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Changing Spring Phenology Dates in the Three-Rivers Headwater Region of the Tibetan Plateau during 1960-2013


doi: 10.1007/s00376-017-6296-y

  • The variation of the vegetation growing season in the Three-Rivers Headwater Region of the Tibetan Plateau has recently become a controversial topic. One issue is that the estimated local trend in the start of the vegetation growing season (SOS) based on remote sensing data is easily affected by outliers because this data series is short. In this study, we determine that the spring minimum temperature is the most influential factor for SOS. The significant negative linear relationship between the two variables in the region is evaluated using Moderate Resolution Imaging Spectroradiometer-Normalized Difference Vegetation Index data for 2000-13. We then reconstruct the SOS time series based on the temperature data for 1960-2013. The regional mean SOS shows an advancing trend of 1.42 d (10 yr)-1 during 1960-2013, with the SOS occurring on the 160th and 151st days in 1960 and 2013, respectively. The advancing trend enhances to 6.04 d (10 yr)-1 during the past 14 years. The spatiotemporal variations of the reconstructed SOS data are similar to those deduced from remote sensing data during the past 14 years. The latter exhibit an even larger regional mean trend of SOS [7.98 d (10 yr-1)] during 2000-13. The Arctic Oscillation is found to have significantly influenced the changing SOS, especially for the eastern part of the region, during 2000-13.
    摘要: 近年来, 三江源植被春季物候期变化趋势的研究存在争议. 遥感数据的长度过短, 在反演植被生长季开始日(SOS)变化趋势时, 研究结果容易受到个别年份极端值的影响. 本文采用2000-2013年的MODIS-NDVI时序数列提取生长季开始日(SOS), 并建立SOS与春季温度间的相关关系;再利用1960-2013年春季温度数据重建过去近五十年SOS的时间序列. 在五十年大背景下讨论SOS 近十年的变化, 结果表明:(1)春季最低温度是与三江源SOS相关性最高的气象因子. 1960-2013年间, 研究地区的SOS呈现显著提前趋势, 从1960年的160日提前到2013年的151日, 增长速率为1.42d/10a;(2)2000-2013年间, SOS提前速率加快, 增长至6.04 d/10a. 遥感反演结果与温度重建结果基本一致, 提前速率为7.98 d/10a;(3)北极涛动对三江源地区的SOS有显著影响, 尤其是在研究地区的东部.
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  • Barnett T. P., J. C. Adam, and D. P. Lettenmaier, 2005: Potential impacts of a warming climate on water availability in snow-dominated regions.Nature,438(7066),303-309, https://doi.org/10.1038/nature04141.10.1038/nature0414121f899ee5a2d9135811dbc4dc2392568http%3A%2F%2Ftreephys.oxfordjournals.org%2Fexternal-ref%3Faccess_num%3D16292301%26amp%3Blink_type%3DMEDhttp://www.nature.com/articles/nature04141
    Chen H., Q. A. Zhu, N. Wu, Y. F. Wang, and C.-H. Peng, 2011a: Delayed spring phenology on the Tibetan plateau may also be attributable to other factors than winter and spring warming,Proceedings of the National Academy of Sciences of the United States of America,108(19),E93, .https://doi.org/10.1073/pnas.110009110810.1073/pnas.1100091108214828153db506b686c8906cb328e9c038f40d08http%3A%2F%2Feuropepmc.org%2Farticles%2FPMC3093455http://www.pnas.org/cgi/doi/10.1073/pnas.1100091108In their recent paper “Winter and spring warming result in delayed spring phenology on the Tibetan Plateau,” Yu et al. (1) reported an interesting but unexpected result that spring phenology initiated retreating in the mid-1990s, despite continued warming for grasslands (both steppe and meadow) on the Tibetan Plateau, and shortening the length of the growing season of the steppe together with an advancing end. Although we have not observed the same phenomenon in our own many years of field studies on the eastern edge of the Tibetan Plateau, we believed that there were indeed some complicated yet poorly understood dynamics...
    Chen H. L., Y. J. Liu, Z. X. Du, Z. Y. Liu, and C. H. Zou, 2011b: The change of growing season of the vegetation in Huanghe-Huaihe-Haihe region and its responses to climate changes.Journal of Applied Meteorological Science,22(4),437-444, . (in Chinese with English abstract)https://doi.org/10.3969/j.issn.1001-7313.2011.04.0610.3724/SP.J.1146.2006.01085b18d4ef3739921f3d08a0490052feb0chttp%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTOTAL-YYQX201104008.htmhttp://en.cnki.com.cn/Article_en/CJFDTOTAL-YYQX201104008.htmUsing the each ten-day data of NOAA/AVHRR from 1981 to 2000 and adopting the Maximum-Slope method,Curve-Fitting method and stepwise regression,the beginning and ending of the growth season of the vegetations in the Huanghe-Huaihe-Haihe(HHH) region are analyzed.Meanwhile,the pixel by pixel I_(NDV) map is worked out with typical bands as the sample.Through analyzing the change of green wave and brown wave,the changing rules of vegetation activities and its responses to climate changes in the past 20 years are revealed.The growth season in HHH region starts at the last 10 days of March with I_(NDV)0.19604 and ends at the first 10 days of November with I_(NDV)0.22899 on average.The tendency is that the growing season has prolonged obviously by starting earlier and earlier,while ending later and later from 1982 to 2000.The average I_(NDV) for the vegetation in the researched area in the past 20 years is generally increasing, especially in spring.According to the I_(NDV) map of typical bands as the sample worked out with the pixel by pixel,the green wave changes from south to north during January-July on the band of 116ºE,the maximum of I_(NDV) occurs in August and decreases gradually after September.On the band of 36ºN,to the east and nearby 116ºE,the I_(NDV) has two peaks because of two crops a year,which is obviously different from other regions.The two peaks occur in April and August and they become clearer and clearer from 1982 to 2000,especially in agriculture area.However,the two peaks are not obvious to the north of 38.5ºN,in the area of Beijing,Tianjin and the mountain areas,and the peak value of early summer is lower than that of autumn.In agriculture area,the peak interval of green wave is broadening with the time passing by,and it's more obviously from south to north.With the time passing by,the green wave of the critical value of the growth season in the area changes from south to north,while the emergence of brown wave prolongs from north to south.According to the stepwise regression result of the every ten-day meteorological data and the I_(NDV) of the typical sampling areas in the HHH region,the I_(NDV) of vegetation is closely related to the temperature and precipitation,especially it's more sensitive to the temperature,and the significance of the correlation is 0.01.The prolonging of vegetation growth season is a main response to climate changes of vegetation activities in target areas.
    Chmielewski F.-M., A. Müller, and E. Bruns, 2004: Climate changes and trends in phenology of fruit trees and field crops in Germany,1961-2000. Agricultural and Forest Meteorology,121(1-2),69-78,.https://doi.org/10.1016/S0168-1923(03)00161-810.1016/S0168-1923(03)00161-8c2e6cfa004ed460bc8b8f3f86f8ebf32http%3A%2F%2Feuropepmc.org%2Fabstract%2FAGR%2FIND43620915http://europepmc.org/abstract/AGR/IND43620915Distinct changes in air temperature since the end of the 1980s have led to clear responses in plant phenology in many parts of the world. In Germany phenological phases of the natural vegetation as well as of fruit trees and field crops have advanced clearly in the last decade of the 20th century. The strongest shift in plant development occurred for the very early spring phases. The late spring phases and summer phases reacted also to the increased temperatures, but they usually show lower trends. Until now the changes in plant development are still moderate, so that no strong impacts on yield formation processes were observed. But further climate changes will probably increase the effect on plants, so that in the future stronger impacts on crop yields are likely.
    Cong N., T. Wang, H. J. Nan, Y. C. Ma, X. H. Wang, and R. B. Myneni, 2013: Changes in satellite-derived spring vegetation green-up date and its linkage to climate in China from 1982 to 2010: A multimethod analysis.Global Change Biology,19(3),881-891, https://doi.org/10.1111/gcb.12077.10.1111/gcb.1207723504844df9cf77b53a2a1e59eb695a8270ad414http%3A%2F%2Feuropepmc.org%2Fabstract%2FMED%2F23504844http://doi.wiley.com/10.1111/gcb.2013.19.issue-3Abstract The change in spring phenology is recognized to exert a major influence on carbon balance dynamics in temperate ecosystems. Over the past several decades, several studies focused on shifts in spring phenology; however, large uncertainties still exist, and one understudied source could be the method implemented in retrieving satellite-derived spring phenology. To account for this potential uncertainty, we conducted a multimethod investigation to quantify changes in vegetation green-up date from 1982 to 2010 over temperate China, and to characterize climatic controls on spring phenology. Over temperate China, the five methods estimated that the vegetation green-up onset date advanced, on average, at a rate of 1.3卤0.6days per decade (ranging from 0.4 to 1.9days per decade) over the last 29years. Moreover, the sign of the trends in vegetation green-up date derived from the five methods were broadly consistent spatially and for different vegetation types, but with large differences in the magnitude of the trend. The large intermethod variance was notably observed in arid and semiarid vegetation types. Our results also showed that change in vegetation green-up date is more closely correlated with temperature than with precipitation. However, the temperature sensitivity of spring vegetation green-up date became higher as precipitation increased, implying that precipitation is an important regulator of the response of vegetation spring phenology to change in temperature. This intricate linkage between spring phenology and precipitation must be taken into account in current phenological models which are mostly driven by temperature.
    de Beurs, K. M., G. M. Henebry, 2008: Northern annular mode effects on the land surface phenologies of northern Eurasia.J. Climate,21(17),4257-4279, .http://doi.org/10.1175/2008JCLI2074.110.1175/2008JCLI2074.1f16360a2c51628dac3c03e20b951d489http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2008JCli...21.4257Dhttp://journals.ametsoc.org/doi/abs/10.1175/2008JCLI2074.1Land surface phenology (LSP) is the spatiotemporal development of the vegetated land surface as revealed by synoptic sensors. Modeling LSP across northern Eurasia reveals the magnitude, significance, and spatial pattern of the influence of the northern annular mode. Here the authors fit simple LSP models to two normalized difference vegetation index (NDVI) datasets and calculate the Spearman rank correlations to link the start of the observed growing season (SOS) and the timing of the peak NDVI with the North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) indices. The relationships between the northern annular mode and weather station data, accumulated precipitation derived from the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) dataset, accumulated growing degree-days (AGDDs) derived from the NCEP09“Department of Energy Atmospheric Model Intercomparison Project (AMIP-II) reanalysis, and the number of snow days from the National Snow and Ice Data Center are investigated. The analyses confirm strong relationships between the temporal behavior of temperature and precipitation and large-scale climatic variability across Eurasia. The authors find widespread influence of the northern annular mode (NAM) on the land surface phenologies across northern Eurasia affecting 20009“300 Mha. The tundra ecoregions were especially impacted with significant results for about a quarter of the biome. The influence of the AO was also extensive (>130 Mha) for the boreal forests. The AO appears to affect the Asian part of northern Eurasia more strongly than the NAO, especially for the NDVI peak position as a function of AGDD. Significant responses of vegetation timing to NAO and AO in northeastern Russia have not been as well documented as the seasonal advancement in Europe. The two Advanced Very High Resolution Radiometer NDVI datasets yield fields of LSP model parameter estimates that are more similar in dates of peak position than in dates for SOS and more similar for AO than for NAO. As a result, the authors conclude that peak position appears to be a more robust characteristic of land surface phenology than SOS to link vegetation dynamics to variability and change in regional and global climates.
    Ding M. J., Y. L. Zhang, X. M. Sun, L. S. Liu, Z. F. Wang, and W. Q. Bai, 2013: Spatiotemporal variation in alpine grassland phenology in the Qinghai-Tibetan plateau from 1999 to 2009.Chinese Science Bulletin,58(3),396-405, https://doi.org/10.1007/s11434-012-5407-5.10.1007/s11434-012-5407-58f7203b24d657bb461eee48d8641400ehttp%3A%2F%2Fkns.cnki.net%2FKCMS%2Fdetail%2Fdetail.aspx%3Ffilename%3Djxtw201303013%26dbname%3DCJFD%26dbcode%3DCJFQhttp://link.springer.com/10.1007/s11434-012-5407-5
    Hu M. Q., F. Mao, H. Sun, and Y. Y. Hou, 2011: Study of normalized difference vegetation index variation and its correlation with climate factors in the three-river-source region.International Journal of Applied Earth Observation and Geoinformation,13(1),24-33, 2010. 06. 003.https://doi.org/10.1016/j.jag.10.1016/j.jag.2010.06.00391642f1ffbe023d68f55a88324aa32cdhttp%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS030324341000067Xhttp://linkinghub.elsevier.com/retrieve/pii/S030324341000067XUsing NOAA/AVHRR 10-day composite NDVI data and 10-day meteorological data, including air temperature, precipitation, vapor pressure, wind velocity and sunshine duration, at 19 weather stations in the three-river-source region in the Qinghai揟ibetan Plateau in China from 1982 to 2000, the variations of NDVI and climate factors were analyzed for the purpose of studying the correlation between climate change and vegetation growth as represented by NDVI in this region. Results showed that the NDVI values in this region gradually grew from the west to the east, and the distribution was consistent with that of moisture status. The growing season came earlier due to climate warming, yet because of the reduction of precipitation, maximal NDVI during 19822000 did not show a significant change. NDVI related positively to air temperature, vapor pressure and precipitation, but negatively related to sunshine duration and wind velocity. Furthermore, the response of NDVI to climate change showed time lags for different climate factors. Water condition and temperature were found to be the most important factors effecting the variation of NDVI during the growing season in both the semi-arid and the semi-humid areas. In addition, NDVI had a better correlation with vapor pressure than with precipitation. The ratio of precipitation to evapotranspiration, representing water gain and loss, can be regarded as a comprehensive index to analyze NDVI and climate change, especially in areas where the water condition plays a dominant role.
    Huang N. E., Z. H. Wu, 2008: A review on Hilbert-Huang transform: Method and its applications to geophysical studies,Rev. Geophys.,46(2),RG2006, https://doi.org/10.1029/2007RG000228.10.1029/2007RG0002281277e5d500146bd6f489c978eba601d0http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2007RG000228%2Ffullhttp://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.470.130[1] Data analysis has been one of the core activities in scientific research, but limited by the availability of analysis methods in the past, data analysis was often relegated to data processing. To accommodate the variety of data generated by nonlinear and nonstationary processes in nature, the analysis method would have to be adaptive. Hilbert-Huang transform, consisting of empirical mode decomposition and Hilbert spectral analysis, is a newly developed adaptive data analysis method, which has been used extensively in geophysical research. In this review, we will briefly introduce the method, list some recent developments, demonstrate the usefulness of the method, summarize some applications in various geophysical research areas, and finally, discuss the outstanding open problems. We hope this review will serve as an introduction of the method for those new to the concepts, as well as a summary of the present frontiers of its applications for experienced research scientists.
