Trends in the thermal growing season throughout the Tibetan Plateau during 1960–2009

Dong, Manyu; Jiang, Yuan; Zheng, Chutao; Zhang, Dayong

doi:10.1016/j.agrformet.2012.07.013

Cited by 101 publications

(73 citation statements)

References 40 publications

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“…In fact, Liu et al (2010) reported that DT 5 for China as a whole has exhibited a rapidly increasing trend since the 1980s. Dong et al (2012) suggested that the daily minimum temperature in spring (March, April, and May) and autumn (September and October) may have a considerable effect on variations in DT.…”

Section: Discussionmentioning

confidence: 99%

“…Nevertheless, studies investigating bio-temperature on the QTP have been rare in recent decades and have focused primarily on the thermal growing season. For example, Liu et al (2006) analysed variation in the length of the thermal growing season over the eastern and central QTP during 1961-2003 on the basis of meteorological data, whereas Dong et al (2012) examined variations at the start, end, and across the length of the growing season throughout the QTP during 1960-2009. In the present study, TWM, TCM, AT 10 , DT 10 , AT 5 , DT 5 , AT 0 , and DT 0 were selected as key indicators of bio-temperature; all these variables have well-defined ecological definitions and have been applied widely in previous studies. Based on these indicators, we investigated temporal and spatial trends in bio-temperature on the QTP from 1961 to 2013.…”

Section: Zhao and S Wumentioning

confidence: 99%

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Spatial and temporal variability of key bio-temperature indicators on the Qinghai-Tibetan Plateau for the period 1961-2013

Zhao

2015

Int. J. Climatol.

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ABSTRACT:The ecosystems of the Qinghai-Tibetan Plateau (QTP) are highly sensitive to climate change, the effects of which can be expressed through alterations to some key thresholds, including bio-temperature. However, to date, few studies have investigated variations in bio-temperature on the QTP in the context of climate change. To address this, the present study selected the following key indicators of bio-temperature: mean temperature of the warmest month (TWM); mean temperature of the coldest month (TCM); accumulated temperature above 10 ∘ C (AT 10 ), 5 ∘ C (AT 5 ), and 0 ∘ C (AT 0 ); and number of days with daily mean temperature above 10 ∘ C (DT 10 ), 5 ∘ C (DT 5 ), and 0 ∘ C (DT 0 ). These indicators were selected owing to their roles in the growth and distribution of vegetation on the QTP. The trends exhibited by these indicators were examined on basis of the data obtained from 71 observation stations on the QTP from 1961 to 2013. The results demonstrate that both TWM and TCM at the regional scale exhibited significant rising trends over this period, although TCM increased at a considerably faster rate. Spatially, the greatest increases in TWM and TCM were observed in the Qaidam basin desert and Guoluo-Naqu alpine shrub meadow regions. Additionally, AT 10 , AT 5 , and AT 0 exhibited increasing trends, with AT 0 increasing most rapidly and exhibiting the most significant changes, followed by AT 5 and then AT 10 . Similarly, DT 0 exhibited a greater overall increase than DT 10 and DT 5 . Spatially, AT and DT exhibited asymmetrical change, i.e. large increases in AT did not correspond to large increases in DT. In general, all the bio-temperature indicators considered indicate pronounced rising trends over this recent five decades. This was likely due to rising air temperatures across the QTP, which have affected the structures and functions of alpine ecosystems.

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Section: Discussionmentioning

confidence: 99%

Section: Zhao and S Wumentioning

confidence: 99%

Spatial and temporal variability of key bio-temperature indicators on the Qinghai-Tibetan Plateau for the period 1961-2013

Zhao

2015

Int. J. Climatol.

