Mark S. Raleigh

Impact of errors in the downwelling irradiances on simulations of snow water equivalent, snow surface temperature, and the snow energy balance

The forcing irradiances (downwelling shortwave and longwave irradiances) are the primary drivers of snowmelt; however, in complex terrain, few observations, the use of estimated irradiances, and the influence of topography and elevation all lead to uncertainties in these radiative fluxes. The impact of uncertainties in the forcing irradiances on simulations of snow is evaluated in idealized modeling experiments. Two snow models of contrasting complexity, the Utah Energy Balance Model (UEB) and the Snow Thermal Model (SNTHERM), are forced with irradiances with prescribed errors of the structure and magnitude representative of those found in methods for estimating the downwelling irradiances. Relatively modest biases have substantial impacts on simulated snow water equivalent (SWE) and surface temperature (Ts) across a range of climates, whereas random noise at the daily scale has a negligible effect on modeled SWE and Ts. Shortwave biases have a smaller SWE impact, due to the influence of albedo, and Ts impact, due to their diurnal cycle, compared to equivalent longwave biases. Warmer sites exhibit greater sensitivity to errors when evaluated using SWE, while colder sites exhibit more sensitivity as evaluated using Ts. The two models displayed different sensitivity and responses to biases. The stability feedback in the turbulent fluxes explains differences in Ts between models in the negative longwave bias scenarios. When the models diverge during melt events, differences in the turbulent fluxes and internal energy change of the snow are found to be responsible. From this analysis, we suggest model evaluations use Ts in addition to SWE.

Mountain system monitoring at Senator Beck Basin, San Juan Mountains, Colorado: A new integrative data source to develop and evaluate models of snow and hydrologic processes

A hydrologic modeling data set is presented for water years 2006 through 2012 from the Senator Beck Basin (SBB) study area. SBB is a high altitude, 291 ha catchment in southwest Colorado exhibiting a continental, radiation-driven, alpine snow climate. Elevations range from 3362 m at the SBB pour point to 4118 m. Two study plots provide hourly forcing data including precipitation, wind speed, air temperature and humidity, global solar radiation, downwelling thermal radiation, and pressure. Validation data include snow depth, reflected solar radiation, snow surface infrared temperature, soil moisture, temperatures and heat flux, and stream discharge. Snow water equivalence and other snowpack properties are captured in snowpack profiles. An example of snow cover model testing using SBB data is discussed. Serially complete data sets are published including both measured data as well as alternative, corrected data and, in conjunction with validation data, expand the physiographic scope of published mountain system hydrologic data sets in support of advancements in snow hydrology modeling and understanding.

Spatial Heterogeneity in Ecologically Important Climate Variables at Coarse and Fine Scales in a High-Snow Mountain Landscape

Climate plays an important role in determining the geographic ranges of species. With rapid climate change expected in the coming decades, ecologists have predicted that species ranges will shift large distances in elevation and latitude. However, most range shift assessments are based on coarse-scale climate models that ignore fine-scale heterogeneity and could fail to capture important range shift dynamics. Moreover, if climate varies dramatically over short distances, some populations of certain species may only need to migrate tens of meters between microhabitats to track their climate as opposed to hundreds of meters upward or hundreds of kilometers poleward. To address these issues, we measured climate variables that are likely important determinants of plant species distributions and abundances (snow disappearance date and soil temperature) at coarse and fine scales at Mount Rainier National Park in Washington State, USA. Coarse-scale differences across the landscape such as large changes in elevation had expected effects on climatic variables, with later snow disappearance dates and lower temperatures at higher elevations. However, locations separated by small distances (∼20 m), but differing by vegetation structure or topographic position, often experienced differences in snow disappearance date and soil temperature as great as locations separated by large distances (>1 km). Tree canopy gaps and topographic depressions experienced later snow disappearance dates than corresponding locations under intact canopy and on ridges. Additionally, locations under vegetation and on topographic ridges experienced lower maximum and higher minimum soil temperatures. The large differences in climate we observed over small distances will likely lead to complex range shift dynamics and could buffer species from the negative effects of climate change.

