The Post-Wildfire Impact of Burn Severity and Age on Black Carbon Snow Deposition and Implications for Snow Water Resources, Cascade Range, Washington

Wildfires in the snow zone affect ablation by removing forest canopy, which enhances surface solar irradiance, and depositing light absorbing particles [LAPs, such as black carbon (BC)] on the snowpack, reducing snow albedo. How variations in BC deposition affects post-wildfire snowmelt timing is poorly known and highly relevant to water resources. We present a field-based analysis of BC variability across five sites of varying burn age and burn severity in the Cascade Range, Washington State, United States. Single particle soot photometer (SP2) analyses of BC snow concentrations were used to assess the impact of BC on snow albedo, and radiative transfer modeling was used to estimate the radiative effect of BC on snowmelt. Results were compared to Snowpack Telemetry (SNOTEL) data from one site that burned in 2012 and another in a proximal unburned forest. We show that post-wildfire forests provide a significant source of BC to the snowpack, and this effect increases by an order of magnitude in regions of high versus low burn severity, and decreased by two orders of magnitude over a decade. There is a shift in the timing of snowmelt, with snow disappearance occurring on average 19 ± 9 days earlier post-wildfire (2013–19) relative to pre-wildfire (1983–2012). This study improves understanding of the connection between wildfire activity and snowmelt, which is of high relevance as climate change models project further decreases in snowpack and increases in wildfire activity in the Washington Cascades

Operational Water Forecast Ability of the HRRR-iSnobal Combination: An Evaluation to Adapt into Production Environments

Operational water-resource forecasters, such as the Colorado Basin River Forecast Center (CBRFC) in the Western United States, currently rely on historical records to calibrate the temperature-index models used for snowmelt runoff predictions. This data dependence is increasingly challenged, with global and regional climatological factors changing the seasonal snowpack dynamics in mountain watersheds. To evaluate and improve the CBRFC modeling options, this work ran the physically based snow energy balance iSnobal model, forced with outputs from the High-Resolution Rapid Refresh (HRRR) numerical weather prediction model across 4 years in a Colorado River Basin forecast region. Compared to in situ, remotely sensed, and the current operational CBRFC model data, the HRRR-iSnobal combination showed well-reconstructed snow depth patterns and magnitudes until peak accumulation. Once snowmelt set in, HRRR-iSnobal showed slower simulated snowmelt relative to observations, depleting snow on average up to 34 d later. The melting period is a critical component for water forecasting. Based on the results, there is a need for revised forcing data input preparation (shortwave radiation) required by iSnobal, which is a recommended future improvement to the model. Nevertheless, the presented performance and architecture make HRRR-iSnobal a promising combination for the CBRFC production needs, where there is a demonstrated change to the seasonal snow in the mountain ranges around the Colorado River Basin. The long-term goal is to introduce the HRRR-iSnobal combination in day-to-day CBRFC operations, and this work created the foundation to expand and evaluate larger CBRFC domains

Accelerated Glacier Melt on Snow Dome, Mount Olympus, Washington, USA, due to Deposition of Black Carbon and Mineral Dust from Wildfire

Assessing the potential for black carbon (BC) and dust deposition to reduce albedo and accelerate glacier melt is of interest in Washington because snow and glacier melt are an important source of water resources, and glaciers are retreating. In August 2012 on Snow Dome, Mount Olympus, Washington, we measured snow surface spectral albedo and collected surface snow samples and a 7 m ice core. The snow and ice samples were analyzed for iron (Fe, used as a dust proxy) via inductively coupled plasma sector field mass spectrometry, total impurity content gravimetrically, BC using a single-particle soot photometer (SP2), and charcoal through microscopy. In the 2012 summer surface snow, BC (54 ± 50 μg/L), Fe (367±236 μg/L) and gravimetric impurity (35 ± 18 mg/L) concentrations were spatially variable, and measured broadband albedo varied between 0.67–0.74. BC and dust concentrations in the ice core 2011 summer horizon were a magnitude higher (BC = 3120 μg/L, Fe = 22000 μg/L, and gravimetric impurity = 1870 mg/L), corresponding to a modeled broadband albedo of 0.45 based on the measured BC and ravimetric impurity concentrations. The Big Hump forest fire is the likely source for the higher concentrations. Modeling constrained by measurements indicates that the all-sky 12 h daily mean radiative forcings in summer 2012 and 2011 range between 37–53Wm_2 and 112–149Wm_2, respectively, with the greater forcings in 2011 corresponding to a 29–38mm/d enhancement in snowmelt. The timing of the forest fire impurity deposition is coincident with an increase in observed discharge in the Hoh River, highlighting the potential for BC and dust deposition on glaciers from forest fires to accelerate melt

