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  • View in gallery

    Map of southwestern Ontario showing Pinery Provincial Park and the eight closest weather stations, along with their respective climatic-distance rankings.

  • View in gallery

    The effect of seasonal climatic anomalies on winter-season [December–February (DJF)] visitation to Pinery Provincial Park (2000–16).

  • View in gallery

    As in Fig. 2, but for spring [March–May (MAM)].

  • View in gallery

    As in Fig. 2, but for summer [June–August (JJA)].

  • View in gallery

    As in Fig. 2, but for autumn [September–November (SON)].

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Using a Multiyear Temporal Climate-Analog Approach to Assess Climate Change Impacts on Park Visitation

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  • 1 Department of Physical and Environmental Sciences, University of Toronto Scarborough, Toronto, Ontario, Canada
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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.

Denotes content that is immediately available upon publication as open access.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Micah J. Hewer, micah.hewer@utoronto.ca

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.

Denotes content that is immediately available upon publication as open access.

© 2019 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Micah J. Hewer, micah.hewer@utoronto.ca

1. Introduction

The strong relationship between weather and climate with outdoor recreation and tourism (ORT) is now widely understood within the international academic literature, as evidenced by the substantive literature now representing the growing field, referred to as “tourism climatology” (de Freitas 1990, 2003; Hewer et al. 2015, 2018). Given the well-documented relationship between weather and climate with ORT, and the growing evidence of a changing global climate system (IPCC 2001, 2007, 2014), numerous climate change impact assessments (CCIAs) have been produced on a global scale—again, as evidenced by the various literature reviews on climate change and tourism that have emerged in recent years (Scott et al. 2012; Gössling et al. 2012; Becken 2013; Kaján and Saarinen 2013; Rosselló-Nadal 2014; Hewer and Gough 2018).

Given the importance of this industry to local, regional, and national economies in Canada and the perceived sensitivity of ORT to weather and climate variability, several CCIAs have been conducted on various activities across the country over the last three decades, as covered in the Canadian literature review by Hewer and Gough (2018). Parks and protected areas as a place for ORT in Canada have received considerable attention within the Canadian scholarship (Hewer and Gough 2018). The very first CCIA on ORT ever published in a peer-reviewed academic journal worldwide was conducted by Wall et al. (1986) and focused on camping in the Canadian province of Ontario. It was not until 20 years later that Jones and Scott (2006a) modeled the potential impact of projected climate change on visitation to Ontario’s provincial parks system. Using the same method, Jones and Scott (2006b) did the same for Canada’s system of national parks. Scott et al. (2007) added a survey-based component to the modeling-based assessment to better understand the impact of direct climate change as well as climate-induced environmental change on visitation to Waterton Lakes National Park in Canada’s Rocky Mountain region. Scott et al. (2008) then applied that same survey-based approach to assessing the potential impact of climate-induced environmental change on park visitor decision-making to Banff, Canada’s most popular national park, forming somewhat of a regional assessment in conjunction with the previous results from Waterton National Park, as both parks are located within the Canadian Rocky Mountains. Most recently, Hewer et al. (2016) obtained daily weather and park visitation data to reassess the impact of projected climate change on park visitation in Ontario, based on a case study of Pinery Provincial Park, to explore differences in the projected impacts related to more current climate science [global climate model (GCM) outputs from IPCC’s (2014) Fifth Assessment Report (AR5) rather than IPCC’s (2001) Third Assessment Report] as well as due to more sophisticated predictive models based on finer temporal scales (actual daily data rather than aggregate monthly data). Although not directly related to visitation, implications can be inferred from several studies that have also modeled considerable climate-induced environmental changes, which are likely to indirectly impact park visitation in Canada (Suffling and Scott 2002; Scott et al. 2002; Lemieux et al. 2007). Detailed discussion concerning the methods and results associated with these previous CCIAs on parks and protected areas in Canada can be found in the literature review by Hewer and Gough (2018).

Although there have been other CCIAs on parks and protected areas conducted outside of Canada, especially within the United States (Richardson and Loomis 2004; Loomis and Richardson 2006; Buckley and Foushee 2012; Albano et al. 2013; Monahan and Fisichelli 2014; Fisichelli et al. 2015), these previous assessments have also relied exclusively on a variety of modeling-based approaches, and many have also been subject to the limitations associated with using aggregate monthly data to model the impact of projected climate change on future park visitation. Modeling-based approaches generally establish statistical relationships between weather and climate, with participation based on monthly or daily data, and then use that statistical relationship to model how participation will respond to a changed climate in the future. The temporal-analog approach, on the other hand, looks for an anomalous climatic event in the past that is likely to become the norm in the future (under projected climate change) and then uses the way participation responded to these climatic anomalies in the past to provide insights into how participation is likely to respond in the future. There has been some empirical evidence produced to suggest that the modeling approach may overestimate the impact of projected climate change on tourism participation when compared with other methods of CCIA (Dawson et al. 2009; Steiger 2011) and especially when the modeling approach is reliant upon coarse-resolution monthly data (Hewer et al. 2016). Because of these contentions, the analog approach has been commended within the international literature as a positive alternative to the modeling approach for CCIAs on ORT. For example, when reviewing tourism demand response studies, Gössling et al. (2012) concluded that modeling studies have a wide range of uncertainties regarding behavioral response, but climate analogs may provide more robust insights. Scott et al. (2012), however, suggested that tourists have the greatest capacity to adapt to the risks and opportunities posed by climate change, a factor responsible for much of the uncertainty in the modeling approach, yet contend that there remains much scope to better understand the adaptive capacity of tourists and tourism operators alike by assessing climate-analog events.

