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The High Mountain Asia glacier contribution to sea-level rise from 2000 to 2050

Published online by Cambridge University Press:  03 March 2016

Liyun Zhao ,
Ran Ding  and
John C. Moore
Liyun Zhao
Affiliation:
College of Global Change and Earth System Science, Beijing Normal University, Beijing, China Joint Center for Global Change Studies, Beijing, China
Ran Ding
Affiliation:
College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
John C. Moore*
Affiliation:
College of Global Change and Earth System Science, Beijing Normal University, Beijing, China Joint Center for Global Change Studies, Beijing, China Arctic Centre, University of Lapland, Rovaniemi, Finland
*
Correspondence: John C. Moore <john.moore.bnu@gmail.com>
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Abstract

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We estimate all the individual glacier area and volume changes in High Mountain Asia (HMA) by 2050 based on Randolph Glacier Inventory (RGI) version 4.0, using different methods of assessing sensitivity to summer temperatures driven by a regional climate model and the IPCC A1B radiative forcing scenario. A large range of sea-level rise variation comes from varying equilibrium-line altitude (ELA) sensitivity to summer temperatures. This sensitivity and also the glacier mass-balance gradients with elevation have the largest coefficients of variability (amounting to ~50%) among factors examined. Prescribing ELA sensitivities from energy-balance models produces the highest sea-level rise (9.2 mm, or 0.76% of glacier volume a–1), while the ELA sensitivities estimated from summer temperatures at Chinese meteorological stations and also from 1°x1° gridded temperatures in the Berkeley Earth database produce 3.6 and 3.8 mm, respectively. Different choices of the initial ELA or summer precipitation lead to 15% uncertainties in modelled glacier volume loss. RGI version 4.0 produces 20% lower sea-level rise than version 2.0. More surface mass-balance observations, meteorological data from the glaciated areas, and detailed satellite altimetry data can provide better estimates of sea-level rise in the future.

Keywords

glacier mass balanceglacier modellingmountain glaciers
Type
Paper
Information
Annals of Glaciology , Volume 57 , Issue 71 , March 2016 , pp. 223 - 231
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © The Author(s) 2016