    Huang, N. E., Coauthors, 1998: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis.Proc. Roy. Soc. A,454(1971),903-995, 1998. 0193.https://doi.org/10.1098/rspa.10.1098/rspa.1998.0193a50afd109a1b2bc2eea6bfae8c24a775http%3A%2F%2Fbioinformatics.oxfordjournals.org%2Fexternal-ref%3Faccess_num%3D10.1098%2Frspa.1998.0193%26amp%3Blink_type%3DDOIhttp://rspa.royalsocietypublishing.org/cgi/doi/10.1098/rspa.1998.0193A new method for analysing nonlinear and non-stationary data has been developed. The key part of the method is the 'empirical mode decomposition' method with which any complicated data set can be decomposed into a finite and often small number of 'intrinsic mode functions' that admit well-behaved Hilbert transforms. This decomposition method is adaptive, and, therefore, highly efficient. Since the decomposition is based on the local characteristic time scale of the data, it is applicable to nonlinear and non-stationary processes. With the Hilbert transform, the 'instrinic mode functions' yield instantaneous frequencies as functions of time that give sharp identifications of imbedded structures. The final presentation of the results is an energy-frequency-time distribution, designated as the Hilbert spectrum. In this method, the main conceptual innovations are the introduction of 'intrinsic mode functions' based on local properties of the signal, which makes the instantaneous frequency meaningful; and the introduction of the instantaneous frequencies for complicated data sets, which eliminate the need for spurious harmonics to represent nonlinear and non-stationary signals. Examples from the numerical results of the classical nonlinear equation systems and data representing natural phenomena are given to demonstrate the power of this new method. Classical nonlinear system data are especially interesting, for they serve to illustrate the roles played by the nonlinear and non-stationary effects in the energy-frequency-time distribution.
    Jeong S.-J., C.-H. Ho, H.-J. Gim, and M. E. Brown, 2011: Phenology shifts at start vs.end of growing season in temperate vegetation over the northern hemisphere for the period 1982-2008. Global Change Biology,17(7),2385-2399, https://doi.org/10.1111/j.1365-2486.2011.02397.x.
    Kong D. D., Q. Zhang, W. L. Huang, and X. H. Gu, 2017: Vegetation phenology change in Tibetan Plateau from 1982 to 2013 and its related meteorological factors.Acta Geographica Sinica,72(1),39-52, 11821/dlxb 201701004. (in Chinese with English abstract)https://doi.org/10.e488e54625951a848de9d699450808efhttp%3A%2F%2Fwww.en.cnki.com.cn%2FArticle_en%2FCJFDTotal-DLXB201701005.htmhttp://www.en.cnki.com.cn/Article_en/CJFDTotal-DLXB201701005.htmUsing NDVI3 g vegetation index, we defined 18 phenological metrics to investigate phenology change in the Tibetan Plateau(TP). Considering heterogeneity of vegetation phenology, we divided TP into 8 vegetation clusters according to 1:1000000 vegetation cluster map. Using partial least regression(PLS) method, we investigated impacts of climate variables such as temperature, precipitation and solar radiation on vegetation phenology. Results indicated that:(1) Turning points of the date of the start of growing season(SOS) metrics are mainly observed during 1997-2000, before which SOS advanced 2-3 d/a. Turning points of the date of the end of growing season(EOS) and length of growing season(LOS) metrics are found during 2005 and 2004- 2007, respectively. Before the turning point, EOS has a delayed tendency of 1- 2 d/10 a, and LOS has a lengthening tendency of 1- 2 d/10 a. After the turning point, the tendency of SOS and EOS metrics is questionable. Meanwhile, lengthening of LOS is not statistically significant;(2) Alpine meadows and alpine shrub meadows are subject to the most remarkable changes. Lengthening LOS of alpine meadow is mainly due to advanced SOS and delayed EOS. Nevertheless, lengthening LOS of alpine shrub meadow is attributed mainly to advanced SOS;(3) Using PLS method, we quantified impacts of meteorological variables such as temperature, precipitation and solar radiation on phenology changes of alpine meadows and alpine shrub meadows, indicating that temperature is the dominant meteorological factor affecting vegetation phenology. In these two regions, autumn of last year and early winter temperature of last year have a positive effect on SOS. Firstly, increased temperature in this period would postpone last year's EOS, and hence indirectly delay SOS of the current year;Secondly, warming autumn and early winter have the potential to negatively impact fulfilment of chilling requirements, leading to delay of SOS. Except summer, minimum temperature has a similar effect on vegetation phenology, when compared to average and maximum temperature.Furthermore, precipitation effects on phenology fluctuate widely across different months.Precipitation of the autumn and winter/spring of the last year has a negative/positive effect on SOS. Besides, precipitation acts as the key driver constraining vegetation growth in August,during which precipitation has a positive impact on EOS. Therefore, solar radiation can exert impacts on vegetation phenology mainly during summer and early fall. Our research will provide a scientific support for the improvement of vegetation phenology model.
    Li Z., Z.-W. Yan, 2009: Homogenized daily mean/maximum/ minimum temperature series for China from 1960-2008.Atmospheric and Oceanic Science Letters,2(4),237-243, 2009. 11446802.https://doi.org/10.1080/16742834.10.1080/16742834.2009.11446802187b89cedb46d974f3418e6ab2a9a247http%3A%2F%2Fcpfd.cnki.com.cn%2FArticle%2FCPFDTOTAL-ZGQX200910004018.htmhttp://www.tandfonline.com/doi/full/10.1080/16742834.2009.11446802Inhomogeneities in the daily mean/maximum/minimum temperature (Tm/Tmax/Tmin) series from 1960-2008 at 549 National Standard Stations (NSSs) in China were analyzed by using the Multiple Analysis of Series for Homogenization (MASH) software package. Typical biases in the dataset were illustrated via the cases of Beijing (BJ), Wutaishan (WT), 眉r眉mqi (UR) and Henan (HN) stations. The homogenized dataset shows a mean warming trend of 0.261/0.193/0.344oC/decade for the annual series of Tm/Tmax/Tmin, slightly smaller than that of the original dataset by 0.006/0.009/0.007oC/decade. However, considerable differences between the adjusted and original datasets were found at the local scale. The adjusted Tmin series shows a significant warming trend almost everywhere for all seasons, while there are a number of stations with an insignificant trend in the original dataset. The adjusted Tm data exhibit significant warming trends annually as well as for the autumn and winter seasons in northern China, and cooling trends only for the summer in the middle reaches of the Yangtze River and parts of central China and for the spring in southwestern China, while the original data show cooling trends at several stations for the annual and seasonal scales in the Qinghai, Shanxi, Hebei, and Xinjiang provinces. The adjusted Tmax data exhibit cooling trends for summers at a number of stations in the mid-lower reaches of the Yangtze and Yellow Rivers and for springs and winters at a few stations in southwestern China, while the original data show cooling trends at three/four stations for the annual/autumn periods in the Qinghai and Yunnan provinces. In general, the number of stations with a cooling trend was much smaller in the adjusted Tm and Tmax dataset than in the original dataset. The cooling trend for summers is mainly due to cooling in August. The results of homogenization using MASH appear to be robust; in particular, different groups of stations with consideration of elevation led to minor effects in the results.
    Li Z., Z. W. Yan, 2010: Application of multiple analysis of series for homogenization to Beijing daily temperature series (1960-2006).Adv. Atmos. Sci.,27(4),777-787, https://doi.org/10.1007/s00376-00909052-0.10.1007/s00376-009-9052-08d19ce207392cf29f51767f64dcf41edhttp%3A%2F%2Fd.wanfangdata.com.cn%2FPeriodical%2Fdqkxjz-e201004006http://link.springer.com/10.1007/s00376-009-9052-0Homogenization of climate observations remains a challenge to climate change researchers, especially in cases where metadata (e.g., probable dates of break points) are not always available. To examine the inffuence of metadata on homogenizing climate data, the authors applied the recently developed Multiple Analysis of Series for Homogenization (MASH) method to the Beijing (BJ) daily temperature series for 1960- 2006 in three cases with different references: (1) 13M-considering metadata at BJ and 12 nearby stations; (2) 13NOM-considering the same 13 stations without metadata; and (3) 21NOM-considering 20 further stations and BJ without metadata. The estimated mean annual, seasonal, and monthly inhomogeneities are similar between the 13M and 13NOM cases, while those in the 21NOM case are slightly different. The detected biases in the BJ series corresponding to the documented relocation dates are as low as -0.71~0C, -0.79~0C, and -0.5~0C for the annual mean in the 3 cases, respectively. Other biases, including those undocumented in metadata, are minor. The results suggest that any major inhomogeneity could be detected via MASH, albeit with minor differences in estimating inhomogeneities based on the different references. The adjusted annual series showed a warming trend of 0.337, 0.316, and 0.365~0C (10 yr)~(-1) for the three cases, respectively, smaller than the estimate of 0.453~0C (10 yr)~(-1) in the original series, mainly due to the relocation-induced biases. The impact of the MASH-type homogenization on estimates of climate extremes in the daily temperature series is also discussed.
    Linderholm H. W., 2006: Growing season changes in the last century.Agricultural and Forest Meteorology,137(1-2),1-14, 2006. 03. 006.https://doi.org/10.1016/j.agrformet.10.1016/j.agrformet.2006.03.006276fceb15928dfa4a8b7356c19c04a71http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0168192306000931http://linkinghub.elsevier.com/retrieve/pii/S0168192306000931An increasing number of studies have reported on shifts in timing and length of the growing season, based on phenological, satellite and climatological studies. The evidence points to a lengthening of the growing season of ca. 1020 days in the last few decades, where an earlier onset of the start is most prominent. This extension of the growing season has been associated with recent global warming. Changes in the timing and length of the growing season (GSL) may not only have far reaching consequences for plant and animal ecosystems, but persistent increases in GSL may lead to long-term increases in carbon storage and changes in vegetation cover which may affect the climate system. This paper reviews the recent literature concerned with GSL variability.
    Liu G. H., Q. H. Tang, X. C. Liu, J. H. Dai, X. Z. Zhang, Q. S. Ge, and Y. Tang, 2014b: Spatiotemporal analysis of ground-based woody plant leafing in response to temperature in temperate eastern China.International Journal of Biometeorology,58(7),1583-1592, https://doi.org/10.1007/s00484-013-0762-8.10.1007/s00484-013-0762-82425366507da745aa0528f7024427292acc61074http%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fpubmed%2F24253665http://link.springer.com/10.1007/s00484-013-0762-8The analysis of woody plant leafing in response to regional-scale temperature variation using ground-based phenology is usually limited by the sparse coverage and missing data of ground observation. In this study, a station-based multispecies method was proposed to generate spatiotemporal variation of woody plant leafing date using ground observations from the Chinese Phenological Observation Network during 1974–1996. The results show that the leafing date had slightly insignificant advance (610.5602day02decade 611 ), and the Arctic Oscillation (AO) index could explain 3602% variance of the spring leafing date anomaly. The leafing date had been substantially delayed (402days) when AO shifted from an extreme high index state (2) in 1989–1990 to a relatively low state (0.1) in 1991–1996. The canonical correlation analysis (CCA) was used to demonstrate the temporal evolutions and spatial structures of interannual variations of the spring temperature and leafing date anomalies. The three CCA spatial patterns of leafing date anomaly are similar to those of spring temperature anomaly. The first spatial pattern shows ubiquitous warming, which is consistent with the ubiquitous advance in leafing date across the study area. The second and third spatial patterns present the regional differences featured by advanced (delayed) leafing associated with high (low) temperature. The results suggest that the spring leafing date anomaly is spatiotemporally coherent with the regional-scale temperature variations. Although we focus here on woody plant leafing in a historical period in temperate eastern China, our station-based multispecies method may be applicable to analysis of the ground-based phenology in response to regional-scale climatic variation in other regions.
    Liu L. M., Y. T. Liang, H. Y. Ma, and J. Huang, 2004: Relationship research between MODIS-NDVI and AVHRR-NDVI.Geomatics and Information Science of Wuhan University,29,307-310, 2004. 04. 006. (in Chinese with English abstract)https://doi.org/10.3321/j.issn:1671-8860.2eaf5b3f0e6c48c43a778d6bb763aaa1http%3A%2F%2Fen.cnki.com.cn%2Farticle_en%2Fcjfdtotal-whch200404006.htmhttp://en.cnki.com.cn/article_en/cjfdtotal-whch200404006.htmThis paper analyzes the relationship between MODIS-NDVI and AVHRR-NDVI of the same region at different time,based on Histogram and Feather Space methods. The results show that their shapes are the same, but MODIS NDVIs have more sensitivity to vegetation, and the range of their values is wider than that of NOAA-AVHRR. They have no strong correlations. It is not a good idea to use AVHRR NDVI directly as MODIS history NDVI.
    Liu X. F., X. F. Zhu, W. Q. Zhu, Y. Z. Pan, C. Zhang, and D. H. Zhang, 2014a: Changes in spring phenology in the three-rivers headwater region from 1999 to 2013.Remote Sensing,6(9),9130-9144, https://doi.org/10.3390/rs6099130.10.3390/rs6099130cbf26224a0f60676d4a896a0782dd560http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2014RemS....6.9130Lhttp://www.mdpi.com/2072-4292/6/9/9130/Vegetation phenology is considered a sensitive indicator of terrestrial ecosystem response to global climate change. We used a satellite-derived normalized difference vegetation index to investigate the spatiotemporal changes in the green-up date over the Three-Rivers Headwater Region (TRHR) from 1999 to 2013 and characterized their driving forces using climatic data sets. A significant advancement trend was observed throughout the entire study area from 1999 to 2013 with a linear tendency of 6.3 days/decade (p lt; 0.01); the largest advancement trend was over the Yellow River source region (8.6 days/decade, p lt; 0.01). Spatially, the green-up date increased from the southeast to the northwest, and the green-up date of 87.4% of pixels fell between the 130th and 150th Julian day. Additionally, about 91.5% of the study area experienced advancement in the green-up date, of which 80.2%, mainly distributed in areas of vegetation coverage increase, experienced a significant advance. Moreover, it was found that the green-up date and its trend were significantly correlated with altitude. Statistical analyses showed that a 1-ºC increase in spring temperature would induce an advancement in the green-up date of 4.2 days. We suggest that the advancement of the green-up date in the TRHR might be attributable principally to warmer and wetter springs.
    Lloyd D., 1990: A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery.Int. J. Remote Sens.,11(12),2269-2279, 1080/ 01431169008955174.https://doi.org/10.10.1080/0143116900895517471339339162a494d3770039b4f760640http%3A%2F%2Fwww.tandfonline.com%2Fdoi%2Fabs%2F10.1080%2F01431169008955174http://www.tandfonline.com/doi/abs/10.1080/01431169008955174The imaging frequency and synoptic coverage of the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) make possible for the first time a phenological approach to vegetation cover classification in which classes are defined in terms of the timing, the duration and the intensity of photosynthetic activity. This approach, which exploits the strong, approximately linear relationship between the amount of solar irradiance absorbed by plant pigments and shortwave vegetation indices calculated from red and near-infrared reflectances, involves a supervised binary decision tree classification of phytophenological variables derived from multidate normalized difference vegetation index (NDVI) imagery. A global phytophenological classification derived from NOAA global vegetation index imagery is presented and discussed. Although interpretation of the various classes is limited considerably by the quality of global vegetation index imagery, the data show clearly the marked temporal asymmetry of terrestrial photosynthetic activity.
