Matthew Sturm

Interdecadal changes in snow depth on Arctic sea ice

Snow plays a key role in the growth and decay of Arctic sea ice. In winter, it insulates sea ice from cold air temperatures, slowing sea ice growth. From spring to summer, the albedo of snow determines how much insolation is absorbed by the sea ice and underlying ocean, impacting ice melt processes. Knowledge of the contemporary snow depth distribution is essential for estimating sea ice thickness and volume, and for understanding and modeling sea ice thermodynamics in the changing Arctic. This study assesses spring snow depth distribution on Arctic sea ice using airborne radar observations from Operation IceBridge for 2009–2013. Data were validated using coordinated in situ measurements taken in March 2012 during the Bromine, Ozone, and Mercury Experiment (BROMEX) field campaign. We find a correlation of 0.59 and root-mean-square error of 5.8 cm between the airborne and in situ data. Using this relationship and IceBridge snow thickness products, we compared the recent results with data from the 1937, 1954–1991 Soviet drifting ice stations. The comparison shows thinning of the snowpack, from 35.1 ± 9.4 to 22.2 ± 1.9 cm in the western Arctic, and from 32.8 ± 9.4 to 14.5 ± 1.9 cm in the Beaufort and Chukchi seas. These changes suggest a snow depth decline of 37 ± 29% in the western Arctic and 56 ± 33% in the Beaufort and Chukchi seas. Thinning is negatively correlated with the delayed onset of sea ice freezeup during autumn.

White water: Fifty years of snow research in WRR and the outlook for the future

Over the past 50 years, 239 papers related to snow have been published in Water Resources Research (WRR). Seminal papers on virtually every facet of snow physics and snow water resources have appeared in the journal. These include papers on drifting snow, the snow surface energy balance, the effect of grain size on albedo, chemical elution, water movement through snow, and canopy interception. In particular, papers in WRR have explored the distribution of snow across different landscapes, providing data, process knowledge, and the basis for virtually all of the distributed snow models in use today. In this paper, I review these key contributions and provide some personal thoughts on what is likely to be the focus and nature of papers published in the next few decades, a period that is likely to see an increasing ability to map snow cover in detail, which should serve as a basis for the further development and improvement of snow models. It will also be an uncertain future, with profound changes in snow climatology predicted. I expect WRR will continue to play a key role in documenting and understanding these important cryospheric changes.

Physical properties of Arctic versus subarctic snow: Implications for high latitude passive microwave snow water equivalent retrievals

Two unique observational data sets are used to evaluate the ability of multi-layer snow emission models to simulate passive microwave brightness temperatures (TB) in high latitude, observation sparse, snow-covered environments. Data were utilized from a coordinated series of 18 sites measured across the subarctic Northwest Territories and Nunavut, Canada in April 2007 during a 1000 km segment of a 4200 km snowmobile traverse from Fairbanks, Alaska to Baker Lake, Nunavut (~64°N). In April 2011, a network of 22 high Arctic sites was sampled across a 60 × 60 km study area on the Fosheim Peninsula, Ellesmere Island (~80°N). In comparison to sites across the subarctic, high Arctic snow was more spatially variable, thinner (site averages between 15 and 25 cm versus 30 to 40 cm), colder (−25°C versus −10°C), composed of fewer layers, had a proportionally higher fraction of wind slabs (storing 57% of the snow water equivalent (SWE) versus 15%), with these slabs comparatively denser (often exceeding 450 g/cm3, compared to 350 g/cm3 in the subarctic). The physical snow measurements were used as inputs to snow emission model simulations. The radiometric difference between simulations of “typical” arctic and subarctic snow reached 30 K at 37 GHz. Sensitivity analysis showed that this TB difference could be partitioned between the effects of physical temperature (~5 K between −25°C and −10°C), wind slab density (~5 K between 0.40and 0.35 g/cm3), and vertical depth hoar fraction (~20 K between 70% and 30% vertical fraction of total snow depth). Model simulations at the satellite scale (625 km2) were produced using the observational spread for snow depth and snow stratigraphy. The range of TB from simulations with varied stratigraphy extended unrealistically far below the magnitude of satellite measured TB, illustrating that the snow depth first guess is very important for SWE retrieval schemes that are based on forward emission model simulations.

Snow bedforms: A review, new data, and a formation model

Snow bedforms, like sand bedforms, consist of various shapes that form under the action of wind on mobile particles. Throughout a year, they can cover up to 11% of the Earth surface, concentrated toward the poles. These forms impact the local surface energy balance and the distribution of precipitation. Only a few studies have concentrated on their genesis. Their size ranges from 2 cm (ripple marks) to 2.5 m tall (whaleback dunes). We counted a total of seven forms that are widely recognized. Among them sastrugi, an erosional shape, is the most widespread. From laser scans, we compared scaling of snow versus sand barchan morphology. We found that both have proportionally the same footprint, but snow barchans are flatter. The key difference is that snow can sinter, immobilizing the bedform and creating an erodible material. Using a model, we investigated the effect of sintering on snow dune dynamics. We found that sintering limits their size because it progressively hardens the snow and requires an ever-increasing wind speed to maintain snow transport. From the literature and results from this model, we have reclassified snow bedforms based on two parameters: wind speed and snow surface conditions. The new data show that snow dune behavior mirrors that of sand dunes, with merging, calving, and collision. However, isolated snow barchans are rare, with most of the snow surfaces encountered in the field consisting of several superimposed bedforms formed sequentially during multiple weather events. Spatially variable snow properties and geometry can explain qualitatively these widespread compound snow surfaces.

Water and life from snow: A trillion dollar science question

Snow provides essential resources/services in the form of water for human use, and climate regulation in the form of enhanced cooling of the Earth. In addition, it supports a thriving winter outdoor recreation industry. To date, the financial evaluation of the importance of snow is incomplete and hence the need for accelerated snow research is not as clear as it could be. With snow cover changing worldwide in several worrisome ways, there is pressing need to determine global, regional, and local rates of snow cover change, and to link these to financial analyses that allow for rational decision making, as risks related to those decisions involve trillions of dollars.

Using an option pricing approach to evaluate strategic decisions in a rapidly changing climate: Black–Scholes and climate change

Nature provides critical ecosystem services on which society and businesses rely, but the effort and cost of utilizing those services can change with the climate. Both climatic trend and variance affect these efforts and costs, creating a complex decision space where uncertain future predictions are the rule. Here, we show how these problems mimic option payoffs and demonstrate a modified version of the Black–Scholes option pricing formula (widely used in finance) to analyze these types of business-climate decisions. We demonstrate the method by (1) examining the viability of building ice roads in the Northwest Territories of Canada, where a strong negative warming trend is underway, and (2) applying it to the problem of the ongoing California drought, estimating expected water costs with and without storage. The method is novel and provides a simple and accessible way to make such assessments to at least a first-order approximation. While our focus here is on business situations where decisions are usually based on money, we suggest that a similar approach could be used beyond the business world in examining risk and attributing that risk to climate variance vs. trend.