Annual Snowfall Research Articles

Classification of risk levels for snow damage estimation considering socioeconomic factors in South Korea

In South Korea, the snowy season spans from October to April, and the annual average snowfall varies significantly depending on specific regions, latitudes, and elevations, ranging from 0 to 260 cm. The average annual snowfall in South Korea is 25.1 cm. Despite of the relatively shallow snowfall depth, over the past decade, South Korea has experienced approximately 120 million dollars in damages attributed to snow-related incidents. In this study, the DPSIR (Driver-Pressure-State-Impact-Response) framework was employed to consider the meteorological and socioeconomic factors to calculate the snow damage vulnerability. A total of 17 indicators were taken into account to comprehend meteorological conditions, socioeconomic factors, and historical damage records from 1994 to 2020. However, due to the limited availability of meteorological observatories and changes in greenhouse design standards, accurately estimating the snow damage amount poses challenges. Therefore, based on the vulnerability, the risk levels were classified into four categories and estimated snow damage generated by the categorized models was compared with those of the model constructed using the entire dataset. The categorized models offer improved estimation results, as the meteorological and socioeconomic characteristics within each category differ and should be addressed separately in modeling. Among the categorized models, the Green zone exhibited the best results, primarily because it did not include outlier snow damage incidents. The developed model in this study could be utilized to mitigate the impact of heavy snowfall and prioritize snow removal regions.

The hotter the better: high mean annual temperature, not seed predation, hastens the decline of invasive Bromus tectorum

AbstractBromus tectorum invasion degrades biodiversity and ecosystem functioning in the Great Basin Desert. To explain, predict, and manage B. tectorum invasion, we must understand the biotic and abiotic factors that influence its establishment and persistence. We explored interactions between two key constraints for invaders in general and B. tectorum in particular: post-dispersal seed predation and climate, including mean annual temperature, rainfall, snowfall, and aridity. At six study sites in undisturbed, climax Artemisia tridentata communities across northern Nevada and Utah, we performed one-time additions of 100 B. tectorum seeds to microsites outfitted with either a dummy (“open”) cage that exposed seeds to rodent foraging, or a functional (“closed”) cage that protected them from rodent foraging. Living Bromus tectorum plants within experimental cages were censused one and 5 years after seed additions. At both 1- and 5-years censuses, and regardless of climate, B. tectorum densities were similar in open and closed microsites, suggesting that rodent foraging did not affect B. tectorum establishment or persistence. Compared to 1-year censuses, B. tectorum counts declined by 47% across all sites after 5 years, but declines were sharpest at sites with the highest mean annual temperatures. Taken together, our findings suggest that undisturbed, climax A. tridentata communities can resist B. tectorum expansion, and this resistance increases with mean annual temperature, not rodent foraging. We suggest that controlling nascent populations of B. tectorum may be particularly urgent in intact climax A. tridentata communities in relatively cool climates, where community-level resistance against B. tectorum persistence is lowest.

Deep learning algorithms for predicting routes of snowplows in European cities

Most cities are faced with annual snowfall but are finding it difficult to deal with their snow plowing activities. Even though winter road maintenance has been studied for many decades, most of the papers do not present models that can be scaled up to allow for incorporation of side constraints that are often met in practical applications. Subject of the paper: Comparison of deep learning algorithms for forecasting snowplow routes in European cities in terms of spatial analysis, routing and traffic flow. The study focuses on extending neural networks including Recurrent Neural Network (RNN), Convolutional Neural Network (CNN) and Graph Neural Network (GNN) to overcome the problematics of snowplow in urban setting. Temporal and spatial data are incorporated using sophisticated models such as Spatio-Temporal Graph Convolutional Networks (STGCNs) using examples that capture the capacity to control the changes in the snowplow routes in reaction to the real-time traffic flow and weather information. The report also looks at how transformer models among other emerging technologies could be harnessed to improve predictive accuracy as well as efficiency. Comparisons made to these other methods evaluating deep learning methods’ benefits and drawbacks and their application to urban infrastructure and traffic flow. The results, therefore, point out that integrating these complicated formulas can go a long way in enhancing the efficiency and safety of snowplowing in the European cities to create better lit and safer transport networks

