Seasonality of Continental Extratropical‐Cyclone Wind Speeds Over Northeastern North America - Chen, Ting‐Chen; Di Luca, Alejandro; Winger, Katja; Laprise, René; Thériault, Julie M. (Geophysical Research Letters, 2022)
Abstract This study investigates the seasonality of near‐surface wind speeds associated with extratropical cyclones (ETCs) over northeastern North America using a global reanalysis data set during 1979–2020. As opposed to most studies that emphasize winter storms, ETCs during the fall exhibit significantly stronger 10‐m winds over this region due to the slightly stronger continental cyclones and significantly weaker low‐level stability during that time of the year. Also, ETCs favor inland lakes and Hudson Bay during the low‐ice‐content fall season, leading to lower surface roughness. Combining these results, we derive simple linear regressions to predict the 10‐m wind speed given three variables: 850‐hPa wind speed, low‐level Richardson number, and surface roughness length. This formula captures the observed seasonality and serves as a valuable tool for cyclone near‐surface wind risk assessment. , Plain Language Summary Extratropical cyclones can bring powerful winds that can cause severe damage to infrastructure. We find that cyclones with severe winds are the most frequent in the fall season over continental northeastern North America. Three reasons are found responsible: stronger continental cyclones, weaker low‐level atmospheric stability, and the lower surface roughness over lakes and Hudson Bay, where cyclones frequently occur in fall. A simple formula that can effectively assess the near‐surface wind speeds associated with cyclones is derived based on these results. , Key Points Extratropical‐cyclone‐associated 10‐m wind speeds are the strongest in the fall season over northeastern North America Besides stronger continental cyclones and 850‐hPa winds, weaker low‐level stability in fall favors stronger 10‐m wind speeds in this region Linear regression using 850‐hPa wind, Richardson number, and surface roughness well predicts cyclones' 10‐m wind speeds and seasonality

A Multi‐Algorithm Analysis of Projected Changes to Freezing Rain Over North America in an Ensemble of Regional Climate Model Simulations - McCray, Christopher D.; Paquin, Dominique; Thériault, Julie M.; Bresson, Émilie (Journal of Geophysical Research: Atmospheres, 2022)
Abstract Freezing rain events have caused severe socioeconomic and ecosystem impacts. An understanding of how these events may evolve as the Earth warms is necessary to adequately adapt infrastructure to these changes. We present an analysis of projected changes to freezing rain events over North America relative to the 1980–2009 recent past climate for the periods during which +2, +3, and +4°C of global warming is attained. We diagnose freezing rain using four precipitation‐type algorithms (Cantin and Bachand, Bourgouin, Ramer, and Baldwin) applied to four simulations of the fifth‐generation Canadian Regional Climate Model (CRCM5) driven by four global climate models (GCMs). We find that the choice of driving GCM strongly influences the spatial pattern of projected change. The choice of algorithm has a comparatively smaller impact, and primarily affects the magnitude but not the sign of projected change. We identify several regions where all simulations and algorithms agree on the sign of change, with increases projected over portions of western Canada and decreases over the central, eastern, and southern United States. However, we also find large regions of disagreement on the sign of change depending on driving GCM and even ensemble member of the same GCM, highlighting the importance of examining freezing rain events in a multi‐member ensemble of simulations driven by multiple GCMs to sufficiently account for uncertainty in projections of these hazardous events. , Plain Language Summary Freezing rain events, or ice storms, can have major impacts on electrical infrastructure, agriculture, and road and air travel. Despite these impacts, relatively little research has been done on how these events may change as the Earth warms. We therefore examine several climate model simulations to determine how the frequency of freezing rain may change at different levels of future global warming. We focus in particular on how sensitive the projected changes are to the method used to identify freezing rain in the model output, as well as to the choice of climate model used to produce the projections. We find strong agreement among methods and models on a decrease in freezing rain frequency over much of the United States (from Texas northeastward to Maine) and an increase in freezing rain frequency over portions of western Canada (Alberta, Saskatchewan, Manitoba). In many other areas, however, the different methods and simulations disagree on the direction of projected change. Our findings highlight the importance of using many different climate models, rather than single simulations, to paint a clearer picture of the level of certainty in projections of freezing rain in the context of global warming. , Key Points Freezing rain is projected to increase in frequency over portions of western and central Canada and decrease over most of the United States The sign of projected changes is not highly sensitive to the precipitation‐type algorithm used to diagnose freezing rain The choice of driving global climate model is a key source of uncertainty in both the sign and magnitude of projected changes

