Articles | Volume 14, issue 12
https://doi.org/10.5194/tc-14-4581-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-14-4581-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Dec 2020
Research article |
| 16 Dec 2020
| 16 Dec 2020
Measurements and modeling of snow albedo at Alerce Glacier, Argentina: effects of volcanic ash, snow grain size, and cloudiness
Julián Gelman Constantin
CORRESPONDING AUTHOR
División de Química Atmosférica, Gerencia de Química, Comisión Nacional de Energía Atómica, Av General Paz 1499, San Martin, B1650KNA Buenos Aires, Argentina
Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina
Lucas Ruiz
IANIGLA, Gobierno de Mendoza, Universidad Nacional de Cuyo, CONICET, CCT-Mendoza, Mendoza, Argentina
Gustavo Villarosa
Instituto Andino Patagónico de Tecnologías Biológicas y Geoambientales (IPATEC), CONICET-UNCo, Bariloche, Argentina
Departamento de Geología y Petróleo, Centro Regional Universitario Bariloche, Universidad Nacional del Comahue, Bariloche, Argentina
Valeria Outes
Instituto Andino Patagónico de Tecnologías Biológicas y Geoambientales (IPATEC), CONICET-UNCo, Bariloche, Argentina
Facundo N. Bajano
División de Química Atmosférica, Gerencia de Química, Comisión Nacional de Energía Atómica, Av General Paz 1499, San Martin, B1650KNA Buenos Aires, Argentina
Cenlin He
Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
Hector Bajano
División de Química Atmosférica, Gerencia de Química, Comisión Nacional de Energía Atómica, Av General Paz 1499, San Martin, B1650KNA Buenos Aires, Argentina
Laura Dawidowski
División de Química Atmosférica, Gerencia de Química, Comisión Nacional de Energía Atómica, Av General Paz 1499, San Martin, B1650KNA Buenos Aires, Argentina
Related authors
No articles found.
Line Rouyet, Tobias Bolch, Francesco Brardinoni, Rafael Caduff, Diego Cusicanqui, Margaret Darrow, Reynald Delaloye, Thomas Echelard, Christophe Lambiel, Cécile Pellet, Lucas Ruiz, Lea Schmid, Flavius Sirbu, and Tazio Strozzi
Earth Syst. Sci. Data, 17, 4125–4157, https://doi.org/10.5194/essd-17-4125-2025, https://doi.org/10.5194/essd-17-4125-2025, 2025
Short summary
Short summary
Rock glaciers are landforms generated by the creep of frozen ground (permafrost) in cold-climate mountains. Mapping rock glaciers contributes to documenting the distribution and the dynamics of mountain permafrost. We compiled inventories documenting the location, the characteristics, and the extent of rock glaciers in 12 mountain regions around the world. In each region, a team of operators performed the work following common rules and agreed on final solutions when discrepancies were identified.
Siyu Zhao, Rong Fu, Kelly Núñez Ocasio, Robert Nystrom, Cenlin He, and Jiaying Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-3591, https://doi.org/10.5194/egusphere-2025-3591, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
The Congo Basin has frequent organized thunderstorms producing much of the region’s rainfall, yet their development remains unclear due to limited data. Using a high-resolution global model, it shows the long-lasting storm is supported by vertical wind shear up to 400 km ahead, explaining up to 65 % of its variance, with the mid-level jet stream playing a role in maintaining the shear. The findings highlight the value of such model in data-sparse regions for examining storms and their impacts.
Yanyan Cheng, Kalli Furtado, Cenlin He, Fei Chen, Alan Ziegler, Song Chen, Matteo Detto, Yuna Mao, Baoxiang Pan, Yoshiko Kosugi, Marryanna Lion, Shoji Noguchi, Satoru Takanashi, Lulie Melling, and Baoqing Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-3898, https://doi.org/10.5194/egusphere-2025-3898, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Tropical land surface processes shape the Earth’s climate, but models often lack accuracy in the tropics due to limited data for validation. We improved the Noah-MP land surface model for the tropics using data from forests in Panama and Malaysia, and an urban site in Singapore. Calibration enhanced simulations of energy and water fluxes, and revealed key vegetation and soil parameters, as well as future directions for model improvement in tropical regions.
Chayan Roychoudhury, Cenlin He, Rajesh Kumar, and Avelino F. Arellano Jr.
Earth Syst. Dynam., 16, 1237–1266, https://doi.org/10.5194/esd-16-1237-2025, https://doi.org/10.5194/esd-16-1237-2025, 2025
Short summary
Short summary
We present a novel data-driven approach to understand how pollution and weather processes interact to influence snowmelt in Asian glaciers and how these interactions are represented in three climate models. Our findings show where models need improvement in predicting snowmelt, particularly dust and its transport. This method can support future model development for reliable predictions in climate-vulnerable regions.
Rajesh Kumar, Piyush Bhardwaj, Cenlin He, Jennifer Boehnert, Forrest Lacey, Stefano Alessandrini, Kevin Sampson, Matthew Casali, Scott Swerdlin, Olga Wilhelmi, Gabriele G. Pfister, Benjamin Gaubert, and Helen Worden
Earth Syst. Sci. Data, 17, 1807–1834, https://doi.org/10.5194/essd-17-1807-2025, https://doi.org/10.5194/essd-17-1807-2025, 2025
Short summary
Short summary
We have created a 14-year hourly air quality dataset at 12 km resolution by combining satellite observations of atmospheric composition with air quality models over the contiguous United States (CONUS). The dataset has been found to reproduce key observed features of air quality over the CONUS. To enable easy visualization and interpretation of county-level air quality measures and trends by stakeholders, an ArcGIS air quality dashboard has also been developed.