The use of analogs to better understand the impacts of weather and climate on society was first introduced by Glantz (1988, 1990, 1991). Analogs help to assess impacts while identifying and characterizing determinants by using what is known about the present to better understand the future (Hewer and Gough 2016b). According to Ford et al. (2010), analog methods have proven useful for helping to understand how climate affects society over time. Analogs can provide critical insights into the interactions between climate and society, including impacts, vulnerability, and adaptive capacity (Hewer and Gough 2016b). It is the contention of Ford et al. (2006) that temporal analogs can be used to better understand how human systems manage and experience risk associated with climatic variability and change. The temporal-analog method assumes that human systems in the near future will respond as they have in the recent past, being influenced by similar conditions and processes (Ford et al. 2009). As a result, Hewer and Gough (2016b) argue that this approach provides empirical evidence to support the analysis of sensitivity, vulnerability, and adaptation in climate change impact assessments.

Despite the potential to offer new insights into future impacts and the effectiveness of adaptations, the analog approach remains underutilized in CCIAs for ORT, apart from a limited number of studies conducted in recent years (Scott 2005; Dawson et al. 2009; Steiger 2011; Hewer and Gough 2016b). According to Dawson et al. (2009), because climate change impacts are assessed during real events, which include human adaptation strategies made during a historical climate anomaly, temporal analogs are useful for identifying potential future climate change impacts. Scott et al. (2012) argue that the ability of temporal climate analogs to capture the full range of supply-side and demand-side adaptations within CCIAs for ORT is a key advantage of this approach. Furthermore, since analogs provide insights into fully contextualized human adaptation, while regression analysis can only offer highly abstracted projections of potential adaptation, this approach has received commendation as an effective alternative to the conventional modeling approach for CCIAs on ORT (Hewer and Gough 2016b).

No study to date has utilized the analog approach for a CCIA on visitation to parks or protected areas. The current study aims to adapt the multiyear temporal climate-analog approach developed by Hewer and Gough (2016b), which was originally applied to zoo attendance in Toronto, Ontario, Canada, to reassess the potential impacts of projected climate change on park visitation, based on a case study of Pinery Provincial Park in southwestern Ontario. The selection of Pinery Provincial Park as a case study for park visitation in Ontario can be merited for several reasons: 1) Pinery has the greatest number of campsites in the province and welcomes the greatest number of visitors each year; 2) Pinery has been the focus of a recent modeling study reliant on daily data (Hewer et al. 2016); and 3) Pinery was also one of the parks selected for the modeling study by Jones and Scott (2006a), based on monthly data. Therefore, Pinery is a meaningful and representative case study within Ontario’s system of provincial parks and will enable insightful comparisons between modeling and analog approaches to CCIA for ORT.

Pinery Provincial Park operates year-round, with activities ranging from ice fishing, snow shoeing, and cross-country skiing in the winter to swimming, hiking, cycling, fishing, and boating in the summer. Visitation peaks in the summer months of July and August, with 93% of annual visitation in 2010 arriving during these two months, according to the last park statistics report published by Ontario Parks (2011). The shoulder season in Ontario parks spans from the Victoria Day holiday long weekend at the end of May to the Thanksgiving holiday long weekend in October (excluding the peak season months of July and August). This report also indicated that visitation to Pinery consisted of 20% day users and 80% overnight campers, with visitors staying at the park for 4 days, on average (Ontario Parks 2011). According to the last campground visitor survey published by Ontario Parks (2012), 83% of visitors to the southwest park region of Ontario, where Pinery is located, were Canadian born, with only 5% of visitors coming from the United States and the other 12% coming from other countries. On average, visitors traveled 230 km to arrive at their destination, and of the 65 908 campers surveyed in 2011, 96% stated that they visited Ontario parks for rest and relaxation, with 82% of visitors to the southwest park region reporting that they went swimming and only 11% having gone fishing (Ontario Parks 2012).

2. Methods

a. Park visitation data

The Ontario Parks agency, operating on behalf of the Ontario Ministry of Natural Resources, agreed to supply daily park visitation data from 1 January 2000 to 31 December 2016 for Pinery Provincial Park on Lambton Shores near Grand Bend in southwestern Ontario, Canada (Fig. 1). These data did not distinguish among different types of visitors (to classify users as individuals or visitors traveling as couples or in family groups). The dataset also did not provide any qualifying information, such as distance traveled to reach the park (to distinguish among visitors that would be classified as local residents, domestic tourists, or international tourists). Furthermore, the dataset did not contain any demographic information such as age or gender of the visitors. These factors have been identified within the tourism climatology literature as having a direct effect on the importance of weather, temperature preferences, and thresholds, as well as weather-based decisions for park visitors in Ontario (Hewer et al. 2017, 2018). In addition, the daily data did not indicate the timing of visitation on a particular day (since it was not recorded on an hourly time scale), which has been found to be important within the tourism climatology literature, especially for the analysis of precipitation events (Scott and Jones 2006, 2007). Despite these limitations, the 17 years of daily park visitation data used in this study provided an excellent historical record from which to conduct an effective empirical investigation into the impact of seasonal climatic anomalies on park visitation in Ontario and to reassess the potential impacts of projected climate, this time using a multiyear temporal climate-analog approach.

Fig. 1.
Fig. 1.

Map of southwestern Ontario showing Pinery Provincial Park and the eight closest weather stations, along with their respective climatic-distance rankings.

