Hydrometeorology, Atmosphere & Extremes



First Author: Lejla Latifovic, McMaster University


Additional Author(s): M. Altaf Arain, McMaster University


Abstract: "Temperate forests are an important global carbon sink. However, various environmental disturbances can impact carbon sequestration. An invasive defoliation attack by the gypsy moth (Lymantria dispar L.) during the growing season of 2021 decimated a mature oak-dominated forest stand situated north of Lake Erie in Southern Ontario, Canada. This forest is >90 years old and is know as CA-TPD site in the Global Water Futures and global FLUXNET networks. In this study, we quantify the extent and severity of the forest disturbance (defoliation) using eddy covariance measurements of net ecosystem productivity (NEP), gross primary productivity (GPP), and ecosystem respiration (RE) in the forest.


Previous research at our site indicated a strong carbon sink suggesting that sequestration capabilities of the forest were resilient to environmental stresses. On average, between 2012 and 2016, the forest was an annual carbon sink of 206 ± 92 g C m−2 yr−1. Current research examines how continued climate variability and the sever attack by Lymantria dispar impacted carbon dynamics and the potential of the stand to remain a strong carbon sink.


The extent to which North American temperate forests remain an important carbon sink will depend on the severity and rate of recovery from forest disturbance and extreme weather events under a changing climate."


Extreme weather events effect on water and energy fluxes and water use efficiency of an evergreen conifer forest in southern Ontario, Canada

First Author: Elizabeth Arango-Ruda


Additional Author(s): M. Altaf Arain


Abstract: Temperate forests play a significant role in the global water and energy cycles. Interactions between forest, energy, and water fluxes are important due to the impact they have on carbon storage, cooling of terrestrial surfaces, and water yield. Mono-specific forest management practices influence evapotranspiration (ET) and the energy partitioning response to extreme weather conditions. Therefore, quantification of evapotranspiration and energy fluxes (water use efficiency) under heat and drought stresses is important for understanding the cooling effect of this managed mono-specific forest under a changing climate. This work presents eighteen years of ET and energy balance components at an 83-year-old white pine managed forest in southern Ontario, Canada. The mean ET over the study period was 446 (±47) mm yr-1. ET showed a positive trend from 2003 to 2010, after which it decreased smoothly. It was not until 2017 that it showed increasing values again before reaching the highest level in 2010 (537 mm), a year with moderately dry, hot conditions. ET was sensitive to active photosynthetic radiation and air temperature and tended to decrease with increasing atmospheric dryness partly due to the inhibitor effect of VPD on transpiration (T) by constraining stomatal closure in plants trees. Prolonged dry periods with increased Ta significantly reduced ET (i.e., 2016). Water table depth (WTD) generally increased with spring recharge, then gradually declined over the growing season. The dry and warm conditions were reflected in WTD (i.e., 2010 and 2012, respectively). The water use efficiency was 0.4 g C kg-1 d-1, indicating high evapotranspiration and carbon storage rates. The energy partitioning was consistent throughout the years. During the growing season, the net radiation peaked, especially in years characterized by hot conditions. The site exhibited higher sensible heat (H) in the early growing season (spring), shifting to latent heat (LE) in the summer when the tree leaves were entirely out. The ground heat flux was small, particularly in 2005 (1.02 W m-2). Evapotranspiration, energy partitioning, and water use efficiency vary according to factors highly influenced by hot and dry periods, leading to significant implications on the water and energy cycles. This response depends upon the multiple interactions inherent to the forest (i.e., forest age, sapwood area dynamics, understory plants, biodiversity, etc.). Our results highlight the importance of extreme weather events such as drought and heat on water fluxes and the coupling of forest carbon and water, which has enormous implications for ecosystem functioning and sustainable management. However, how this affects the global hydrological cycle, freshwater resources, and global climate remains uncertain.


Can a Multi-Angle Snowflake Camera detect mixed precipitation?

