Changes In Snow Water Equivalent Research Articles

Snow water equivalent retrieval over Idaho – Part 2: Using L-band UAVSAR repeat-pass interferometry

Abstract. This study evaluates using interferometry on low-frequency synthetic aperture radar (SAR) images to monitor snow water equivalent (SWE) over seasonal and synoptic scales. We retrieved SWE changes from nine pairs of SAR images, mean 8 d temporal baseline, captured by an L-band aerial platform, NASA's Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), over central Idaho as part of the NASA SnowEx 2020 and 2021 campaigns. The retrieved SWE changes were compared against coincident in situ measurements (SNOTEL and snow pits from the SnowEx field campaign) and to 100 m gridded SnowModel modeled SWE changes. The comparison of in situ to retrieved measurements shows a strong Pearson correlation (R=0.80) and low RMSE (0.1 m, n=64) for snow depth change and similar results for SWE change (RMSE = 0.04 m, R=0.52, n=57). The comparison between retrieved SWE changes to SnowModel SWE change also showed good correlation (R=0.60, RMSD = 0.023 m, n=3.2×106) and especially high correlation for a subset of pixels with no modeled melt and low tree coverage (R=0.72, RMSD = 0.013 m, n=6.5×104). Finally, we bin the retrievals for a variety of factors and show decreasing correlation between the modeled and retrieved values for lower elevations, higher incidence angles, higher tree percentages and heights, and greater cumulative melt. This study builds on previous interferometry work by using a full winter season time series of L-band SAR images over a large spatial extent to evaluate the accuracy of SWE change retrievals against both in situ and modeled results and the controlling factors of the retrieval accuracy.

Performance of precipitation phase partitioning methods and their impact on snowpack evolution in a humid continental climate

AbstractAccurate estimations of the precipitation phase at the surface are critical for hydrological and snowpack modelling in cold regions. Precipitation phase partitioning methods (PPMs) vary in their ability to estimate the precipitation phase at around 0°C and can significantly impact simulations of snowpack accumulation and melt. The goal of this study is to evaluate PPMs of varying complexity using high‐quality observations of precipitation phase and to assess the impact on snowpack simulations. We used meteorological data collected in Edmundston, New Brunswick, Canada, during the 2021 Saint John River Experiment on Cold Season Storms (SAJESS). These data were combined with manual observations of snow depth. Five PPMs commonly used in hydrological models were tested against observations from a laser‐optical disdrometer and a Micro Rain Radar. Most PPMs produced similar accuracy in estimating only rainfall and snowfall. Mixed precipitation was the most difficult phase to predict. The multi‐physics model Crocus was then used to simulate snowpack evolution and to diagnose model sensitivity to snowpack accumulation processes (PPM, snowfall density, and snowpack compaction). Sixteen snowpack accumulation periods, including nine warm accumulation events (average temperatures above −2°C) were observed during the study period. When considering all accumulation events, simulated changes in snow water equivalent (SWE) were more sensitive to the type of PPM used, whereas simulated changes in snow depth were more sensitive to uncertainties in snowfall density. Choice of PPM was the main source of model sensitivity for changes in SWE and snow depth when only considering warm events. Overall, this study highlights the impact of precipitation phase estimations on snowpack accumulation at the surface during near‐0°C conditions.

Estimating snow accumulation and ablation with L-band interferometric synthetic aperture radar (InSAR)

