scholarly journals Response of snowpack to +2°C global warming in Hokkaido, Japan

2019 ◽  
Vol 66 (255) ◽  
pp. 83-96
Author(s):  
Yuta Katsuyama ◽  
Masaru Inatsu ◽  
Tatsuo Shirakawa
Keyword(s):  
Global Warming ◽  
Model Evaluation ◽  
Snow Water Equivalent ◽  
Large Decrease ◽  
Present Climate ◽  
Model Driven ◽  
The North ◽  
Snow Water ◽  
Forced Simulation ◽  
Moderate Bias

AbstractThe response of snowpack to a +2°C global warming relative to the present climate was estimated in Hokkaido, Japan, using a physical snowpack model driven by dynamically downscaled (DDS) data, after model evaluation. The evaluation revealed that the snowpack model successfully reproduced the height of snow cover (HS), snow water equivalent (SWE) and snow-covered days (SCDs), but had a moderate bias in the thickness ratios of melt form (MF) and hoar category (HC). The DDS-forced simulation predicted that the seasonal-maximum HS and SWE would decrease by 30–40% in the southwestern and eastern parts of Hokkaido due to a large decrease in snowfall during the accumulation period, and that the HS and SWE in the north would decrease, albeit not significantly due to uncertain atmospheric forcing. The number of SCDs in Hokkaido was predicted to decline by ~30 d. Additionally, ~50% of snowpack thickness during a season would be MF in most areas, whereas HC would be <50% all over Hokkaido.

Extreme snow events along the coast of the northeast United States: Potential changes due to global warming

2020 ◽  
pp. 1-46
Author(s):  
Guoxing Chen ◽  
Wei-Chyung Wang ◽  
Chao-Tzuen Cheng ◽  
Huang-Hsiung Hsu
Keyword(s):  
United States ◽  
New York ◽  
Global Warming ◽  
Snow Water Equivalent ◽  
Vertical Motion ◽  
Economic Activities ◽  
Northeast United States ◽  
The North ◽  
Snow Water ◽  
Mean Climate

AbstractWinter extreme snowstorm events along the coast of the northeast United States incur significant impacts on social and economic activities, and their potential changes under global warming are of great concern. Here, we adopted the pseudo global warming approach to investigate the responses of 93 events identified in our previous observational analysis. The study was conducted by contrasting two sets of WRF simulations for each event: the first set driven by ERA-Interim reanalysis and the second set by ERA data superimposed with mean-climate changes simulated from HiRAM historical (1980–2004) and future (2075–2099; RCP8.5) runs. Results reveal that the warming together with increased moisture tends to decrease the snowfall along the coast but increase the rainfall throughout the region. For example, the number of events having daily snow-water-equivalent larger than 10 mm day-1 at Boston, New York City, Philadelphia and Washington D.C. is decreased by 47, 46, 30 and 33%, respectively. The compensating changes in snowfall and rainfall lead to total precipitation increased in 3 southern cities but decreased in Boston. In addition, the southwestward shift of regional precipitation distribution is in coherence with the enhancement/reduction of upward vertical motion in the south/north and the movement of cyclone centers (westward in 58% of events and southward in 72%). Finally, perhaps more adversely, because of the northward retreat of the 0°C and the expansion of near-freezing zone, the number of events with mixed rain-snow and freezing precipitations in the north (especially the inland area) is increased.

scholarly journals Simulation of Snow Water Equivalent (SWE) Using Thermodynamic Snow Models in Québec, Canada

2009 ◽  
Vol 10 (6) ◽  
pp. 1447-1463 ◽  
Author(s):  
A. Langlois ◽  
J. Kohn ◽  
A. Royer ◽  
P. Cliche ◽  
L. Brucker ◽  
...  
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Meteorological Data ◽  
Regional Scale ◽  
Correlation Coefficients ◽  
Reanalysis Data ◽  
Regional Variability ◽  
The North ◽  
Snow Water ◽  
Regional Reanalysis

