Amount Of Snowfall Research Articles

How does a change in climate variability impact the Greenland ice sheet surface mass balance?

Abstract. Given the long response time of ice sheets, simulations of the Greenland ice sheet typically exceed the availability of input climate data to reliably simulate the fast processes underlying surface mass balance. Strong feedback processes are known to make the mass balance sensitive to inter- and intra-annual variability. Even simulations with climate models do not always cover the full period of interest, motivating bridging these gaps using relatively coarsely resolved climate reconstructions or temporal interpolation methods. However, both of these approaches usually only provide information about the climatological average but not variability. We investigate how this simplification impacts the surface mass balance using the BErgen Snow SImulator. The model was run for up to 500 years using the same atmospheric climatology but different synthetic variabilities. While changing inter-annual variations has an impact of less than 5 % on the surface mass balance of the Greenland ice sheet, neglecting intra-annual variability by using a daily climatology causes a 40 % change in mass balance. Decomposing the total effect into contributions from different input variables, the biggest contributor is precipitation followed by temperature. Using a daily climatology, a small amount of snowfall every day overestimates the albedo and thus surface mass balance (SMB). We propose a correction that re-captures the effect of intermittent precipitation, reducing the SMB overestimation to 15 %–25 %. We conclude that simulations of the Greenland surface mass and energy balance should be forced with a transient climate, in particular for models that are calibrated with transient data.

Hourly estimation of water reaching the ground surface in snow-covered regions of Japan using mesoscale meteorological data with the heat balance method

In snow-covered regions, landslides, including debris flows, frequently result from snowmelt and cause significant hazards. Because these snowmelt-induced landslides are triggered by increasing groundwater level and pore-water pressure acting on the slip surface due to infiltration of snowmelt, sometimes mixed with rainwater, accurate estimation of water reaching the ground surface (abbreviated MR in this study) on an hourly basis is essential for developing countermeasures such as an early warning system. This study introduces a model for estimating hourly MR, as well as snow water equivalent and snow depth, using mesoscale meteorological data provided by the Japan Meteorological Agency. The model considers four processes: precipitation, snow accumulation and densification, snowmelt, and infiltration. In the snowmelt process, snowmelt is estimated using the heat balance method. We applied the model to a snow observation station in Niigata Prefecture, central Japan, where MR, snow water equivalent, and snow depth were observed during the winter seasons of 2017–18 through 2020–21. Comparisons of observed and calculated values demonstrated that the model successfully reproduced the observations, regardless of the amount of snowfall in individual winter seasons. We propose that this model can estimate hourly MR, snow water equivalent, and snow depth at any location in snow-covered regions where on-site meteorological and snow observations are unavailable.

Changes in precipitation phases based on the multi-discrimination method in the Tibetan Plateau

Global warming is rapidly changing the precipitation structure, and the shift in precipitation phases is even more pronounced in the Tibetan Plateau (TP), a region sensitive to global climate change. The accuracy of precipitation phase discrimination is essential for studying precipitation variability. Based on the observed data from 112 meteorological stations in the study area obtained from the China Meteorological Administration (CMA) and the National Climatic Data Center (NCDC) before 1980, we compared and assessed six methods for distinguishing precipitation phases and the optimal method was determined to complete the precipitation phase data after 1980 in the study area. Subsequently, the changes in precipitation amount, intensity, and frequency were analyzed by classifying them into light, moderate, heavy, and extreme precipitation using the percentile threshold discrimination method. It found that the dynamic threshold temperature method (presented by Ding et al., 2014) was the most effective to distinguish precipitation phases in the TP, and its performance was further improved from 80.9% to 86.1% after using frequency intersection and probability guarantee methods. Precipitation was mainly concentrated in July. Meanwhile, the proportion of snowfall to total precipitation (S/P) was approximately 9%. From 1961 to 2020, the amount of total precipitation, rainfall and snowfall in the TP exhibited increasing trends, while the sleet demonstrated a persistent decline. Spatial and temporal heterogeneity existed in the changes of precipitation of different phases. The average single precipitation amount increased. Moreover, the changes in the amount and frequency of extreme precipitation were more pronounced. Increased frequency and amount of both rainfall and snowfall were distributed in the central and eastern parts of the study area with a more significant proportion. In contrast, the peripheral areas witnessed a decrease. The spatial distribution of frequency and amount of different precipitation intensities was comparable to that of the total precipitation. This study offers novel procedure into changes of precipitation phases at high altitudes, which will inform future research on climate change and water resource management.