Abstract. Snow is a critical water resource for the western United States and many regions across the globe. However, our ability to accurately measure and monitor changes in snow mass from satellite remote sensing, specifically its water equivalent, remains a challenge. To confront these challenges, NASA initiated the SnowEx program, a multiyear effort to address knowledge gaps in snow remote sensing. During SnowEx 2020, the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) team acquired an L-band interferometric synthetic aperture radar (InSAR) data time series to evaluate the capabilities and limitations of repeat-pass L-band InSAR for tracking changes in snow water equivalent (SWE). The goal was to develop a more comprehensive understanding of where and when L-band InSAR can provide SWE change estimates, allowing the snow community to leverage the upcoming NASA–ISRO (NASA–Indian Space Research Organization) SAR (NISAR) mission. Our study analyzed three InSAR image pairs from the Jemez Mountains, NM, between 12 and 26 February 2020. We developed a snow-focused multi-sensor method that uses UAVSAR InSAR data synergistically with optical fractional snow-covered area (fSCA) information. Combining these two remote sensing datasets allows for atmospheric correction and delineation of snow-covered pixels within the radar swath. For all InSAR pairs, we converted phase change values to SWE change estimates between the three acquisition dates. We then evaluated InSAR-derived retrievals using a combination of fSCA, snow pits, meteorological station data, in situ snow depth sensors, and ground-penetrating radar (GPR). The results of this study show that repeat-pass L-band InSAR is effective for estimating both snow accumulation and ablation with the proper measurement timing, reference phase, and snowpack conditions.

Reasons for East Siberia Winter Snow Water Equivalent Increase in the Recent Decades

With the rapid warming in the past few decades, the snow water equivalent (SWE) in winter and spring decreased generally over the Northern Hemisphere, but an increasing trend occurred in some areas, especially in east Siberia. In this paper, we analyze the sources and reasons for the SWE increase in east Siberia in winter since 1979 and document projected future SWE changes in this region. The winter SWE changes in east Siberia were not significant over the past four decades until the 2000s, and the SWE increased rapidly thereafter. The SWE increase after the 2000s is mainly contributed by SWE in November, followed by that in winter, and attributed to the increase in snowfall. With the moisture budget diagnosis, we found that the atmospheric dynamic-induced moisture convergence (vertical motion effect and horizontal advection of moisture) are the reasons that contributed to the winter snowfall increase in east Siberia. As east Siberia is cold in winter, even under the high radiative forcing scenario, precipitation in east Siberia will continue to increase and be dominated by snowfall until the 2060s. Thereafter, with the rainfall increase and the accelerated snowmelt due to rising temperature, precipitation will gradually shift to rainfall type and the SWE may turn to decrease.

Observed and Modeled Changes in Snow Water Equivalent in the Northern Dvina Basin

Observed and Modeled Changes in Snow Water Equivalent in the Northern Dvina Basin

An Overview of Snow Water Equivalent: Methods, Challenges, and Future Outlook

The snow depth or snow water equivalent affects water, carbon, and energy cycles as well as surface–atmosphere interactions. Therefore, the global monitoring of spatiotemporal changes in snow water equivalent is a crucial issue, which is performed by characterizing the macrophysical, microstructural, optical, and thermal characteristics of the snowpack. This paper is a review of the retrieval methods of snow water equivalent in three main categories, including in situ measurements, reconstruction approaches, and space-borne measurements, along with their basic concepts, advantages, and uncertainties. Since satellite observations are the most important tool used to detect snow properties, the paper focuses on inversion models and techniques using microwave remote sensing. The inversion models, based on various theoretical foundations, are classified into empirical, statistical, and physical (emission) models, and the techniques are described in four groups: iterative methods, lookup table, machine learning, and data assimilation approaches. At the end, the available global and regional gridded products providing the spatiotemporal maps of snow water equivalent with different resolutions are presented, as well as approaches for improving the snow data.

Spatial Trend and Impact of Snowmelt Rate in Spring across China’s Three Main Stable Snow Cover Regions over the Past 40 Years Based on Remote Sensing

