Ahlström, A., and Coauthors, 2015: The dominant role of semiarid ecosystems in the trend and variability of the land CO2 sink. Science, 348, 895–899, https://doi.org/10.1126/science.aaa1668.
Barman, R., A. K. Jain, and M. L. Liang, 2014: Climate-driven uncertainties in modeling terrestrial gross primary production: A site level to global-scale analysis. Global Change Biology, 20, 1394–1411, https://doi.org/10.1111/gcb.12474.
Beer, C., and Coauthors, 2010: Terrestrial gross carbon dioxide uptake: Global distribution and Covariation with climate. Science, 329, 834–838, https://doi.org/10.1126/science.1184984.
Betts, R. A., C. A. Burton, R. A. Feely, M. Collins, C. D. Jones, and A. J. Wiltshire, 2021: ENSO and the carbon cycle. El Niño Southern Oscillation in a Changing Climate, M. J. McPhaden et al., Eds., John Wiley & Sons, Inc.
Chen, X. J., X. G. Mo, S. Hu, and S. X. Liu, 2017: Contributions of climate change and human activities to ET and GPP trends over North China Plain from 2000 to 2014. Journal of Geographical Sciences, 27, 661–680, https://doi.org/10.1007/s11442-017-1399-z.
Cho, M.-H., G.-H. Lim, and H.-J. Song, 2014: The effect of the wintertime arctic oscillation on springtime vegetation over the northern high latitude region. Asia-Pacific Journal of Atmospheric Sciences, 50, 567–573, https://doi.org/10.1007/S13143-014-0046-1.
Dannenberg, M. P., E. K. Wise, M. Janko, T. Hwang, and W. K. Smith, 2018: Atmospheric teleconnection influence on North American land surface phenology. Environmental Research Letters, 13, 034029, https://doi.org/10.1088/1748-9326/aaa85a.
Forkel, M., N. Carvalhais, C. Rödenbeck, R. Keeling, M. Heimann, K. Thonicke, S. Zaehle, and M. Reichstein, 2016: Enhanced seasonal CO2 exchange caused by amplified plant productivity in northern ecosystems. Science, 351, 696–699, https://doi.org/10.1126/science.aac4971.
Frederiksen, C. S. and X. Zheng, 2004: Variability of seasonalmean fields arising from intraseasonal variability. Part 2, application to nh winter circulations. Climate Dyn., 23, 193–206, https://doi.org/10.1007/s00382-004-0429-6.
Friedlingstein, P., and Coauthors, 2020: Global carbon budget 2020. Earth System Science Data, 12, 3269–3340, https://doi.org/10.5194/essd-12-3269-2020.
Gong, D.-Y., J. Yang, S.-J. Kim, Y. Q. Gao, D. Guo, T. J. Zhou, and M. Hu, 2011: Spring Arctic Oscillation-East Asian summer monsoon connection through circulation changes over the western North Pacific. Climate Dyn., 37, 2199–2216, https://doi.org/10.1007/s00382-011-1041-1.
Harris, I., T. J. Osborn, P. Jones, and D. Lister, 2020: Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Scientific Data, 7, 109, https://doi.org/10.6084/m9.figshare.11980500.
Houghton, R. A., 2000: Interannual variability in the global carbon cycle. J. Geophys. Res., 105, 20121–20130, https://doi.org/10.1029/2000JD900041.
Hughes, J. K., P. J. Valdes, and R. Betts, 2006: Dynamics of a global-scale vegetation model. Ecological Modelling, 198, 452–462, https://doi.org/10.1016/j.ecolmodel.2006.05.020.
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.
Kim, J.-S., J.-S. Kug, and S.-J. Jeong, 2017: Author Correction: Intensification of terrestrial carbon cycle related to El Niño—Southern Oscillation under greenhouse warming. Nature Communications, 8, 207, https://doi.org/10.1038/s41467-017-02461-9.
Le Quéré, C., and Coauthors, 2018: Global carbon budget 2017. Earth System Science Data, 10, 405–448, https://doi.org/10.5194/essd-10-405-2018.
Li, Y. Y., and Coauthors, 2021: Response of growing season gross primary production to El Niño in different phases of the pacific decadal oscillation over Eastern China based on Bayesian model averaging. Adv. Atmos. Sci., 38, 1580–1595, https://doi.org/10.1007/s00376-021-0265-1.
Lorenz, E. N., 1956: Empirical orthogonal functions and statistical weather prediction. Statistical Forecast Project Report 1, 49 pp.
Ma, J., X. M. Xiao, R. H. Miao, Y. Li, B. Q. Chen, Y. Zhang, and B. Zhao, 2019: Trends and controls of terrestrial gross primary productivity of China during 2000–2016. Environmental Research Letters, 14, 084032, https://doi.org/10.1088/1748-9326/ab31e4.
Muller, W. A., C. Frankignoul, and N. Chouaib, 2008: Observed decadal tropical Pacific-North Atlantic teleconnections. Geophys. Res. Lett., 35, L24810, https://doi.org/10.1029/2008GL035901.
