Arctic precipitation is another key climate variable, which, like cloud cover, is still inadequately determined. The density of observing stations is generally quite low, particularly over the Canadian Arctic Archipelago, the Greenland Ice Sheet and the Arctic Ocean, making it difficult to obtain accurate regional precipitation estimates. The station density problem is becoming more severe as fiscal constraints in Canada and Russia have led to the closure of many stations. In recognition of network deficiencies, various techniques have been explored to compile Arctic-specific data sets

January April

January April

Figure 2.24 Mean cloud cover (%) from the APP-x data set (1982-99) for January, April, July and October (courtesy of J. Key, NOAA, Madison, WI).

that make use of output from numerical weather prediction models (Chen et al., 1997; Serreze et al., 2003b). A number of gridded global data sets providing Arctic coverage have been developed based on satellite retrievals or blending satellite retrievals with station gauge data and numerical weather model output (e.g., Xie and Arkin, 1997; Huffman etal., 1997, 2001). However, Arctic results are of questionable value; while satellite retrievals are confounded by snow-covered surfaces, insufficient attention has been paid to the problem of gauge catch of solid precipitation. The latter issue primarily revolves around gauge under-catch of solid precipitation. The most important environmental variable influencing catch efficiency is wind speed (Yang et al., 2001). Different gauge and shield combinations have been and continue to be used, which vary in catch efficiency, especially for high wind speeds. Errors can reach 50-100% in cold, windy environments such as the Arctic. This has created artificial discontinuities in cold-region precipitation amounts within countries and across national borders.

Efforts have been made to provide data sets with bias adjustments, mostly with respect to monthly totals. Legates and Willmott (1990) provide a global gridded climatology. Groisman et al. (1991) and Mekis and Hogg (1999) provide adjusted monthly station data sets for the former Soviet Union and Canada, respectively. Adjustments are generally climatological in that they represent constant multipliers to raw monthly precipitation totals. Corrections require information on gauge type, mean winds and site conditions. Yang (1999) performed daily corrections to the Russian NP records. Different data sets may also include adjustments for the neglect of trace amounts, as well as evaporation and wetting losses (moisture that sticks to the inside of the gauge after it is emptied). The neglect of trace precipitation can be important in the Arctic, as total precipitation amounts are low in many areas.

Figure 2.25 shows the pattern of mean annual precipitation across the Arctic. This is a best-faith attempt on our part, based on bias-adjusted gauge data from several different data sources, bias-adjusted output from a numerical weather prediction model, and satellite retrievals over open-ocean areas. In general, annual precipitation totals for much of the Arctic are rather low, but amounts vary widely. Totals of less than 200 mm are found over the Canadian Arctic Archipelago and Beaufort Sea. Over the central Arctic Ocean, mean totals are around 300-400 mm. The largest amounts in the Arctic, exceeding 1000 mm and locally much higher, are found over the Atlantic sector, southeast Greenland and coastal Scandinavia. This is essentially a reflection of the northward extension of the primary North Atlantic cyclone track and Icelandic Low (Chapter 4), associated convergences of moisture into the region and local orographic uplift of moist air.

There is a strong seasonality in Arctic precipitation as well as liquid/solid contributions, which is addressed in Chapter 6 along with precipitation mechanisms. This chapter also provides a closer focus on Greenland. For the present, it is sufficient to state that for the Atlantic sector of the Arctic, precipitation has a cold season maximum and summer minimum, in accord with seasonality in the strength of the North Atlantic cyclone track. By contrast, most land areas and the central Arctic Ocean exhibit a summer maximum and cold season minimum. The summer maximum over land areas is strongly associated with surface evaporation. The summer maximum for the central Arctic Ocean, by contrast, is a response to the seasonal maximum in vapor flux convergence.

Renewable Energy Eco Friendly

Renewable Energy Eco Friendly

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.

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