Articles | Volume 12, issue 5
https://doi.org/10.5194/tc-12-1595-2018
© Author(s) 2018. 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-12-1595-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 May 2018
Research article |
| 03 May 2018
| 03 May 2018
Observations and simulations of the seasonal evolution of snowpack cold content and its relation to snowmelt and the snowpack energy budget
Keith S. Jennings
CORRESPONDING AUTHOR
Geography Department, University of Colorado Boulder, 260 UCB, Boulder, CO 80309, USA
Institute of Arctic and Alpine Research, University of Colorado Boulder, 450 UCB, Boulder, CO 80309, USA
Timothy G. F. Kittel
Institute of Arctic and Alpine Research, University of Colorado Boulder, 450 UCB, Boulder, CO 80309, USA
Noah P. Molotch
Geography Department, University of Colorado Boulder, 260 UCB, Boulder, CO 80309, USA
Institute of Arctic and Alpine Research, University of Colorado Boulder, 450 UCB, Boulder, CO 80309, USA
NASA Jet Propulsion Laboratory, 4800 Oak Grove Drive, Pasadena, CA 91109, USA
Related authors
Ryan W. Webb, Keith Jennings, Stefan Finsterle, and Steven R. Fassnacht
The Cryosphere, 15, 1423–1434, https://doi.org/10.5194/tc-15-1423-2021, https://doi.org/10.5194/tc-15-1423-2021, 2021
Short summary
Short summary
We simulate the flow of liquid water through snow and compare results to field experiments. This process is important because it controls how much and how quickly water will reach our streams and rivers in snowy regions. We found that water can flow large distances downslope through the snow even after the snow has stopped melting. Improved modeling of snowmelt processes will allow us to more accurately estimate available water resources, especially under changing climate conditions.
Keith S. Jennings and Noah P. Molotch
Hydrol. Earth Syst. Sci., 23, 3765–3786, https://doi.org/10.5194/hess-23-3765-2019, https://doi.org/10.5194/hess-23-3765-2019, 2019
Short summary
Short summary
There is a wide variety of modeling methods to designate precipitation as rain, snow, or a mix of the two. Here we show that method choice introduces marked uncertainty to simulated snowpack water storage (> 200 mm) and snow cover duration (> 1 month) in areas that receive significant winter and spring precipitation at air temperatures at and near freezing. This marked uncertainty has implications for water resources management as well as simulations of past and future hydroclimatic states.
Baptiste Vandecrux, Jason E. Box, Andreas P. Ahlstrøm, Signe B. Andersen, Nicolas Bayou, William T. Colgan, Nicolas J. Cullen, Robert S. Fausto, Dominik Haas-Artho, Achim Heilig, Derek A. Houtz, Penelope How, Ionut Iosifescu Enescu, Nanna B. Karlsson, Rebecca Kurup Buchholz, Kenneth D. Mankoff, Daniel McGrath, Noah P. Molotch, Bianca Perren, Maiken K. Revheim, Anja Rutishauser, Kevin Sampson, Martin Schneebeli, Sandy Starkweather, Simon Steffen, Jeff Weber, Patrick J. Wright, Henry Jay Zwally, and Konrad Steffen
Earth Syst. Sci. Data, 15, 5467–5489, https://doi.org/10.5194/essd-15-5467-2023, https://doi.org/10.5194/essd-15-5467-2023, 2023
Short summary
Short summary
The Greenland Climate Network (GC-Net) comprises stations that have been monitoring the weather on the Greenland Ice Sheet for over 30 years. These stations are being replaced by newer ones maintained by the Geological Survey of Denmark and Greenland (GEUS). The historical data were reprocessed to improve their quality, and key information about the weather stations has been compiled. This augmented dataset is available at https://doi.org/10.22008/FK2/VVXGUT (Steffen et al., 2022).
Oliver Wigmore and Noah P. Molotch
Earth Syst. Sci. Data, 15, 1733–1747, https://doi.org/10.5194/essd-15-1733-2023, https://doi.org/10.5194/essd-15-1733-2023, 2023
Short summary
Short summary
We flew a custom-built drone fitted with visible, near-infrared and thermal cameras every week over a summer season at Niwot Ridge in Colorado's alpine tundra. We processed these images into seamless orthomosaics that record changes in snow cover, vegetation health and the movement of water over the land surface. These novel datasets provide a unique centimetre resolution snapshot of ecohydrologic processes, connectivity and spatial and temporal heterogeneity in the alpine zone.
Ryan W. Webb, Keith Jennings, Stefan Finsterle, and Steven R. Fassnacht
The Cryosphere, 15, 1423–1434, https://doi.org/10.5194/tc-15-1423-2021, https://doi.org/10.5194/tc-15-1423-2021, 2021
Short summary
Short summary
We simulate the flow of liquid water through snow and compare results to field experiments. This process is important because it controls how much and how quickly water will reach our streams and rivers in snowy regions. We found that water can flow large distances downslope through the snow even after the snow has stopped melting. Improved modeling of snowmelt processes will allow us to more accurately estimate available water resources, especially under changing climate conditions.
Keith S. Jennings and Noah P. Molotch
Hydrol. Earth Syst. Sci., 23, 3765–3786, https://doi.org/10.5194/hess-23-3765-2019, https://doi.org/10.5194/hess-23-3765-2019, 2019
Short summary
Short summary
There is a wide variety of modeling methods to designate precipitation as rain, snow, or a mix of the two. Here we show that method choice introduces marked uncertainty to simulated snowpack water storage (> 200 mm) and snow cover duration (> 1 month) in areas that receive significant winter and spring precipitation at air temperatures at and near freezing. This marked uncertainty has implications for water resources management as well as simulations of past and future hydroclimatic states.
