Articles | Volume 18, issue 1
https://doi.org/10.5194/tc-18-17-2024
© Author(s) 2024. 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-18-17-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Jan 2024
Research article |
| 02 Jan 2024
| 02 Jan 2024
Evaluation of reanalysis data and dynamical downscaling for surface energy balance modeling at mountain glaciers in western Canada
Department of Earth Ocean and Atmospheric Sciences (EOAS), The University of British Columbia, Vancouver, Canada
Valentina Radić
Department of Earth Ocean and Atmospheric Sciences (EOAS), The University of British Columbia, Vancouver, Canada
Rachel H. White
Department of Earth Ocean and Atmospheric Sciences (EOAS), The University of British Columbia, Vancouver, Canada
Mekdes Ayalew Tessema
Department of Earth Ocean and Atmospheric Sciences (EOAS), The University of British Columbia, Vancouver, Canada
Related authors
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Rachel H. White
EGUsphere, https://doi.org/10.5194/egusphere-2024-966, https://doi.org/10.5194/egusphere-2024-966, 2024
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Mid-latitude atmospheric jet streams sometimes create 'waveguides', thought to increase the chance of quasi-stationary waves — atmospheric circulation patterns that lead to extreme weather events. I describe a new algorithm for identifying atmospheric waveguides, and show maps of waveguide frequency and strength. Waveguide strength is associated with an increased probability of quasi-stationary waves, although not in all regions; the connection is particularly strong over Europe during summer.
Jonathan P. Conway, Jakob Abermann, Liss M. Andreassen, Mohd Farooq Azam, Nicolas J. Cullen, Noel Fitzpatrick, Rianne H. Giesen, Kirsty Langley, Shelley MacDonell, Thomas Mölg, Valentina Radić, Carleen H. Reijmer, and Jean-Emmanuel Sicart
The Cryosphere, 16, 3331–3356, https://doi.org/10.5194/tc-16-3331-2022, https://doi.org/10.5194/tc-16-3331-2022, 2022
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We used data from automatic weather stations on 16 glaciers to show how clouds influence glacier melt in different climates around the world. We found surface melt was always more frequent when it was cloudy but was not universally faster or slower than under clear-sky conditions. Also, air temperature was related to clouds in opposite ways in different climates – warmer with clouds in cold climates and vice versa. These results will help us improve how we model past and future glacier melt.
Sam Anderson and Valentina Radić
Hydrol. Earth Syst. Sci., 26, 795–825, https://doi.org/10.5194/hess-26-795-2022, https://doi.org/10.5194/hess-26-795-2022, 2022
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We develop and interpret a spatiotemporal deep learning model for regional streamflow prediction at more than 200 stream gauge stations in western Canada. We find the novel modelling style to work very well for daily streamflow prediction. Importantly, we interpret model learning to show that it has learned to focus on physically interpretable and physically relevant information, which is a highly desirable quality of machine-learning-based hydrological models.
Erica Madonna, David S. Battisti, Camille Li, and Rachel H. White
Weather Clim. Dynam., 2, 777–794, https://doi.org/10.5194/wcd-2-777-2021, https://doi.org/10.5194/wcd-2-777-2021, 2021
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The amount of precipitation over Europe varies substantially from year to year, with impacts on crop yields and energy production. In this study, we show that it is possible to infer much of the winter precipitation and temperature signal over Europe by knowing only the frequency of occurrence of certain atmospheric circulation patterns. The results highlight the importance of (daily) weather for understanding and interpreting seasonal signals.
Noel Fitzpatrick, Valentina Radić, and Brian Menounos
The Cryosphere, 13, 1051–1071, https://doi.org/10.5194/tc-13-1051-2019, https://doi.org/10.5194/tc-13-1051-2019, 2019
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Measurements of surface roughness are rare on glaciers, despite being an important control for heat exchange with the atmosphere and surface melt. In this study, roughness values were determined through measurements at multiple locations and seasons and found to vary across glacier surfaces and to differ from commonly assumed values in melt models. Two new methods that remotely determine roughness from digital elevation models returned good performance and may facilitate improved melt modelling.
Mekdes Ayalew Tessema, Valentina Radić, Brian Menounos, and Noel Fitzpatrick
The Cryosphere Discuss., https://doi.org/10.5194/tc-2018-154, https://doi.org/10.5194/tc-2018-154, 2018
Preprint withdrawn
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To force physics-based models of glacier melt, meteorological variables and energy fluxes are needed at or in vicinity of the glaciers in question. In the absence of observations detailing these variables, the required forcing is commonly derived by downscaling the coarse-resolution output from global climate models (GCMs). This study investigates how the downscaled fields from GCMs can successfully resolve the local processes driving surface melting at three glaciers in British Columbia.
Valentina Radić, Brian Menounos, Joseph Shea, Noel Fitzpatrick, Mekdes A. Tessema, and Stephen J. Déry
The Cryosphere, 11, 2897–2918, https://doi.org/10.5194/tc-11-2897-2017, https://doi.org/10.5194/tc-11-2897-2017, 2017
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Our overall goal is to improve the numerical modeling of glacier melt in order to better predict the future of glaciers in Western Canada and worldwide.
Most commonly used models rely on simplifications of processes that dictate melting at a glacier surface, in particular turbulent processes of heat exchange. We compared modeled against directly measured turbulent heat fluxes at a valley glacier in British Columbia, Canada, and found that more improvements are needed in all the tested models.
