms with time-dependent stochastic radiative transfer theory for remote estimations of forest biomass. J. Geophys. Res., doi: 10.1029/2002JD003288
  41. Potter et al., 2003. Major disturbance events in terrestrial ecosystems detected using global satellite data sets. Global Change Biology, 9(7): 1005-1021.
  42. Nemani et al., 2003. Climate driven increases in global net primary production from 1981 to 1991. Science, 300:1560-1563.
  43. Zhuang et al., 2003. Carbon cycling in extratropical terrestrial ecosystems of the northern hemisphere during the 20th century: A modeling analysis of the influences of soil thermal dynamics. Tellus, 55B: 751-776.
  44. Lucht etal., 2002. Climatic control of the high-latitude vegetationgreening trend and Pinatubo effect. Science, 296:1687-1689.
  45. Dong et al., 2002. Remote sensing of boreal and temperate forest woody biomass: Carbon pools, Sources and Sinks, Remote Sens. Environ. 84:393-410.
  46. Myneni and Dong et al., 2001. A large carbon sink in the woody biomass of northern forests. Proc. Natl. Acad. Sci. USA., 98(26): 14784-14789.
    supplemental information