Evaluation of CMIP5 earth system models in reproducing leaf area index and vegetation cover over the tibetan plateau

Journal of Meteorological Research

Volumn 28, Issue 6, 2014, Pages 1041-1060

  Bao, Yan ^a,b   Gao, Yanhong ^b   Lü, Shihua ^b   Wang, Qingxia ^b   Zhang, Shaobo ^b   Xu, Jianwei ^b   Li, Ruiqing ^b   Li, Suosuo ^b   Ma, Di ^b   Meng, Xianhong ^b   Chen, Hao ^b   Chang, Yan ^b  

^a NANJING UNIVERSITY OF INFORMATION SCIENCE AND TECHNOLOGY   (China)

^b INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

Author keywords

Coupled Model Intercomparison Project Phase 5 (CMIP5); Dynamic global vegetation model (DGVM); Earth system model (ESM); Tibetan Plateau; Vegetation cover

Indexed keywords

ANNUAL VARIATION; CLIMATE MODELING; LEAF AREA INDEX; PHOTOSYNTHESIS; PRECIPITATION (CLIMATOLOGY); SPATIAL DISTRIBUTION; VEGETATION COVER; VEGETATION DYNAMICS;

CHINA; QINGHAI-XIZANG PLATEAU; YELLOW RIVER;

EID: 84925196780     PISSN: 20956037     EISSN: 21980934     Source Type: Journal    
DOI: 10.1007/s13351-014-4023-5     Document Type: Article

References (50)