    Luo C. F., J. Wang, M. L. Liu, and Z. J. Liu, 2014: Analysis on the change of grassland coverage in the source region of three rivers during 2000-2012,IOP Conference Series: Earth and Environmental Science,Vol. 17,No. 1, p. 012062., IOP Publishing.10.1088/1755-1315/17/1/012062c95df6f7f9eb749d1aa1a9386a429d23http%3A%2F%2Fwww.ingentaconnect.com%2Fcontent%2Fiop%2Fees%2F2014%2F00000017%2F00000001%2Fart012062http://www.ingentaconnect.com/content/iop/ees/2014/00000017/00000001/art012062The Source Region of Three Rivers (SRTR) has very important ecological functions which form an ecological security barrier for China's Qinghai-Tibet plateau. As the biggest nationally occuring nature reserve region in China, the ecological environment here is very fragile. In SRTR the grassland coverage is an effective detector to reflect the ecological environment condition, because it records the changing process of climatic and environmental sensitively. In recent years SRTR has been suffering pressures from both nature and social pressures. With MODIS data the study monitored the grassland coverage continuously in SRTR from 2000 to 2012. The density-model was adapted to estimate grassland coverage degree firstly. Then the degree of change and the change intensity, change type were used to judge the grassland coverage change trend comprehensively. For grassland coverage there was natural change annual or within the year, and the degree of change was used to judge if there was change or not. The grassland has another important characteristic, annual fluctuation, and it can be differed from sustained changes with change type. For grassland coverage, such continuous change, like improvement or degradation, and to what extent, has more guidance sense on specific production practice. On the base of change type and degree of change, change intensity was used to identify the change trend of the grassland coverage. The analysis results from our study show that steady state and fluctuation are two main change trends for the vegetation coverage in SRTR from 2000 to 2012. The conclusion of this paper can provide references in response to environment change research and in the regional ecological environmental protection project in SRTR
    Peng, S. B., Coauthors, 2004: Rice yields decline with higher night temperature from global warming.Proceedings of the National Academy of Sciences of the United States of America,101(27),9971-9975, https://doi.org/10.1073/pnas.0403720101.10.1073/pnas.040372010115226500d6882a296063c987a7101a3759a6d444http%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fpubmed%2F15226500%2Fhttp://www.pnas.org/cgi/doi/10.1073/pnas.0403720101The impact of projected global warming on crop yields has been evaluated by indirect methods using simulation models. Direct studies on the effects of observed climate change on crop growth and yield could provide more accurate information for assessing the impact of climate change on crop production. We analyzed weather data at the International Rice Research Institute Farm from 1979 to 2003 to examine temperature trends and the relationship between rice yield and temperature by using data from irrigated field experiments conducted at the International Rice Research Institute Farm from 1992 to 2003. Here we report that annual mean maximum and minimum temperatures have increased by 0.35ºC and 1.13ºC, respectively, for the period 1979-2003 and a close linkage between rice grain yield and mean minimum temperature during the dry cropping season (January to April). Grain yield declined by 10% for each 1ºC increase in growing-season minimum temperature in the dry season, whereas the effect of maximum temperature on crop yield was insignificant. This report provides a direct evidence of decreased rice yields from increased nighttime temperature associated with global warming.
    Peterson B. J., R. M. Holmes, J. W. McClelland , C. J. Vörösmarty, R. B. Lammers, A. I. Shiklomanov, I. A. Shiklomanov, and S. Rahmstorf, 2002: Increasing river discharge to the arctic ocean.Science,298(5601),2171-2173, https://doi.org/10.1126/science.1077445.10.1126/science.1077445ecbf790f46deb3c9f67db785418dcdf1http%3A%2F%2Fwww.jstor.org%2Fstable%2F10.2307%2F3833063%3FSearch%3Dyes%26amp%3BresultItemClick%3Dtrue%26amp%3BsearchText%3Dau%3A%26amp%3BsearchText%3D%22Alexander%2520I.%2520Shiklomanov%22%26amp%3BsearchUri%3D%252Faction%252FdoBasicSearch%253Fymod%253DYour%252Binbound%252Blink%252Bdid%252Bnot%252Bhave%252Ban%252Bexact%252Bmatch%252Bin%252Bour%252Bdatabase.%252BBut%252Bbased%252Bon%252Bthe%252Belements%252Bwe%252Bcould%252Bmatch%25252C%252Bwe%252Bhave%252Breturned%252Bthe%252Bfollowing%252Bresults.%2526amp%253BQuery%253Dau%253A%252522Alexander%252BI.%252BShiklomanov%252522%2526amp%253Bsi%253D1http://www.sciencemag.org/cgi/doi/10.1126/science.1077445
    Piao S. L., J. Y. Fang, L. M. Zhou, P. Ciais, and B. Zhu, 2006: Variations in satellite-derived phenology in China's temperate vegetation.Global Change Biology,12(4),672-685, https://doi.org/10.1111/j.1365-2486.2006.01123.x.10.1111/j.1365-2486.2006.01123.x097865fa2592ff1b76b65dbaed3e2080http%3A%2F%2Fci.nii.ac.jp%2Fnaid%2F30014844016http://www.blackwell-synergy.com/toc/gcb/12/4The relationship between vegetation phenology and climate is a crucial topic in global change research because it indicates dynamic responses of terrestrial ecosystems to climate changes. In this study, we investigate the possible impact of recent climate changes on growing season duration in the temperate vegetation of China, using the advanced very high resolution radiometer (AVHRR)/normalized difference vegetation index (NDVI) biweekly time-series data collected from January 1982 to December 1999 and concurrent mean temperature and precipitation data. The results show that over the study period, the growing season duration has lengthened by 1.16 days yr 611 in temperate region of China. The green-up of vegetation has advanced in spring by 0.79 days yr 611 and the dormancy delayed in autumn by 0.37 days yr 611 . The dates of onset for phenological events are most significantly related with the mean temperature during the preceding 2–3 months. A warming in the early spring (March to early May) by 1°C could cause an earlier onset of green-up of 7.5 days, whereas the same increase of mean temperature during autumn (mid-August through early October) could lead to a delay of 3.8 days in vegetation dormancy. Variations in precipitation also influenced the duration of growing season, but such influence differed among vegetation types and phenological phases.
    Piao S. L., P. Friedlingstein, P. Ciais, N. Viovy, and J. Demarty, 2007: Growing season extension and its impact on terrestrial carbon cycle in the Northern Hemisphere over the past 2 decades. Global Biogeochemical Cycles 21(3), https://doi.org/10.1029/2006GB002888.10.1029/2006GB00288819def0f1cafa3dc3ee4e886c6910a768http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2006GB002888%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1029/2006GB002888/fullA number of studies have suggested that the growing season duration has significantly lengthened during the past decades, but the connections between phenology variability and the terrestrial carbon (C) cycle are far from clear. In this study, we used the "ORganizing Carbon and Hydrology In Dynamic Ecosystems" (ORCHIDEE) process based ecosystem model together with observed climate data to investigate spatiotemporal changes in phenology and their impacts on carbon fluxes in the Northern Hemisphere (>25ºN) during 1980-2002. We found that the growing season length (GSL) has increased by 0.30 days yr(R= 0.27, P = 0.010), owing to the combination of an earlier onset in spring (0.16 days yr) and a later termination in autumn (0.14 days yr). Trends in the GSL are however highly variable across the regions. In Eurasia, there is a significant trend toward earlier vegetation green-up with an overall advancement rate of 0.28 days yr(R= 0.32, P = 0.005), while in North America there is a significantly delayed vegetation senescence by 0.28 days yr(R= 0.26, P = 0.013) during the study period. Our results also suggested that the GSL strongly correlates with annual gross primary productivity (GPP) and net primary productivity (NPP), indicating that longer growing seasons may eventually enhance vegetation growth. A 1-day extension in GSL leads to an increase in annual GPP of 5.8 gC myr(or 0.6% per day), and an increase in NPP of 2.8 gC myrper day. However, owing to enhanced soil carbon decomposition accompanying the GPP increase, a change in GSL correlates only poorly with a change in annual net ecosystem productivity (NEP).
    Piao S. L., M. D. Cui, A. P. Chen, X. H. Wang, P. Ciais, J. Liu, and Y. H. Tang, 2011a: Altitude and temperature dependence of change in the spring vegetation green-up date from 1982 to 2006 in the Qinghai-Xizang plateau.Agricultural and Forest Meteorology,151(12),1599-1608, 2011. 06. 016.https://doi.org/10.1016/j.agrformet.10.1016/j.agrformet.2011.06.016bb8d27819eded445f140b69c421bf055http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0168192311002097http://linkinghub.elsevier.com/retrieve/pii/S0168192311002097Research in phenology change has been one heated topic of current ecological and climate change study. In this study, we use satellite derived NDVI (Normalized Difference Vegetation Index) data to explore the spatio-temporal changes in the timing of spring vegetation green-up in the Qinghai-Xizang (Tibetan) Plateau from 1982 to 2006 and to characterize their relationship with elevation and temperature using concurrent satellite and climate data sets. At the regional scale, no statistically significant trend of the vegetation green-up date is observed during the whole study period ( R 2 02=020.00, P 02=020.95). Two distinct periods of green-up changes are identified. From 1982 to 1999, the vegetation green-up significantly advanced by 0.8802days02year 611 ( R 2 02=020.56, P 02<020.001). In contrast, from 1999 to 2006, a marginal delaying trend is evidenced ( R 2 02=020.44, P 02=020.07), suggesting that the persistent trend towards earlier vegetation green-up in spring between 1980s and 1990s was stalled during the first decade of this century. This shift in the tendency of the vegetation green-up seems to be related to differing temperature trends between these two periods. Statistical analysis shows that the average onset of vegetation green-up over the Qinghai-Xizang Plateau would advance by about 4.1 days in response to 102°C increase of spring temperature. In addition, results from our analysis indicate that the spatial patterns of the vegetation green-up date and its change since 1982 are altitude dependent. The magnitude of the vegetation green-up advancement during 1982–1999, and of its postponement from 1999 to 2006 significantly increases along an increasing elevation gradient.
    Piao S. L., X. H. Wang, P. Ciais, B. Zhu, T. Wang, and J. Liu, 2011b: Changes in satellite-derived vegetation growth trend in temperate and boreal Eurasia from 1982 to 2006.Global Change Biology,17(10),3228-3239, https://doi.org/10.1111/j.1365-2486.2011.02419.x.10.1111/j.1365-2486.2011.02419.xc10259f80234c1de2841f456097043c3http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1111%2Fj.1365-2486.2011.02419.x%2Ffullhttp://doi.wiley.com/10.1111/gcb.2011.17.issue-10Abstract Monitoring changes in vegetation growth has been the subject of considerable research during the past several decades, because of the important role of vegetation in regulating the terrestrial carbon cycle and the climate system. In this study, we combined datasets of satellite-derived Normalized Difference Vegetation Index (NDVI) and climatic factors to analyze spatio-temporal patterns of changes in vegetation growth and their linkage with changes in temperature and precipitation in temperate and boreal regions of Eurasia (> 23.5°N) from 1982 to 2006. At the continental scale, although a statistically significant positive trend of average growing season NDVI is observed (0.5 × 10 613 year 611 , P = 0.03) during the entire study period, there are two distinct periods with opposite trends in growing season NDVI. Growing season NDVI has first significantly increased from 1982 to 1997 (1.8 × 10 613 year 611 , P < 0.001), and then decreased from 1997 to 2006 (611.3 × 10 613 year 611 , P = 0.055). This reversal in the growing season NDVI trends over Eurasia are largely contributed by spring and summer NDVI changes. Both spring and summer NDVI significantly increased from 1982 to 1997 (2.1 × 10 613 year 611 , P = 0.01; 1.6 × 10 613 year 611 P < 0.001, respectively), but then decreased from 1997 to 2006, particularly summer NDVI which may be related to the remarkable decrease in summer precipitation (612.7mmyr 611 , P = 0.009). Further spatial analyses supports the idea that the vegetation greening trend in spring and summer that occurred during the earlier study period 1982–1997 was either stalled or reversed during the following study period 1997–2006. But the turning point of vegetation NDVI is found to vary across different regions.
    Qian C., Z. H. Wu, X. B. Fu, and T. J. Zhou, 2010: On multi-timescale variability of temperature in China in modulated annual cycle reference frame.Adv. Atmos. Sci.,27,1169-1182, https://doi.org/10.1007/s00376-009-9121-4.10.1007/s00376-009-9121-470dba7d494ca54ecb5e774521669d7bfhttp%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2Fs00376-009-9121-4http://link.springer.com/10.1007/s00376-009-9121-4The traditional anomaly (TA) reference frame and its corresponding anomaly for a given data span changes with the extension of data length. In this study, the modulated annual cycle (MAC), instead of the widely used climatological mean annual cycle, is used as an alternative reference frame for computing climate anomalies to study the multi-timescale variability of surface air temperature (SAT) in China based on homogenized daily data from 1952 to 2004. The Ensemble Empirical Mode Decomposition (EEMD) method is used to separate daily SAT into a high frequency component, a MAC component, an interannual component, and a decadal-to-trend component. The results show that the EEMD method can reflect historical events reasonably well, indicating its adaptive and temporally local characteristics. It is shown that MAC is a temporally local reference frame and will not be altered over a particular time span by an extension of data length, thereby making it easier for physical interpretation. In the MAC reference frame, the low frequency component is found more suitable for studying the interannual to longer timescale variability (ILV) than a 13-month window running mean, which does not exclude the annual cycle. It is also better than other traditional versions (annual or summer or winter mean) of ILV, which contains a portion of the annual cycle. The analysis reveals that the variability of the annual cycle could be as large as the magnitude of interannual variability. The possible physical causes of different timescale variability of SAT in China are further discussed.
    Reed B. C., J. F. Brown, D. Vand erZee, T. R. Loveland , J. W. Merchant, and D. O. Ohlen, 1994: Measuring phenological variability from satellite imagery.Journal of Vegetation Science,5(5),703-714, https://doi.org/10.2307/3235884.10.2307/323588488e4d48ff8881553002c0528f84b7bf5http%3A%2F%2Fwww.jstor.org%2Fstable%2F3235884http://doi.wiley.com/10.2307/3235884Abstract Abstract. Vegetation phenological phenomena are closely related to seasonal dynamics of the lower atmosphere and are therefore important elements in global models and vegetation monitoring. Normalized difference vegetation index (NDVI) data derived from the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer (AVHRR) satellite sensor offer a means of efficiently and objectively evaluating phenological characteristics over large areas. Twelve metrics linked to key phenological events were computed based on time-series NDVI data collected from 1989 to 1992 over the conterminous United States. These measures include the onset of greenness, time of peak NDVI, maximum NDVI, rate of greenup, rate of senescence, and integrated NDVI. Measures of central tendency and variability of the measures were computed and analyzed for various land cover types. Results from the analysis showed strong coincidence between the satellite-derived metrics and predicted phenological characteristics. In particular, the metrics identified interannual variability of spring wheat in North Dakota, characterized the phenology of four types of grasslands, and established the phenological consistency of deciduous and coniferous forests. These results have implications for large-area land cover mapping and monitoring. The utility of remotely sensed data as input to vegetation mapping is demonstrated by showing the distinct phenology of several land cover types. More stable information contained in ancillary data should be incorporated into the mapping process, particularly in areas with high phenological variability. In a regional or global monitoring system, an increase in variability in a region may serve as a signal to perform more detailed land cover analysis with higher resolution imagery.