S. H. Mernild, W. H. Lipscomb, D. B. Bahr, V. Radić, and M. Zemp
The Cryosphere, 7, 1565–1577, https://doi.org/10.5194/tc-7-1565-2013, https://doi.org/10.5194/tc-7-1565-2013, 2013
Related subject area
Discipline: Glaciers | Subject: Energy Balance Obs/Modelling
Brief Communication: Accurate and autonomous snow water equivalent measurements using a cosmic ray sensor on a Himalayan glacier
Surface heat fluxes at coarse blocky Murtèl rock glacier (Engadine, eastern Swiss Alps)
Modeling of surface energy balance for Icelandic glaciers using remote-sensing albedo
Strategies for regional modeling of surface mass balance at the Monte Sarmiento Massif, Tierra del Fuego
Long-term firn and mass balance modelling for Abramov Glacier in the data-scarce Pamir Alay
The surface energy balance during foehn events at Joyce Glacier, McMurdo Dry Valleys, Antarctica
Sub-seasonal variability of supraglacial ice cliff melt rates and associated processes from time-lapse photogrammetry
Cloud forcing of surface energy balance from in situ measurements in diverse mountain glacier environments
Modelling glacier mass balance and climate sensitivity in the context of sparse observations: application to Saskatchewan Glacier, western Canada
Understanding monsoon controls on the energy and mass balance of glaciers in the Central and Eastern Himalaya
SNICAR-ADv4: a physically based radiative transfer model to represent the spectral albedo of glacier ice
Firn changes at Colle Gnifetti revealed with a high-resolution process-based physical model approach
Seasonal and interannual variability of melt-season albedo at Haig Glacier, Canadian Rocky Mountains
Surface energy fluxes on Chilean glaciers: measurements and models
Using 3D turbulence-resolving simulations to understand the impact of surface properties on the energy balance of a debris-covered glacier
Incorporating moisture content in surface energy balance modeling of a debris-covered glacier
Surface melt and the importance of water flow – an analysis based on high-resolution unmanned aerial vehicle (UAV) data for an Arctic glacier
Glacio-hydrological melt and run-off modelling: application of a limits of acceptability framework for model comparison and selection
Navaraj Pokhrel, Patrick Wagnon, Fanny Brun, Arbindra Khadka, Tom Matthews, Audrey Goutard, Dibas Shrestha, Baker Perry, and Marion Réveillet
EGUsphere, https://doi.org/10.5194/egusphere-2024-1760, https://doi.org/10.5194/egusphere-2024-1760, 2024
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We studied snow processes in the accumulation area of Mera Glacier (Central Himalaya, Nepal) by deploying a cosmic ray counting sensor that allows to track the evolution of the snow water equivalent. We suspect significant surface melting, water percolation and refreezing within the snowpack, that might be missed by traditional mass balance surveys.
Dominik Amschwand, Martin Scherler, Martin Hoelzle, Bernhard Krummenacher, Anna Haberkorn, Christian Kienholz, and Hansueli Gubler
The Cryosphere, 18, 2103–2139, https://doi.org/10.5194/tc-18-2103-2024, https://doi.org/10.5194/tc-18-2103-2024, 2024
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Rock glaciers are coarse-debris permafrost landforms that are comparatively climate resilient. We estimate the surface energy balance of rock glacier Murtèl (Swiss Alps) based on a large surface and sub-surface sensor array. During the thaw seasons 2021 and 2022, 90 % of the net radiation was exported via turbulent heat fluxes and only 10 % was transmitted towards the ground ice table. However, early snowmelt and droughts make these permafrost landforms vulnerable to climate warming.
Andri Gunnarsson, Sigurdur M. Gardarsson, and Finnur Pálsson
The Cryosphere, 17, 3955–3986, https://doi.org/10.5194/tc-17-3955-2023, https://doi.org/10.5194/tc-17-3955-2023, 2023
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A model was developed with the possibility of utilizing satellite-derived daily surface albedo driven by high-resolution climate data to estimate the surface energy balance (SEB) for all Icelandic glaciers for the period 2000–2021.
Franziska Temme, David Farías-Barahona, Thorsten Seehaus, Ricardo Jaña, Jorge Arigony-Neto, Inti Gonzalez, Anselm Arndt, Tobias Sauter, Christoph Schneider, and Johannes J. Fürst
The Cryosphere, 17, 2343–2365, https://doi.org/10.5194/tc-17-2343-2023, https://doi.org/10.5194/tc-17-2343-2023, 2023
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Calibration of surface mass balance (SMB) models on regional scales is challenging. We investigate different calibration strategies with the goal of achieving realistic simulations of the SMB in the Monte Sarmiento Massif, Tierra del Fuego. Our results show that the use of regional observations from satellite data can improve the model performance. Furthermore, we compare four melt models of different complexity to understand the benefit of increasing the processes considered in the model.
Marlene Kronenberg, Ward van Pelt, Horst Machguth, Joel Fiddes, Martin Hoelzle, and Felix Pertziger
The Cryosphere, 16, 5001–5022, https://doi.org/10.5194/tc-16-5001-2022, https://doi.org/10.5194/tc-16-5001-2022, 2022
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The Pamir Alay is located at the edge of regions with anomalous glacier mass changes. Unique long-term in situ data are available for Abramov Glacier, located in the Pamir Alay. In this study, we use this extraordinary data set in combination with reanalysis data and a coupled surface energy balance–multilayer subsurface model to compute and analyse the distributed climatic mass balance and firn evolution from 1968 to 2020.
Marte G. Hofsteenge, Nicolas J. Cullen, Carleen H. Reijmer, Michiel van den Broeke, Marwan Katurji, and John F. Orwin
The Cryosphere, 16, 5041–5059, https://doi.org/10.5194/tc-16-5041-2022, https://doi.org/10.5194/tc-16-5041-2022, 2022
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In the McMurdo Dry Valleys (MDV), foehn winds can impact glacial meltwater production and the fragile ecosystem that depends on it. We study these dry and warm winds at Joyce Glacier and show they are caused by a different mechanism than that found for nearby valleys, demonstrating the complex interaction of large-scale winds with the mountains in the MDV. We find that foehn winds increase sublimation of ice, increase heating from the atmosphere, and increase the occurrence and rates of melt.
Marin Kneib, Evan S. Miles, Pascal Buri, Stefan Fugger, Michael McCarthy, Thomas E. Shaw, Zhao Chuanxi, Martin Truffer, Matthew J. Westoby, Wei Yang, and Francesca Pellicciotti
The Cryosphere, 16, 4701–4725, https://doi.org/10.5194/tc-16-4701-2022, https://doi.org/10.5194/tc-16-4701-2022, 2022
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Ice cliffs are believed to be important contributors to the melt of debris-covered glaciers, but this has rarely been quantified as the cliffs can disappear or rapidly expand within a few weeks. We used photogrammetry techniques to quantify the weekly evolution and melt of four cliffs. We found that their behaviour and melt during the monsoon is strongly controlled by supraglacial debris, streams and ponds, thus providing valuable insights on the melt and evolution of debris-covered glaciers.