Citation: Weather, Climate, and Society 11, 2; 10.1175/WCAS-D-18-0025.1

b. Historical weather data

It has been common practice for previous studies in tourism climatology and CCIAs on ORT to obtain relevant weather data from the closest meteorological station in proximity to the tourism attraction being studied (Jones and Scott 2006a,b; Hewer et al. 2016; Hewer and Gough 2016a,c). Yet, there has been growing criticism for this method of selecting a representative weather station for use in tourism climatology case studies, since certain stations have been located considerable distances from the attraction itself and may be considered to be nonrepresentative because of the presence of microclimates associated with many tourism destinations, especially parks and protected areas (Rutty and Andrey 2014; Rutty and Scott 2014). Conveniently, this study was well positioned to take advantage of a recently introduced method for selecting the most representative weather station (Hewer and Gough 2016b), since there were daily meteorological data recorded by a former Environment Canada weather station at the study site itself, for the period from 1 January 1980 to 31 December 1983 (4 years). This study required daily weather data from 1981 to 2010 (30 years) to establish the baseline climate conditions for the region, as well as daily weather data from 2000 to 2016 (17 years) to assess the impact of seasonal climatic anomalies on park visitation. Following the method detailed by Hewer and Gough (2016b), instead of selecting a weather station based on physical distance alone, this study ranked the eight closest weather stations (Fig. 1) in proximity to Pinery Provincial Park, based on their ability to represent average monthly climate conditions recorded at the former park station from 1980 to 1983. Daily weather data were available for each of the test stations from 1980 to 1983 and were used to conduct the climatic-distance analysis to select a representative weather station for this study.

Using the same method for establishing climatic distance that was first introduced and described in detail by Hewer and Gough (2016b), each test station was ranked on its ability to mirror climate conditions at the park during the period from 1980 to 1983 (Table 1). The formula used for calculating climatic distance for each of the weather variables considered was
eq1
where the variables are defined in Hewer and Gough (2016b). It is understood from the literature that temperature is the greatest climatic predictor of park visitation in North America (Jones and Scott 2006a,b; Scott et al. 2007; Loomis and Richardson 2006; Albano et al. 2013; Fisichelli et al. 2015; Hewer et al. 2016). For this reason, climatic distance was evaluated on the basis of two temperature variables [maximum temperature (Tmax) and minimum temperature (Tmin)] and one only precipitation variable [total precipitation (totP)]. Therefore, the ability of the test station to mirror temperature conditions was weighted more heavily within the climatic-distance metric than the ability to mirror precipitation conditions. Of the eight stations evaluated, two resulted in the same mean rank for climatic distance and thus are recorded as 5a and 5b. Woodstock was given the closer ranking (5a) over Stratford (5b) because it recorded the closest Tmax Cdist. Several interesting findings emerge from the climatic-distance analysis. First, there appears to be somewhat of a theoretical threshold: when physical distance becomes greater than 50 km from the study site, the climatic distance is also greater than test sites located fewer than 50 km from the study site. Furthermore, the station that was physically closest to Pinery (Exeter, Ontario) was only the fourth-closest climatically. This is important to note, as the Exeter station was selected to inform the CCIA on visitation to Pinery as conducted by Hewer et al. (2016). Additionally, the Sarnia, Ontario, station, which was selected to inform the CCIA on Pinery by Jones and Scott (2006a), was only the third-closest station, both physically and climatically. From this analysis, the Strathroy, Ontario, weather station was selected for this CCIA, and daily temperature and precipitation data were retrieved from Environment Canada for the baseline period from 1981 to 2010, as well as for the analysis period from 2000 to 2016.
Table 1.

Using the climatic-distance metric to rank and select representative weather stations for Pinery Provincial Park.

Table 1.

c. Climate change projections

To employ a temporal climate-analog approach for a CCIA on participation in ORT, it is necessary to refer to the available projections for future climate in this region. This study uses the selective-ensemble approach that was first introduced by Hewer and Gough (2016a), for which GCM output from the Intergovernmental Panel on Climate Change’s (IPCC 2013) AR5 can be obtained from the Coupled Model Intercomparison Project of the World Climate Research Programme. There are 40 different GCMs available to provide projections of future climate change from the most recent IPCC assessment (IPCC 2014). Beyond these 40 different GCMs, each of the modeling centers provides future projections for up to four different representative concentration pathways (RCPs), which describe how greenhouse gas concentrations could evolve over the twenty-first century and thereby influence global climate in the future. Fenech et al. (2007) outlined the many approaches that have been developed to provide some direction for determining which of the future projections of climate available for impact assessments should be used in planning. According to the IPCC (2010), when compared with historical observed gridded data, climate projections using the ensemble approach have been shown to come closest to replicating the historical climate. It was the contention of Hewer et al. (2016) in using this approach that it is best to plan for the average climate change from all the climate model projections by using a mean of all the models to reduce the uncertainty associated with any individual model. The argument was based on the notion that the individual model biases effectively offset one another when considered together (Fenech et al. 2007; IPCC 2010; Hewer et al. 2016).