Annual frequencies of freezing rain diagnosed from the fifth-generation Canadian Regional Climate Model (CRCM5) for the past and future climates - McCray, Christopher D.; Paquin, Dominique; Thériault, Julie M.; Bresson, Émilie (Federated Research Data Repository / dépôt fédéré de données de recherche, 2022)
This dataset contains output of yearly frequencies (hours) of freezing rain identified using four precipitation-type algorithms applied to output of the fifth-generation Canadian Regional Climate Model (CRCM5) run at Ouranos. Algorithms are applied to three-hourly output of eight simulations of four dynamically-downscaled global climate models (GCMs) on a 0.22° horizontal grid over the North American domain. Simulations for 1980-2005 are forced with observed greenhouse gas concentrations, with data for 2006-2099 using the RCP 8.5 greenhouse gas concentration trajectory. Each occurrence of freezing rain identified in the model output is multiplied by 3 for comparison with hourly observations. These data are associated with the article "A multi-algorithm analysis of projected changes to freezing rain over North America in an ensemble of regional climate model simulations" by McCray et al., submitted in 2022 to the Journal of Geophysical Research: Atmospheres.
Keywords: CRCM5; Climate change; Climate model; Climate modelling; Dynamical Downscaling; Freezing rain; Ice storms; Modélisation climatique; Precipitation type

The Severe Multi-Day October 2019 Snow Storm Over Southern Manitoba, Canada - Hanesiak, John; Stewart, Ronald; Painchaud-Niemi, Dylan; Milrad, Shawn; Liu, George; Vieira, Michael; Theriault, Julie; Cholette, Melissa; Ziolkowski, Kyle (Atmosphere-Ocean, 2022)

WINTRE-MIX: CFI Climate Sentinels Arboretum Parsivel Disdrometer Data. Version 1.0 - Fraser, D.; Bigras, È.; Gyakum, J.; Lachapelle, M.; Meunier, V.; Thériault, J.; Thompson, H.; Girouard, M.; Low, Y.; Wray, J. (UCAR/NCAR - Earth Observing Laboratory, 2022)
This dataset contains raw data collected from an OTT Parsivel laser disdrometer installed at a climate sentinel (Arboretum) in the Saint Lawrence River Valley. The data is available from 1 Nov 2021 to 31 March 2022 (inclusive) to support the Winter Precipitation Type Research Multi-Scale Experiment (WINTRE-MIX). The instrument provides histograms of hydrometeor size and fallspeed. The Arboretum site is located on the southwestern tip of Montreal Island near the confluence of the Ottawa River and the St. Lawrence River. Several other sites also collected Parsivel data during WINTRE-MIX.
Artwork Size: 1 data file, 1 ancillary/documentation file, 1775 MiB Pages: 1 data file, 1 ancillary/documentation file, 1775 MiB

WINTRE-MIX: CFI Climate Sentinels Gault Parsivel Disdrometer Data. Version 1.0 - Fraser, D.; Bigras, È.; Gyakum, J.; Lachapelle, M.; Meunier, V.; Thériault, J.; Thompson, H.; Girouard, M.; Low, Y.; Wray, J. (UCAR/NCAR - Earth Observing Laboratory, 2022)
This dataset contains raw data collected from an OTT Parsivel laser disdrometer installed at a climate sentinel (Gault) in the Saint Lawrence River Valley The data is available from 1 Nov 2021 to 31 March 2022 (inclusive) to support the Winter Precipitation Type Research Multi-Scale Experiment (WINTRE-MIX). The instrument provides histograms of hydrometeor size and fallspeed. The Gault site is located behind Mont-Saint-Hilaire, about an hour’s drive east of Montreal. Other sites also collected Parsivel data during WINTRE-MIX.
Artwork Size: 1 data file, 1 ancillary/documentation file, 1812 MiB Pages: 1 data file, 1 ancillary/documentation file, 1812 MiB