Relationship between Synoptic Weather Patterns and Topography on Snowfall in the Idaho Mountainous Regions during the SNOWIE Project

Abstract Snowpack melting is a crucial water resource for local ecosystems, agriculture, and hydropower in the Intermountain West of the United States. Glaciogenic seeding, a method widely used in mountain regions to enhance precipitation, has been subject to numerous field studies aiming to understand and validate this mechanism. However, investigating precipitation distribution and amounts in mountainous areas is complicated due to the intricate interplay of synoptic circulation patterns and local complex topography. These interactions significantly influence microphysical processes, ultimately affecting the amount and distribution of surface precipitation. To address these challenges, this study leverages Weather Research and Forecasting (WRF) Model simulations, providing high-resolution (900 m), hourly data, spanning the Payette region of Idaho from January to March 2017. We applied the self-organizing map approach to categorize the most representative synoptic circulation patterns and conducted a multiscale analysis to explore their associated environmental conditions and microphysical processes, aiming to assess the cloud seeding potential. The analysis identified four primary synoptic patterns: cold zonal flow (CZF), cold southwesterly flow (CSWF), warm zonal flow (WZF), and warm southwesterly flow (WSWF), constituting 21.3%, 23.1%, 30.0%, and 25.5%, respectively. CSWF and WSWF demonstrated efficiency in generating natural precipitation. These patterns were characterized by abundant supercooled liquid water (SLW) and ice particles, facilitating cloud droplet growth through seeder–feeder processes. On the other hand, CZF exhibited the least SLW and limited potential for cloud seeding, while WZF displayed a lower ice water content but substantial SLW in the diffusion/dendritic growth layer, suggesting a favorable scenario for cloud seeding. Significance Statement Understanding snowfall amounts and distribution in the mountains and how it is linked to topography, synoptic flow, and microphysical processes will help in the development of effective strategies for cloud seeding operations, managing runoff, reservoir, and mitigating flood risks, garnering substantial interest from stakeholders and the government agencies.

Economics of Snow Accumulation on Photovoltaic Modules

The growth in photovoltaic (PV) module installations over the past decade has prompted a critical need to examine the economic implications of snow accumulation on solar energy production. The aim of this study is to quantify the economic impact of snow accumulation on PV modules in different regions and environmental conditions and to identify effective mitigation strategies for enhancing power generation efficiency and reliability of PV systems. It was found that snow accumulation on PV modules can lead to annual losses of 1% to 12% depending on the environmental conditions and geographic location. A financial analysis related to maintenance costs associated with snow accumulation on PV modules is also presented. A two-fold methodology of quantitative data analysis and interviews conducted with PV system operators is used for this purpose. In addition, the extent of snow accumulation financial losses in the U.S. is categorized based on the snowfall amount and solar market segment, suggesting an annual loss of at least USD 313M in utility and residential solar sectors. Furthermore, various currently employed active and passive snow mitigation strategies are presented in detail, describing their shortcomings and advantages. Finally, prospects on the need for developing reliable and cost-effective snow mitigation strategies for solar panels are discussed, paving the path for future studies.

The disproportionate impact of enhanced evaporation from melting arctic sea ice on cold-season land precipitation trends

Diminishing Arctic sea ice has led to enhanced evaporation from the Arctic marginal seas (AMS), which is expected to alter precipitation over land. In this work, AMS evaporation is numerically tracked to quantify its contribution to cold-season (October–March) precipitation over land in the Northern Hemisphere during 1980–2021. Results show a significant 32% increase in AMS moisture contribution to land precipitation, corresponding to a 16% increase per million square km loss of sea ice area. Especially over the high-latitude land, despite the fractional contribution of AMS to precipitation being relatively low (8%), the augmented AMS evaporation contributed disproportionately (42%) to the observed upward trend in precipitation. Notably, northern East Siberia exhibited a substantial rise in both the amount and fraction of extreme snowfall sourced from the AMS. Our findings underscore the importance of the progressively ice-free Arctic as an important contributor to the escalating levels of cold-season precipitation and snowfall over northern high-latitude land.