    Root T. L., J. T. Price, K. R. Hall, S. H. Schneider, C. Rosenzweig, and J. A. Pounds, 2003: Fingerprints of global warming on wild animals and plants.Nature,421(6918),57-60, https://doi.org/10.1038/nature01333.10.1038/nature01333125119523f450e5d0989e54c8735d952626c2b9dhttp%3A%2F%2Feuropepmc.org%2Fabstract%2Fmed%2F12511952http://www.nature.com/doifinder/10.1038/nature01333Over the past 100 years, the global average temperature has increased by approximately 0.6 degrees C and is projected to continue to rise at a rapid rate. Although species have responded to climatic changes throughout their evolutionary history, a primary concern for wild species and their ecosystems is this rapid rate of change. We gathered information on species and global warming from 143 studies for our meta-analyses. These analyses reveal a consistent temperature-related shift, or 'fingerprint', in species ranging from molluscs to mammals and from grasses to trees. Indeed, more than 80% of the species that show changes are shifting in the direction expected on the basis of known physiological constraints of species. Consequently, the balance of evidence from these studies strongly suggests that a significant impact of global warming is already discernible in animal and plant populations. The synergism of rapid temperature rise and other stresses, in particular habitat destruction, could easily disrupt the connectedness among species and lead to a reformulation of species communities, reflecting differential changes in species, and to numerous extirpations and possibly extinctions.
    Shen M. G., 2011: Spring phenology was not consistently related to winter warming on the Tibetan Plateau.Proceedings of the National Academy of Sciences of the United States of America,108(19),E91-E92, https://doi.org/10.1073/pnas.1018390108.10.1073/pnas.101839010821482816dc97b218eacbd7f25e975f31f951ff58http%3A%2F%2Feuropepmc.org%2Farticles%2FPMC3093521%2Fhttp://www.pnas.org/cgi/doi/10.1073/pnas.1018390108ABSTRACT Not Available Bibtex entry for this abstract Preferred format for this abstract (see Preferences) Find Similar Abstracts: Use: Authors Title Return: Query Results Return items starting with number Query Form Database: Astronomy Physics arXiv e-prints
    Shen M. G., Z. Z. Sun, S. P. Wang, G. X. Zhang, W. D. Kong, A. P. Chen, and S. L. Piao, 2013: No evidence of continuously advanced green-up dates in the Tibetan Plateau over the last decade,Proceedings of the National Academy of Sciences of the United States of America,110(26),E2329, 1073/pnas. 1304625110.https://doi.org/10.10.1073/pnas.1304625110259760f17472394900ada6d8f75f8f61http%3A%2F%2Fnsr.oxfordjournals.org%2Fcgi%2Fijlink%3FlinkType%3DFULL%26amp%3BjournalCode%3Dpnas%26amp%3Bresid%3D110%2F26%2FE2329http://www.pnas.org/cgi/doi/10.1073/pnas.1304625110
    Shen M. G., G. X. Zhang, N. Cong, S. P. Wang, W. D. Kong, and S. L. Piao, 2014: Increasing altitudinal gradient of spring vegetation phenology during the last decade on the Qinghai-Tibetan Plateau.Agricultural and Forest Meteorology,189-190,71-80, 2014. 01. 003.https://doi.org/10.1016/j.agrformet.10.1016/j.agrformet.2014.01.003dfd1e9fec0d8c6fabf3aca9f0faaf16bhttp%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0168192314000045http://linkinghub.elsevier.com/retrieve/pii/S0168192314000045Spring vegetation phenology in temperate and cold regions is widely expected to advance with increasing temperature, and is often used to indicate regional climatic change. The Qinghai–Tibetan Plateau (QTP) has recently experienced intensive warming, but strongly contradictory evidence exists regarding changes in satellite retrievals of spring vegetation phenology. We investigated spatio-temporal variations in green-up date on the QTP from 2000 to 2011, as determined by five methods employing vegetation indices from each of the four sources: three Normalized Difference Vegetation Index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR), Système Pour l’Observation de la Terre (SPOT), MODerate resolution Imaging Spectroradiometer (MODIS), and the Enhanced Vegetation Index (EVI) from MODIS. Results indicate that, at the regional scale, all vegetation indices and processing methods consistently found no significant temporal trend (all P>0.05). This insignificance resulted from substantial spatial heterogeneity of trends in green-up date, with a notably delay in the southwest region, and widespread advancing trend in the other areas, despite a region-wide temperature increase. These changes doubled the altitudinal gradient of green-up date, from 0.63 days 100m611 in the early 2000s to 1.30 days 100m611 in the early 2010s. The delays in the southwest region and at high altitudes were likely caused by the decline in spring precipitation, rather than the increasing spring temperature, suggesting that spring precipitation may be an important regulator of spring phenological response to climatic warming over a considerable area of the QTP. Consequently, a delay in spring vegetation phenology in the QTP may not necessarily indicate spring cooling. Furthermore, the phenological changes retrieved from the widely used AVHRR NDVI differed from those retrieved from SPOT and MODIS NDVIs and MODIS EVI, necessitating the use of multiple datasets when monitoring vegetation dynamics from space.
    Shen, M. G., Coauthors, 2015: Evaporative cooling over the Tibetan Plateau induced by vegetation growth.Proceedings of the National Academy of Sciences of the United States of America,112(30),9299-9304, https://doi.org/10.1073/pnas.1504418112.10.1073/pnas.1504418112c452a122725bfc984ed85d44a8e996bfhttp%3A%2F%2Fnsr.oxfordjournals.org%2Fcgi%2Fijlink%3FlinkType%3DABST%26amp%3BjournalCode%3Dpnas%26amp%3Bresid%3D112%2F30%2F9299http://www.pnas.org/lookup/doi/10.1073/pnas.1504418112
    Song C.-Q., S.-C. You, L.-H. Ke, G.-H. Liu, and X.-K. Zhong, 2011: Spatio-temporal variation of vegetation phenology in the Northern Tibetan Plateau as detected by MODIS remote sensing.Chinese Journal of Plant Ecology,35(8),853-863, 2011. 00853. (in Chinese with English abstract)https://doi.org/10.3724/SP.J.1258.10.3724/SP.J.1258.2011.00853a9141022d7aea1596364aae757f57e37http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTOTAL-ZWSB201108011.htm<![CDATA[http://pub.chinasciencejournal.com/article/getArticleRedirect.action?doiCode=10.3724/SP.J.1258.2011.00853]]>Aim Estimating regional variation in vegetation phenology from time-series remote sensing data is important in global climate change studies. However,there are few studies on vegetation phenology for the Qinghai-Tibet Pla-teau and most are based on field records of stations. Methods We utilized the dynamic threshold method to explore vegetation phenological metrics (greenup date,length of season and senescence date) of typical grassland in the Northern Tibetan Plateau. We used time-series TERRA/MODIS EVI data for 2001-2010 reconstructed by the asymmetric Gaussian function fitting method to analyze spatial pattern and differentiation of vegetation phenology and its inter-annual variation and to examine the relationship between phenological variation and climate changes. Important findings The spatial pattern of date of vegetation greenup was embodied by transition from southeast to northwest and vertical zonation in the mountainous topography of the southeast. The vegetation greenup date in approximately sixty percent of the northern Tibetan Plateau had advanced, especially in high mountains. Inter-annual variation of vegetation senescence date was not obvious, and most of the region had natural inter-annual fluctuations.The variation of growing season length is influenced by greenup and senescence dates, but was chiefly affected by advanced greenup date lengthening the growing season. Among the four different climatic zones in the study area, the mountain and valley Nagqu sub-arctic and sub-humid zone and the southern Qinghai sub-arctic and semi-arid zone had the most apparent advanced greenup date and prolonged growing season.Based on measured data from weather stations, increased temperature appears to be a critical factor contributing to earlier greenup and prolonged growing season;however,the relationship between precipitation fluctuations and phenological variation was unclear.
    Thompson D. W. J., J. M. Wallace, 1998: The arctic oscillation signature in the wintertime geopotential height and temperature fields.Geophys. Res. Lett.,25(9),1297-1300, https://doi.org/10.1029/98GL00950.10.1029/98GL00950adf244c1165dc0c5b3e8ecc1d4c5e7fehttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F98GL00950%2Fpdfhttp://doi.wiley.com/10.1029/98GL00950The leading empirical orthogonal function of the wintertime sea-level pressure field is more strongly coupled to surface air temperature fluctuations over the Eurasian continent than the North Atlantic Oscillation (NAO). It resembles the NAO in many respects; but its primary center of action covers more of the Arctic, giving it a more zonally symmetric appearance. Coupled to strong fluctuations at the 50-hPa level on the intraseasonal, interannual, and interdecadal time scales, this rctic Oscillation (AO) can be interpreted as the surface signature of modulations in the strength of the polar vortex aloft. It is proposed that the zonally asymmetric surface air temperature and mid-tropospheric circulation anomalies observed in association with the AO may be secondary baroclinic features induced by the land-sea contrasts. The same modal structure is mirrored in the pronounced trends in winter and springtime surface air temperature, sea-level pressure, and 50-hPa height over the past 30 years: parts of Eurasia have warmed by as much as several K, sea-level pressure over parts of the Arctic has fallen by 4 hPa, and the core of the lower stratospheric polar vortex has cooled by several K. These trends can be interpreted as the development of a systematic bias in one of the atmosphere's dominant, naturally occurring modes of variability.
    Tian L. Q., 2015: The research of green-up date in the Qinghai-Tibetan plateau based on remote sensing technology. M.S. thesis, Northwest A&F University, (in Chinese)
    Wang L. X., H. L. Chen, Q. Li, and Y. D. Wu, 2010: Research advances in plant phenology and climate. Acta Ecologica Sinica, 30( 2), 447- 454. (in Chinese with English abstract)29da1733fad3630b9036ef72a7f52782http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F283832495_Research_advances_in_plant_phenology_and_climatehttp://www.researchgate.net/publication/283832495_Research_advances_in_plant_phenology_and_climateOf many environmental factors,the climate is the most important and active factor influencing plant phenology and its changes.In this paper,we first of all examine the relationship among the plant phenology,the climate and the climate change.Since numerical modeling is an important tool to quantify this relationship,we have further documented the recent research advances at home and abroad in areas of plant phenology and phenology simulation.It is shown that among climate variables,temperature is the most important factor to impact on the plant phenology.When water become a stress factor,its effect on the phenology is also significant.During the recent 50 years,the worldwide plant phenologies tend to have spring phenology advance and autumn phenology delay or slight delay,resulting in the growing season prolong for most plants.As a result of global warming,the temperature rising in winter and spring mainly leads to the spring phenology advance and the growing season prolong thereby.To further understand quantitative relationships between climate forcing and phenology response,we suggest further researches to be conducted in the areas such as,but not limited to,phenology mechanism,phenology relationships with climates and their changes,phenology modeling,and remote sensing applications.http://www.researchgate.net/publication/283832495_Research_advances_in_plant_phenology_and_climate
    Wang T., S. S. Peng, X. Lin, and J. F. Chang, 2013a: Declining snow cover may affect spring phenological trend on the Tibetan Plateau.Proceedings of the National Academy of Sciences of the United States of America,110,E2854-E2855, https://doi.org/10.1073/pnas.1306157110.10.1073/pnas.130615711023739768814eacc5bcd86c8a0c2999d5cb68293ehttp%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fpubmed%2F23739768http://www.pnas.org/cgi/doi/10.1073/pnas.1306157110Zhang et al. (1) report that the Tibetan Plateau experienced a continuous advancing start of green-up date (SOS) from 1982 to 2011 based on the merged Global Inventory Modeling and Mapping Studies (GIMMS)-based with Moderate Resolution Imaging Spectroradiometer (MODIS) [Système Pour l’Observation de la Terre vegetation (SPOT-VGT)]-based SOSs. It challenged...
    Wang Y. T., L. Q. Yu, F. G. Wang, N. Wang, and J. Liu, 2013b: Effects of low-temperature stress on chlorophyll fluorescence parameters of alfalfa in returning green period.Chinese Journal of Grassland,35(1),29-34, 2013. 01. 005. (in Chinese with English abstract)https://doi.org/10.3969/j.issn.1673-5021.c6e2ce3515918b8ff6c2d6cff5aa24e8http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTOTAL-ZGCD201301004.htmhttp://en.cnki.com.cn/Article_en/CJFDTOTAL-ZGCD201301004.htmIn order to reveal the influence of low temperature in spring on alfalfa in returning green period,using the controlled temperature to simulate the low temperature in spring,the room temperature(25℃)as the control,the changes of chlorophyll fluorescence parameters of alfalfa in returning green period treated at-5℃ for 4h,-5℃ for 12h,-6.5℃ for 8h and-8℃ for 4h were studied.The results showed that after slight cryogenic stress,the chlorophyll fluorescence parameters of alfalfa leaves did not have significant difference;but at the-6.5℃ for 8h and-8℃ for 4h,the Fm,Fv/Fm,Fv/Fo decreased,and the lower the temperature,the more the chlorophyll fluorescence parameters changed.It indicated that photo inhibition happened in alfalfa leaves at lower temperature,and the changes of chlorophyll fluorescence parameters were closely related with cold resistance.
    White M. A., P. F. Thornton, and S. W. Running, 1997: A continental phenology model for monitoring vegetation responses to interannual climatic variability.Global Biogeochemical Cycles,11(2),217-234, https://doi.org/10.1029/97GB00330.10.1029/97GB00330b77139e4ef624b3ea216518e78d7104dhttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F97GB00330%2Ffullhttp://doi.wiley.com/10.1029/97GB00330Regional phenology is important in ecosystem simulation models and coupled biosphere/atmosphere models. In the continental United States, the timing of the onset of greenness in the spring (leaf expansion, grass green-up) and offset of greenness in the fall (leaf abscission, cessation of height growth, grass brown-off) are strongly influenced by meteorological and climatological conditions. We developed predictive phenology models based on traditional phenology research using commonly available meteorological and climatological data. Predictions were compared with satellite phenology observations at numerous 20 km 20 km contiguous landcover sites. Onset mean absolute error was 7.2 days in the deciduous broadleaf forest (DBF) biome and 6.1 days in the grassland biome. Offset mean absolute error was 5.3 days in the DBF biome and 6.3 days in the grassland biome. Maximum expected errors at a 95% probability level ranged from 10 to 14 days. Onset was strongly associated with temperature summations in both grassland and DBF biomes; DBF offset was best predicted with a photoperiod function, while grassland offset required a combination of precipitation and temperature controls. A long-term regional test of the DBF onset model captured field-measured interannual variability trends in lilac phenology. Continental application of the phenology models for 1990-1992 revealed extensive interannual variability in onset and offset. Median continental growing season length ranged from a low of 129 days in 1991 to a high of 146 days in 1992. Potential uses of the models include regulation of the timing and length of the growing season in large-scale biogeochemical models and monitoring vegetation response to interannual climatic variability.
    Wu Z. H., N. E. Huang, 2011: Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in Adaptive Data Analysis 1(1), 1142/ S1793536909000047.https://doi.org/10.10.1142/S1793536909000047425b93a6b7ea9a69bc737ba441138940http%3A%2F%2Fwww.worldscientific.com%2Fdoi%2Fabs%2F10.1142%2FS1793536909000047http://www.worldscientific.com/doi/abs/10.1142/S1793536909000047A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. Finite, not infinitesimal, amplitude white noise is necessary to force the ensemble to exhaust all possible solutions in the sifting process, thus making the different scale signals to collate in the proper intrinsic mode functions (IMF) dictated by the dyadic filter banks. As EEMD is a timeu2013space analysis method, the added white noise is averaged out with sufficient number of trials; the only persistent part that survives the averaging process is the component of the signal (original data), which is then treated as the true and more physical meaningful answer. The effect of the added white noise is to provide a uniform reference frame in the timeu2013frequency space; therefore, the added noise collates the portion of the signal of comparable scale in one IMF. With this ensemble mean, one can separate scales naturally without any a priori subjective criterion selection as in the intermittence test for the original EMD algorithm. This new approach utilizes the full advantage of the statistical characteristics of white noise to perturb the signal in its true solution neighborhood, and to cancel itself out after serving its purpose; therefore, it represents a substantial improvement over the original EMD and is a truly noise-assisted data analysis (NADA) method.
