Study Confirms that Dust Deposited on Snow Melts Faster and Models do not Detect this Effect!

A study has shown that accumulated snow on mountains melts faster when it is covered in dust. This problem is now forcing us to develop an evolution in water forecasting models.

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The dust cover on snow changes the energy balance by being darker and absorbing more light.

In places plagued by periodic droughts, such as the western United States, mountain snow cover is an essential source of water for both drinking and agricultural production. The annual cycle begins with spring meltwater that nourishes rivers as temperatures recover after winter.

Current models are based on statistical relationships that assume the future will be like the past.

With more pronounced variations in temperatures and precipitation, models that predict the behavior of river flows are essential for planning. This example is multiplied in different places on the planet. However as indicated in EOS , scientists warn that current fusion models are still stuck in the past and that there are parts of the process that have not been integrated.

McKenzie Skiles, a snow researcher at the University of Utah, points out that “ current models are based on statistical relationships that assume the future will be like the past. " In reality, it points to a world where some aspects of climate change are increasingly noticeable in the behavior of variables such as temperature and precipitation.

Dust on snow changes the energy balance

McKenzie Skiles led a study published in Environmental Research Letters that draws attention to one variable that is especially critical for snow forecasting models to adapt to a rapidly evolving world: that is snowfield dust accumulation . The dust, being darker than the underlying snow, absorbs more energy from the Sun and accelerates melting.

Rapid snow melt is a problem because snow accumulates in mountains and protects the ground from the Sun's heat , Skiles told EOS. When snow melts quickly, the soil loses this protective layer and dries out early in the season. Researchers worked on this phenomenon in Utah in 2021 and 2022. At that time, water levels in the Great Salt Lake reached a record high , driven by increased consumption and prolonged drought. Dust from the exposed lake bed fell onto snow from the adjacent Wasatch Mountains.

Observations have shown that dust from the Great Salt Lake accelerated the melting of the Wasatch by 17 days during the 2022 melt season. This data was published in June 2023. “The landscape is drier, so any additional moisture that arrives is basically absorbed by the landscape. to return to the Great Salt Lake," Skiles explained. Somehow, the dust in the snow modifies the process that, in the long run, dries out the environment even more.

A loop that feeds itself

The truth is that the process is amplified as a continuous feedback loop: with less water flowing into the lake, the dry bed expands and more dust is swept into the Wasatch snowpack. The cycle repeats itself over and over again.

These results support numerous studies conducted between 2010 and 2018 in the Colorado Rockies. In the San Juan Mountains, dust bursts from the Colorado Plateau accelerated snowmelt by 3 to 5 weeks and were correlated with melt forecast errors.

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Researchers collected data on how dust affected snowmelt in Utah's Wasatch Mountains.

Knowing this set of parameters, the models must be adjusted to better explain the situation and overestimate the volumes of water available. The researchers point out that despite the significant impact of dust on the rate of snowmelt, many river forecast models, including those from NOAA, do not take it into account. But work is already underway in this direction, at least in the United States, and this will likely spread to other countries.

At the Centro de Predicción Fluvial de la Cuenca del Colorado (CBRFC), hydrologists are already updating models to change this situation. According to John Lhotak, a CBRFC hydrologist who was not involved in the Utah study, one strategy is to increase the temperature of the models, as adding a little more heat simulates the impact of dust. These calibrations are based on historical dust event data.