WINTRE-MIX: CFI Climate Sentinels Icing Detector Data. Version 1.0 - Fraser, D.; Bigras, È.; Gyakum, J.; Lachapelle, M.; Meunier, V.; Thériault, J.; Thompson, H.; Girouard, M.; Low, Y.; Wray, J. (UCAR/NCAR - Earth Observing Laboratory, 2022)
This dataset contains ice thickness data collected by ice detectors installed at various climate sentinels within the Saint Lawrence River Valley for the WINTRE-MIX field project. The names of four stations for which ice accretion data are available in ‘CFI_Climate_Sentinels_Icing_Detector_Data.nc’ are given in Table 1 of the readme documentation, along with their corresponding four-letter identifiers.
Artwork Size: 1 data file, 1 ancillary/documentation file, 16 MiB Pages: 1 data file, 1 ancillary/documentation file, 16 MiB

WINTRE-MIX: CFI Climate Sentinels Hotplate Precipitation Gauge data. Version 1.0 - Girouard, M.; Lachapelle, M.; Thériault, J.; Gyakum, J.; Bigras, È.; Meunier, V.; Thompson, H.; Fraser, D.; Wray, J.; Low, Y. (UCAR/NCAR - Earth Observing Laboratory, 2022)
This dataset includes hotplate precipitation gauge data from 4 different sites sitting in the St. Lawrence River Valley. The hotplate data were obtained by the K63 Hotplate Total Precipitation Gauge. The instruments belonged to Université du Québec à Montréal (UQAM) and McGill University. UQAM has one hotplate permanently installed on the rooftop of the President-Kennedy building, in downtown Montreal. Another hotplate was temporarily deployed by the UQAM team in the instrument yard of UQTR as part of the WINTRE-MIX field campaign. McGill’s instruments are permanently installed in the instrument yards of Gault and Arboretum.
Artwork Size: 1 data file, 2 ancillary/documentation files, 49 MiB Pages: 1 data file, 2 ancillary/documentation files, 49 MiB

WINTRE-MIX: CFI Climate Sentinels Snow Depth Data. Version 1.0 - Girouard, M.; Lachapelle, M.; Thompson, H.; Bigras, È.; Meunier, V.; Fraser, D.; Low, Y.; Wray, J.; Thériault, J.; Gyakum, J. (UCAR/NCAR - Earth Observing Laboratory, 2022)
This dataset includes snow depth and snow water equivalent data from 4 sites in the St. Lawrence River Valley collected for the WINTRE-MIX field project. The snow depth data were obtained by the SDMS40: Multipoint Scanning Snowfall Sensor and the SR50A Snow-Depth Sensor. The CS725 Snow-Water Equivalent Sensor measured the snow water equivalent data. This dataset includes measurements done at 4 different sites: UQAM-PK (UQAM), Trois-Rivières, Gault and Arboretum.
Artwork Size: 1 data file, 4 ancillary/documentation files, 21 MiB Pages: 1 data file, 4 ancillary/documentation files, 21 MiB

How Well Are We Measuring Snow Post-SPICE? - Kochendorfer, John; Earle, Michael; Rasmussen, Roy; Smith, Craig; Yang, Daqing; Morin, Samuel; Mekis, Eva; Buisan, Samuel; Roulet, Yves-Alain; Landolt, Scott; Wolff, Mareile; Hoover, Jeffery; et al. (Bulletin of the American Meteorological Society, 2022)
Abstract Accurate snowfall measurements are necessary for meteorology, hydrology, and climate research. Typical uses include creating and calibrating gridded precipitation products, the verification of model simulations, driving hydrologic models, input into aircraft deicing processes, and estimating streamflow runoff in the spring. These applications are significantly impacted by errors in solid precipitation measurements. The recent WMO Solid Precipitation Intercomparison Experiment (SPICE) attempted to characterize and reduce some of the measurement uncertainties through an international effort involving 15 countries utilizing over 20 types and models of precipitation gauges from various manufacturers. Key results from WMO-SPICE are presented herein. Recent work and future research opportunities that build on the results of WMO-SPICE are also highlighted.

WINTRE-MIX: CFI Climate Sentinels Arboretum MRR-2 raw data. Version 1.0 - Lachapelle, M.; Fraser, D.; Bigras, È.; Gyakum, J.; Meunier, V.; Thériault, J.; Thompson, H.; Girouard, M.; Low, Y.; Wray, J. (UCAR/NCAR - Earth Observing Laboratory, 2022)
This dataset contains raw data from a METEK vertically profiling K-band Micro Rain Radar (MRR-2) installed at the climate sentinel in the Arboretum forest reserve (ARBO), about 30 km west of Montréal downtown, Québec, Canada. The data were collected as part of the Winter Precipitation Type Research Multi-scale Experiment (WINTRE-MIX) field project held in February and March of 2022. The instrument used to collect the data in this dataset provides vertical profiles of reflectivity, Doppler velocity, and spectrum width. The site is located near the confluence of the Ottawa River and the St. Lawrence River. Several other sites also collected MRR data during WINTRE-MIX.
Artwork Size: 181 data files, 3 ancillary/documentation files, 28 GiB Pages: 181 data files, 3 ancillary/documentation files, 28 GiB

