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- Author or Editor: Barrie Bonsal x
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Abstract
Precipitation responses over Canada associated with the two extreme phases of the Southern Oscillation (SO), namely El Niño and La Niña, are identified. Using the best available precipitation data from 1911 to 1994, both the spatial and temporal behavior of the responses are analyzed from the El Niño/La Niña onset to several seasons afterward. Composite and correlation analyses indicate that precipitation over a large region of southern Canada extending from British Columbia, through the prairies, and into the Great Lakes region is significantly influenced by the SO phenomenon. The results show a distinct pattern of negative (positive) precipitation anomalies in this region during the first winter following the onset of El Niño (La Niña) events. During this same period, significant positive precipitation anomalies occur over the northern prairies and southeastern Northwest Territories in association with El Niño events. Statistical significance of the responses is tested by the Student’s t-test and the Wilcoxon rank-sum test, while field significance is established through the Monte Carlo procedure. All of the significant precipitation anomalies can be explained by the associated upper-atmospheric flow patterns, which during the first winter following the onset of El Niño (La Niña) events resemble the positive (negative) phase of the Pacific–North American (PNA) pattern. Significant correlations between Southern Oscillation index (SOI) values and the observed precipitation anomalies over southern Canada suggest the possibility of developing a long-range forecasting technique for Canadian precipitation based on the occurrence and evolution of the various phases of the SO.
Abstract
Precipitation responses over Canada associated with the two extreme phases of the Southern Oscillation (SO), namely El Niño and La Niña, are identified. Using the best available precipitation data from 1911 to 1994, both the spatial and temporal behavior of the responses are analyzed from the El Niño/La Niña onset to several seasons afterward. Composite and correlation analyses indicate that precipitation over a large region of southern Canada extending from British Columbia, through the prairies, and into the Great Lakes region is significantly influenced by the SO phenomenon. The results show a distinct pattern of negative (positive) precipitation anomalies in this region during the first winter following the onset of El Niño (La Niña) events. During this same period, significant positive precipitation anomalies occur over the northern prairies and southeastern Northwest Territories in association with El Niño events. Statistical significance of the responses is tested by the Student’s t-test and the Wilcoxon rank-sum test, while field significance is established through the Monte Carlo procedure. All of the significant precipitation anomalies can be explained by the associated upper-atmospheric flow patterns, which during the first winter following the onset of El Niño (La Niña) events resemble the positive (negative) phase of the Pacific–North American (PNA) pattern. Significant correlations between Southern Oscillation index (SOI) values and the observed precipitation anomalies over southern Canada suggest the possibility of developing a long-range forecasting technique for Canadian precipitation based on the occurrence and evolution of the various phases of the SO.
Abstract
Most of the globe has experienced significant warming trends that have been attributed to anthropogenic climate change. However, these rates of warming are also influenced by short-term climate fluctuations driven by atmospheric circulation dynamics, resulting in inconsistent trend magnitudes in both time and space. This research evaluated winter (Dec to Feb) temperature trends over 1950-2020 at 91 climate stations across British Columbia (BC), Alberta (AB), and Saskatchewan (SK), Canada, and determined the components attributed to thermodynamic and dynamic [atmospheric circulation] factors. A synoptic climatological approach was used to classify atmospheric circulation patterns in the mid-troposphere, relate those patterns to surface temperature, and evaluate changes in frequency. Moderate-high temperature increases over 71 years were found for most of the region, averaging 3.1°C in southern SK to 4.1°C in central-northern AB, and a maximum of 5.8°C in northern BC. Low-moderate increases were found for southern BC, averaging 1.2°C. Changes in atmospheric circulation accounted for 29% and 31% of observed temperature changes in central-northern BC and AB, respectively. Dynamic factors were a moderate driver in southern AB (18%) and central-northern SK (13%), and low in southern SK (5%). Negative dynamic contributions in southern BC (−6%), suggest atmospheric circulation changes counteracted thermodynamically-driven temperature changes. Results were consistent with trend analyses, indicating this method is well-suited for trend detection and identification of thermodynamic and dynamic drivers. Results of this research improve our understanding of the magnitude of winter temperature changes critical for informing adaptation and climate-related policy decisions.
