Temporal variability in the thermal requirements for vegetation phenology on the Tibetan plateau and its implications for carbon dynamics

Abstract

Static thermal requirements (T req ) are widely used to model the timing of phenology, yet may significantly bias phenological projections under future warming conditions, since recent studies argue that climate warming will increase T req for triggering vegetation phenology. This study investigates the temporal trend and inter-annual variation of T req derived from satellite-based spring and autumn phenology for the alpine and temperate vegetation on the Tibetan Plateau from 1982 to 2011. While we detected persistent warming in both spring and autumn across this time period, we did not find a corresponding long-term increase in T req for most of the study area. Instead, we found a substantial interannual variability of T req that could be largely explained by interannual variations in other climatic factors. Specifically, the number of chilling days and fall temperature were robust variables for predicting the dynamics of T req for spring onset and autumn senescence, respectively. Phenology models incorporating a dynamic T req algorithm performed slightly better than those with static T req values in reproducing phenology derived from SPOT-VGT NDVI data. To assess the degree to which T req variation affects large-scale phenology and carbon cycling projections, we compared the output from versions of the Terrestrial Ecosystem Model that incorporated static and dynamic T req values in their phenology algorithms. Under two contrasting future climate scenarios, the dynamic T req setting reduced the projected growing season length by up to 1–3 weeks by the late twenty-first century, leading to a maximum reduction of 8.9 % in annual net primary production and ~15 % in cumulative net ecosystem production for this region. Our study reveals that temporal dynamics of T req meaningfully affect the carbon dynamics on the Tibetan Plateau, and should thus be considered in future ecosystem carbon modeling.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Reference

  1. Bennie J, Kubin E, Wiltshire A, Huntley B, Baxter R (2010) Predicting spatial and temporal patterns of bud budburst and spring frost risk in north‐west Europe: the implications of local adaptation to climate. Glob Chang Biol 16(5):1503–1514

    Article  Google Scholar 

  2. Chinese Academy of Sciences (2001) Vegetation Atlas of China. Science Press, Beijing

  3. Cleland EE, Allen JM, Crimmins TM, et al. (2012) Phenological tracking enables positive species responses to climate change. Ecology 93:1765–1771

    Article  Google Scholar 

  4. Diez JM, Ibáñez I, Miller-Rushing AJ, et al. (2012) Forecasting phenology: from species variability to community patterns. Ecol Lett 15:545–553

    Article  Google Scholar 

  5. Eccel E, Rea R, Caffarra A, Crisci A (2009) Risk of spring frost to apple production under future climate scenarios: the role of phenological acclimation. Int J Biometeorol 53:273–286

    Article  Google Scholar 

  6. Fang J, Yang Y, Ma W, Mohammat A, Shen H (2010) Ecosystem carbon stocks and their changes in China’s grasslands. Science China Life Sciences 53:757–765

  7. Frauenfeld OW, Zhang T, Serreze MCCD (2005) Climate change and variability using European Centre for Medium-Range Weather Forecasts reanalysis (ERA-40) temperatures on the Tibetan Plateau. J Geophys Res Atmos 110. doi:10.1029/2004JD005230

  8. Fu YH, Piao S, Vitasse Y, et al. (2015) Increased heat requirement for leaf flushing in temperate woody species over 1980–2012: effects of chilling, precipitation and insolation. Glob Chang Biol 21:2687–2697

    Article  Google Scholar 

  9. Fu YH, Piao S, Zhao H, Jeong S-J, Wang X, Vitasse Y, Ciais P, Janssens IA (2014) Unexpected role of winter precipitation in determining heat requirement for spring vegetation green-up at northern middle and high latitudes. Glob Chang Biol 20:3743–3755

  10. Hoffmann AA, Sgro CM (2011) Climate change and evolutionary adaptation. Nature 470(7335):479–485

    Article  Google Scholar 

  11. Inouye DW, Wielgolaski FE (2013) Phenology at high altitudes. In: Phenology: an integrative environmental science. Springer, Netherlands, pp 249–272

  12. Jeong S-J, Medvigy D, Shevliakova E, Malyshev S (2013) Predicting changes in temperate forest budburst using continental-scale observations and models. Geophys Res Lett 40:359–364

    Article  Google Scholar 

  13. Jeong S-J, Medvigy D, Shevliakova E, Malyshev SCG (2012) Uncertainties in terrestrial carbon budgets related to spring phenology. J Geophys Res Biogeosci 117. doi:10.1029/2011JG001868

  14. Jin Z, Zhuang Q, He J-S, Luo T, Shi Y (2013) Phenology shift from 1989 to 2008 on the Tibetan Plateau: an analysis with a process-based soil physical model and remote sensing data. Clim Chang 119:435–449

    Article  Google Scholar 

  15. Körner C (2007) Significance of temperature in plant life. Plant Growth and Climate Change. Blackwell Publishing Ltd, pp. 48–69

  16. Körner C, Paulsen J (2004) A world‐wide study of high altitude treeline temperatures. J Biogeogr 31(5):713–732

    Article  Google Scholar 

  17. Krinner G, Viovy N, de Noblet-Ducoudré N, et al. (2005) A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Glob Biogeochem Cycles 19. doi:10.1029/2003GB002199

  18. Linderholm HW (2006) Growing season changes in the last century. Agric For Meteorol 137:1–14

    Article  Google Scholar 

  19. Maisongrande P, Duchemin B, Dedieu G (2004) VEGETATION/SPOT: an operational mission for the Earth monitoring; presentation of new standard products. Int J Remote Sens 25(1):9–14