WINTRE-MIX: CFI Climate Sentinels Gault MRR-2 Processed Data. Version 1.0 - Lachapelle, M.; Fraser, D.; Bigras, È.; Gyakum, J.; Meunier, V.; Thériault, J.; Thompson, H.; Girouard, M.; Low, Y.; Wray, J. (UCAR/NCAR - Earth Observing Laboratory, 2022)
This dataset contains post-processed data from a METEK vertically profiling K-band Micro Rain Radar (MRR-2) installed at the climate sentinel in the Gault Nature Reserve (GAUL), about 30 km east of Montréal, Québec. The data were collected as part of the Winter Precipitation Type Research Multi-scale Experiment (WINTRE-MIX) field project held in February and March of 2022. The instrument provides vertical profiles of reflectivity, Doppler velocity, and spectrum width. The site is located at the southern flank of Mont-Saint-Hilaire, a mountain with an elevation of about 400 m (above mean sea-level). Several other sites also collected MRR data during WINTRE-MIX.
Artwork Size: 181 data files, 3 ancillary/documentation files, 94 GiB Pages: 181 data files, 3 ancillary/documentation files, 94 GiB

WINTRE-MIX: CFI Climate Sentinels Gault MRR-2 Raw Data. Version 1.0 - Lachapelle, M.; Fraser, D.; Bigras, È.; Gyakum, J.; Meunier, V.; Thériault, J.; Thompson, H.; Girouard, M.; Low, Y.; Wray, J. (UCAR/NCAR - Earth Observing Laboratory, 2022)
This dataset contains raw data from a METEK vertically profiling K-band Micro Rain Radar (MRR-2) installed at the climate sentinel in the Gault Nature Reserve (GAUL), about 30 km east of Montréal, Québec.The data were collected as part of the Winter Precipitation Type Research Multi-scale Experiment (WINTRE-MIX) field project held in February and March of 2022. The instrument provides vertical profiles of reflectivity, Doppler velocity, and spectrum width. The site is located at the southern flank of Mont-Saint-Hilaire, a mountain with an elevation of about 400 m (above mean sea-level). Several other sites also collected MRR data during WINTRE-MIX.
Artwork Size: 181 data files, 3 ancillary/documentation files, 28 GiB Pages: 181 data files, 3 ancillary/documentation files, 28 GiB

WINTRE-MIX: CFI Climate Sentinels Trois-Rivières MRR-Pro data. Version 1.0 - Lachapelle, M.; Girouard, M.; Thompson, H.; Thériault, J. (UCAR/NCAR - Earth Observing Laboratory, 2022)
This dataset contains data from a METEK vertically profiling K-band Micro Rain Radar Pro (MRR-Pro) that was temporarily installed at the Université du Québec à Trois-Rivières (UQTR) campus during February and March 2022 to support the Winter Precipitation Type Research Multi-Scale Experiment (WINTRE-MIX). The instrument provides vertical profiles of reflectivity, Doppler velocity, and spectrum width. The site sits in the St. Lawrence River Valley. Several other sites also collected MRR data during WINTRE-MIX.
Artwork Size: 59 data files, 2 ancillary/documentation files, 11 GiB Pages: 59 data files, 2 ancillary/documentation files, 11 GiB

WINTRE-MIX: CFI Climate Sentinels UQAM-PK MRR-2 Processed Data. Version 1.0 - Lachapelle, M.; Girouard, M.; Thompson, H.; Thériault, J. (UCAR/NCAR - Earth Observing Laboratory, 2022)
This dataset contains processed data from a METEK vertically profiling K-band Micro Rain Radar (MRR-2) permanently installed on the rooftop of UQAM President-Kennedy building in Montréal downtown, Québec. The instrument provides vertical profiles of reflectivity, Doppler velocity, and spectrum width. The site sits in the St. Lawrence River Valley. Several other sites also collected MRR data during WINTRE-MIX.
Artwork Size: 149 data files, 3 ancillary/documentation files, 73 GiB Pages: 149 data files, 3 ancillary/documentation files, 73 GiB

