Commerce | Tourism » What Could Tomorrows Weather and Climate Look like for Tourism in the South West of England

Source: http://www.doksinet NATIONAL CASE STUDY Cost-a South West: What could tomorrow’s weather and climate look like for tourism in the South West of England? Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England Authors Emma Whittlesea, South West Tourism, ewhittlesea@swtourism.orguk Bas Amelung, Amelung Advies, bas@amelung.nl January 2010 Maps and GIS Outputs Vivienne Sharland, Environment Agency, Vivienne.sharland@environment-agencygovuk Acknowledgements This research was initiated and co-ordinated by South West Tourism to inform the review of the regional Tourism Strategy and was conducted in partnership with the South West Environment Agency and Amelung Advies, a consultancy who has worked with the Tourism Climate Index and other tourism and climate indices for the last six years. The work was supported by members of the Climate SouthWest tourism sector group and specific thanks go to the following for their input into the process:

Alex, Webb, Climate SouthWest Mark Gallani, Met Office Murray Simpson, University of Oxford Paul Haydon, South West Tourism Tim Coles, University of Exeter Maps Please note the maps in this report have been produced by the SW Environment Agency and are reproduced from Ordnance Survey material with the permission of Ordnance Survey on behalf of the Controller of Her Majestys Stationery Office Crown copyright. Unauthorised reproduction infringes Crown copyright and may lead to prosecution or civil proceedings. Environment Agency, 100026380, 2009 The scale for the maps is: 1:1,251,622 Page 2 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England Contents Page Summary 1 Introduction 4 2 Study Area 7 3 Tourism Climate Indices 3.1 Tourism Climate Index 3.2 Beach Climate Index 3.3 Index Limitations 7 7 9 10 4 Method 4.1 Downloading the UKCP09 climate variables 4.2 Filling the gaps – missing climate variables from UKCP09 4.21 Wind 4.22

Minimum relative humidity 4.23 Hours of sunshine 4.3 Creating the 1970s baseline 4.4 Calculating the Climate Indices (TCI and BCI) 4.5 Identifying the 10%, 50% and 90% probabilities 4.6 Using a Geographic Information System to present the TCI results 4.7 Identify key climate variable thresholds and use the ‘threshold detector’ to indicate the likelihood of extreme weather events 11 11 12 15 16 17 19 19 5 Results 5.1 Tourism Climate Index 5.2 Presentation of the results using GIS 5.3 Difference in the 10%, 50% and 90% probability levels 5.4 Beach Climate Index 5.5 Threshold Detector 5.6 Review of Tourism Data 20 20 21 25 27 28 30 6 Conclusions 38 7 Next Steps and Recommendations 40 8 Links and Further Information 42 9 References 44 Page 3 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England Summary Tourism in the South West of England plays a vital role in local communities and economies. Changes in the climate and weather

will directly and indirectly affect tourism as it is a crucial component of the tourism offer. Projected changes will need careful consideration in both regional and local tourism development, management and planning. This research investigates the 2009 UK Climate Projections (UKCP09) for the South West region and explores the likely impact on tourism comfort and seasonality in the 2020’s and 2050’s. The results show that the future climate should improve for tourism activity across the South West and could work to extend the tourist season, however alongside this the incidence in severe weather events is projected to increase. Both of these findings bring a series of challenges and management issues for strategic planners and businesses over the coming years. Can the adverse ‘costs’ be kept to a minimum and a low carbon tourism industry that benefits the environment, the community and the economy be designed and developed for the future? This needs further investigation at the

destination level and requires a sensitive and proactive response in order to maintain and protect the natural environment and local communities which attract people to the South West in the first instance. In 2050 there could be a different tourism climate and tourist type and the industry will need to change accordingly. This research is one of the first applications of the UKCP09 data set which provides an improved resolution of 25km grid squares in comparison to the 50km grid used in UKCP02. There have been many UKCP02 research projects that have influenced policy but no tourism-specific ones that the UK Climate Impacts Partnership is aware. This work will inform the refresh of the South West regional tourism strategy ‘Towards 2015’ (South West Tourism, 2005) which strives to protect the environment; improve the quality of life of local people; take advantage of the regions existing strengths; and create a long-term and sustainable industry. 1. Introduction The South West of

England is Britain’s foremost holiday destination with UK residents alone making 20.46 million trips to the South West in 2007, surpassing all other English regions as well as Scotland and Wales (Visit Britain, 2007). The tourism sector of the South Wests economy is one of its largest industries, with its 22.7 million staying visitors and 96 million day visitors accounting for approximately 8% of the South West GVA (Gross Value Added) and supporting around 240,000 jobs (South West Tourism, 2007). The tourism industry in the South West is significant both locally and nationally. This in part is due to its southerly location offering warmer climes and also its landscape, beautiful coastline and its wealth of natural and historic features. A large part of the region - rural and urban - is concerned with satisfying the needs of visitors, from the natural landscape to accommodation providers, activities and experiences, attractions and events, historic buildings, food and drink, transport

and a multitude of shopping opportunities. Tourism is climate-sensitive and changes in our weather, seasons and climate will impact on the tourism industry affecting the health of destinations, choice of trip and tourist spending. A recent survey of non-visitor and lapsed visitors to the South West (South West Tourism, 2009) highlighted the ‘lack of guaranteed sunshine’ as a key reason why they are not travelling to the SW for their holiday. Poor and unpredictable weather forms part of the value equation and leads to greater entertainment costs. This is also evidenced by a recent IPPR research report ‘Consumer Power’ where participants overwhelmingly prefer to take holidays abroad because of the lack of sunshine and the amount of rain in the UK, Page 4 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England identifying good, hot weather as a key ingredient for a holiday which cannot be guaranteed in the UK (Platt and Retallack, 2009).