Rapid Laurentide Ice Sheet growth preceding the Last Glacial Maximum due to summer snowfall

There has been extensive research into the nonlinear responses of the Earth system to astronomical forcing during the last glacial cycle. However, the speed and spatial geometry of ice sheet expansion to its largest extent at the Last Glacial Maximum 21 thousand years ago remains uncertain. Here we use an Earth system model with interactive ice sheets to show that distinct initial North American (Laurentide) ice sheets at 38 thousand years ago converge towards a configuration consistent with the Last Glacial Maximum due to feedbacks between atmospheric circulation and ice sheet geometry. Notably, ice advance speed and spatial pattern in our model are controlled by the amount of summer snowfall, which is dependent on moisture transport pathways from the North Atlantic warm pool linked to ice sheet geometry. The consequence of increased summer snowfall on the surface mass balance of the ice sheet is not only the direct increase in accumulation but the indirect reduction in melt through the snow/ice–albedo feedback. These feedbacks provide an effective mechanism for ice growth for a range of initial ice sheet states and may explain the rapid North American ice volume increase during the last ice age and potentially driving growth during previous glacial periods.

Spatiotemporal Variability of Extreme Precipitation Events and Associated Atmospheric Processes Over Dronning Maud Land, East Antarctica

AbstractWe investigate the spatial and temporal variability of extreme precipitation events (EPEs) in the Dronning Maud Land (DML) sector of Antarctica using high‐resolution ECMWF ERA5 reanalysis data. This study examines the spatial occurrence of EPEs across DML, focusing particularly on six locations spanning the coastal and interior parts of the area. The largest snowfall amounts are usually found on eastward‐facing slopes in the coastal zone. EPEs occur predominantly in north‐easterly to easterly flows, leading to enhanced precipitation on the windward side of the orographic features with a steep gradient. Wind during EPEs was found to be more directionally consistent in the coastal area than in the interior. An east‐west couplet of a mid‐tropospheric ridge and low‐pressure center is essential for steering warm moist maritime airmasses into the DML region before EPEs. Approximately 40% of EPEs result from atmospheric rivers (ARs), narrow bands of moist air originating at subtropical latitudes, which provide the greatest daily precipitation amounts. From 1979 to 2018, much of the DML experienced a statistically significant (p < 0.05) increase in the number of EPEs per year, along with increased precipitation from the EPEs. These trends were associated with significant changes in moisture availability and poleward meridional winds in the Atlantic sector of the Southern Ocean. The inter‐annual variability in the number of EPEs is primarily dictated by regional atmospheric variability, while the influence of the Southern Oscillation Index and Southern Annular Mode is limited.

Sensitivities of hydrological processes under climate warming and Landuse/landcover change in the Lhasa basin, Tibetan plateau

Study Region: The Lhasa River Basin (LRB) on the Tibetan Plateau.Study Focus: This study assessed the influence of climate change and landuse/landcover (LULC) change on hydrological processes using the water balancing simulation model (WaSiM). We used multiple data sources in a multi-objective calibration and validation approach, such as streamflow, snow cover, and glacier inventory, to minimize the uncertainties in the simulation results. Heat transfer in the soil was implemented in this study, which is crucial for permafrost regions.New Hydrological Insights for the Region: The results revealed spatiotemporal variation of precipitation change induced by climate warming in the last few decades. Runoff, actual evapotranspiration, and glacier melt are experiencing an increased trend. Meanwhile, decreased trends are signaled for snowmelt and water storage changes. Snowmelt showed an earlier peak that changed from June to May, which, combined with increased rainfall, resulted in an earlier peak of runoff in the basin. Rainfall is the main factor affecting runoff generation in LRB, while snowmelt and storage change are driven by the amount of snowfall, and actual evapotranspiration and glacier melt are most influenced by temperature. The annual discharge in LRB is projected to decrease, ranging from 20% to 39%, in response to the total permafrost degradation. Finally, the apparent variations in LULC exhibited in this study reveal a variety of water cycle and hydrological implications.