A Comparison of Methods for Filling Gaps in Hourly Near-Surface Air Temperature Data

AbstractNear-surface air temperature observations often have periods of missing data, and many applications using these datasets require filling in all missing periods. Multiple methods are available to fill missing data, but the comparative accuracy of these approaches has not been assessed. In this comparative study, five techniques were used to fill in missing temperature data: spatiotemporal correlations in the form of empirical orthogonal functions (EOFs), time series diurnal interpolation, and three variations of lapse rate–based filling. The method validation used sets of hourly surface temperature observations in complex terrain from five regions. The most accurate method for filling missing data depended on the number of available stations and the number of hours of missing data. Spatiotemporal correlations using EOF reconstruction were most accurate provided that at least 16 stations were available. Temporal interpolation was the most accurate method when only one or two stations were available ...

Evaluating observational methods to quantify snow duration under diverse forest canopies

Forests cover almost 40% of the seasonally snow-covered regions in North America. However, operational snow networks are located primarily in forest clearings, and optical remote sensing cannot see through tree canopies to detect forest snowpack. Due to the complex influence of the forest on snowpack duration, ground observations in forests are essential. We therefore consider the effectiveness of different strategies to observe snow-covered area under forests. At our study location in the Pacific Northwest, we simultaneously deployed fiber-optic cable, stand-alone ground temperature sensors, and time-lapse digital cameras in three diverse forest treatments: control second-growth forest, thinned forest, and forest gaps (one tree height in diameter). We derived fractional snow-covered area and snow duration metrics from the colocated instruments to assess optimal spatial resolution and sampling configuration, and snow duration differences between forest treatments. The fiber-optic cable and the cameras indicated that mean snow duration was 8 days longer in the gap plots than in the control plots (p < 0.001). We conducted Monte Carlo experiments for observing mean snow duration in a 40 m forest plot, and found the 95% confidence interval was ±5 days for 10 m spacing between instruments and ±3 days for 6 m spacing. We further tested the representativeness of sampling one plot per treatment group by observing snow duration across replicated forest plots at the same elevation, and at a set of forest plots 250 m higher. Relative relationships between snow duration in the forest treatments are consistent between replicated plots, elevation, and two winters of data.

How Does Availability of Meteorological Forcing Data Impact Physically Based Snowpack Simulations

AbstractPhysically based models facilitate understanding of seasonal snow processes but require meteorological forcing data beyond air temperature and precipitation (e.g., wind, humidity, shortwave radiation, and longwave radiation) that are typically unavailable at automatic weather stations (AWSs) and instead are often represented with empirical estimates. Research is needed to understand which forcings (after temperature and precipitation) would most benefit snow modeling through expanded observation or improved estimation techniques. Here, the impact of forcing data availability on snow model output is assessed with data-withholding experiments using 3-yr datasets at well-instrumented sites in four climates. The interplay between forcing availability and model complexity is examined among the Utah Energy Balance (UEB), the Distributed Hydrology Soil Vegetation Model (DHSVM) snow submodel, and the snow thermal model (SNTHERM). Sixty-four unique forcing scenarios were evaluated, with different assumptio...

A first overview of SnowEx ground-based remote sensing activities during the winter 2016–2017

NASA SnowExs goal is estimating how much water is stored in Earths terrestrial snow-covered regions. To that end, two fundamental questions drive the mission objectives: (a) What is the distribution of snow-water equivalent (SWE), and the snow energy balance, among different canopy and topographic situations?; and (b) What is the sensitivity and accuracy of different SWE sensing techniques among these different areas? In situ, ground-based and airborne remote sensing observations were collected during winter 2016–2017 in Colorado to provide the scientific community with data needed to work on these key questions. An intensive period of observations occurred in February 2017 during which over 30 remote sensing instruments were used. Their observations were coordinated with in situ measurements from snowpits (e.g. profiles of stratigraphy, density, grain size and type, specific surface area, temperature) and along transects (mainly for snow depth measurements). Both remote sensing and in situ data will be archived and publicly distributed by the National Snow and Ice Data Center at nsidc.org/data/snowex.