    Xia J. J., Z. W. Yan, 2014: Changes in the local growing season in eastern China during 1909-2012.SOLA,10(1),163-166, 2151/sola. 2014- 034.https://doi.org/10.10.2151/sola.2014-034ba52c32e8d34821f739fab54279e9db1http%3A%2F%2Fci.nii.ac.jp%2Fnaid%2F130004694334http://jlc.jst.go.jp/DN/JST.JSTAGE/sola/2014-034?lang=en&amp;from=CrossRef&amp;type=abstractThe lack of long-term daily observations limited the study of changes in thermal growing season in China during the 20th century. Changes in the Local Growing Season (LGS) are analyzed based on a set of homogenized monthly temperature series back to the 19th century at 16 stations in eastern China. The analysis contains three steps: (1) to calculate the LGS indices (including the start and end dates) based on the daily temperature records at the 16 stations for the period 1960-2011; (2) to establish a linear relationship between the LGS indices and monthly temperature records; (3) to reconstruct the long-term LGS index series based on this linear relationship and the long-term monthly temperature records. It is found that, in eastern China, start (end) of LGS exhibits an advancing (a delaying) trend of 1.0 (0.5) days per decade, resulting in a lengthening trend of growing season of 1.5 days per decade, for the period 1909-2012. Changes in LGS indices are not monotonic but with mutidecadal variability. In particular, enhanced LGS-lengthening trends occurred during 1910-1940 and 1965-2012. Especially from 1965 onward, the LGS has been significantly extended by 3.5 days per decade, of which about 35% is contributed from mutidecadal variability.
    Xia J. J., Z. W. Yan, and P. L. Wu, 2013: Multidecadal variability in local growing season during 1901-2009.Climate Dyn.,41(2),295-305, https://doi.org/10.1007/s00382-012-1438-5.10.1007/s00382-012-1438-5acd4fad075308a733bb87a25a8eaf261http%3A%2F%2Flink.springer.com%2F10.1007%2Fs00382-012-1438-5http://link.springer.com/10.1007/s00382-012-1438-5Global warming exerts a lengthening effect on the growing season, with observational evidences emerging from different regions over the world. However, the difficulty for a global overview of this effect for the last century arises from limited availability of the long-term daily observations. In this study, we find a good linear relationship between the start (end) date of local growing season (LGS) and the monthly mean temperature in April (October) using the global gridded daily temperature dataset for 1960-1999. Using homogenized daily temperature records from nine stations where the time series go back to the beginning of the twentieth century, we find that the rate of change in the start (end) date of the LGS for per degree warming in April (October) mean temperature keeps nearly constant throughout the time. This enables us to study LGS changes during the last century using global gridded monthly mean temperature data. The results show that during the period 1901-2009, averaged over the observation areas, the LGS length has increased by a rate of 0.89 days decade, mainly due to an earlier start (0.58 days decade). This is smaller than those estimates for the late half of the twentieth century, because of multidecadal climate variability (MDV). A MDV component of the LGS index series is extracted by using Ensemble Empirical Mode Decomposition method. The MDV exhibits significant positive correlation with the Atlantic Multi-decadal Oscillation (AMO) over most of the Northern Hemisphere lands, but negative in parts of North America and Western Asia for start date of LGS. These are explained by analyzing differences in atmospheric circulation expressed by sea level pressure departures between the warm and cool phases of AMO. It is suggested that MDV in association with AMO accelerates the lengthening of LGS in Northern Hemisphere by 53 % for the period 1980-2009.
    Xia J. J., Z. W. Yan, G. S. Jia, H. Q. Zeng, P. D. Jones, and W. Z. Zhou, 2015: Projections of the advance in the start of the growing season during the 21st century based on CMIP5 simulations.Adv. Atmos. Sci.,32(6),831-838, https://doi.org/10.1007/s00376-014-4125-0.10.1007/s00376-014-4125-048b545bb5d341fe88708b9719ca3bce8http%3A%2F%2Fd.wanfangdata.com.cn%2FPeriodical_dqkxjz-e201506009.aspxhttp://link.springer.com/10.1007/s00376-014-4125-0由于人为的大气的温室效应的全球温暖推进了在第 20 世纪期间越过地球种季节(求救) 的植被的开始,是著名的。在为在某些排出物情形下面的第 21 世纪的 SOS 的进一步的变化的设计(代表性的集中小径, RCP ) 为改进全球温暖的后果的理解是有用的。在这研究,我们首先评估在 SOS 之间的一种线性关系(使用规范的差别植被索引定义) 并且为为 19822008 的北半球的大多数陆地区域的 4 月温度。从最近的最先进的全球联合气候模型在 RCP 下面基于这种关系和 4 月温度的整体设计,我们在第 21 世纪期间为大多数北半球的陆地区域在 SOS 显示出可能的变化。在约 204059, SOS 愿望在 RCP8.5 下面在 RCP2.6,在 RCP4.5 下面的 8.4 天,和 10.1 天下面到 4.7 天进展了,相对 19852004。在 208099,它将在 RCP8.5 下面在 RCP2.6,在 RCP4.5 下面的 11.3 天,和 21.6 天下面到 4.3 天进展。求救进展的地理模式更加依赖于 SOS 的温度敏感的。越大温度敏感,越 larger SOS 的 date-shift-rate。
    Yan Z. W., J. J. Xia, C. Qian, and W. Zhou, 2011: Changes in seasonal cycle and extremes in China during the period 1960-2008.Adv. Atmos. Sci.,28,269-283, https://doi.org/10.1007/s00376-010-0006-3.10.1007/s00376-010-0006-3ab5cdbbd1581c9660c73d28c49c73971http%3A%2F%2Fkns.cnki.net%2FKCMS%2Fdetail%2Fdetail.aspx%3Ffilename%3Ddqjz201102004%26dbname%3DCJFD%26dbcode%3DCJFQhttp://link.springer.com/10.1007/s00376-010-0006-3
    Yu F. F., K. P. Price, J. Ellis, and P. J. Shi, 2003: Response of seasonal vegetation development to climatic variations in eastern central Asia.Remote Sensing of Environment,87(1),42-54, .http://doi.org/10.1016/S0034-4257(03)00144- 510.1016/S0034-4257(03)00144-57c940c867ebde2b7b0a5364876506c76http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0034425703001445http://linkinghub.elsevier.com/retrieve/pii/S0034425703001445Meteorological records show that central Asia has experienced one of the strongest warming signals in the world over the last 30 years. The objective of this study was to examine the seasonal vegetation response to the recent climatic variation on the Mongolian steppes, the third largest grassland in the world. The onset date of green-up for central Asia was estimated using time-series analysis of advanced very high resolution radiometer (AVHRR) normalized difference vegetation index (NDVI) biweekly composite data collected between January 1982 and December 1991. Monthly precipitation and mean temperature data (1982–1990) were acquired from 19 meteorological stations throughout the grasslands of the eastern Mongolian steppes in China. Our results showed that while the taiga forest north of the Mongolian steppes (>50°N) experienced an earlier onset of green-up during the study period, a later onset was observed at the eastern and northern edges of the Gobi Desert (40°N–50°N). Responses of different vegetation types to climatic variability appeared to vary with vegetation characteristics and spring soil moisture availability of specific sites. Plant stress caused by drought was the most significant contributor to later vegetation green-up as observed from satellite imagery over the desert steppe. Areas with greater seasonal soil moisture greened up earlier in the growing season. Our results suggested that water budget limitations determine the pattern of vegetation responses to atmospheric warming.
    Yu H. Y., E. Luedeling, and J. C. Xu, 2010: Winter and spring warming result in delayed spring phenology on the Tibetan Plateau.Proceedings of the National Academy of Sciences of the United States of America,107(51),22 151-22 156, https://doi.org/10.1073/pnas.1012490107.10.1073/pnas.1012490107211158337678a57df5dc18d49e946f799088cbc1http%3A%2F%2Fwww.jstor.org%2Fstable%2F25757032http://www.pnas.org/cgi/doi/10.1073/pnas.1012490107Climate change has caused advances in spring phases of many plant species. Theoretically, however, strong warming in winter could slow the fulfillment of chilling requirements, which may delay spring phenology. This phenomenon should be particularly pronounced in regions that are experiencing rapid temperature increases and are characterized by highly temperature-responsive vegetation. To test this hypothesis, we used the Normalized Difference Vegetation Index ratio method to determine the beginning, end, and length of the growing season of meadow and steppe vegetation of the Tibetan Plateau in Western China between 1982 and 2006. We then correlated observed phenological dates with monthly temperatures for the entire period on record. For both vegetation types, spring phenology initially advanced, but started retreating in the mid-1990s in spite of continued warming. Together with an advancing end of the growing season for steppe vegetation, this led to a shortening of the growing period. Partial least-squares regression indicated that temperatures in both winter and spring had strong effects on spring phenology. Although warm springs led to an advance of the growing season, warm conditions in winter caused a delay of the spring phases. This delay appeared to be related to later fulfillment of chilling requirements. Because most plants from temperate and cold climates experience a period of dormancy in winter, it seems likely that similar effects occur in other environments. Continued warming may strengthen this effect and attenuate or even reverse the advancing trend in spring phenology that has dominated climate-change responses of plants thus far.
    Zeng H. Q., G. S. Jia, and H. Epstein, 2011: Recent changes in phenology over the northern high latitudes detected from multi-satellite data,Environmental Research Letters,6,045508, https://doi.org/10.1088/1748-9326/6/4/045508.10.1088/1748-9326/6/4/045508af9b04a5a6f6e8fc9a75ea247ec8fd96http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2011ERL.....6d5508Zhttp://stacks.iop.org/1748-9326/6/i=4/a=045508?key=crossref.451b039c1f33c963d9a36a74ed10b07ePhenology of vegetation is a sensitive and valuable indicator of the dynamic responses of terrestrial ecosystems to climate change. Therefore, to better understand and predict ecosystems dynamics, it is important to reduce uncertainties in detecting phenological changes. Here, changes in phenology over the past several decades across the northern high-latitude region (≥60°N) were examined by calibrating and analyzing time series of the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Very High Resolution Radiometer (AVHRR). Over the past decade (2000–10), an expanded length of the growing season (LOS) was detected by MODIS, largely due to an earlier start of the growing season (SOS) by 4.7 days per decade and a delayed end of the growing season (EOS) by 1.6 days per decade over the northern high latitudes. There were significant differences between North America and Eurasia in phenology from 2000 to 2010 based on MODIS data (SOS: 02=0221, 02=0249.02, 02<020.0001; EOS: 02=0221, 02=0249.25, 02<020.0001; LOS: 02=0221, 02=0279.40, 02<020.0001). In northern America, SOS advanced by 11.5 days per decade, and EOS was delayed by 2.2 days per decade. In Eurasia, SOS advanced by 2.7 days per decade, and EOS was delayed by 3.5 days per decade. SOS has likely advanced due to the warming Arctic during April and May. Our results suggest that in recent decades the longer vegetation growing seasons can be attributed to more advanced SOS rather than delayed EOS. AVHRR detected longer LOS over the past three decades, largely related to delayed EOS rather than advanced SOS. These two datasets are significantly different in key phenological parameters (SOS: 02=0217, 02=0214.63, 02=020.0015; EOS: 02=0217, 02=0238.69, 02<020.0001; LOS: 02=0217, 02=0216.47, 02=020.0009) from 2000 to 2008 over the northern high latitudes. Thus, further inter-calibration between the sensors is needed to resolve the inconsistency and to better understand long-term trends of vegetation growth in the Arctic.
    Zhang G. L., Y. J. Zhang, J. W. Dong, and X. M. Xiao, 2013a: Green-up dates in the Tibetan Plateau have continuously advanced from 1982 to 2011.Proceedings of the National Academy of Sciences of the United States of America,110(11),4309-4314, https://doi.org/10.1073/pnas.1210423110.10.1073/pnas.121042311023440201e87512326586bd20b6b2943c5d5c07achttp%3A%2F%2Feuropepmc.org%2Farticles%2FPMC3600495%2Fhttp://www.pnas.org/lookup/doi/10.1073/pnas.1210423110As the Earth's third pole, the Tibetan Plateau has experienced a pronounced warming in the past decades. Recent studies reported that the start of the vegetation growing season (SOS) in the Plateau showed an advancing trend from 1982 to the late 1990s and a delay from the late 1990s to 2006. However, the findings regarding the SOS delay in the later period have been questioned, and the reasons causing the delay remain unknown. Here we explored the alpine vegetation SOS in the Plateau from 1982 to 2011 by integrating three long-term time-series datasets of Normalized Difference Vegetation Index (NDVI): Global Inventory Modeling and Mapping Studies (GIMMS, 1982-2006), SPOT VEGETATION (SPOT-VGT, 1998-2011), and Moderate Resolution Imaging Spectroradiometer (MODIS, 2000-2011). We found GIMMS NDVI in 2001-2006 differed substantially from SPOT-VGT and MODIS NDVIs and may have severe data quality issues in most parts of the western Plateau. By merging GIMMS-based SOSs from 1982 to 2000 with SPOT-VGT-based SOSs from 2001 to 2011 we found the alpine vegetation SOS in the Plateau experienced a continuous advancing trend at a rate of similar to 1.04 d.y(-1) from 1982 to 2011, which was consistent with observed warming in springs and winters. The satellite-derived SOSs were proven to be reliable with observed phenology data at 18 sites from 2003 to 2011; however, comparison of their trends was inconclusive due to the limited temporal coverage of the observed data. Longer-term observed data are still needed to validate the phenology trend in the future.
    Zhang G. L., J. W. Dong, Y. J. Zhang, and X. M. Xiao, 2013b: Reply to Shen et al.: No evidence to show nongrowing season NDVI affects spring phenology trend in the Tibetan Plateau over the last decade. Proceedings of the National Academy of Sciences of the United States of America,110(26),E2330-E2331, https://doi.org/10.1073/pnas.1305593110.10.1093/ejcts/ezs5042300218577830cbfb2cb06fc3c484ef5bea13228http%3A%2F%2Fejcts.oxfordjournals.org%2Fcontent%2F43%2F3%2F660.fullhttp://ejcts.oxfordjournals.org/content/43/3/660.fullA response from the author of the article "Incidence of post-thoracotomy pain: a comparison between total intravenous anaesthesia and inhalation anaesthesia," in the previous issue is presented.
    Zhang X. Y., M. A. Friedl, C. B. Schaaf, A. H. Strahler, J. C. F. Hodges, F. Gao, B. C. Reed, and A. Huete, 2003: Monitoring vegetation phenology using MODIS.Remote Sensing of Environment,84(3),471-475, .http://doi.org/10.1016/S0034-4257(02)00135-910.1016/S0034-4257(02)00135-9a9a6bbd62f615dcf208a5aba80714cb8http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0034425702001359http://linkinghub.elsevier.com/retrieve/pii/S0034425702001359ABSTRACT Accurate measurements of regional to global scale vegetation dynamics (phenology) are required to improve models and understanding of inter-annual variability in terrestrial ecosystem carbon exchange and climate&ndash;biosphere interactions. Since the mid-1980s, satellite data have been used to study these processes. In this paper, a new methodology to monitor global vegetation phenology from time series of satellite data is presented. The method uses series of piecewise logistic functions, which are fit to remotely sensed vegetation index (VI) data, to represent intra-annual vegetation dynamics. Using this approach, transition dates for vegetation activity within annual time series of VI data can be determined from satellite data. The method allows vegetation dynamics to be monitored at large scales in a fashion that it is ecologically meaningful and does not require pre-smoothing of data or the use of user-defined thresholds. Preliminary results based on an annual time series of Moderate Resolution Imaging Spectroradiometer (MODIS) data for the northeastern United States demonstrate that the method is able to monitor vegetation phenology with good success.
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Manuscript received: 01 December 2016
Manuscript revised: 09 June 2017
Manuscript accepted: 14 August 2017
通讯作者: 陈斌, bchen63@163.com
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Changing Spring Phenology Dates in the Three-Rivers Headwater Region of the Tibetan Plateau during 1960-2013

