Bastos, A., S. W. Running, C. Gouveia, and R. M. Trigo, 2013: The global NPP dependence on ENSO: La Niña and the extraordinary year of 2011. J. Geophys. Res.: Biogeosci., 118, 1247−1255, https://doi.org/10.1002/jgrg.20100.
Bastos, A., and Coauthors, 2018: Impact of the 2015/2016 El Niño on the terrestrial carbon cycle constrained by bottom-up and top-down approaches. Philos. Trans. Roy. Soc. B: Biol. Sci., 373, 20170304, https://doi.org/10.1098/rstb.2017.0304.
Cavaleri, M. A., A. P. Coble, M. G. Ryan, W. L. Bauerle, H. W. Loescher, and S. F. Oberbauer, 2017: Tropical rainforest carbon sink declines during El Niño as a result of reduced photosynthesis and increased respiration rates. New Phytologist, 216, 136−149, https://doi.org/10.1111/nph.14724.
Chang, J. F., and Coauthors, 2017: Benchmarking carbon fluxes of the ISIMIP2a biome models. Environmental Research Letters, 12, 045002, https://doi.org/10.1088/1748-9326/aa63fa.
Cox, P. M., D. Pearson, B. B. Booth, P. Friedlingstein, C. Huntingford, C. D. Jones, and C. M. Luke, 2013: Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability. Nature, 494, 341−344, https://doi.org/10.1038/nature11882.
Dan, L., F. Q. Cao, and R. Gao, 2015: The improvement of a regional climate model by coupling a land surface model with eco-physiological processes: A case study in 1998. Climatic Change, 129, 457−470, https://doi.org/10.1007/s10584-013-0997-8.
Ding, Y. H., and J. C. L. Chan, 2005: The East Asian summer monsoon: An overview. Meteorol. Atmos. Phys., 89, 117−142, https://doi.org/10.1007/s00703-005-0125-z.
Ding, Y. H., Z. Y. Wang, and Y. Sun, 2008: Inter‐decadal variation of the summer precipitation in East China and its association with decreasing Asian summer monsoon. Part I: Observed evidences. International Journal of Climatology, 28, 1139−1161, https://doi.org/10.1002/joc.1615.
Fan, K., Y. Liu, and H. Chen, 2012: Improving the prediction of the East Asian summer monsoon: New approaches. Wea. Forecasting, 27, 1017−1030, https://doi.org/10.1175/WAF-D-11-00092.1.
Fang, Y. Y., and Coauthors, 2017: Global land carbon sink response to temperature and precipitation varies with ENSO phase. Environmental Research Letters, 12, 064007, https://doi.org/10.1088/1748-9326/aa6e8e.
Feng, J., L. Wang, and W. Chen, 2014: How does the East Asian summer monsoon behave in the decaying phase of El Niño during different PDO phases? J. Climate, 27, 2682−2698, https://doi.org/10.1175/JCLI-D-13-00015.1.
Gao, H., S. Yang, A. Kumar, Z.-Z. Hu, B. H. Huang, Y. Q. Li, and B. Jha, 2011: Variations of the East Asian Mei-Yu and simulation and prediction by the NCEP Climate Forecast System. J. Climate, 24, 94−108, https://doi.org/10.1175/2010JCLI3540.1.
Gao, H., W. Jiang, and W. J. Li, 2014: Changed relationships between the East Asian summer monsoon circulations and the summer rainfall in eastern China. Journal of Meteorological Research, 28, 1075−1084, https://doi.org/10.1007/s13351-014-4327-5.
Gough, C. M., 2011: Terrestrial primary production: Fuel for life. Nature Education Knowledge, 3, 28.
Gregg, J. S., R. J. Andres, and G. Marland, 2008: China: Emissions pattern of the world leader in CO2 emissions from fossil fuel consumption and cement production. Geophys. Res. Lett., 35, L08806, https://doi.org/10.1029/2007GL032887.
Gu, G. J., and R. F. Adler, 2011: Precipitation and temperature variations on the interannual time scale: Assessing the impact of ENSO and volcanic eruptions. J. Climate, 24, 2258−2270, https://doi.org/10.1175/2010JCLI3727.1.
Harris, I., P. D. Jones, T. J. Osborn, and D. H. Lister, 2014: Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 Dataset. International Journal of Climatology, 34, 623−642, https://doi.org/10.1002/joc.3711.
Hoeting, J. A., D. Madigan, A. E. Raftery, and C. T. Volinsky, 1999: Correction to: Bayesian model averaging: A tutorial'' [Statist. Sci. 14 (1999), no. 4, 382−417; MR 2001a:62033]. Statistical Science, 15, 193−195, https://doi.org/10.1214/ss/1009212814.
Huntzinger, D. N., and Coauthors, 2013: The north american carbon program multi-scale synthesis and terrestrial model intercomparison project-part 1: Overview and experimental design. Geoscientific Model Development, 6, 2121−2133, https://doi.org/10.5194/gmd-6-2121-2013.
