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William A. Gough

Abstract

A newly developed precipitation phase metric is used to detect the impact of urbanization on the nature of precipitation at Toronto, Ontario, Canada, by contrasting the relative amounts of rain and snow. 162 years of observed precipitation data were analyzed to classify the nature of winter season precipitation for the Canadian city of Toronto. In addition shorter records were examined for nearby climate stations in less urbanized areas in and near Toronto. For Toronto, all winters from 1849 to 2010 as well as three climate normal periods (1961-1990, 1971-2000, 1981-2010) were thus categorized for the Toronto climate record. The results show that Toronto winters have become increasingly “rainy” across these time-periods in a statistically significant fashion, consistent with a warming climate. Toronto was compared to the other less urban sites to tease out the impacts of the urban heat island from larger scale warming. This yielded an estimate of 19 to 27% of the Toronto shift in precipitation type (snow to rain) that can be attributed to urbanization for coincident time periods. Other regions characterized by similar climates and urbanization with temperatures near the freezing point are likely to experience similar climatic changes expressed as a change in the phase of winter season precipitation.

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William A. Gough

Abstract

A new thermal metric is examined that is based on the ratio of day-to-day warm and cold surface temperature transitions. Urban and rural sites in Canada are examined using this new metric for the minimum temperature, maximum temperature, and mean temperature of the day. A distinctive signature emerges for “peri-urban” landscapes—landscapes at the urban–rural interface—and thus may provide a useful and relatively easy way to detect such environments using the current and historical climate records. A climatological basis for the presence of these distinct thermal signatures in peri-urban landscapes is proposed.

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Conor I. Anderson and William A. Gough

Abstract

Globally, 2014 and 2015 were the two warmest years on record. At odds with these global records, eastern Canada experienced pronounced annual cold anomalies in both 2014 and 2015, especially during the 2013/14 and 2014/15 winters. This study sought to contextualize these cold winters within a larger climate context in Toronto, Ontario, Canada. Toronto winter temperatures (maximum T max, minimum T min, and mean T mean) for the 2013/14 and 2014/15 seasons were ranked among all winters for three periods: 1840/41–2015 (175 winters), 1955/56–2015 (60 winters), and 1985/86–2015 (30 winters), and the average warming trend for each temperature metric during these three periods was analyzed using the Mann–Kendall test and Thiel–Sen slope estimation. The winters of 2013/14 and 2014/15 were the 34th and 36th coldest winters in Toronto since record-keeping began in 1840; however these events are much rarer, relatively, over shorter periods of history. Overall, Toronto winter temperatures have warmed considerably since winter 1840/41. The Mann–Kendall analysis showed statistically significant monotonic trends in winter T max, T min, and T mean over the last 175 and 60 years. These trends notwithstanding, there has been no clear signal in Toronto winter temperature since 1985/86. However, there was a statistically significant increase in the diurnal temperature range in that period, indicating an expansion of winter extremes. It is proposed that the possible saturation of urban heat island–related warming in Toronto may partially explain this increase in variation. Also, anomalies in the position of the polar jet stream over Toronto during these cold events are identified. No direct influence of major teleconnections on Toronto winter temperature is found.

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Micah J. Hewer and William A. Gough

Abstract

Because of the perceived weather sensitivity of park visitation in Ontario, Canada, several previous assessments have examined the impact of climate change. However, these assessments have predominantly been based on modeling approaches (regression analysis). The current study uses a multiyear temporal climate-analog approach to reassess the impact of climate change on visitation to Pinery Provincial Park in southwestern Ontario based on the observed effects of historical climatic anomalies on park visitation from 2000 to 2016. Consideration was also given to major events such as the North American terror attacks on 11 September 2001 and the confounding effect that events such as this may have had on the results. There were no statistically significant relationships (at the 95% confidence level) between seasonal climatic anomalies and park visitation in Ontario during the winter or spring seasons. There was a weak statistical relationship between anomalously warm summer seasons and park visitation, when compared to summer seasons with climatically normal temperatures; however, the presence of nonclimatic variables may have confounded these results, producing a false positive. Autumn-season park visitation was most sensitive to climatic anomalies, with the warmest temperatures causing visitation to increase by 37%, the wettest conditions causing visitation to decrease by 11%, and the driest conditions resulting in a 24% increase. These observed seasonal temperature anomalies represent temporal climate analogs for projected climate change across the span of the twenty-first century. Thus, the results of this study suggest that previous assessments may have overestimated the positive impacts of projected climate change on park visitation in this region.

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Jerry Y. Jien, William A. Gough, and Ken Butler

Abstract

The interannual variability of tropical cyclone (TC) activity due to El Niño–Southern Oscillation (ENSO) in the main development region of the eastern North Pacific basin has received scant attention. Herein the authors classify years of El Niño, La Niña, and neutral conditions using the multivariate ENSO index (MEI). Storm measurements of the net tropical cyclone activity index and power dissipation index are used to summarize the overall seasonal TC activity and TC intensity between 1971 and 2012. Both measures are found to be statistically dependent on the ENSO phases in the basin’s main development region. However, when the area is longitudinally divided, only the western portion of the development region experienced a significant difference (p < 0.05). Specifically, El Niño years are characterized by more frequent, more intense events compared to La Niña conditions for this subregion. Correlation analyses on the relationships between the MEI and both TC indices demonstrate correlations between ENSO and TC activity and intensity that are statistically significant (p < 0.05) only in the western region. These relationships have the potential to improve the short-term forecast of the local TC activity and intensity on a seasonal basis for public awareness and disaster preparation.