  • 1. Key Laboratory of Regional Climate-Environment for East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
  • 2. Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100107, China
  • 3. University of Chinese Academy of Sciences, Beijing 100049, China

Abstract: The variation of the vegetation growing season in the Three-Rivers Headwater Region of the Tibetan Plateau has recently become a controversial topic. One issue is that the estimated local trend in the start of the vegetation growing season (SOS) based on remote sensing data is easily affected by outliers because this data series is short. In this study, we determine that the spring minimum temperature is the most influential factor for SOS. The significant negative linear relationship between the two variables in the region is evaluated using Moderate Resolution Imaging Spectroradiometer-Normalized Difference Vegetation Index data for 2000-13. We then reconstruct the SOS time series based on the temperature data for 1960-2013. The regional mean SOS shows an advancing trend of 1.42 d (10 yr)-1 during 1960-2013, with the SOS occurring on the 160th and 151st days in 1960 and 2013, respectively. The advancing trend enhances to 6.04 d (10 yr)-1 during the past 14 years. The spatiotemporal variations of the reconstructed SOS data are similar to those deduced from remote sensing data during the past 14 years. The latter exhibit an even larger regional mean trend of SOS [7.98 d (10 yr-1)] during 2000-13. The Arctic Oscillation is found to have significantly influenced the changing SOS, especially for the eastern part of the region, during 2000-13.

