Rain-on-snow events over North America based on two Canadian regional climate models

Jeong, Do Un; Sushama, Laxmi

doi:10.1007/s00382-017-3609-x

Cited by 92 publications

(61 citation statements)

References 42 publications

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“…Here we present a comprehensive analysis of how the surface water balance responds to atmospheric forcing under AR and non‐AR conditions. Consistent with previous studies, we found that the ROS process can heavily modulate the snowpack response (Il Jeong & Sushama, ; McCabe et al, ; Musselman et al, ). Also, we quantitatively attribute the ROS effect on snowpack and runoff to surface air temperature (on average 3.49 mm/day of snow ablation) and radiation (0.97 mm/day of snow ablation) associated with ARs.…”

Section: Discussionsupporting

confidence: 92%

Impact of Atmospheric Rivers on Surface Hydrological Processes in Western U.S. Watersheds

et al. 2019

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Atmospheric rivers (ARs) can significantly modulate surface hydrological processes through the extreme precipitation they produce. However, there is a lack of comprehensive evaluation of ARs' impact on surface hydrology. This study uses a high‐resolution regional climate simulation to quantify the impact of ARs on surface hydrological processes across the western U.S. watersheds. The model performance is evaluated through extensive comparison against observations. Our analysis indicates that ARs produce heavy precipitation but suppress evapotranspiration. Snowpack ablates more during ARs, with higher air temperature and increased longwave radiation playing the primary and secondary roles, respectively. At the 0 °C to 10 °C temperature range, ARs increase the probability of snow ablation from 0.33 to 0.57. The runoff‐to‐precipitation ratio is primarily controlled by antecedent soil moisture, but it almost doubles in the northwestern watersheds due to the intensification of snow ablation during AR events. From the analysis of the relationship between the hydrological responses and different meteorological factors, precipitation, temperature, and radiation are identified as the key drivers that distinguish the hydrologic responses between AR and non‐AR events. Lastly, analysis of ARs and total runoff at annual scale and 1 April snowpack and winter precipitation shows that ARs explain 30% to 60% of the variability of annual total runoff and sharpen the seasonality of water resources availability in the west coast mountain watersheds.

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Section: Discussionsupporting

confidence: 92%

Impact of Atmospheric Rivers on Surface Hydrological Processes in Western U.S. Watersheds

et al. 2019

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“…The Arctic is warming at twice the rate of lower latitudes and is predicted to experience elevated levels of precipitation with a higher proportion falling as rain (Olsen et al 2011, Bintanja and Selten 2014, Bring et al 2016, Vihma et al 2016. As a consequence, the snow-covered season is shortening, spring conditions are becoming more variable, and rain-on-snow events are increasing in frequency (Putkonen et al 2009, Jeong and Sushama 2017, Langlois et al 2017, Mallory and Boyce 2017. These changes in temperature and hydrology are transforming northern ecosystems, with profound implications for wildlife that are not well understood , 2011b, Chapin et al 2004, Post et al 2009).…”

Section: Introductionmentioning

confidence: 99%

Navigating snowscapes: scale‐dependent responses of mountain sheep to snowpack properties

Mahoney

Liston

LaPoint

et al. 2018

Ecological Applications

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Winters are limiting for many terrestrial animals due to energy deficits brought on by resource scarcity and the increased metabolic costs of thermoregulation and traveling through snow. A better understanding of how animals respond to snow conditions is needed to predict the impacts of climate change on wildlife. We compared the performance of remotely sensed and modeled snow products as predictors of winter movements at multiple spatial and temporal scales using a data set of 20,544 locations from 30 GPS-collared Dall sheep (Ovis dalli dalli) in Lake Clark National Park and Preserve, Alaska, USA from 2005 to 2008. We used daily 500-m MODIS normalized difference snow index (NDSI), and multi-resolution snow depth and density outputs from a snowpack evolution model (SnowModel), as covariates in step selection functions. We predicted that modeled snow depth would perform best across all scales of selection due to more informative spatiotemporal variation and relevance to animal movement. Our results indicated that adding any of the evaluated snow metrics substantially improved model performance and helped characterize winter Dall sheep movements. As expected, SnowModel-simulated snow depth outperformed NDSI at fine-to-moderate scales of selection (step scales < 112 h). At the finest scale, Dall sheep selected for snow depths below mean chest height (<54 cm) when in low-density snows (100 kg/m ), which may have facilitated access to ground forage and reduced energy expenditure while traveling. However, sheep selected for higher snow densities (>300 kg/m ) at snow depths above chest height, which likely further reduced energy expenditure by limiting hoof penetration in deeper snows. At moderate-to-coarse scales (112-896 h step scales), however, NDSI was the best-performing snow covariate. Thus, the use of publicly available, remotely sensed, snow cover products can substantially improve models of animal movement, particularly in cases where movement distances exceed the MODIS 500-m grid threshold. However, remote sensing products may require substantial data thinning due to cloud cover, potentially limiting its power in cases where complex models are necessary. Snowpack evolution models such as SnowModel offer users increased flexibility at the expense of added complexity, but can provide critical insights into fine-scale responses to rapidly changing snow properties.

