ABSTRACT
Global temperatures are increasing, and regional precipitation patterns are changing. Snow is an excellent indicator of regional climate change; 50 years of temperature and precipitation data were analysed from weather stations located within the five most populated cities of Ontario (Canada). Recorded measurements for temperature and precipitation were converted into binary values to indicate the frequency of rain days, snow days, wet days (when total precipitation is greater than 0.2 mm) and freezing days (when the average temperature is less than 0 °C); then, these values were summed over each winter season from 1970/71 to 2019/20. The snow-day fraction was calculated from the seasonal totals by dividing the total number of snow days by the total number of wet days. Historical trends were detected using Pearson's R, Kendall's Tau and Spearman's Rho. Differences in mean values between the first decade (1971-1980) and the last decade (2011-2020) within the time series for the snow-day fraction and total freezing days were determined using Student's t-tests. During the winter season in southern Ontario (December 1 to March 31), total snow days, total wet days, the snow-day fraction and freezing days were all decreasing at statistically significant rates (90 to 99% confidence levels) across four of the five cities studied (Toronto, Ottawa, Hamilton and London). Mississauga was the exception, being the only city where rain days were increasing, but no trends were detected for snow days or wet days. The snow-day fraction was decreasing in Mississauga but not at a statistically significant rate, despite freezing days decreasing at the greatest rate compared to the other four cities. Total freezing days were highly correlated with the snow-day fraction during the winter season, being able to explain 61 to 76 percent of the observed variability, where Mississauga recorded the weakest correlation and London recorded the strongest correlation.
ABSTRACT
Coastal tourism is impacted by regional environmental change, including sea-level rise and climatic change. A case study at five Japanese beaches was conducted to determine the relative difference in coastal and climatic changes that are important for recreational beach tourism. Future climate conditions and projections of shoreline changes due to sea-level rise were estimated using ensembles of 14 and 21 Global Circulation Models (GCMs), respectively. The Holiday Climate Index (HCI:Beach) specific to recreational beach use was used to assess historical and future climatic suitability while beach loss area was used to quantify physical changes to beach conditions. Diminished beaches with improved climate conditions were observed at all sites except Yonehara, which had both diminished beach and climate conditions. Comparing HCI:Beach scores relative to historical conditions revealed a change of -2.4 to +10%, while beach losses were typically >60%. These results highlight that beach tourism suitability at the study sites will likely exhibit greater changes due to sea-level rise, compared to those driven by changing atmospheric conditions. Supplementary Information: The online version contains supplementary material available at 10.1007/s10113-022-01906-2.
ABSTRACT
Wind speed analysis is important for informing airport operation and safety. Many communities in the Hudson Bay and Labrador regions (Canada) are remote communities that rely heavily on aircraft for passenger and freight movement. Historical trends in average daily wind speed and maximum daily wind speed from 1971 to 2010 were examined to identify patterns of change and determine how these changes may influence aviation in six northern communities across Hudson Bay and Labrador in Canada. Significant increases in average wind speed and maximum wind speed were found for some of the months and seasons of the year for the Hudson Bay region, along with a significant decrease in those variables for the Labrador communities. Average wind speeds at multiple locations are approaching the threshold (18.5 km/h or 10 knots) when take-off and landing would be restricted to one direction. The results of this study agree with previous research that examined historical patterns for wind speed in these regions but calls into question climate change impact assessments that suggest wind speeds will continue to increase under future climatic conditions for this study area. Future research is needed to further analyse shifts in prevailing wind directions and changes in the frequency of extreme wind conditions, to better understand the potential impacts of projected climate change on this climatic variable and the implications these changes may have on applied sectors, such as aviation.
Subject(s)
Aviation , Wind , Climate Change , Newfoundland and Labrador , SeasonsABSTRACT
This study further develops and finally validates the Climate Model Confidence Index (CMCI) as a simple and effective metric for evaluating and ranking the ability of climate models to reproduce historical climate conditions. Modelled daily climate data outputs from two different statistical downscaling techniques (PCIC: Pacific Climate Impacts Consortium; SDSM: Statistical Down-Scaling Model) are compared with observational data recorded by Environment Canada weather stations located in Kelowna, BC (Canada), for the period from 1969 to 2005. Using daily data (N > 13,000), Student's t-tests determined if there were statistically significant differences between the modelled and observed means while ANOVA F-tests identified differences between variances. Using aggregated annual data (N = 37), CMCI values were also calculated for the individual model runs from each statistical downscaling technique. Climate model outputs were ranked according to the absolute value of the t statistics. The 20 SDSM ensembles outperformed the 27 PCIC models for both minimum and maximum temperatures, while PCIC outperformed SDSM for total precipitation. Linear regression determined the correlation between the absolute value of the t statistics and the corresponding CMCI values (R 2 > 0.99, P < 0.001). Rare discrepancies (< 10% of all model rankings) between the t statistic and CMCI rankings occurred at the third decimal place and resulted in a one rank difference between models. These discrepancies are attributed to the precision of the t tests which rely on daily data and consider observed as well as modelled variance, whereas the simplicity and utility of the CMCI are demonstrated by only requiring annual data and observed variance to calculate.
ABSTRACT
Parks and protected areas represent an important resource for tourism in Canada, in which camping is a common recreational activity. The important relationship between weather and climate with recreation and tourism has been widely acknowledged within the academic literature. Howbeit, the need for activity-specific assessments has been identified as an on-going need for future research in the field of tourism climatology. Furthermore, very little is known about the interrelationships between personal characteristics and socio-demographics with weather preferences and behavioural thresholds. This study uses a stated climate preferences approach (survey responses) to explore differences in the importance of weather and related weather-based decisions among summer campers in Ontario parks. Statistically significant differences were found among campers for each of the four dependent variables tested in this study. Physically active campers placed greater importance on weather but were still more tolerant of adverse weather conditions. Older campers placed greater importance on weather. Campers travelling shorter distances placed greater importance on weather and were more likely to leave the park early due to adverse weather. Campers staying for longer periods of time were less likely to leave early due to weather and were willing to endure longer durations of adverse weather conditions. Beginner campers placed greater importance on weather, were more likely to leave early due to weather and recorded lower temporal weather thresholds. The results of this study contribute to the study of tourism climatology by furthering understanding of how personal characteristics such as gender, age, activity selection, trip duration, distance travelled, travel experience and life cycles affect weather preferences and decisions, focusing this time on recreational camping in a park tourism context.
Subject(s)
Camping/psychology , Weather , Adolescent , Adult , Decision Making , Female , Humans , Male , Middle Aged , Ontario , Parks, Recreational , Young AdultABSTRACT
Based on a case study of the Toronto Zoo (Canada), multivariate regression analysis, involving both climatic and social variables, was employed to assess the relationship between daily weather and visitation. Zoo visitation was most sensitive to weather variability during the shoulder season, followed by the off-season and, then, the peak season. Temperature was the most influential weather variable in relation to zoo visitation, followed by precipitation and, then, wind speed. The intensity and direction of the social and climatic variables varied between seasons. Temperatures exceeding 26 °C during the shoulder season and 28 °C during the peak season suggested a behavioural threshold associated with zoo visitation, with conditions becoming too warm for certain segments of the zoo visitor market, causing visitor numbers to decline. Even light amounts of precipitation caused average visitor numbers to decline by nearly 50 %. Increasing wind speeds also demonstrated a negative influence on zoo visitation.