Ito, A., 2011: Decadal variability in the terrestrial carbon budget caused by the Pacific Decadal Oscillation and Atlantic Multidecadal Oscillation. J. Meteor. Soc. Japan, 89, 441−454, https://doi.org/10.2151/jmsj.2011-503.
Jung, M., K. Henkel, M. Herold, and G. Churkina, 2006: Exploiting synergies of global land cover products for carbon cycle modeling. Remote Sensing of Environment, 101, 534−553, https://doi.org/10.1016/j.rse.2006.01.020.
Jung, M., and Coauthors, 2011: Global patterns of land‐atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations. J. Geophys. Res.: Biogeosci., 116, G00J07, https://doi.org/10.1029/2010JG001566.
Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc., 77, 437−472, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.
Li, X. L., and Coauthors, 2013: Estimation of gross primary production over the terrestrial ecosystems in China. Ecological Modelling, 261−262, 80−92, https://doi.org/10.1016/j.ecolmodel.2013.03.024.
Liu, H. W., T. J. Zhou, Y. X. Zhu, and Y. H. Lin, 2012: The strengthening East Asia summer monsoon since the early 1990s. Chinese Science Bulletin, 57, 1553−1558, https://doi.org/10.1007/s11434-012-4991-8.
Liu, J. J., and Coauthors, 2017: Contrasting carbon cycle responses of the tropical continents to the 2015-2016 El Niño. Science, 358, eaam5690, https://doi.org/10.1126/science.aam5690.
Ma, Z. G., 2007: The interdecadal trend and shift of dry/wet over the central part of North China and their relationship to the Pacific Decadal Oscillation (PDO). Chinese Science Bulletin, 52, 2130−2139, https://doi.org/10.1007/s11434-007-0284-z.
Mantua, N. J., S. R. Hare, Y. Zhang, J. M. Wallace, and R. C. Francis, 1997: A Pacific interdecadal climate oscillation with impacts on salmon production. Bull. Amer. Meteor. Soc., 78, 1069−1080, https://doi.org/10.1175/1520-0477(1997)078<1069:APICOW>2.0.CO;2.
Nash, J. E., and J. V. Sutcliffe, 1970: River flow forecasting through conceptual models part I—A discussion of principles. Journal of Hydrology, 10, 282−290, https://doi.org/10.1016/0022-1694(70)90255-6.
Neuman, S. P., 2003: Maximum likelihood Bayesian averaging of uncertain model predictions. Stochastic Environmental Research and Risk Assessment, 17, 291−305, https://doi.org/10.1007/s00477-003-0151-7.
Peng, S. S., and Coauthors, 2015: Benchmarking the seasonal cycle of CO2 fluxes simulated by terrestrial ecosystem models. Global Biogeochemical Cycles, 29, 46−64, https://doi.org/10.1002/2014GB004931.
Piao, S. L., and Coauthors, 2014: Evidence for a weakening relationship between interannual temperature variability and northern vegetation activity. Nature Communications, 5, 5018, https://doi.org/10.1038/ncomms6018.
Qian, C., and T. J. Zhou, 2014: Multidecadal variability of North China aridity and its relationship to PDO during 1900-2010. J. Climate, 27, 1210−1222, https://doi.org/10.1175/JCLI-D-13-00235.1.
Qian, X., B. Qiu, and Y. G. Zhang, 2019: Widespread decline in vegetation photosynthesis in Southeast Asia due to the prolonged drought during the 2015/2016 El Niño. Remote Sensing, 11, 910, https://doi.org/10.3390/rs11080910.
Raftery, A. E., T. Gneiting, F. Balabdaoui, and M. Polakowski, 2005: Using Bayesian model averaging to calibrate forecast ensembles. Mon. Wea. Rev., 133, 1155−1174, https://doi.org/10.1175/MWR2906.1.
Schwalm, C. R., C. A. Williams, K. Schaefer, I. Baker, G. J. Collatz, and C. Rödenbeck, 2011: Does terrestrial drought explain global CO2 flux anomalies induced by El Niño? Biogeosciences, 8, 2493−2506, https://doi.org/10.5194/bg-8-2493-2011.
Shao, J. J., and Coauthors, 2016: Uncertainty analysis of terrestrial net primary productivity and net biome productivity in China during 1901−2005. J. Geophys. Res.: Biogeosci., 121, 1372−1393, https://doi.org/10.1002/2015JG003062.
Taylor, K. E., 2001: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res.: Atmos., 106, 7183−7192, https://doi.org/10.1029/2000JD900719.
Vrugt, J. A., 2016: MODELAVG: A MATLAB toolbox for postprocessing of model ensembles. Department of Civil and Environmental Engineering, University of California Irvine, 1−69.
Wang, B., J. Li, and Q. He, 2017: Variable and robust East Asian monsoon rainfall response to El Niño over the past 60 years (1957−2016). Adv. Atmos. Sci., 34, 1235−1248, https://doi.org/10.1007/s00376-017-7016-3.
Wang, J., N. Zeng, and M. R. Wang, 2016: Interannual variability of the atmospheric CO2 growth rate: Roles of precipitation and temperature. Biogeosciences, 13, 2339−2352, https://doi.org/10.5194/bg-13-2339-2016.