Historical patterns of snow cover and snowmelt are shifting due to climate warming and perhaps some human activities, threatening natural water resources and the ecological environment. Passive microwave remote sensing provides quantitative data for snow mass evaluation. Here, we evaluated the long-term impact of climate warming on snowmelt rates, using snow water equivalent (SWE) datasets derived from passive microwave remotely sensed data over China’s three main stable snow cover regions during the past 40 years (1981–2020). The results showed that higher ablation rates in spring were found in locations with a deeper SWE because of high snowmelt rates that occurred in late spring and early summer in areas with a deeper snowpack. Annual maximum SWE (snow water equivalent) has declined across two out of the three main mountains of China’s snow cover regions over the past 40 years under climate warming. The maximum and mean snowmelt rate was ca. 30 and 3 mm/day, respectively, over the three regions. Further, due to SWE being reduced in these deep snowpack areas, moderate and high rates of snowmelt showed trends of decline after 2000. Accordingly, an earlier snow onset day (average 0.6~0.7 day/a) and slower snowmelt rates characterized the mountainous areas across the three main snow cover regions. The slower snowmelt rate is also closely related to vegetation improvement over the three main stable snow cover regions. Therefore, not only vegetation in spring but also streamflow and other ecological processes could be affected by the pronounced changes in SWE and snowmelt rates. These findings strengthen our understanding of how to better assess ecological and environmental changes towards the sustainable use of freshwater resources in spring and earlier summer months in snow-rich alpine regions.

Wildfire Impacts on Snowpack Phenology in a Changing Climate Within the Western U.S.

AbstractSnowpack in the western U.S. is critical for water supply and is threatened by wildfires, which are becoming larger and more common. Numerous studies have examined impacts of wildfire on snow water equivalent (SWE), but many of these studies are limited in the number of observation locations, and they have sometimes produced conflicting results. The objective of this study is to distinguish the net effects of wildfires on snowpack from those of climate. We consider 45 burned sites from the Snow Telemetry network. For each burned site, unburned control sites are identified from the same level III ecoregion. Impacts of climate changes on snowpack are analyzed by comparing pre‐fire and post‐fire snow water equivalent at the unburned sites. Combined climate and wildfire effects are considered by comparing pre‐fire and post‐fire SWE at the burned sites. Wildfire impacts are then isolated by taking the difference between the burned and unburned sites. The wildfire‐induced changes in SWE are also compared to several possible controlling variables including burn severity, leaf‐area index change, dominant pre‐fire tree genus, years since the fire, and site elevation. On average, wildfires have advanced melt‐out dates 9 days and maximum SWE dates 6 days and reduced annual maximum SWE by 10% across the sites considered. On average, the combined effects of climate and wildfire have advanced melt‐out and maximum SWE dates approximately 14 and 10 days, respectively, while decreasing annual maximum SWE by approximately 10%.

Projected Changes in the Tibetan Plateau Snowpack Resulting From Rising Global Temperatures

AbstractThe snowpack is a critical component of the hydrologic cycle in cold regions, the change in which becomes important for proper planning and management of water. The Tibetan Plateau provides significant amount of water to most Asian rivers, and consequently the downstream population is dependent on its availability. Despite its importance, potential change in snowpack in this region due to climate change is poorly understood to date, largely because of remoteness and the orographic complexity of the area. This study inspects the impact of climate change on snowpack change over the Tibetan Plateau considering historical simulations (1981–2004), near future projections (2041–2064), and far future projections (2071–2094) from global climate models (GCMs) and regional climate models (RCMs) of derived temperature, precipitation, and snow water equivalent (SWE). A multivariate nesting bias correction approach (MRNBC) was employed to correct possible biases in GCM and RCM derived temperature, precipitation, and SWE jointly over multiple time scales to preserve interdependencies among the variables while enabling simulation of year‐to‐year persistence, which is of importance in water security assessments. MRNBC reduced the bias in model simulations significantly and improved projections of the snow climatology. The results indicate that the annual maximum spell of snow‐free days will increase over the region whereas the snowy day fraction will decrease in the future compared to the historical period. In addition, annual SWE are noted to be decreasing in both the near future and far future with respect to historical averages. Changes in SWE will result from warming temperatures and also from changes in precipitation, which will lead to more rainfall than snowfall thus affecting snowmelt processes.

