Preinstrumental data

Prior to the late 1800s, there were few systematic measurements of snow depth. However, routine snow-cover-related observations were made of variables such as the number of days with snowfall, number of days with snow on the ground, or first/last dates of snow on the ground. The presence of snow on the ground is one of the earliest regularly recorded snow-cover observations. In Great Britain, for example, regular observations of "snow lying" (when snow covers more than half the ground at 0900 GMT) began in 1912 (Jackson, 1978b) but it was only in the late 1940s that snow depth was routinely recorded (Jackson, 1978a). It has been shown (Foster, 1986; Robinson, 1991; Brown and Goodison, 1996) that regionally averaged in situ observations of snow-cover duration agree closely with satellite observations for open, non-mountainous areas. This offers the potential for using historical in situ observations to extend the more recent satellite-based observations of snow-cover extent back to the early part of the twentieth century (Frei etal.,1999; Brown, 2000). Observations of related parameters such as the first and last dates of snow on the ground, or dates of the first thaw have been made for extended periods of time at a number of locations around the world. For example, Ball (1992) was able to investigate spring thaw variability from 1715 to 1840 at York Factory and Churchill, Canada, using records of the first date of snow thaw made by Hudson Bay Company personnel. Information on snow-cover conditions can also be inferred from observations of the number of days when snow or solid precipitation fell. For example, Manley (1969) used data on the "frequency of occurrence of days with snow or sleet observed" in the UK to construct a normalized "snowiness index" back to the 1660s. Manley found good agreement between the index and snow on the ground at elevations above 300 m, and on the basis of this relationship was able to conclude that in the severe winter of 1695, some upland Aberdeenshire farms probably had snow cover for five months.

Inferences concerning past snow-cover conditions can also be made from various biophysical markers that are sensitive to snow cover or to snowmelt runoff. For example, Lavoie and Payette (1992) used changes in basal abrasion levels and changes in growth forms of black spruce to document an increase in twentieth-century snow cover along the treeline in subarctic Quebec. Paleo reconstruction of lake water levels in semi-arid areas with snowmelt-fed reservoirs (e.g. Vance et al., 1992) can also provide insights into spring snowpack conditions over time periods spanning several thousand years.

5.2.2 Snow depth

Snow depth is the most obvious property of a snowpack, but it is one of the less useful values from a water and energy budget perspective since snow depth can change independently from snow mass due to processes such as settling, metamorphism, and melt/refreeze events (Fitzharris etal., 1992). Nonetheless, daily snow depth data are used in a multitude of applications such as estimating building snow loads, snow clearing contracts, winter survival of crops, ground frost penetration, and biological studies. Daily snow depth is considered a high priority variable for global climate monitoring, validation of climate models, and climate change impact assessment (Cihlar etal., 1997).

Manual snow depth measurements are made using a ruler or with one or more fixed snow stakes (Fig. 5.1c). Where daily observations of the total depth of snow on the ground are made by ruler, some judgement is required to obtain a "representative" value. This is evident in the instructions to Canadian observers (AES, 1977):

The total depth of snow on the ground at the time of the observation shall be determined, (in whole centimetres) by making a series of measurements and taking the average. The area selected for the measurement shall be chosen with a view to avoiding drifts. Care shall be taken to ensure that the total depth is measured including the depth of any layers of ice which are present.

Determining a representative snow depth for an incomplete, patchy snow cover is particularly subjective and the Canadian manual for weather observations offers no instructions or guidance on how to do this. According to Doesken and Judson (1997), a visual average should be determined for the area surrounding the weather station, and when the snow cover is less than 50%, the depth recorded as a trace. This problem is avoided when a series of fixed stakes are used for measuring snow depth. However, care is required when reading fixed stakes as snow can preferentially accumulate or melt around a fixed object. Even where snow depth observing practices are carried out consistently, the measurements are only truly representative of the snow conditions at the measuring site. Weather observing sites are usually located in open areas, often at airports, thus the data are mainly representative of exposed sites.

Usually manual snow depth measurements are taken once per day in the morning or afternoon, although twice daily readings are made in some countries. The timing of the depth observation is not critical, but it can create inhomogeneities in derived snow-cover data series such as the number of days with snow on the ground if not followed consistently over time e.g. a shift from PM to AM readings occurred in the United States in the 1960s (D. Robinson, personal communication, 1998). Since a snowpack is more likely to experience melting and settling during the daytime, especially in situations of small amounts of new snow on the ground from overnight snowfall events, a shift to morning readings could introduce a spurious increase in snow-cover statistics such as the number of days with snow on the ground.