Wang, J., N. Zeng, M. R. Wang, F. Jiang, H. M. Wang, and Z. Q. Jiang, 2018: Contrasting terrestrial carbon cycle responses to the 1997/98 and 2015/16 extreme El Niño events. Earth System Dynamics, 9, 1−14, https://doi.org/10.5194/esd-9-1-2018.
Wang, S. Q., and Coauthors, 2015: Improving the light use efficiency model for simulating terrestrial vegetation gross primary production by the inclusion of diffuse radiation across ecosystems in China. Ecological Complexity, 23, 1−13, https://doi.org/10.1016/j.ecocom.2015.04.004.
Wang, W. L., and Coauthors, 2013: Variations in atmospheric CO2 growth rates coupled with tropical temperature. Proceedings of the National Academy of Sciences of the United States of America, 110, 13061−13066, https://doi.org/10.1073/pnas.1219683110.
Wasserman, L., 2000: Bayesian model selection and model averaging. Journal of Mathematical Psychology, 44, 92−107, https://doi.org/10.1006/jmps.1999.1278.
Wei, Y., and Coauthors, 2014: The North American carbon program multi-scale synthesis and terrestrial model intercomparison project-Part 2: Environmental driver data. Geoscientific Model Development, 7, 2875−2893, https://doi.org/10.5194/gmd-7-2875-2014.
Wharton, S., and M. Falk, 2016: Climate indices strongly influence old-growth forest carbon exchange. Environmental Research Letters, 11, 044016, https://doi.org/10.1088/1748-9326/11/4/044016.
Yan, H., S. Q. Wang, A. Huete, and H. H. Shugart, 2019: Effects of light component and water stress on photosynthesis of Amazon rainforests during the 2015/2016 El Niño drought. J. Geophys. Res.: Biogeosci., 124, 1574−1590, https://doi.org/10.1029/2018JG004988.
Yang, F. L., and K. M. Lau, 2004: Trend and variability of China precipitation in spring and summer: Linkage to sea-surface temperatures. International Journal of Climatology, 24, 1625−1644, https://doi.org/10.1002/joc.1094.
Yang, J., H. Q. Tian, S. F. Pan, G. S. Chen, B. W. Zhang, and S. Dangal, 2018: Amazon drought and forest response: Largely reduced forest photosynthesis but slightly increased canopy greenness during the extreme drought of 2015/2016. Global Change Biology, 24, 1919−1934, https://doi.org/10.1111/gcb.14056.
Yang, X., and M. X. Wang, 2000: Monsoon ecosystems control on atmospheric CO2 interannual variability: Inferred from a significant positive correlation between year-to-year changes in land precipitation and atmospheric CO2 growth rate. Geophys. Res. Lett., 27, 1671−1674, https://doi.org/10.1029/1999GL006073.
Yu, G. R., X. F. Wen, X. M. Sun, B. D. Tanner, X. Lee, and J. Y. Chen, 2006: Overview of ChinaFLUX and evaluation of its eddy covariance measurement. Agricultural and Forest Meteorology, 137, 125−137, https://doi.org/10.1016/j.agrformet.2006.02.011.
Yu, G. R., Z. Chen, S. L. Piao, C. H. Peng, P. Ciais, Q. F. Wang, X. R. Li, and X. J. Zhu, 2014: High carbon dioxide uptake by subtropical forest ecosystems in the East Asian monsoon region. Proceedings of the National Academy of Sciences of the United States of America, 111, 4910−4915, https://doi.org/10.1073/pnas.1317065111.
Zhang, L., and Coauthors, 2016: Evaluation of the Community Land Model simulated carbon and water fluxes against observations over ChinaFLUX sites. Agricultural and Forest Meteorology, 226−227, 174−185, https://doi.org/10.1016/j.agrformet.2016.05.018.
Zhang, L., and Coauthors, 2019a: Interannual variability of terrestrial net ecosystem productivity over China: Regional contributions and climate attribution. Environmental Research Letters, 14, 014003, https://doi.org/10.1088/1748-9326/aaec95.
Zhang, X. Z., and Coauthors, 2018: Dominant regions and drivers of the variability of the global land carbon sink across timescales. Global Change Biology, 24, 3954−3968, https://doi.org/10.1111/gcb.14275.
Zhang, Y. L., M. P. Dannenberg, T. Hwang, and C. H. Song, 2019b: El Niño-southern oscillation-induced variability of terrestrial gross primary production during the satellite era. J. Geophys. Res.: Biogeosci., 124, 2419−2431, https://doi.org/10.1029/2019JG005117.
Zhao, M. S., F. A. Heinsch, R. R. Nemani, and S. W. Running, 2005: Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sensing of Environment, 95, 164−176, https://doi.org/10.1016/j.rse.2004.12.011.
Zhao, T. B., and C. B. Fu, 2006: Comparison of products from ERA-40, NCEP-2, and CRU with station data for summer precipitation over China. Adv. Atmos. Sci., 23, 593−604, https://doi.org/10.1007/s00376-006-0593-1.