Terrestrial Carbon Cycle

The Greening Earth

  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. Meng et al., 2023. Climate change increases carbon allocation to leaves in early leaf green-up. Ecol. Lett., doi: 10.1111/ele.14205
  4. Tucker et al., 2023. Sub-continental-scale carbon stocks of individual trees in African drylands. Nature, doi: 10.110.1038/s41586-022-05653-6
  5. Zhu et al., 2021. Comment on “Recent global decline of CO2 fertilization effects on vegetation photosynthesis”. Science, doi: 10.1126/science.abg5673
  6. Huang et al., 2020. Spatial and temporal variations in global soil respiration and their relationships with climate and land cover. Sci. Adv., 6, eabb8508
  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. Yang et al., 2020. Interannual Variability of Carbon Uptake of Secondary Forests in the Brazilian Amazon (2004‐2014). Global Biogeochem. Cycles, doi:10.1029/2019GB006396
  9. Piao et al., 2019. Characteristics, drivers and feedbacks of global greening. Nature Reviews Earth and Environment, doi: 10.1038/s43017-019-0001-x
  10. 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
  11. Huang et al., 2019. Air temperature optima of vegetation productivity across global biomes. Nature Ecol. Evolution, doi:10.1038/s41559-019-0838-x
  12. Fan et al., 2019. Satellite-observed pantropical carbon dynamics. Nature Plants, doi:10.1038/s41477-019-0478-9
  13. Winkler et al., 2019. Investigating the applicability of emergent constraints. Earth System Dynamics, doi:10.5194/esd-10-501-2019
  14. Park et al., 2019. Changes in timing of seasonal peak photosynthetic activity in northern ecosystems. Global Change Biology, doi:10.1111/gcb.14638
  15. Tømmervik et al., 2019. Legacies of Historical Exploitation of Natural Resources Are More Important Than Summer Warming for Recent Biomass Increases in a Boreal–Arctic Transition Region. Ecosystems, doi:10.1007/s10021-019-00352-2
  16. Yang et al., 2018. Post-drought decline of the Amazon carbon sink. Nature Communications, doi:10.1038/s41467-018-05668-6
  17. Piao et al., 2018. Lower land-use emissions responsible for increased net land carbon sink during the slow warming period. Nature Geoscience, doi:10.1038/s41561-018-0204-7
  18. Bastos et al., 2018. Impact of the 2015/2016 El Nino on the terrestrial carbon cycle constrained by bottom-up and top-down approaches, Phil. Trans. R. Soc. B 373: 20170304.http://dx.doi.org/10.1098/rstb.2017.0304
  19. Li et al., 2018. Recent Changes in Global Photosynthesis and Terrestrial Ecosystem Respiration Constrained From Multiple Observations. Geophys. Res. Lett., doi:10.1002/2017GL076622
  20. Tong et al., 2018. Increased vegetation growth and carbon stock in China karst via ecological engineering. Nature Sustainability, https://doi.org/10.1038/s41893-017-0004-x
  21. Zhu et al., 2016. Greening of the Earth and its Drivers. Nature Climate Change, doi:10.1038/nclimate3004
  22. 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
  23. 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
  24. Li et al., 2016. Reducing uncertainties in decadal variability of the global carbon budget with multiple datasets. PNAS, doi: 10.1073/pnas.1603956113
  25. 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
  26. Ukkola et al., 2015. Reduced streamflow in water-stressed climates consistent with CO2 effects on vegetation. Nature Climate Change, 2015 (DOI: 10.1038/NCLIMATE2831)
  27. 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)
  28. 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)
  29. 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)
  30. 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)
  31. 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.
  32. Tan et al., 2014. Seasonally different response of photosynthetic activity to daytime and night-time warming in the Northern Hemisphere, Global Change Biology, (doi:
    10.1111/gcb.12724)
  33. 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
  34. 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).
  35. 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.
  36. Poulter et al., 2014. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle, Nature, 2014 (doi:10.1038/nature13376)
  37. Zhou et al., 2014. Widespread decline of Congo rainforest greenness in the past decade, Nature, 2014 (doi: 10.1038/nature13265)
  38. 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)
  39. 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)
  40. Wang et al., 2014. A two-fold increase of carbon cycle sensitivity to tropical temperature variations, Nature, 2014 (doi: 10.1038/nature12915)
  41. Ciais et al., 2013. Carbon and Other Biogeochemical Cycles, IPCC AR5 Chapter 6, 2013.
  42. Xu et al., 2013. Temperature and vegetation seasonality diminishment over northern lands. Nature Climate Change, doi: 10.1038/NCLIMATE1836
    Supplementary Information
  43. Peng et al., 2013. Asymmetric effects of daytime and night-time warming on Northern Hemisphere vegetation, Nature, doi: 10.1038/nature12434
  44. Wang et al., 2013.Variations in atmospheric CO2 growth rates coupled with tropical temperature, Proc. Natl. Acad. Sci. USA,  doi: 10.1073/pnas.1219683110
  45. Ichii et al., 2013 Recent changes in terrestrial gross primary productivity in Asia from 1982 to 2011, Remote Sens., doi: 10.3390/rs5116043
  46. 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
  47. 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
  48. 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
  49. Mao et al., 2013. Global Latitudinal-Asymmetric Vegetation Growth Trends and Their Driving Mechanisms: 1982-2009, Remote Sens., doi:10.3390/rs5031484
  50. Hashimoto et al., 2012. Exploring Simple Algorithms for Estimating Gross Primary Production in Forested Areas from Satellite Data, Remote Sens., doi:10.3390/rs4010303
  51. 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
  52. 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
  53. Yu et al., 2010. Regional distribution of forest height and biomass from multisensor data fusion, J. Geophys. Res.,  doi:10.1029/2009JG000995
  54. 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
  55. Hashimoto et al., 2004. El Nin˜o–Southern Oscillation–induced variability in terrestrial carbon cycling. J. Geophys. Res., doi:10.1029/2004JD004959
  56. Kotchenova et al., 2004. Lidar remote sensing for modelling net primary productivity of deciduous forests. Remote Sens. Environ., 92: 158-172.
  57. Potter et el., 2003. Satellite data help predict terrestrial carbon sinks. EOS, 84(46): pages 502 & 508.
  58. 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.
  59. Potter et al., 2003. Global teleconnections of climate to terrestrial carbon flux. J. Geophys. Res., doi: 10.1029/2002JD002979
  60. 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
  61. Potter et al., 2003. Major disturbance events in terrestrial ecosystems detected using global satellite data sets. Global Change Biology, 9(7): 1005-1021.
  62. Nemani et al., 2003. Climate driven increases in global net primary production from 1981 to 1991. Science, 300:1560-1563.
  63. 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.
  64. Lucht etal., 2002. Climatic control of the high-latitude vegetationgreening trend and Pinatubo effect. Science, 296:1687-1689.
  65. Dong et al., 2002. Remote sensing of boreal and temperate forest woody biomass: Carbon pools, Sources and Sinks, Remote Sens. Environ. 84:393-410.
  66. 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