Diurnal asymmetry of surface temperature trends over India

Kumar, Krishan; Kumar, Kireet; Pant, G. B.

doi:10.1029/94gl00007

Cited by 211 publications

(127 citation statements)

References 6 publications

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“…Observed long-term summer and autumn (or monsoon) cooling is mostly consistent with the earlier reports for the study basin Archer, 2005, 2006;Khattak et al, 2011;Sheikh et al, 2009), as well as those for the neighboring regions, such as Nepal, Himalayas (Sharma et al, 2000;Cook et al, 2003), northwest India (Kumar et al, 1994), the Tibetan Plateau (Liu and Chen, 2000), central China (Hu et al, 2003), and central Asia (Briffa et al, 2001).…”

Section: Cooling Trendssupporting

confidence: 79%

Prevailing climatic trends and runoff response from Hindukush–Karakoram–Himalaya, upper Indus Basin

Böhner²,

2017

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Abstract. Largely depending on the meltwater from the Hindukush-Karakoram-Himalaya, withdrawals from the upper Indus Basin (UIB) contribute half of the surface water availability in Pakistan, indispensable for agricultural production systems, industrial and domestic use, and hydropower generation. Despite such importance, a comprehensive assessment of prevailing state of relevant climatic variables determining the water availability is largely missing. Against this background, this study assesses the trends in maximum, minimum and mean temperatures, diurnal temperature range and precipitation from 18 stations (1250-4500 m a.s.l.) for their overlapping period of record (1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) and, separately, from six stations of their long-term record . For this, a Mann-Kendall test on serially independent time series is applied to detect the existence of a trend, while its true slope is estimated using the Sen's slope method. Further, locally identified climatic trends are statistically assessed for their spatial-scale significance within 10 identified subregions of the UIB, and the spatially (field-) significant climatic trends are then qualitatively compared with the trends in discharge out of corresponding subregions. Over the recent period (1995-2012), we find warming and drying of spring (field-significant in March) and increasing early melt season discharge from most of the subregions, likely due to a rapid snowmelt. In stark contrast, most of the subregions feature a field-significant cooling within the monsoon period (particularly in July and September), which coincides well with the main glacier melt season. Hence, a decreasing or weakly increasing discharge is observed from the corresponding subregions during mid-to late melt season (particularly in July). Such tendencies, being largely consistent with the long-term trends , most likely indicate dominance of the nival but suppression of the glacial melt regime, altering overall hydrology of the UIB in future. These findings, though constrained by sparse and short observations, largely contribute in understanding the UIB melt runoff dynamics and address the hydroclimatic explanation of the "Karakoram Anomaly".

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Section: Cooling Trendssupporting

confidence: 79%

Prevailing climatic trends and runoff response from Hindukush–Karakoram–Himalaya, upper Indus Basin

Böhner²,

2017

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“…The observed cooling trend at these stations in the basin corresponds with results for minimum temperature reported by Fowler and Archer [40] for the upper Indus Basin. The predominantly increasing trend in [41] and has also been reported in parts of India [42]. This contradicts general global patterns whereby faster increases in TMX than TMN yield decreasing DTR [27,43].…”

Section: Discussioncontrasting

confidence: 50%

Analysis of Temperature Trends in Sutluj River Basin, India

At¹,

Sharif²,

ARCHER³

2014

J Earth Sci Clim Change

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“…Adequate care has been taken in ensuring the homogeneity of the data. The outliers in the monthly temperature data were identified as such if the station's monthly temperature values were greater/lesser than the long term mean by ±3 times standard deviations of the corresponding month [3]. The identified outliers were compared with the nearest station data and then ensured that it was really outlier or not.…”

Section: Data and Methods Of Analysismentioning

confidence: 99%

“…The outliers identified in this manner have been considered as missing data and these account for less than 5% of the total number of data points. The missing values were then interpolated using the data from the neighboring stations by a regression technique [3], as well as inverse distance squared weighted average algorithm [6].…”

Section: Data and Methods Of Analysismentioning

confidence: 99%

Long Term Temperature Trends at Major, Medium, Small Cities and Hill Stations in India during the Period 1901-2013

Kothawale¹,

Deshpande²,

Kolli³

2016

AJCC

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Industrialization and urbanization are the most dominant causal factors for long-term changes in surface air temperatures. To examine this fact, the long term changes in the surface-air temperatures have been evaluated by the linear trend for the different periods, i.e. and recent period 1971-2013 as rapid industrialization was observed during the recent four decades. In the present study, seasonal and annual mean, maximum and minimum temperature data of 36 stations for the period 1901-2013 have been used. These stations are classified into 4 groups, namely major, medium, small cities and hill stations. During the period 1901-1970, less than 50% stations from each group showed a significant increasing trend in annual mean temperature, whereas in the recent period 1971-2013, more than 80% stations from all the groups except small city group showed a significant increasing trend. The minimum temperature increased faster than that of the maximum temperature over major and medium cities, while maximum temperature increased faster than the minimum temperature over the small cities and hill stations. The annual mean temperature of all the coastal stations showed a significant increasing trend and positive correlation with Precipitable Water Vapour (PWV). The effect of PWV is more pronounced on minimum temperature than that of the maximum.

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Diurnal asymmetry of surface temperature trends over India

Cited by 211 publications

References 6 publications

Prevailing climatic trends and runoff response from Hindukush–Karakoram–Himalaya, upper Indus Basin

Prevailing climatic trends and runoff response from Hindukush–Karakoram–Himalaya, upper Indus Basin

Analysis of Temperature Trends in Sutluj River Basin, India

Long Term Temperature Trends at Major, Medium, Small Cities and Hill Stations in India during the Period 1901-2013

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