Alden C. Adolph

A longer vernal window: the role of winter coldness and snowpack in driving spring transitions and lags

Climate change is altering the timing and duration of the vernal window, a period that marks the end of winter and the start of the growing season when rapid transitions in ecosystem energy, water, nutrient, and carbon dynamics take place. Research on this period typically captures only a portion of the ecosystem in transition and focuses largely on the dates by which the system wakes up. Previous work has not addressed lags between transitions that represent delays in energy, water, nutrient, and carbon flows. The objectives of this study were to establish the sequence of physical and biogeochemical transitions and lags during the vernal window period and to understand how climate change may alter them. We synthesized observations from a statewide sensor network in New Hampshire, USA, that concurrently monitored climate, snow, soils, and streams over a three-year period and supplemented these observations with climate reanalysis data, snow data assimilation model output, and satellite spectral data. We found that some of the transitions that occurred within the vernal window were sequential, with air temperatures warming prior to snow melt, which preceded forest canopy closure. Other transitions were simultaneous with one another and had zero-length lags, such as snowpack disappearance, rapid soil warming, and peak stream discharge. We modeled lags as a function of both winter coldness and snow depth, both of which are expected to decline with climate change. Warmer winters with less snow resulted in longer lags and a more protracted vernal window. This lengthening of individual lags and of the entire vernal window carries important consequences for the thermodynamics and biogeochemistry of ecosystems, both during the winter-to-spring transition and throughout the rest of the year.

Major fraction of black carbon is flushed from the melting New Hampshire snowpack nearly as quickly as soluble impurities

Seasonal snowpacks accumulate impurities derived from atmospheric aerosols and trace gases throughout the winter and release them during snow melt. Previous field and laboratory studies have shown that a snowpack can lose up to 80% of the soluble ion burden in the first 20% of the melt, an event commonly known as an ionic pulse. Other studies have concluded that particulate impurities (e.g. black carbon (BC)) concentrate in surface layers during melt which can have important implications for snowpack albedo. However, model and field studies have indicated that meltwater scavenging efficiency of BC in melting snowpacks is still an area of uncertainty. To quantify BC melt dynamics and the release of soluble impurities, we collected and analyzed near daily chemical profiles in the snowpack at three sites during two winters in New Hampshire, United States of America. We observe an ionic pulse and a pulse of BC from the snowpack at the onset of melt; up to 62% of BC leaves within the first 24% of the melt. Surface concentrations of BC are higher than seasonal medians at the end of the winter season, but surface enhancements do not appear to be closely linked to decreases in snow-water equivalence caused by melting.

Dominance of grain size impacts on seasonal snow albedo at open sites in New Hampshire

Snow cover serves as a major control on the surface energy budget in temperate regions due to its high reflectivity compared to underlying surfaces. Winter in the northeastern United States has changed over the last several decades, resulting in shallower snowpacks, fewer days of snow cover, and increasing precipitation falling as rain in the winter. As these climatic changes occur, it is imperative that we understand current controls on the evolution of seasonal snow albedo in the region. Over three winter seasons between 2013 and 2015, snow characterization measurements were made at three open sites across New Hampshire. These near-daily measurements include spectral albedo, snow optical grain size determined through contact spectroscopy, snow depth, snow density, black carbon content, local meteorological parameters, and analysis of storm trajectories using the Hybrid Single-Particle Lagrangian Integrated Trajectory model. Using analysis of variance, we determine that land-based winter storms result in marginally higher albedo than coastal storms or storms from the Atlantic Ocean. Through multiple regression analysis, we determine that snow grain size is significantly more important in albedo reduction than black carbon content or snow density. And finally, we present a parameterization of albedo based on days since snowfall and temperature that accounts for 52% of variance in albedo over all three sites and years. Our improved understanding of current controls on snow albedo in the region will allow for better assessment of potential response of seasonal snow albedo and snow cover to changing climate.

Autonomous rover for polar science support and remote sensing

This paper reports outcomes of recent field deployments of the solar-powered Cool Robot, which was developed as an autonomous platform for towing or carrying scientific instruments in Greenland and Antarctica. This simple 70-kg four-wheel drive, solar-electric vehicle executes autonomous surveys via GPS waypoint following, while towing or carrying payloads of over 40 kg. Cool Robot conducted over 175 km of autonomous surveys to study atmospheric chemistry at the snow-air interface in the vicinity of Summit camp. The robot also conducted local grid surveys of the Summit ice layer in order to evaluate the potential of using ground penetrating radar to estimate the density of melt channels in the ice layer. Our key objectives were to demonstrate the operational value of a rover to locate subsurface features and the use of autonomous rovers in collecting scientific data from long-duration surveys. The Cool Robot operated reliably at -30 C, and demonstrated good oversnow mobility and adequate GPS accuracy for waypoint-following on a 5-10 meter grid. The deployment results demonstrate that autonomous vehicles have great potential to improve the efficiency of polar science.