Keith N. Musselman, Noah P. Molotch, and Steven A. Margulis
The Cryosphere, 11, 2847–2866, https://doi.org/10.5194/tc-11-2847-2017, https://doi.org/10.5194/tc-11-2847-2017, 2017
Short summary
Short summary
We present a study of how melt rates in the California Sierra Nevada respond to a range of warming projected for this century. Snowfall and melt were simulated for historical and modified (warmer) snow seasons. Winter melt occurs more frequently and more intensely, causing an increase in extreme winter melt. In a warmer climate, less snow persists into the spring, causing spring melt to be substantially lower. The results offer insight into how snow water resources may respond to climate change.
Dominik Schneider, Noah P. Molotch, and Jeffrey S. Deems
The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-167, https://doi.org/10.5194/tc-2017-167, 2017
Revised manuscript not accepted
Short summary
Short summary
New data from the ongoing Airborne Snow Observatory (ASO) provides an unprecedented look at the spatial and temporal patterns of snow water content (SWE) over multiple years in California, USA. We found that relationships between SWE, snow covered area, and topography transfer between years at accuracy levels equivalent to those from models generated from ASO data collected on the day of interest. This research provides a first attempt at extending the value of ASO beyond the observations.
Felix C. Seidel, Karl Rittger, S. McKenzie Skiles, Noah P. Molotch, and Thomas H. Painter
The Cryosphere, 10, 1229–1244, https://doi.org/10.5194/tc-10-1229-2016, https://doi.org/10.5194/tc-10-1229-2016, 2016
Short summary
Short summary
Quantifying the snow albedo effect is an important step to predict water availability as well as changes in climate and sea level. We use imaging spectroscopy to determine optical properties of mountain snow. We find an inverse relationship between snow albedo and grain size as well as between elevation and grain size. Under strong melt conditions, however, we show that the optical-equivalent snow grain size increases slower than expected at lower elevations and we explain possible reasons.
A. A. Harpold, J. A. Marshall, S. W. Lyon, T. B. Barnhart, B. A. Fisher, M. Donovan, K. M. Brubaker, C. J. Crosby, N. F. Glenn, C. L. Glennie, P. B. Kirchner, N. Lam, K. D. Mankoff, J. L. McCreight, N. P. Molotch, K. N. Musselman, J. Pelletier, T. Russo, H. Sangireddy, Y. Sjöberg, T. Swetnam, and N. West
Hydrol. Earth Syst. Sci., 19, 2881–2897, https://doi.org/10.5194/hess-19-2881-2015, https://doi.org/10.5194/hess-19-2881-2015, 2015
Short summary
Short summary
This review's objective is to demonstrate the transformative potential of lidar by critically assessing both challenges and opportunities for transdisciplinary lidar applications in geomorphology, hydrology, and ecology. We find that using lidar to its full potential will require numerous advances, including more powerful open-source processing tools, new lidar acquisition technologies, and improved integration with physically based models and complementary observations.
Related subject area
Discipline: Snow | Subject: Energy Balance Obs/Modelling
Modeling snowpack dynamics and surface energy budget in boreal and subarctic peatlands and forests
Estimating degree-day factors of snow based on energy flux components
Understanding wind-driven melt of patchy snow cover
An 11-year record of wintertime snow-surface energy balance and sublimation at 4863 m a.s.l. on the Chhota Shigri Glacier moraine (western Himalaya, India)
Sensitivity of modeled snow grain size retrievals to solar geometry, snow particle asphericity, and snowpack impurities
Metamorphism of snow on Arctic sea ice during the melt season: impact on spectral albedo and radiative fluxes through snow
GABLS4 intercomparison of snow models at Dome C in Antarctica
Divergence of apparent and intrinsic snow albedo over a season at a sub-alpine site with implications for remote sensing
Modelling surface temperature and radiation budget of snow-covered complex terrain
Snow model comparison to simulate snow depth evolution and sublimation at point scale in the semi-arid Andes of Chile
Brief communication: Evaluation of multiple density-dependent empirical snow conductivity relationships in East Antarctica
Effect of small-scale snow surface roughness on snow albedo and reflectance
Impact of forcing on sublimation simulations for a high mountain catchment in the semiarid Andes
Intercomparison and improvement of two-stream shortwave radiative transfer schemes in Earth system models for a unified treatment of cryospheric surfaces
A key factor initiating surface ablation of Arctic sea ice: earlier and increasing liquid precipitation
Forcing the SURFEX/Crocus snow model with combined hourly meteorological forecasts and gridded observations in southern Norway
Jari-Pekka Nousu, Matthieu Lafaysse, Giulia Mazzotti, Pertti Ala-aho, Hannu Marttila, Bertrand Cluzet, Mika Aurela, Annalea Lohila, Pasi Kolari, Aaron Boone, Mathieu Fructus, and Samuli Launiainen
The Cryosphere, 18, 231–263, https://doi.org/10.5194/tc-18-231-2024, https://doi.org/10.5194/tc-18-231-2024, 2024
Short summary
Short summary
The snowpack has a major impact on the land surface energy budget. Accurate simulation of the snowpack energy budget is difficult, and studies that evaluate models against energy budget observations are rare. We compared predictions from well-known models with observations of energy budgets, snow depths and soil temperatures in Finland. Our study identified contrasting strengths and limitations for the models. These results can be used for choosing the right models depending on the use cases.
Muhammad Fraz Ismail, Wolfgang Bogacki, Markus Disse, Michael Schäfer, and Lothar Kirschbauer
The Cryosphere, 17, 211–231, https://doi.org/10.5194/tc-17-211-2023, https://doi.org/10.5194/tc-17-211-2023, 2023
Short summary
Short summary
Fresh water from mountainous catchments in the form of snowmelt and ice melt is of critical importance especially in the summer season for people living in these regions. In general, limited data availability is the core concern while modelling the snow and ice melt components from these mountainous catchments. This research will be helpful in selecting realistic parameter values (i.e. degree-day factor) while calibrating the temperature-index models for data-scarce regions.