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“…Missing data were filled using linear regressions based on data from neighbour stations. The data time series had no missing data and the highest correlation with the former data during the same period (Dong et al, 2012).…”

Section: Environmental Variablesmentioning

confidence: 99%

Climate change challenges the current conservation strategy for the giant panda

Shen

Pimm

Feng

et al. 2015

Biological Conservation

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a b s t r a c tThe global total of protected areas to conserve biodiversity is increasing steadily, while numerous studies show that they are broadly effective. That said, how will current conservation strategies work, given the current and expected changes to the global climate? The giant panda is a conservation icon and exceptional efforts protect its remaining habitats. It provides a unique case study to address this question. There are many studies on the projected loss of habitats as climate warms, but few consider the geographical arrangement of future habitats, current protected area, and species' dispersal abilities. Most alarmingly, we expect much greater habitat fragmentation after climate change. Here, we combine long-term data on giant pandas with climate-change scenarios to predict future habitat loss and distribution in the Min Shan of Sichuan and Gansu, China. We employ metapopulation capacity as a mechanistic measure of a species' response to habitat fragmentation. The results show that climate changes will lead to 16.3 ± 1.4 (%) losses of giant panda habitats. Alarmingly, 11.4% of the remaining habitat fragments would be smaller than the extinction threshold area as the extent of fragmentation increases nearly fourfold. The projected fragmentation of giant panda habitats predicts 9% lower effectiveness inside the protected area network compared with that outside of reserves. A 35% reduction will occur in future effectiveness of reserve networks. The results challenge the long-term effectiveness of protected areas in protecting the species' persistence. They indicate a need for integrating both natural processes and dynamic threats over a simple reliance on individual static natural reserves.

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“…most regions have warmed substantially whereas Southwest China has cooled slightly from 1901 onwards (IPCC, ). The increases of GDD and GS could be explained by temperature increase (Menzel and Fabian, ; Dong et al , ; Mix et al , ), and then indirectly by the change of radiative and anthropogenic forcing that cause temperature change (Zhao et al , ). However, compared with the strong warming since the 1970s, as recorded by 16 stations across eastern China (Zhao et al , ), there is a lag in both GDD and GS anomalies, which have strongly increased since the mid‐1990s.…”

Section: Discussionmentioning

confidence: 99%

A new method for generating the thermal growing degree‐days and season in China during the last century

Yin

Deng

2016

Intl Journal of Climatology

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Changes in the thermal variables of growing degree-days (GDDs) and season have substantial effects on vegetation growth and distribution. The magnitude and spatial distribution of changes in GDDs and season in China during the past century are still uncertain, mainly due to limited daily observations before 1950. In this study, a site-specific multiple linear regression method is developed based on the homogenized daily temperature data of 536 meteorological stations in China for 1960-2010, in order to estimate the annual variables of GDDs and season with only monthly temperature. The method is robust for all stations and interannual variability. Since it could accurately simulate the observed values in 2011, with relative errors of −0.02 and 0.96% for GDDs and growing season (GS), respectively. We simulated the thermal variables based on this method using gridded monthly temperature data for 1901-2010. The interannual variations of simulated GDDs and GS matched well with that of observed values from 1960 to 2010, with high correlation coefficients of 0.99 and 0.92, respectively. Overall, the GDDs and season showed significant (p < 0.01) increasing trends of 11.88 ∘ C·day 10 years −1 and 0.69 day 10 years −1 , respectively, in China as a whole, with the trends increasing from 1991 to 2010. Moreover, all eco-geographical zones showed positive trends during 1901-2010. The GS was more prolonged in northern zones, and degree-days were more increased in north-subtropical, south-subtropical, and tropical zones. However, both the GDDs and season have decreased in the part of Southwest China in the mid-subtropical zone over the past century.

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Trends in the thermal growing season throughout the Tibetan Plateau during 1960–2009

Cited by 101 publications

References 40 publications

Spatial and temporal variability of key bio-temperature indicators on the Qinghai-Tibetan Plateau for the period 1961-2013

Spatial and temporal variability of key bio-temperature indicators on the Qinghai-Tibetan Plateau for the period 1961-2013

Climate change challenges the current conservation strategy for the giant panda

A new method for generating the thermal growing degree‐days and season in China during the last century

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