Possible Impacts of Climate Change on Wind Gusts under Downscaled Future Climate Conditions over Ontario, Canada

Abstract

Hourly/daily wind gust simulation models and regression-based downscaling methods were developed to assess possible impacts of climate change on future hourly/daily wind gust events over the province of Ontario, Canada. Since the climate/weather validation process is critical, a formal model result verification process has been built into the analysis to ascertain whether the methods are suitable for future projections. The percentage of excellent and good simulations among all studied seven wind gust categories ranges from 94% to 100% and from 69% to 95%, respectively, for hourly and daily wind gusts, for both model development and validation.The modeled results indicate that frequencies of future hourly/daily wind gust events are projected to increase late this century over the study area under a changing climate. For example, across the study area, the annual mean frequency of future hourly wind gust events $28, $40, and $70 km h 21 for the period 2081-2100 derived from the ensemble of downscaled eight-GCM A2 simulations is projected to be about 10%-15%, 10%-20%, and 20%-40% greater than the observed average during the period 1994-2007, respectively. The corresponding percentage increase for future daily wind gust events is projected to be ,10%, ;10%, and 15%-25%. Inter-GCM-model and interscenario uncertainties of future wind gust projections were quantitatively assessed. On average, projected percentage increases in frequencies of future hourly/daily wind gust events $28 and $40 km h 21 are about 90%-100% and 60%-80% greater than inter-GCMmodel-interscenario uncertainties, respectively. For wind gust events $70 km h 21 , the corresponding projected percentage increases are about 25%-35% greater than the interscenario uncertainties and are generally similar to inter-GCM-model uncertainties.

Current gust forecasting techniques, developments and challenges

“…Recent studies that do address climate impacts include Cheng et al (2012aCheng et al ( , 2014, who employ statistical downscaling from a global circulation model (GCM) and a gust factor vs. wind speed characteristic, Seregina et al (2014) (discussed above), and Hewston and Dorling (2011), who use UK routine observation sites for historic gust climate variability and a regional climate model's daily maximum wind as a proxy for gust in projections to a future climate.…”

“…They then constructed relationships between Weibull distribution parameters for extremes of the (sustained) wind and those for extreme gusts, so that synthetic gusts could be obtained at further sites reporting only sustained wind, enabling a more comprehensive gust return period analysis using a 10-year dataset. Others include Hewston and Dorling (2011), Thorarinsdottir and Johnson (2012), Cheng et al (2012aCheng et al ( , 2014, , , Efthimiou et al (2017b), Efthimiou et al (2017a).…”

Evaluating the effects of land use and cover change on the decrease of surface wind speed over China in recent 30 years using a statistical downscaling method

“…Statistical downscaling is a valid method to diminish the error in climate prediction versus observation that was induced by the local characteristics missing in the general circulation models or regional climate models (Busuioc et al 1999;Murphy 1999;Wilby et al 1999;Schubert 1998;Winkler et al 1997). A wide range of wind variables have been predicted (Kirchmeier et al 2014), including wind speed (De Rooy and Kok 2004), u and v components (Monahan 2012), wind gusts (Cheng et al 2012), maximum wind speed (Yan et al 2002), and energy density (Pryor et al 2005), and some SDMs include neural networks (Sailor et al 2000), probability methods (Kirchmeier et al 2014), multivariable linear regression methods , the combined statistical-dynamical methods (Najac et al 2011), and multiple linear regression models (Haas and Pinto 2012;Goubanova et al 2010). These studies show that statistical downscaling can accurately capture large-scale information and regional climatic characteristic based on long-term observation data (predictand) and reanalysis data (predictor).…”

Combined Use of Wind-Driven Rain Load and Potential Evaporation to Evaluate Moisture Damage Risk: Case Study on the Parliament Buildings in Ottawa, Canada

“…Precipitation duration was assumed to remain constant. Furthermore, the effects of climate change on mean wind velocity were assumed to be relatively modest [3,50], so no additional modification was made for the wind-flow conditions. We note that the calculated catch ratios for the building can also be used for WDR predictions with climate change, since catch ratios are given as a function of horizontal rain intensity and wind speed for different wind directions.…”