Luuk D. van der Valk, Adriaan J. Teuling, Luc Girod, Norbert Pirk, Robin Stoffer, and Chiel C. van Heerwaarden
The Cryosphere, 16, 4319–4341, https://doi.org/10.5194/tc-16-4319-2022, https://doi.org/10.5194/tc-16-4319-2022, 2022
Short summary
Short summary
Most large-scale hydrological and climate models struggle to capture the spatially highly variable wind-driven melt of patchy snow cover. In the field, we find that 60 %–80 % of the total melt is wind driven at the upwind edge of a snow patch, while it does not contribute at the downwind edge. Our idealized simulations show that the variation is due to a patch-size-independent air-temperature reduction over snow patches and also allow us to study the role of wind-driven snowmelt on larger scales.
Arindan Mandal, Thupstan Angchuk, Mohd Farooq Azam, Alagappan Ramanathan, Patrick Wagnon, Mohd Soheb, and Chetan Singh
The Cryosphere, 16, 3775–3799, https://doi.org/10.5194/tc-16-3775-2022, https://doi.org/10.5194/tc-16-3775-2022, 2022
Short summary
Short summary
Snow sublimation is an important component of glacier surface mass balance; however, it is seldom studied in detail in the Himalayan region owing to data scarcity. We present an 11-year record of wintertime snow-surface energy balance and sublimation characteristics at the Chhota Shigri Glacier moraine site at 4863 m a.s.l. The estimated winter sublimation is 16 %–42 % of the winter snowfall at the study site, which signifies how sublimation is important in the Himalayan region.
Zachary Fair, Mark Flanner, Adam Schneider, and S. McKenzie Skiles
The Cryosphere, 16, 3801–3814, https://doi.org/10.5194/tc-16-3801-2022, https://doi.org/10.5194/tc-16-3801-2022, 2022
Short summary
Short summary
Snow grain size is important to determine the age and structure of snow, but it is difficult to measure. Snow grain size can be found from airborne and spaceborne observations by measuring near-infrared energy reflected from snow. In this study, we use the SNICAR radiative transfer model and a Monte Carlo model to examine how snow grain size measurements change with snow structure and solar zenith angle. We show that improved understanding of these variables improves snow grain size precision.
Gauthier Vérin, Florent Domine, Marcel Babin, Ghislain Picard, and Laurent Arnaud
The Cryosphere, 16, 3431–3449, https://doi.org/10.5194/tc-16-3431-2022, https://doi.org/10.5194/tc-16-3431-2022, 2022
Short summary
Short summary
Snow physical properties on Arctic sea ice are monitored during the melt season. As snow grains grow, and the snowpack thickness is reduced, the surface albedo decreases. The extra absorbed energy accelerates melting. Radiative transfer modeling shows that more radiation is then transmitted to the snow–sea-ice interface. A sharp increase in transmitted radiation takes place when the snowpack thins significantly, and this coincides with the initiation of the phytoplankton bloom in the seawater.
Patrick Le Moigne, Eric Bazile, Anning Cheng, Emanuel Dutra, John M. Edwards, William Maurel, Irina Sandu, Olivier Traullé, Etienne Vignon, Ayrton Zadra, and Weizhong Zheng
The Cryosphere, 16, 2183–2202, https://doi.org/10.5194/tc-16-2183-2022, https://doi.org/10.5194/tc-16-2183-2022, 2022
Short summary
Short summary
This paper describes an intercomparison of snow models, of varying complexity, used for numerical weather prediction or academic research. The results show that the simplest models are, under certain conditions, able to reproduce the surface temperature just as well as the most complex models. Moreover, the diversity of surface parameters of the models has a strong impact on the temporal variability of the components of the simulated surface energy balance.
Edward H. Bair, Jeff Dozier, Charles Stern, Adam LeWinter, Karl Rittger, Alexandria Savagian, Timbo Stillinger, and Robert E. Davis
The Cryosphere, 16, 1765–1778, https://doi.org/10.5194/tc-16-1765-2022, https://doi.org/10.5194/tc-16-1765-2022, 2022
Short summary
Short summary
Understanding how snow and ice reflect solar radiation (albedo) is important for global climate. Using high-resolution topography, darkening from surface roughness (apparent albedo) is separated from darkening by the composition of the snow (intrinsic albedo). Intrinsic albedo is usually greater than apparent albedo, especially during melt. Such high-resolution topography is often not available; thus the use of a shade component when modeling mixtures is advised.
Alvaro Robledano, Ghislain Picard, Laurent Arnaud, Fanny Larue, and Inès Ollivier
The Cryosphere, 16, 559–579, https://doi.org/10.5194/tc-16-559-2022, https://doi.org/10.5194/tc-16-559-2022, 2022
Short summary
Short summary
Topography controls the surface temperature of snow-covered, mountainous areas. We developed a modelling chain that uses ray-tracing methods to quantify the impact of a few topographic effects on snow surface temperature at high spatial resolution. Its large spatial and temporal variations are correctly simulated over a 50 km2 area in the French Alps, and our results show that excluding a single topographic effect results in cooling (or warming) effects on the order of 1 °C.
Annelies Voordendag, Marion Réveillet, Shelley MacDonell, and Stef Lhermitte
The Cryosphere, 15, 4241–4259, https://doi.org/10.5194/tc-15-4241-2021, https://doi.org/10.5194/tc-15-4241-2021, 2021
Short summary
Short summary
The sensitivity of two snow models (SNOWPACK and SnowModel) to various parameterizations and atmospheric forcing biases is assessed in the semi-arid Andes of Chile in winter 2017. Models show that sublimation is a main driver of ablation and that its relative contribution to total ablation is highly sensitive to the selected albedo parameterization and snow roughness length. The forcing and parameterizations are more important than the model choice, despite differences in physical complexity.