Jonathan P. Conway, Jakob Abermann, Liss M. Andreassen, Mohd Farooq Azam, Nicolas J. Cullen, Noel Fitzpatrick, Rianne H. Giesen, Kirsty Langley, Shelley MacDonell, Thomas Mölg, Valentina Radić, Carleen H. Reijmer, and Jean-Emmanuel Sicart
The Cryosphere, 16, 3331–3356, https://doi.org/10.5194/tc-16-3331-2022, https://doi.org/10.5194/tc-16-3331-2022, 2022
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We used data from automatic weather stations on 16 glaciers to show how clouds influence glacier melt in different climates around the world. We found surface melt was always more frequent when it was cloudy but was not universally faster or slower than under clear-sky conditions. Also, air temperature was related to clouds in opposite ways in different climates – warmer with clouds in cold climates and vice versa. These results will help us improve how we model past and future glacier melt.
Christophe Kinnard, Olivier Larouche, Michael N. Demuth, and Brian Menounos
The Cryosphere, 16, 3071–3099, https://doi.org/10.5194/tc-16-3071-2022, https://doi.org/10.5194/tc-16-3071-2022, 2022
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This study implements a physically based, distributed glacier mass balance model in a context of sparse direct observations. Carefully constraining model parameters with ancillary data allowed for accurately reconstructing the mass balance of Saskatchewan Glacier over a 37-year period. We show that the mass balance sensitivity to warming is dominated by increased melting and that changes in glacier albedo and air humidity are the leading causes of increased glacier melt under warming scenarios.
Stefan Fugger, Catriona L. Fyffe, Simone Fatichi, Evan Miles, Michael McCarthy, Thomas E. Shaw, Baohong Ding, Wei Yang, Patrick Wagnon, Walter Immerzeel, Qiao Liu, and Francesca Pellicciotti
The Cryosphere, 16, 1631–1652, https://doi.org/10.5194/tc-16-1631-2022, https://doi.org/10.5194/tc-16-1631-2022, 2022
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The monsoon is important for the shrinking and growing of glaciers in the Himalaya during summer. We calculate the melt of seven glaciers in the region using a complex glacier melt model and weather data. We find that monsoonal weather affects glaciers that are covered with a layer of rocky debris and glaciers without such a layer in different ways. It is important to take so-called turbulent fluxes into account. This knowledge is vital for predicting the future of the Himalayan glaciers.
Chloe A. Whicker, Mark G. Flanner, Cheng Dang, Charles S. Zender, Joseph M. Cook, and Alex S. Gardner
The Cryosphere, 16, 1197–1220, https://doi.org/10.5194/tc-16-1197-2022, https://doi.org/10.5194/tc-16-1197-2022, 2022
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Snow and ice surfaces are important to the global climate. Current climate models use measurements to determine the reflectivity of ice. This model uses physical properties to determine the reflectivity of snow, ice, and darkly pigmented impurities that reside within the snow and ice. Therefore, the modeled reflectivity is more accurate for snow/ice columns under varying climate conditions. This model paves the way for improvements in the portrayal of snow and ice within global climate models.
Enrico Mattea, Horst Machguth, Marlene Kronenberg, Ward van Pelt, Manuela Bassi, and Martin Hoelzle
The Cryosphere, 15, 3181–3205, https://doi.org/10.5194/tc-15-3181-2021, https://doi.org/10.5194/tc-15-3181-2021, 2021
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In our study we find that climate change is affecting the high-alpine Colle Gnifetti glacier (Swiss–Italian Alps) with an increase in melt amounts and ice temperatures.
In the near future this trend could threaten the viability of the oldest ice core record in the Alps.
To reach our conclusions, for the first time we used the meteorological data of the highest permanent weather station in Europe (Capanna Margherita, 4560 m), together with an advanced numeric simulation of the glacier.
Shawn J. Marshall and Kristina Miller
The Cryosphere, 14, 3249–3267, https://doi.org/10.5194/tc-14-3249-2020, https://doi.org/10.5194/tc-14-3249-2020, 2020
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Surface-albedo measurements from 2002 to 2017 from Haig Glacier in the Canadian Rockies provide no evidence of long-term trends (i.e., the glacier does not appear to be darkening), but there are large variations in albedo over the melt season and from year to year. The glacier ice is exceptionally dark in association with forest fire fallout but is effectively cleansed by meltwater or rainfall. Summer snowfall plays an important role in refreshing the glacier surface and reducing summer melt.
Marius Schaefer, Duilio Fonseca-Gallardo, David Farías-Barahona, and Gino Casassa
The Cryosphere, 14, 2545–2565, https://doi.org/10.5194/tc-14-2545-2020, https://doi.org/10.5194/tc-14-2545-2020, 2020
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Chile hosts glaciers in a large range of latitudes and climates. To project future ice extent, a sound quantification of the energy exchange between atmosphere and glaciers is needed. We present new data for six Chilean glaciers belonging to three glaciological zones. In the Central Andes, the main energy source for glacier melt is the incoming solar radiation, while in southern Patagonia heat provided by the mild and humid air is also important. Total melt rates are higher in Patagonia.
Pleun N. J. Bonekamp, Chiel C. van Heerwaarden, Jakob F. Steiner, and Walter W. Immerzeel
The Cryosphere, 14, 1611–1632, https://doi.org/10.5194/tc-14-1611-2020, https://doi.org/10.5194/tc-14-1611-2020, 2020
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Drivers controlling melt of debris-covered glaciers are largely unknown. With a 3D turbulence-resolving model the impact of surface properties of debris on micrometeorological variables and the conductive heat flux is shown. Also, we show ice cliffs are local melt hot spots and that turbulent fluxes and local heat advection amplify spatial heterogeneity on the surface.This work is important for glacier mass balance modelling and for the understanding of the evolution of debris-covered glaciers.
Alexandra Giese, Aaron Boone, Patrick Wagnon, and Robert Hawley
The Cryosphere, 14, 1555–1577, https://doi.org/10.5194/tc-14-1555-2020, https://doi.org/10.5194/tc-14-1555-2020, 2020
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Rocky debris on glacier surfaces is known to affect the melt of mountain glaciers. Debris can be dry or filled to varying extents with liquid water and ice; whether debris is dry, wet, and/or icy affects how efficiently heat is conducted through debris from its surface to the ice interface. Our paper presents a new energy balance model that simulates moisture phase, evolution, and location in debris. ISBA-DEB is applied to West Changri Nup glacier in Nepal to reveal important physical processes.