GNSS signal-based snow water equivalent determination for different snowpack conditions along a steep elevation gradient

Abstract. Snow water equivalent (SWE) can be measured using low-cost Global Navigation Satellite System (GNSS) sensors with one antenna placed below the snowpack and another one serving as a reference above the snow. The underlying GNSS signal-based algorithm for SWE determination for dry- and wet-snow conditions processes the carrier phases and signal strengths and additionally derives liquid water content (LWC) and snow depth (HS). So far, the algorithm was tested intensively for high-alpine conditions with distinct seasonal accumulation and ablation phases. In general, snow occurrence, snow amount, snow density and LWC can vary considerably with climatic conditions and elevation. Regarding alpine regions, lower elevations mean generally earlier and faster melting, more rain-on-snow events, and shallower snowpack. Therefore, we assessed the applicability of the GNSS-based SWE measurement at four stations along a steep elevation gradient (820, 1185, 1510 and 2540 m a.s.l.) in the eastern Swiss Alps during two winter seasons (2018–2020). Reference data of SWE, LWC and HS were collected manually and with additional automated sensors at all locations. The GNSS-derived SWE estimates agreed very well with manual reference measurements along the elevation gradient, and the accuracy (RMSE = 34 mm, RMSRE = 11 %) was similar under wet- and dry-snow conditions, although significant differences in snow density and meteorological conditions existed between the locations. The GNSS-derived SWE was more accurate than measured with other automated SWE sensors. However, with the current version of the GNSS algorithm, the determination of daily changes of SWE was found to be less suitable compared to manual measurements or pluviometer recordings and needs further refinement. The values of the GNSS-derived LWC were robust and within the precision of the manual and radar measurements. The additionally derived HS correlated well with the validation data. We conclude that SWE can reliably be determined using low-cost GNSS sensors under a broad range of climatic conditions, and LWC and HS are valuable add-ons.

Wideband Backscattering From Alpine Snow Cover: A Full-Season Study

This article experimentally investigates relationships between copol backscattering at a wide range of frequencies (L- to Ka-bands) and snow–ground state parameters (SPs) in different evolution phases during the full winter cycle of 2019/2020. Backscattering coefficients from 1 to 40 GHz, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> snow–ground SPs, and meteorological data are measured at the Davos-Laret Remote Sensing Field Laboratory (Switzerland). Relative strengths of the snow–ground system’s three primary scattering elements (air–snow interface, snow volume, and snow–ground interface) on backscattering are assessed. An anticorrelation between reasonably high snow wetness and backscattering coefficient is found, especially at higher microwave frequencies. For small amounts of snow wetness, backscatter coefficients at L- and S-bands are intensified via increasing snow volume and snow surface scattering. Snow–ground SPs influence backscattering according to their characteristic time scales of temporal evolution. Under dry snow conditions and at low and intermediate frequencies, ground permittivity is the major influencer of backscatter at a time scale of roughly two weeks. Snowfall is the major influencer of backscatter at a time scale of a few hours to a few days. The findings of this article are valuable to the development of retrieval algorithms using machine learning while maintaining a grasp on the ongoing physical processes. Another key message is that multifrequency active microwave measurements are critical to maximize the number of retrievable SPs and their estimation accuracy. For example, while Ka-band performs well in the detection of snow cover, L-band measurements are more responsive to changes of snow water equivalent (SWE) under moist or wet snow conditions.

The Effects of Dry Snow on the SAR Impulse Response and Feasibility for Single Channel Snow Water Equivalent Estimation