Minghu Ding, Tong Zhang, Diyi Yang, Ian Allison, Tingfeng Dou, and Cunde Xiao
The Cryosphere, 15, 4201–4206, https://doi.org/10.5194/tc-15-4201-2021, https://doi.org/10.5194/tc-15-4201-2021, 2021
Short summary
Short summary
Measurement of snow heat conductivity is essential to establish the energy balance between the atmosphere and firn, but it is still not clear in Antarctica. Here, we used data from three automatic weather stations located in different types of climate and evaluated nine schemes that were used to calculate the effective heat diffusivity of snow. The best solution was proposed. However, no conductivity–density relationship was optimal at all sites, and the performance of each varied with depth.
Terhikki Manninen, Kati Anttila, Emmihenna Jääskeläinen, Aku Riihelä, Jouni Peltoniemi, Petri Räisänen, Panu Lahtinen, Niilo Siljamo, Laura Thölix, Outi Meinander, Anna Kontu, Hanne Suokanerva, Roberta Pirazzini, Juha Suomalainen, Teemu Hakala, Sanna Kaasalainen, Harri Kaartinen, Antero Kukko, Olivier Hautecoeur, and Jean-Louis Roujean
The Cryosphere, 15, 793–820, https://doi.org/10.5194/tc-15-793-2021, https://doi.org/10.5194/tc-15-793-2021, 2021
Short summary
Short summary
The primary goal of this paper is to present a model of snow surface albedo (brightness) accounting for small-scale surface roughness effects. It can be combined with any volume scattering model. The results indicate that surface roughness may decrease the albedo by about 1–3 % in midwinter and even more than 10 % during the late melting season. The effect is largest for low solar zenith angle values and lower bulk snow albedo values.
Marion Réveillet, Shelley MacDonell, Simon Gascoin, Christophe Kinnard, Stef Lhermitte, and Nicole Schaffer
The Cryosphere, 14, 147–163, https://doi.org/10.5194/tc-14-147-2020, https://doi.org/10.5194/tc-14-147-2020, 2020
Cheng Dang, Charles S. Zender, and Mark G. Flanner
The Cryosphere, 13, 2325–2343, https://doi.org/10.5194/tc-13-2325-2019, https://doi.org/10.5194/tc-13-2325-2019, 2019
Tingfeng Dou, Cunde Xiao, Jiping Liu, Wei Han, Zhiheng Du, Andrew R. Mahoney, Joshua Jones, and Hajo Eicken
The Cryosphere, 13, 1233–1246, https://doi.org/10.5194/tc-13-1233-2019, https://doi.org/10.5194/tc-13-1233-2019, 2019
Short summary
Short summary
The variability and potential trends of rain-on-snow events over Arctic sea ice and their role in sea-ice losses are poorly understood. This study demonstrates that rain-on-snow events are a critical factor in initiating the onset of surface melt over Arctic sea ice, and onset of spring rainfall over sea ice has shifted to earlier dates since the 1970s, which may have profound impacts on ice melt through feedbacks involving earlier onset of surface melt.
Hanneke Luijting, Dagrun Vikhamar-Schuler, Trygve Aspelien, Åsmund Bakketun, and Mariken Homleid
The Cryosphere, 12, 2123–2145, https://doi.org/10.5194/tc-12-2123-2018, https://doi.org/10.5194/tc-12-2123-2018, 2018
Short summary
Short summary
Knowledge of the snow reservoir is important for energy production and water resource management. In this study, a detailed snow model is run over southern Norway with two different sets of forcing data. The results show that forcing data consisting of post-processed data from a numerical weather model (observations assimilated into the raw weather predictions) are most promising for snow simulations when larger regions are evaluated.
Cited articles
Albert, M. R. and McGilvary, W. R.: Thermal effects due to air flow and vapor
transport in dry snow, J. Glaciol., 38, 273–281, 1992.
Anderson, E. A.: Development and testing of snow pack energy balance
equations, Water Resour. Res., 4, 19–37, 1968.
Anderson, E. A.: A point of energy and mass balance model of snow cover, NOAA
technical report NWS, 19, 150 pp., 1976.
Andreadis, K. M., Storck, P., and Lettenmaier, D. P.: Modeling snow
accumulation and ablation processes in forested environments, Water Resour.
Res., 45, W05429, https://doi.org/10.1029/2008WR007042, 2009.
Angström, A. K.: A study of the radiation of the atmosphere: based upon
observations of the nocturnal radiation during expeditions to Algeria and to
California, Smithsonian Institution, Washington, DC, 159 pp., 1915.
Armstrong, R. L. and Armstrong, B. R.: Snow and avalanche climates of the
western United States: a comparison of maritime, intermountain and
continental conditions, IAHS Publ., 162, 281–294, 1987.
Armstrong, R. L. and Brun, E.: Snow and climate: physical processes, surface
energy exchange and modeling, Cambridge University Press, Cambridge, UK,
2008.
Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.: Potential impacts of a
warming climate on water availability in snow-dominated regions, Nature, 438,
303–309, 2005.
Barnhart, T. B., Molotch, N. P., Livneh, B., Harpold, A. A., Knowles, J. F.,
and Schneider, D.: Snowmelt rate dictates streamflow, Geophys. Res. Lett.,
43, 8006–8016, 2016.
Bartelt, P. and Lehning, M.: A physical SNOWPACK model for the Swiss
avalanche warning: Part I: numerical model, Cold Reg. Sci. Technol., 35,
123–145, 2002.
Bengtsson, L.: Percolation of meltwater through a snowpack, Cold Reg. Sci.
Technol., 6, 73–81, 1982.
Berg, N. H.: Blowing snow at a Colorado alpine site: measurements and
implications, Arctic Alpine Res., 18, 147–161, 1986.
