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This dataset contains the observation data used to prepare the thesis of Mathieu Lachapelle. It contains radar data, laser-optical disdrometer data, standard meteorological data, manual observations, and macrophotography recorded during four ice pellet events that occurred in 2019 and 2020. The ice pellet episodes occurred in the Montreal region and most observational data were collected at UQAM-PK weather station, on the rooftop of President Kennedy building, in Downtown Montreal. More documentation is available in the READMEs provided with the dataset. Cette base de données contient les données d'observation utilisées pour rédiger la thèse de Mathieu Lachapelle. Elle inclut des données radar, des données d'un disdromètre optique, des mesures météorologiques de base, des observations manuelles et des macro photographies collectées pendant quatre épisodes de grésil qui se sont produit en 2019 et en 2020. Les épisodes de grésil ont eu lieu dans la région de Montréal et la plupart des données d'observation ont été collectées à la station météo UQAM-PK, installée sur le toit du bâtiment Président-Kennedy au centre-ville de Montréal. Davantage de documentation est accessible via les fichiers READMEs inclus dans la base de données.

Abstract Accurate estimations of the precipitation phase at the surface are critical for hydrological and snowpack modelling in cold regions. Precipitation phase partitioning methods (PPMs) vary in their ability to estimate the precipitation phase at around 0°C and can significantly impact simulations of snowpack accumulation and melt. The goal of this study is to evaluate PPMs of varying complexity using high‐quality observations of precipitation phase and to assess the impact on snowpack simulations. We used meteorological data collected in Edmundston, New Brunswick, Canada, during the 2021 Saint John River Experiment on Cold Season Storms (SAJESS). These data were combined with manual observations of snow depth. Five PPMs commonly used in hydrological models were tested against observations from a laser‐optical disdrometer and a Micro Rain Radar. Most PPMs produced similar accuracy in estimating only rainfall and snowfall. Mixed precipitation was the most difficult phase to predict. The multi‐physics model Crocus was then used to simulate snowpack evolution and to diagnose model sensitivity to snowpack accumulation processes (PPM, snowfall density, and snowpack compaction). Sixteen snowpack accumulation periods, including nine warm accumulation events (average temperatures above −2°C) were observed during the study period. When considering all accumulation events, simulated changes in snow water equivalent ( SWE ) were more sensitive to the type of PPM used, whereas simulated changes in snow depth were more sensitive to uncertainties in snowfall density. Choice of PPM was the main source of model sensitivity for changes in SWE and snow depth when only considering warm events. Overall, this study highlights the impact of precipitation phase estimations on snowpack accumulation at the surface during near‐0°C conditions.

Abstract Precipitation changes modify C, N, and P cycles, which regulate the functions and structure of terrestrial ecosystems. Although altered precipitation affects above‐ and belowground C:N:P stoichiometry, considerable uncertainties remain regarding plant–microbial nutrient allocation strategies under increased (IPPT) and decreased (DPPT) precipitation. We meta‐analyzed 827 observations from 235 field studies to investigate the effects of IPPT and DPPT on the C:N:P stoichiometry of plants, soils, and microorganisms. DPPT reduced leaf C:N ratio, but increased the leaf and root N:P ratios reflecting stronger decrease of P compared with N mobility in soil under drought. IPPT increased microbial biomass C (+13%), N (+15%), P (26%), and the C:N ratio, whereas DPPT decreased microbial biomass N (−12%) and the N:P ratio. The C:N and N:P ratios of plant leaves were more sensitive to medium DPPT than to IPPT because drought increased plant N content, particularly in humid areas. The responses of plant and soil C:N:P stoichiometry to altered precipitation did not fit the double asymmetry model with a positive asymmetry under IPPT and a negative asymmetry under extreme DPPT. Soil microorganisms were more sensitive to IPPT than to DPPT, but they were more sensitive to extreme DPPT than extreme IPPT, consistent with the double asymmetry model. Soil microorganisms maintained stoichiometric homeostasis, whereas N:P ratios of plants follow that of the soils under altered precipitation. In conclusion, specific N allocation strategies of plants and microbial communities as well as N and P availability in soil critically mediate C:N:P stoichiometry by altered precipitation that need to be considered by prediction of ecosystem functions and C cycling under future climate change scenarios.

Abstract Canada's boreal forests, which occupy approximately 30% of boreal forests worldwide, play an important role in the global carbon budget. However, there is little quantitative information available regarding the spatiotemporal changes in the drought‐induced tree mortality of Canada's boreal forests overall and their associated impacts on biomass carbon dynamics. Here, we develop spatiotemporally explicit estimates of drought‐induced tree mortality and corresponding biomass carbon sink capacity changes in Canada's boreal forests from 1970 to 2020. We show that the average annual tree mortality rate is approximately 2.7%. Approximately 43% of Canada's boreal forests have experienced significantly increasing tree mortality trends (71% of which are located in the western region of the country), and these trends have accelerated since 2002. This increase in tree mortality has resulted in significant biomass carbon losses at an approximate rate of 1.51 ± 0.29 MgC ha −1  year −1 (95% confidence interval) with an approximate total loss of 0.46 ± 0.09 PgC year −1 (95% confidence interval). Under the drought condition increases predicted for this century, the capacity of Canada's boreal forests to act as a carbon sink will be further reduced, potentially leading to a significant positive climate feedback effect.

