Trend Identification in Twentieth-Century U.S. Snowfall: The Challenges

Abstract

There is an increasing interest in examining long-term trends in measures of snow climatology. An examination of the U.S. daily snowfall records for 1900-2004 revealed numerous apparent inconsistencies. For example, long-term snowfall trends among neighboring lake-effect stations differ greatly from insignificant to ϩ100% century Ϫ1 . Internal inconsistencies in the snow records, such as a lack of upward trends in maximum seasonal snow depth at stations with large upward trends in snowfall, point to inhomogeneities. Nationwide, the frequency of daily observations with a 10:1 snowfall-to-liquid-equivalent ratio declined from 30% in the 1930s to a current value of around 10%, a change that is clearly due to observational practice. There then must be biases in cold-season liquid-equivalent precipitation, or snowfall, or both. An empirical adjustment of snow-event, liquid-equivalent precipitation indicates that the potential biases can be statistically significant.Examples from this study show that there are nonclimatic issues that complicate the identification of and significantly change the trends in snow variables. Thus, great care should be taken in interpretation of time series of snow-related variables from the Cooperative Observer Program (COOP) network. Furthermore, full documentation of optional practices should be required of network observers so that future users of these data can properly account for such practices.

Role of extreme snowfall events in interannual variability of snowfall accumulation in the western United States

“…Conversely, this method may overestimate daily SFE by not accounting for the augmentation of snowpack by precipitation that falls as rain and subsequently freezes in the snowpack and results in positive gains in SWE. To address this, we identified days when SWE increased and minimum temperature was greater than 3.7 C (based on Dai [2008]) and eliminated SFE for these days (0.05% of days [Kunkel et al, 2007], we used an approach to estimate daily SFE that extends the empirical precipitation phase probability function of Dai [2008] to daily time scales using daily mean temperature and daily precipitation amount (see Appendix A for details). Resulting daily SFE values estimated at COOP stations less than 2.54 mm (the resolution of SNOTEL SFE measurements) were set to 0 for compatibility with SNOTEL data.…”

Discussion of Influences on Snow Water Equivalent at Utah Snow Courses

“…As such, sites are commonly located in or below tree-line [33], thereby increasing the potential impact of vegetation increases over the observation period [22]. Additional sources of bias include: (2) site physical changes leading to localized scour or deposition of the snowpack [34,35] or snow compaction, such as from increases in recreation usage or road relocation near sites [36]; (3) weather modification from cloud seeding [37][38][39], pollutants [40][41][42], or dust storms that decrease the snowpack albedo [43,44]; (4) measurement timing and technique [45][46][47]; and (5) sensor changes [23,48]. A detailed review of how each of these potential sources of bias may affect SWE data at snow courses and SNOTEL sites is provided in [36].…”

“…A detailed review of how each of these potential sources of bias may affect SWE data at snow courses and SNOTEL sites is provided in [36]. As each of these factors may introduce similar-looking biases, site-specific bias features must be quantified before attributing changes over time to any particular cause [36,45]. This paper focuses primarily on the first form of bias presented above: the effect of vegetation changes on SWE data obtained at measurement sites.…”

Random threshold driven tail dependence measures with application to precipitation data analysis

“…Data modeling and inference can be found in Smith (2003), Naveau et al (2005), Falk and Michel (2006), Kunkel et al (2007), Beniston et al (2007), Cooley, Nychka and Naveau (2007), Bel, Bacro and Lantuejoul (2007), Elek and Márkus (2007), Yiou et al (2008), Zhang (2008a), Smith and Stephenson (2009), Naveau, Zhang and Zhu (2011), and Gilleland, Brown and Ammann (2013, among others. Several papers, such as Peng (1999), Draisma et al (2004) and Ferreria and de Haan (2004) focused on rare events modeling, particularly of Dutch coastal wind and water level extremes.…”