Extreme snowfall variations in the Southeastern Tibetan Plateau under warming climate

Snowfall is a critical component of the Earth system and an important indicator and amplifier of climate change. Climate warming is reducing the seasonal snowpack globally, which could have catastrophic consequences for the regions in high dependence on snow for water recharge. However, the climate influences on extreme snowfall events, which significantly impact humans, are still poorly understood. This study uses the Tibetan Plateau snowpack observation dataset combined with NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) climate data to investigate the spatial and temporal characteristics of snowfall, and to explore its response mechanism to climate change and future change trends across the Lower Yarlung Zangbo River (LYZR) in Southeastern Tibetan Plateau (SETP). Annual snowfall decreased at a rate of 1.05 mm per decade (P < 0.05) during the historical period (1960–2014). Relative to 1960–2014, annual snowfall would decline of ∼38 % and ∼ 73 % by the end of the 21st century under the SSP245 and SSP585 scenarios, respectively. At the same time, a general decrease in extreme snowfall averaged the LYZR is expected for historical and future periods, but it showed notable spatial variations. The regional increase in extreme snowfall is mainly distributed along the valley of Brahmaputra-Yarlung Zangbo. In addition, precipitation accounted for 66.5 % of the snowfall variations during the historical period. Meanwhile, according to future warming, temperature would dominate snowfall variations, contributing 56.66 % to 72.92 % during 2015–2100. The projected response of mean and extreme snowfall to climate change indicates that it is a double-edged sword. To scientifically address the risks and challenges posed by snowfall changes in alpine regions, it is imperative to limit future climate warming.

An East Antarctic, sub-annual resolution water isotope record from the Mount Brown South Ice core

We report high resolution measurements of the stable water isotope ratios (δ18O, δD) from the Mount Brown South ice core (MBS, 69.11° S 86.31° E). The record covers the period 873 - 2009 CE with sub-annual temporal resolution. Preliminary analyses of surface cores have shown the Mount Brown South site has relatively high annual snowfall accumulation (0.3 metres ice equivalent) with a seasonal bias toward lower snowfall during austral summer. Precipitation at the site is frequently related to intense, short term synoptic scale events from the mid-latitudes of the southern Indian Ocean. Higher snowfall regimes are associated with easterly winds, while lower snowfall regimes are associated with south-easterly winds. Isotope ratios are measured with Infra-Red Cavity Ring Down Spectroscopy, calibrated on the VSMOW/SLAP scale and reported on the MBS2023 time scale interpolated accordingly. We provide estimates for measurement precision and internal accuracy for δ18O and δD.

Evaluation of weather and environmental factors and their association with cutaneous melanoma incidence: A national ecological study

BackgroundCutaneous melanoma (CM) is a significant contributor to skin cancer-related mortality globally and in Canada. Despite the well-established link between ultraviolet (UV) radiation exposure and skin cancer risk, there remains a gap in population-level interventions and persistent misconceptions about sun exposure and impact of environment on individual behavior. ObjectiveThe current study provides an ecological analysis using latest available data (2011-2017) to define geographic/environmental contributors to the CM landscape in Canada. MethodsUtilizing Canadian Cancer Registry (CCR) and Canadian Urban Environmental Health Research Consortium (CANUE) data, we analyzed 39,605 CM cases occurring in Canada from 2011-2017. Environmental data, including UV radiation, greenspace (NDVI), temperature, heat events, and precipitation was used to evaluate the effect of environment on CM incidence rates across Forward Sortation Area (FSA) postal codes. ResultsFSAs with increased CM incidence were associated with higher annual average temperature, snowfall, heat events, NDVI, and vitamin D-weighted UV exposure. Conversely, factors associated with decreased incidence included an increased annual highest temperature, rain precipitation, and a longer duration of heat events. LimitationsThis study is subject to ecological bias and findings should be interpreted with caution ConclusionThis study further substantiates associations between specific environmental factors and CM incidence.