First Author: Hadleigh D. Thompson, Département des sciences de la Terre et de l’atmosphère, Université du Québec à Montréal

Additional Author(s): Julie M. Thériault, Département des sciences de la Terre et de l’atmosphère, Université du Québec à Montréal; Nicolas R. Leroux, Département des sciences de la Terre et de l’atmosphère, Université du Québec à Montréal


Abstract: Measuring solid precipitation phase and type at the surface is crucial for better understanding their formation mechanism and improving their representation in models. Such measurements are commonly taken manually and, when possible, with macrophotography. The Multi-Angle Camera (MASC) allows for automatic observations of solid precipitation type at the surface without an observer being present. The MASC has previously been deployed in regions such as the Colorado Rockies, the Swiss Alps, and Antarctica, yet not in a mixed phased precipitation orientated campaign. The Saint John River Experiment on Cold Season Storms (SAJESS) provided such an opportunity, with an intensive observation period (IOP) running March-April 2021 in Edmundston, New Brunswick. During this period, five storms with mixed-phase (both rain and snow) precipitation occurred. Here, we explore the potential for the MASC to contribute to mixed-phase precipitation observations by delineating between images of solid and liquid particles. After 980 hours of operation, a previously published supervised detection algorithm was used to process over 93,000 collected images, with the algorithm detecting solid particles 92% of the time during the mixed phased events. This contrasts with manual observations for the same period that recoded 50/50 rain and snow observations, implying that liquid particles may be seen as solid by the MASC. During snow events, however, when only solid particles were recorded during manual observations, MASC data compare favorably in both precipitation type and timing. Ongoing work includes further analysis of MASC data, including particle size and fall speed measured during rain-only events, with the aim of improving the detection algorithms' ability to identify liquid particles. This research highlights the current limitations, yet also the potential, of the MASC to identify mixed precipitation.


Establishing Reflectivity-Snowfall Relationships for Different Hydrometeor Particle Size Distributions in the Fortress Mountain Snow Laboratory

First Author: André Bertoncini, Centre for Hydrology, University of Saskatchewan, Canmore and Global Institute for Water Security (GIWS), University of Saskatchewan, Saskatoon


Additional Author(s): Julie M. Thériault, Centre ESCER, Département des Sciences de la Terre et de l’Atmosphère, Université du Québec à Montréal (UQAM), Montréal; John Pomeroy, Centre for Hydrology, University of Saskatchewan, Canmore and Global Institute for Water Security (GIWS), University of Saskatchewan, Saskatoon


Abstract: Accurate estimation of precipitation fields remains a grand challenge in cold regions hydrology due to the sparseness of precipitation gauges and lack of quantitative precipitation estimation from ground based weather radars. It is even more challenging in cold regions mountains due to blockage of ground based weather radars, and the complexity of modelling precipitation. Satellite remote sensing provides an alternative to precipitation monitoring in complex terrain when retrieval algorithms are able to represent a range of hydrometeor types. The Global Precipitation Measurement (GPM) satellite constellation has been successfully monitoring liquid precipitation since early 2014 through the Integrated Multi-SatellitE Retrievals for GPM (IMERG) algorithm. IMERG uses a constellation of satellites with passive microwave and infrared sensors to estimate precipitation, which is intercalibrated using GPM’s core platform Dual-frequency Precipitation Radar (DPR). The DPR sensor has been working efficiently to retrieve liquid precipitation because reflectivity-rainfall relationships are well established due to a more uniform Particle Size Distribution (PSD); however, DPR’s snowfall retrieval has been suboptimal because of the diversity of PSDs, especially in complex terrain. For example, the same radar reflectivity can represent differing precipitation rates for different PSDs. Therefore, proper PSD-specific reflectivity-snowfall (Z-S) relationships need to be established to improve the accuracy of DPR’s snowfall retrievals and, consequently, IMERG precipitation estimates. This study aims to develop PSD-specific Z-S relationships to improve satellite snowfall estimates in the complex terrain of the Fortress Mountain Snow Laboratory, Canadian Rockies, Alberta. Observations from instruments located between 2100 and 2310 m above sea level included a Parsivel-2 optical disdrometer to determine hydrometeor phase and PSD at ~ 3 m above the surface; a Micro Rain Radar-2 (MRR-2) to establish Z-S relationships for each classified PSD at the near-surface and at different levels of the atmosphere; and a network of Alter-shielded weighing precipitation gauges, including one next to the Parsivel-2 and MRR-2, to quantify the precipitation rate used on the Z-S relationship equations. The study period comprises matching Parsivel-2 and MRR-2 observed events during the Storms Across the Continental Divide Experiment (SPADE), April-June 2019, and between January 2020 and March 2022. Preliminary results during SPADE events indicate that snowfall rate percentage differences between PSD-specific and general Z-S relationships range from -51% to 18%. This study’s findings explore the biases of satellite precipitation in cold mountain river basins. Improved precipitation satellite estimates can supplement precipitation gauges and modelling to yield an improved characterization of snowfall in mountains.