1. Abramowitz, G., R. Leuning, M. Clark, et al., 2008: Evaluating the performance of land surface models. J. Climate, 21, 5468–5481.
2. Anav, A., G. Murray-Tortarolo, P. Friedlingstein, et al., 2013a: Evaluation of land surface models in reproducing satellite derived leaf area index over the high-latitude Northern Hemisphere. Part II: Earth system models. Remote Sens., 5, 3637–3661.
3. Anav, A., P. Friedlingstein, M. Kidston, et al., 2013b: Evaluating the land and ocean components of the global carbon cycle in the CMIP5 Earth System Models. J. Climate, 26, 6801–6843.
4. Arneth, A., S. P. Harrison, S. Zaehle, et al., 2010: Terrestrial biogeochemical feedbacks in the climate system. Nature Geosci., 3, 525–532.
5. Bathiany, S., M. Claussen, V. Brovkin, et al., 2010: Combined biogeophysical and biogeochemical effects of large-scale forest cover changes in the MPI earth system model. Biogeosci. Discuss., 7, 1383–1399.
6. Blyth, E., D. Clark, R. Ellis, et al., 2011: A comprehensive set of benchmark tests for a land surface model of simultaneous fluxes of water and carbon at both the global and seasonal scale. Geosci. Model Dev., 4, 255–269.
7. Brovkin, V., L. Boysen, T. Raddatz, et al., 2013: Evaluation of vegetation cover and land-surface albedo in MPI-ESM CMIP5 simulations. J. Adv. Modeling Earth Syst., 5, 48–57.
8. Brunke, M. A., C. W. Fairall, X. B. Zeng, et al., 2003: Which bulk aerodynamic algorithms are least problematic in computing ocean surface turbulent fluxes? J. Climate, 16, 619–635.
9. Cadule, P., P. Friedlingstein, L. Bopp, et al., 2010: Benchmarking coupled climate-carbon models against long-term atmospheric CO2 measurements. Global Biogeochem. Cy., 24, doi: 10.1029/2009GB003556.
10. Collins, W., N. Bellouin, M. Doutriaux-Boucher, et al., 2011: Development and evaluation of an earthsystem model—HadGEM2. Geosci. Model Dev. Discuss., 4, 997–1062.
11. Cramer, W., 2011: ISLSCP II IGBP NPP output from terrestrial biogeochemistry models. ISLSCP Initiative II Collection. Data Set. Hall, F. G., G. Collatz, B. Meeson, et al., Eds., Oak Ridge National Laboratory Distributed Active Center, Oak Ridge, Tennessee, U. S. A., doi: 10.3334/ORNLDAAC/1027.
12. Cramer, W., D. W. Kicklighter, A. Bondeau, et al., 1999: Comparing global models of terrestrial net primary productivity (NPP): Overview and key results. Global Change Biology, 5(S1), doi: 10.1046/j.1365-2486.1999.00009.x.
13. Dan, L., J. J. Ji, and Y. He, 2007: Use of ISLSCP II data to intercompare and validate the terrestrial net primary production in a land surface model coupled to a general circulation model. J. Geophys. Res. Atmos. (1984–2012), 112, doi: 10.1029/2006JD007721.
14. Decker, M., M. A. Brunke, Z. Wang, et al., 2012: Evaluation of the reanalysis products from GSFC, NCEP, and ECMWF using flux tower observations. J. Climate, 25, 1916–1944.
15. Fisher, J. B., K. P. Tu, and D. D. Baldocchi, 2008: Global estimates of the land-atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites. Remote Sens. Environ., 112, 901–919.
16. Gleckler, P. J., K. E. Taylor, and C. Doutriaux, 2008: Performance metrics for climate models. J. Geophys. Res. Atmos. (1984–2012), 113, doi: 10.1029/2007JD008972.
17. Hansen, M. C., R. S. DeFries, J. R. G. Townshend, et al., 2003: Global percent tree cover at a spatial resolution of 500 meters: First results of the MODIS vegetation continuous fields algorithm. Earth Interact., 7, 1–15.
18. IPCC, 2007: Summary for Policymakers. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Solomon, S., et al., Eds., Cambridge University Press, 996 pp.
19. Jung, M., M. Reichstein, and A. Bondeau, 2009: Towards global empirical upscaling of FLUXNET eddy covariance observations: Validation of a model tree ensemble approach using a biosphere model. Biogeosci., 6, 2001–2013.
20. Jung, M., M. Reichstein, H. A. Margolis, et al., 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. (2005–2012), 116, doi: 10.1029/2010JG001566.
21. Kang, H.-S., Y. K. Xue, and G. J. Collatz, 2007: Impact assessment of satellite-derived leaf area index datasets using a general circulation model. J. Climate, 20, 993–1015.
22. Kato, T., Y. H. Tang, S. Gu, et al., 2004: Carbon dioxide exchange between the atmosphere and an alpine meadow ecosystem on the Qinghai-Tibetan Plateau, China. Agric. Forest Meteor., 124, 121–134.
23. Lawrence, D. M., K. W. Oleson, M. G. Flanner, et al., 2011: Parameterization improvements and functional and structural advances in version 4 of the Community Land Model. J. Adv. Modeling Earth Syst., 3, doi: 10.1029/2011MS00045.
24. Lawrence, P. J., and T. N. Chase, 2007: Representing a new MODIS consistent land surface in the Community Land Model (CLM 3.0). J. Geophys. Res. Biogeosci. (2005–2012). 112, doi: 10.1029/2006JG000168.