WINTRE-MIX: CFI Climate Sentinels UQAM-PK MRR-2 Raw Data. Version 1.0 - Lachapelle, M.; Girouard, M.; Thompson, H.; Thériault, J. (UCAR/NCAR - Earth Observing Laboratory, 2022)
This dataset contains raw data from a METEK vertically profiling K-band Micro Rain Radar (MRR-2) permanently installed on the rooftop of UQAM President-Kennedy building in Montréal downtown, Québec. The instrument provides vertical profiles of reflectivity, Doppler velocity, and spectrum width. The site sits in the St. Lawrence River Valley. Several other sites also collected MRR data during WINTRE-MIX.
Artwork Size: 149 data files, 3 ancillary/documentation files, 21 GiB Pages: 149 data files, 3 ancillary/documentation files, 21 GiB

WINTRE-MIX: CFI Climate Sentinels UQAM-PK Parsivel Disdrometer Data. Version 1.0 - Lachapelle, M.; Girouard, M.; Thompson, H.; Thériault, J. (UCAR/NCAR - Earth Observing Laboratory, 2022)
This dataset contains raw data from an OTT Parsivel laser disdrometer permanently installed on the rooftop of UQAM President-Kennedy building in Montréal downtown, Québec. The instrument provides histograms of hydrometeor size and fallspeed. The site sits in the St. Lawrence River Valley. Several other sites also collected Parsivel data during WINTRE-MIX 2022.
Artwork Size: 1 data file, 2 ancillary/documentation files, 1585 MiB Pages: 1 data file, 2 ancillary/documentation files, 1585 MiB

WINTRE-MIX: Trois-Rivieres Parsivel Disdrometer Data. Version 1.0 - Lachapelle, M.; Girouard, M.; Thompson, H.; Thériault, J. (UCAR/NCAR - Earth Observing Laboratory, 2022)
This dataset contains raw data from an OTT Parsivel laser disdrometer that was temporarily installed at the Université du Québec à Trois-Rivières (UQTR) campus from December 2021 to April 2022 to support the Winter Precipitation Type Research Multi-Scale Experiment (WINTRE-MIX). The instrument provides histograms of hydrometeor size and fallspeed. The site sits in the St. Lawrence River Valley. Several other sites also collected Parsivel data during WINTRE-MIX.
Artwork Size: 1 data file, 2 ancillary/documentation files, 1320 MiB Pages: 1 data file, 2 ancillary/documentation files, 1320 MiB

WINTRE-MIX: Manual Hydrometeor Photographs Dataset. Version 1.0 - Lachapelle, M.; Han, B.; Minder, J. R.; Winters, A.; Baiman, R.; Thériault, J.; Gyakum, J.; Wray, J. (UCAR/NCAR - Earth Observing Laboratory, 2022)
Manual hydrometeor macro photographs were collected during the Winter Precipitation Type Research Multi-Scale Experiment (WINTRE-MIX) between 01 Feb – 15 March 2022. The macro photographs were collected by manual ground observation teams from the University at Albany (UAlbany), University of Colorado Boulder (CU), Université du Québec à Montréal (UQAM), and McGill University (McGill). Sections in the readme provide information on the camera setup, protocol, and dataset file formats, as well as limitations associated with the data.
Artwork Size: 33 data files, 1 ancillary/documentation file, 41 GiB Pages: 33 data files, 1 ancillary/documentation file, 41 GiB

