Defining snow, world class


Winter started out peculiarly. Many Alaskans had no snow to shovel at all. At the same time they could watch news coverage of cars buried by blizzards in the lower 48.

emming specimen, Gates of the Arctic National Park and Preserve, National Park Service
emming specimen, Gates of the Arctic National Park and Preserve, National Park Service
Magnified surface hoar crystals / Image Matt Sturm
Magnified surface hoar crystals / Image Matt Sturm

The unusual weather conditions were a strange state of affairs for Arctic plants and animals adapted to survive and even thrive in snowy conditions. Lemmings, for instance, require snow pack in which to dig tunnels and hide from predators. Left in the open they would freeze to death; in their homes under the snow they benefit from sensible and latent heat released from the ground over winter and trapped by the snow. That heat causes snow to metamorphosize into beautiful crunchy crystals called depth hoar. Lemmings burrow and den in the depth hoar, and even consume it for liquid… after all, there’s no tap water under there. The lemming population provides food for everything from snowy owls to foxes stalking prey in the winter landscape.

To understand snow’s role in climate

Snow expert Matthew Sturm, geophysics professor at the University of Alaska Fairbanks Geophysical Institute, says the Arctic as we know it is best described as white.

“For 9-10 months of the year it’s a white world covered with snow; that’s the natural state of that Arctic world. Summer is the peculiarity. We tend to focus on the summer because things grow then, life comes back. But when you think about a 12-month calendar, the Arctic is white. You know, that’s why snow is important.” ~ Matthew Sturm

Matthew Sturm, geophysicist
Matthew Sturm, geophysicist

He’s so passionate about snow that even when planning a snowmobile trip with old friends, snow science still figures into the mix. In Finding the Arctic (University of Alaska Press, 2012), the story of a 2,500 mile snowmobile journey from Alaska to Hudson’s Bay, Sturm outlines a voyage, a tale including his quest to understand the role of snow in climate.

“The Snowstar [2007] Expedition did not start out as a scientific mission, but because we would be traversing 2,500 miles of wilderness for which very little information about snow was available, we could not resist the chance to gather new data.” ~ Finding the Arctic by Matthew Sturm

It was a timely journey. No matter how many words different languages and dialects might have for snow, the bulk of Arctic snow is so distant and difficult and hard to reach that we lack an extensive data pool chronicling the stuff. And science needs data.

“As the world became increasingly aware that a warming climate was altering the Arctic environment, these snow studies had taken on a new sense of urgency and importance, and the results of the work made their way into computer models designed to predict global warming.” ~ Finding the Arctic by Matthew Sturm

The snow classification system

How much do we really know about snow in the Arctic? It’s a region warming twice as fast as the rest of the world. And while we don’t yet perfectly understand how the Arctic modulates feedbacks in earth systems between the atmosphere and ocean, we know that it has been a major player in past global-level changes. Think of the Ice Ages. Chronicling and understanding the Arctic now can help inform accurate predictions about future climate evolution.

“Everybody knows that the Arctic is snowy but I bet they think that the snow is a lot deeper snow than it actually is, and that it snows a lot more than it actually does. Five or six snowfalls a year and that’s the whole Arctic.” ~ Matthew Sturm

These insights show that it’s not necessarily how much it snows that matters. Probably more defining are the characteristics of the snow and how the snow interacts with its local environment.

Layers of snow in a snow pit indicated by Glen Liston, geophysicist, Colorado State University / FrontierScientists footage
Layers of snow in a snow pit indicated by Glen Liston, geophysicist, Colorado State University / FrontierScientists footage

“The differences in properties, depths, and hardness have hugely different ecological impacts. … Snow affects animals, plants, and humans. The other big thing it affects is climate. Once you start studying snow you find yourself connected to almost everything out there in the Arctic.” ~ Matthew Sturm

A Seasonal Snow Cover Classification System for Local to Global Applications
A Seasonal Snow Cover Classification System for Local to Global Applications [Figure 1]
A paper titled ‘A Seasonal Snow Cover Classification System for Local to Global Applications’ published in the Journal of Climate in 1995 by Matthew Sturm, Jon Holmgren and Glen E. Liston is the standard world-wide for how scientists look at seasonal snow across the globe. Snow is classified based on characteristics like density, crystal grain size, depth and temperature. Seasonal snow cover classes are: Tundra, Taiga, Alpine, Maritime, Ephemeral, Prairie, or Mountain. This approach integrates physics and math as it helps us understand the physical material of snow in its varied environments.