25. Liang, S. L., X. Zhao, S. H. Liu, et al., 2013: A long-term global land surface satellite (GLASS) dataset for environmental studies. Int. J. Digital Earth, 6(Sup1), 5–33.
26. Monserud, R. A., and R. Leemans, 1992: Comparing global vegetation maps with the Kappa statistic. Ecological Modeling, 62, 275–293.
27. Neale, R. B., C. Chen, A. Gettelman, et al., 2010: Description of the NCAR Community Atmosphere Model (CAM 5.0). NCAR Tech. Note NCAR/TN- 486+STR. 274 pp.
28. Piao, S. L., J. Y. Fang, and J. S. He, 2006: Variations in vegetation net primary production in the Qinghai- Xizang Plateau, China, from 1982 to 1999. Climatic Change, 74, 253–267.
29. Piao, S. L., J. Y. Fang, P. Ciais, et al., 2009: The carbon balance of terrestrial ecosystems in China. Nature, 458, 1009–1013.
30. Piao, S. L., P. Ciais, Y. Huang, et al., 2010: The impacts of climate change on water resources and agriculture in China. Nature, 467, 43–51.
31. Port, U., V. Brovkin, and M. Claussen, 2012: The role of dynamic vegetation cover in future climate change. Earth Syst. Dyn. Discuss., 3, 485–522.
32. Poulter, B., P. Ciais, E. Hodson, et al., 2011: Plant functional type mapping for earth system models. Geosci. Model Dev. Discuss., 4, 2081–2121.
33. Randerson, J. T., F. M. Hoffman, P. E. Thornton, et al., 2009: Systematic assessment of terrestrial biogeochemistry in coupled climate-carbon models. Global Change Biology, 15, 2462–2484.
34. Scherrer, S. C., 2011: Present-day interannual variability of surface climate in CMIP3 models and its relation to future warming. Int. J. Climatol., 31, 1518–1529.
35. Shao, P., X. P. Zeng, K. Sakaguchi, et al., 2013: Terrestrial carbon cycle: Climate relations in eight CMIP5 earth system models. J. Climate, 26, 8744–8764.
36. Sitch, S., P. M. Cox, W. J. Collins, et al., 2007: Indirect radiative forcing of climate change through ozone effects on the land-carbon sink. Nature, 448, 791–794.
37. Tan, K., P. Ciais, S. Piao, et al., 2010: Application of the ORCHIDEE global vegetation model to evaluate biomass and soil carbon stocks of Qinghai- Tibetan grasslands. Global Biogeochem. Cy., 24, doi: 10.1029/2009GB003530.
38. Taylor, K. E., 2001: Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res. Atmos. (1984–2012), 106, 7183–7192.
39. Thornton, P. E., and N. E. Zimmermann, 2007: An improved canopy integration scheme for a land surface model with prognostic canopy structure. J. Climate, 20, 3902–3923.
40. Wang, A. H., and X. B. Zeng, 2012: Evaluation of multireanalysis products with in situ observations over the Tibetan Plateau. J. Geophys. Res. Atmos. (1984–2012), 117, doi: 10.1029/2011JD016553.
41. Watanabe, S., T. Hajima, K. Sudo, et al., 2011: MIROCESM: Model description and basic results of CMIP5- 20c3m experiments. Geosci. Model Dev. Discuss., 4, 1063–1128.
42. Watson, D. J., 1947: Comparative physiological studies on the growth of field crops. I: Variation in net assimilation rate and leaf area between species and varieties, and within and between years. Ann. Bot., 11, 41–76.
43. Wittig, V. E., E. A. Ainsworth, S. L. Naidu, et al., 2009: Quantifying the impact of current and future tropospheric ozone on tree biomass, growth, physiology and biochemistry: A quantitative meta-analysis. Global Change Biology, 15, 396–424.
44. Wu Jia and Gao Xuejie, 2013: A gridded daily observation dataset over China region and comparison with the other datasets. Chinese Geophys., 56, 1102–1111.(in Chinese)
45. Xiao, Z. Q., S. L. Liang, J. D. Wang, et al., 2014: Use of general regression neural networks for generating the GLASS leaf area index product from time-series MODIS surface reflectance. Geosci. Remote Sens., 52, 209–223, doi: 10.1109/TGRS.2013.2237780.
46. Yu, H., E. Luedeling, J. Xu, 2010: Winter and spring warming result in delayed spring phenology on the Tibetan Plateau. Proc. Natl. Acad. Sci. USA, 107, 22151–22156.
47. Zhang, P., M. Hirota, H. Shen, et al., 2009: Characterization of CO2 flux in three Kobresia meadows differing in dominant species. J. Plant Ecol., 2, 187–196.
48. Zhao, M. S., F. A. Heinsch, R. R. Nemani, et al., 2005: Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sens. Environ., 95, 164–176
49. Zhao, M. S., S. W. Running, and R. R. Nemani, 2006: Sensitivity of Moderate Resolution Imaging Spectroradiometer (MODIS) terrestrial primary production to the accuracy of meteorological reanalyses. J. Geophys. Res. Biogeosci. (2005–2012), 111, doi: 10.1029/2004JG000004.
50. Zhao, M. S., and S. W. Running, 2010: Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science, 329, 940–943.