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Chinese Science Bulletin, Volume 64 , Issue 27 : 2885-2893(2019) https://doi.org/10.1360/TB-2019-0085

Change of snow and ice melting time in High Mountain Asia

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  • ReceivedMay 22, 2019
  • AcceptedAug 19, 2019
  • PublishedSep 19, 2019

Abstract

High Mountain Asia (HMA) is very sensitive to climate changes. In HMA, air temperature and precipitation shifts or increases are reflected in the timing of snowmelt onset. In this study, a long-term series (1979–2018) of snow melt onset time is first derived using spaceborne microwave radiometer data, following which the long-term trend of snow and ice-melt time of Tienshan, Altay, Karakorom, Hindu Kush, and Pamir is analyzed. Previous studies proposed many algorithms for detecting snowmelt onset and freeze-up using microwave remote sensing. The previously proposed algorithms were extensively applied to polar regions (including the Antarctic, Siberia, Greenland, and sea ice surface), where the impact of topography or mixed pixel problem is relatively small. However, for high mountain regions, complex topography and a potential mixed pixel issue would result in a very complicated and noisy satellite-observed active and passive microwave remote sensing signal. This study thus proposes a recently developed snow and ice-melt detection algorithm in which, for passive microwave data, a median filter is first applied to the original signal (brightness temperature) to suppress random, small, or short-duration signal variations. The differential average derivative of a particular date is then calculated using the time series first-order derivative as the average first-order derivative of a specific count of observations after this particular date. Generally, the differential average derivative is an indicator of sudden changes in time series brightness temperature. Results show that the melt onset time in the majority of HMA occurs earlier, except in the case of the Karakorom Mountains and a part of the West Kunlun Mountains. Moreover, the melt onset time derived from satellite morning (6:00 local time) pass data shows that the melt onset time of Karakorom, the West Kulun Mountains, and southeast Tibet remains stable, or even occurs later. The trend of southeast Tibet is unique, with its earlier melt based on afternoon (18:00 local time) pass data and its later melt based on morning pass data. This suggests that the melt–refreeze period of southeast Tibet is increasing together with the increasing diurnal temperature difference. Then, a 2-m air temperature in ERA5 reanalysis data is used for comparison with melt onset time for validation and analysis. ERA5 is the latest climate reanalysis produced by ECMWF, providing hourly data on many atmospheric, land-surface, and sea-state parameters. A strong correlation exists between monthly average air temperatures and melt onset time, with the maximum linear fit R2 of 0.76. This strong correlation indicates the good data quality of ERA5 reanalysis and melt onset time. The unique trend of southeast Tibet can also be explained using ERA5 reanalysis data, which show that the monthly mean daily maximum air temperature is increasing, but the monthly mean daily minimum air temperature is decreasing. This renders a slope of the linear fit of melt onset data in the period of 1988–2018 in the whole HMA region. Because the satellite overpasses time difference, data from only 1988 to 2018 is used for trend analysis. The analysis of the relationship between the melt onset time change rate and elevation shows that the areas with an earlier melt are almost all located in low-elevation regions, and the rate of melt time change is positively correlated with elevation. This suggests that low-elevation regions are more affected by climate changes. This study provides objective evidence of the impact of climate change on the cryospheric system in HMA.


Funded by

第二次青藏高原综合科学考察研究(2019QZKK0206)

国家自然科学基金(41871266)


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  • Figure 1

    (Color online) The snow and ice melt time (DOY) of Pamir, Karakorom, western Kunkun Mountains and part of western Himalaya derived from QuikSCAT (a) and SMM/I (b) data

  • Figure 2

    (Color online) The five typical snow and ice covered region in HMA and their time series snow and ice melt onset time. (a) The five typical regions; (b)−(f) the time series snow and ice melt time of the five typical regions

  • Figure 3

    (Color online) The time series monthly mean daily maximum air temperature during snow and ice melting season of the five typical regions and its correlation with remote sensing retrieved snow and ice melt onset date. (a) The time series ERA5 monthly mean daily maximum air temperature of the five typical regions in snow melting season; (b)−(f) the correlation between ERA5 monthly mean daily maximum air temperature and snow/ice melt onset date derived by remote sensing

  • Figure 4

    (Color online) The time series monthly mean daily maximum and minimum air temperature of March in southeast Tibet

  • Figure 5

    (Color online) The change rate of snow/ice melt onset date over HMA. (a) Derived from afternoon pass SSM/I and SSMI/S data; (b) derived from morning pass SSM/I and SSMI/S data. Basemap is from Google Earth

  • Figure 6

    (Color online) The relationship between snow/ice melt onset date chage rate and elevation. (a) Morning pass data; (b) afternoon pass data. The solid line indicates fit from current data, and dash line indicates fit from afternoon or morning data

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