Berghuijs, W. R., Woods, R. A., and Hrachowitz, M.: A precipitation shift
from snow towards rain leads to a decrease in streamflow, Nat. Clim. Change,
4, 583–586, 2014.
Blanken, P. D., Williams, M. W., Burns, S. P., Monson, R. K., Knowles, J.,
Chowanski, K., and Ackerman, T.: A comparison of water and carbon dioxide
exchange at a windy alpine tundra and subalpine forest site near Niwot Ridge,
Colorado, Biogeochemistry, 95, 61–76, 2009.
Blöschl, G. and Kirnbauer, R.: Point snowmelt models with different
degrees of complexity – internal processes, J. Hydrol., 129, 127–147, 1991.
Boone, A. and Etchevers, P.: An intercomparison of three snow schemes of
varying complexity coupled to the same land surface model: Local-scale
evaluation at an Alpine site, J. Hydrometeorol., 2, 374–394, 2001.
Brooks, P. D. and Williams, M. W.: Snowpack controls on nitrogen cycling and
export in seasonally snow-covered catchments, Hydrol. Process., 13,
2177–2190, 1999.
Burns, S. P., Molotch, N. P., Williams, M. W., Knowles, J. F., Seok, B.,
Monson, R. K., Turnipseed, A. A., and Blanken, P. D.: Snow Temperature
Changes within a Seasonal Snowpack and Their Relationship to Turbulent Fluxes
of Sensible and Latent Heat, J. Hydrometeorol., 15, 117–142,
https://doi.org/10.1175/JHM-D-13-026.1, 2014.
Caine, N.: Streamflow patterns in the alpine environment of North Boulder
Creek, Colorado Front Range, Z. Geomorphol. Supp., 104, 27–42, 1996.
Cherkauer, K. A., Bowling, L. C., and Lettenmaier, D. P.: Variable
infiltration capacity cold land process model updates, Global Planet. Change,
38, 151–159, https://doi.org/10.1016/S0921-8181(03)00025-0, 2003.
Christensen, N. S., Wood, A. W., Voisin, N., Lettenmaier, D. P., and Palmer,
R. N.: The effects of climate change on the hydrology and water resources of
the Colorado River basin, Clim. Change, 62, 337–363, 2004.
Clark, M. P., Nijssen, B., and Luce, C. H.: An analytical test case for snow
models, Water Resour. Res., 53, 909–922, https://doi.org/10.1002/2016WR019672, 2017.
Cline, D. W.: Snow surface energy exchanges and snowmelt at a continental,
midlatitude Alpine site, Water Resour. Res., 33, 689–701, 1997.
Clow, D. W., Williams, M. W., and Schuster, P. F.: Increasing aeolian dust
deposition to snowpacks in the Rocky Mountains inferred from snowpack, wet
deposition, and aerosol chemistry, Atmos. Environ., 146, 183–194,
https://doi.org/10.1016/j.atmosenv.2016.06.076, 2016.
Colbeck, S. C.: Air movement in snow due to windpumping, J. Glaciol., 35,
209–213, 1989a.
Colbeck, S. C.: Snow-crystal growth with varying surface temperatures and
radiation penetration, J. Glaciol., 35, 23–29, 1989b.
Crawford, T. M. and Duchon, C. E.: An improved parameterization for
estimating effective atmospheric emissivity for use in calculating daytime
downwelling longwave radiation, J. Appl. Meteorol., 38, 474–480, 1999.
Déry, S. J. and Brown, R. D.: Recent Northern Hemisphere snow cover
extent trends and implications for the snow-albedo feedback, Geophys. Res.
Lett., 34, L22504, https://doi.org/10.1029/2007GL031474, 2007.
DeWalle, D. R. and Rango, A.: Principles of snow hydrology, Cambridge
University Press, Cambridge, UK, 2008.
Dilley, A. C. and O'Brien, D. M.: Estimating downward clear sky long-wave
irradiance at the surface from screen temperature and precipitable water,
Q. J. Roy. Meteor. Soc., 124, 1391–1401, 1998.
Erickson, T. A., Williams, M. W., and Winstral, A.: Persistence of
topographic controls on the spatial distribution of snow in rugged mountain
terrain, Colorado, United States, Water Resour. Res., 41, W04014,
https://doi.org/10.1029/2003WR002973, 2005.
Essery, R., Morin, S., Lejeune, Y., and Ménard, C. B.: A comparison of
1701 snow models using observations from an alpine site, Adv. Water Resour.,
55, 131–148, https://doi.org/10.1016/j.advwatres.2012.07.013, 2013.
Etchevers, P., Martin, E., Brown, R., Fierz, C., Lejeune, Y., Bazile, E.,
Boone, A., Dai, Y.-J., Essery, R., Fernandez, A. Gusev, Y., Jordan, R.,
Koren, V., Kowalczyk, E., Nasonova, N. O., Pyles, R. D., Schlosser, A.,
Shmakin, A. B., Smirnova, T. G., Strasser, U., Verseghy, D., Yamazaki, T.,
and Yang, Z.-L.: Validation of the energy budget of an alpine snowpack
simulated by several snow models (SnowMIP project), Ann. Glaciol., 38,
150–158, 2004.
Flerchinger, G. N., Xaio, W., Marks, D., Sauer, T. J., and Yu, Q.: Comparison
of algorithms for incoming atmospheric long-wave radiation, Water Resour.
Res., 45, W03423, https://doi.org/10.1029/2008WR007394, 2009.
Förster, K., Meon, G., Marke, T., and Strasser, U.: Effect of
meteorological forcing and snow model complexity on hydrological simulations
in the Sieber catchment (Harz Mountains, Germany), Hydrol. Earth Syst. Sci.,
18, 4703–4720, https://doi.org/10.5194/hess-18-4703-2014, 2014.