Investigation of winter weather crash injury severity using winter storm classification techniques

This study employs a unique methodology for analyzing seasonal traffic crashes by first identifying adverse winter-weather events using traditional traffic-crash data (from Nebraska) and correlating them to detailed meteorological data for subsequent analyses. Specifically, the novel component of this analysis utilized a state-specific winter storm and seasonal severity index metric in conjunction with classifications of storm type. Both crash and meteorological data were combined to determine if the amount of snowfall and/or storm intensity/severity could be a predictor of the number and severity of crashes. Analysis was performed to identify weather conditions causing highway safety concerns. The important takeaway of the study was that most winter weather-related vehicular crashes in Nebraska were associated with relatively minor winter weather conditions. The reported crashes typically occurred with relatively low snowfall amounts due to residual snowfall on the ground, emphasizing the importance of continuing winter maintenance operations activities and public service announcements long after a storm has passed through the region. Modeling crash injury severity revealed that icy pavements, higher duration of snowfall, work zones, and higher number of trucks and buses involved in a crash were linked to higher injury severity; higher visibility was associated with a greater chance of crashes involving visible injuries but a lower likelihood of crashes involving disabling injury/fatality. Snowfall was associated with more visible injury crashes, whereas snow-depth was linked to fewer visible injury and disabling injury/fatality crashes. The study's limitations include the spatial and temporal aggregation of weather data and a lack of detailed winter roadway maintenance/clearance activity data.

Decreasing trends of mean and extreme snowfall in High Mountain Asia

Intense warming profoundly alters precipitation phase patterns and intensity in High Mountain Asia (HMA). While snowfall climatology and precipitation extremes have been studied, there is a lack of understanding of snowfall extremes within HMA. Here, we investigate the spatial and temporal variability of non-extreme and extreme snowfall in hydrological years 1979–2020 using multi-source meteorological data, compare weather systems during extreme and non-extreme snowfall events, and identify key circulation factors that influence fluctuations in mean annual snowfall and extreme snowfall. The snowfall amount (−0.13 d/mm), days (−0.56 d/a), and fraction (−0.0012) were significantly reduced in HMA, with a shorter snowfall season (−0.52 d/a). Some extreme snowfall metrics (maximum 1-day snowfall and maximum 3-day snowfall) were insensitive to climate change, whereas the maximum consecutive snowfall days (−0.007 d/a), snowfall amount (−0.0023 mm/a), heavy snowfall days (S95pD; 0.0087 d/a), and extremely heavy snowfall days (S99pD; −0.1019 d/a) showed significant decreases. Synthetic analyses show that extreme snowfall events were more likely to occur within a narrow temperature range (−5 °C to 3 °C) with higher relative humidity and precipitation compared to non-extreme events. A stepwise regression method was used to determine that the fluctuation in the average annual snowfall was closely related to the Atlantic Multidecadal Oscillation, whereas the variation in extreme snowfall was mainly influenced by the Southern Oscillation Index. Our research provides a reference for assessing the potential impacts of climate change on a regional scale for risk management and disaster adaptation.

The Extraordinary March 2022 East Antarctica “Heat” Wave. Part II: Impacts on the Antarctic Ice Sheet

Abstract Between 15 and 19 March 2022, East Antarctica experienced an exceptional heat wave with widespread 30°–40°C temperature anomalies across the ice sheet. In Part I, we assessed the meteorological drivers that generated an intense atmospheric river (AR) that caused these record-shattering temperature anomalies. Here, we continue our large collaborative study by analyzing the widespread and diverse impacts driven by the AR landfall. These impacts included widespread rain and surface melt that was recorded along coastal areas, but this was outweighed by widespread high snowfall accumulations resulting in a largely positive surface mass balance contribution to the East Antarctic region. An analysis of the surface energy budget indicated that widespread downward longwave radiation anomalies caused by large cloud-liquid water contents along with some scattered solar radiation produced intense surface warming. Isotope measurements of the moisture were highly elevated, likely imprinting a strong signal for past climate reconstructions. The AR event attenuated cosmic ray measurements at Concordia, something previously never observed. Last, an extratropical cyclone west of the AR landfall likely triggered the final collapse of the critically unstable Conger Ice Shelf while further reducing an already record low sea ice extent. Significance Statement Using our diverse collective expertise, we explored the impacts from the March 2022 heat wave and atmospheric river across East Antarctica. One key takeaway is that the Antarctic cryosphere is highly sensitive to meteorological extremes originating from the midlatitudes and subtropics. Despite the large positive temperature anomalies driven from strong downward longwave radiation, this event led to huge amounts of snowfall across the Antarctic interior desert. The isotopes in this snow of warm airmass origin will likely be detectable in future ice cores and potentially distort past climate reconstructions. Even measurements of space activity were affected. Also, the swells generated from this storm helped to trigger the final collapse of an already critically unstable Conger Ice Shelf while further degrading sea ice coverage.

