We present a new approach to monitoring the water content of falling snow over large areas using time series of water pressure in lakes whose catchments freeze in winter. We have successfully demonstrated our method in the lowland Finnish Arctic, coastal Greenland and high-mountain settings in the Swiss Alps and the Indian Himalayas. We were able to measure snow accumulation and its uncertainty through snowfalls with little bias and with an uncertainty comparable to or better than that achievable by other instruments. Critically though, our method inherently represents the whole surface area of the lakes studied, and ours have all been many orders of magnitude larger than existing precipitation gauges such as pluviometers and snow pillows. One of our instrumented lakes alone, at nearly 300 square km, has an area about a million times larger than all existing global pluviometers combined. The large spatial scale of our measurements makes them directly comparable to the grid cells of weather and climate models. Together, the scale and accuracy of our new method reveal, and can eliminate, two of the outstanding sources of uncertainty in developing, calibrating and validating weather models. We find, for example, snowfall biases of up to 100% in operational forecast models AROME Arctic and COSMO-1. Seasonally-frozen lakes are widely distributed at high latitudes and are particularly common in mountain ranges, hence our new method is particularly well suited to the widespread, autonomous monitoring of winter precipitation in remote areas that are largely unmonitored today. This is potentially transformative in reducing uncertainty in regional precipitation and runoff in seasonally cold climates.
Topic : Theme 1: Cryosphere monitoring.
Reference : T1-E9
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