Parag Joshi, Tzu-Shun Lin, Cenlin He, and Katia Lamer
EGUsphere, https://doi.org/10.5194/egusphere-2025-1751, https://doi.org/10.5194/egusphere-2025-1751, 2025
Short summary
Short summary
Study revisits urban representation (using canopy models & bulk parameterization) in the Weather Research & Forecasting model. We propose methods to identify evaluable parameters via field measurements and found inconsistencies between UCM physics and code implementation. Simulations reveal small errors can significantly impact outputs, highlighting the need for precise physics implementation.
Mohamed Hamitouche, Giorgia Fosser, Alessandro Anav, Cenlin He, and Tzu-Shun Lin
Hydrol. Earth Syst. Sci., 29, 1221–1240, https://doi.org/10.5194/hess-29-1221-2025, https://doi.org/10.5194/hess-29-1221-2025, 2025
Short summary
Short summary
This study evaluates how different methods of simulating runoff impact river flow predictions globally. By comparing seven approaches within the Noah-Multi-parameterisation (Noah-MP) land surface model, we found significant differences in accuracy, with some methods underestimating or overestimating runoff. The results are crucial for improving water resource management and flood prediction. Our work highlights the need for precise modelling to better prepare for climate-related challenges.
Cenlin He, Tzu-Shun Lin, David M. Mocko, Ronnie Abolafia-Rosenzweig, Jerry W. Wegiel, and Sujay V. Kumar
EGUsphere, https://doi.org/10.5194/egusphere-2024-4176, https://doi.org/10.5194/egusphere-2024-4176, 2025
Short summary
Short summary
This study integrates the refactored community Noah-MP version 5.0 model with the NASA Land Information System (LIS) version 7.5.2 to streamline the synchronization, development, and maintenance of Noah-MP within LIS and to enhance their interoperability and applicability. The model benchmarking and evaluation results reveal key model strengths and weaknesses in simulating land surface quantities and show implications for future model improvements.
Dario Di Santo, Cenlin He, Fei Chen, and Lorenzo Giovannini
Geosci. Model Dev., 18, 433–459, https://doi.org/10.5194/gmd-18-433-2025, https://doi.org/10.5194/gmd-18-433-2025, 2025
Short summary
Short summary
This paper presents the Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool (ML-AMPSIT), a computationally efficient tool that uses machine learning algorithms for sensitivity analysis in atmospheric models. It is tested with the Weather Research and Forecasting (WRF) model coupled with the Noah-Multiparameterization (Noah-MP) land surface model to investigate sea breeze circulation sensitivity to vegetation-related parameters.
Sarah E. Hancock, Daniel J. Jacob, Zichong Chen, Hannah Nesser, Aaron Davitt, Daniel J. Varon, Melissa P. Sulprizio, Nicholas Balasus, Lucas A. Estrada, María Cazorla, Laura Dawidowski, Sebastián Diez, James D. East, Elise Penn, Cynthia A. Randles, John Worden, Ilse Aben, Robert J. Parker, and Joannes D. Maasakkers
Atmos. Chem. Phys., 25, 797–817, https://doi.org/10.5194/acp-25-797-2025, https://doi.org/10.5194/acp-25-797-2025, 2025
Short summary
Short summary
We quantify 2021 methane emissions in South America at up to 25 km × 25 km resolution using satellite methane observations. We find a 55 % upward adjustment to anthropogenic emission inventories, including those reported to the UN Framework Convention on Climate Change under the Paris Agreement. Our estimates match inventories for Brazil, Bolivia, and Paraguay but are much higher for other countries. Livestock emissions (65 % of anthropogenic emissions) show the largest discrepancies.
Jorge E. Pachón, Mariel A. Opazo, Pablo Lichtig, Nicolas Huneeus, Idir Bouarar, Guy Brasseur, Cathy W. Y. Li, Johannes Flemming, Laurent Menut, Camilo Menares, Laura Gallardo, Michael Gauss, Mikhail Sofiev, Rostislav Kouznetsov, Julia Palamarchuk, Andreas Uppstu, Laura Dawidowski, Nestor Y. Rojas, María de Fátima Andrade, Mario E. Gavidia-Calderón, Alejandro H. Delgado Peralta, and Daniel Schuch
Geosci. Model Dev., 17, 7467–7512, https://doi.org/10.5194/gmd-17-7467-2024, https://doi.org/10.5194/gmd-17-7467-2024, 2024
Short summary
Short summary
Latin America (LAC) has some of the most populated urban areas in the world, with high levels of air pollution. Air quality management in LAC has been traditionally focused on surveillance and building emission inventories. This study performed the first intercomparison and model evaluation in LAC, with interesting and insightful findings for the region. A multiscale modeling ensemble chain was assembled as a first step towards an air quality forecasting system.
Wenfu Tang, Louisa K. Emmons, Helen M. Worden, Rajesh Kumar, Cenlin He, Benjamin Gaubert, Zhonghua Zheng, Simone Tilmes, Rebecca R. Buchholz, Sara-Eva Martinez-Alonso, Claire Granier, Antonin Soulie, Kathryn McKain, Bruce C. Daube, Jeff Peischl, Chelsea Thompson, and Pieternel Levelt
Geosci. Model Dev., 16, 6001–6028, https://doi.org/10.5194/gmd-16-6001-2023, https://doi.org/10.5194/gmd-16-6001-2023, 2023
Short summary
Short summary
The new MUSICAv0 model enables the study of atmospheric chemistry across all relevant scales. We develop a MUSICAv0 grid for Africa. We evaluate MUSICAv0 with observations and compare it with a previously used model – WRF-Chem. Overall, the performance of MUSICAv0 is comparable to WRF-Chem. Based on model–satellite discrepancies, we find that future field campaigns in an eastern African region (30°E–45°E, 5°S–5°N) could substantially improve the predictive skill of air quality models.
