Author:
Richardson Alasdair,Carr Rachel,Cook Simon
Abstract
Abstract
Tropical Andean glaciers are highly sensitive to climate change and are impacted by the El Niño Southern Oscillation (ENSO). However, glaciological data are scarce, meaning that there are substantial knowledge gaps in the response of Andean glaciers to future anthropogenic and ENSO forcing and these are crucial to address, as glaciers represent a key water source for downstream populations and ecosystems. Here we integrated data from glaciological field studies, remote sensing, statistical analysis and glacier modelling to analyse the response of two Andean glaciers (Zongo and Shallap) to ENSO and their potential sensitivity to a range of climate forcing scenarios. Both glaciers retreated and experienced increasingly negative mass balance between the 1990s and the 2010s and responded strongly and rapidly to contemporary ENSO forcing, although this relationship evolved over time. Sensitivity experiments demonstrate that Shallap and Zongo are highly sensitive to ENSO forcing scenarios and the combination of ENSO and climate warming can cause rapid ice loss under the most extreme scenarios. Results also demonstrate the strong sensitivity of both glaciers to changes in the equilibrium line altitude, whereby rapid ice loss occurred when melt extended into present-day accumulation areas.
Publisher
Cambridge University Press (CUP)
Reference101 articles.
1. ENSO impact on hydrology in Peru;Lavado-Casimiro;Advances in Geosciences,2013
2. Response of surface topography to basal variability along glacial flowlines;Ng;Journal of Geophysical Research: Earth Surface,2018
3. Changes of the tropical glaciers throughout Peru between 2000 and 2016 – mass balance and area fluctuations;Seehaus;The Cryosphere,2019
4. Evaluating glacier fluctuations in Cordillera Blanca (Peru) by remote sensing between 1987 and 2016 in the context of ENSO;Silverio;Archives des Sciences,2017
5. Remote sensing of glaciers in the tropical Andes: a review;Veettil;International Journal of Remote Sensing,2017