Characteristics of Precipitation Particles and Microphysical Processes during the 11–12 January 2020 Ice Pellet Storm in the Montréal Area, Québec, Canada - Lachapelle, Mathieu; Thériault, Julie M. (Monthly Weather Review, 2022)
Abstract Freezing rain and ice pellets are particularly difficult to forecast when solid precipitation is completely melted aloft. This study addresses this issue by investigating the processes that led to a long-duration ice pellet event in Montreal, Québec, Canada, on 11–12 January 2020. To do so, a benchmark model initialized with ERA5 data is used to show that solid precipitation was completely melted below the melting layer, which discards partial melting from the possible ice pellet formation processes. Macro photography of precipitation reveals that small columnar crystals (∼200 μ m) and ice pellets occurred simultaneously for more than 10 h. The estimation of ice crystal number concentration using macro photographs and laser-optical disdrometer data suggests that all supercooled drops could have refrozen by contact freezing with ice crystals. Rimed ice pellets also indicate ice supersaturation in the subfreezing layer. Given these observations, the formation of ice pellets and ice crystals was probably promoted by secondary ice production and the horizontal advection of ice crystals below the melting layer, as we illustrate using a conceptual model. Overall, these findings demonstrate how ice nucleation processes at temperatures near 0°C can drastically change the precipitation phase and the impact of a storm. Significance Statement Ice pellets are generally formed when snow particles partially melt while falling through a warm layer aloft before completely refreezing in a cold layer closer to the surface. Ice pellets can also be formed when snow particles completely melt aloft, but freezing rain is often produced in such conditions. On 11–12 January 2020, ice pellets were produced during more than 10 h in Montreal, Quebec, Canada. Macro photographs of the precipitation particles show that ice pellets occurred simultaneously with small ice crystals. Most of the ice pellets were produced while snow particles were completely melted aloft. The supercooled drops probably refroze due to collisions with the ice crystals that could have been advected by the northeasterly winds near the surface.

WINTRE-MIX: CFI Climate Sentinels Pyranometer and Radiation Data. Version 1.0 - Low, Y.; Lachapelle, M.; Gyakum, J.; Bigras, È.; Meunier, V.; Thériault, J.; Thompson, H.; Girouard, M.; Fraser, D.; Wray, J. (UCAR/NCAR - Earth Observing Laboratory, 2022)
The Sentinel Radiation MetData provided in situ measurements of radiation fluxes for the Winter Precip Type Research Multi-scale Experiment (WINTRE-MIX) are included in this dataset. Pyranometer and radiation data are provided in netCDF format. Data were collected from the following 4 sites: Gault, Arboretum, UQAM-PK and Trois-Rivières.
Artwork Size: 4 data files, 1 ancillary/documentation file, 20 MiB Pages: 4 data files, 1 ancillary/documentation file, 20 MiB

WINTRE-MIX: CFI Climate Sentinels Pressure, Wind, Temperature, Relative Humidity, and Precipitation Data. Version 1.0 - Low, Y.; Lachapelle, M.; Kinnard, C.; Gyakum, J.; Bigras, È.; Meunier, V.; Thériault, J.; Thompson, H.; Girouard, M.; Fraser, D.; Wray, J. (UCAR/NCAR - Earth Observing Laboratory, 2022)
The Sentinel Non-Radiation MetData provided in situ measurements of meteorological variables (such as 2-meter temperature, relative humidity, wind speed and direction, and precipitation) collected for the the Winter Precip Type Research Multi-scale Experiment (WINTRE-MIX) are included in this dataset. Data are provided in netCDF format. Data were collected from the following 4 sites: Gault, Arboretum, UQAM-PK and Trois-Rivières.
Artwork Size: 1 data file, 1 ancillary/documentation file, 61 MiB Pages: 1 data file, 1 ancillary/documentation file, 61 MiB

Quantifying the Impact of Precipitation-Type Algorithm Selection on the Representation of Freezing Rain in an Ensemble of Regional Climate Model Simulations - McCray, Christopher D.; Thériault, Julie M.; Paquin, Dominique; Bresson, Émilie (Journal of Applied Meteorology and Climatology, 2022)
Abstract Given their potentially severe impacts, understanding how freezing rain events may change as the climate changes is of great importance to stakeholders including electrical utility companies and local governments. Identification of freezing rain in climate models requires the use of precipitation-type algorithms, and differences between algorithms may lead to differences in the types of precipitation identified for a given thermodynamic profile. We explore the uncertainty associated with algorithm selection by applying four algorithms (Cantin and Bachand, Baldwin, Ramer, and Bourgouin) offline to an ensemble of simulations of the fifth-generation Canadian Regional Climate Model (CRCM5) at 0.22° grid spacing. First, we examine results for the CRCM5 driven by ERA-Interim reanalysis to analyze how well the algorithms reproduce the recent climatology of freezing rain and how results vary depending on algorithm parameters and the characteristics of available model output. We find that while the Ramer and Baldwin algorithms tend to be better correlated with observations than Cantin and Bachand or Bourgouin, their results are highly sensitive to algorithm parameters and to the number of pressure levels used. We also apply the algorithms to four CRCM5 simulations driven by different global climate models (GCMs) and find that the uncertainty associated with algorithm selection is generally similar to or greater than that associated with choice of driving GCM for the recent past climate. Our results provide guidance for future studies on freezing rain in climate simulations and demonstrate the importance of accounting for uncertainty between algorithms when identifying precipitation type from climate model output. Significance Statement Freezing rain events and ice storms can have major consequences, including power outages and dangerous road conditions. It is therefore important to understand how climate change might affect the frequency and severity of these events. One source of uncertainty in climate studies of these events is related to the choice of algorithm used to detect freezing rain in model output. We compare the frequency of freezing rain identified using four different algorithms and find sometimes large differences depending on the algorithm chosen over some regions. Our findings highlight the importance of taking this source of uncertainty into account and will provide researchers with guidance as to which algorithms are best suited for climate studies of freezing rain.