Cenlin He, Prasanth Valayamkunnath, Michael Barlage, Fei Chen, David Gochis, Ryan Cabell, Tim Schneider, Roy Rasmussen, Guo-Yue Niu, Zong-Liang Yang, Dev Niyogi, and Michael Ek
Geosci. Model Dev., 16, 5131–5151, https://doi.org/10.5194/gmd-16-5131-2023, https://doi.org/10.5194/gmd-16-5131-2023, 2023
Short summary
Short summary
Noah-MP is one of the most widely used open-source community land surface models in the world, designed for applications ranging from uncoupled land surface and ecohydrological process studies to coupled numerical weather prediction and decadal climate simulations. To facilitate model developments and applications, we modernize Noah-MP by adopting modern Fortran code and data structures and standards, which substantially enhance model modularity, interoperability, and applicability.
Zhe Zhang, Yanping Li, Fei Chen, Phillip Harder, Warren Helgason, James Famiglietti, Prasanth Valayamkunnath, Cenlin He, and Zhenhua Li
Geosci. Model Dev., 16, 3809–3825, https://doi.org/10.5194/gmd-16-3809-2023, https://doi.org/10.5194/gmd-16-3809-2023, 2023
Short summary
Short summary
Crop models incorporated in Earth system models are essential to accurately simulate crop growth processes on Earth's surface and agricultural production. In this study, we aim to model the spring wheat in the Northern Great Plains, focusing on three aspects: (1) develop the wheat model at a point scale, (2) apply dynamic planting and harvest schedules, and (3) adopt a revised heat stress function. The results show substantial improvements and have great importance for agricultural production.
Wenfu Tang, Simone Tilmes, David M. Lawrence, Fang Li, Cenlin He, Louisa K. Emmons, Rebecca R. Buchholz, and Lili Xia
Atmos. Chem. Phys., 23, 5467–5486, https://doi.org/10.5194/acp-23-5467-2023, https://doi.org/10.5194/acp-23-5467-2023, 2023
Short summary
Short summary
Globally, total wildfire burned area is projected to increase over the 21st century under scenarios without geoengineering and decrease under the two geoengineering scenarios. Geoengineering reduces fire by decreasing surface temperature and wind speed and increasing relative humidity and soil water. However, geoengineering also yields reductions in precipitation, which offset some of the fire reduction.
Melisa Diaz Resquin, Pablo Lichtig, Diego Alessandrello, Marcelo De Oto, Darío Gómez, Cristina Rössler, Paula Castesana, and Laura Dawidowski
Earth Syst. Sci. Data, 15, 189–209, https://doi.org/10.5194/essd-15-189-2023, https://doi.org/10.5194/essd-15-189-2023, 2023
Short summary
Short summary
We explored the performance of the random forest algorithm to predict CO, NOx, PM10, SO2, and O3 air quality concentrations and comparatively assessed the monitored and modeled concentrations during the COVID-19 lockdown phases. We provide the first long-term O3 and SO2 observational dataset for an urban–residential area of Buenos Aires in more than a decade and study the responses of O3 to the reduction in the emissions of its precursors because of its relevance regarding emission control.
Dalei Hao, Gautam Bisht, Karl Rittger, Edward Bair, Cenlin He, Huilin Huang, Cheng Dang, Timbo Stillinger, Yu Gu, Hailong Wang, Yun Qian, and L. Ruby Leung
Geosci. Model Dev., 16, 75–94, https://doi.org/10.5194/gmd-16-75-2023, https://doi.org/10.5194/gmd-16-75-2023, 2023
Short summary
Short summary
Snow with the highest albedo of land surface plays a vital role in Earth’s surface energy budget and water cycle. This study accounts for the impacts of snow grain shape and mixing state of light-absorbing particles with snow on snow albedo in the E3SM land model. The findings advance our understanding of the role of snow grain shape and mixing state of LAP–snow in land surface processes and offer guidance for improving snow simulations and radiative forcing estimates in Earth system models.
Huilin Huang, Yun Qian, Ye Liu, Cenlin He, Jianyu Zheng, Zhibo Zhang, and Antonis Gkikas
Atmos. Chem. Phys., 22, 15469–15488, https://doi.org/10.5194/acp-22-15469-2022, https://doi.org/10.5194/acp-22-15469-2022, 2022
Short summary
Short summary
Using a clustering method developed in the field of artificial neural networks, we identify four typical dust transport patterns across the Sierra Nevada, associated with the mesoscale and regional-scale wind circulations. Our results highlight the connection between dust transport and dominant weather patterns, which can be used to understand dust transport in a changing climate.
Aldo Bertone, Chloé Barboux, Xavier Bodin, Tobias Bolch, Francesco Brardinoni, Rafael Caduff, Hanne H. Christiansen, Margaret M. Darrow, Reynald Delaloye, Bernd Etzelmüller, Ole Humlum, Christophe Lambiel, Karianne S. Lilleøren, Volkmar Mair, Gabriel Pellegrinon, Line Rouyet, Lucas Ruiz, and Tazio Strozzi
The Cryosphere, 16, 2769–2792, https://doi.org/10.5194/tc-16-2769-2022, https://doi.org/10.5194/tc-16-2769-2022, 2022
Short summary
Short summary
We present the guidelines developed by the IPA Action Group and within the ESA Permafrost CCI project to include InSAR-based kinematic information in rock glacier inventories. Nine operators applied these guidelines to 11 regions worldwide; more than 3600 rock glaciers are classified according to their kinematics. We test and demonstrate the feasibility of applying common rules to produce homogeneous kinematic inventories at global scale, useful for hydrological and climate change purposes.