摘要: 近年来, 三江源植被春季物候期变化趋势的研究存在争议. 遥感数据的长度过短, 在反演植被生长季开始日(SOS)变化趋势时, 研究结果容易受到个别年份极端值的影响. 本文采用2000-2013年的MODIS-NDVI时序数列提取生长季开始日(SOS), 并建立SOS与春季温度间的相关关系;再利用1960-2013年春季温度数据重建过去近五十年SOS的时间序列. 在五十年大背景下讨论SOS 近十年的变化, 结果表明:(1)春季最低温度是与三江源SOS相关性最高的气象因子. 1960-2013年间, 研究地区的SOS呈现显著提前趋势, 从1960年的160日提前到2013年的151日, 增长速率为1.42d/10a;(2)2000-2013年间, SOS提前速率加快, 增长至6.04 d/10a. 遥感反演结果与温度重建结果基本一致, 提前速率为7.98 d/10a;(3)北极涛动对三江源地区的SOS有显著影响, 尤其是在研究地区的东部.

1. Introduction
  • Vegetation plays a key role in the terrestrial ecosystem. For example, changes in the vegetation growing season influence crop yields (Chmielewski et al., 2004; Peng et al., 2004), water cycling (Peterson et al., 2002Barnett et al., 2005) and carbon cycling (Piao et al., 2007, 2011a), and hence provide feedback to climate change (Shen et al., 2015). Therefore, it is important to monitor the interannual and seasonal variations of vegetation for eco-environmental assessment (Chen et al., 2011a). The Three-Rivers Headwater Region (TRHR) is in the hinterland of the Tibetan Plateau, which fosters the Yangtze, Yellow and Lancang rivers with important ecological functions (Hu et al., 2011; Luo et al., 2014). Detection of shifts in the TRHR growing season has become an increasingly interesting topic from the perspective of climate change under global warming (Liu et al., 2004; Zeng et al., 2011).

    In general, the annual variation of the vegetation growing season in the mid-to-high latitudes depends mainly on the date of the start of the vegetation growing season (SOS; Linderholm, 2006), as opposed to the end of growing season (Piao et al., 2007; Piao et al., 2011a; Song et al., 2011). In recent years, studies of vegetation seasons in the TRHR have focused on the SOS dates (Song et al., 2011; Wang et al., 2013a; Liu et al., 2014b). These studies often used the phenological definition of growing seasons based on satellite remote sensing data of the normalized difference vegetation index (NDVI), owing to their fine spatial resolution (Liu et al., 2004; Yu et al., 2010; Ding et al., 2013; Zhang et al., 2013a). However, the satellite data record is short for studying long-term trends of change. Many studies have investigated the relationship between SOS and other variables such as temperature (Root et al., 2003; Piao et al., 2006; Jeong et al., 2011), precipitation (Piao et al., 2011b; Shen et al., 2014), snow depth (Zhang et al., 2013b) and altitude (Shen et al., 2014). It has been evidenced that the negative correlation between the spring temperature and SOS is particularly significant in the TRHR (Piao et al., 2011a; Tian, 2015). (de Beurs and Henebry, 2008) reported that the negative correlation between SOS in the Asian part of northern Eurasia and atmospheric circulation such as the Arctic Oscillation (AO) is significant. Whether and how the AO exerts influence on SOS in a particular region such as the TRHR remains an interesting and open topic.

    It is notable that previous results regarding the trend of SOS during the last decade on the Tibetan Plateau remain controversial. For example, some researchers have reported a continuous advancement in SOS on the Tibetan Plateau during 1982-2011 (Song et al., 2011; Zeng et al., 2011; Zhang et al., 2013a), whereas others have argued that there is no evidence to indicate such a conclusion (Shen et al., 2013). In particular, (Shen, 2011) reported an SOS-delaying trend from 1998 to 2003 and an advancement from 2003 to 2009, based on Syst\`eme Pour l'Observation de la Terre Vegetation (SPOT-VGT) NDVI data.

    Three issues are relevant to this controversy. First, the quality of the Advanced Very High Resolution Radiometer (AVHRR) NDVI data (since 1982) is not as good as that of SPOT-VGT NDVI data (since 1998) and Moderate Resolution Imaging Spectroradiometer (MODIS) data (since 2000), as discussed by (Zhang et al., 2013a), particularly for alpine steppe and alpine meadow regions during 2001-06. Second, although better-quality NDVI data from MODIS and SPOT-VGT have been available since 2000 and 1998 respectively (Shen, 2011; Song et al., 2011; Zhang et al., 2013a; Shen et al., 2014), the time series of these datasets are still too short for robust estimation of long-term trends. The estimate of a trend in vegetation activity can be easily biased by individual outliers (Zhang et al., 2013b). Having removed these outliers, (Zhang et al., 2013b) reported that the advancing trend of SOS in the study region based on SPOT-VGT NDVI data during 1998-2011 is statistically significant. Third, the SOS dates defined using different methods can differ considerably, and hence can influence the estimate of a trend in a time series. In fact, even if based on the same AVHRR dataset, large uncertainty remains in estimating the phenological trend in a region using different methods. The trend can range from 0.4 to 1.9 d (10 yr)-1 in China (Cong et al., 2013).

    The main objective of the present study is to incorporate long-term series of local temperature records to investigate the long-term trend of SOS in the TRHR during 1960-2013 by using a novel method of time series analysis to define SOS, and understand the changes in SOS during the last decade as part of the long-term series of SOS. A preliminary analysis of the effect of the AO on the long-term series of SOS in the TRHR is made to understand the possible driving mechanism via large-scale atmospheric circulation. The data and methods used are described in section 2; the results are illustrated in section 3; and the conclusions and further discussion are given in section 4.

2. Data and methods
  • The daily mean/maximum/minimum surface air temperature (\(T_\rm mean/T_\max/T_\min\)) records from 28 National Standard Stations in the TRHR (31°39'-36°16'N, 89°24'-102°23'E) during the period 1960-2013 are used in this study. The altitudes of all the stations are between 1800 m and 4500 m (Fig. 1). This dataset has been homogenized and updated by (Li and Yan, 2009).

    Figure 1.  Temperature observations recorded at 28 National Standard Stations. The 17 green points indicate stations satisfying the criterion for reconstruction outlined in section 2.2.2. The 8 yellow points indicate the stations used after further data processing outlined in section 3.2.2. The 3 red points indicate the stations eliminated in section 3.2.2.

    The NDVI data used in this study is the MODIS-NDVI product MODI3C1, which can be retrieved from the online Data Pool of the NASA Land Processes Distributed Active Archive Center at https://lpdaac.usgs.gov/dataset_discovery/ modis/modis_products_table/mod13c1. This dataset has a spatial resolution of 0.05°× 0.05° and a temporal resolution of 16 days for the period 2000-13. The average NDVI values within a box area of 1° latitude × 1° longitude, centered at each temperature station, are applied.

    The AO index is defined as the mean deviation from the average sea level pressure measured throughout the Northern Hemisphere at longitudes north of 20°N (Thompson and Wallace, 1998). It is obtained from the NOAA Climate Prediction Center (www.cdc.noaa.gov/correlation/ao.data). The mean AO data in winter (December-January-February) and spring (March-April-May) during 1960-2013 are used.

  • 2.2.1. Determining the SOS date

    Several methods can be used to determine the SOS date from the seasonal variation in NDVI for one year (Lloyd, 1990; Reed et al., 1994; White et al., 1997; Yu et al., 2003; Zhang et al., 2003). In the present study, the SOS date is defined in a manner similar to that reported by (Piao et al., 2006). First, the 14-year-average seasonal cycle of NDVI is calculated for the entire study area. Second, a threshold of NDVI is defined at the time when the rate of change in NDVI reaches its maximum in the mean annual cycle. For each year, the SOS date is defined as the first day when the value of NDVI exceeds this threshold. The rate of change in NDVI is calculated as \begin{equation} {\rm NDVI}_{{\rm ratio}}(t)=[{\rm NDVI}(t+1)-{\rm NDVI}(t)]/{\rm NDVI}(t) , \ \ (1)\end{equation}

    where t is time (temporal resolution of 16 days). (Piao et al., 2006) analyzed biweekly NDVI time series data from January to September to determine the SOS dates for each year. We apply the ensemble empirical mode decomposition (EEMD) method to filter out the seasonal cycles from the NDVI series, rather than the polynomial function used by (Piao et al., 2006). EEMD is a type of time-varying algorithm used for analyzing nonlinear and nonstationary time series such as those of climate (Huang et al., 1998; Huang and Wu, 2008; Qian et al., 2010; Yan et al., 2011; Xia and Yan, 2014). In this study, we apply the EEMD method to extract the seasonal component of NDVI datasets and smooth the NDVI data within 16-day windows. The specific steps are described in (Qian et al., 2010). Figure 2 shows a better fit of the multi-year average NDVI seasonal curve based on EEMD than that obtained by the six-degree polynomial function, which influences the determination of the SOS date. The procedure and algorithm of EEMD are summarized in (Qian et al., 2010), (Wu and Huang, 2011), and (Huang and Wu, 2008).

    Figure 2.  Multi-year average NDVI seasonal cycle fitted using EEMD (red) and a six-degree polynomial function (green) for station 52868. The blue points indicate the original NDVI data. The horizontal black line indicates the threshold of NDVI at the time when the rate of NDVI change reaches its maximum in the mean annual cycle. The vertical lines show the SOS based on the EEMD fitting (red) and the six-degree polynomial function fitting (green) multi-year average NDVI seasonal cycle. The enlarged portion of the image shows that the SOS based on EEMD (red) is closer to the original NDVI data (blue points).

    2.2.2. Establishing a linear relationship between spring temperature and SOS

    In general, the pre-season temperature has a strong correlation with SOS (Yu et al., 2003; Piao et al., 2011a; Xia et al., 2013, 2015). For each station, we choose the minimum value of the SOS date during 2000-13 as the base day. Similar to (Yu et al., 2003), we use the term "pre-season" to refer to a period before the base day, and we analyze the correlation between SOS and the mean temperature of the pre-season period. We set the length of the pre-season (N) from 1 to 100. Then, by using the least-squares method, we calculate the linear regression coefficient between SOS and the pre-season temperature with different N values for the period 2000-13 for each station.

    The results show that the number of stations with significant correlation (P<0.1) is largest when N is set to 40. We suggest that, for each station, if SOS and the pre-season temperature, with N from 30 to 50, show a significant linear relationship under the 0.1 significance level, as defined as the criterion for reconstruction, the station qualifies for reconstructing the SOS time series.

