Research ArticleCLIMATOLOGY

Acceleration of ice loss across the Himalayas over the past 40 years

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Science Advances  19 Jun 2019:
Vol. 5, no. 6, eaav7266
DOI: 10.1126/sciadv.aav7266
  • Fig. 1 Map of glacier locations and geodetic mass balances for the 650 glaciers.

    Circle sizes are proportional to glacier areas, and colors delineate clean-ice, debris-covered, and lake-terminating categories. Insets indicate ice loss, quantified as geodetic mass balances (m w.e. year−1) plotted for individual glaciers along a longitudinal transect during 1975–2000 and 2000–2016. Both inset plots are horizontally aligned with the map view. Gray error bars are 1σ uncertainty, and the yellow trend is the (area-weighted) moving-window mean, using a window size of 30 glaciers.

  • Fig. 2 Comparison of ice losses between 1975–2000 and 2000–2016 for the 650 glaciers.

    (A) Histograms of individual glacier geodetic mass balances (m w.e. year−1) during 1975–2000 (mean = −0.21, SD = 0.15) and 2000–2016 (mean = −0.41, SD = 0.24). Shaded regions behind the histograms are fitted normal distributions. (B) Result of dividing the modern (2000–2016) mass balances by the historical (1975–2000) mass balances for each glacier, showing the resulting distribution of the mass balance change (ratio) between the two intervals (mean = 2.01, SD = 1.36). In this case, the shaded region is a fitted kernel distribution. (C) Altitudinal distributions of ice thickness change (m year−1) separated into 50-m elevation bins during the two intervals. (D) Normalized altitudinal distributions of ice thickness change. Normalized elevations are defined as (zz2.5)/(z97.5z2.5), where z is elevation and subscripts indicate elevation percentiles. This scales all glaciers by their elevation range (i.e., after scaling, glacier termini = 0 and headwalls = 1), allowing for more consistent comparison of ice thickness changes across glaciers with different elevation ranges. Note the abrupt inflection point in the 2000–2016 profile at ~0.1; this is likely due to retreating glacier termini. Shaded regions in the altitudinal distributions indicate the SEM estimated as σz/nz, where σz is the SD of the thinning rate for each 50-m elevation bin and nz is the number of independent measurements when accounting for spatial autocorrelation (see Materials and Methods).

  • Fig. 3 Comparison between clean-ice (<33% debris-covered area) and debris-covered (≥33% debris-covered area) glaciers for seven subregions.

    Circle sizes are proportional to glacier areas, colors delineate clean-ice versus debris-covered categories, and boxplots indicate geodetic mass balance (m w.e. year−1). Box center marks (red lines) are medians; box bottom and top edges indicate the 25th and 75th percentiles, respectively; whiskers indicate q75 + 1.5 ⋅ (q75q25) and q25 − 1.5 ⋅ (q75q25), where subscripts indicate percentiles and “+” symbols are outliers.

  • Fig. 4 Altitudinal distributions of ice thickness change (m year−1) for the 650 glaciers.

    Glaciers are separated by time interval (top) and category (<33% versus ≥33% debris-covered area) (bottom). (A) Altitudinal distributions of ice thickness change for clean-ice glaciers during 1975–2000 and 2000–2016. The y axes are normalized elevation as in Fig. 2. (B) Same as (A), but for debris-covered glaciers. (C) Altitudinal distributions of ice thickness change during 1975–2000 for clean-ice and debris-covered glaciers. (D) Same as (C), but for 2000–2016. (E) Altitudinal distributions of glacierized area for both glacier categories. Elevational extent of debris cover varies widely between individual glaciers, but is mostly concentrated in lower ablation zones. The clean-ice category includes 478 glaciers and the debris-covered category includes 124 glaciers.

  • Fig. 5 Compilation of previously published instrumental temperature records in HMA.