Harder, P. and Pomeroy, J.: Estimating precipitation phase using a
psychrometric energy balance method, Hydrol. Process., 27, 1901–1914,
https://doi.org/10.1002/hyp.9799, 2013.
Harpold, A. A. and Molotch, N. P.: Sensitivity of soil water availability to
changing snowmelt timing in the western US, Geophys. Res. Lett., 42,
8011–8020, 2015.
Helgason, W. and Pomeroy, J.: Problems Closing the Energy Balance over a
Homogeneous Snow Cover during Midwinter, J. Hydrometeorol., 13, 557–572,
https://doi.org/10.1175/JHM-D-11-0135.1, 2011.
Henn, B., Raleigh, M. S., Fisher, A., and Lundquist, J. D.: A Comparison of
Methods for Filling Gaps in Hourly Near-Surface Air Temperature Data,
J. Hydrometeorol., 14, 929–945, https://doi.org/10.1175/JHM-D-12-027.1, 2012.
Hood, E., Williams, M., and Cline, D.: Sublimation from a seasonal snowpack
at a continental, mid-latitude alpine site, Hydrol. Process., 13, 1781–1797,
1999.
Jennings, K. and Jones, J. A.: Precipitation-snowmelt timing and snowmelt
augmentation of large peak flow events, western Cascades, Oregon, Water
Resour. Res., 51, 7649–7661, https://doi.org/10.1002/2014WR016877, 2015.
Jennings, K. S., Winchell, T. S., Livneh, B., and Molotch, N. P.: Spatial
variation of the rain-snow temperature threshold across the Northern
Hemisphere, Nat. Commun., 9, 1148, https://doi.org/10.1038/s41467-018-03629-7, 2018.
Jepsen, S. M., Molotch, N. P., Williams, M. W., Rittger, K. E., and Sickman,
J. O.: Interannual variability of snowmelt in the Sierra Nevada and Rocky
Mountains, United States: Examples from two alpine watersheds, Water Resour.
Res., 48, W02529, https://doi.org/10.1029/2011WR011006, 2012.
Kampf, S. K. and Lefsky, M. A.: Transition of dominant peak flow source from
snowmelt to rainfall along the Colorado Front Range: Historical patterns,
trends, and lessons from the 2013 Colorado Front Range floods, Water Resour.
Res., 52, 407–422, 2016.
Kapnick, S. and Hall, A.: Causes of recent changes in western North American
snowpack, Clim. Dynam., 38, 1885–1899, 2012.
Kirchner, J. W.: Getting the right answers for the right reasons: Linking
measurements, analyses, and models to advance the science of hydrology, Water
Resour. Res., 42, W03S04, https://doi.org/10.1029/2005WR004362, 2006.
Kittel, T.: The Development and Analysis of Climate Datasets for National
Park Science and Management: A Guide to Methods for Making Climate Records
Useful and Tools to Explore Critical Questions, available at:
https://irma.nps.gov/DataStore/Reference/Profile/2169763 (last access:
6 March 2018), 2009.
Kittel, T. G. F., Williams, M. W., Chowanski, K., Hartman, M., Ackerman, T.,
Losleben, M., and Blanken, P. D.: Contrasting long-term alpine and subalpine
precipitation trends in a mid-latitude North American mountain system,
Colorado Front Range, USA, Plant Ecol. Divers., 8, 607–624,
https://doi.org/10.1080/17550874.2016.1143536, 2015.
Knowles, J. F., Blanken, P. D., Williams, M. W., and Chowanski, K. M.: Energy
and surface moisture seasonally limit evaporation and sublimation from
snow-free alpine tundra, Agr. Forest Meteorol., 157, 106–115,
https://doi.org/10.1016/j.agrformet.2012.01.017, 2012.
Knowles, J. F., Harpold, A. A., Cowie, R., Zeliff, M., Barnard, H. R., Burns,
S. P., Blanken, P. D., Morse, J. F., and Williams, M. W.: The relative
contributions of alpine and subalpine ecosystems to the water balance of a
mountainous, headwater catchment, Hydrol. Process., 29, 4794–4808,
https://doi.org/10.1002/hyp.10526, 2015.
Knowles, N., Dettinger, M. D., and Cayan, D. R.: Trends in snowfall versus
rainfall in the western United States, J. Climate, 19, 4545–4559, 2006.
Lapo, K. E., Hinkelman, L. M., Raleigh, M. S., and Lundquist, J. D.: Impact
of errors in the downwelling irradiances on simulations of snow water
equivalent, snow surface temperature, and the snow energy balance, Water
Resour. Res., 51, 1649–1670, 2015.
Lehning, M., Bartelt, P., Brown, B., Fierz, C., and Satyawali, P.: A physical
SNOWPACK model for the Swiss avalanche warning: Part II. Snow microstructure,
Cold Reg. Sci. Technol., 35, 147–167, 2002a.
Lehning, M., Bartelt, P., Brown, B., and Fierz, C.: A physical SNOWPACK model
for the Swiss avalanche warning: Part III: Meteorological forcing, thin layer
formation and evaluation, Cold Reg. Sci. Technol., 35, 169–184, 2002b.
Liston, G. E. and Elder, K.: A meteorological distribution system for
high-resolution terrestrial modeling (MicroMet), J. Hydrometeorol., 7,
217–234, 2006.
Litaor, M. I., Williams, M., and Seastedt, T. R.: Topographic controls on snow
distribution, soil moisture, and species diversity of herbaceous alpine
vegetation, Niwot Ridge, Colorado, J. Geophys. Res., 113, G02008,
https://doi.org/10.1029/2007JG000419, 2008.
Livneh, B., Xia, Y., Mitchell, K. E., Ek, M. B., and Lettenmaier, D. P.: Noah
LSM Snow Model Diagnostics and Enhancements, J. Hydrometeorol., 11, 721–738,
https://doi.org/10.1175/2009JHM1174.1, 2010.