Nemani, R. R., C. D. Keeling, H. Hashimoto, W. M. Jolly, S. C. Piper, C. J. Tucker, R. B. Myneni, and S. W. Running, 2003: Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science, 300, 1560–1563, https://doi.org/10.1126/science.1082750.
Nian, D., N. M. Yuan, K. R. Ying, G. Liu, Z. T. Fu, Y. J. Qi, and C. L. F. Franzke, 2020: Identifying the sources of seasonal predictability based on climate memory analysis and variance decomposition. Climate Dyn., 55, 3239–3252, https://doi.org/10.1007/s00382-020-05444-7.
Oleson, K. W., and Coauthors, 2010: Technical Description of version 4.0 of the Community Land Model (CLM) (No. NCAR/TN-478+STR). University Corporation for Atmospheric Research, https://doi.org/10.5065/D6FB50WZ.
Peng, J., L. Dan, and M. Huang, 2014: Sensitivity of global and regional terrestrial carbon storage to the direct CO2 effect and climate change based on the CMIP5 model intercomparison. PLoS One, 9, e95282, https://doi.org/10.1371/journal.pone.0095282.
Peng, J., and L. Dan, 2015: Impacts of CO2 concentration and climate change on the terrestrial carbon flux using six global climate-carbon coupled models. Ecological Modelling, 304, 69–83, https://doi.org/10.1016/j.ecolmodel.2015.02.016.
Peng, J., Y.-P. Wang, B. Z. Houlton, L. Dan, B. Pak, and X. B. Tang, 2020: Global carbon sequestration is highly sensitive to model-based formulations of nitrogen fixation. Global Biogeochemical Cycles, 34, e2019GB006296, https://doi.org/10.1029/2019GB006296.
Peng, J., L. Dan, K. R. Ying, S. Yang, X. B. Tang, and F. Q. Yang, 2021: China’s interannual variability of net primary production is dominated by the central china region. J. Geophys. Res., 126, e2020JD033362, https://doi.org/10.1029/2020JD033362.
Piao, S. L., J. Y. Fang, P. Ciais, P. Peylin, Y. Huang, S. Sitch, and T. Wang, 2009a: The carbon balance of terrestrial ecosystems in China. Nature, 458, 1009–1013, https://doi.org/10.1038/nature07944.
Piao, S. L., P. Ciais, P. Friedlingstein, N. de Noblet-Ducoudré, P. Cadule, N. Viovy, and T. Wang, 2009b: Spatiotemporal patterns of terrestrial carbon cycle during the 20th century. Global Biogeochemical Cycles, 23, GB4026, https://doi.org/10.1029/2008GB003339.
Piao, S. L., and Coauthors, 2013: Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends. Global Change Biology, 19, 2117–2132, https://doi.org/10.1111/gcb.12187.
Piao, S. L., and Coauthors, 2020: Interannual variation of terrestrial carbon cycle: Issues and perspectives. Global Change Biology, 26, 300–318, https://doi.org/10.1111/gcb.14884.
Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670.
Reimer, J. J., R. Vargas, D. Rivas, G. Gaxiola-Castro, J. M. Hernandez-Ayon, and R. Lara-Lara, 2015: Sea surface temperature influence on terrestrial gross primary production along the southern California current. PLoS One, 10, e0125177, https://doi.org/10.1371/journal.pone.0125177.
Richardson, A. D., D. Y. Hollinger, J. D. Aber, S. V. Ollinger, and B. H. Braswell, 2007: Environmental variation is directly responsible for short- but not long-term variation in forest-atmosphere carbon exchange. Global Change Biology, 13, 788–803, https://doi.org/10.1111/j.1365-2486.2007.01330.x.
Schaefer, K., A. S. Denning, and O. Leonard, 2005: The winter Arctic Oscillation, the timing of spring, and carbon fluxes in the Northern Hemisphere. Global Biogeochemical Cycles, 19, GB3017, https://doi.org/10.1029/2004GB002336.
Shen, B. Z., Z. D. Lin, R. Y. Lu, and Y. Lian, 2011: Circulation anomalies associated with interannual variation of early- and late-summer precipitation in Northeast China. Science China Earth Sciences, 54, 1095–1104, https://doi.org/10.1007/s11430-011-4173-6.
Sitch, S., and Coauthors, 2003: Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Global Change Biology, 9, 161–185, https://doi.org/10.1046/j.1365-2486.2003.00569.x.
Smith, B., I. C. Prentice, and M. T. Sykes, 2001: Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space. Global Ecology and Biogeography, 10, 621–637.
Tharammal, T., G. Bala, N. Devaraju, and R. Nemani, 2019: A review of the major drivers of the terrestrial carbon uptake: Model-based assessments, consensus, and uncertainties. Environmental Research Letters, 14, 093005, https://doi.org/10.1088/1748-9326/ab3012.
Wang, B., J. Yang, T. J. Zhou, and B. Wang, 2008: Interdecadal changes in the major modes of Asian-Australian Monsoon variability: Strengthening relationship with ENSO since the late 1970s. J. Climate, 21, 1771–1789, https://doi.org/10.1175/2007JCLI1981.1.