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Vincent Y. S. Cheng, George B. Arhonditsis, David M. L. Sills, William A. Gough, and Heather Auld

Abstract

Destruction and fatalities from recent tornado outbreaks in North America have raised considerable concerns regarding their climatic and geographic variability. However, regional characterization of tornado activity in relation to large-scale climatic processes remains highly uncertain. Here, a novel Bayesian hierarchical framework is developed for elucidating the spatiotemporal variability of the factors underlying tornado occurrence in North America. It is demonstrated that regional variability of tornado activity can be characterized using a hierarchical parameterization of convective available potential energy, storm relative helicity, and vertical wind shear quantities. It is shown that the spatial variability of tornado occurrence during the warm summer season can be explained by convective available potential energy and storm relative helicity alone, while vertical wind shear is clearly better at capturing the spatial variability of the cool season tornado activity. The results suggest that the Bayesian hierarchical modeling approach is effective for understanding the regional tornadic environment and in forming the basis for establishing tornado prognostic tools in North America.

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Vincent Y. S. Cheng, George B. Arhonditsis, David M. L. Sills, Heather Auld, Mark W. Shephard, William A. Gough, and Joan Klaassen

Abstract

The number of tornado observations in Canada is believed to be significantly lower than the actual occurrences. To account for this bias, the authors propose a Bayesian modeling approach founded upon the explicit consideration of the population sampling bias in tornado observations and the predictive relationship between cloud-to-ground (CG) lightning flash climatology and tornado occurrence. The latter variable was used as an indicator for quantifying convective storm activity, which is generally a precursor to tornado occurrence. The CG lightning data were generated from an 11-yr lightning climatology survey (1999–2009) from the Canadian Lightning Detection Network. The results suggest that the predictions of tornado occurrence in populated areas are fairly reliable with no profound underestimation bias. In sparsely populated areas, the analysis shows that the probability of tornado occurrence is significantly higher than what is represented in the 30-yr data record. Areas with low population density but high lightning flash density demonstrate the greatest discrepancy between predicted and observed tornado occurrence. A sensitivity analysis with various grid sizes was also conducted. It was found that the predictive statements supported by the model are fairly robust to the grid configuration, but the population density per grid cell is more representative to the actual population density at smaller resolution and therefore more accurately depicts the probability of tornado occurrence. Finally, a tornado probability map is calculated for Canada based on the frequency of tornado occurrence derived from the model and the estimated damage area of individual tornado events.

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Nirnimesh Kumar, James A. Lerczak, Tongtong Xu, Amy F. Waterhouse, Jim Thomson, Eric J. Terrill, Christy Swann, Sutara H. Suanda, Matthew S. Spydell, Pieter B. Smit, Alexandra Simpson, Roland Romeiser, Stephen D. Pierce, Tony de Paolo, André Palóczy, Annika O’Dea, Lisa Nyman, James N. Moum, Melissa Moulton, Andrew M. Moore, Arthur J. Miller, Ryan S. Mieras, Sophia T. Merrifield, Kendall Melville, Jacqueline M. McSweeney, Jamie MacMahan, Jennifer A. MacKinnon, Björn Lund, Emanuele Di Lorenzo, Luc Lenain, Michael Kovatch, Tim T. Janssen, Sean Haney, Merrick C. Haller, Kevin Haas, Derek J. Grimes, Hans C. Graber, Matt K. Gough, David A. Fertitta, Falk Feddersen, Christopher A. Edwards, William Crawford, John Colosi, C. Chris Chickadel, Sean Celona, Joseph Calantoni, Edward F. Braithwaite III, Johannes Becherer, John A. Barth, and Seongho Ahn

Abstract

The inner shelf, the transition zone between the surf zone and the mid shelf, is a dynamically complex region with the evolution of circulation and stratification driven by multiple physical processes. Cross-shelf exchange through the inner shelf has important implications for coastal water quality, ecological connectivity, and lateral movement of sediment and heat. The Inner-Shelf Dynamics Experiment (ISDE) was an intensive, coordinated, multi-institution field experiment from Sep.-Oct. 2017, conducted from the mid shelf, through the inner shelf and into the surf zone near Point Sal, CA. Satellite, airborne, shore- and ship-based remote sensing, in-water moorings and ship-based sampling, and numerical ocean circulation models forced by winds, waves and tides were used to investigate the dynamics governing the circulation and transport in the inner shelf and the role of coastline variability on regional circulation dynamics. Here, the following physical processes are highlighted: internal wave dynamics from the mid shelf to the inner shelf; flow separation and eddy shedding off Point Sal; offshore ejection of surfzone waters from rip currents; and wind-driven subtidal circulation dynamics. The extensive dataset from ISDE allows for unprecedented investigations into the role of physical processes in creating spatial heterogeneity, and nonlinear interactions between various inner-shelf physical processes. Overall, the highly spatially and temporally resolved oceanographic measurements and numerical simulations of ISDE provide a central framework for studies exploring this complex and fascinating region of the ocean.

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