Storms and Precipitation Across the continental Divide Experiment (SPADE) - Thériault, Julie M.; Leroux, Nicolas R.; Stewart, Ronald E.; Bertoncini, André; Déry, Stephen J.; Pomeroy, John W.; Thompson, Hadleigh D.; Smith, Hilary; Mariani, Zen; Desroches-Lapointe, Aurélie; Mitchell, Selina; Almonte, Juris (Bulletin of the American Meteorological Society, 2022)
Abstract The Canadian Rockies are a triple-continental divide, whose high mountains are drained by major snow-fed and rain-fed rivers flowing to the Pacific, Atlantic, and Arctic Oceans. The objective of the April–June 2019 Storms and Precipitation Across the continental Divide Experiment (SPADE) was to determine the atmospheric processes producing precipitation on the eastern and western sides of the Canadian Rockies during springtime, a period when upslope events of variable phase dominate precipitation on the eastern slopes. To do so, three observing sites across the divide were instrumented with advanced meteorological sensors. During the 13 observed events, the western side recorded only 25% of the eastern side’s precipitation accumulation, rainfall occurred rather than snowfall, and skies were mainly clear. Moisture sources and amounts varied markedly between events. An atmospheric river landfall in California led to moisture flowing persistently northward and producing the longest duration of precipitation on both sides of the divide. Moisture from the continental interior always produced precipitation on the eastern side but only in specific conditions on the western side. Mainly slow-falling ice crystals, sometimes rimed, formed at higher elevations on the eastern side (>3 km MSL), were lifted, and subsequently drifted westward over the divide during nonconvective storms to produce rain at the surface on the western side. Overall, precipitation generally crossed the divide in the Canadian Rockies during specific spring-storm atmospheric conditions although amounts at the surface varied with elevation, condensate type, and local and large-scale flow fields.

Meteorological Factors Responsible for Major Power Outages during a Severe Freezing Rain Storm over Eastern Canada - Thériault, Julie M.; McFadden, Vanessa; Thompson, Hadleigh D.; Cholette, Mélissa (Journal of Applied Meteorology and Climatology, 2022)
Abstract Winter precipitation is the source of many inconveniences in many regions of North America, for both infrastructure and the economy. The ice storm that hit the Canadian Maritime Provinces on 24–26 January 2017 remains one of the most expensive in history for the province of New Brunswick. Up to 50 mm of freezing rain caused power outages across the province, depriving up to one-third of New Brunswick residences of electricity, with some outages lasting 2 weeks. This study aims to use high-resolution atmospheric modeling to investigate the meteorological conditions during this severe storm and their contribution to major power outages. The persistence of a deep warm layer aloft, coupled with the slow movement of the associated low pressure system, contributed to widespread ice accumulation. When combined with the strong winds observed, extensive damage to electricity networks was inevitable. A 2-m temperature cold bias was identified between the simulation and the observations, in particular during periods of freezing rain. In the northern part of New Brunswick, cold-air advection helped keep temperatures below 0°C, while in southern regions, the 2-m temperature increased rapidly to slightly above 0°C because of radiational heating. The knowledge gained in this study on the processes associated with either maintaining or stopping freezing rain will enhance the ability to forecast and, in turn, to mitigate the hazards associated with those extreme events. Significance Statement A slow-moving low pressure system produced up to 50 mm of freezing rain for 31 h along the east coast of New Brunswick, Canada, on 24–26 January 2017, causing unprecedented power outages. Warm-air advection aloft, along with a combination of higher wind speeds and large amounts of ice accumulation, created ideal conditions for severe freezing rain. The storm began with freezing rain along the entire north–south cross section of eastern New Brunswick and changed to rain only in the south, when local temperatures increased to >0°C. Near-surface cold-air advection kept temperatures below 0°C in the north. Warming from the latent heat produced by freezing contributed to persistent near-0°C conditions during freezing rain.

