Abstract
AbstractA temperature reconstruction in the glacierized Himalaya over the past centuries using glacial length fluctuation records is challenging due to the abundance of debris-covered glaciers and a scarcity of glacial length fluctuation data. Using idealized flowline model simulations, we show that supraglacial debris cover significantly alters the length fluctuations only when the debris cover is very thick. An expanded database of length fluctuation records for 43 glaciers in the Himalaya and Karakoram is compiled and a standard linear inversion procedure is applied to a subset of 34 glaciers in this database. The reconstructed temperature anomaly during 1860-2010 indicates a continued warming of the region with a total temperature change of ~1.6 K. A close resemblance of the regional temperature anomaly to global trends is seen.
Highlights
The detailed nature and magnitude of the temperature changes taking place in the glacierized parts of the Himalaya over the past century are largely unknown due to the scarcity of long-term instrumental temperature records
Using an idealized numerical flowline model, we demonstrate that a large class of debris-covered glaciers in the Himalaya can be described by the same original Oerlemans model despite the known peculiarities of such glaciers as noted above
The Eastern and Central Himalayan glaciers are mainly influenced by the Indian summer monsoon (ISM), receiving their maximum precipitation in the summer, and are summer-accumulation type glaciers (Ageta and Higuchi, 1984)
Summary
The detailed nature and magnitude of the temperature changes taking place in the glacierized parts of the Himalaya over the past century are largely unknown due to the scarcity of long-term instrumental temperature records. The unknown relative contributions of precipitation and temperature changes add to the challenges Despite such complexities, the success of a simple linear model due to Oerlemans (2001, 2005), hereafter referred to as the Oerlemans model, is remarkable. The time-independent coefficients are conveniently expressed as a climate sensitivity, c, and a response time, These coefficients are a function of average slope of glacier surface, s, glacier length, l, and glacier-wide average annual precipitaptiofififni , P, and may be parameterizedpfiafififsififififififfififoifififlififlifiofifififwifififis: c 1⁄4 P=ð0:00204sÞ and 1⁄4 3233:33=ðs P l ð1 þ 20sÞÞ where s is dimensionless, l is in metres and P in m aÀ 1 (Leclercq and Oerlemans, 2012). This model has been used for individual glaciers as well, but c and are explicitly evaluated from a robust dynamical ice flow model in these cases (e.g. Adhikari and others, 2011)
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