시스템 다이내믹스를 이용한 재난관리자원의 동원 및 응원 시뮬레이션

In this study, a disaster management resource simulation model between local governments was suggested using the system dynamics methodology. At this time, the subject of analysis was selected as the stockpile of deicing agents for each district in Seoul. Based on statistical data, a scenario was set in which the expected amount of heavy snowfall reached level 3, and the response to heavy snowfall reached level 4 for 5 days in the 11 districts of Seoul. The simulation shows that in areas with heavy snow, the deicing agent stock falls below the minimum by the 4.5th day, and in areas without heavy snow, it happens by the 14.5th day. Additionally, it was estimated that the Seoul Disaster Resource Integrated Management Center required about 2,155 tons of deicing agents. This study is meaningful because it can be used to set the RTO(Recovery Time Objective) for the mobilization and support of deicing agents. Furthermore, it can be used to interconnect disaster management resources by suggesting an appropriate stockpile of deicing agents for the Seoul Disaster Resource Integrated Management Center.

Control of the temperature signal in Antarctic proxies by snowfall dynamics

Abstract. Antarctica, the coldest and driest continent, is home to the largest ice sheet, whose mass is predominantly recharged by snowfall. A common feature of polar regions is the warming associated with snowfall, as moist oceanic air and cloud cover increase the surface temperature. Consequently, snow that accumulates on the ice sheet is deposited under unusually warm conditions. Here we use a polar-oriented regional atmospheric model to study the statistical difference between average and snowfall-weighted temperatures. During snowfall, the warm anomaly scales with snowfall amount, with the strongest sensitivity occurring at low-accumulation sites. Heavier snowfall in winter helps to decrease the annual snowfall-weighted temperature, but this effect is overwritten by the event-scale warming associated with precipitating atmospheric systems, which particularly contrast with the extremely cold conditions that occur in winter. Consequently, the seasonal range of snowfall-weighted temperature is reduced by 20 %. On the other hand, the annual snowfall-weighted temperature shows 80 % more interannual variability than the annual temperature due to the irregularity of snowfall occurrence and its associated temperature anomaly. Disturbances of the apparent annual temperature cycle and interannual variability have important consequences for the interpretation of water isotopes in precipitation, which are deposited with snowfall and commonly used for paleotemperature reconstructions from ice cores.

Synoptic Climatology of Central U.S. Snowfall

Abstract Prior research evaluating snowfall conditions and temporal trends in the United States often acknowledges the role of various synoptic-scale weather systems in governing snowfall variability. While synoptic classifications have been performed in other regions of North America in applications to snowfall, there remains a need for enhanced understanding of the atmospheric mechanisms of snowfall in the central United States. Here we conduct a novel synoptic climatological investigation of the weather systems responsible for snowfall in the central United States from 1948 to 2021 focused on their identification and the quantification of associated snowfall totals and events. Ten unique synoptic weather types (SWTs) were identified, each resulting in distinct regions of enhanced snowfall across the study domain aligning with regions of sufficiently cold air temperatures and forcing mechanisms. While a substantial proportion of seasonal snowfall is attributed to SWTs associated with surface troughs and/or midlatitude cyclones, in portions of the southeastern and western study domain, as much as 70% of seasonal snowfall occurs during systems with high pressure centers as the domain’s synoptic-scale forcing. Easterly flow, potentially resulting in topographic uplift from high pressure to east of the domain, was associated with between 15% and 25% of seasonal snowfall in Nebraska and South Dakota. On average, 64.8% of the SWT occurrences resulted in snowfall within the study region, ranging between 40.1% and 93.5% by SWT. Synoptic climatological investigations provide valuable insights into the unique weather systems that generate hydroclimatic variability. Significance Statement By evaluating the weather patterns that are responsible for snowfall in the central United States, key insights can be gained into how and why snowfall varies and potentially changes over space and time. Using an approach that categorizes weather patterns based on their similarities, here 10 unique snowfall-producing weather patterns are identified and analyzed from 1948 to 2021. Each pattern resulted in different snowfall amounts across the central United States, varying substantially spatially and within the calendar year. Approximately 65% of the time that these weather patterns occur, snowfall is observed in the region. The majority of snowfall-producing weather patterns are associated with low pressure systems, but in some regions up to 70% of snowfall is associated with instances of high pressure in which winds can cause upward motions associated with topography.