Wang, B., J.-Y. Lee, and B. Q. Xiang, 2015: Asian summer monsoon rainfall predictability: A predictable mode analysis. Climate Dyn., 44, 61–74, https://doi.org/10.1007/s00382-014-2218-1.
Wang, Y. P., and Coauthors, 2011: Diagnosing errors in a land surface model (CABLE) in the time and frequency domains. Journal of Geophysical Research: Biogeosciences, 116, G01034.
Wieder, W. R., C. C. Cleveland, W. K. Smith, and K. Todd-Brown, 2015: Future productivity and carbon storage limited by terrestrial nutrient availability. Nature Geoscience, 8, 441–444, https://doi.org/10.1038/ngeo2413.
Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences. Academic Press, 467 pp.
Woodward, F. I., T. M. Smith, and W. R. Emanuel, 1995: A global land primary productivity and phytogeography model. Global Biogeochemical Cycles, 9, 471–490, https://doi.org/10.1029/95GB02432.
Wu, R. G., and B. Wang, 2002: A contrast of the East Asian summer Monsoon-ENSO relationship between 1962–77 and 1978–93. J. Climate, 15, 3266–3279, https://doi.org/10.1175/1520-0442(2002)015<3266:ACOTEA>2.0.CO;2.
Wu, Z. W., B. Wang, J. P. Li, and F.-F. Jin, 2009: An empirical seasonal prediction model of the east Asian summer monsoon using ENSO and NAO. J. Geophys. Res., 114, D18120, https://doi.org/10.1029/2009JD011733.
Yang, Q., Z. G. Ma, X. G. Fan, Z.-L. Yang, Z. F. Xu, and P. L. Wu, 2017: Decadal modulation of precipitation patterns over eastern China by sea surface temperature anomalies. J. Climate, 30, 7017–7033, https://doi.org/10.1175/JCLI-D-16-0793.1.
Yao, Y. T., and Coauthors, 2018: Spatiotemporal pattern of gross primary productivity and its covariation with climate in China over the last thirty years. Global Change Biology, 24, 184–196, https://doi.org/10.1111/gcb.13830.
Ying, K. R., T. B. Zhao, X.-W. Quan, X. G. Zheng, and C. S. Frederiksen, 2015: Interannual variability of autumn to spring seasonal precipitation in eastern China. Climate Dyn., 45, 253–271, https://doi.org/10.1007/s00382-014-2411-2.
Ying, K. R., T. B. Zhao, X. G. Zheng, X.-W. Quan, C. S. Frederiksen, and M. X. Li, 2016: Predictable signals in seasonal mean soil moisture simulated with observation-based atmospheric forcing over China. Climate Dyn., 47, 2373–2395, https://doi.org/10.1007/s00382-015-2969-3.
Ying, K. R., X. G. Zheng, T. B. Zhao, C. S. Frederiksen, and X.-W. Quan, 2017: Identifying the predictable and unpredictable patterns of spring-to-autumn precipitation over eastern China. Climate Dyn., 48, 3183–3206, https://doi.org/10.1007/s00382-016-3258-5.
Ying, K. R., C. S. Frederiksen, T. B. Zhao, X. G. Zheng, Z. Xiong, X. Yi, and C. X. Li, 2018: Predictable and unpredictable modes of seasonal mean precipitation over Northeast China. Climate Dyn., 50, 3081–3095, https://doi.org/10.1007/s00382-017-3795-6.
Zhang, X. Z., P. J. Rayner, Y.-P. Wang, J. D. Silver, X. J. Lu, B. Pak, and X. G. Zheng, 2016: Linear and nonlinear effects of dominant drivers on the trends in global and regional land carbon uptake: 1959 to 2013. Geophys. Res. Lett., 43, 1607–1614, https://doi.org/10.1002/2015GL067162.
Zhang, L., and Coauthors, 2019: 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, A. Z., and G. S. Jia, 2020: ENSO-driven reverse coupling in interannual variability of pantropical water availability and global atmospheric CO2 growth rate. Environmental Research Letters, 15, 034006, https://doi.org/10.1088/1748-9326/ab66cc.
Zheng, X. G., and R. E. Basher, 1999: Structural time series models and trend detection in global and regional temperature series. J. Climate, 12, 2347–2358, https://doi.org/10.1175/1520-0442(1999)012<2347:STSMAT>2.0.CO;2.
Zheng, X., D. M. Straus, and C. S. Frederiksen, 2008: Variance decomposition approach to the prediction of the seasonal mean circulation: Comparison with dynamical ensemble prediction using NCEP’s CFS. Quart. J. Roy. Meteor. Soc., 134, 1997–2009, https://doi.org/10.1002/qj.330.
Zhu, Z. C., S. L. Piao, Y. Y. Xu, A. Bastos, P. Ciais, and S. S. Peng, 2017: The effects of teleconnections on carbon fluxes of global terrestrial ecosystems. Geophys. Res. Lett., 44, 3209–3218, https://doi.org/10.1002/2016GL071743.