Elevation dependent precipitation changes in historical and future times in the Himalayan Upper Ganga Basin, India

This study examines long-term (1991–2092) precipitation trends in the Upper Ganga Basin (UGB) across multiple elevations, using historical data (1991–2022) and future Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model datasets (2022–2092). By identifying the top five best-fit models from 29 climate model datasets, this study assesses historical patterns and project future changes in the Near-Future-Term (NFT) (2023–2056) and Far-Future-Term (FFT) (2061–2092) under moderate and extreme scenarios. Based on the evaluation of 29 CMIP6 climate models precipitation products, five models namely: BCC-CSM2-MR, CMCC-CM2-SR5, INM-CM5–0, KIOST-ESM, and NESM3 were selected as the best models describing precipitation over UGB and future precipitation of these models were bias corrected using linear scaling bias correction approach. Also it was observed that in the UGB region usually the climate models are much more reliable in capturing the precipitation pattern in lower and mid elevation regions as compared to higher-elevation regions. Further analysis of best five climate models' future precipitation indicates a substantial increase in wet spell occurrences in the south-western part of UGB, while the north-western and central regions are expected to see a rise in dry spell counts, although dry spell increase is insignificant. The mid-elevation range from 2200 to 4050 m which historically receives the most precipitation and is expected to continue experiencing the highest volume of precipitation in the future. The lower elevation range (287–1500 m) has anticipated the most substantial increase in future precipitation. Analysis suggests a future rise in low precipitation events (2.5 mm/day to 10 mm/day) at high elevations (4050 m to 7399 m) and an increase in events exceeding 10 mm/day in mid to low elevations (287 m to 4050 m). Although an increase in low precipitation events is expected across the entire UGB, it is particularly noteworthy that the region between 287 m to 4050 m is likely to experience a greater prevalence of these events. Historically, high elevations witnessed most of the years with over 20 days of annual snowfall. However, future scenarios indicate a prominent shift, predominantly anticipating <20 days per year of snowfall, signifying a significant change in snowfall patterns within the UGB. An analysis of snowfall patterns within the UGB indicates that historically, in the elevation range of 1500 m to 2200 m, some years have experienced 3–7 days of snowfall per year, however, under the SSP245 (NFT) scenario, this is projected to decrease to 2–5 days per year. Conversely, in scenarios SSP245 (FFT), SSP585 (NFT), and SSP585 (FFT), it is anticipated that there will be no snowfall in this elevation range in the future.

Evaluating the impact of climatic changes on streamflow in headwater mountain catchments with varying human pressure. An example from the Tatra Mountains (Western Carpathians)

Study regionTatra Mountains, Western Carpathians Study focusOur study aims to assess the impact of climatic changes on streamflow in headwater mountain catchments with varying human pressure. The research bases on hydrological and climatological data from a period 1971–2020 and includes trend analysis, wavelet analysis and paired catchment observations, comparing semi-natural and human-altered catchments. New hydrological insights for the regionAir temperature has increased (+0.4°C decade−1) in the region. Precipitation totals remained unchanged, however annual snowfall totals decreased (−69 mm decade−1, ∼6.3%) at the expense of rainfalls, especially in winter. Air temperature and precipitation exhibited altitude-dependent changes. Snow depths remained stable; however, snow cover duration has shortened (−2 to −4 days decade−1). The observed climatic changes have led to an increase in annual low flows (+7–11.5% decade−1), as well as winter low, average, and high flows, affecting the timing of snowmelt runoff. Streamflow changes were more pronounced in semi-natural catchments. Climatic-driven trends in streamflow were more evident in winter, whereas trends associated with human activities and land use were detected throughout the year. Discharge was more coherent with precipitation in human-altered catchments, and weaker in semi-natural catchments. The intensification of annual cycle in precipitation and semiannual cycle in snow cover at high altitudes, along with the intensification of semiannual cycle in streamflow, provide evidence of ongoing climatic changes.

