Advances in Atmospheric Sciences

, Volume 30, Issue 6, pp 1549–1559 | Cite as

Earth System Model FGOALS-s2: Coupling a dynamic global vegetation and terrestrial carbon model with the physical climate system model

  • Jun Wang (王 军)
  • Qing Bao (包 庆)
  • Ning Zeng
  • Yimin Liu (刘屹岷)
  • Guoxiong Wu (吴国雄)
  • Duoying Ji (纪多颖)
Article

Abstract

Earth System Models (ESMs) are fundamental tools for understanding climate-carbon feedback. An ESM version of the Flexible Global Ocean-Atmosphere-Land System model (FGOALS) was recently developed within the IPCC AR5 Coupled Model Intercomparison Project Phase 5 (CMIP5) modeling framework, and we describe the development of this model through the coupling of a dynamic global vegetation and terrestrial carbon model with FGOALS-s2. The performance of the coupled model is evaluated as follows.

The simulated global total terrestrial gross primary production (GPP) is 124.4 PgC yr−1 and net primary production (NPP) is 50.9 PgC yr−1. The entire terrestrial carbon pools contain about 2009.9 PgC, comprising 628.2 PgC and 1381.6 PgC in vegetation and soil pools, respectively. Spatially, in the tropics, the seasonal cycle of NPP and net ecosystem production (NEP) exhibits a dipole mode across the equator due to migration of the monsoon rainbelt, while the seasonal cycle is not so significant in Leaf Area Index (LAI). In the subtropics, especially in the East Asian monsoon region, the seasonal cycle is obvious due to changes in temperature and precipitation from boreal winter to summer. Vegetation productivity in the northern mid-high latitudes is too low, possibly due to low soil moisture there.

On the interannual timescale, the terrestrial ecosystem shows a strong response to ENSO. The model-simulated Niño3.4 index and total terrestrial NEP are both characterized by a broad spectral peak in the range of 2–7 years. Further analysis indicates their correlation coefficient reaches −0.7 when NEP lags the Niño3.4 index for about 1–2 months.