Eleanor A. Bash and Brian J. Moorman
The Cryosphere, 14, 549–563, https://doi.org/10.5194/tc-14-549-2020, https://doi.org/10.5194/tc-14-549-2020, 2020
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High-resolution measurements from unmanned aerial vehicle (UAV) imagery allowed for examination of glacier melt model performance in detail at Fountain Glacier. This work capitalized on distributed measurements at 10 cm resolution to look at the spatial distribution of model errors in the ablation zone. Although the model agreed with measurements on average, strong correlation was found with surface water. The results highlight the contribution of surface water flow to melt at this location.
Jonathan D. Mackay, Nicholas E. Barrand, David M. Hannah, Stefan Krause, Christopher R. Jackson, Jez Everest, and Guðfinna Aðalgeirsdóttir
The Cryosphere, 12, 2175–2210, https://doi.org/10.5194/tc-12-2175-2018, https://doi.org/10.5194/tc-12-2175-2018, 2018
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We apply a framework to compare and objectively accept or reject competing melt and run-off process models. We found no acceptable models. Furthermore, increasing model complexity does not guarantee better predictions. The results highlight model selection uncertainty and the need for rigorous frameworks to identify deficiencies in competing models. The application of this approach in the future will help to better quantify model prediction uncertainty and develop improved process models.
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, b
Abrams, M., Crippen, R., and Fujisada, H.: ASTER Global Digital Elevation Model (GDEM) and ASTER Global Water Body Dataset (ASTWBD), Remote Sens., 12, 1156, https://doi.org/10.3390/rs12071156, 2020. a
Alduchov, O. A. and Eskridge, R. E.: Improved Magnus` form approximation of saturation vapor pressure, Tech. rep., Department of Commerce, Asheville, NC (United States), https://doi.org/10.2172/548871, 1997. a
Anderson, J., Hardy, E., J., R., and Witmer, R.: A land use and land cover classification system for use with remote sensor data, Tech. Rep. 964, USGS Publications Warehouse, https://doi.org/10.3133/pp964, 1976. a
Anderson, S. and Radić, V.: Identification of local water resource vulnerability to rapid deglaciation in Alberta, Nat. Clim. Change, 10, 933–938, https://doi.org/10.1038/s41558-020-0863-4, 2020. a, b
Andreassen, L. M., van den Broeke, M. R., Giesen, R. H., and Oerlemans, J.: A 5 year record of surface energy and mass balance from the ablation zone of Storbreen, Norway, J. Glaciol., 54, 245–258, https://doi.org/10.3189/002214308784886199, 2008. a
Annor, T., Lamptey, B., Wagner, S., Oguntunde, P., Arnault, J., Heinzeller, D., and Kunstmann, H.: High-resolution long-term WRF climate simulations over Volta Basin. Part 1: validation analysis for temperature and precipitation, Theor. Appl. Climatol., 133, 829–849, https://doi.org/10.1007/s00704-017-2223-5, 2018. a
Arendt, A., Walsh, J., and Harrison, W.: Changes of glaciers and climate in northwestern North America during the late twentieth century, J. Climate, 22, 4117 – 4134, https://doi.org/10.1175/2009JCLI2784.1, 2009. a
Arndt, A., Scherer, D., and Schneider, C.: Atmosphere Driven Mass-Balance Sensitivity of Halji Glacier, Himalayas, Atmosphere, 12, 426, https://doi.org/10.3390/atmos12040426, 2021. a
Azam, M. F. and Srivastava, S.: Mass balance and runoff modelling of partially debris-covered Dokriani Glacier in monsoon-dominated Himalaya using ERA5 data since 1979, J. Hydrol., 590, 125432, https://doi.org/10.1016/j.jhydrol.2020.125432, 2020. a
Brock, B. W., Willis, I. C., and Sharp, M. J.: Measurement and parameterization of albedo variations at Haut Glacier d'Arolla, Switzerland, J. Glaciol., 46, 675–688, https://doi.org/10.3189/172756500781832675, 2000. a
Bryan, G. H., Wyngaard, J. C., and Fritsch, J. M.: Resolution requirements for the simulation of deep moist convection, Mon. Weather Rev., 131, 2394, https://doi.org/10.1175/1520-0493(2003)131<2394:RRFTSO>2.0.CO;2, 2003. a
Clarke, G. K. C., Jarosch, A. H., Anslow, F. S., Radić, V., and Menounos, B.: Projected deglaciation of Western Canada in the twenty-first century, Nat. Geosci., 8, 372–377, https://doi.org/10.1038/ngeo2407, 2015. a, b
Collier, E., Maussion, F., Nicholson, L. I., Mölg, T., Immerzeel, W. W., and Bush, A. B. G.: Impact of debris cover on glacier ablation and atmosphere–glacier feedbacks in the Karakoram, The Cryosphere, 9, 1617–1632, https://doi.org/10.5194/tc-9-1617-2015, 2015. a, b, c, d
Collins, W., Rasch, P., Boville, B., Hack, J., Mccaa, J., Williamson, D., and Kiehl, J.: Description of the NCAR community atmosphere model (CAM 3.0), NCAR Technical Note, TN-464+STR, 2004. a
Craig, G. C. and Dörnbrack, A.: Entrainment in cumulus clouds: What resolution is cloud-resolving?, J. Atmos. Sci., 65, 3978, https://doi.org/10.1175/2008JAS2613.1, 2008. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitart, F.: The ERA-Interim reanalysis: configuration and performance of the data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
Denby, B. and Smeets, C. J. P. P.: Derivation of Turbulent Flux Profiles and Roughness Lengths from Katabatic Flow Dynamics, J. Appl. Meteorol., 39, 1601–1612, https://doi.org/10.1175/1520-0450(2000)039<1601:DOTFPA>2.0.CO;2, 2000. a
Devine, K. A. and Mekis, E.: Field accuracy of Canadian rain measurements, Atmosphere-Ocean, 46, 213–227, https://doi.org/10.3137/ao.460202, 2008. a