The basis of the rationale for using the selective-ensemble approach introduced by Hewer and Gough (2016a) rather than the full ensemble approach applied by Hewer et al. (2016) is that it has been generally accepted that climate models can be evaluated based on their ability to reproduce baseline conditions (Randall et al. 2007). Furthermore, it has been acknowledged that some climate models perform better in certain regions than they do in others (Macadam et al. 2010). For this reason, Hewer and Gough (2016a,b) argued that it is unreasonable to create a “full” ensemble including all the available GCMs when it is evident that some models are unable to reproduce past climate for the study region. Using this logic, they contend that it is more appropriate to evaluate each model individually on the basis of its ability to reproduce past climate and then to rank and select the best three models to create a “selective” ensemble from these top-performing models. For greater detail pertaining to the process and statistical method of ranking and selecting GCMs for CCIAs, including the use of the Gough–Fenech confidence index (GFCI) and the creation of selective ensembles, see Hewer and Gough (2016a). As a product of this approach, Table 2 presents the selective ensemble of seasonal GCM outputs for Tmax and totP at Strathroy from 2011 to 2100 under RCP4.5 and RCP8.5. Note that RCP4.5 represents a low radiative forcing, stabilization scenario, whereas RCP8.5 represents a high radiative forcing, increased emissions scenario (IPCC 2014).

Table 2.

Selective ensemble of seasonal climate change projections for Strathroy from GCM outputs ranked and selected using the GFCI.

Table 2.

d. The effect of seasonal climatic anomalies

Following the methods detailed by Hewer and Gough (2016b), seasonal climatic anomalies were identified by determining the seasonal climate normals (30-yr averages) for both daily maximum temperature (Tmax) and daily total precipitation (totP), but this time at Strathroy weather station from 1981 to 2010. After the climate normals were identified for each season, it was then determined which years (if any) recorded anomalously warm, dry, or wet winters, springs, summers, and autumns. Within the tourism climatology literature (Scott 2005; Dawson et al. 2009; Steiger 2011), CCIAs for ORT that have applied similar methodological approaches (temporal climate analogs) relied on only one anomalous season or year at a time for their analysis. This limitation may be subject to potential bias from unidentified nonclimatic factors that may have either increased or decreased participation in that same season/year, but the results would be attributed to the anomalous climate conditions nonetheless (generating a false positive). From the 17 years of historical daily visitation data supplied by Ontario Parks, the current study was able to successfully identify two climatic anomalies for each season upon which to base conclusions, thereby reducing the potential confounding effect that nonclimatic factors occurring in a selected year may have had on the reported results. In this analysis, within each season, the two years that recorded the warmest temperatures (from 2000 to 2016) along with the two years that recorded temperatures closest to the seasonal average (relative to the 1981–2010 baseline) were identified. Next, still within each season, the two years that recorded the greatest amount of total precipitation (the two wettest seasons) along with the two years that recorded the lowest amount of total precipitation (the two driest seasons) were also identified and compared.

In accordance with the methods outlined by Hewer and Gough (2016b) and as described therein, the analysis was conducted individually for each season, and the same methods were repeated each time. For the first step, daily park visitation data were aggregated to seasonal averages to assess the impact of seasonal climatic anomalies using the standard climatic interpretation of seasons in the region: winter (December, January, and February), spring (March, April, and May), summer (June, July, and August, and autumn (September, October, and November). Next, the total number of annual park visitors within each season was graphed over time. To explore the stationarity of total seasonal park visitation over time, the equation for the slope of the linear trend line was determined, and a simple linear regression analysis was conducted. This analysis identified whether total seasonal visitation had been increasing or decreasing over time and whether the observed trend was statistically significant, which guided the selection of climate analogs and climate normals. If there was no statistically significant trend associated with total annual park visitation in that season, then there was no need to defer from selecting a climatically anomalous or normal year that occurred at opposite temporal ends of the time series. However, if the linear trend was statistically significant, then it would be beneficial to avoid comparing years from opposite sides of the time series since they may have been negatively or positively influenced by the slope of the linear trend line (in this case, time itself becomes the potentially confounding variable).

Still following the methods described by Hewer and Gough (2016b), once the effects of the climatic anomalies within each season were visually observed within the graphs, a series of statistical tests were employed to determine if the observed differences were statistically significant. For temperature, the daily data from the two years that represented the climatic anomalies were tested against the daily data from the two years that represented climate normals to see if there were significant differences between the variances and means of the two groups (using F tests and t tests, respectively). For precipitation, daily data from the two years representing the wettest seasons were tested against the two years representing the driest seasons. Once again, F tests and t tests were used to determine if there were significant differences between the variances and means of the two groups, respectively.

e. Using temporal climate analogs to assess future climate change impacts

The impact of projected climate change on park visitation was assessed using the multiyear temporal climate-analog approach introduced by Hewer and Gough (2016b). For this task, the averaged climatic anomaly recorded between the two years in each season was cross referenced against climate change projections for across the twenty-first century in this region, based on the selective ensemble of seasonal GCM outputs. It was then possible to provide predictions concerning the impact on park visitation when these climatic anomalies may become climate normals in the future, under projected climate change.

Potential impacts of projected climate change on future park visitation are presented as either an increase or decrease in total seasonal visits, as captured by the effect that seasonal climatic anomalies had in the past. These impacts corresponded to a temporal climate analog for the 2020s, 2050s, or 2080s, depending on a range of potential climate change impacts (RCP4.5–RCP8.5). Furthermore, the statistical significance of these historical effects and therefore the meaningfulness of potential future impacts were also presented by means of the P values associated with the tests of significant differences.

3. Results

a. The effect of seasonal climatic anomalies

1) Winter

Figure 2 presents the time series data for total winter visits and average winter temperatures (Tmax) from 2000 to 2016. Winter-season precipitation anomalies (totP) are also identified on the time series graph. Looking at the statistically significant (correlation coefficient squared R2 = 0.445; P = 0.003) negative slope of the linear trend line for winter season visits, it is apparent that time itself could act as a confounding variable within the analysis of this season, especially if comparing years that are far apart within the time series.

Fig. 2.
Fig. 2.

