Análisis de la cobertura nival y el albedo y su relación con el fenómeno ENSO y la evolución del permafrost en los estratovolcanes Coropuna y Chachani (Región Arequipa)

La cobertura nival y glaciar de los volcanes Chachani y Coropuna ha experimentado un rápido retroceso en las últimas décadas debido al cambio climático. Este trabajo tiene como objetivo analizar la relación entre la cobertura nival y el albedo en Chachani y Coropuna, y fenómeno El Niño Oscilación Sur (ENSO) mediante imágenes de alta resolución temporal y baja resolución espacial. La técnica desarrollada en el procesamiento de imágenes ha permitido extraer información relaciona con las anomalías de variables climáticas mediante filtros en el dominio temporal y de frecuencia. Los resultados de las correlaciones muestran periodos alrededor de tres años relacionando al fenómeno ENSO con la cobertura nival en las áreas de estudio

Paleoclimatic reconstruction during the Little Ice Age in the Llanganuco basin, Cordillera Blanca (Peru)

The Equilibrium Line Altitude (ELA, m) is a good indicator for the impact of climate change on tropical glaciers , because it varies in time and space depending on changes in temperature and/or precipitation.The estimation of the ELA and paleoELA using the Area x Altitude Balance Ratio method (AABR; Osmaston, 2005) requires knowing the surface and hypsometry of glaciers or paleoglaciers (Benn et al. 2005) and the Balance Ratio (BR) correct (Rea, 2009). In the Llanganuco basin (~ 9°3´S; 77°37´W) there are very well preserved moraines near the current glaciers front. These deposits provide information to reconstruct the extent of paleoglaciers since the Little Ice Age (LIA) and deduce some paleo-climatic variables. The goal of this work has been to reconstruct the paleotemperature (°C) during LIA, deduced from the difference between ELA AABR2016 and paleoELA AABRLIA. The paleoclimatic reconstruction was carried out in 6 phases: Phase 1) Development of a detailed geomorphological map (scale 1/10,000), in order to identify glacial landforms (advance moraines and polished rocks) which, due to their geomorphological context, can be considered of LIA, so palaeoglaciers can be delimited. Current glacial extension was done using dry season, high resolution satellite images. Phase 2) Glacial bedrock Reconstruction from glacier surface following the GLABTOP methodology (Linsbauer et al 2009). Phase 3) 3D reconstruction of paleoglacial surface using GLARE tool, based on bed topography and flow lines for each defined paleoglacial (Pellitero et al., 2016). As perfect plasticity model does not reflect the tension generated by the side walls of the valley, form factors were calculated based on the glacier thickness, lateral moraines and the geometry of the valley following the equation proposed by Nye (1952), adjusting the thicknesses generated in the paleoglacial front. Phase 4) Calculation of BR in a reference glacier (Artesonraju; 8° 56’S; 77º38’W), near to the study area, using the product BR = b • z • s, where BR= Balance Ratio; b= mass balance measured in fieldwork 2004-2014 (m); z= average altitude (meters) and s= surface (m2) of each altitude band of the glacier (with intervals of 100 m altitude). A value BR = 2.3 was estimated. Phase 5) Automatic reconstruction of the ELA AABR2016 and paleoELA AABRLIA using ELA Calculation tool (Pellitero et al. 2015) after 3D reconstruction of the glacial and paleoglacial surface in phases 2 and 3. Phase 6) Estimation of paleotemperature during LIA by solving the equation of Porter et al. (1995): ∆T (°C)= ∆ELA • ATLR, where ∆T= air temperature depression (ºC); ∆ELA = variation of ELA AABR 2016-LIA and ATLR = Air Temperature Lapse Rate, using the average global value of the Earth (0.0065 °C/m), considered valid for tropics. The results obtained were: ELA AABR2016= 5260m, paleoELA AABRLIA= 5084m, and ∆T = 1.1 °C. The reconstruction of air paleotemperature is consistent with different studies that have estimated values between 1–2 °C colder than the present, with intense rainfall (Matthews & Briffa, 2005; Malone et al., 2015)

Modelo hipsométrico de la deglaciación futura de la Cuenca Paltay (Cordillera Blanca, Perú)

Este trabajo es un ensayo para evaluar la deglaciación de la cuenca Paltay (9°22’S; 77º22’W) en 2100, suponiendo cuatro escenarios de calentamiento global para ese año: +1 °C, +2 °C, +3 °C y +4 °C. El ensayo se realizó a lo largo de cuatro fases: Fase 1) Cálculo del BR en un glaciar de referencia, Artesonraju (8°56’S; 77º38’W), próximo al área de estudio de este trabajo. Dicho cálculo se realizó mediante el producto BR=b·z·s, donde BR es el Balance Ratio; b el balance de masa 2004-2014 (mm) medido en el campo; z la altitud media (m s.n.m.) y s la superficie (m2 ) de cada banda altitudinal del glaciar (con intervalos de 100 m). De ese modo se estimó un valor BR=2.3. Fase 2) Delimitación de los glaciares de la cuenca Paltay en 2016 y reconstrucción de su altitud de la línea de equilibrio (ELA, msnm). Con esa finalidad se utilizó la herramienta programada por Pellitero et al (2015), que indicó como resultado ELA2016=5189 m. Fase 3) Cálculo de las ELAs de los glaciares de la cuenca Paltay en 2100, correspondientes a cada incremento hipotético de temperatura. Se dedujeron despejando la ecuación de Porter et al., (1995): ∆T (°C)= ∆ELA·GVT, donde ∆T es la variación de la temperatura del aire (°C); ∆ELA la variación de la ELA 2016-2100 y GVT es el gradiente vertical de la temperatura del aire, empleando el valor global medio de la Tierra (0.065 °C/m). Con ese método se obtuvieron las siguientes estimaciones: ELA2100(+1 °C)=5342 m; ELA2100(+2 °C)=5496 m; ELA2100(+3 °C)=5650 m y ELA2100(+4 °C)=5804 m. Fase 4) Realización de 10 iteraciones regresivas para cada ELA2100, que permitieron calcular los siguientes porcentajes de reducción de superficie de los glaciares de la cuenca Paltay (en 2100 con respecto a 2016): 31% para +1 °C; 85% para +2 °C; 89% para +3 °C y 95% para +4 °C. La validación del método requerirá realizar nuevos ensayos en otras áreas de estudio

Boost glacier monitoring

Glacier-mass changes are a reliable indicator of climate change. On behalf of the worldwide network of glacier observers, we urge parties to the United Nations Framework Convention on Climate Change to boost international cooperation in monitoring these changes, and to include the results in the Paris agreement’s global stocktake. Since 1960, glaciers have lost more than 9,000 gigatonnes of ice worldwide — the equivalent of a 20-metre-thick layer with the area of Spain. This melting alone — as distinct from that of the Greenland and Antarctic ice sheets — has raised global sea level by almost 3 centimetres, contributing 25–30% of the total rise (M. Zemp et al. Nature 568, 382–386; 2019). The present rate of melting is unprecedented. Several mountain ranges are likely to lose most of their glaciers this century. And we face the loss of almost all glaciers by 2300 (B. Marzeion et al. Cryosph. 6, 1295–1322; 2012). Glacier shrinkage will severely affect freshwater availability and increase the risk of local geohazards. Global sea-level rise will result in the displacement of millions of people in coastal regions and in the loss of life, livelihoods and cultural- heritage sites. The systematic monitoring of glaciers has been internationally coordinated for 125 years. Continuing to do so will document progress in limiting climate change for current and future generations