Snow water equivalent (SWE) is an important surface parameter for understanding a number of Earth system processes. Synthetic aperture radar (SAR) has considerable potential for measuring SWE of dry-snow because SAR can penetrate through the snow to the ground surface and is both amplitude- and phase-sensitive to refraction from the snow. Previous work on refraction-based SWE measurement by SAR has utilized the repeat-pass InSAR phase signal to estimate changes in SWE that occur between SAR acquisitions. These are subject to temporal decorrelation effects and consider only the refraction that occurs along the SAR beam center rather than the entire synthetic aperture. This study examines the refractive effect of dry-snow along the synthetic aperture and its impact on SAR image formation including defocusing and phase bias of the system impulse response. Snow phase compensation during time domain processing to recover the snow-free impulse response function (IRF) is described and demonstrated. The feasibility of using the mapdrift and image sharpness autofocus methods to estimate SWE is examined, and the effect of key system parameters on the estimation performance is derived. Experimental validation of the method was conducted by acquiring L-band data with the Simon Fraser University (SFU) Airborne SAR System over a pair of corner reflectors installed on the Kluane icefield in northwestern Canada. Results from both simulations and the icefield experiment are presented and compared including an analysis of errors affecting the estimation.

Investigation of Environmental Effects on Coherence Loss in SAR Interferometry for Snow Water Equivalent Retrieval

Interferometric Synthetic Aperture Radar (InSAR) is a promising tool for the retrieval of Snow Water Equivalent (SWE) from space. Due to refraction, the interferometric phase changes with snow depth and density, which is exploited by the InSAR method. While the method was first proposed two decades ago, qualitative research using experimental data analyzing factors affecting retrieval performance remains scarce. In this work a tower-based 1-10 GHz, fully polarimetric SAR with InSAR capabilities was used to analyze the effect of meteorological events (air temperature, precipitation intensity, and wind) on the observed temporal decorrelation of interferometric image pairs, at L-, S-, C- and X-bands. These factors were found to be causes of decorrelation in snow, being the temperature the critical variable in the case of snowmelt events. Of the analyzed bands, L-band presented the best coherence conservation properties. Additionally, the phase change between pairs with sufficient coherence was applied to generate estimates of changes in SWE, studying the retrieval errors at different bands and over different temporal baselines. SWE accumulation was calculated from 6 hours up to 12 days temporal baseline over a non-vegetated area. SWE accumulation profiles were successfully reconstructed for short temporal baselines and low frequencies, while an increase in the retrieval error was observed for high frequencies and long temporal baselines, indicating the limitations of higher frequencies for repeat-pass InSAR retrieval. The analysis was also reproduced over a forested area at L-band with similar results as to the non-vegetated area.

In Situ Determination of Dry and Wet Snow Permittivity: Improving Equations for Low Frequency Radar Applications

Extensive efforts have been made to observe the accumulation and melting of seasonal snow. However, making accurate observations of snow water equivalent (SWE) at global scales is challenging. Active radar systems show promise, provided the dielectric properties of the snowpack are accurately constrained. The dielectric constant (k) determines the velocity of a radar wave through snow, which is a critical component of time-of-flight radar techniques such as ground penetrating radar and interferometric synthetic aperture radar (InSAR). However, equations used to estimate k have been validated only for specific conditions with limited in situ validation for seasonal snow applications. The goal of this work was to further understand the dielectric permittivity of seasonal snow under both dry and wet conditions. We utilized extensive direct field observations of k, along with corresponding snow density and liquid water content (LWC) measurements. Data were collected in the Jemez Mountains, NM; Sandia Mountains, NM; Grand Mesa, CO; and Cameron Pass, CO from February 2020 to May 2021. We present empirical relationships based on 146 snow pits for dry snow conditions and 92 independent LWC observations in naturally melting snowpacks. Regression results had r2 values of 0.57 and 0.37 for dry and wet snow conditions, respectively. Our results in dry snow showed large differences between our in situ observations and commonly applied equations. We attribute these differences to assumptions in the shape of the snow grains that may not hold true for seasonal snow applications. Different assumptions, and thus different equations, may be necessary for varying snowpack conditions in different climates, suggesting that further testing is necessary. When considering wet snow, large differences were found between commonly applied equations and our in situ measurements. Many previous equations assume a background (dry snow) k that we found to be inaccurate, as previously stated, and is the primary driver of resulting uncertainty. Our results suggest large errors in SWE (10–15%) or LWC (0.05–0.07 volumetric LWC) estimates based on current equations. The work presented here could prove useful for making accurate observations of changes in SWE using future InSAR opportunities such as NISAR and ROSE-L.