Manual observations of daily snow depth have been carried out in association with regular meteorological observation programs at synoptic and climate stations in most countries with a seasonal snow cover. Typically, these networks evolved

Measure For Snow Accumulation
Figure 5.1. Instruments for measuring snow accumulation and melt (Fitzharris et al., 1992). Reproduced with permission from the New Zealand Hydrological Society.

in response to needs for weather and climate information in support of economic activities and the spatial distribution of stations tends to follow the population distribution, with poor spatial coverage in mountainous and remote areas. In general, the number of stations reporting snow depth increases over time during much of the twentieth century, with a reduction in the 1980s and 1990s in many countries in response to budgetary cutbacks. A number of countries have systematic snow depth observations going back to the late 1800s, e.g. Switzerland (Fohn, 1990), USA (Easterling et al., 1999), the former Soviet Union (Armstrong, 2001), and Finland (Kuusisto, 1984). When working with these data sets it is important to remember that snow depth observations are subject to the same sources of inhomogeneity as other climatological elements e.g. changes in station location, changes in observing time, changes in measuring units, changes in observers, and urban effects (warming and dirtying of snow from pollution). Robinson (1989) developed quality control procedures to check the internal consistency of daily snow depth observations for U.S. data. The procedure compares the observed change in snow depth over 24 hours to the expected change based on the observed snowfall and air temperature. Brown and Braaten (1998) applied a slightly modified version of this approach to Canadian daily snow depth data and found that over 98% of non-zero snow depth observations satisfied the tests for internal consistency.

Automated measurement of snow depth is possible with the use of ultrasonic snow depth sensors. These are placed above the snow surface, and compute the distance to the snow surface from the time taken for a pulse of sound to reach the snow surface and be reflected back (Fig. 5.1f). On-board electronics compute the speed of sound as a function of the ambient air temperature. As outlined in Pomeroy and Gray (1995), anomalous measurements may occur during falling or blowing snow, and the ultrasound cannot distinguish newly fallen, low density snow. At other times snow depth can be measured with an accuracy of ~1 cm, and the sensor can be interrogated at frequent regular time intervals (e.g. hourly) to obtain a detailed history of snow depth changes from settling, wind erosion and melt that is particularly useful for the validation of physical snow process models. The other major advantage of the automated depth sensor is that the snow layer is undisturbed. When the snow layer is gone, ultrasonic sensors continue to faithfully record the height of the growing vegetation. This requires the application of quality control logic to remove spurious "depths." A criticism of the auto sensor is that it only measures snow depth at "one point," a circular area with diameter 0.2-2 m depending on the height of the sensor above the snow surface (Pomeroy and Gray, 1995). This makes careful site selection essential.

Daily snow depth is a versatile measurement in that a variety of snow-cover statistics can be derived from daily depth observations for monitoring snow cover. These include: the duration of snow on the ground for various depth thresholds;

the dates of the start and end of continuous snow cover; the maximum snow depth; and the date of maximum snow depth. Monitoring these kinds of statistics provides more detailed information on changes in snowpack amount and timing (Goodison and Walker, 1993) than traditional monthly statistics such as the average or median snow depth. A summary of snow data sources for climate studies was provided by Groisman and Davies (2001). Major compilations of historical daily snow depth data have been published for the USA (Easterling et al., 1999), the former Soviet Union (Armstrong, 2001), and Canada (MSC, 2000). There is relatively little published snow depth data for Europe and Asia, although there are published records of monthly snow-cover duration at selected stations for the People's Republic of China (Shiyan et al., 1997). The U.S. National Snow and Ice Data Center (NSIDC) is continually working to rescue important snow data sets and make them freely available to the research community. The reader should visit their website to check the latest data sets as well as the U.S. Carbon Dioxide Data Information and Analysis Center (CDIAC), which has made a number of important snow data sets available for downloading, e.g. Easterling et al. (1999) and Shiyan et al. (1997). U.S. daily snow depth data are archived at the National Climatic Data Center (NCDC) and were compiled and analyzed by Heim (1998) to produce a monthly snowfall and snow-cover climatology for 5525 stations in the contiguous U.S.A. and Alaska.

The World Meteorological Organization (WMO) Global Telecommunication System (GTS), which transmits synoptic and aviation weather data internationally, includes daily snow depth observations in its messages. These observations are used by a number of operational meteorological agencies to generate daily snow depth fields for input to numerical weather prediction models. However, on any given day only a small fraction of the stations observing snow depth may actually report the data over the GTS. Snow depth and snow-cover information are included in the global climatological data summaries and standard climate normals, which are compiled on a regular basis by the WMO.

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Renewable Energy 101

Renewable Energy 101

Renewable energy is energy that is generated from sunlight, rain, tides, geothermal heat and wind. These sources are naturally and constantly replenished, which is why they are deemed as renewable. The usage of renewable energy sources is very important when considering the sustainability of the existing energy usage of the world. While there is currently an abundance of non-renewable energy sources, such as nuclear fuels, these energy sources are depleting. In addition to being a non-renewable supply, the non-renewable energy sources release emissions into the air, which has an adverse effect on the environment.

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