Abstract
Most of the globe has experienced significant warming trends that have been attributed to anthropogenic climate change. However, these rates of warming are also influenced by short-term climate fluctuations driven by atmospheric circulation dynamics, resulting in inconsistent trend magnitudes in both time and space. This research evaluated winter (Dec to Feb) temperature trends over 1950-2020 at 91 climate stations across British Columbia (BC), Alberta (AB), and Saskatchewan (SK), Canada, and determined the components attributed to thermodynamic and dynamic [atmospheric circulation] factors. A synoptic climatological approach was used to classify atmospheric circulation patterns in the mid-troposphere, relate those patterns to surface temperature, and evaluate changes in frequency. Moderate-high temperature increases over 71 years were found for most of the region, averaging 3.1°C in southern SK to 4.1°C in central-northern AB, and a maximum of 5.8°C in northern BC. Low-moderate increases were found for southern BC, averaging 1.2°C. Changes in atmospheric circulation accounted for 29% and 31% of observed temperature changes in central-northern BC and AB, respectively. Dynamic factors were a moderate driver in southern AB (18%) and central-northern SK (13%), and low in southern SK (5%). Negative dynamic contributions in southern BC (−6%), suggest atmospheric circulation changes counteracted thermodynamically-driven temperature changes. Results were consistent with trend analyses, indicating this method is well-suited for trend detection and identification of thermodynamic and dynamic drivers. Results of this research improve our understanding of the magnitude of winter temperature changes critical for informing adaptation and climate-related policy decisions.
Abstract
Recent advances in artificial intelligence (AI) and explainable AI (XAI) have created opportunities to better predict and understand drought processes. This study uses a machine learning approach for understanding the drivers of drought severity and extent in the Canadian Prairies from 2005 to 2019 using climate and satellite data. The model is trained on the Canadian Drought Monitor (CDM), an extensive dataset produced by expert analysis of drought impacts across various sectors that enables a more comprehensive understanding of drought. Shapley additive explanation (SHAP) is used to understand model predictions during emerging or worsening drought conditions, providing insight into the key determinants of drought. The results demonstrate the importance of capturing spatiotemporal autocorrelation structures for accurate drought characterization and elucidates the drought time scales and thresholds that optimally separate each CDM severity category. In general, there is a positive relationship between the severity of drought and the time scale of the anomalies. However, high-severity droughts are also more complex and driven by a multitude of factors. It was found that satellite-based evaporative stress index (ESI), soil moisture, and groundwater were effective predictors of drought onset and intensification. Similarly, anomalous phases of large-scale atmosphere–ocean dynamics exhibit teleconnections with Prairie drought. Overall, this investigation provides a better understanding of the physical mechanisms responsible for drought in the Prairies, provides data-driven thresholds for estimating drought severity that could improve future drought assessments, and offers a set of early warning indicators that may be useful for drought adaptation and mitigation.
Significance Statement
This work is significant because it identifies drivers of drought onset and intensification in an agriculturally and economically important region of Canada. This information can be used in the future to improve early warning for adaptation and mitigation. It also uses state-of-the-art machine learning techniques to understand drought, including a novel approach called SHAP probability values to improve interpretability. This provides evidence that machine learning models are not black boxes and should be more widely considered for understanding drought and other hydrometeorological phenomena.
Abstract
Recent advances in artificial intelligence (AI) and explainable AI (XAI) have created opportunities to better predict and understand drought processes. This study uses a machine learning approach for understanding the drivers of drought severity and extent in the Canadian Prairies from 2005 to 2019 using climate and satellite data. The model is trained on the Canadian Drought Monitor (CDM), an extensive dataset produced by expert analysis of drought impacts across various sectors that enables a more comprehensive understanding of drought. Shapley additive explanation (SHAP) is used to understand model predictions during emerging or worsening drought conditions, providing insight into the key determinants of drought. The results demonstrate the importance of capturing spatiotemporal autocorrelation structures for accurate drought characterization and elucidates the drought time scales and thresholds that optimally separate each CDM severity category. In general, there is a positive relationship between the severity of drought and the time scale of the anomalies. However, high-severity droughts are also more complex and driven by a multitude of factors. It was found that satellite-based evaporative stress index (ESI), soil moisture, and groundwater were effective predictors of drought onset and intensification. Similarly, anomalous phases of large-scale atmosphere–ocean dynamics exhibit teleconnections with Prairie drought. Overall, this investigation provides a better understanding of the physical mechanisms responsible for drought in the Prairies, provides data-driven thresholds for estimating drought severity that could improve future drought assessments, and offers a set of early warning indicators that may be useful for drought adaptation and mitigation.
Significance Statement
This work is significant because it identifies drivers of drought onset and intensification in an agriculturally and economically important region of Canada. This information can be used in the future to improve early warning for adaptation and mitigation. It also uses state-of-the-art machine learning techniques to understand drought, including a novel approach called SHAP probability values to improve interpretability. This provides evidence that machine learning models are not black boxes and should be more widely considered for understanding drought and other hydrometeorological phenomena.