Snow Level From Post‐Processing of Atmospheric Model Improves Snowfall Estimate and Snowpack Prediction in Mountains - Vionnet, V.; Verville, M.; Fortin, V.; Brugman, M.; Abrahamowicz, M.; Lemay, F.; Thériault, J. M.; Lafaysse, M.; Milbrandt, J. A. (Water Resources Research, 2022)
Abstract In mountains, the precipitation phase greatly varies in space and time and affects the evolution of the snow cover. Snowpack models usually rely on precipitation‐phase partitioning methods (PPMs) that use near‐surface variables. These PPMs ignore conditions above the surface thus limiting their ability to predict the precipitation phase at the surface. In this study, the impact on snowpack simulations of atmospheric‐based PPMs, incorporating upper atmospheric information, is tested using the snowpack scheme Crocus. Crocus is run at 2.5‐km grid spacing over the mountains of southwestern Canada and northwestern United States and is driven by meteorological fields from an atmospheric model at the same resolution. Two atmospheric‐based PPMs were considered from the atmospheric model: the output from a detailed microphysics scheme and a post‐processing algorithm determining the snow level and the associated precipitation phase. Two ground‐based PPMs were also included as lower and upper benchmarks: a single air temperature threshold at 0°C and a PPM using wet‐bulb temperature. Compared to the upper benchmark, the snow‐level based PPM improved the estimation of snowfall occurrence by 5% and the simulation of snow water equivalent (SWE) by 9% during the snow melting season. In contrast, due to missing processes, the microphysics scheme decreased performances in phase estimate and SWE simulations compared to the upper benchmark. These results highlight the need for detailed evaluation of the precipitation phase from atmospheric models and the benefit for mountain snow hydrology of the post‐processed snow level. The limitations to drive snowpack models at slope scale are also discussed. , Plain Language Summary The partitioning of precipitation between rainfall and snowfall is a crucial component of the evolution of the snowpack in mountains. Most snowpack models use the air temperature and humidity near the surface to derive the precipitation phase. However, the phase at the surface is strongly influenced by processes such as melting and refreezing of falling hydrometeors that occur above the surface. Atmospheric models simulate these processes and the corresponding phase at the surface. However, snowpack models rarely use this information. In this study, we considered two estimates of precipitation phase from an atmospheric model and tested them with a physically‐based snow model over the mountains of southwestern Canada and northwestern United States. The results were compared with traditional approaches using the air temperature and humidity near the surface to derive the precipitation phase. Our results showed that the precipitation phase associated with the snow level obtained from the atmospheric model improved snowfall estimate and snowpack prediction compared to the traditional approaches. In contrast, the cloud/precipitation scheme of the atmospheric model decreased performance in phase estimate and snow simulations due to missing physical processes. Our study highlights that snowpack predictions in the mountains can be improved if valuable information is obtained from atmospheric models. , Key Points Estimates of precipitation phase from an atmospheric model were used to drive snow simulations with a detailed snowpack model Snowfall prediction and snowpack modeling are improved by using the snow level from post‐processing of the atmospheric model Direct precipitation phase from the microphysics scheme does not improve snow simulations compared to simpler rain‐snow partitioning schemes

WINTRE-MIX Field Collected Sounding Data. Version 4.0 - Winters, A.; Minder, J. R.; Han, B.; Thériault, J.; Lachapelle, M.; Gyakum, J.; Wray, J.; Baiman, R. (UCAR/NCAR - Earth Observing Laboratory, 2022)
Sounding data collected during the WINTRE-MIX project field phase are included in this dataset. This dataset has soundings from the University of Colorado (CU) DOWs, McGill University at Gault, St Jean sur Richelieu, University at Albany (UA) DOWs, Université du Québec à Montréal (UQAM), and UA Essex sites. data file names are of the form "upperair.sounding.YYYYMMDDHHMM.siteName.[txt or csv]" where the YYYMMDDHHMM indicates the date and time of the sounding and the siteName indicates the site source and location. See the documentation for more information on this dataset.
Artwork Size: 179 data files, 6 ancillary/documentation files, 115 MiB Pages: 179 data files, 6 ancillary/documentation files, 115 MiB