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“…In general, there exist strong regional differences in future projections of RoS occurrence, as such changes depend on both the air temperature and associated precipitation phase (rain or snowfall) as well as the areal extent and thickness of the snowpack: In midlatitude and lowland regions, the frequency of RoS events is expected to decrease due to snowpack declines. By contrast, RoS frequency is expected to increase in high latitude and mountainous regions due to a shift from snowfall to rain but persistent seasonal snow cover (Freudiger et al, ; Jeong & Sushama, ; McCabe et al, ; Musselman et al, ; Surfleet & Tullos, ; Ye et al, ). Furthermore, projected changes in RoS frequencies are depending on the investigated climate scenario and time horizon (Beniston & Stoffel, ; Morán‐Tejeda et al, ) and might differ for different seasons (Jeong & Sushama, ).…”

Section: Introductionmentioning

confidence: 99%

“…By contrast, RoS frequency is expected to increase in high latitude and mountainous regions due to a shift from snowfall to rain but persistent seasonal snow cover (Freudiger et al, ; Jeong & Sushama, ; McCabe et al, ; Musselman et al, ; Surfleet & Tullos, ; Ye et al, ). Furthermore, projected changes in RoS frequencies are depending on the investigated climate scenario and time horizon (Beniston & Stoffel, ; Morán‐Tejeda et al, ) and might differ for different seasons (Jeong & Sushama, ). For Switzerland, Köplin et al () predict a diversification of flood types in the wintertime as well as an increase of RoS floods in the future.…”

Section: Introductionmentioning

confidence: 99%

Improving Medium‐Range Forecasts of Rain‐on‐Snow Events in Prealpine Areas

Fehlmann

Gascón

Rohrer

et al. 2019

Water Resources Research

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Rain‐on‐snow (RoS) events have caused severe floods in mountainous catchments in the recent past. Challenges in forecasting such events are uncertainties in meteorological input variables, the accurate estimation of snow cover and deficits in process understanding during runoff formation. Here, we evaluate the potential of the European Centre for Medium‐Range Weather Forecasts Integrated Forecasting System (ECMWF IFS) to forecast RoS disposition (i.e., minimum rainfall amounts, initial snow cover, and meltwater contribution) several days ahead. We thereby evaluate forecasts of rain and snowfall with disdrometer observations and show that ensemble‐based forecasts have larger potential than the high‐resolution forecast of ECMWF IFS. Then, we use ECMWF IFS weather forecasts as input to a conceptual hydrological model, which is calibrated using estimates of snow‐covered area (SCA), snow water equivalent (SWE), and discharge observations. We show that the forecast skill of this model chain is reasonably high with respect to SCA and SWE, even several days ahead. However, a number of RoS events are missed in the forecast, mainly due to an underestimation of rainfall amounts. These misses can be reduced by lowering the rainfall amount threshold for the forecast as compared to the analysis, being accompanied by only a moderate increase in false alarm rates. In contrast, the forecast of RoS disposition is found to be less sensitive to thresholds of initial snow cover and meltwater contribution. We conclude that valuable disposition warnings for RoS events can be issued several days ahead, and we illustrate this idea with a case study.

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Rain-on-snow events over North America based on two Canadian regional climate models

Abstract: take into consideration ROS events in water resources management adaptation strategies for future climate.

Cited by 92 publications

References 42 publications

Impact of Atmospheric Rivers on Surface Hydrological Processes in Western U.S. Watersheds

Impact of Atmospheric Rivers on Surface Hydrological Processes in Western U.S. Watersheds

Navigating snowscapes: scale‐dependent responses of mountain sheep to snowpack properties

Improving Medium‐Range Forecasts of Rain‐on‐Snow Events in Prealpine Areas

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