The effect of seasonal climatic anomalies on winter-season [December–February (DJF)] visitation to Pinery Provincial Park (2000–16).

Citation: Weather, Climate, and Society 11, 2; 10.1175/WCAS-D-18-0025.1

The anomalously warm winters of 2006 and 2012 were on average 2.86°C warmer than the seasonal average from 1981 to 2010. Winter season visits were 8% lower during these warm winters, when compared witho the climatically normal winters of 2008 and 2013. However, there were no statistical differences between means for daily visitation data from years with anomalously warm winters compared to normal winters (t = −0.504; P = 0.307). The anomalously wet winters of 2006 and 2008 experienced an average of 40% more precipitation than the seasonal average from 1981 to 2010 and may have caused winter visits to decrease by 26% relative to the seasonal average from 2000 to 2016. However, the anomalously dry winters of 2002 and 2015 experienced 34% less precipitation than the seasonal average and were also associated with a 14% decline in winter visits when compared with the seasonal average. Nonetheless, there were not any statistical differences between means for daily visitation data from the years with anomalously wet winters when compared with anomalously dry winters (t = −1.150; P = 0.125).

2) Spring

Figure 3 graphs the time series data for total spring visits and average spring Tmax from 2000 to 2016. Spring-season precipitation anomalies are also identified on the time series graph. The slope of the linear trend line for spring-season visits from 2000 to 2016 was not statistically significant (R2 = 0.002; P = 0.862); therefore, it is unlikely that time itself would act as a confounding variable within this season’s analysis.

Fig. 3.
Fig. 3.

As in Fig. 2, but for spring [March–May (MAM)].

Citation: Weather, Climate, and Society 11, 2; 10.1175/WCAS-D-18-0025.1

The anomalously warm springs of 2010 and 2012 were, on average, 3.03°C warmer than the seasonal average from 1981 to 2010. Spring-season visits were 3% higher during these warm springs when compared with the climatically normal springs of 2001 and 2004. However, there were no statistical differences between means for daily visitation data from years with anomalously warm springs when compared with climatically normal springs (t = −0.146; P = 0.442). The anomalously wet springs of 2009 and 2011 experienced an average of 50% more precipitation than the seasonal average from 1981 to 2010 and may have caused spring visits to decrease by 10% when compared with the seasonal average from 2000 to 2016. The anomalously dry springs of 2002 and 2014 experienced 36% less precipitation than the seasonal average and were associated with a 3% increase in spring visits when compared with the seasonal average. Nonetheless, there were not any statistical differences between means for daily visitation data from the years with anomalously wet springs when compared with anomalously dry springs (t = −0.783; P = 0.217).

3) Summer

Figure 4 graphs the time series data for total summer visits and average summer Tmax from 2000 to 2016. Summer-season precipitation anomalies are also identified on the time series graph. The slope of the linear trend line for summer-season visits from 2000 to 2016 was not statistically significant (R2 = 0.014; P = 0.656); therefore, it is unlikely that time itself will serve as a confounding variable within this season’s analysis. There is a nonclimatic event that must be acknowledged within this time series because it is very likely to confound the results of this season’s analysis: the lingering effect of the 11 September 2001 North American terror attacks on the World Trade Center in New York City, New York. This event has been recognized within the tourism literature as having a negative effect on travel patterns (Araña and León 2008), and it is very likely that during the summer of 2002 visits were abnormally low as a result (A. MacKenzie 2017, personal communication). The effect of this event is not isolated to the summer of 2002 alone: dips in visitation during the autumn of 2001 (Fig. 5) and even more so in the spring of 2002 (Fig. 3) are also apparent. However, the fact that the summer of 2002 was the second hottest summer on record from 2000 to 2016 is what makes this event particularly confounding for this analysis.

Fig. 4.
Fig. 4.

As in Fig. 2, but for summer [June–August (JJA)].

Citation: Weather, Climate, and Society 11, 2; 10.1175/WCAS-D-18-0025.1

Fig. 5.
Fig. 5.

As in Fig. 2, but for autumn [September–November (SON)].

Citation: Weather, Climate, and Society 11, 2; 10.1175/WCAS-D-18-0025.1

The anomalously warm summers of 2002 and 2005 were, on average, 1.94°C warmer than the seasonal average from 1981 to 2010. Summer-season visits were an average of 10% lower during these warm summers when compared with the climatically normal summers of 2006 and 2007. Differences between daily data from years with anomalously warm summers when compared with normal summers were statistically significant (t = −2.479; P = 0.007). The anomalously wet summers of 2000 and 2015 experienced an average of 57% more precipitation than the seasonal average from 1981 to 2010 and may have caused summer visits to decrease by 14% when compared with the seasonal average from 2000 to 2016. However, the anomalously dry summers of 2001 and 2002 experienced 48% less precipitation than the seasonal average and were also associated with a 12% decrease in summer visits when compared with the seasonal average. Nonetheless, there were not any statistical differences between daily data from the years with anomalously wet summers when compared with anomalously dry summers (t = −0.392; P = 0.348).

4) Autumn

Figure 5 presents the time series data for total autumn visits and average autumn Tmax from 2000 to 2016. Autumn-season precipitation anomalies are also identified on the time series graph. The slope of the linear trend line for autumn-season visits from 2000 to 2016 was not statistically significant (R2 = 0.029; P = 0.515); therefore, it is unlikely that time itself will serve as a confounding variable within this season’s analysis.