Duchon, C. E. and Biddle, C. J.: Undercatch of tipping-bucket gauges in high rain rate events, Adv. Geosci., 25, 11–15, https://doi.org/10.5194/adgeo-25-11-2010, 2010. a
Dudhia, J.: Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model, J. Atmos. Sci., 46, 3077–3107, https://doi.org/10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2, 1989. a, b
Earth Resources Observation And Science (EROS) Center: Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) [Data set], https://doi.org/10.5066/F7J38R2N (last access: 1 August 2020), 2017. a, b, c, d
Ebrahimi, S. and Marshall, S. J.: Surface energy balance sensitivity to meteorological variability on Haig Glacier, Canadian Rocky Mountains, The Cryosphere, 10, 2799–2819, https://doi.org/10.5194/tc-10-2799-2016, 2016. a
Eidhammer, T., Booth, A., Decker, S., Li, L., Barlage, M., Gochis, D., Rasmussen, R., Melvold, K., Nesje, A., and Sobolowski, S.: Mass balance and hydrological modeling of the Hardangerjøkulen ice cap in south-central Norway, Hydrol. Earth Syst. Sci., 25, 4275–4297, https://doi.org/10.5194/hess-25-4275-2021, 2021. a, b, c
Erler, A., Peltier, W., and d'Orgeville, M.: Dynamically downscaled high resolution hydro-climate projections for Western Canada, J. Climate, 28, 423–450, https://doi.org/10.1175/JCLI-D-14-00174.1, 2014. a
Erler, A. R., Peltier, W. R., and D’Orgeville, M.: Dynamically downscaled high-resolution hydroclimate projections for Western Canada, J. Climate, 28, 423–450, https://doi.org/10.1175/JCLI-D-14-00174.1, 2015. a
Fitzpatrick, N.: An investigation of surface energy balance and turbulent heat flux on mountain glaciers (T), PhD thesis, University of British Columbia, Vancouver, https://open.library.ubc.ca/collections/ubctheses/24/items/1.0375764 (last access: 15 January 2023), 2018. a
Friedl, M. and Sulla-Menashe, D.: MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid [Data set], NASA EOSDIS Land Processes DAAC, 2004. a
Gbode, I. E., Dudhia, J., Ogunjobi, K. O., and Ajayi, V. O.: Sensitivity of different physics schemes in the WRF model during a West African monsoon regime, Theor. Appl. Climatol., 136, 733–751, https://doi.org/10.1007/s00704-018-2538-x, 2019. a
Gerard, L.: An integrated package for subgrid convection, clouds and precipitation compatible with meso-gamma scales, Q. J. Roy. Meteor. Soc., 133, 711–730, https://doi.org/10.1002/qj.58, 2007. a
Gerber, F., Besic, N., Sharma, V., Mott, R., Daniels, M., Gabella, M., Berne, A., Germann, U., and Lehning, M.: Spatial variability in snow precipitation and accumulation in COSMO–WRF simulations and radar estimations over complex terrain, The Cryosphere, 12, 3137–3160, https://doi.org/10.5194/tc-12-3137-2018, 2018. a
Giese, A., Rupper, S., Keeler, D., Johnson, E., and Forster, R.: Indus river basin glacier melt at the subbasin scale, Front. Earth Sci., 10, https://doi.org/10.3389/feart.2022.767411, 2022. a
Gillett, S. and Cullen, N. J.: Atmospheric controls on summer ablation over Brewster Glacier, New Zealand, Int. J. Climatol., 31, 2033–2048, https://doi.org/10.1002/joc.2216, 2011. a
Gochis, D., Barlage, M., Cabell, R., Casali, M., Dugger, A., FitzGerald, K., McAllister, M., McCreight, J., RafieeiNasab, A., Read, L., Sampson, K., Yates, D., and Zhang, Y.: The WRF-Hydro modeling system technical description, (Version 5.2.0), Tech. rep., NCAR, https://ral.ucar.edu/sites/default/files/public/projects/wrf-hydro/technical-description-user-guide/wrf-hydrov5.2technicaldescription.pdf (last access: 15 January 2023), 2020. a
Goger, B., Stiperski, I., Nicholson, L., and Sauter, T.: Large-eddy simulations of the atmospheric boundary layer over an Alpine glacier: Impact of synoptic flow direction and governing processes, Q. J. Roy. Meteor. Soc., 148, 1319–1343, https://doi.org/10.1002/qj.4263, 2022. a, b
Grell, G. A.: Prognostic Evaluation of Assumptions Used by Cumulus Parameterizations, Mon. Weather Rev., 121, 764–787, https://doi.org/10.1175/1520-0493(1993)121<0764:PEOAUB>2.0.CO;2, 1993. a, b
Grell, G. A. and Dévényi, D.: A generalized approach to parameterizing convection combining ensemble and data assimilation techniques, Geophys. Res. Lett., 29, 38-1–38-4, https://doi.org/10.1029/2002GL015311, 2002. a, b
Gualtieri, G.: Reliability of ERA5 Reanalysis Data for Wind Resource Assessment: A Comparison against Tall Towers, Energies, 14, 4169, https://doi.org/10.3390/en14144169, 2021. a
He, C., Valayamkunnath, P., Barlage, M., Chen, F., Gochis, D., Cabell, R., Schneider, T., Rasmussen, R., Niu, G.-Y., Yang, Z.-L., Niyogi, D., and Ek, M.: The Community Noah-MP Land Surface Modeling System Technical Description Version 5.0, Tech. rep., NCAR/UCAR, https://doi.org/10.5065/EW8G-YR95, 2023. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1959 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47 (last access: 27 June 2021), 2018. a, b
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a, b, c
Hirose, J. M. and Marshall, S. J.: Glacier meltwater contributions and glaciometeorological regime of the Illecillewaet River Basin, British Columbia, Canada, Atmosphere-Ocean, 51, 416–435, https://doi.org/10.1080/07055900.2013.791614, 2013. a
Hock, R. and Holmgren, B.: A distributed surface energy-balance model for complex topography and its application to Storglaciären, Sweden, J. Glaciol., 51, 25–36, https://doi.org/10.3189/172756505781829566, 2005. a
Hong, S.-Y., Noh, Y., and Dudhia, J.: A new vertical diffusion package with an explicit treatment of entrainment processes, Mon. Weather Rev., 134, 2318–2341, https://doi.org/10.1175/MWR3199.1, 2006. a