Abstract
Anthropogenic climate change–induced snowpack loss is affecting streamflow predictability, as it becomes less dependent on the initial snowpack conditions and more dependent on meteorological forecasts. We assess future changes to seasonal streamflow predictability over two large river basins, Liard and Athabasca in western Canada, by approximating streamflow response from the Variable Infiltration Capacity (VIC) hydrologic model with the Bayesian regularized neutral network (BRNN) machine learning emulator. We employ the BRNN emulator in a testbed ensemble streamflow prediction system by treating VIC-simulated snow water equivalent (SWE) as a known predictor and precipitation and temperature from GCMs as ensemble forecasts, thereby isolating the effect of SWE on streamflow predictability. We assess warm-season mean and maximum flow predictability over 2041–70 and 2071–2100 future periods against the1981–2010 historical period. The results indicate contrasting patterns of change, with the predictive skills for mean flow generally declining for the two basins, and marginally increasing or decreasing for the headwater subbasins. The predictive skill for maximum flow declines for the relatively warmer Athabasca basin and improves for the colder Liard basin and headwater subbasins. While the decreasing skill for the Athabasca is attributable to substantial loss in SWE, the improvement for the Liard and headwaters can be attributed to an earlier maximum flow timing that reduces the forecast horizon and offsets the effect of SWE loss. Overall, while the future change in SWE does affect the streamflow prediction skill, the loss of SWE alone is not a sufficient condition for the reduction in streamflow predictability.
Significance Statement
The purpose of this study is to evaluate potential changes in seasonal streamflow predictability in relation to snowpack change under future climate. This is highly relevant because snowpack storage provides a means of predicting available freshet water supply, as well as peak flow events in cold regions. We use a machine learning model as an emulator of a hydrologic model in a testbed ensemble prediction system. Our results provide insights on hydroclimatic controls and interactions that affect future streamflow predictability across two river basins in western Canada. We conclude that besides snowpack, predictability depends on a number of other factors (basin/subbasin characteristics, streamflow variables, and future periods), and the loss of snowpack alone is not a sufficient condition for the reduction in streamflow predictability.
Abstract
Anthropogenic climate change–induced snowpack loss is affecting streamflow predictability, as it becomes less dependent on the initial snowpack conditions and more dependent on meteorological forecasts. We assess future changes to seasonal streamflow predictability over two large river basins, Liard and Athabasca in western Canada, by approximating streamflow response from the Variable Infiltration Capacity (VIC) hydrologic model with the Bayesian regularized neutral network (BRNN) machine learning emulator. We employ the BRNN emulator in a testbed ensemble streamflow prediction system by treating VIC-simulated snow water equivalent (SWE) as a known predictor and precipitation and temperature from GCMs as ensemble forecasts, thereby isolating the effect of SWE on streamflow predictability. We assess warm-season mean and maximum flow predictability over 2041–70 and 2071–2100 future periods against the1981–2010 historical period. The results indicate contrasting patterns of change, with the predictive skills for mean flow generally declining for the two basins, and marginally increasing or decreasing for the headwater subbasins. The predictive skill for maximum flow declines for the relatively warmer Athabasca basin and improves for the colder Liard basin and headwater subbasins. While the decreasing skill for the Athabasca is attributable to substantial loss in SWE, the improvement for the Liard and headwaters can be attributed to an earlier maximum flow timing that reduces the forecast horizon and offsets the effect of SWE loss. Overall, while the future change in SWE does affect the streamflow prediction skill, the loss of SWE alone is not a sufficient condition for the reduction in streamflow predictability.
Significance Statement
The purpose of this study is to evaluate potential changes in seasonal streamflow predictability in relation to snowpack change under future climate. This is highly relevant because snowpack storage provides a means of predicting available freshet water supply, as well as peak flow events in cold regions. We use a machine learning model as an emulator of a hydrologic model in a testbed ensemble prediction system. Our results provide insights on hydroclimatic controls and interactions that affect future streamflow predictability across two river basins in western Canada. We conclude that besides snowpack, predictability depends on a number of other factors (basin/subbasin characteristics, streamflow variables, and future periods), and the loss of snowpack alone is not a sufficient condition for the reduction in streamflow predictability.