Chaman Gul, Shichang Kang, Siva Praveen Puppala, Xiaokang Wu, Cenlin He, Yangyang Xu, Inka Koch, Sher Muhammad, Rajesh Kumar, and Getachew Dubache
Atmos. Chem. Phys., 22, 8725–8737, https://doi.org/10.5194/acp-22-8725-2022, https://doi.org/10.5194/acp-22-8725-2022, 2022
Short summary
Short summary
This work aims to understand concentrations, spatial variability, and potential source regions of light-absorbing impurities (black carbon aerosols, dust particles, and organic carbon) in the surface snow of central and western Himalayan glaciers and their impact on snow albedo and radiative forcing.
Paula Castesana, Melisa Diaz Resquin, Nicolás Huneeus, Enrique Puliafito, Sabine Darras, Darío Gómez, Claire Granier, Mauricio Osses Alvarado, Néstor Rojas, and Laura Dawidowski
Earth Syst. Sci. Data, 14, 271–293, https://doi.org/10.5194/essd-14-271-2022, https://doi.org/10.5194/essd-14-271-2022, 2022
Short summary
Short summary
This work presents the results of the first joint effort of South American and European researchers to generate regional maps of emissions. The PAPILA dataset is a collection of annual emission inventories of reactive gases (CO, NOx, NMVOCs, NH3, and SO2) from anthropogenic sources in the region for the period 2014–2016. This was developed on the basis of the CAMS-GLOB-ANT v4.1 dataset, enriching it with derived data from locally available emission inventories for Argentina, Chile, and Colombia.
Mark G. Flanner, Julian B. Arnheim, Joseph M. Cook, Cheng Dang, Cenlin He, Xianglei Huang, Deepak Singh, S. McKenzie Skiles, Chloe A. Whicker, and Charles S. Zender
Geosci. Model Dev., 14, 7673–7704, https://doi.org/10.5194/gmd-14-7673-2021, https://doi.org/10.5194/gmd-14-7673-2021, 2021
Short summary
Short summary
We present the technical formulation and evaluation of a publicly available code and web-based model to simulate the spectral albedo of snow. Our model accounts for numerous features of the snow state and ambient conditions, including the the presence of light-absorbing matter like black and brown carbon, mineral dust, volcanic ash, and snow algae. Carbon dioxide snow, found on Mars, is also represented. The model accurately reproduces spectral measurements of clean and contaminated snow.
Wenfu Tang, Benjamin Gaubert, Louisa Emmons, Yonghoon Choi, Joshua P. DiGangi, Glenn S. Diskin, Xiaomei Xu, Cenlin He, Helen Worden, Simone Tilmes, Rebecca Buchholz, Hannah S. Halliday, and Avelino F. Arellano
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-864, https://doi.org/10.5194/acp-2020-864, 2020
Revised manuscript not accepted
Short summary
Short summary
A specific demonstration of the potential use of correlative information from carbon monoxide to refine estimates of regional carbon dioxide emissions from fossil fuel combustion.
Cited articles
Aas, K. S., Dunse, T., Collier, E., Schuler, T. V., Berntsen, T. K., Kohler, J., and Luks, B.: The climatic mass balance of Svalbard glaciers: a 10-year simulation with a coupled atmosphere–glacier mass balance model, The Cryosphere, 10, 1089–1104, https://doi.org/10.5194/tc-10-1089-2016, 2016. a
Alloway, B. V., Pearce, N. J. G., Villarosa, G., Outes, V., and Moreno, P. I.:
Multiple melt bodies fed the AD 2011 eruption of Puyehue-Cordón
Caulle, Chile, Scientific Reports, 5, 17589, https://doi.org/10.1038/srep17589,
2015. a, b, c
Bond, T. C., Doherty, S. J., Fahey, D. W., Forster, P. M., Berntsen, T.,
Deangelo, B. J., Flanner, M. G., Ghan, S., Kärcher, B., Koch, D. M.,
Kinne, S., Kondo, Y., Quinn, P. K., Sarofim, M. C., Schultz, M. G., Schulz,
M., Venkataraman, C., Zhang, H., Zhang, S., Bellouin, N., Guttikunda, S. K.,
Hopke, P. K., Jacobson, M. Z., Kaiser, J. W., Klimont, Z., Lohmann, U.,
Schwarz, J. P., Shindell, D. T., Storelvmo, T., Warren, S. G., and Zender,
C. S.: Bounding the role of black carbon in the climate system: A scientific
assessment, J. Geophys. Res.-Atmos., 118, 5380–5552,
https://doi.org/10.1002/jgrd.50171,
2013. a, b, c, d
Brandt, R. E., Warren, S. G., and Clarke, A. D.: A controlled snowmaking
experiment testing the relation between black carbon content and reduction of
snow albedo, J. Geophys. Res., 116, D08109,
https://doi.org/10.1029/2010JD015330,
2011. a
Brock, B., Rivera, A., Casassa, G., Bown, F., and Acuña, C.: The surface
energy balance of an active ice-covered volcano: Villarrica Volcano, southern
Chile, Ann. Glaciol., 45, 104–114, https://doi.org/10.3189/172756407782282372,
2007. a, b
Brock, B. W., Willis, I. C., and Sharp, M. J.: Measurement and
parameterisation of albedo variations at Haut Glacier d ’Arolla,
Switzerland, J. Glaciol., 46, 675–688,
https://doi.org/10.3189/172756506781828746, 2000. a, b, c
Carmagnola, C. M., Domine, F., Dumont, M., Wright, P., Strellis, B., Bergin, M., Dibb, J., Picard, G., Libois, Q., Arnaud, L., and Morin, S.: Snow spectral albedo at Summit, Greenland: measurements and numerical simulations based on physical and chemical properties of the snowpack, The Cryosphere, 7, 1139–1160, https://doi.org/10.5194/tc-7-1139-2013, 2013. a, b, c, d
Cereceda-Balic, F., Vidal, V., Moosmüller, H., and Lapuerta, M.:
Reduction of snow albedo from vehicle emissions at Portillo, Chile, Cold
Reg. Sci. Technol., 146, 43–52,
https://doi.org/10.1016/j.coldregions.2017.11.008,
2018. a, b
Collier, E., Mölg, T., Maussion, F., Scherer, D., Mayer, C., and Bush, A. B. G.: High-resolution interactive modelling of the mountain glacier–atmosphere interface: an application over the Karakoram, The Cryosphere, 7, 779–795, https://doi.org/10.5194/tc-7-779-2013, 2013. a
Conway, H., Gades, A., and Raymond, C. F.: Albedo of dirty snow during
conditions of melt, Water Resour. Res., 32, 1713–1718,
https://doi.org/10.1029/96WR00712, 1996. a, b
Córdoba, G., Villarosa, G., Sheridan, M. F., Viramonte, J. G., Beigt, D., and Salmuni, G.: Secondary lahar hazard assessment for Villa la Angostura, Argentina, using Two-Phase-Titan modelling code during 2011 Cordón Caulle eruption, Nat. Hazards Earth Syst. Sci., 15, 757–766, https://doi.org/10.5194/nhess-15-757-2015, 2015. a
Cuffey, K. M. and Paterson, W. S. B.: The physics of glaciers, Academic
Press, 4th editio edn., ISBN
9780123694614, 2010. a
Doherty, S. J., Grenfell, T. C., Forsström, S., Hegg, D. L., Brandt,
R. E., and Warren, S. G.: Observed vertical redistribution of black carbon
and other insoluble light-absorbing particles in melting snow, J.