Hugonnet, R., McNabb, R., Berthier, E., Menounos, B., Nuth, C., Girod, L., Farinotti, D., Huss, M., Dussaillant, I., Brun, F., and Kääb, A.: Accelerated global glacier mass loss in the early twenty-first century, Nature, 592, 726–731, https://doi.org/10.1038/s41586-021-03436-z, 2021. a
Hwang, C.-L. and Yoon, K.: Methods for Multiple Attribute Decision Making, in: Multiple Attribute Decision Making: Methods and Applications A State-of-the-Art Survey, 58–191, Springer, Berlin, Heidelberg, https://doi.org/10.1007/978-3-642-48318-9_3, 1981. a
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., and Collins, W. D.: Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models, J. Geophys. Res.-Atmos., 113, D13, https://doi.org/10.1029/2008JD009944, 2008. a, b
Janjić, Z. I.: The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes, Mon. Weather Rev., 122, 927–945, https://doi.org/10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2, 1994. a, b, c, d
Janjić, Z. I.: The surface layer in the NCEP eta model, in: Eleventh Conference on Numerical Weather Prediction, Norfolk, VA, 19–23 August, Amererican Meteor Society, Boston, MA, 345–355, 1996. a
Janjić, Z. I.: Nonsingular implementation of the Mellor–Yamada Level 2.5 scheme in the NCEP meso model, NCEP Office Note, 436, 2002. a
Jiménez, P. A., Dudhia, J., González-Rouco, J. F., Navarro, J., Montávez, J. P., and García-Bustamante, E.: A Revised Scheme for the WRF Surface Layer Formulation, Mon. Weather Rev., 140, 898–918, https://doi.org/10.1175/MWR-D-11-00056.1, 2012. a
Jung, Y. and Lin, Y.-L.: Assessment of a regional-scale weather model for hydrological applications in South Korea, Environ. Nat. Resour. Res., 6, 28, https://doi.org/10.5539/enrr.v6n2p28, 2016. a
Kain, J. S.: The Kain-Fritsch convective parameterization: An update, J. Appl. Meteorol., 43, 170–181, https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2, 2004. a
Kinnard, C., Larouche, O., Demuth, M. N., and Menounos, B.: Modelling glacier mass balance and climate sensitivity in the context of sparse observations: application to Saskatchewan Glacier, western Canada, The Cryosphere, 16, 3071–3099, https://doi.org/10.5194/tc-16-3071-2022, 2022. a
Kronenberg, M., van Pelt, W., Machguth, H., Fiddes, J., Hoelzle, M., and Pertziger, F.: Long-term firn and mass balance modelling for Abramov Glacier in the data-scarce Pamir Alay, The Cryosphere, 16, 5001–5022, https://doi.org/10.5194/tc-16-5001-2022, 2022. a
Larsen, C. F., Motyka, R. J., Arendt, A. A., Echelmeyer, K. A., and Geissler, P. E.: Glacier changes in southeast Alaska and northwest British Columbia and contribution to sea level rise, J. Geophys. Res.-Earth Surf., 112, F1, https://doi.org/10.1029/2006JF000586, 2007. a
Li, Y., Li, Z., Zhang, Z., Chen, L., Kurkute, S., Scaff, L., and Pan, X.: High-resolution regional climate modeling and projection over western Canada using a weather research forecasting model with a pseudo-global warming approach, Hydrol. Earth Syst. Sci., 23, 4635–4659, https://doi.org/10.5194/hess-23-4635-2019, 2019. a
Liu, C., Ikeda, K., Thompson, G., Rasmussen, R., and Dudhia, J.: High-resolution simulations of wintertime precipitation in the Colorado Headwaters region: Sensitivity to physics parameterizations, Mon. Weather Rev., 139, 3533–3553, https://doi.org/10.1175/MWR-D-11-00009.1, 2011. a
Lundquist, J., Hughes, M., Gutmann, E., and Kapnick, S.: Our Skill in Modeling Mountain Rain and Snow is Bypassing the Skill of Our Observational Networks, B. Am. Meteorol. Soc., 100, 2473–2490, https://doi.org/10.1175/BAMS-D-19-0001.1, 2019. a
MacDougall, A. H., Wheler, B. A., and Flowers, G. E.: A preliminary assessment of glacier melt-model parameter sensitivity and transferability in a dry subarctic environment, The Cryosphere, 5, 1011–1028, https://doi.org/10.5194/tc-5-1011-2011, 2011. a
Marshall, S. J. and Miller, K.: Seasonal and interannual variability of melt-season albedo at Haig Glacier, Canadian Rocky Mountains, The Cryosphere, 14, 3249–3267, https://doi.org/10.5194/tc-14-3249-2020, 2020. a, b, c
Martens, B., Schumacher, D. L., Wouters, H., Muñoz-Sabater, J., Verhoest, N. E. C., and Miralles, D. G.: Evaluating the land-surface energy partitioning in ERA5, Geosci. Model Dev., 13, 4159–4181, https://doi.org/10.5194/gmd-13-4159-2020, 2020. a
Marzeion, B., Hock, R., Anderson, B., Bliss, A., Champollion, N., Fujita, K., Huss, M., Immerzeel, W. W., Kraaijenbrink, P., Malles, J.-H., Maussion, F., Radić, V., Rounce, D. R., Sakai, A., Shannon, S., van de Wal, R., and Zekollari, H.: Partitioning the Uncertainty of Ensemble Projections of Global Glacier Mass Change, Earth's Future, 8, e01470, https://doi.org/10.1029/2019EF001470, 2020. a
Matsui, T., Zhang, S. Q., Lang, S. E., Tao, W.-K., Ichoku, C., and Peters-Lidard, C. D.: Impact of radiation frequency, precipitation radiative forcing, and radiation column aggregation on convection-permitting West African monsoon simulations, Clim. Dynam., 55, 193–213, https://doi.org/10.1007/s00382-018-4187-2, 2018. a
Max, M.-D. C. and Suarez, M. J.: An Efficient Thermal Infrared Radiation Parameterization For Use In General Circulation Models, NASA Technical Memorandum, 3, 85, https://archive.org/details/nasa_techdoc_19950009331 (last access: 1 December 2022), 1994. a
Mesinger, F.: Forecasting upper tropospheric turbulence within the framework of the Mellor-Yamada 2.5 closure, in: Res. Activ. in Atmos. and Ocean. Mod., WMO, Geneva, CAS/JSC WGNE, vol. 18, 4.28–4.29, 1993. a