Statistical Modeling of Monthly and Seasonal Michigan Snowfall Based on Machine Learning: A Multiscale Approach

Abstract Snow is an important component of Earth’s climate system, and snowfall intensity and variation often significantly impact society, the environment, and ecosystems. Understanding monthly and seasonal snowfall intensity and variations is challenging because of multiple controlling mechanisms at different spatial and temporal scales. Using 65 years of in situ snowfall observation, we evaluated seven machine learning algorithms for modeling monthly and seasonal snowfall in the Lower Peninsula of Michigan (LPM) based on selected environmental and climatic variables. Our results show that the Bayesian additive regression tree (BART) has the best fitting (R2 = 0.88) and out-of-sample estimation skills (R2 = 0.58) for the monthly mean snowfall followed by the random forest model. The BART also demonstrates strong estimation skills for large monthly snowfall amounts. Both BART and the random forest models suggest that topography, local/regional environmental factors, and teleconnection indices can significantly improve the estimation of monthly and seasonal snowfall amounts in the LPM. These statistical models based on machine learning algorithms can incorporate variables at multiple scales and address nonlinear responses of snowfall variations to environmental/climatic changes. It demonstrated that the multiscale machine learning techniques provide a reliable and computationally efficient approach to modeling snowfall intensity and variability.

Impact of Severe Weather on Operations of a Radiation Oncology Department

In 2022, two major storms in Western New York brought blizzard conditions and historic snowfall amounts to the region. Beyond the immediate impact to life and property, these weather events resulted in disruptions to cancer care as a result of hazardous travel conditions, and facility closures as a result of regional travel bans. We sought to understand the impact of these disruptive incidents on departmental operations to develop better contingency plans for future occurrences. The radiation treatment machine schedules for a large academic center in Western New York, as well as a neighboring affiliated satellite clinic were queried for daily treatment adherence rates from November 1, 2022 through February 4, 2023. We classified "severe weather days" based on travel advisories issued by the National Weather Service or from local county officials. Out of a total of 68 working days during the study period, 15 days were classified as having severe weather. We compared the percentage of scheduled patients treated (adherence rates) on severe weather days compared with days without severe weather. Statistical analysis was performed using a two-tailed Z-test for proportions. Under routine operating conditions, out of a total of 7045 scheduled treatment visits, we found a mean daily treatment adherence rate of 93.57%. On severe weather days, out of a total of 2093 scheduled treatments, we found a mean daily treatment adherence rate of 77.83%. Out of these 15 days, the satellite clinic closed for a total of 3 days, while the academic center closed for 1 day. Excluding the dates of facility closure, overall treatment adherence rate was 84.76% on severe weather days (Risk Ratio: 0.91). We found that the difference in treatment adherence rates between regular and severe weather days was statistically significant (p<0.001), even when excluding dates of facility closure. This study demonstrates a significant reduction in treatment adherence rates around severe winter storms, even when excluding dates of facility closure. We suggest that preparation for such incidents be a part of facility contingency planning. Weekend openings can be considered for closures ≥ 2 days, or sustained adherence rates of 85% or less for 3 or more days.

Duration and ice thickness of a Late Holocene outlet glacier advance near Narsarsuaq, southern Greenland

Abstract. Greenland Ice Sheet (GrIS) outlet glaciers are currently losing mass, leading to sea level rise. Reconstructions of past outlet glacier behavior through the Holocene help us better understand how they respond to climate change. Kiattuut Sermiat, a southern Greenland outlet glacier near Narsarsuaq, is known to have experienced an unusually large Late Holocene advance that culminated at ∼1600 cal yr BP and exceeded the glacier's Little Ice Age extent. We report sedimentary records from two lakes at slightly different elevations in an upland valley adjacent to Kiattuut Sermiat. These reveal when the outlet glacier's surface elevation was higher than during the Little Ice Age and constrain the associated outlet glacier surface elevation. We use bulk sediment geochemistry, magnetic susceptibility, color, texture, and the presence of aquatic plant macrofossils to distinguish between till, glaciolacustrine sediments, and organic lake sediments. Our 14C results above basal till recording regional deglaciation skew slightly old due to a reservoir effect but are generally consistent with regional deglaciation occurring ∼ 11 000 cal yr BP. Neoglacial advance of Kiattuut Sermiat is recorded by deposition of glaciolacustrine sediments in the lower-elevation lake, which we infer was subsumed by an ice-dammed lake that formed along the glacier's margin just after ∼ 3900 cal yr BP. This timing is consistent with several other glacial records in Greenland showing neoglacial cooling driving advance between ∼ 4500–3000 cal yr BP. Given that glaciolacustrine sediments were deposited only in the lower-elevation lake, combined with glacial geomorphological evidence in the valley containing these lakes, we estimate the former ice margin's elevation to have been ∼ 670 m a.s.l., compared with ∼ 420 m a.s.l. today. The ice-dammed lake persisted until the glacier surface fell below this elevation at ∼ 1600 cal yr BP. The retreat timing contrasts with overall evidence for cooling and glacier advance in the region at that time, so we infer that Kiattuut Sermiat's retreat may have resulted from reduced snowfall amounts and/or local glaciological complexity. High sensitivity to precipitation changes could also explain the relatively limited Little Ice Age advance of Kiattuut Sermiat compared with the earlier neoglacial advance.