Analysis of rapid snow and ice cover loss in mountain glaciers of arid and semi-arid regions using remote sensing data

This article investigates the effects of climate change on snow cover and glacier in mountainous glacial areas in northern region of Iran. Due to the lack of glacier measuring stations, accurately assessing changes in snow cover over the past two decades is a challenge for water experts. This research instead utilized remote sensing data from the MODIS sensor on the Terra satellite in Google Earth Engine (GEE) system covering the period from 2000 to 2020. The annual temperature changes near the Earth's surface, using ERA5 data, showed a significant increase of 1.5 °C in average temperature over the past thirty years. The Mann-Kendall test confirms the statistical significance of the temperature rise, with a significance value of 3.5 and a p-value of 0.002. For MODIS data validation, two methods of overall accuracy and the Kappa coefficient were used with 93% and 85% of accuracy. While the annual snowfall and volume have remained relatively stable over the past 20 years, the findings indicate a decreasing trend of snow cover during warmer months (summer) due to rising air temperatures. The findings show a strong negative correlation coefficient of −0.8 between snow cover and annual average temperature. The results indicate a 30% decrease in summer snow cover compared to the spatial extent of two decades ago. The rapid snowmelt poses a significant threat to permanent glaciers in the region as the protective snow cover diminishes, putting these glaciers at risk of completely melting in the future. The loss of these glaciers will have severe consequences for the environment, including impacts on local water supplies, river flows, and the ecosystem.

Assessment of Water Resources under Climate Change in Western Hindukush Region: A Case Study of the Upper Kabul River Basin

This study aims to estimate the surface runoff and examine the impact of climate change on water resources in the Upper Kabul River Basin (UKRB). A hydrological model was developed using the Soil and Water Assessment Tool (SWAT) from 2009 to 2019. The monthly calibration was conducted on streamflow in six stations for the period from 2010 to 2016, and the results were validated from 2017 to 2018 based on available observed data. The hydrological sensitivity parameters were further prioritized using SWAT-CUP. The uncertainty of the model was analyzed by the 95% Prediction Uncertainty (95PPU). Future projections were analyzed for the 2040s (2030–2049) and 2090s (2080–2099) compared to the baseline period (1986–2005) under two representation concentration pathways (RCP4.5, RCP8.5). Four Regional Climate Models (RCMs) were bias-corrected using the linear scaling bias correction method. The modeling results exhibited a very reasonable fit between the estimated and observed runoff in different stations, with NS values ranging from 0.54 to 0.91 in the calibration period. The future mean annual surface runoff exhibited an increase in the 2040s and 2090s compared to the baseline under both RCPs of 4.5 and 8.5 due to an increase in annual precipitation. The annual precipitation is projected to increase by 5% in the 2040s, 1% in the 2090s under RCP4.5, and by 9% in the 2040s and 2% in the 2090s under RCP8.5. The future temperature is also projected to increase and consequently lead to earlier snowmelt, resulting in a shift in the seasonal runoff peak to earlier months in the UKRB. However, the shifts in the timing of runoff could lead to significant impacts on water availability and exacerbate the water stress in this region, decreasing in summer runoff and increasing in the winter and spring runoffs. The future annual evapotranspiration is projected to increase under both scenarios; however, decreases in annual snowfall, snowmelt, sublimation, and groundwater recharge are predicted in the UKRB.

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.

Anthropogenic influence has significantly affected snowfall changes in Eurasia

It has been widely reported that anthropogenic influences are detectable with high confidence in global warming. However, whether human activities have an impact on snowfall changes is still unclear. Here we based on phase 6 of the Coupled Model Intercomparison Project (CMIP6) multi-forcing dataset and the regularized optimal fingerprint method, the detection and attribution of various grades of snowfall (including annual snowfall (>0.1 mm/day), light snowfall (<2.5 mm/day), intense snowfall (>5 mm/day), and their corresponding days) changes in Eurasia (20°-90°N, 10°W-180°E) were carried out. Results show that anthropogenic activity forcing (ANT) and greenhouse gas forcing (GHG) well reproduced the spatial-temporal characteristics of snowfall indices. The ANT influence is robustly detected in the decreased trend of snowfall days (snowday), light snowfall, and light snowfall days (light_day) at the 90% confidence interval, clearly separated from the natural forcing. Moreover, the GHG signals are detectable for decreases in the three snowfall indices, which only could be distinguished from the natural and aerosol forcings for snowday and light snowfall. Thus, anthropogenic activities may considerably account for the decreases in snowday, light snowfall, and light_day across Eurasia, wherein the changes in the first two indices dominated by GHG emissions. However, human influence detections fail for intense snowfall, and it is hard to detect on regional scales, except for North Asia. Finally, by the end of this century (2081–2100), the observation-constrained projections based on the detection and attribution analysis under two SSPs (new Shared Socioeconomic Pathway) scenarios exhibit that the scaled snowday, light snowfall, and light_day are expected to decrease by approximately 13.9 days (28.3 days), 24.8% (48.7%), and 4.3 days (8.8 days) under the SSP2–4.5 (SSP5–8.5) scenario with reference to the current climate (1995–2014). Our study highlights the need to improve climate model performance in simulating extreme snowfall to clear whether and to what extent human influence impacts it.