Radiative Forcing by Dust and Black Carbon on the Juneau Icefield, Alaska

Here we present the first known data set on black carbon (BC) and mineral dust concentrations in snow from the Juneau Icefield (JIF) in southeastern Alaska, where glacier melt rates are among the highest on Earth. In May 2016, concentrations of BC (0.4–3.1 μg/L) and dust (0.2–34 mg/L) were relatively low and decreased toward the interior of the JIF. The associated radiative forcing (RF) averaged 4 W/m2. In July, after 10 weeks of exposure, the aged snow surface had substantially higher concentrations of BC (2.1–14.8 μg/L) and dust (11–72 mg/L) that were not spatially distributed by elevation or distance from the coast. RF by dust and BC ranged from 70 to 130 W/m2 (87 W/m2 average) across the JIF in July, and RF was dominated by dust. The associated median snow water equivalent reduction in the July samples is estimated at 10–18 mm/day, potentially advancing melt on the scale of days to weeks. Aging of the snow surface in summer likely resulted in a positive feedback of melt consolidation, enhanced solar absorption and melting, and further concentration of surface particles. Regional projections of warming temperatures and increased rain at the expense of snow make it likely that summer season darkening will become a more important contributor to the high melt rates on the JIF. Further studies are needed to elucidate the spatiotemporal occurrence of various light‐absorbing particles on the JIF, and models of ice field wastage should incorporate their associated RF

Sensitivity of modeled snow grain size retrievals to solar geometry, snow particle asphericity, and snowpack impurities

Snow grain size is an important metric to determine snow age and metamorphism, but it is difficult to measure. The effective grain size can be derived from spaceborne and airborne radiance measurements due to strong attenuation of near-infrared energy by ice. Consequently, a snow grain size inversion technique that uses hyperspectral radiances and exploits variations in the 1.03&thinsp;&micro;m ice absorption feature was previously developed for use with airborne imaging spectroscopy. Previous studies have since demonstrated the effectiveness of the technique, though there has yet to be a quantitative assessment of the retrieval sensitivity to snowpack impurities, ice particle shape, or solar geometry. In this study, we use the Snow, Ice, and Aerosol Radiative (SNICAR) model and a Monte Carlo photon tracking model to examine the sensitivity of snow grain size retrievals to changes in dust and black carbon content, anisotropic reflectance, changes in solar illumination angle (&theta;0), and scattering asymmetry parameter (g) associated with different particle shapes. Our results show that changes in these variables can produce large grain size errors, especially when the effective grain size exceeds 500&thinsp;&micro;m. Dust content of 1000&thinsp;ppm induces errors exceeding 800&thinsp;&micro;m, with the highest biases associated with small particles. Aspherical ice particles and perturbed solar zenith angles produce maximum biases of &sim;540&thinsp;&micro;m and &sim;400&thinsp;&micro;m, respectively, when spherical snow grains and &theta;0=60∘ are assumed in the generation of the retrieval calibration curve. Retrievals become highly sensitive to viewing angle when reflectance is anisotropic, with biases exceeding 1000&thinsp;&micro;m in extreme cases. Overall, we show that a more detailed understanding of snowpack state and solar geometry improves the precision when determining snow grain size through hyperspectral remote sensing. &nbsp;</p

Interpreting Sentinel-1 SAR Backscatter Signals of Snowpack Surface Melt/Freeze, Warming, and Ripening, Through Field Measurements and Physically-Based SnowModel