Lundy, C. C., Brown, R. L., Adams, E. E., Birkeland, K. W., and Lehning, M.:
A statistical validation of the SNOWPACK model in a Montana climate, Cold
Reg. Sci. Technol., 33, 237–246, 2001.
Mankin, J. S., Viviroli, D., Singh, D., Hoekstra, A. Y., and Diffenbaugh, N.
S.: The potential for snow to supply human water demand in the present and
future, Environ. Res. Lett., 10, 114016,
https://doi.org/10.1088/1748-9326/10/11/114016, 2015.
Marks, D. and Dozier, J.: Climate and energy exchange at the snow surface in
the alpine region of the Sierra Nevada: 2. Snow cover energy balance, Water
Resour. Res., 28, 3043–3054, 1992.
Marks, D. and Winstral, A.: Comparison of snow deposition, the snow cover
energy balance, and snowmelt at two sites in a semiarid mountain basin,
J. Hydrometeorol., 2, 213–227, 2001.
Marks, D., Dozier, J., and Davis, R. E.: Climate and Energy Exchange at the
Snow Surface in the Alpine Region of the Sierra Nevada 1. Meteorological
Measurements and Monitoring, Water Resour. Res., 28, 3029–3042, 1992.
Marks, D., Winstral, A., Flerchinger, G., Reba, M., Pomeroy, J., Link, T.,
and Elder, K.: Comparing simulated and measured sensible and latent heat
fluxes over snow under a pine canopy to improve an energy balance snowmelt
model, J. Hydrometeorol., 9, 1506–1522, 2008.
Meek, D. W. and Hatfield, J. L.: Data quality checking for single station
meteorological databases, Agr. Forest Meteorol., 69, 85–109, 1994.
Meromy, L., Molotch, N. P., Williams, M. W., Musselman, K. N., and Kueppers,
L. M.: Snowpack-climate manipulation using infrared heaters in subalpine
forests of the Southern Rocky Mountains, USA, Agr. Forest Meteorol., 203,
142–157, https://doi.org/10.1016/j.agrformet.2014.12.015, 2015.
Molotch, N. P., Blanken, P. D., Williams, M. W., Turnipseed, A. A., Monson,
R. K., and Margulis, S. A.: Estimating sublimation of intercepted and
sub-canopy snow using eddy covariance systems, Hydrol. Process., 21,
1567–1575, 2007.
Molotch, N. P., Brooks, P. D., Burns, S. P., Litvak, M., Monson, R. K.,
McConnell, J. R., and Musselman, K.: Ecohydrological controls on snowmelt
partitioning in mixed-conifer sub-alpine forests, Ecohydrology, 2, 129–142,
2009.
Molotch, N. P., Barnard, D. M., Burns, S. P., and Painter, T. H.: Measuring
spatiotemporal variation in snow optical grain size under a subalpine forest
canopy using contact spectroscopy, Water Resour. Res., 52, 7513–7522,
https://doi.org/10.1002/2016WR018954, 2016.
Mosier, T. M., Hill, D. F., and Sharp, K. V.: How much cryosphere model
complexity is just right? Exploration using the conceptual cryosphere
hydrology framework, The Cryosphere, 10, 2147–2171,
https://doi.org/10.5194/tc-10-2147-2016, 2016.
Mote, P. W., Hamlet, A. F., Clark, M. P., and Lettenmaier, D. P.: Declining
mountain snowpack in western North America*, B. Am. Meteorol. Soc., 86,
39–49, 2005.
Musselman, K. N., Clark, M. P., Liu, C., Ikeda, K., and Rasmussen, R.: Slower
snowmelt in a warmer world, Nat. Clim. Change, 7, 214–219,
https://doi.org/10.1038/nclimate3225, 2017.
Obled, C. and Rosse, B.: Mathematical models of a melting snowpack at an
index plot, J. Hydrol., 32, 139–163, https://doi.org/10.1016/0022-1694(77)90123-8,
1977.
Painter, T. H., Deems, J. S., Belnap, J., Hamlet, A. F., Landry, C. C., and
Udall, B.: Response of Colorado River runoff to dust radiative forcing in
snow, P. Natl. Acad. Sci. USA, 107, 17125–17130, 2010.
Pederson, G. T., Gray, S. T., Woodhouse, C. A., Betancourt, J. L., Fagre, D.
B., Littell, J. S., Watson, E., Luckman, B. H., and Graumlich, L. J.: The
unusual nature of recent snowpack declines in the North American Cordillera,
Science, 333, 332–335, 2011.
Raleigh, M. S., Lundquist, J. D., and Clark, M. P.: Exploring the impact of
forcing error characteristics on physically based snow simulations within a
global sensitivity analysis framework, Hydrol. Earth Syst. Sci., 19,
3153–3179, https://doi.org/10.5194/hess-19-3153-2015, 2015.
Raleigh, M. S., Livneh, B., Lapo, K., and Lundquist, J. D.: How Does
Availability of Meteorological Forcing Data Impact Physically Based Snowpack
Simulations?, J. Hydrometeorol., 17, 99–120, https://doi.org/10.1175/JHM-D-14-0235.1,
2016.
Rasmussen, R., Baker, B., Kochendorfer, J., Meyers, T., Landolt, S., Fischer,
A. P., Black, J., Thériault, J. M., Kucera, P., Gochis, D., Smith, C., Nitu, R., Hall, M., Ikeda, K., and Gutmann, E.:
How well are we measuring snow: The NOAA/FAA/NCAR winter precipitation test
bed, B. Am. Meteorol. Soc., 93, 811–829, 2012.
Regonda, S. K., Rajagopalan, B., Clark, M., and Pitlick, J.: Seasonal cycle
shifts in hydroclimatology over the western United States, J. Climate, 18,
372–384, 2005.