Abstract Snow cover plays a key role in the climate system by influencing the transfer of energy and mass between the soil and the atmosphere. In particular, snow water equivalent (SWE) is of primary importance for climatological and hydrological processes and is a good indicator of climate variability and change. Efforts to quantify SWE over land from spaceborne passive microwave measurements have been conducted since the 1980s, but a more suitable method has yet to be developed for hemispheric-scale studies. Tools such as snow thermodynamic models allow for a better understanding of the snow cover and can potentially significantly improve existing snow products at the regional scale. In this study, the use of three snow models [SNOWPACK, CROCUS, and Snow Thermal Model (SNTHERM)] driven by local and reanalysis meteorological data for the simulation of SWE is investigated temporally through three winter seasons and spatially over intensively sampled sites across northern Québec. Results show that the SWE simulations are in agreement with ground measurements through three complete winter seasons (2004/05, 2005/06, and 2007/08) in southern Québec, with higher error for 2007/08. The correlation coefficients between measured and predicted SWE values ranged between 0.72 and 0.99 for the three models and three seasons evaluated in southern Québec. In subarctic regions, predicted SWE driven with the North American Regional Reanalysis (NARR) data fall within the range of measured regional variability. NARR data allow snow models to be used regionally, and this paper represents a first step for the regionalization of thermodynamic multilayered snow models driven by reanalysis data for improved global SWE evolution retrievals.

Spatial Variability in Seasonal Snowpack Trends across the Rio Grande Headwaters (1984–2017)

2020 ◽  
Vol 21 (11) ◽  
pp. 2713-2733 ◽  
Author(s):  
Graham A. Sexstone ◽  
Colin A. Penn ◽  
Glen E. Liston ◽  
Kelly E. Gleason ◽  
C. David Moeser ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Forest Canopy ◽  
Snow Water Equivalent ◽  
Rio Grande ◽  
Winter Precipitation ◽  
Time Step ◽  
The North ◽  
Spatial Coverage ◽  
Snow Water ◽  
Snowpack Properties

AbstractThis study evaluated the spatial variability of trends in simulated snowpack properties across the Rio Grande headwaters of Colorado using the SnowModel snow evolution modeling system. SnowModel simulations were performed using a grid resolution of 100 m and 3-hourly time step over a 34-yr period (1984–2017). Atmospheric forcing was provided by phase 2 of the North American Land Data Assimilation System, and the simulations accounted for temporal changes in forest canopy from bark beetle and wildfire disturbances. Annual summary values of simulated snowpack properties [snow metrics; e.g., peak snow water equivalent (SWE), snowmelt rate and timing, and snow sublimation] were used to compute trends across the domain. Trends in simulated snow metrics varied depending on elevation, aspect, and land cover. Statistically significant trends did not occur evenly within the basin, and some areas were more sensitive than others. In addition, there were distinct trend differences between the different snow metrics. Upward trends in mean winter air temperature were 0.3°C decade−1, and downward trends in winter precipitation were −52 mm decade−1. Middle elevation zones, coincident with the greatest volumetric snow water storage, exhibited the greatest sensitivity to changes in peak SWE and snowmelt rate. Across the Rio Grande headwaters, snowmelt rates decreased by 20% decade−1, peak SWE decreased by 14% decade−1, and total snowmelt quantity decreased by 13% decade−1. These snow trends are in general agreement with widespread snow declines that have been reported for this region. This study further quantifies these snow declines and provides trend information for additional snow variables across a greater spatial coverage at finer spatial resolution.

Observation of snow water equivalent in the North catchment area of Lake Biwa

2011 ◽  
Vol 16 (3) ◽  
pp. 215-221 ◽  
Author(s):  
Iwaki Maho ◽  
Hida Yoshifumi ◽  
Ueno Ken’ichi ◽  
Saijyou Mayumi
Keyword(s):  
Catchment Area ◽  
Snow Water Equivalent ◽  
Lake Biwa ◽  
The North ◽  
Snow Water

scholarly journals A quantitative method to decompose SWE differences between regional climate models and reanalysis datasets

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Yun Xu ◽  
Andrew Jones ◽  
Alan Rhoades
Keyword(s):  
Sierra Nevada ◽  
Climate Models ◽  
Snow Water Equivalent ◽  
Driving Forces ◽  
Regional Climate ◽  
Model Development ◽  
Regional Climate Models ◽  
The North ◽  
Snow Water ◽  
Regional Downscaling