Monitoring lake ice phenology from CYGNSS: Algorithm development and assessment using Qinghai Lake, Tibet Plateau, as a case study

First Author: Yusof Ghiasi

Additional Author(s): Claude R. Duguay; Justin Murfitt, Wu Yuhao, Milad Asgarimehr


Abstract: "There has been a rapidly growing interest in the use of Global Navigation Satellite System Reflectometry (GNSS-R) to monitor a variety of geophysical parameters over the last two decades. However, within cryosphere and hydrosphere studies, few have yet been dedicated to the retrieval of lake ice cover, which is an important physical feature playing a role in climate and affecting the economy and livelihood of northern regions. In this paper, GNSS-R technique is employed for assessing seasonal timing of annual ice cover (lake ice phenology) for Qinghai Lake, Tibet Plateau. To this aim, the Signal-to-Noise Ratio (SNR) values obtained from the Cyclone GNSS (CYGNSS) constellation from August 2018 to March 2022 (V. 3.0) were used to examine how reflected GNSS signals are modified by changing lake ice surface properties and the freezing/thawing states of the lake. A moving t-test (MTT) algorithm applied to SNR timeseries over three ice seasons allowed for detection of lake ice at daily temporal resolution. A strong agreement was found between ice phenology records derived from CYGNSS and those obtained from the visual interpretation of Moderate Resolution Imaging Spectroradiometer (MODIS) color composite images. Over the three years of observations, the error for CYGNSS freeze-up timing ranged from 0 to 8 days with an average of 2.5 days. However, the error for breakup timing ranged from 2 to 8 days with an average of 2.5 days and showed the sensitivity of CYGNSS to the onset of spring melt before moving into open water conditions. Moreover, all seven t-score spikes appeared within the freeze-up and breakup periods visually obtained from MODIS images. In addition, results showed a drop in SNR values in the presence of ice cover compared to those from open water. We find that this incongruity with previous GNSS-R studies over sea ice, which have shown a higher reflection power from the sea ice surface, is due to differences in salinity and roughness of frozen lakes and oceans."

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First Author: Peter Wasswa,Institute for Water Research, Rhodes University, Grahamstown-South Africa


Additional Author(s): Jane Tanner,Institute for Water Research, Rhodes University, Grahamstown-South Africa


Abstract: In this study, hydrological drought in Uganda was characterized based on Gravity Recovery and Climate Experiment (GRACE) total water storage (TWS) values incorporated with soil moisture values of Global Land Data Assimilation System (GLDAS) masking out the area around Lake Victoria. A standardized GRACE-derived Index (SGI) approach was adopted where water storage deficits were computed by subtracting the GRACE data set from the total soil moisture component anomalies of (10cm and 40cm) of the GLDAS dataset. The new cumulative TWS deficits were normalized, then a new Standardized GRACE-derived Index (SGI) was developed for Uganda (from April 2002 to July 2016). The method builds on previous research by considering threshold values of the Standardized Precipitation Index (SPI). The results show that entire Uganda had severe drought events during (June 2008, August 2010, September 2011, and October 2013), though drought events occurred in July 2016. SGI showed that the Northern and North-Eastern parts of the country received more severe hydrological drought events concerning other parts of the country. The SGI used in this study showed that 82.5% of the hydrological drought events were under near-normal conditions, 12.5% under moderate dry, 2.6 % under severe dry, and 2.4 % under extreme dry conditions. The study results demonstrate that GRACE data sets are useful for reconstructing the TWS time series for a national level, from which hydrological drought can be characterized, and for investigating spatial and temporal trends in groundwater storage conditions.


Investigation of wet snow events leading to power outages over New Brunswick using convection-permitting simulations