Quantifying the snowfall variations in the Third Pole region from 1980 to 2020

Snowfall is critical in the mountainous cryosphere cycle and is vulnerable to climate change. Due to data-scarcity and strong surface heterogeneity, its information is not sufficiently revealed across the Third Pole (TP) under warming. Accordingly, this study investigated the spatiotemporal variability of snowfall and its entropy information across the TP from 1980 to 2020 based on a new high-accuracy snowfall dataset (TPHiS), which was estimated from a long-term high-resolution precipitation dataset for the TP (TPHiPr) and High Asia Refined analysis version 2 reanalysis data (HARv2) using a threshold temperature method. The results indicated that the correlation coefficient, mean bias, and root-mean-square error of the daily TPHiS were 0.77, 0.03 mm/daily, and 0.18 mm/daily, respectively, compared against in-situ observation, illustrating that this dataset performs better than other snowfall datasets. The annual mean snowfall measured 259.38 mm across the TP and experienced a slight negative trend (−0.77 mm/decade, p > 0.05). A significantly positive trend in annual snowfall was observed in the intersection of the West Kunlun, Karakoram, and Western Inner Tibet, with elevations over 5500 m. In contrast, a significantly negative trend prevailed in the Pamir, Hengduan Shan, and West and Central Himalaya intersections, especially at elevations varied from 3000 to 4500 m. Compared with other seasons, a significantly negative trend in snowfall was observed in summer (−1.94 mm/decade, p < 0.05). Change in snowfall frequency entropy was more significant than that in snowfall amount entropy. A significantly positive trend in light snowfall frequency dominated the augmentation in snowfall frequency, causing a negative trend in snowfall frequency entropy, while a substantial reduction in snowfall frequency associated with the decline in light snowfall frequency induced a positive trend in snowfall frequency entropy. The outcomes provide a finer snowfall dataset for regional cryospheric research and might inform sustainable water resource management in alpines.

Climatology of the Mount Brown South ice core site in East Antarctica: implications for the interpretation of a water isotope record

Abstract. Water stable isotope records from ice cores (δ18O and δD) are a critical tool for constraining long-term temperature variability at high latitudes. However, precipitation in Antarctica consists of semi-continuous small events and intermittent extreme events. In regions of high accumulation, this can bias ice core records towards recording the synoptic climate conditions present during extreme precipitation events. In this study we utilise a combination of ice core data, reanalysis products, and models to understand how precipitation intermittency impacts the temperature records preserved in an ice core from Mount Brown South in East Antarctica. Extreme precipitation events represent only the largest 10 % of all precipitation events, but they account for 52 % of the total annual snowfall at this site, leading to an overrepresentation of these events in the ice core record. Extreme precipitation events are associated with high-pressure systems in the mid-latitudes that cause increased transport of warm and moist air from the southern Indian Ocean to the ice core site. Warm temperatures associated with these events result in a +4.8 ∘C warm bias in the mean annual temperature when weighted by daily precipitation, and water isotopes in the Mount Brown South ice core are shown to be significantly correlated with local temperature when this precipitation-induced temperature bias is included. The Mount Brown South water isotope record spans more than 1000 years and will provide a valuable regional reconstruction of long-term temperature and hydroclimate variability in the data-sparse southern Indian Ocean region.