Snow conditions in northern Europe: the dynamics of interannual variability versus projected long-term change

Abstract. Simulations by the EURO-CORDEX (European branch of the Coordinated Regional Climate Downscaling Experiment) regional climate models indicate a widespread future decrease in snow water equivalent (SWE) in northern Europe. This concurs with the negative interannual correlation between SWE and winter temperature in the southern parts of the domain but not with the positive correlation observed further north and over the Scandinavian mountains. To better understand these similarities and differences, interannual variations and projected future changes in SWE are attributed to anomalies or changes in three factors: total precipitation, the snowfall fraction of precipitation and the fraction of accumulated snowfall that remains on the ground (the snow-on-ground fraction). In areas with relatively mild winter climate, the latter two terms govern both the long-term change and interannual variability, resulting in less snow with higher temperatures. In colder areas, however, interannual SWE variability is dominated by variations in total precipitation. Since total precipitation is positively correlated with temperature, more snow tends to accumulate in milder winters. Still, even in these areas, SWE is projected to decrease in the future due to the reduced snowfall and snow-on-ground fractions in response to higher temperatures. Although winter total precipitation is projected to increase, its increase is smaller than would be expected from the interannual covariation of temperature and precipitation and is therefore insufficient to compensate the lower snowfall and snow-on-ground fractions. Furthermore, interannual SWE variability in northern Europe in the simulated warmer future climate is increasingly governed by variations in the snowfall and snow-on-ground fractions and less by variations in total precipitation.

Measuring Changes in Snowpack SWE Continuously on a Landscape Scale Using Lake Water Pressure

AbstractThe seasonal snowpack is a globally important water resource that is notoriously difficult to measure. Existing instruments make measurements of falling or accumulating snow water equivalent (SWE) that are susceptible to bias, and most represent only a point in the landscape. Furthermore, the global array of SWE sensors is too sparse and too poorly distributed adequately to constrain snow in weather and climate models. We present a new approach to monitoring snowpack SWE from time series of lake water pressure. We tested our method in the lowland Finnish Arctic and in an alpine valley and high-mountain cirque in Switzerland and found that we could measure changes in SWE and their uncertainty through snowfalls with little bias and with an uncertainty comparable to or better than that achievable by other instruments. More importantly, our method inherently senses change over the whole lake surface, an area in this study up to 10.95 km2, or 274 million times larger than the nearest pluviometer. This large scale makes our measurements directly comparable to the grid cells of weather and climate models. We find, for example, snowfall biases of up to 100% in operational forecast models AROME-Arctic and COSMO-1. Seasonally frozen lakes are widely distributed at high latitudes and are particularly common in mountain ranges, hence our new method is particularly well suited to the widespread, autonomous monitoring of snow-water resources in remote areas that are largely unmonitored today. This is potentially transformative in reducing uncertainty in regional precipitation and runoff in seasonally cold climates.

Impact of global warming on snow in ski areas: A case study using a regional climate simulation over the interior western United States

AbstractA high-resolution (4 km) regional climate simulation conducted with the Weather Research and Forecast (WRF) model is used to investigate potential impacts of global warming on skiing conditions in the interior western United States (IWUS). Recent past and near-future climate conditions are compared. The past climate period is from November 1981 to October 2011. The future climate applies to a 30-year period centered on 2050. A pseudo global warming approach is used, with the driver re-analysis dataset perturbed by the CMIP5 ensemble mean model guidance. Using the 30-year retrospective simulation, a vertical adjustment technique is used to determine meteorological parameters in the complex terrain where ski areas are located. For snow water equivalent (SWE), Snow Telemetry sites close to ski areas are used to validate the technique and apply a correction to SWE in ski areas. The vulnerability to climate change is assessed for 71 ski areas in the IWUS considering SWE, artificially produced snow, temperature, and rain. 20 of the ski areas will tend to have fewer than 100 days per season with sufficient natural and artificial snow for skiing. These ski areas are located at either low elevations or low latitudes making these areas the most vulnerable to climate change. Throughout the snow season, natural SWE decreases significantly at the low elevations and low latitudes. At higher elevations changes in SWE are predicted to not be significant in the mid-season. In mid-February, SWE decreases by 11.8% at the top elevations of ski areas while it decreases by 25.8% at the base elevations.