References

Arendt, A and 77 others (2012) Randolph Glacier Inventory[v2.0]: A Dataset of Global Glacier Outlines. (GLIMS Technical Report) Global Land Ice Measurements from Space, Boulder CO. Digital MediaGoogle Scholar
Arendt, A and 80 others (2014) Randolph Glacier Inventory: A Dataset of Global Glacier Outlines: Version 4.0. (GLIMS Technical Report) Global Land Ice Measurements from Space, Boulder CO. Digital mediaGoogle Scholar
Bolch, T, Menounos, B and Wheate, RD (2010) Landsat-based inventory of glaciers in western Canada, 1985–2005. Remote Sens. Environ., 114(1), 127–137 (doi: 10.1016/j.rse.2009.08.015)Google Scholar
Bolch, T and 11 others (2012) The state and fate of Himalayan Glaciers. Science, 336, 310 (doi: 10.1126/science.1215828)CrossRefGoogle ScholarPubMed
Braithwaite, RJ (1984) Calculation of degree-days for glacier-climate research. Z. Gletscherkd. Glazialgeol., 20, 1–20 Google Scholar
Cuffey, KM and Paterson, WSB (2010) The physics of glaciers, 4th edn. Academic Press, Amsterdam Google Scholar
Fujita, K, Ageta, Y, Pu, J and Yao, T (2000) Mass balance of Xiao Dongkemadi Glacier on the central Tibetan Plateau from 1989 to 1995. Ann. Glaciol., 31, 159–163 (doi: 10.3189/172756400818 20075)Google Scholar
Gao, X, Shi, Y, Zhang, D and Giorgi, F (2012) Climate change in China in the 21st century as simulated by a high resolution regional climate model. Chinese Sci. Bull., 57, 1188–1195 (doi: 10.1007/s11434-011-4935-8)Google Scholar
Gardner, AS and 15 others (2013) A reconciled estimate of glacier contributions to sea level rise: 2003 to 2009. Science, 340(6134), 852–857 (doi: 10.1126/science.1234532)CrossRefGoogle ScholarPubMed
Grinsted, A (2013) An estimate of global glacier volume. Cryosphere, 7, 141–151 (doi: 10.5194/tc-7-141-2013)Google Scholar
Jarvis, A, Reuter, HI, Nelson, A and Guevara, E (2008) Hole-filled seamless SRTM data V4. International Centre for Tropical Agriculture (CIAT), Cali http://srtm.csi.cgiar.org Google Scholar
Kääb, A, Treichler, D, Nuth, C and Berthier, E (2015) Brief Communication. Contending estimates of 2003–2008 glacier mass balance over the Pamir–Karakoram–Himalaya. Cryosphere, 9, 557–564 (doi: 10.5194/tc-9-557-2015)Google Scholar
Kayastha, RB, Ageta, Y, Nakawo, M, Fujita, K, Sakai, A and Matsuda, Y (2003) Positive degree-day factors for ice ablation on four glaciers in the Nepalese Himalayas and Qinghai–Tibetan Plateau. Bull. Glaciol. Res., 20, 29–40 Google Scholar
Kienholz, C, Hock, R and Arendt, A (2013) A new semi-automatic approach for dividing glacier complexes into individual glaciers. J. Glaciol., 59(217), 925–937 (doi: 10.3189/2013JoG 12J138)CrossRefGoogle Scholar
Kotlyakov, BM and Krenke, AN (1982) Investigation of the hydrological conditions of alpine regions by glaciological methods. IAHS Publ. 138 (Symposium at Exeter 1982 – Hydrological Aspects of Alpine and High Mountain Areas), 31–42 Google Scholar
Krenke, AN and Khodakov, VG (1997) On the correlation between glacier melting and air temperature. In Kotlyakov, VM ed. 34 selected papers on main ideas of Soviet glaciology (1940s– 1980s). Glaciological Association, Moscow, 191–207 Google Scholar
Liu, C and Ding, L (1988) A primary calculation of temperature and precipitation in Tienshan mountains, China. J. Glaciol. Geocryol., 10(2), 151–160 [in Chinese with English summary]Google Scholar
Marzeion, B, Jarosch, AH and Hofer, M (2012) Past and future sealevel change from the surface mass balance of glaciers. Cryosphere, 6, 1295–1322 (doi: 10.5194/tc-6-1295-2012)Google Scholar
Moore, JC, Grinsted, A, Zwinger, T and Jevrejeva, S (2013) Semiempirical and process-based global sea level projections. Rev. Geophys., 51, 1–39 (doi: 10.1002/rog.20015)Google Scholar
Nuimura, T and 12 others (2015) The GAMDAM Glacier Inventory: a quality controlled inventory of Asian glaciers. Cryosphere, 9, 849–864 (doi: 10.5194/tc-9-849-2015)CrossRefGoogle Scholar
Pieczonka, T and Bolch, T (2015) Region-wide glacier mass budgets and area changes for the Central Tien Shan between ~1975 and 1999 using Hexagon KH-9 imagery. Global Planet. Change, 128, 1–13 (doi: 10.1016/j.gloplacha.2014.11.014)Google Scholar
Pu, JC and 6 others (2008) Rapid decrease of mass balance observed in the Xiao (Lesser) Dongkemadi Glacier, in the central Tibetan Plateau. Hydrol. Process., 22(16), 2953–2958 (doi: 10.1002/hyp.6865. 2008)Google Scholar
Radić, V, Bliss, A, Beedlow, AC, Hock, R, Miles, E and Cogley, JG (2014) Regional and global projections of the 21st century glacier mass changes in response to climate scenarios from GCMs. Climate Dyn., 42, 37–58 (doi: 10.1007/s00382-013-1719-7)Google Scholar
Rohde, R and 8 others (2013) A new estimate of the average Earth surface land temperature spanning 1753 to 2011. Geoinfor Geostat: An Overview, 1(1) (doi: 10.4172/gigs.1000101)Google Scholar
Rupper, S and Roe, G (2008) Glacier changes and regional climate: a mass and energy balance approach. J. Climate, 21, 5384–5401 (doi: 10.1175/2008JCLI2219.1)Google Scholar
Shi, Y, Liu, YC and Kang, E (2009) The Glacier Inventory of China. Ann. Glaciol., 50(53), 1–4 (doi: 10.3189/172756410790595831)Google Scholar
Wei, Y and Fang, Y (2013) Spatio-temporal characteristics of global warming in the Tibetan Plateau during the last 50 years based on a generalised temperature zone-elevation model. PLoS ONE, 8(4), e60044 (doi: 10.1371/journal.pone. 0060044)Google Scholar
Zhang, Y, Liu, S, Xie, CW and Ding, Y (2006) Observed degree-day factors and their spatial variation on glaciers in western China. Ann. Glaciol., 43, 301–306 (doi: 10.3189/172756406781811952)CrossRefGoogle Scholar
Zhao, L, Ding, R and Moore, JC (2014) Glacier volume and area change by 2050 in high mountain Asia. Global Planet. Change, 122, 197–207 (doi: 10.1016/j.gloplacha.2014.08.006)Google Scholar