Mills, C. M.: Modification of the Weather Research and Forecasting Model's treatment of sea ice albedo over the Arctic Ocean, WRF 3.3.1 Code Submission Doc., https://publish.illinois.edu/catrinmills/files/2012/10/Mills_WRFIceAlbedoProj_Summary.pdf (last access: 10 December 2021), 2011. a
Milovac, J., Warrach-Sagi, K., Behrendt, A., Späth, F., Ingwersen, J., and Wulfmeyer, V.: Investigation of PBL schemes combining the WRF model simulations with scanning water vapor differential absorption lidar measurements, J. Geophys. Res.-Atmos., 121, 624–649, https://doi.org/10.1002/2015JD023927, 2016. a
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A.: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave, J. Geophys. Res.-Atmos., 102, 16663–16682, https://doi.org/10.1029/97JD00237, 1997. a
Mölg, T., Großhauser, M., Hemp, A., Hofer, M., and Marzeion, B.: Limited forcing of glacier loss through land-cover change on Kilimanjaro, Nat. Clim. Change, 2, 254–258, https://doi.org/10.1038/nclimate1390, 2012. a
Morrison, H., Thompson, G., and Tatarskii, V.: Impact of Cloud Microphysics on the Development of Trailing Stratiform Precipitation in a Simulated Squall Line: Comparison of One- and Two-Moment Schemes, Mon. Weather Rev., 137, 991–1007, https://doi.org/10.1175/2008MWR2556.1, 2009. a
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021. a, b, c
Mukherjee, K., Menounos, B., Shea, J., Mortezapour, M., Ednie, M., and Demuth, M. N.: Evaluation of surface mass-balance records using geodetic data and physically-based modelling, Place and Peyto glaciers, western Canada, J. Glaciol., 276, 1–18, https://doi.org/10.1017/jog.2022.83, 2022. a, b
Nakanishi, M. and Niino, H.: An improved Mellor–Yamada Level-3 model: Its numerical stability and application to a regional prediction of advection fog, Bound.-Lay. Meteorol., 119, 397–407, https://doi.org/10.1007/s10546-005-9030-8, 2006. a, b
Nakanishi, M. and Niino, H.: Development of an improved turbulence closure model for the atmospheric boundary layer, J. Meteorol. Soc. Japan. Ser. II, 87, 895–912, https://doi.org/10.2151/jmsj.87.895, 2009. a, b
NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team: ASTER Global Digital Elevation Model V003 [Data set], NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/ASTER/ASTGTM.003 (last access: 1 August 2020), 2019. a
Nash, J. and Sutcliffe, J.: River flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970. a
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements, J. Geophys. Res.-Atmos., 116, D12, https://doi.org/10.1029/2010JD015139, 2011. a, b
Noël, B., van de Berg, W. J., Lhermitte, S., Wouters, B., Machguth, H., Howat, I., Citterio, M., Moholdt, G., Lenaerts, J. T. M., and van den Broeke, M. R.: A tipping point in refreezing accelerates mass loss of Greenland's glaciers and ice caps, Nat. Commun., 8, 14730, https://doi.org/10.1038/ncomms14730, 2017. a
Nossent, J. and Bauwens, W.: Application of a normalized Nash-Sutcliffe efficiency to improve the accuracy of the Sobol' sensitivity analysis of a hydrological model, in: EGU General Assembly Conference Abstracts, EGU General Assembly Conference Abstracts, p. 237, 2012. 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/10.1017/S0022143000002574, 1998. a
Olson, J. B., Kenyon, J. S., Angevine, W. A., Brown, J. M., and Pagowski, Mariusz abd Sušelj, K.: A description of the MYNN-EDMF Scheme and the coupling to other components in WRF–ARW, OAA Technical Memorandum OAR GSD, 61, 37, https://doi.org/10.25923/n9wm-be49, 2019. a, b
Pervin, L. and Gan, T. Y.: Sensitivity of physical parameterization schemes in WRF model for dynamic downscaling of climatic variables over the MRB, J. Water Climate Change, 12, 1043–1058, https://doi.org/10.2166/wcc.2020.036, 2020. a
Petch, J. C.: Sensitivity studies of developing convection in a cloud-resolving model, Q. J. Roy. Meteor. Soc., 132, 345–358, https://doi.org/10.1256/qj.05.71, 2006. a
Radić, V., Bliss, A., Beedlow, A. C., Hock, R., Miles, E., and Cogley, J. G.: Regional and global projections of twenty-first century glacier mass changes in response to climate scenarios from global climate models, Clim. Dynam., 42, 37–58, https://doi.org/10.1007/s00382-013-1719-7, 2014. a
Radić, V., Menounos, B., Shea, J., Fitzpatrick, N., Tessema, M. A., and Déry, S. J.: Evaluation of different methods to model near-surface turbulent fluxes for a mountain glacier in the Cariboo Mountains, BC, Canada, The Cryosphere, 11, 2897–2918, https://doi.org/10.5194/tc-11-2897-2017, 2017. a, b, c, d, e
RGI Consortium: Randolph glacier inventory – A dataset of global glacier outlines, version 6, NSIDC: National Snow and Ice Data Center [data set], https://doi.org/10.7265/4m1f-gd79 (last access: 1 May 2022), 2017. a, b
Rounce, D. R., Hock, R., Maussion, F., Hugonnet, R., Kochtitzky, W., Huss, M., Berthier, E., Brinkerhoff, D., Compagno, L., Copland, L., Farinotti, D., Menounos, B., and McNabb, R. W.: Global glacier change in the 21st century: Every increase in temperature matters, Science, 379, 78–83, https://doi.org/10.1126/science.abo1324, 2023. a, b
Schindler, D. W. and Donahue, W. F.: An impending water crisis in Canada's western prairie provinces, P. Natl. Acad. Sci. USA, 103, 7210–7216, https://doi.org/10.1073/pnas.0601568103, 2006. a