Scenario Projections of Changes in Snow Water Equivalent Due to Possible Climate Changes in Different Regions of the Earth

The study was carried out under the international Earth System Model–Snow Model Intercomparison Project on ten experimental well-instrumented snow sites in different parts of the Earth and aimed to examine the ability of the SWAP land surface model, developed by the authors, to reproduce the current characteristics of snow cover dynamics. A procedure was developed for scenario projecting of changes in the characteristics of snow cover formation in the XXI century with the use of series of meteorological forcing data, calculated with the use of atmospheric and oceanic general circulation models. The developed procedure was used to evaluate changes in the characteristics of snow cover formation in the XXI century in the areas of snow site locations in the context of current climate changes.

Examining the atmospheric radiative and snow-darkening effects of black carbon and dust across the Rocky Mountains of the United States using WRF-Chem

Abstract. The Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) is run to quantify the in-snow and atmospheric radiative effects of black carbon (BC) and dust on a convective-allowing (4 km) grid for water year 2009 across a large area of the Rocky Mountains. The snow-darkening effect (SDE) due to the deposition of these light-absorbing particles (LAPs) on surface snow enhances snowmelt by 3 to 12 mm during late spring and early summer, effectuating surface runoff increases (decreases) prior to (after) June. Meanwhile, aerosol–radiation interactions (ARIs) associated with LAPs generally dim the surface from incoming solar energy, introducing an energy deficit at the surface and leading to snowpack preservation by 1 to 5 mm. Surface runoff alterations brought forth by LAP ARI are of opposite phase to those associated with LAP SDEs, and the BC SDE drives a majority of the surface energy and hydrological perturbations. More generally, changes in snow water equivalent (SWE) brought forth by LAP effects are more a result of perturbations to the surface energy budget rather than changes in precipitation amount or type. It is also found that perturbations to the surface energy budget by dust ARI can differ in sign from those of BC ARI, with the former being positive, enhancing snow melting, and changing runoff.

Snowpack response in the Assiniboine-Red River basin associated with projected global warming of 1.0 °C to 3.0 °C

This study assesses snow response in the Assiniboine-Red River basin, located in the Lake Winnipeg watershed, due to anthropogenic climate change. We use a process-based distributed snow model driven by an ensemble of eight statistically downscaled global climate models (GCMs) to project future changes under policy-relevant global mean temperature (GMT) increases of 1.0 °C to 3.0 °C above the pre-industrial period. Results indicate that basin scale seasonal warmings generally exceed the GMT increases, with greater warming in winter months. The majority of GCMs project wetter winters and springs, and drier summers, while autumn could become either drier or wetter. An analysis of snow water equivalent (SWE) responses under GMT changes reveal higher correlations of snow cover duration (SCD), snowmelt rate, maximum SWE (SWEmax) and timing of SWEmax with winter and spring temperatures compared to precipitation, implying that these variables are predominantly temperature controlled. Consequently, under the GMT increases from 1.0 °C to 3.0 °C, the basin will experience successively shorter SCD, slower snowmelt, smaller monthly SWE and SWEmax, earlier SWEmax, and a transition from snow-dominated to rain-snow hybrid regime. Further, while the winter precipitation increases for some GCMs compensate the temperature-driven changes in SWE, the increases for most GCMs occur as rainfall, thus limiting the positive contribution to snow storage. Overall, this study provides a detailed diagnosis of the snow regime changes under the policy-relevant GMT changes, and a basis for further investigations on water quantity and quality changes.