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.
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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
Chinese Academy of Sciences (2001) Vegetation Atlas of China. Science Press, Beijing
Cleland EE, Allen JM, Crimmins TM, et al. (2012) Phenological tracking enables positive species responses to climate change. Ecology 93:1765–1771
Diez JM, Ibáñez I, Miller-Rushing AJ, et al. (2012) Forecasting phenology: from species variability to community patterns. Ecol Lett 15:545–553
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
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
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
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
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
Hoffmann AA, Sgro CM (2011) Climate change and evolutionary adaptation. Nature 470(7335):479–485
Inouye DW, Wielgolaski FE (2013) Phenology at high altitudes. In: Phenology: an integrative environmental science. Springer, Netherlands, pp 249–272
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
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
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
Körner C (2007) Significance of temperature in plant life. Plant Growth and Climate Change. Blackwell Publishing Ltd, pp. 48–69
Körner C, Paulsen J (2004) A world‐wide study of high altitude treeline temperatures. J Biogeogr 31(5):713–732
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
Linderholm HW (2006) Growing season changes in the last century. Agric For Meteorol 137:1–14
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
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
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
Peñuelas J, Filella I (2009) Phenology feedbacks on climate change. Science 324(5929):887–888
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
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
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
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
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
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
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
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
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
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
Webster M, Sokolov A, Reilly J, et al. (2012) Analysis of climate policy targets under uncertainty. Clim Chang 112:569–583
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
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
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
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
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
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
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.
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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
- Root Mean Square Error
- Normalize Difference Vegetation Index
- Normalize Difference Vegetation Index Data
- Vegetation Phenology
- Thermal Requirement