    Article  Google Scholar 

  20. Migliavacca M, Galvagno M, Cremonese E, et al. (2011) Using digital repeat photography and eddy covariance data to model grassland phenology and photosynthetic CO2 uptake. Agric For Meteorol 151:1325–1337

    Article  Google Scholar 

  21. Pau S, Wolkovich EM, Cook BI, Davies TJ, Kraft NJB, Bolmgren K, Betancourt JL, Cleland EE (2011) Predicting phenology by integrating ecology, evolution and climate science. Glob Chang Biol 17:3633–3643

    Article  Google Scholar 

  22. Peñuelas J, Filella I (2009) Phenology feedbacks on climate change. Science 324(5929):887–888

    Article  Google Scholar 

  23. Piao S, Cui M, Chen A, Wang X, Ciais P, Liu J, Tang Y (2011) Altitude and temperature dependence of change in the spring vegetation green-up date from 1982 to 2006 in the Qinghai-Xizang Plateau. Agric For Meteorol 151:1599–1608

    Article  Google Scholar 

  24. Piao S, Tan K, Nan H, Ciais P, Fang J, Wang T, Vuichard N, Zhu B (2012) Impacts of climate and CO2 changes on the vegetation growth and carbon balance of Qinghai–Tibetan grasslands over the past five decades. Glob Planet Chang 98–99:73–80

    Article  Google Scholar 

  25. Richardson AD, Anderson RS, Arain MA, et al. (2012) Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program Site Synthesis. Glob Chang Biol 18:566–584

    Article  Google Scholar 

  26. Richardson AD, Keenan TF, Migliavacca M, Ryu Y, Sonnentag O, Toomey M (2013) Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric For Meteorol 169:156–173

    Article  Google Scholar 

  27. Shen M, Piao S, Cong N, Zhang G, Jassens IA (2015) Precipitation impacts on vegetation spring phenology on the Tibetan Plateau. Glob Chang Biol 21:3647–3656

    Article  Google Scholar 

  28. Shen M, Tang Y, Chen J, Yang W (2012) Specification of thermal growing season in temperate China from 1960 to 2009. Clim Chang 114:783–798

    Article  Google Scholar 

  29. Sitch S, Smith B, Prentice IC, et al. (2003) Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Glob Chang Biol 9:161–185

    Article  Google Scholar 

  30. Sokolov AP, Stone PH, Forest CE, et al. (2009) Probabilistic Forecast for Twenty-First-Century Climate Based on Uncertainties in Emissions (Without Policy) and Climate Parameters. J Clim 22:5175–5204

    Article  Google Scholar 

  31. Tang J, Zhuang Q (2011) Modeling soil thermal and hydrological dynamics and changes of growing season in Alaskan terrestrial ecosystems. Clim Chang 107:481–510

    Article  Google Scholar 

  32. Tucker CJ, Pinzon JE, Brown ME, et al. (2005) An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int J Remote Sens 26:4485–4498

    Article  Google Scholar 

  33. Webster M, Sokolov A, Reilly J, et al. (2012) Analysis of climate policy targets under uncertainty. Clim Chang 112:569–583

    Article  Google Scholar 

  34. White MA, Beurs D, Kirsten M, et al. (2009) Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982–2006. Glob Chang Biol 15(10):2335–2359

    Article  Google Scholar 

  35. Yu H, Luedeling E, Xu J (2010) Winter and spring warming result in delayed spring phenology on the Tibetan Plateau. Proc Natl Acad Sci 107:22151–22156

    Article  Google Scholar 

  36. Zhang G, Zhang Y, Dong J, Xiao X (2013) Green-up dates in the Tibetan Plateau have continuously advanced from 1982 to 2011. Proc Natl Acad Sci 110(11):4309–4314

    Article  Google Scholar 

  37. Zhu W, Tian H, Xu X, Pan Y, Chen G, Lin W (2012) Extension of the growing season due to delayed autumn over mid and high latitudes in North America during 1982–2006. Glob Ecol Biogeogr 21(2):260–271

  38. Zhuang Q, Romanovsky VE, McGuire AD (2001) Incorporation of a permafrost model into a large‐scale ecosystem model: Evaluation of temporal and spatial scaling issues in simulating soil thermal dynamics. J Geophys Res-Atmos 106(D24):33649–33670

    Article  Google Scholar 

  39. Zhuang Q, He J, Lu Y, Ji L, Xiao J, Luo T (2010) Carbon dynamics of terrestrial ecosystems on the Tibetan Plateau during the twentieth century: an analysis with a process-based biogeochemical model. Glob Ecol Biogeogr 19:649–662

    Google Scholar 

Download references

Acknowledgments

We thank Zhiyao Tang, Shilong Piao and Yue Shi for helpful comments on the manuscript. This research is supported with a NSF project (DEB- #0919331), the NSF Carbon and Water in the Earth Program (NSF-0630319), the NASA Land Use and Land Cover Change program (NASA-NNX09AI26G), Department of Energy (DE-FG02-08ER64599), and the NSF Division of Information & Intelligent Systems (NSF-1028291), funded to Q.Z.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Qianlai Zhuang.

Electronic supplementary material

ESM 1

(PDF 862 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Jin, Z., Zhuang, Q., Dukes, J.S. et al. Temporal variability in the thermal requirements for vegetation phenology on the Tibetan plateau and its implications for carbon dynamics. Climatic Change 138, 617–632 (2016). https://doi.org/10.1007/s10584-016-1736-8

Download citation

Keywords

  • Root Mean Square Error
  • Normalize Difference Vegetation Index
  • Normalize Difference Vegetation Index Data
  • Vegetation Phenology
  • Thermal Requirement