The anomalously warm autumns of 2007 and 2016 were, on average, 2.39°C warmer than the seasonal average from 1981 to 2010. Autumn-season visits were 37% higher during these warm autumns when compared with the climatically normal autumns of 2000 and 2003. Differences between daily data from years with anomalously warm autumns when compared with climatically normal autumns were statistically significant (t = 2.156; P = 0.016). The anomalously wet autumns of 2002 and 2006 experienced an average of 49% more precipitation than the seasonal average from 1981 to 2010 and may have caused autumn visits to decrease by 11% when compared with the seasonal average from 2000 to 2016. The anomalously dry autumns of 2004 and 2007 experienced 39% less precipitation than the seasonal average and were also associated with a 24% increase in autumn visits when compared with the seasonal average. Furthermore, there were statistical differences between daily data from the years with anomalously wet autumns when compared with anomalously dry autumns (t = −2.137; P = 0.017).

b. Temporal climate-analog approach to climate change impact assessment

Table 3 presents the results of the multiyear temporal climate-analog approach to assessing the potential impacts of projected climate change on visitation to Pinery Provincial Park in southwestern Ontario. During the winter season, two anomalously warm winters were identified, representing a temporal climate analog for the 2050s under projected climate change. Although winter-season visitation was 8% lower during these warm winters, the differences in visits between warm and normal winters were not statistically significant. Therefore, although an additional 3.0°C of warming under projected climate change may cause winter-season visitation to further decline, the impacts cannot be predicted with enough confidence (95% confidence level; P < 0.05), based on how visitation responded to this magnitude of warming in the past. For precipitation anomalies, both the two wettest and the two driest winters were associated with fewer-than-average winter visits. Although the wettest winters had even fewer visits than the driest winters, the differences in visits between the two groups were not statistically significant. Furthermore, winter-season precipitation is not projected to increase by more than 20% over the course of the twenty-first century; therefore, it is unlikely that projected changes in precipitation under future climate change will have a direct measurable impact on winter-season visitation to this park, based on these results.

Table 3.

Seasonal climatic anomalies, temporal climate analogs, and impacts on park visitation.

Table 3.

During the spring season, temperature anomalies were observed representing temporal climate analogs for warming projected under future climate change by the 2080s. When spring seasons associated with a 3.0°C warming were experienced in the past, spring visitation increased by 3%; however, the differences in visits between warm springs and normal springs were not statistically significant. Therefore, although we may expect spring-season visitation to increase if we experience future warming of such magnitude, we cannot with a sufficient degree of confidence predict these impacts. For precipitation, the wettest two spring seasons were associated with fewer-than-average visits, and the driest two spring seasons were associated with more-than-average visits. However, the differences in visits between the two groups were not statistically significant. Furthermore, spring-season precipitation is not expected to increase beyond 20%; therefore, based on these results, it is again unlikely that the projected changes in precipitation under future climate change will result in a measurable impact on park visitation during the spring season.

During the summer season, once again, two anomalously warm summers were identified, this time only representing a temporal climate analog for the 2020s under projected climate change. When these summer seasons associated with a 2.0°C warming were experienced in the past, visitation was 10% lower than during summers with normal temperatures. The differences in visits were statistically significant, suggesting that we could predict these impacts with enough confidence. However, the greatest dip in summer-season visitation associated with anomalously warm temperatures occurred in 2002, which very likely was due to the residual effect of the 11 September 2001 terror attacks, therefore confounding the summer-season analysis. For precipitation, the wettest two summer seasons as well as the driest two summers were both associated with fewer-than-average visits. Although the wettest summers were associated with even fewer visits than the driest summers, the differences in visits between the two groups were not statistically significant. Furthermore, summer-season precipitation is not expected to decrease by more than 15% over the course of the twenty-first century; therefore, it is unlikely that the projected changes in precipitation under future climate change will result in a measurable impact on park visitation during the summer season, based on these results.

The most significant and meaningful results generated by this study were associated with the effect of seasonal climatic anomalies on park visitation during the autumn season. During the autumn season, two anomalously warm autumns were identified, representing a temporal climate analog for the 2050s under projected climate change. When these autumn seasons associated with a 2.4°C warming were experienced in the past, visitation was 37% higher than during autumns with normal temperatures. Furthermore, the differences in visits were statistically significant, suggesting that we could predict these impacts with a sufficient degree of confidence. For precipitation, the wettest two autumn seasons were associated with fewer-than-average visits and the driest two autumn seasons were associated with higher-than-average visitation. This time, the differences in visits between the two groups were statistically significant. Although autumn-season precipitation is projected to remain relatively unchanged over the course of the twenty-first century, there remains much uncertainty associated with modeling future precipitation under projected climate change, especially when relying on coarse temporal and spatial resolutions from GCM outputs. Therefore, the results of this study suggest that if autumn-season precipitation does change under future climate conditions, there will likely be measurable impacts on autumn-season visitation, with wetter climates resulting in fewer visits, for example.