Geophys. Res.-Atmos., 118, 5553–5569, https://doi.org/10.1002/jgrd.50235,
2013. a
Doherty, S. J., Hegg, D. A., Johnson, J. E., Quinn, P. K., Schwarz, J. P.,
Dang, C., and Warren, S. G.: Causes of variability in light absorption by
particles in snow at sites in Idaho and Utah, J. Geophys.
Res., 121, 4751–4768, https://doi.org/10.1002/2015jd024375, 2016. a
Dussaillant, I., Berthier, E., Brun, F., Masiokas, M., Hugonnet, R., Favier,
V., Rabatel, A., Pitte, P., and Ruiz, L.: Two decades of glacier mass loss
along the Andes, Nature Geosci., 12, 802–808,
https://doi.org/10.1038/s41561-019-0432-5,
2019. a
Ernst, M., Holst, H., Winter, M., and Altermatt, P. P.: SunCalculator: A
program to calculate the angular and spectral distribution of direct and
diffuse solar radiation, Solar Energy Materials and Solar Cells, 157,
913–922, https://doi.org/10.1016/J.SOLMAT.2016.08.008,
2016. a, b
Flanner, M. G.: Arctic climate sensitivity to local black carbon, J.
Geophys. Res.-Atmos., 118, 1840–1851, https://doi.org/10.1002/jgrd.50176, 2013. a
Flanner, M. G. and Zender, C. S.: Linking snowpack microphysics and albedo
evolution, J. Geophys. Res., 111, D12208,
https://doi.org/10.1029/2005JD006834,
2006. a
Garreaud, R. D., Vuille, M., Compagnucci, R., and Marengo, J.: Present-day
South American climate, Palaeogeogr. Palaeoclimatol. Palaeoecol.,
281, 180–195, https://doi.org/10.1016/j.palaeo.2007.10.032, 2009. a
Ginot, P., Dumont, M., Lim, S., Patris, N., Taupin, J.-D., Wagnon, P., Gilbert, A., Arnaud, Y., Marinoni, A., Bonasoni, P., and Laj, P.: A 10 year record of black carbon and dust from a Mera Peak ice core (Nepal): variability and potential impact on melting of Himalayan glaciers, The Cryosphere, 8, 1479–1496, https://doi.org/10.5194/tc-8-1479-2014, 2014. a, b, c, d, e, f
Gueymard, C.: Une paramétrisation de la luminance
énergétique du ciel clair en fonction de la turbidité,
Atmosphere-Ocean, 24, 1–15, https://doi.org/10.1080/07055900.1986.9649237,
1986. a
Gueymard, C.: An anisotropic solar irradiance model for tilted surfaces and
its comparison with selected engineering algorithms, Sol. Energ., 38,
367–386, https://doi.org/10.1016/0038-092X(87)90009-0,
1987. a
Gueymard, C. A.: Parameterized transmittance model for direct beam and
circumsolar spectral irradiance, Sol. Energ., 71, 325–346,
https://doi.org/10.1016/S0038-092X(01)00054-8, 2001. a
Hadley, O. L. and Kirchstetter, T. W.: Black-carbon reduction of snow albedo,
Nature Climate Change, 2, 437–440, https://doi.org/10.1038/nclimate1433, 2012. a, b, c
Hansen, J., Sato, M., Ruedy, R., Nazarenko, L., Lacis, A., Schmidt, G. A.,
Russell, G., Aleinov, I., Bauer, M., Bauer, S., Bell, N., Cairns, B., Canuto,
V., Chandler, M., Cheng, Y., Genio, A. D., Faluvegi, G., Fleming, E., Friend,
A., Hall, T., Jackman, C., Kelley, M., Kiang, N., Koch, D., Lean, J., Lerner,
J., Lo, K., Menon, S., Miller, R., Minnis, P., Novakov, T., Oinas, V.,
Perlwitz, J., Perlwitz, J., Rind, D., Romanou, A., Shindell, D., Stone, P.,
Sun, S., Tausnev, N., Thresher, D., Wielicki, B., Wong, T., Yao, M., and
Zhang, S.: Efficacy of climate forcings, J. Geophys. Res.,
110, D18104, https://doi.org/10.1029/2005JD005776,
2005. a
He, C.: EarthSciCode/SNICARv2: Release of SNICARv2.1 (Version v2.1), Zenodo, https://doi.org/10.5281/zenodo.4319016, 2020. a
He, C. and Flanner, M.: Snow Albedo and Radiative Transfer: Theory, Modeling,
and Parameterization, in: Springer Series in Light Scattering (Volume 5),
edited by: Kokhanovsky, A. A., pp. 67–133, Springer, Cham,
https://doi.org/10.1007/978-3-030-38696-2_3,
2020. a
He, C., Takano, Y., Liou, K.-N., Yang, P., Li, Q., and Chen, F.: Impact of
Snow Grain Shape and Black Carbon–Snow Internal Mixing on Snow Optical