    (A) Regional temperature anomalies, relative to the 1980–2009 mean temperatures for each record. The yellow trend (23) from the quality-controlled and homogenized climate datasets LSAT-V1.1 and CGP1.0 recently developed by the China Meteorological Administration (CMA), using approximately 94 meteorological stations located throughout the Hindu Kush Himalayan region. The orange trend (44) is from a similar CMA dataset derived from 81 stations more concentrated on the eastern Tibetan Plateau. The blue trend (24) is from three decades of temperature data from 13 mountain stations located on the southern slopes of the central Himalayas. The black trend is the 5-year moving mean. (B) Temperature anomalies from high-elevation stations at the Chhota Shigri glacier terminus (25); Dingri station in the Everest region (26); average from the Kanzalwan, Drass, and Patseo stations (45); and average of 16 stations above 4000 m elevation on the Tibetan Plateau and eastern Himalayas (46). Here, temperature anomalies are relative to the mean of each record. The gray trend line is the 5-year moving mean. (C) Difference in mean temperature (°C) between the two intervals, i.e., the mean of the 2000–2016 interval relative to the mean of the 1975–2000 interval.

  • Table 1 Himalaya-wide geodetic mass balances (m w.e. year−1).

    1975–20002000–20161975–2016
    All glaciers−0.22 ± 0.13−0.43 ± 0.14−0.31 ± 0.13
    Clean-ice−0.19 ± 0.07−0.38 ± 0.08−0.27 ± 0.07
    Debris-covered−0.24 ± 0.06−0.44 ± 0.08−0.32 ± 0.06
    Lake-terminating−0.33 ± 0.07−0.56 ± 0.08−0.40 ± 0.07

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/5/6/eaav7266/DC1

    Fig. S1. Comparison of Himalayan temperature trends and regional mass balance with benchmark mid-latitude glaciers and a global average trend.

    Fig. S2. Coverage of glacierized area in the Himalayas.

    Fig. S3. Trend fit examples for two large glaciers using ASTER DEMs during 2000–2016, histograms of ASTER pixel counts and timespans per stack (glacier averages), and outlier thresholds.

    Fig. S4. Illustration of uncertainty estimation procedure for a single iteration/glacier and Himalaya-wide sensitivity tests.

    Fig. S5. Geodetic mass balances during 1975–2000 and 2000–2016 plotted against various parameters.

    Fig. S6. Log-log plot of glacier volumes versus areas used to estimate the total ice mass present in our region of study.

    Fig. S7. Analysis of elevation change for nonglacier pixels (stable terrain) during both intervals.

    Fig. S8. Thickness change maps used in the analysis.

    Fig. S9. Thickness change maps for the three remaining Himalayan regions.

    Table S1. Geodetic mass balance comparisons with prior studies.

    References (4770)

  • Supplementary Materials

    This PDF file includes:

    • Fig. S1. Comparison of Himalayan temperature trends and regional mass balance with benchmark mid-latitude glaciers and a global average trend.
    • Fig. S2. Coverage of glacierized area in the Himalayas.
    • Fig. S3. Trend fit examples for two large glaciers using ASTER DEMs during 2000–2016, histograms of ASTER pixel counts and timespans per stack (glacier averages), and outlier thresholds.
    • Fig. S4. Illustration of uncertainty estimation procedure for a single iteration/glacier and Himalaya-wide sensitivity tests.
    • Fig. S5. Geodetic mass balances during 1975–2000 and 2000–2016 plotted against various parameters.
    • Fig. S6. Log-log plot of glacier volumes versus areas used to estimate the total ice mass present in our region of study.
    • Fig. S7. Analysis of elevation change for nonglacier pixels (stable terrain) during both intervals.
    • Fig. S8. Thickness change maps used in the analysis.
    • Fig. S9. Thickness change maps for the three remaining Himalayan regions.
    • Table S1. Geodetic mass balance comparisons with prior studies.
    • References (4770)

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