The transition of a cold winter snowpack to one that is ripe and contributing to runoff is crucial to gauge for water resource management, but is highly variable in space and time. Snow surface melt/freeze cycles, associated with diurnal fluctuations in radiative inputs, are hallmarks of this transition. C-band synthetic aperture radar (SAR) reliably detects meltwater in the snowpack. Sentinel-1 (S1) C-band SAR offers consistent acquisition patterns that allow for diurnal investigations of melting snow. We used over 50 snow pit observations from 2020 in Grand Mesa, Colorado, USA, to track temperature and wetness in the snowpack as a function of depth and time during snowpack phases of warming, ripening, and runoff. We also ran the physically-based SnowModel, which provided a spatially and temporally continuous independent indication of snowpack conditions. Snowpack phases were identified and corroborated by comparing field measurements with SnowModel outputs. Knowledge of snowpack warming, ripening, and runoff phases was used to interpret diurnal changes in S1 backscatter values. Both field measurements and SnowModel simulations suggested that S1 SAR was not sensitive to the initial snowpack warming phase on Grand Mesa. In the ripening and runoff phases, the diurnal cycle in S1 SAR co-polarized backscatter was affected by both surface melt/freeze as well as the conditions of the snowpack underneath (ripening or ripe). The ripening phase was associated with significant increases in morning backscatter values, likely due to volume scattering from surface melt/freeze crusts, as well as significant decreases in evening backscatter values associated with snowmelt. During the runoff phase, both morning and evening backscatter decreased compared to reference values. These unique S1 diurnal signatures, and their interpretations using field measurements and SnowModel outputs, highlight the capacities and limitations of S1 SAR to understand snow surface states and bulk phases, which may offer runoff forecasting or energy balance model validation or parameterization, especially useful in remote or sparsely-gauged alpine basins

Improving snow albedo processes in WRF/SSiB regional climate model to assess impact of dust and black carbon in snow on surface energy balance and hydrology over western U.S.

Two important factors that control snow albedo are snow grain growth and presence of light‐absorbing impurities (aerosols) in snow. However, current regional climate models do not include such processes in a physically based manner in their land surface models. We improve snow albedo calculations in the Simplified Simple Biosphere (SSiB) land surface model coupled with the Weather Research and Forecasting (WRF) regional climate model (RCM), by incorporating the physically based SNow ICe And Radiative (SNICAR) scheme. SNICAR simulates snow albedo evolution due to snow aging and presence of aerosols in snow. The land surface model is further modified to account for deposition, movement, and removal by meltwater of such impurities in the snowpack. This paper presents model development technique, validation with in situ observations, and preliminary results from RCM simulations investigating the impact of such impurities in snow on surface energy and water budgets. By including snow‐aerosol interactions, the new land surface model is able to realistically simulate observed snow albedo, snow grain size, dust in snow, and surface water and energy balances in offline simulations for a location in western U.S. Preliminary results with the fully coupled RCM show that over western U.S., realistic aerosol deposition in snow induces a springtime average radiative forcing of 16 W/m2 due to a 6% albedo reduction, a regional surface warming of 0.84°C, and a snowpack reduction of 11 mm.Key PointsIncluding snow aging and aerosols in snow improves offline and WRF snow simulationsDust and black/organic carbon exerts nontrivial radiative forcing in western U.S.RCM simulation shows temperature increase and snow mass loss from aerosols in snowPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111782/1/jgrd52045.pd

High Mountain Areas

The cryosphere (including, snow, glaciers, permafrost, lake and river ice) is an integral element of high-mountain regions, which are home to roughly 10% of the global population. Widespread cryosphere changes affect physical, biological and human systems in the mountains and surrounding lowlands, with impacts evident even in the ocean. Building on the IPCC’s Fifth Assessment Report (AR5), this chapter assesses new evidence on observed recent and projected changes in the mountain cryosphere as well as associated impacts, risks and adaptation measures related to natural and human systems. Impacts in response to climate changes independently of changes in the cryosphere are not assessed in this chapter. Polar mountains are included in Chapter 3, except those in Alaska and adjacent Yukon, Iceland, and Scandinavia, which are included in this chapter