Properties: Parameterizations for Climate Models, J. Climate, 30,
10019–10036, https://doi.org/10.1175/JCLI-D-17-0300.1,
2017. a, b, c, d
He, C., Flanner, M. G., Chen, F., Barlage, M., Liou, K.-N., Kang, S., Ming, J., and Qian, Y.: Black carbon-induced snow albedo reduction over the Tibetan Plateau: uncertainties from snow grain shape and aerosol–snow mixing state based on an updated SNICAR model, Atmos. Chem. Phys., 18, 11507–11527, https://doi.org/10.5194/acp-18-11507-2018, 2018. a, b, c, d
Hock, R.: A distributed temperature-index ice- and snowmelt model including
potential direct solar radiation, J. Glaciol., 45, 101–111,
https://doi.org/10.3189/s0022143000003087, 1999. a, b
Huss, M.: Mass balance of Pizolgletscher, Geogr. Helv., 65, 80–91,
https://doi.org/10.5194/gh-65-80-2010,
2010. a
Huss, M., Bauder, A., Funk, M., and Hock, R.: Determination of the seasonal
mass balance of four Alpine glaciers since 1865, J. Geophys.
Res., 113, F01015, https://doi.org/10.1029/2007JF000803,
2008. a, b, c
IPCC: IPCC Special Report on the Ocean and Cryosphere in a Changing
Climate, available at: https://www.ipcc.ch/report/srocc/ (last access: 15 September 2020), 2019. a
Kasten, F. and Czeplak, G.: Solar and terrestrial radiation dependent on the
amount and type of cloud, Sol. Energ., 24, 177–189,
https://doi.org/10.1016/0038-092X(80)90391-6, 1980. a
Koch, D. M., Menon, S., Del Genio, A., Ruedy, R., Alienov, I., and Schmidt,
G. A.: Distinguishing Aerosol Impacts on Climate over the Past Century, J.
Climate, 22, 2659–2677, https://doi.org/10.1175/2008JCLI2573.1,
2009. a
Krinner, G., Boucher, O., and Balkanski, Y.: Ice-free glacial northern Asia
due to dust deposition on snow, Climate Dyn., 27, 613–625,
https://doi.org/10.1007/s00382-006-0159-z,
2006. a, b, c
Le Bas, M. J., Le Maitre, R. W., Streckeisen, A., and Zanettin, B.: A chemical
classification of volcanic rocks based on the total alkali-silica diagram,
J. Petrol., 27, 745–750, https://doi.org/10.1093/petrology/27.3.745, 1986. a
Li, X., Kang, S., He, X., Qu, B., Tripathee, L., Jing, Z., Paudyal, R., Li, Y.,
Zhang, Y., Yan, F., Li, G., and Li, C.: Light-absorbing impurities
accelerate glacier melt in the Central Tibetan Plateau, Sci. Total
Environ., 587–588, 482–490, https://doi.org/10.1016/j.scitotenv.2017.02.169, 2017. a
Libois, Q., Picard, G., France, J. L., Arnaud, L., Dumont, M., Carmagnola, C. M., and King, M. D.: Influence of grain shape on light penetration in snow, The Cryosphere, 7, 1803–1818, https://doi.org/10.5194/tc-7-1803-2013, 2013. a, b
Malmros, J. K., Mernild, S. H., Wilson, R., Tagesson, T., and Fensholt, R.:
Snow cover and snow albedo changes in the central Andes of Chile and
Argentina from daily MODIS observations (2000–2016), Remote Sens.
Environ., 209, 240–252, https://doi.org/10.1016/J.RSE.2018.02.072, 2018. a
Ménégoz, M., Krinner, G., Balkanski, Y., Boucher, O., Cozic, A., Lim, S., Ginot, P., Laj, P., Gallée, H., Wagnon, P., Marinoni, A., and Jacobi, H. W.: Snow cover sensitivity to black carbon deposition in the Himalayas: from atmospheric and ice core measurements to regional climate simulations, Atmos. Chem. Phys., 14, 4237–4249, https://doi.org/10.5194/acp-14-4237-2014, 2014. a, b, c, d, e
Molina, L. T., Gallardo, L., Andrade, M., Baumgardner, D.,
Borbor-Córdova, M., Bórquez, R., Casassa, G., Cereceda-Balic, F.,
Dawidowski, L., Garreaud, R., Huneeus, N., Lambert, F., McCarty, J., Mc Phee,
J., Mena-Carrasco, M., Raga, G. B., Schmitt, C. G., and Schwarz, J. P.:
Pollution and its impacts on the South American Cryosphere (PISAC), Earth's
Future, 3, 345–369, https://doi.org/10.1002/2015EF000311, 2015. a
Oerlemans, J. and Knap, W. H.: A 1 year record of global radiation and albedo
in the ablation zone of Morteratschgletscher, Switzerland, J.