4. Discussion

The results of this study suggest that when an average warming of 2.6°C was experienced during the winter, spring, and summer seasons, the mixed effect on total park visitation was determined to be negligible because of the lack of statistically significant differences between groups when comparing total visitation during the anomalously warm seasons with the climatically normal seasons. However, the additional 26 401 visitors reported during the autumn seasons, which were associated with an average warming of 2.4°C, did represent a meaningful impact as evidenced by the statistically significant differences that were recorded. The additional visitors arriving during the autumn season represent a 4% increase in total annual visitation to Pinery Provincial Park, given that this park recorded an average of 588 927 annual visitors from 2000 to 2016. These findings stand in stark contrast to those from the earlier modeling studies by Jones and Scott (2006a) as well as by Hewer et al. (2016). Jones and Scott (2006a) used regression models based on aggregated monthly visitation and climate data to conclude that a future warming of an additional 1°–9°C would result in an increase to annual visitation at Pinery Provincial Park by 8%–68%. Hewer et al. (2016) used regression models based on actual daily weather and visitation data and arrived at a more conservative prediction, suggesting that a future warming ranging from an additional 1°–5°C would result in a 3%–15% increase in annual park visitation. To illustrate an even closer comparison between studies, based on the monthly data modeling approach (Jones and Scott 2006a), it was suggested that a 2.5°C warming would likely result in a 20% increase in annual visitation to Pinery Provincial Park, spread across all four seasons but most pronounced during the spring and autumn seasons, whereas the daily data modeling approach (Hewer et al. 2016) suggested that a 2.5°C warming would result in a 7.5% increase to annual park visitation, again mainly affecting spring and autumn season visitation. This analog approach, on the other hand, indicates that an average annual warming of 2.5°C would only result in a 4% increase in park visitation and that the increase would be isolated to the autumn season only. Thus, the results of this study appear to be in partial agreement with the daily modeling approach of Hewer et al. (2016) but question their model outputs suggesting that spring-season visitation will significantly increase under warmer conditions. These results are contrary to the suggestion that a warmer climate will lead to increased visitation across all four seasons, as modeled by Jones and Scott (2006a).

Most of the previous CCIAs for ORT that relied on the temporal climate-analog approach focused on the alpine ski industry. Within the tourism climatology literature (Dawson et al. 2009; Steiger 2011), the temporal climate-analog approach has revealed more conservative assessments concerning the impact of projected climate change on ski-season length and lift-ticket sales when compared with the results of corresponding modeling studies (Scott et al. 2008; Abegg et al. 2007). In the context of urban zoos and aquariums, Hewer and Gough (2016b) found that the analog approach suggested greater climate change impacts on attendance levels than the previous modeling studies had predicted (Aylen et al. 2014; Hewer and Gough 2016a). Therefore, when comparing modeling-based and analog-based assessments for this park, the difference between approaches is like the findings for the alpine ski industry, where the analysis of analog events produced more conservative results than did modeling-based studies.

There is also an opportunity for some interesting comparisons between two studies using the same multiyear temporal climate-analog approach for a CCIA on two different ORT contexts within the same geographic and climatic region. When Hewer and Gough (2016b) first presented this approach, the results were highly significant (statistically), and the impacts of seasonal climatic anomalies were quite pronounced on zoo attendance levels at the Toronto Zoo in southern Ontario. Although similar findings were expected when this same method was applied to visitation at Pinery Provincial Park, which is also in southern Ontario, the results were quite different. Hewer and Gough (2016b) reported statistically significant differences in zoo attendance levels between anomalously warm years and climatically normal years for all seasons except the autumn season, whereas the current study found that only the autumn season recorded statistically significant differences in park visitation between the two groups within the analysis. Thus, Hewer and Gough (2016b) concluded that zoo attendance was highly sensitive to seasonal climatic anomalies and very likely to be considerably impacted by future climate change, apart from during the autumn season. The current study must conclude that park visitation is not highly sensitive to seasonal climatic anomalies and that the direct impact of future climatic change on park visitation may be negligible, apart from the autumn season. However, this conclusion does not account for indirect impacts on visitation associated with climate-induced environmental changes (Scott et al. 2008). A possible explanation for these different results between tourism contexts is related to the nature of the recreational activities being assessed. Zoo attendance generally requires a lower amount of time and money to be invested in the venture, a shorter degree of planning is involved, and travel distances are shorter to reach the destination. Park visitation, on the other hand, generally requires greater levels of time commitment and increased monetary costs, greater distances must be traveled to reach the destination, and a longer period of planning is involved. A comparison of these two different tourism contexts using the same method provides considerable support to the theoretical contentions that, as the degree of planning, distance traveled, length of stay, and monetary cost increase, the corresponding weather sensitivities of and potential climate change impacts on tourist decision-making and tourism demand responses decrease (Jafari 1987; Hoxter and Lester 1988). These findings also provide further rationale for future research that will add to the growing body of literature that now provides empirical evidence concerning the effect of distance traveled, trip costs, and length of stay on weather sensitivities and potential climate change impacts (Rutty and Scott 2016; Hewer et al. 2017, 2018).

The findings of this study were not in agreement with the expectations of Dwyer (1988) and Smith (1993), who theorized that, when anomalously warm winters occurred, tourism participation would increase and that, when anomalously warm summers occurred, tourism participation would decrease. There are two likely explanations as to why these seemingly sound theoretical expectations did not hold true in the context of park visitation. First, winter-season visitation to Pinery Provincial Park is climatically dependent upon cold temperatures, the preservation of ice, and the formation of snow because of the nature of the recreational activities pursued by visitors at this site during the winter months (i.e., snow shoeing, cross-country skiing, and ice fishing). Second, during the summer months that represent the peak visitation season, the role of the camping reservation system and the inclusive nature of park visitation data likely hide the effect of uncomfortably warm temperatures on park visitor decision-making. For instance, most campers must book sites as early as 6 months in advance to secure a week of summer vacation camping at this park; therefore, the presence of above-average temperatures during that week may not deter campers who have already invested considerable amounts of time and money (including scheduled vacation time) for the trip. Furthermore, it is unclear to what degree the park visitation data capture actual visits versus booked reservations. So, if visitors decided not to come or left early as a result of a severe heat wave, would the visitation data reflect this last-minute change or simply record those campers as present on the basis of their paid reservation? Parks already operating at capacity during the peak season can present a research concern, especially if the study design expects or models an increase in visitation during the peak season. This was not a concern for the current study because the expected result was a decrease in visitation resulting from anomalously warm temperatures during the summer months of the peak season at Pinery, as theorized by Dwyer (1988) and Smith (1993). It can be reasonably expected that day users would be more sensitive to these short-term weather anomalies whereas overnight campers would be more likely to respond to long-term climatic changes. Since visitors to Pinery are historically 20% day users and 80% overnight campers (Ontario Parks 2011), these demographics may also explain why the results from the spring and summer seasons were not statistically significant, potentially hiding future impacts on visitation under projected climate change.