    In addition, we define N with the largest correlation coefficient as the optimal pre-season length, which is used to reconstruct the SOS time series. Thus, the optimal pre-season length can differ among stations.

    2.2.3. Reconstructing the 50-year series of SOS

    The regression coefficient (a) of SOS onto the mean temperature during the optimal pre-season temperature is calculated for each station for the period 2000-13 as \begin{equation} D=a\delta T+D_{0}(T_{0}) , \end{equation} where D0 is the mean SOS date depending on T0, the mean optimal pre-season temperature; δ T is the temperature anomaly with respect to T0; and D is the SOS date corresponding to the temperature. On the basis of these regression coefficients and the optimal pre-season temperature records for 1960-2013, we can reconstruct the SOS time series for the same period for a given location near each station.

3. Results
  • We calculate the correlation coefficient between SOS and the optimal pre-season temperature based on \(T_\min\), Tmax and T mean records. The \(T_\min\)-based optimal pre-season temperature index results in the most negative correlation with SOS for most of the stations (Fig. 3a). Figure 3b shows that the linear correlation between regional mean SOS and the optimal pre-season minimum temperature is significant. Figure 3c illustrates that the regional mean spring minimum temperature during 1960-2013 has an increasing trend of 0.30°C (10 yr)-1. The warming trend during 2000-13 enhances up to 0.92°C (10 yr)-1 and is significant at most stations. The warming trend during the last 14 years corresponds well with the rapid advancement of SOS during the same period.

    Of all stations, 17, or 61% (Fig. 1), show a significant (P<0.1) linear relationship between SOS and the optimal pre-season \(T_\min\). Thus, for these 17 stations, the SOS time series are reconstructed on the basis of the optimal pre-season temperature and regression coefficient a (Table 1), using the method described in section 2.2.3.

    Figure 3.  (a) Boxplot of the correlation coefficients between SOS and the pre-season temperature for 28 stations in the TRHR during 2000-13, based on \(T_\min\), Tmax and T mean separately. The bottom and top of the boxes are the lower and upper quartiles, respectively; the bar near the middle of the box represents the median; and the solid points represent the outliers. The horizontal line indicates the significant criterion (P<0.1) of -0.457; the degrees of freedom is 12. (b) Linear correlation between mean SOS and mean optimal pre-season \(T_\min\) averaged over 17 stations. (c) Regional mean spring \(T_\min\) series, marked with a linear trend during 2000-13 (dotted line) and 1960-2013 (solid lines).

  • It should be noted that 11 stations do not qualify for the aforementioned reconstruction analysis. Considering the limited number of stations in the TRHR, we utilize as many stations as possible to obtain relatively reasonable results. We explore the reason for the lack of an SOS-temperature relationship for these stations, and we examine the potential of utilizing as many data sources as possible.

    3.2.1. Effect of outliers

    (Zhang et al., 2013b) reported that the outliers of NDVI in 1998 and 2001 might considerably influence the SOS trend estimates. In addition, outliers may also influence the regression between the optimal pre-season temperature and SOS for the 11 unqualified stations. Figure 4 shows an example that the trends of both temperature and SOS are statistically significant at P<0.1 after removing the year 2002. Having analyzed the correlation between the optimal pre-season temperature and SOS after removing each year during 2000-13, we determined that 4 (52856, 56064, 56125 and 56146) of the 11 stations exhibit significantly improved correlation up to the criterion for reconstruction after removing the year 2002 (Figs. 5a-d). In contrast, the correlation is not enhanced after removing any one of the other years. This suggests possible bad records in either station temperature or remote sensing data in the year 2002. We therefore calculate the regression coefficients and reconstruct the SOS time series for these four stations back to the 1960s, according to the revised relation. To facilitate subsequent discussion, this method is referred to as Method 1.

    Figure 4.  The (a) Optimal pre-season temperature (\(T_\min\); 52943, for example) and (b) SOS (56144, for example). Linear trends are statistically significant (P<0.1) after removing the data for 2002.

    Figure 5.  Improvement in the reconstruction-required relation for eight stations based on further data processing. Method 1 involves removing outliers in 2002 and Method 2 uses temperature data at a neighboring station. In each panel, the curves show the correlation coefficients between SOS and pre-season temperature, in which the length of pre-season changes from 1 to 100 days. The horizontal line indicates the (P<0.1) significant criterion of the correlation coefficient.

    3.2.2. Effect of local spring temperature data quality

    Previous studies have reported that observed climate data may be flawed because of artificial error, transference and changes to instrument types (Li and Yan, 2009; Li and Yan, 2010). In this study, we define each of the 11 unqualified stations as a base station, and we analyze the relationship between the SOS of the base station and the optimal pre-season temperature of each of the 28 stations. Taking station 52825 as an example (Fig. 6), the correlation coefficients between the SOS at 52818 and the optimal pre-season temperature at adjacent stations, such as 52908, are higher and meet the criterion for reconstruction, whereas that between the SOS and local optimal pre-season temperature at the same station (52818) does not. This result could be due to temporally flawed local observation records at this station. Therefore, we use the temperature data at the adjacent station, 52908, which exhibits the highest correlation, to replace those at 52818 for calculating the regression coefficients and reconstructing the long-term SOS series during 1960-2013 for station 52818. We refer to this method as Method 2. Similarly, we apply this method to 7 out of the 11 unqualified stations to reconstruct the local long-term SOS series (Table 2).

    Having applied the aforementioned two methods for excluding the influence of bad data and choosing the processing method with higher correlation for each station, we can add eight more stations for reconstructing the SOS series back to 1960, based on Method 2 for seven stations and Method 1 for one station (Table 2). The correlation between the SOS and pre-season \(T_\min\) data for the eight stations is improved and meets the criterion for reconstruction after further data processing (Fig. 5a-h). Three stations (55299, 56137 and 56144) still do not qualify, which implies other factors of influence beyond the scope of consideration in the present analysis. Nevertheless, the results of the 25 chosen stations (Tables 1 and 2) can form an effective base for an overview of the changes in SOS in the TRHR during 1960-2013.

  • Using the method described in section 2.2.3, the SOS time series for the period 1960-2013 are reconstructed for the 25 chosen stations. The geographical pattern of mean SOS dates based on the reconstructed data is consistent with that based on MODIS-NDVI during 2000-13 (r=0.999, P<0.05, where r is the spatial correlation coefficient; Figs. 7a and c). Moreover, the correlation between the spatial distribution of the SOS trends based on MODIS-NDVI and the reconstruction is significant (r=0.628, P<0.05). In particular, the stations in the central, southwest and northwest regions of the study area have higher spatial correlation, while those in the northeast and southeast regions are weaker (Figs. 7b and d). These results reinforce the reasonability of the reconstructed SOS for most stations in the TRHR.

    Figure 6.  Correlation coefficients between the SOS of base station 52825 and the pre-season \(T_\min\) at 52825 (red solid line), 52908 (red dotted line) and the other 26 stations (black dotted lines) in the TRHR during 2000-13. Horizontal red line indicates the significant criterion (P<0.1) for the correlation coefficients.

    Figure 7.  Spatial distributions of average SOS dates and linear trends based on (a, b) MODIS-NDVI during 2000-13, (c, d) reconstruction during 2000-13 and (e, f) reconstruction during 1960-2013. Black circles indicate stations with non-significant advancing trends. Dots show the stations with significant trends. Units for SOS date: order number of the day in a year.

    Tables 1 and 2 show the equations of linear regression between the pre-season \(T_\min\) and SOS for the chosen 25 stations. Most of the stations show an advancing SOS trend for the period 1960-13. Figure 7e shows that the geographical pattern of mean SOS has generally earlier dates in the east in early April than those in the west by the end of May in the TRHR during 1960-2013. These results are consistent with the results of 2000-13 (r=0.992, P<0.05). The spatial distribution of the SOS trends during the last half-century is also consistent with that during the last decade (r=0.669, P<0.05), particularly in the central and northeast regions of the study area (r=0.878, P<0.05). The SOS trend maxima in the study area are distributed mostly in the east, at 1.8-2.9 d (10 yr)-1, in contrast to those in the central and southwest regions at 0.3-1.3 d (10 yr)-1 during 1960-2013 (Fig. 7f).

    As shown in Fig. 8, the regional mean SOS series during 1960-2013 indicate an advancing trend of 1.42 d (10 yr)-1 (R2=0.357, P<0.05, where R2 is the coefficient of determination), with SOS occurring on approximately the 160th day of the year in 1960 and the 151st day in 2013. The reconstructed SOS series for the region is similar to that based on the remote sensing data for the last 14 years, although the latter appears to be more variable from year to year. The regional mean advancing trend of SOS in the TRHR during the last 14 years is quite large, supporting the conclusion of (Zhang et al., 2013a). The regional mean reconstructed SOS series exhibits an advancing trend of 6.04 d (10 yr)-1 during 2000-13, with the SOS occurring on approximately the 158th day of the year in 2000 and the 152nd day in 2013. The trend based on the remote sensing data is even larger, at 7.98 d (10 yr)-1.

  • According to a sequential Mann-Kendall test (α=0.05), a significant jump in spring temperature in the TRHR is apparent in the late 1980s. This jump is similar in timing to the increase in the AO index for February-April. Negative correlation between the AO index for spring (especially for March) and regional SOS is found for the last 10 years, with the largest correlation coefficient (r=-0.260, P>0.1) for March, especially for the eastern part of the region (r=-0.496, P<0.1), during 2000-13 (Fig. 9a). The result suggests that the AO could exert its influence but is hardly a dominant factor for SOS in the study region. The influence of the AO exists mainly for winter temperature. The positive correlation between the winter AO index and regional spring surface air temperature is not significant, though the correlation with winter temperature is significant during 1960-2013 (r=0.268, P<0.1) (Fig. 9b). When the AO index is in a high-value period, the mean surface air temperature in winter in the TRHR is also high. This preliminary relationship is useful for exploring the possibility of a link between SOS in the TRHR and large-scale atmospheric circulation.

    Figure 8.  Interannual variations in the reconstructed SOS for the entire study area from 1960 to 2013 (black dotted curve). The black (red) line indicates the linear trend of the reconstructed SOS for the period 1960-2013 (2000-13). Red points indicate the SOS dates calculated on the basis of MODIS-NDVI datasets. The red dotted line indicates the linear trend.

    Figure 9.  (a) Linear correlation between mean SOS and AO index in March over most stations in the eastern part of the TRHR region during 2000-13. (b) Linear correlation between winter AO index and winter \(T_\min\) of the TRHR region during 1960-2013.

4. Conclusions and discussion
  • In the TRHR, the spring minimum temperature is the most influential factor for SOS, and the negative correlation is significant during the last decade. The increasing temperature during spring is key to enhancing the enzyme activity of vegetation and therefore accelerating this phenological process (Wang et al., 2010). Specifically, it leads to an earlier occurrence of photoinhibition, which takes place at low temperature, and hence causes earlier green-up of the vegetation (Wang et al., 2013b). The present result is useful for studying growing season and terrestrial ecosystem responses to climate change. In this study, we reconstruct SOS time series based on temperature data for 1960-2013 and investigate the spatiotemporal change of SOS in the TRHR during the last 50 years. The regional mean SOS shows an advancing trend of 1.42 d (10 yr)-1 during 1960-2013, which enhances up to 6.04 d (10 yr)-1 during the last 14 years. These results support the conclusion of (Zhang et al., 2013a) and (Song et al., 2011), whereas trends of delay (Yu et al., 2010) and reversal (Shen, 2011) are not found during the last 10 years. The methods used in our study reduce the impact of individual outliers in short series of satellite data when calculating the trend of SOS, and the results are meaningful when attempting to clarify controversies in the SOS trend in the TRHR during the last decade.

    In addition, we attempt to identify possible links between SOS in the TRHR and large-scale atmospheric circulation via the AO. Preliminary results indicate a negative correlation between the AO index for spring (especially March) and SOS for the last 10 years, with the correlation particularly significant in the eastern part of the region. This builds upon the conclusion of (de Beurs and Henebry, 2008), albeit the relatively weak correlation implies more factors of influence are involved in this region. The possibility of teleconnection mechanisms involving multiple factors is deserving of further study.

    In short, the present study establishes a linear relationship between pre-season temperature and SOS in the TRHR. The correlation is significant in most of the study region, and the reconstructed results reflect the long-term SOS trend in the TRHR during the last 50 years. Further research using accurate spring phenology models would still beneficial. It also remains interesting to study the synergistic effect of multiple factors on SOS, such as temperature (Root et al., 2003; Piao et al., 2006; Jeong et al., 2011), precipitation (Piao et al., 2011b; Shen et al., 2014) and the vegetation component (Chen et al., 2011b), as well as atmospheric circulation. The effects of climate variables on spring phenology might be discontinuous and synergistic (Kong et al., 2017) and are also deserving of further study.

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