Glaciol., 44, 231–238, https://doi.org/10.3189/s0022143000002574, 1998. a, b
Painter, T. H., Skiles, S. M., Deems, J. S., Bryant, A. C., and Landry, C. C.:
Dust radiative forcing in snow of the Upper Colorado River Basin: 1. A 6
year record of energy balance, radiation, and dust concentrations, Water
Resour. Res., 48, W07521, https://doi.org/10.1029/2012WR011985,
2012. a, b, c
Painter, T. H., Flanner, M. G., Kaser, G., Marzeion, B., VanCuren, R. A., and
Abdalati, W.: End of the Little Ice Age in the Alps forced by industrial
black carbon, P. Natl. Acad. Sci., 110,
15216–15221, https://doi.org/10.1073/pnas.1302570110,
2013. a, b
Qian, Y., Yasunari, T. J., Doherty, S. J., Flanner, M. G., Lau, W. K. M., Jing,
M., Wang, H., Wang, M., Warren, S. G., and Zhang, R.: Light-absorbing
Particles in Snow and Ice: Measurement and Modeling of Climatic and
Hydrological impact, Adv. Atmos. Sci., 32, 64–91,
https://doi.org/10.1007/s00376-014-0010-0, 2015. a, b, c
Reckziegel, F., Bustos, E., Mingari, L., Báez, W., Villarosa, G., Folch,
A., Collini, E., Viramonte, J., Romero, J., and Osores, S.: Forecasting
volcanic ash dispersal and coeval resuspension during the April–May 2015
Calbuco eruption, J. Volcanol. Geoth. Res., 321,
44–57, https://doi.org/10.1016/j.jvolgeores.2016.04.033,
2016. a, b, c
Romero, J. E., Morgavi, D., Arzilli, F., Daga, R., Caselli, A., Reckziegel, F.,
Viramonte, J., Díaz-Alvarado, J., Polacci, M., Burton, M., and
Perugini, D.: Eruption dynamics of the 22–23 April 2015 Calbuco Volcano
(Southern Chile): Analyses of tephra fall deposits, J. Volcanol. Geoth. Res., 317, 15–29, https://doi.org/10.1016/j.jvolgeores.2016.02.027,
2016. a
Rowe, P. M., Cordero, R. R., Warren, S. G., Stewart, E., Doherty, S. J.,
Pankow, A., Schrempf, M., Casassa, G., Carrasco, J., Pizarro, J., MacDonell,
S., Damiani, A., Lambert, F., Rondanelli, R., Huneeus, N., Fernandoy, F., and
Neshyba, S.: Black carbon and other light-absorbing impurities in snow in
the Chilean Andes, Scientific Reports, 9, 4008,
https://doi.org/10.1038/s41598-019-39312-0,
2019. a, b, c
Ruiz, L., Berthier, E., Masiokas, M., Pitte, P., and Villalba, R.: First
surface velocity maps for glaciers of Monte Tronador, North Patagonian Andes,
derived from sequential Pléiades satellite images, J.
Glaciol., 61, 908–922, https://doi.org/10.3189/2015JoG14J134, 2015. a, b
Ruiz, L., Berthier, E., Viale, M., Pitte, P., and Masiokas, M. H.: Recent geodetic mass balance of Monte Tronador glaciers, northern Patagonian Andes, The Cryosphere, 11, 619–634, https://doi.org/10.5194/tc-11-619-2017, 2017. a
Schaefer, M., Fonseca-Gallardo, D., Farías-Barahona, D., and Casassa, G.: Surface energy fluxes on Chilean glaciers: measurements and models, The Cryosphere, 14, 2545–2565, https://doi.org/10.5194/tc-14-2545-2020, 2020. a
Schmitt, C. G., All, J. D., Schwarz, J. P., Arnott, W. P., Cole, R. J., Lapham, E., and Celestian, A.: Measurements of light-absorbing particles on the glaciers in the Cordillera Blanca, Peru, The Cryosphere, 9, 331–340, https://doi.org/10.5194/tc-9-331-2015, 2015. a
Schneider, C. A., Rasband, W. S., and Eliceiri, K. W.: NIH Image to ImageJ: 25
years of image analysis, Nature Methods, 9, 671–675 , https://doi.org/10.1038/nmeth.2089, 2012. a
Sicart, J. E., Ribstein, P., Wagnon, P., and Brunstein, D.: Clear-sky albedo
measurements on a sloping glacier surface: A case study in the Bolivian
Andes, J. Geophys. Res., 106, 31729–31737,
https://doi.org/10.1029/2000JD000153, 2001. a
Skiles, S. M. and Painter, T. H.: Daily evolution in dust and black carbon
content, snow grain size, and snow albedo during snowmelt, Rocky Mountains,
Colorado, J. Glaciol., 63, 118–132, https://doi.org/10.1017/jog.2016.125,
2017. a, b
Skiles, S. M., Flanner, M., Cook, J. M., Dumont, M., and Painter, T. H.:
Radiative forcing by light-absorbing particles in snow, Nat. Clim. Change, 8, 964–971,
https://doi.org/10.1038/s41558-018-0296-5, 2018. a
Sold, L., Huss, M., Machguth, H., Joerg, P. C., Leysinger Vieli, G., Linsbauer,
A., Salzmann, N., Zemp, M., and Hoelzle, M.: Mass Balance Re-analysis of
Findelengletscher, Switzerland; Benefits of Extensive Snow Accumulation
Measurements, Front. Earth Sci., 4, 18, https://doi.org/10.3389/feart.2016.00018,
2016. a
Toyos, G., Mingari, L., Pujol, G., and Villarosa, G.: Investigating the nature
of an ash cloud event in Southern Chile using remote sensing: volcanic
eruption or resuspension?, Remote Sens. Lett., 8, 146–155,
https://doi.org/10.1080/2150704X.2016.1239281,
2017. a
Tuzet, F., Dumont, M., Lafaysse, M., Picard, G., Arnaud, L., Voisin, D., Lejeune, Y., Charrois, L., Nabat, P., and Morin, S.: A multilayer physically based snowpack model simulating direct and indirect radiative impacts of light-absorbing impurities in snow, The Cryosphere, 11, 2633–2653, https://doi.org/10.5194/tc-11-2633-2017, 2017. a
Villarosa, G., Outes, V., Delménico, A., Beigt, D., Cottet, J., Toyos,
G., Horwell, C. J., Damby, D. E., Najorka, J., Arretche, M., Wilson, T., and
Stewart, C.: Impacts after the 2015 Calbuco eruption in Argentina and their
relation to tephra deposit characteristics and climatic variables, in:
Cities on Volcanoes 9, Puerto Varas, Chile, 2016. a, b
Vionnet, V., Brun, E., Morin, S., Boone, A., Faroux, S., Le Moigne, P., Martin, E., and Willemet, J.-M.: The detailed snowpack scheme Crocus and its implementation in SURFEX v7.2, Geosci. Model Dev., 5, 773–791, https://doi.org/10.5194/gmd-5-773-2012, 2012. a, b, c
Warren, S. G. and Wiscombe, W. J.: A Model for the Spectral Albedo of Snow.