Shannon, S., Smith, R., Wiltshire, A., Payne, T., Huss, M., Betts, R., Caesar, J., Koutroulis, A., Jones, D., and Harrison, S.: Global glacier volume projections under high-end climate change scenarios, The Cryosphere, 13, 325–350, https://doi.org/10.5194/tc-13-325-2019, 2019. a
Shirai, T., Enomoto, Y., Watanabe, M., and Arikawa, T.: Sensitivity analysis of the physics options in the Weather Research and Forecasting model for typhoon forecasting in Japan and its impacts on storm surge simulations, Coast. Eng. J., 64, 506–532, https://doi.org/10.1080/21664250.2022.2124040, 2022. a
Sicart, J. E., Wagnon, P., and Ribstein, P.: Atmospheric controls of the heat balance of Zongo Glacier (16∘ S, Bolivia), J. Geophys. Res.-Atmos., 110, D12106, https://doi.org/10.1029/2004JD005732, 2005. a, b
Skamarock, W. C. and Klemp, J. B.: A time-split nonhydrostatic atmospheric model for weather research and forecasting applications, J. Comput. Phys., 227, 3465–3485, https://doi.org/10.1016/j.jcp.2007.01.037, 2008. a, b, c, d
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., Wang, W., Powers, J. G., Duda, M. G., Barker, D. M., and Huang, X.-Y.: A description of the Advanced Research WRF Version 4.1, Tech. rep., UCAR/NCAR, https://doi.org/10.5065/1dfh-6p97, 2019. a, b, c, d
Srivastava, S. and Azam, M. F.: Mass- and Energy-Balance Modeling and Sublimation Losses on Dokriani Bamak and Chhota Shigri Glaciers in Himalaya Since 1979, Front. Water, 4, https://doi.org/10.3389/frwa.2022.874240, 2022. a
Stergiou, I., Tagaris, E., and Sotiropoulou, R.-E. P.: Sensitivity assessment of WRF parameterizations over Europe, Proceedings, 1, 119, https://doi.org/10.3390/ecas2017-04138, 2017. a
Stull, R. B.: Practical meteorology: An algebra-based survey of atmospheric science, Department of Earth, Ocean & Atmospheric Sciences, University of British Columbia, Vancouver, BC, https://doi.org/10.14288/1.0300441, 2015. a
Suzuki, K. and Zupanski, M.: Uncertainty in solid precipitation and snow depth prediction for Siberia using the Noah and Noah-MP land surface models, Front. Earth Sci., 12, 672–682, https://doi.org/10.1007/s11707-018-0691-2, 2018. a
Tarek, M., Brissette, F. P., and Arsenault, R.: Evaluation of the ERA5 reanalysis as a potential reference dataset for hydrological modelling over North America, Hydrol. Earth Syst. Sci., 24, 2527–2544, https://doi.org/10.5194/hess-24-2527-2020, 2020. a
Tewari, M., Chen, F., Wang, W., Dudhia, J., Lemone, A., Mitchell, E., Ek, M., Gayno, G., Wegiel, W., and Cuenca, R. H.: Implementation and verification of the united N land surface model in the WRF model, in: 20th Conference on Weather Analysis and Forecasting/16th Conference on Numerical Weather Prediction, 11–15, 2004. a, b
Thompson, G. and Eidhammer, T.: A study of aerosol impacts on clouds and precipitation development in a large winter cyclone, J. Atmos. Sci., 71, 3636–3658, https://doi.org/10.1175/JAS-D-13-0305.1, 2014. a
Thompson, G., Field, P., Rasmussen, R., and Hall, W.: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization, Mon. Weather Rev., 136, 5095–5115, https://doi.org/10.1175/2008MWR2387.1, 2008. a, b
Tzeng, G. and Huang, J.: Multiple Attribute Decision Making: Methods and Applications, A Chapman & Hall book, Taylor & Francis, ISBN 9781439861578, 2011. a
Wagner, J. S., Gohm, A., and Rotach, M. W.: The impact of horizontal model grid resolution on the boundary layer structure over an idealized valley, Mon. Weather Rev., 142, 3446–3465, https://doi.org/10.1175/MWR-D-14-00002.1, 2014. a
Wang, X., Tolksdorf, V., Otto, M., and Scherer, D.: WRF-based dynamical downscaling of ERA5 reanalysis data for High Mountain Asia: Towards a new version of the High Asia Refined analysis, Int. J. Climatol., 41, 743–762, https://doi.org/10.1002/joc.6686, 2021. a, b
WRF Community: Weather Research and Forecasting (WRF) Model, WRF Community [code], https://doi.org/10.5065/D6MK6B4K, 2000. a
Yang, Z.-L., Niu, G.-Y., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Longuevergne, L., Manning, K., Niyogi, D., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 2. Evaluation over global river basins, J. Geophys. Res.-Atmos., 116, https://doi.org/10.1029/2010JD015140, 2011. a, b
Zemp, M., Frey, H., Gärtner-Roer, I., Nussbaumer, S. U., Hoelzle, M., Paul, F., Haeberli, W., Denzinger, F., Ahlstrøm, A. P., Anderson, B., and et al.: Historically unprecedented global glacier decline in the early 21st century, J. Glaciol., 61, 745–762, https://doi.org/10.3189/2015JoG15J017, 2015. a
Zeyaeyan, S., Fattahi, E., Ranjbar, A., Azadi, M., and Vazifedoust, M.: Evaluating the effect of physics schemes in WRF simulations of summer rainfall in North West Iran, Climate, 5, 48, https://doi.org/10.3390/cli5030048, 2017. a
Zhang, Y., Hemperly, J., Meskhidze, N., and Skamarock, W. C.: The Global Weather Research and Forecasting (GWRF) model: Model evaluation, sensitivity study, and future year simulation, Atmos. Climate Sci., 02, 231–253, https://doi.org/10.4236/acs.2012.23024, 2012. a
Short summary
Our study increases our confidence in using reanalysis data for reconstructions of past glacier melt and in using dynamical downscaling for long-term simulations from global climate models to project glacier melt. We find that the surface energy balance model, forced with reanalysis and dynamically downscaled reanalysis data, yields <10 % difference in the modeled total melt energy when compared to the same model being forced with observations at our glacier sites in western Canada.
Our study increases our confidence in using reanalysis data for reconstructions of past glacier...