Observed and projected trends in spring flood discharges for the Upper Harricana River, eastern boreal Canada

Study regionIn northwestern Québec, the Upper Harricana River is representative of the Abitibi Plains’ hydrological dynamics over the last 250 years. Study focusPlanning for future spring flood risks involves uncertainties. This research presents a multicentury evaluation of changes in spring mean discharge and flood drivers using streamflow reconstruction (1771–2016), observations (1915–2020) and projections (2021–2100). New hydrological insights for the regionUsing a downscaled CMIP5 ensemble of 10 global climate models (GCMs), generalized additive mixed modeling of mean spring discharge projections matched those of an independent mechanistic model and eight GCMs projected variability in spring discharge by 2100 to be similar to the historical variability reconstructed for the last 250 years across the Abitibi Plains. Results indicate that the projected decline in snow cover (–20 to –30% annual snowfall) and rise in winter and spring temperature may be offset by a greater contribution of rainfall to spring high discharge (+100 to +125 mm). However, two GCMs projected an increase in the magnitude and frequency of high mean spring discharge for the Abitibi Plains. By investigating future mean spring discharge for the Upper Harricana River in reference to past reconstructed variability, this study provides insights to inform the future management of regional water resources. The importance of estimating future regional flood risks from the behavior of multi-model ensembles is highlighted.

Changing Nature of High‐Impact Snowfall Events in Eastern North America

AbstractSnowstorms cause substantial disruption in the eastern United States and Canada each winter. While reductions in annual snowfall are projected over most of this region due to anthropogenic global warming, daily snowfall extremes that have the greatest impact may not decrease in the same manner. We examine changes to two extreme snowfall metrics: the 95th percentile of daily snowfall (SF95, cm) and the number of events during which 10% of the mean annual snowfall is exceeded during a single day (TC10, events yr−1). We explore changes to these metrics in two ensembles of the fifth‐generation Canadian Regional Climate Model, including four 0.22° (≈25 km) simulations driven by different coupled general circulation models as well as the higher‐resolution (0.11°, ≈12 km) ClimEx ensemble, driven by 50 members of a large initial‐condition ensemble of one global model. We find that while mean annual snowfall is projected to decrease over our domain, SF95 is projected to remain relatively constant, suggesting that the most extreme daily snowfalls currently observed are likely to occur even in a warmer future climate. The region of the largest TC10 values exhibits a northward shift, with a larger percentage of annual snowfall occurring during a few large events along the U.S.‐Canada border. These projected changes to the nature of snowfall events may have important socioeconomic consequences in this densely populated region of North America.

Assessing hydrological sensitivity to future climate change over the Canadian southern boreal forest

This study develops a physically based hydrological process model using the Cold Region Hydrological Modelling (CRHM) platform to simulate the water budget storages and fluxes over the Canadian southern boreal forest (SBF). Evaluation of the CRHM-based model in a well-gauged SBF basin, White Gull Creek (WGC), Saskatchewan, Canada, indicated quite good performance in reproducing historical observations of streamflow, snow water equivalent (SWE), evapotranspiration (ET) and soil moisture without parameter calibration from streamflow. The entire SBF was then evenly divided into 2243 virtual basins, each of which was structured and parameterized with the same land cover, soil and hydrological parameters as the WGC basin, but with local latitude and topography in order to examine the sensitivity of governing hydrological processes to future climate variability and perturbation. Hydrological sensitivity in the virtual basins was assessed by examining the differences between hydrological simulations driven by 4-km gridded convection-permitting Weather Research and Forecasting (WRF) outputs in the current period (ctrl, 2001–2013) and a Pseudo Global Warming period (pgw, 2087–2099). The WRF simulation in the pgw period was forced by a perturbation of the same boundary conditions from ERA reanalysis data as for the ctrl period, and the perturbation was based on the ensemble-mean of projected changes from the CMIP5 RCP 8.5 emission scenario. Results showed that temperature would increase by 4.5℃ to 7℃ over the SBF but increases in annual precipitation of 15–24% would more than compensate for the effects of warming on runoff generation and result in greater streamflow volumes. Annual streamflow volumes would increase by 64 mm (35%) and 95 mm (16%) in the west and east, and by 48 mm (17%) in the central SBF. Annual snowfall and maximum SWE would decrease by 89–109 mm (∼29%) in the east, 3–8 mm (∼6%) in the west, and 31–50 mm (∼20%) in the central SBF. Annual mean soil moisture storage would decrease by 54–56 mm (27%) in the west and central, and by only 37 mm (14%) in the east SBF. Decreases in soil moisture would be caused by reduced soil freezing and enhanced thawing under future warming which would increase soil water loss from ET, subsurface runoff and percolation into groundwater storage. The larger sensitivity of streamflow and snow processes in the east SBF is partly due to the wetter climate and the larger increase in annual precipitation, the later also buffered the sensitivity of soil moisture to warming. These results show that the SBF would switch to a higher water yield region, dominated by rainfall-runoff fed streamflow over a longer snow-free season, and provide first-order guidance for sustainable water management of the SBF in the future.