Abstract The simulation of snow water equivalent (SWE) remains difficult for regional climate models. Accurate SWE simulation depends on complex interacting climate processes such as the intensity and distribution of precipitation, rain-snow partitioning, and radiative fluxes. To identify the driving forces behind SWE difference between model and reanalysis datasets, and guide model improvement, we design a framework to quantitatively decompose the SWE difference contributed from precipitation distribution and magnitude, ablation, temperature and topography biases in regional climate models. We apply this framework within the California Sierra Nevada to four regional climate models from the North American Coordinated Regional Downscaling Experiment (NA-CORDEX) run at three spatial resolutions. Models generally predict less SWE compared to Landsat-Era Sierra Nevada Snow Reanalysis (SNSR) dataset. Unresolved topography associated with model resolution contribute to dry and warm biases in models. Refining resolution from 0.44° to 0.11° improves SWE simulation by 35%. To varying degrees across models, additional difference arises from spatial and elevational distribution of precipitation, cold biases revealed by topographic correction, uncertainties in the rain-snow partitioning threshold, and high ablation biases. This work reveals both positive and negative contributions to snow bias in climate models and provides guidance for future model development to enhance SWE simulation.

scholarly journals Projected Changes in Snow Water Equivalent over the Tibetan Plateau under Global Warming of 1.5° and 2°C

2020 ◽  
Vol 33 (12) ◽  
pp. 5141-5154
Author(s):  
Qinglong You ◽  
Fangying Wu ◽  
Hongguo Wang ◽  
Zhihong Jiang ◽  
Nick Pepin ◽  
...  
Keyword(s):  
Global Warming ◽  
Tibetan Plateau ◽  
Snow Water Equivalent ◽  
Coupled Model ◽  
The Tibetan Plateau ◽  
Model Simulations ◽  
The Future ◽  
Snow Water ◽  
Positive Correlations ◽  
Projected Changes

AbstractSnow water equivalent (SWE) is a critical parameter for characterizing snowpack, which has a direct influence on the hydrological cycle, especially over high terrain. In this study, SWE from 18 coupled model simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) is validated against the Canadian Sea Ice and Snow Evolution Network (CanSISE) SWE. The model simulations under RCP8.5 and RCP4.5 are employed to investigate projected changes in spring/winter SWE over the Tibetan Plateau (TP) under global warming of 1.5° and 2°C. Most CMIP5 models overestimate the CanSISE SWE. A decrease in mean spring/winter SWE for both RCPs over most regions of the TP is predicted in the future, with most significant reductions over the western TP, consistent with pronounced warming in that region. This is supported by strong positive correlations between SWE and mean temperature in the future in both seasons. Compared with the preindustrial period, spring/winter SWE over the TP under global warming of 1.5° and 2°C will reduce significantly, at faster rates than over China as a whole and the Northern Hemisphere. SWE changes over the TP do not show a simple elevation dependency under global warming of 1.5° and 2°C, with maximum changes in the elevation band of 4000–4500 m. Moreover, there are also strong positive correlations between projected SWE and historical mean SWE, indicating that the initial conditions of SWE are an important parameter of future SWE under specific global warming scenarios.

scholarly journals Severe floods in Nemunas River Delta

Baltica ◽  
2019 ◽  
Vol 31 (2) ◽  
pp. 89-99
Author(s):  
Gintaras Valiuškevičius ◽  
Edvinas Stonevičius ◽  
Gintautas Stankūnavičius ◽  
Janina Brastovickytė-Stankevič
Keyword(s):  
Snow Water Equivalent ◽  
High Water ◽  
River Delta ◽  
Flood Events ◽  
The North ◽  
Severe Flood ◽  
Snow Water ◽  
Terrain Elevation ◽  
Original Interpretation ◽  
Severe Flooding

The river delta regions are usually most vulnerable to flooding due to small changes in terrain elevation and river – sea interaction. The trends of increased frequency of flooding and an increased duration of the high water events are evident in many regions. In this study, we analyse the most extreme (severe) flood events in the Nemunas Delta region of Lithuania. The study focuses on the causes of floods and their changes over 1926–2016. Analysing specific case studies and comparing them with related studies of other researchers, we present an original interpretation of the variability of flood parameters. The aim of the study is to demonstrate that the analysis of flood events must be based on the identification of the drivers of individual floods. This is especially true for the lower reaches and the delta regions of rivers situated within the North European Plain. Historically, an intense melting of snow appeared to be the main cause of severe flooding in this region. The results of this study, however, show that the situation has rapidly changed over the last 30 years and large areas can be flooded even if the snow water equivalent over the whole basin is relatively low.

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