Boots on the ground

That’s not an easy task. The Arctic is cold, difficult and costly to navigate. Much of our information comes from earth-observing satellites. But often, satellites can only provide broad views. If you’ve ever shoveled you know that snow isn’t merely present or absent. It’s patchy, diverse, shallow or deep, densely packed or airy. To get the gritty details Sturm says that on-the-ground measurements like the ones taken annually throughout the basin of Alaska’s Imnaviat Creek are vital.

“This is such a dynamic landscape and pieces of it function very differently. And that’s the real reason we have to be in a place like Imnavait (a research site high in the Alaskan Arctic). The snow down near the creek is doing something utterly different than the snow on the ridgetop, right? It doesn’t do any good to look at this over a 25 kilometer pixel. That’s averaging, it’s like going to a supermarket and saying ‘There is food here.’ Right, well, there is produce on one side and there is medicine somewhere else. That’s what the Arctic is really doing, and when we just say that there is food here it is too gross a generalization.” ~ Matthew Sturm

Snow data collected on-site is precious for refining models that utilize satellite-gained snow information. So knowledge collected on the ground at Imnaviat Creek, or during the Snowstar Expeditions, seems specific but actually creates an enormous gain in improving supercomputer-driven models.

“Many things about snow melt can’t be measured with autonomous data, you literally have to tromp around with snow shoes and a ruler or a measuring device or a coring tube.” ~ Matthew Sturm

Taking measurements

As snow melts it progresses rapidly through distinct stages; the more measurements that can be taken in short time intervals during the melting process, the better.

When scientists research snow at more permanent study locations like Imnaviat Creek they set up a multitude of tests. Hourly measurements of snow temperature including snow-ground interface temperature are recorded at multiple locations, as well as local meteorology (weather conditions) and solar radiation (measuring sunlight). This provides all sorts of information about temperature gradient, vapor pressure gradient, density, stratigraphy (layers) and snow profiles.

When researching snow during expeditions the scientists have a lot of ground to cover. Snowstar efforts involved digging snow pits: exposing a vertical plane of snow to assess the nature of the crystals and the snow density, depth, and temperature in each snow layer. A near-infrared camera was employed to help document the crystals’ grain size. They took snow depth transects, measuring snow depth at many intervals along a line. They also cored lakes, drilling down to extract cylindrical samples of ice – also allowing for analysis of layers.

A Seasonal Snow Cover Classification System for Local to Global Applications
A Seasonal Snow Cover Classification System for Local to Global Applications [Table 2]
Using the system

Comparing common snow measurements to local conditions like air temperature, precipitation and wind speed allow the snow class (Tundra, Taiga, Alpine, Maritime, Ephemeral, Prairie, or Mountain) to be determined from more readily obtained weather records. Once you’ve classified the snow type, you can infer information about snow that might be time consuming to measure or even difficult to reach.

The paper gives the example that the thermal conductivity, air permeability, grain size distribution and strength of taiga snow measured in

Caribou and windswept snow / FrontierScientists footage
Caribou and snowy ridge / FrontierScientists footage

Alaska is comparable to taiga snow in Siberia. The converse is also true: knowing what properties snow has can allow a researcher to infer information about the climatic regime where the snow is located. Snow cover that’s thin, composed of wind slab and depth hoar, and shows little evidence of features impacted by melting results from the flat windy frigid regions dominated by tundra.

Although at first glance details about snow may seem small, they are vital for refining our interpretation of the Arctic and its role in our world at large.

“Snow is everywhere in the North, it’s been here a long time, it’s pervasive and lasts two-thirds of the year. It’s like asking about the air we breathe. It’s so important there’s no getting around it.” ~ Matthew Sturm

Laura Nielsen

Frontier Scientists: presenting scientific discovery in the Arctic and beyond

Arctic Snow project