Abstract
In the spring and early summer of 2011, the Assiniboine River basin in Canada experienced an extreme flood that was unprecedented in terms of duration and severity. The flood had significant socioeconomic impacts and caused over $1 billion (Canadian dollars) in damage. Contrary to what one might expect for such an extreme flood, individual precipitation events before and during the 2011 flood were not extreme; instead, it was the cumulative impact and timing of precipitation events going back to the summer of 2010 that played a key role in the 2011 flood. The summer and fall of 2010 were exceptionally wet, resulting in above-normal soil moisture levels at the time of freeze-up. This was followed by record high snow water equivalent values in March and April 2011. Cold temperatures in March delayed the spring melt, resulting in the above-average spring freshet occurring close to the onset of heavy rains in May and June. The large-scale atmospheric flow during May and June 2011 favored increased cyclone activity in the region, which produced an anomalously large number of heavy rainfall events over the basin. All of these factors combined generated extreme flooding. Japanese 55-year Reanalysis Project (JRA-55) data are used to quantify the relative importance of snowmelt and spring precipitation in contributing to the unprecedented flood and to demonstrate how the 2011 flood was unique compared to previous floods. This study can be used to validate and improve flood forecasting techniques over this important basin; the findings also raise important questions regarding floods in a changing climate over basins that experience pluvial and nival flooding.
Abstract
In the spring and early summer of 2011, the Assiniboine River basin in Canada experienced an extreme flood that was unprecedented in terms of duration and severity. The flood had significant socioeconomic impacts and caused over $1 billion (Canadian dollars) in damage. Contrary to what one might expect for such an extreme flood, individual precipitation events before and during the 2011 flood were not extreme; instead, it was the cumulative impact and timing of precipitation events going back to the summer of 2010 that played a key role in the 2011 flood. The summer and fall of 2010 were exceptionally wet, resulting in above-normal soil moisture levels at the time of freeze-up. This was followed by record high snow water equivalent values in March and April 2011. Cold temperatures in March delayed the spring melt, resulting in the above-average spring freshet occurring close to the onset of heavy rains in May and June. The large-scale atmospheric flow during May and June 2011 favored increased cyclone activity in the region, which produced an anomalously large number of heavy rainfall events over the basin. All of these factors combined generated extreme flooding. Japanese 55-year Reanalysis Project (JRA-55) data are used to quantify the relative importance of snowmelt and spring precipitation in contributing to the unprecedented flood and to demonstrate how the 2011 flood was unique compared to previous floods. This study can be used to validate and improve flood forecasting techniques over this important basin; the findings also raise important questions regarding floods in a changing climate over basins that experience pluvial and nival flooding.
Abstract
Freshwater supplies in most western Canadian watersheds are threatened by the warming of temperatures because it alters the snow-dominated hydrologic patterns which characterize these cold regions. In this study, we used datasets from 12 climate simulations associated to seven global climate models and four future scenarios and participating in the Coupled Model Intercomparison Project Phase 6, to calculate and assess the historical and future temporal patterns of 13 hydroclimate indicators relevant to water resources management. We conducted linear long-term trend and change analyses on their annual time series, to provide insight into the potential regional impacts of the detected changes on water availability for all users. We implemented our framework with the Alberta oil sands region in Canada, to support the monitoring of environmental changes in this region, relative to the established baseline 1985-2014. Our analysis indicates a persistent increase in the occurrence of extreme hot temperatures, fewer extreme cold temperatures, and an increase in warm spells and heatwaves, while precipitation-related indices show minor changes. Consequently, deficits in regional water availability during summer and water-year periods, as depicted by the Standardized Precipitation Evapotranspiration indices, are expected. The combined effects of the strong climate warming signals and the small increases in precipitation annual amounts generally detected in this study, suggest that drier conditions may become severe and frequent in the Alberta oil sands region. The challenging climate change risks identified for this region should therefore be continuously monitored, updated, and integrated to support a sustainable management for all water users.
Abstract
Freshwater supplies in most western Canadian watersheds are threatened by the warming of temperatures because it alters the snow-dominated hydrologic patterns which characterize these cold regions. In this study, we used datasets from 12 climate simulations associated to seven global climate models and four future scenarios and participating in the Coupled Model Intercomparison Project Phase 6, to calculate and assess the historical and future temporal patterns of 13 hydroclimate indicators relevant to water resources management. We conducted linear long-term trend and change analyses on their annual time series, to provide insight into the potential regional impacts of the detected changes on water availability for all users. We implemented our framework with the Alberta oil sands region in Canada, to support the monitoring of environmental changes in this region, relative to the established baseline 1985-2014. Our analysis indicates a persistent increase in the occurrence of extreme hot temperatures, fewer extreme cold temperatures, and an increase in warm spells and heatwaves, while precipitation-related indices show minor changes. Consequently, deficits in regional water availability during summer and water-year periods, as depicted by the Standardized Precipitation Evapotranspiration indices, are expected. The combined effects of the strong climate warming signals and the small increases in precipitation annual amounts generally detected in this study, suggest that drier conditions may become severe and frequent in the Alberta oil sands region. The challenging climate change risks identified for this region should therefore be continuously monitored, updated, and integrated to support a sustainable management for all water users.