Historical regional climate changes in Japan in winter as assessed by a 5-km regional climate model with a land surface process

We investigate historical regional climate changes in Japan from 1959 to 2020, analyzing a high-resolution dynamical downscaling forced by the Japanese 55-year Reanalysis (JRA-55). One-year continuous simulations are conducted by the non-hydrostatic regional climate model with a land surface model that includes the snow accumulation process, which enables us to evaluate the seasonal variation of snow cover in all of Japan. Our simulation reproduces interannual variations of the annual mean surface air temperature and annual total precipitation, and it shows rapid warming since around 1980. The annual maximum snow depth and annual maximum daily snowfall show significant decreasing trends at lower elevations on the Japan Sea sides of eastern and western Japan. Areas at higher elevations in eastern Japan show no trend in the maximum snow depth and a significant increasing trend in the maximum daily snowfall. In northern Japan, altitudinal dependencies in snow depth and snowfall changes are smaller on the Japan Sea side than on the Pacific Ocean side: the Japan Sea side shows insignificant changes in snow depth at all elevations, while the Pacific Ocean side shows decreasing and significant increasing trends at lower and higher elevations, respectively. The total number of snow-covered days is decreasing at most elevations in all regions, while the rate of decrease is smaller at higher elevations and latitudes. Composite analyses of annual maximum daily snowfall events at one prefectural city facing the Japan Sea in central Japan indicate that heavy daily snowfall occurs when the Japan Polar air mass Convergence Zone appears over the Japan Sea and the snowfall amounts show increasing and decreasing trends over the mountainous and coastal areas, respectively, due to historical warming and moistening. Our results are basically consistent with previous studies that have focused on future changes in snow depth and snowfall.

Historic climate change trends and impacts on crop yields in key agricultural areas of the prairie provinces in Canada: a literature review

The objective of this literature review was to compile research findings on climate change and its impacts on crop production in Prairie Provinces of Canada. Our search strategy included finding primary literature articles from various databases. Seven articles reported increases in average and minimum air temperature over time in the Prairie Provinces of Canada. Increases in maximum air temperature were smaller than that for minimum air temperature. Growing degree days and corn heat units also increased over time, which has allowed for potential expansion of corn growth northwards. While overall increases in average annual precipitation and growing season precipitation have occurred in Canada between 1900 and 2021, western Canada showed increases in some regions but decreases in others. Off-season precipitation and snow cover duration in Canada have decreased since 1950. The number of frost-free days has increased across Canada, on the Prairies and southern Saskatchewan since 1900. Annual snowfall has decreased since 1950 and across Canada the annual maximum snow depth has also decreased. Overall, studies focusing on the Prairie Provinces in Canada have shown accelerated changes in several climate parameters over time, affecting cropping areas and crop yields.

Bandwidth selection for kernel density estimation of fat-tailed and skewed distributions

Applied researchers using kernel density estimation have worked with optimal bandwidth rules that invariably assumed that the reference density is Normal (optimal only if the true underlying density is Normal). We offer four new optimal bandwidth rules-of-thumb based on other infinitely supported distributions: Logistic, Laplace, Student's t and Asymmetric Laplace. Additionally, we propose a psuedo rule-of-thumb (ROT) bandwidth based on a Gram-Charlier expansion of the unknown reference density that is linked to the empirical skewness and kurtosis of the data. The intellectual investment needed to implement these new optimal bandwidths is practically zero. We discuss the behaviour of these bandwidths as it links to differences in skewness and kurtosis to the Normal reference ROT. We further propose model selection criteria for bandwidth choice when the true underlying density is unknown. The performance of these new ROT bandwidths are assessed in a variety of Monte Carlo simulations as well as in two empirical illustrations, the well known data set of annual snowfall in Buffalo, New York, and a timely example on stock market trading.