Hourly mass and snow energy balance measurements from Mammoth Mountain, CA USA, 2011–2017

• Published in: Earth System Science Data
• Main Authors: E. H. Bair, R. E. Davis, J. Dozier
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2018
• Subjects: Environmental sciences; GE1-350; Geology; QE1-996.5; Cold Regions Research and Engineering Laboratory
• Online Access: https://doi.org/10.5194/essd-10-549-2018; https://doaj.org/article/c9f3ad0769b4430d96c3f7d206215451

The mass and energy balance of the snowpack govern its evolution. Direct measurement of these fluxes is essential for modeling the snowpack, yet there are few sites where all the relevant measurements are taken. Mammoth Mountain, CA USA, is home to the Cold Regions Research and Engineering Laboratory and University of California – Santa Barbara Energy Site (CUES), one of five energy balance monitoring sites in the western US. There is a ski patrol study site on Mammoth Mountain, called the Sesame Street Snow Study Plot, with automated snow and meteorological instruments where new snow is hand-weighed to measure its water content. There is also a site at Mammoth Pass with automated precipitation instruments. For this dataset, we present a clean and continuous hourly record of selected measurements from the three sites covering the 2011–2017 water years. Then, we model the snow mass balance at CUES and compare model runs to snow pillow measurements. The 2011–2017 period was marked by exceptional variability in precipitation, even for an area that has high year-to-year variability. The driest year on record, and one of the wettest years, occurred during this time period, making it ideal for studying climatic extremes. This dataset complements a previously published dataset from CUES containing a smaller subset of daily measurements. In addition to the hand-weighed SWE, novel measurements include hourly broadband snow albedo corrected for terrain and other measurement biases. This dataset is available with a digital object identifier: https://doi.org/10.21424/R4159Q .