II: Snow Containing Atmospheric Aerosols, J. Atmos. Sci., 37, 2734–2745,
https://doi.org/10.1175/1520-0469(1980)037<2734:AMFTSA>2.0.CO;2,
1980. a
Williamson, C. J., Cameron, K. A., Cook, J. M., Zarsky, J. D., Stibal, M., and
Edwards, A.: Glacier Algae: A Dark Past and a Darker Future, Front.
Microbiol., 10, 524, https://doi.org/10.3389/fmicb.2019.00524,
2019. a
Wiscombe, W. J. and Warren, S. G.: A Model for the Spectral Albedo of Snow. I:
Pure Snow, J. Atmos. Sci., 37, 2712–2733,
https://doi.org/10.1175/1520-0469(1980)037<2712:AMFTSA>2.0.CO;2,
1980. a
Wittmann, M., Groot Zwaaftink, C. D., Steffensen Schmidt, L., Guðmundsson, S., Pálsson, F., Arnalds, O., Björnsson, H., Thorsteinsson, T., and Stohl, A.: Impact of dust deposition on the albedo of Vatnajökull ice cap, Iceland, The Cryosphere, 11, 741–754, https://doi.org/10.5194/tc-11-741-2017, 2017. a, b, c
Wright, P., Bergin, M., Dibb, J., Lefer, B., Domine, F., Carman, T.,
Carmagnola, C., Dumont, M., Courville, Z., Schaaf, C., and Wang, Z.:
Comparing MODIS daily snow albedo to spectral albedo field measurements in
Central Greenland, Remote Sens. Environ., 140, 118–129,
https://doi.org/10.1016/j.rse.2013.08.044,
2014. a, b
Xu, B., Cao, J., Joswiak, D. R., Liu, X., Zhao, H., and He, J.:
Post-depositional enrichment of black soot in snow-pack and accelerated
melting of Tibetan glaciers, Environ. Res. Lett., 7, 014022,
https://doi.org/10.1088/1748-9326/7/1/014022, 2012. a
Young, C. L., Sokolik, I. N., Flanner, M. G., and Dufek, J.: Surface radiative
impacts of ash deposits from the 2009 eruption of Redoubt volcano, J. Geophys. Res.-Atmos., 119, 11387–11397,
https://doi.org/10.1002/2014JD021949,
2014. a, b, c
Zemp, M., Frey, H., Gärtner-Roer, I., Nussbaumer, S. U., Hoelzle, M.,
Paul, F., Haeberli, W., Denzinger, F., Ahlstrøm, A. P., Anderson, B.,
Bajracharya, S., Baroni, C., Braun, L. N., Cáceres, B. E., Casassa, G.,
Cobos, G., Dávila, L. R., Delgado Granados, H., Demuth, M. N., Espizua,
L. E., Fischer, A., Fujita, K., Gadek, B., Ghazanfar, A., Hagen, J. O.,
Holmlund, P., Karimi, N., Li, Z., Pelto, M., Pitte, P., Popovnin, V. V.,
Portocarrero, C. a., Prinz, R., Sangewar, C. V., Severskiy, I., Sigurðsson,
O., Soruco, A., Usubaliev, R., and Vincent, C.: Historically unprecedented
global glacier decline in the early 21st century, J. Glaciol., 61,
745–762, https://doi.org/10.3189/2015JoG15J017, 2015.
a
Zhang, Y., Kang, S., Sprenger, M., Cong, Z., Gao, T., Li, C., Tao, S., Li, X., Zhong, X., Xu, M., Meng, W., Neupane, B., Qin, X., and Sillanpää, M.: Black carbon and mineral dust in snow cover on the Tibetan Plateau, The Cryosphere, 12, 413–431, https://doi.org/10.5194/tc-12-413-2018, 2018. a, b, c, d, e, f
Zhuravleva, T. B. and Kokhanovsky, A. A.: Influence of surface roughness on
the reflective properties of snow, J. Quant. Spectrosc.
Ra., 112, 1353–1368, https://doi.org/10.1016/J.JQSRT.2011.01.004,
2011. a
Short summary
We present the results of two field campaigns and modeling activities on the impact of atmospheric particles on Alerce Glacier (Argentinean Andes). We found that volcanic ash remains at different snow layers several years after eruption, increasing light absorption on the glacier surface (with a minor contribution of soot). This leads to 36 % higher annual glacier melting. We find remarkably that volcano eruptions in 2011 and 2015 have a relevant effect on the glacier even in 2016 and 2017.
We present the results of two field campaigns and modeling activities on the impact of...