In this study, in which weather and attendance data have been aggregated to the seasonal time scale, even extreme changes in total precipitation had no significant effect on total park visitation (apart from during the autumn season). This finding has important implications for the role of temporal scale within research design for studies in tourism climatology and echoes the discussion of Hewer and Gough (2016b) in this regard. In looking at the tourism climatology literature, when previous studies analyzed daily weather and participation data total precipitation was found to be a significant predictor variable (Scott and Jones 2006, 2007; Hewer et al. 2016). As a result, these previous modeling approach studies reported findings that were in line with the expectations set forth by de Freitas (1990, 2003, 2015), emphasizing the overriding effect that the physical component of tourism climate has on visitor satisfaction and tourist behavior. This study supports the contentions of Hewer and Gough (2016b) who stated that “when working with aggregated data, such as monthly, seasonal or annual averages, the statistical relationship between temperature and participation becomes more emphasised; while other important climatic variables may no longer appear significant.” This has led some to conclude that temperature is the single most important variable in relation to tourist satisfaction and behavior (Mieczkowski 1985; Maddison 2001; Lise and Tol 2002; Hamilton et al. 2005; Bigano et al. 2006). However, Hewer and Gough (2016b) argued that the importance of temporal scale in research design and the implications for the influence that certain climatic predictor variables may have on tourism demand when analyzed at a finer temporal scale should not be overlooked—a contention to which this study adds further support. To this end, examining the impact of extreme weather events on daily park visitation remains an important area of future research.

A limitation of the analog approach is the inability to project conditions that may influence the exposure unit (park visitation, in this case) in the future, such as technological advances, changing behavioral responses, and changing demographics, as well as increasing energy prices for transportation and operations (Dawson et al. 2009). However, the modeling approach often utilized for CCIAs on ORT is also subject to these limitations. Analogs are seldom available to assess the impacts of the upper ranges of projected climate change, since few analog situations have occurred that are representative of long-range modeled climate futures under high greenhouse gas emissions and subsequent radiative forcing, such as warming that exceeds 4°C (Hewer and Gough 2016b). This limitation is one that, although embodying a great degree of uncertainty, can be overcome by using the modeling approach. Furthermore, Gössling et al. (2012) acknowledged that even the analog approach cannot fully capture the adaptive capacity of tourists, especially in relation to human acclimatization to thermal stress. Another limitation was acknowledged by Steiger (2011), who suggested that, as climate change increases the frequency and occurrence of extreme events (seasonal climatic anomalies, in this case), tourists will gain further experience with these kinds of seasons and may no longer respond to these events as they once did in the past. Furthermore, although visitors appeared to be relatively insensitive to these short-term seasonal climatic anomalies in this study, this result does not necessarily mean that visitors will continue to respond this way once these short-term weather anomalies become characteristic of long-term climatic normals. This is another limitation of the analog approach that must be acknowledged, especially regarding potential shifts in the seasonality of tourism participation resulting from the timing of ideal climatic conditions, like those projected by Scott et al. (2004) using the tourism climate index in conjunction with future climate change scenarios. Nonetheless, a continuation of strategic research is needed to reevaluate past assessments and fill important knowledge gaps in the field of tourism climatology as well as for CCIAs on ORT (Hewer and Gough 2016b). Such endeavors will provide more accurate and reliable information for governments and businesses involved with and reliant upon these industry sectors.

Last, given that the analysis was limited by only 17 years of daily park visitation data, there were occurrences in which the seasons that represented anomalous temperature conditions were also associated with anomalous precipitation conditions. Specifically, one of the warmest winters was also the wettest (2006), one of the warmest springs was also the driest (2012), one of the warmest summers was also the driest (2002), and one of the warmest autumns was also the driest (2007). Although the potential for these to confound the results must be acknowledged as a limitation to the study design, there are several reasons why confidence can still be placed in the findings. One is that the use of a multiyear temporal climate-analog approach in which data from two anomalous years were averaged and then compared with two normal years effectively mitigates the influence that any one year has on the study results. Furthermore, the effect of seasonal precipitation anomalies on total park visitation was determined to be insignificant (statistically) for three of the four seasons (all but the autumn season). If a dataset were available in which more observed anomalies were recorded, care could be taken to avoid selecting temperature anomalies that occurred during the same season that precipitation anomalies occurred, and vice versa.

Acknowledgments

We thank the Ontario Ministry of Natural Resources and the Ontario Parks Agency for agreeing to supply daily park visitation data and to allow use of such data for this study. There were two Ministry and Parks employees that were especially helpful in this regard that we also thank by name: Alistair MacKenzie and Tanya Berkers. We also thank Professor Daniel Scott at the University of Waterloo for his help in filling in some of the gaps in the park visitation data record from his own archive of research data and for his continued support and guidance throughout Dr. Hewer’s academic career.

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