First Author: Caio Ruman, Université du Québec à Montreal


Additional Author(s): Julie Thériault, Université du Québec à Montreal


Abstract: Snowstorms are a common occurrence in Atlantic Canada, often causing damage to infrastructure and power outages. However, not all intense snow events cause power outages. When those events occur near 0°C temperatures, a mix of precipitation often occurs, including wet snow. Wet snow is heavier, and when associated with moderate wind speeds, leads to increased accretion, making the fall of trees and power lines more likely. This work investigates why only some events cause power outages and what is the role of wet snow as a factor behind the power outages over New Brunswick, and how well the CONUS-WRF simulations represent those events. CONUS-WRF is a high-resolution, convection-permitting simulation with 4 km horizontal resolution starting in October 2000 and running until September 2013, encompassing the continental US and Southern Canada. The preliminary analysis defines wet snow as snow precipitation that occurred when the 2-m temperature was higher than -4°C. From a list of power outage events supplied by Énergie NB Power, we investigated those events using the CONUS-WRF simulations and the observation data from selected stations in New Brunswick from Environment and Climate Change Canada. A total of 42 winter storms that caused power outages were verified, with 26 associated with wet snow. Of the 42 storms, 31 were well simulated, being able to reproduce the occurrence of precipitation and the near 0°C temperatures. Up to 22 wet snowstorms are included in the 31 well-simulated storms and 64% had wind greater than 8 m/s. The wind in 15 of those 31 storms showed a negative bias, with most of those occurrences in the southern cities of New Brunswick, Moncton, and Saint John. Next, daily snow data was used to define a threshold for the intensity of extreme snow events over New Brunswick. The threshold ranged from 18-22 cm, with higher values in the center of New Brunswick, from central (Fredericton) to the Northeast coast. These were used to develop a spatial distribution of the monthly and yearly number of snow events over the region. On average, for the 2000-2013 period and for events lasting at least 2 days, the northern region of New Brunswick had 8 to 12 snow events a year, while the southern region has between 3 and 6 snow events. Overall, this study will help to better understand the atmospheric conditions associated with snowstorms leading to long power outages over New Brunswick.

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Seasonal and Spatial Changes in Hail Frequency and Associated Thermodynamic Mechanisms in WRF-HAILCAST Simulations

First Author: Daniel Betancourt, University of Manitoba, Department of Environment and Geography, Centre for Earth Observation Science


Additional Author(s): John Hanesiak, University of Manitoba, Department of Environment and Geography, Centre for Earth Observation Science, Julian Brimelow, Meteorological Service of Canada, Environment and Climate Change Canada, Yanping Li, University of Saskatchewan, Zhenhua Li, University of Saskatchewan, George Liu, University of Manitoba, Department of Environment and Geography


Abstract: Hail is a destructive severe weather phenomena that can cause considerable damage to property and agricultural crops. It is therefore important to understand current patterns in the occurrence of severe hail, and how these may shift in the future due to anthropogenic warming. In this study, a coupled cloud-hail and hail-growth model (HAILCAST) was forced with high resolution (4 km) convective-permitting Weather Research and Forecasting (WRF) model output in a control (CTRL) simulation and Pseudo-Global Warming (PGW) scenario for the period 2000-2013 over the Northern Plains and Canadian Prairies. The PGW approach involves applying future climate perturbations derived from CMIP5 to the lateral and initial boundary conditions (ERA-Interim) of the WRF-CTRL simulation [Liu et al., Climate Dyn., 49, 71-95 (2017)]. As such, it allows for an evaluation of thermodynamic drivers on the changes in occurrence of severe weather parameters including hail. Competing processes – greater moisture and instability on one hand, and higher temperatures aloft (leading to increased thermodynamic capping) result in considerable heterogeneity in terms of hail increases versus decreases across the region. Additionally, these different scenarios appear to be organized spatially and seasonally - with increases in hail frequency outpacing decreases during the shoulder seasons, and the opposite during the late summer. Thermodynamic driving mechanisms are examined.


High-Resolution Regional Climate Modeling and Projection over Western Canada using a Weather Research Forecasting Model with a Pseudo-Global Warming Approach

First Author: Yanping Li, University of Saskatchewan

Additional Author(s): Zhenhua Li, University of Saskatchewan

Abstract: "Climate change poses great risks to western Canada's ecosystem and socioeconomical development. To assess these hydroclimatic risks under high-end emission scenario RCP8.5, this study used the Weather Research Forecasting (WRF) model at a convection-permitting (CP) 4 km resolution to dynamically downscale the mean projection of a 19-member CMIP5 ensemble by the end of the 21st century. The CP simulations include a retrospective simulation (CTL, 2000–2015) for verification forced by ERA-Interim and a pseudo-global warming (PGW) for climate change projection forced with climate change forcing (2071–2100 to 1976–2005) from CMIP5 ensemble added on ERA-Interim. The retrospective WRF-CTL's surface air temperature simulation was evaluated against Canadian daily analysis ANUSPLIN, showing good agreements in the geographical distribution with cold biases east of the Canadian Rockies, especially in spring. WRF-CTL captures the main pattern of observed precipitation distribution from CaPA and ANUSPLIN but shows a wet bias near the British Columbia coast in winter and over the immediate region on the lee side of the Canadian Rockies. The WRF-PGW simulation shows significant warming relative to CTL, especially over the polar region in the northeast during the cold season, and in daily minimum temperature. Precipitation changes in PGW over CTL vary with the seasons: in spring and late autumn precipitation increases in most areas, whereas in summer in the Saskatchewan River basin and southern Canadian Prairies, the precipitation change is negligible or decreased slightly. With almost no increase in precipitation and much more evapotranspiration in the future, the water availability during the growing season will be challenging for the Canadian Prairies. WRF-PGW shows an increase in high-intensity precipitation events and shifts the distribution of precipitation events toward more extremely intensive events in all seasons. Due to this shift in precipitation intensity to the higher end in the PGW simulation, the seemingly moderate increase in the total amount of precipitation in summer east of the Canadian Rockies may underestimate the increase in flooding risk and water shortage for agriculture. The change in the probability distribution of precipitation intensity also calls for innovative bias-correction methods to be developed for the application of the dataset when bias correction is required. High-quality meteorological observation over the region is needed for both forcing high-resolution climate simulation and conducting verification. The high-resolution downscaled climate simulations provide abundant opportunities both for investigating local-scale atmospheric dynamics and for studying climate impacts on hydrology, agriculture, and ecosystems."