Key words

Earth System Model (ESM) Dynamic Global Vegetation Model (DGVM) carbon cycle seasonal cycle interannual variability 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bacastow, R. B., 1976: Modulation of atmospheric carbon dioxide by the Southern Oscillation. Nature, 261, 116–118.CrossRefGoogle Scholar
  2. Bao, Q., Y. M. Liu, T. J. Zhou, Z. Z. Wang, G. X. Wu, and P. F. Wang, 2006: The sensitivity of the spectral atmospheric general circulation model of LASG/IAP to the land process. Chinese J. Atmos. Sci., 30, 1077–1099. (in Chinese)Google Scholar
  3. Bao, Q., G. X. Wu, Y. M. Liu, J. Yang, Z. Z. Wang, and T. J. Zhou, 2010: An introduction to the coupled model FGOALS1.1-s and its performance in East Asia. Adv. Atmos. Sci., 27, 1131–1142, doi: 10.1007/s00376-010-9177-1.CrossRefGoogle Scholar
  4. Bao, Q., and Coauthors, 2012: The flexible global ocean-atmosphere-land system model, spectral version: FGOALS-s2. Adv. Atmos. Sci., 30(3), 561–576, doi: 10.1007/s00376-012-2113-9.CrossRefGoogle Scholar
  5. Beer, C., and Coauthors, 2010: Terrestrial gross carbon dioxide uptake: Global distribution and covariation with climate. Science, 329, 834–838.CrossRefGoogle Scholar
  6. Bousquet, P., P. Peylin, P. Ciais, C. L. Quere, P. Friedlingstein, and P. P. Tans, 2000: Regional changes in cabon dioxide fluxes of land and oceans since 1980. Science, 290, 1342–1346.CrossRefGoogle Scholar
  7. Brovkin, V., A. Ganopolski, and Y. Svirezhev, 1997: A continuous climate-vegetation classification for use in climate-biosphere studies. Ecological Modelling, 101, 251–261.CrossRefGoogle Scholar
  8. Collins, W. D., and Coauthors, 2006: The community climate system model version 3 (CCSM3). J. Climate, 19, 2122–2143.CrossRefGoogle Scholar
  9. Cox, P. M., 2001: Description of the “TRIFFID” dynamic global vegetation model. Hadley Center Tech. Note 24, 1–16.Google Scholar
  10. Cox, P. M., R. A. Betts, C. D. Jones, S. A. Spall, and I. J. Totterdell, 2000: Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model. Nature, 408, 184–187.CrossRefGoogle Scholar
  11. Cramer, W., and Coauthors, 1999: Comparing global models of terrestrial net primary productivity (NPP): Overview and key results. Global Change Biology, 5, 1–15.CrossRefGoogle Scholar
  12. Denman, K. L., and Coauthors, 2007: Couplings between changes in the climate system and biogeochemistry. Climate Change 2007: The Physical Science Basis. Solomon et al., Eds., Cambridge University Press, Cambridge, 499–587.Google Scholar
  13. Dufresne, J. L., P. Friedlingstein, M. Berthelot, L. Bopp, P. Ciais, L. Fairhead, H. Le Treut, and P. Monfray, 2002: On the magnitude of positive feedback between future climate change and the carbon cycle. Geophys. Res. Lett., 29(10), doi: 10.1029/2001GL013777.Google Scholar
  14. Foley, J. A., I. C. Prentice, N. Ramankutty, S. Levis, D. Pollard, S. Sitch, and A. Haxeltine, 1996: An integrating biosphere model of land surface processes, terrestrial carbon balance, and vegetation dynamics. Global Biogeochemical Cycles, 10, 603–628.CrossRefGoogle Scholar
  15. Friedlingstein, P., and Coauthors, 2006: Climate-carbon cycle feedback analysis: Results from the C4MIP model intercomparison. J. Climate, 19, 3337–3353.CrossRefGoogle Scholar
  16. Hagemann, S., 2002: An improved land surface parameter dataset for global and regional climate models. Max Planck Inst. Meteorol (MPI) Rep., 336, 1–21.Google Scholar
  17. Heimann, M., and M. Reichstein, 2008: Terrestrial ecosystem carbon dynamics and climate feedbacks. Nature, 451, 289–292.CrossRefGoogle Scholar
  18. Ji, J. J., 1995: A climate-vegetation interaction model: Simulating physical and biological processes at the surface. Journal of Biogeography, 22, 445–451.CrossRefGoogle Scholar
  19. Jones, C. D., M. Collins, P. M. Cox, and S. A. Spall, 2001: The carbon cycle response to ENSO: A coupled climate-carbon cycle and model study. J. Climate, 14, 4113–4129.CrossRefGoogle Scholar
  20. Keeling, C. D., and R. Revelle, 1985: Effects of EL Nino/Southern Oscillation on the atmospheric content of carbon dioxide. Meteoritics, 20, 437–450.Google Scholar
  21. Keeling, C. D., R. B. Bacastow, A. E. Bainbridge, C. A. Ekdahl, J. R., P. R. Guenther, and L. S. Waterman, 1976: Atmospheric carbon dioxide variations at Mauna Loa pbservatory, Hawaii. Tellus, 28, 538–551.CrossRefGoogle Scholar
  22. Levis, S., G. B. Bonan, M. Vertenstein, and K. W. Oleson, 2004: The community land model’s dynamic global vegetation model (CLM-DGVM): Technical description and user’s guide. NCAR Tech. Note, NCAR/TN-459+IA, 64pp.Google Scholar
  23. Li, Y. C., and Y. F. Xu, 2012: Uptake and storage of anthropogenic CO2 in the Pacific Ocean estimated using two modeling approaches. Adv. Atmos. Sci., 29, 795–809, doi: 10.1007/s00376-012-1170-4.CrossRefGoogle Scholar
  24. Mitchell, T. D., and P. D. Jones, 2005: An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Inter. J. Climatol., 25, 693–712.CrossRefGoogle Scholar
  25. Myneni, R. B., R. R. Nemani, and S. W. Running, 1997. Algorithm for the estimation of global land cover, LAI and FPAR based on radiative transfer models. IEEE Trans. Geosc. Remote Sens., 35, 1380–1393.CrossRefGoogle Scholar
  26. Oleson, K. W., and Coauthors, 2004: Technical Description of the Community Land Model (CLM). NCAR/TN-461+STR, 174pp.Google Scholar
  27. Qian, H. F., R. Joseph, and N. Zeng, 2008: Response of the terrestrial carbon cycle to the El Nino-Southern Oscillation. Tellus, 60B, 537–550.Google Scholar
  28. Qian, H. F., R. Joseph, and N. Zeng, 2009: Enhanced terrestrial carbon uptake in the northern high latitudes in the 21st century from the coupled carbon cycle climate model intercomparison project model projections. Global Change Biology, 16, 641–656.CrossRefGoogle Scholar
  29. Saha, S., and Coauthors, 2010: The NCEP climate forecast system reanalysis. Bull. Amer. Meteor. Soc., 91, 1015–1057.CrossRefGoogle Scholar
  30. Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2009: A summary of the CMIP5 experiment design. [Available online at http://cmip-pcmdi.llnl.gov/cmip5/docs/TaylorCMIP5design.pdf.]Google Scholar
  31. Zeng, N., 2003: Glacial-interglacial atmospheric CO2 change — The glacial burial hypothesis. Adv. Atmos. Sci., 20, 677–693.CrossRefGoogle Scholar
  32. Zeng, N., H. F. Qian, E. Munoz, and R. Iacono, 2004: How strong is carbon cycle-climate feedback under global warming? Geophys. Res. Lett., 31, L20203, doi: 10.1029/2004GL020904.CrossRefGoogle Scholar
  33. Zeng, N., A. Mariotti, and P. Wetzel, 2005: Terrestrial mechanisms of interannual CO2 variability. Global Biogeochemical Cycles, 19, GB1016, doi: 10.1029/2004GB002273.CrossRefGoogle Scholar
  34. Zhang, X. H., G. Y. Shi, H. Liu, and Y. Q. Yu, 2000: IAP Global Ocean-Atmosphere-Land System Model. Science Press, Beijing, 252pp. (in Chinese)Google Scholar

Copyright information

© Chinese National Committee for International Association of Meteorology and Atmospheric Sciences, Institute of Atmospheric Physics, Science Press and Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jun Wang (王 军)
    • 1
    • 2
  • Qing Bao (包 庆)
    • 1
  • Ning Zeng
    • 3
  • Yimin Liu (刘屹岷)
    • 1
  • Guoxiong Wu (吴国雄)
    • 1
  • Duoying Ji (纪多颖)
    • 4
  1. 1.State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  2. 2.University of the Chinese Academy of SciencesBeijingChina
  3. 3.Department of Atmospheric and Oceanic Science and Earth System Science Interdisciplinary CenterUniversity of MarylandCollege ParkUSA
  4. 4.College of Global Change and Earth System ScienceBeijing Normal UniversityBeijingChina

Personalised recommendations