Rutter, N., Essery, R., Pomeroy, J., et al.: Evaluation of forest snow
processes models (SnowMIP2), J. Geophys. Res., 114, D06111,
https://doi.org/10.1029/2008JD011063, 2009.
Schlögl, S., Marty, C., Bavay, M., and Lehning, M.: Sensitivity of
Alpine3D modeled snow cover to modifications in DEM resolution, station
coverage and meteorological input quantities, Environ. Model. Softw., 83,
387–396, https://doi.org/10.1016/j.envsoft.2016.02.017, 2016.
Schmucki, E., Marty, C., Fierz, C., and Lehning, M.: Evaluation of modelled
snow depth and snow water equivalent at three contrasting sites in
Switzerland using SNOWPACK simulations driven by different meteorological
data input, Cold Reg. Sci. Technol., 99, 27–37, 2014.
Seligman, Z. M., Harper, J. T., and Maneta, M. P.: Changes to Snowpack Energy
State from Spring Storm Events, Columbia River Headwaters, Montana, J.
Hydrometeorol., 15, 159–170, https://doi.org/10.1175/JHM-D-12-078.1, 2014.
Serreze, M. C., Clark, M. P., Armstrong, R. L., McGinnis, D. A., and
Pulwarty, R. S.: Characteristics of the western United States snowpack from
snowpack telemetry (SNOTEL) data, Water Resour. Res., 35, 2145–2160, 1999.
Sexstone, G. A., Clow, D. W., Stannard, D. I., and Fassnacht, S. R.:
Comparison of methods for quantifying surface sublimation over seasonally
snow-covered terrain, Hydrol. Process., 30, 3373–3389,
https://doi.org/10.1002/hyp.10864, 2016.
Skiles, S. M., Painter, T. H., Deems, J. S., Bryant, A. C., and Landry, C.
C.: Dust radiative forcing in snow of the Upper Colorado River Basin:
2. Interannual variability in radiative forcing and snowmelt rates, Water
Resour. Res., 48, W07522, https://doi.org/10.1029/2012WR011986, 2012.
Slater, A. G., Schlosser, C. A., Desborough, C. E., et al.: The
representation of snow in land surface schemes: Results from PILPS 2(d),
J. Hydrometeorol., 2, 7–25, 2001.
Slater, A. G., Lawrence, D. M., and Koven, C. D.: Process-level model
evaluation: a snow and heat transfer metric, The Cryosphere, 11, 989–996,
https://doi.org/10.5194/tc-11-989-2017, 2017.
Stewart, I. T.: Changes in snowpack and snowmelt runoff for key mountain
regions, Hydrol. Process., 23, 78–94, 2009.
Sturm, M., Holmgren, J., and Liston, G. E.: A seasonal snow cover
classification system for local to global applications, J. Climate, 8,
1261–1283, 1995.
Sturm, M., Holmgren, J., König, M., and Morris, K.: The thermal
conductivity of seasonal snow, J. Glaciol., 43, 26–41, 1997.
Trujillo, E. and Molotch, N. P.: Snowpack regimes of the Western United
States, Water Resour. Res., 50, 5611–5623, https://doi.org/10.1002/2013WR014753, 2014.
United States Army Corps of Engineers: Snow hydrology, North Pacific
Division, Corps of Engineers, U.S. Army, Portland, Or., 1956.
Walker, D. A., Halfpenny, J. C., Walker, M. D., and Wessman, C. A.: Long-term
studies of snow-vegetation interactions, BioScience, 43, 287–301, 1993.
Walker, M. D., Webber, P. J., Arnold, E. H., and Ebert-May, D.: Effects of
interannual climate variation on aboveground phytomass in alpine vegetation,
Ecology, 75, 393–408, 1994.
Wigmosta, M. S., Vail, L. W., and Lettenmaier, D. P.: A distributed
hydrology-vegetation model for complex terrain, Water Resour. Res., 30,
1665–1679, 1994.
Williams, M.: Snow cover profile data for Niwot Ridge, Green Lakes Valley
from 1993/2/26 – ongoing, weekly to biweekly, available at:
http://niwot.colorado.edu/index.php/data/data/snow-cover-profile-data-for-niwot-ridge-and-green-lakes-valley-
- -1993-ongoi (last access: 17 April 2018), 2016.
Williams, M. W., Bardsley, T., and Rikkers, M.: Overestimation of snow depth
and inorganic nitrogen wetfall using NADP data, Niwot Ridge, Colorado, Atmos.
Environ., 32, 3827–3833, 1998.
Williams, M. W., Cline, D., Hartman, M., and Bardsley, T.: Data for snowmelt
model development, calibration, and verification at an alpine site, Colorado
Front Range, Water Resour. Res., 35, 3205–3209, 1999.
Winchell, T. S., Barnard, D. M., Monson, R. K., Burns, S. P., and Molotch, N.
P.: Earlier snowmelt reduces atmospheric carbon uptake in midlatitude
subalpine forests, Geophys. Res. Lett., 43, 8160–8168, 2016.
Yang, D., Goodison, B. E., Metcalfe, J. R., Louie, P., Leavesley, G.,
Emerson, D., Hanson, C. L., Golubev, V. S., Elomaa, E., Gunther, T., and
others: Quantification of precipitation measurement discontinuity induced by
wind shields on national gauges, Water Resour. Res., 35, 491–508, 1999.
Short summary
We show through observations and simulations that cold content, a key part of the snowpack energy budget, develops primarily through new snowfall. We also note that cold content damps snowmelt rate and timing at sub-seasonal timescales, while seasonal melt onset is controlled by the timing of peak cold content and total spring precipitation. This work has implications for how cold content is represented in snow models and improves our understanding of its effect on snowmelt processes.
We show through observations and simulations that cold content, a key part of the snowpack...