GWF-2022-Poster-Physical Response of the 2013 Alberta Flood to Global Warming-Xiaohui Zhao

Physical Response of the 2013 Alberta Flood to Global Warming

First Author: Xiaohui Zhao, Global Institute for Water Security, University of Saskatchewan


Additional Author(s): Yanping Li, Global Institute for Water Security, University of Saskatchewan; School of Environment and Sustainability, University of Saskatchewan


Abstract: "The greenhouse gas induced warming increases the water holding capacity of the atmosphere, which likely results in enhanced extreme precipitations. Extreme precipitations and associated flooding often cause significant societal disruption; therefore, it is imperative to study the changes of extreme precipitation under climate warming scenario. Most previous studies have focused on the frequency and intensity changes of extreme precipitation events from a statistical or climatological perspective. However, few studies have investigated the physical response of an extreme precipitation event to future climate warming.

During 19-22 June 2013, an extreme precipitation event happened in Alberta, which brought severe flooding and socio-economic damage to many locations in southern Alberta. This work aims to investigate the physical responses of the 2013 Alberta heavy rainfall event to future climate by conducting high-resolution pseudo-global warming WRF simulations. The control experiment is forced with 6-h European Centre for Medium-Range Weather Forecast Interim (ERA-Interim) reanalysis data; the sensitivity experiment is forced with 6-h perturbed ERA-Interim reanalysis data (i.e., ERA-Interim reanalysis + climate change signals derived from 5 global climate models under the Representative Concentration Pathway 8.5 emission scenario). This poster will present results from the two simulations."


Investigation of the climatology of low-level jets over North America in a high-resolution WRF simulation

First Author: Xiao Ma, University of Saskatchewan

Additional Author(s): Yanping Li, University of Saskatchewan; Zhenhua Li, Global Institute for Water Security

Abstract: The Low-level Jet (LLJ) is an important atmospheric phenomenon over North America and significantly impacts on local weather and climate. In this study, we use a 4-km convection-permitting Weather Research Forecasting (WRF) simulation over 13 years (2000-2013) to investigate the climatological features of LLJs. A high-resolution model better represents orography and the underlying surface that strongly affect winds in the boundary layer. The simulation domain covers the continental US and the neighboring portions of Canada and Mexico. The study characterizes the spatial distribution and seasonal and diurnal fluctuations of northerly/southerly LLJs' frequencies. Our algorithm successfully identified the previously well-known large-scale features of North American LLJs like southerly Great Plain LLJs and summer northerly Pacific Coast LLJs. Besides, the high-resolution simulation also provides new climatic characteristics of weaker and smaller-scale LLJs near complex terrains. Wintertime northerly Rockies LLJs were confined in limited foothill regions.


A Novel Bias-Correction Method for High-Resolution Regional Climate Model

First Author: Zhenhua Li, Global Institute for Water Security


Additional Author(s): Yanping Li, Global Institute for Water Security; Lintao Li, Global Institute for Water Security


Abstract: "Convection-permitting regional climate models can provide better representations

of physical processes, especially convection and underlying surface

heterogeneity, in the climate system and provide more detailed climate

projections at higher temporal and spatial resolution. However, biases still

exist in high-resolution RCM simulations due to their deficiency in

representations of sub-grid processes and unavoidable parameterization schemes.

The RCM dynamical downscaling of future climate projection, therefore, needs

bias-correction before their application. We present a new method to

bias-correct the dynamically downscaled climate projection by

convection-permitting WRF. The method, based on MBCn and machine

learning,preserves the large-scale features of observed patterns in reanalysis

with added detail from the RCM simulations. It also maintains the climate change

signals between the future projection and the historical simulation."