Abstract Freezing precipitation has major consequences for ground and air transportation, the health of citizens, and power networks. Previous studies using coarse resolution climate models have shown a northward migration of freezing rain in the future. Increased model resolution can better define local topography leading to improved representation of conditions that are favorable for freezing rain. The goal of this study is to examine the climatology and characteristics of future freezing rain events using very-high resolution climate simulations. Historical and pseudo-global warming simulations with a 4-km horizontal grid length were used and compared with available observations. Simulations revealed a northerly shift of freezing rain occurrence, and an increase in the winter. Freezing rain was still shown to occur in the Saint-Lawrence River Valley in a warmer climate, primarily due to stronger wind channeling. Up to 50% of the future freezing rain events also occurred in present day climate within 12 h of each other. In northern Maine, they are typically shorter than 6 h in current climate and longer than 6 h in warmer conditions due to the onset of precipitation during low-pressure systems occurrences. The occurrence of freezing rain also locally increases slightly north of Québec City in a warmer climate because of freezing rain that is produced by warm rain processes. Overall, the study shows that high-resolution regional climate simulations are needed to study freezing rain events in warmer climate conditions, because high horizontal resolutions better define small-scale topographic features and local physical mechanisms that have an influence on these events.

Abstract During near-0°C surface conditions, diverse precipitation types (p-types) are possible, including rain, drizzle, freezing rain, freezing drizzle, ice pellets, wet snow, snow, and snow pellets. Near-0°C precipitation affects wide swaths of the United States and Canada, impacting aviation, road transportation, power generation and distribution, winter recreation, ecology, and hydrology. Fundamental challenges remain in observing, diagnosing, simulating, and forecasting near-0°C p-types, particularly during transitions and within complex terrain. Motivated by these challenges, the field phase of the Winter Precipitation Type Research Multiscale Experiment (WINTRE-MIX) was conducted from 1 February to 15 March 2022 to better understand how multiscale processes influence the variability and predictability of p-type and amount under near-0°C surface conditions. WINTRE-MIX took place near the U.S.–Canadian border, in northern New York and southern Quebec, a region with plentiful near-0°C precipitation influenced by terrain. During WINTRE-MIX, existing advanced mesonets in New York and Quebec were complemented by deployment of 1) surface instruments, 2) the National Research Council Convair-580 research aircraft with W- and X-band Doppler radars and in situ cloud and aerosol instrumentation, 3) two X-band dual-polarization Doppler radars and a C-band dual-polarization Doppler radar from the University of Illinois, and 4) teams collecting manual hydrometeor observations and radiosonde measurements. Eleven intensive observing periods (IOPs) were coordinated. Analysis of these WINTRE-MIX IOPs is illuminating how synoptic dynamics, mesoscale dynamics, and microscale processes combine to determine p-type and its predictability under near-0°C conditions. WINTRE-MIX research will contribute to improving nowcasts and forecasts of near-0°C precipitation through evaluation and refinement of observational diagnostics and numerical forecast models.

Abstract The contraction of species range is one of the most significant symptoms of biodiversity loss worldwide. While anthropogenic activities and habitat alteration are major threats for several species, climate change should also be considered. For species at risk, differentiating the effects of human disturbances and climate change on past and current range transformations is an important step towards improved conservation strategies. We paired historical range maps with global atmospheric reanalyses from different sources to assess the potential effects of recent climate change on the observed northward contraction of the range of boreal populations of woodland caribou ( Rangifer tarandus caribou ) in Quebec (Canada) since 1850. We quantified these effects by highlighting the discrepancies between different southern limits of the caribou's range (used as references) observed in the past and reconstitutions obtained through the hindcasting of the climate conditions within which caribou are currently found. Hindcasted southern limits moved ~105 km north over time under all reanalysis datasets, a trend drastically different from the ~620 km reported for observed southern limits since 1850. The differences in latitudinal shift through time between the observed and hindcasted southern limits of distribution suggest that caribou range recession should have been only 17% of what has been observed since 1850 if recent climate change had been the only disturbance driver. This relatively limited impact of climate reinforces the scientific consensus stating that caribou range recession in Quebec is mainly caused by anthropogenic drivers (i.e. logging, development of the road network, agriculture, urbanization) that have modified the structure and composition of the forest over the past 160 years, paving the way for habitat‐mediated apparent competition and overharvesting. Our results also call for a reconsideration of past ranges in models aiming at projecting future distributions, especially for endangered species.