Drivers of Snowfall Accumulation in the Central Idaho Mountains Using Long-Term High-Resolution WRF Simulations

Abstract The western United States region, an economic and agricultural powerhouse, is highly dependent on winter snowpack from the mountain west. Coupled with increasing water and renewable electricity demands, the predictability and viability of snowpack resources in a changing climate are becoming increasingly important. In Idaho, specifically, up to 75% of the state’s electricity production comes from hydropower, which is dependent on the timing and volume of spring snowmelt. While we know that 1 April snowpack is declining from SNOTEL observations and is expected to continue to decline as indicated by GCM predictions, our ability to understand the variability of snowfall accumulation and distribution at the regional level is less robust. In this paper, we analyze snowfall events using 0.9-km-resolution WRF simulations to understand the variability of snowfall accumulation and distribution in the mountains of Idaho between 1 October 2016 and 31 April 2017. Various characteristics of snowfall events throughout the season are evaluated, including the spatial coverage, event durations, and snowfall rates, along with the relationship between cloud microphysical variables—particularly liquid and ice water content—on snowfall amounts. Our findings suggest that efficient snowfall conditions—for example, higher levels of elevated supercooled liquid water—can exist throughout the winter season but are more impactful when surface temperatures are near or below freezing. Inefficient snowfall events are common, exceeding 50% of the total snowfall events for the year, with some of those occurring in peak winter. For such events, glaciogenic cloud seeding could make a significant impact on snowpack development and viability in the region. Significance Statement The purpose and significance of this study is to better understand the variability of snowfall event accumulation and distribution in the Payette Mountains region of Idaho as it relates to the local topography, the drivers of snowfall events, the cloud microphysical properties, and what constitutes an efficient or inefficient snowfall event (i.e., its ability to convert atmospheric liquid water into snowfall). As part of this process, we identify how many snowfall events in a season are inefficient to determine the number of snowfall events in a season that are candidates for enhancement by glaciogenic cloud seeding.

Simulations of a Heavy Snowfall Event in Xinjiang via the WRF Model Coupled with Different Land Surface Parameterization Schemes

Frequent heavy snowfall in Xinjiang plays an important role in the land water cycle. In this study, 18 groups of simulation experiments are conducted on the heavy snowfall event in Xinjiang during 9–13 December of 2015 using the Weather Research and Forecasting (WRF) model. In these experiments, the combination of six land surface parameterization schemes (the Noah scheme, Noah-MP scheme, RUC scheme, CLM4 scheme, PX scheme, and TD scheme) with three microphysical parameterization schemes (the WSM6 scheme, Thompson scheme, and Lin scheme) are adopted, where the observed snowfall data are used for performance evaluation. Results show that the simulated snowfall intensity and snowfall range in different areas are very sensitive to the selection of the land surface scheme. The snowfall in southern Xinjiang is overestimated by almost all six schemes, where the Noah-MP scheme performs more reasonably than the others. The Noah scheme shows its advantage in northwestern Xinjiang. The three different microphysical schemes vary significantly in producing snowfall amount. The WSM6 scheme produced the largest snowfall amount, and the Lin scheme resulted in the smallest snowfall amount. In addition, the accumulated snowfall amounts above 10 mm are generally underestimated by all six land surface schemes, while the accumulated snowfall amounts below 10 mm are overestimated by most of the schemes. The Noah-MP scheme performs the best in the simulation of the snowfall amount in the whole region. However, the Noah scheme shows an advantage in areas with a large snowfall amount.