However it is worth noting the implication this has in terms of the increased distance travelled (much of it by plane) and consequent generation of greenhouse gases in order to get this reliability. One of the most vulnerable areas to changes in the weather is the day visitor market which currently attracts around 96 million day visitors to the South West per annum, contributing £4.1 billion (47%) towards the total tourism spend in the region (South West Tourism, 2007) In summary, the UKCP09 show the climate of the South West region will become warmer with high summer temperatures becoming more frequent, and very cold winters becoming increasingly rare. Winters will become wetter, whilst summers will become drier Relative sea level continues to rise and the frequency and intensity of extreme weather events (e.g storms, flooding, heatwaves and drought) will increase. The South West has a significant proportion of its tourism infrastructure located in coastal and riverside locations and

could therefore be disproportionately vulnerable to extreme weather, coastal erosion and rising sea levels. Hotter drier summers for the South West are likely to mean more visitors to the region, in turn leading to a bigger tourism market, improved economy and more jobs. As the title of this research implies, this could also ‘cost’ the South West through increased congestion, insensitive development and pressure on natural resources, local infrastructure, services and supplies. Extreme weather will bring other pressures and challenges. For example in July 2007 Gloucestershire received 1! times the average July rainfall in just one day causing widespread flooding that affected many visitors, businesses and vital service sites, leaving many without power and water for days. The costs were estimated in the region of £50 million and the tourism industry was significantly affected with some tourism businesses ceasing trading because of the physical impacts and lost trade. David

Garfitt, a pleasure boat operator vocalised this in the press ‘Tewkesbury is open for business and we need those tourists more than we’ve ever needed them before’ (Gloucestershire Gazette, 2007). The heatwave in August 2003 brought high temperatures of 31+°C attracting record numbers of visitors to Bournemouth and Poole leading to accommodation that was full and beaches that were packed. However it also pushed local infrastructure, facilities and services to its limits. The weekend attracted 20% more traffic than usual leading to pollution that was more than double the Government Health Limit. There were queues for car parks, around 700 parking tickets were issued, access for emergency vehicles was blocked and the heat caused health issues for many (SWCCIP). As this clearly illustrates the projected climate change will not only affect tourism through changes in thermal conditions, but also through ecosystem change, impacts on infrastructure and services, effect on access and

transport prices, and even changes in economic growth and prosperity (Stern, 2007).The impacts will present both challenges and opportunities in varying degrees across the region depending on the business location, type and its vulnerability. The impacts can be split into four broad categories and are summarised in table1 below (Simpson et al., 2008) Table 1: Summary of Impacts. Direct climatic impacts • • • • Suitability of locations – geographical redistribution of tourists Seasonality and tourism demand (length and quality) Destination loyalty and tourist preference Infrastructure damage Page 5 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England Indirect environmental change impacts Impact of mitigation policy on tourist mobility Indirect societal change impacts • • • • • • • • • • • • • • • • • • • Emergency preparedness Business interruptions Operating and insurance costs

(profitability) Biodiversity Loss (terrestrial and marine) Sea Level Rise Increased coastal erosion and inundation Disease Water availability Affect natural and cultural resources Damage to infrastructure and changes in planning and services Altered agricultural production Impacts on competitors Travel costs Transport mode Destination Choice Tourist flows and travel patterns Global/Regional Economic Impacts Local Livelihoods (reduced discretionary wealth) Increased Security Risks (social and political instability) Source: Simpson et al, 2008 In addition to the direct climate impacts, the emergence of climate change mitigation policies are likely to lead to much higher fuel prices, so that transport costs will become an even more important factor in the overall cost of a holiday, in particular for trips involving air travel. This will affect the relative competitiveness of destinations in favour of destinations that are closer to the tourist markets. For the UK, which is relatively

dependent on air travel for outbound international holiday-making, this should strengthen the position of domestic destinations, such as the South West. This aspect of climate change adds to the projected improvement of climatic conditions, and to the projected deterioration of climatic conditions in competing destinations around the Mediterranean that are popular with tourists for the UK. The projections are all likely to require a shift in public sector and business planning and attitude towards risk, service delivery and the products being marketed and delivered. There are destinations and businesses in the South West that are already facing management challenges from excessive visitor pressure in peak season to feeling the impacts of extreme weather events. The regional tourism strategy for the South West ‘Towards 2015’ is undergoing a review and it is important to explore the potential impact of climate change to inform this process. Climate change could bring opportunities

and threats for tourism and understanding what changes may lay ahead can help the regional strategy and the industry respond. The current strategy predicts a rise in tourism numbers and has a seasonality related target to increase the number and value of visitors in the off-peak and off-season periods. The strategy also recognises that the projected growth will also bring challenges around visitor management, community and environmental impact, and the need to create a long-term and sustainable industry. To date there does not appear to have been any detailed research to understand how future changes in our climate may affect tourism in the South West of England specifically. The aim of this case study is to begin this process and apply the UKCP09 projections to the Tourism Climate Index developed by Mieczkowski (1985) to consider the potential effects on tourism in the South West for the 2020s and 2050s. Page 6 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate

Change in SW England 2. Study Area This study covers the Government Office administrative South West region of England which includes Gloucestershire, Bristol, Bath, Wiltshire, Somerset, Dorset, Bournemouth, Poole, Devon, Cornwall and the Isles of Scilly. To cover this area, the research uses data from 46 UKCP 25km grid squares as illustrated by figure 1. Figure1: Overlaying the UKCP09 grid to a map of the SW (showing grid ID numbers) Source: Environment Agency 3. Tourism Climatic Indices This research will work to investigate what the likely impacts of climate change could be for tourism, applying the latest UKCP09 climate projections to the Tourism Climatic Index (TCI) first developed by Mieczkowski in 1985, and in addition to consider the Beach Climate Index developed by Morgan et al. in 2000 3.1 Tourism Climate Index (TCI) The TCI allows quantitative evaluation of a region’s climate for the purpose of general tourism activity. The TCI is based on the notion of “human

comfort” and consists of five subindices, each calculated from one or two monthly climate variables The five sub-indices and their constituent variables are summarised in table 2 and are as follows: (1) daytime comfort index (maximum daily temperature [in ºC] and minimum daily relative humidity [%]), (2) daily comfort index (mean daily temperature [ºC] and mean daily relative humidity [%]), (3) precipitation (total precipitation, in mm), (4) sunshine (total hours of sunshine), and (5) wind (average wind speed, in km/h). The index is weighted and computed as follows: Page 7 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England TCI = 8CID + 2CIA + 4R + 4S + 2W Where CID = daytime comfort index, CIA = daily comfort index, R = precipitation, S = sunshine, and W = wind speed. With an optimal rating for each variable of 5.0, the maximum value of the index is 100 Based on a location’s index value, its suitability for tourism activity is then

rated on a scale from –30 to 100. Mieczkowski (1985) divided this scale into 10 categories, ranging from ideal (90 to 100), excellent (80 to 89), and very good (70 to 79) to extremely unfavorable (10 to 19) and impossible (9 to –30). In this study, a TCI value of 70 or higher is considered attractive to the ‘typical’ tourist engaged in relatively light activities such as sightseeing and shopping. The TCI applies only to these more general forms of tourism activity and is not applicable to more climate-dependent activities such as winter sports. Furthermore, the TCI cannot be used to predict tourist arrivals. The index is designed solely to indicate levels of climatic comfort for tourism activity and does not take into consideration the existence and quality of vital tourism infrastructure such as transportation and attractions. Thus, a region with a high TCI may experience low levels of tourism arrivals, and vice versa, because a multitude of other factors besides climatic

conditions influence tourism activity. Tables 2 and 3 illustrate the components of the index and the rating scale for tourism comfort. Table 2: Components of Mieczkowski’s Tourism Climatic Index Source: Adapted from Mieczkowski (1985, pp. 228-229) Sub-index Daytime comfort index Daily comfort index Precipitation Sunshine Wind speed Variable(s) Maximum daily temperature (ºC) Minimum daily relative humidity (%) Mean daily temperature(ºC) Mean daily relative humidity (%) Precipitation (mm) Daily duration of sunshine (hours) Wind speed (km/h) Table 3: Tourism Climatic Index Rating System In recent times, the TCI has been used to explore the possible effects of climate change on the climatic suitability for tourism in various areas, including the world as a whole (Amelung Page 8 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England et al 2007), Canada (Scott et al., 2004), the Mediterranean (Amelung and Viner, 2006), and Northwestern Europe

(Nicholls and Amelung, 2008). General findings were that climate change is expected to cause a poleward shift of suitability scores, with conditions in areas around the equator deteriorating year-round, conditions in higher latitudes improving yearround (mainly in spring, summer and autumn), and TCI scores in middle latitudes such as the Mediterranean decreasing in summer, and improving in the shoulder seasons. The study on Northwestern Europe suggested a significant lengthening of the season in the SW of England, with the number of (very) good months (TCI>70) increasing from 0-2 in the 1970s to up to 4-5 at the end of the century in a scenario of rapid climate change. Mieczkowski (1985) explicitly suggested in his paper that the TCI could be adapted to other tourism activities than sightseeing and adjustments to different climate requirements could be made by changing the rating schemes and/or the weights attached to the sub-indices. The TCI that was fully developed in the original

paper, and that is used in this study, is the one for sightseeing, one of the most common tourism activities. Mieczkowski designed the TCI to be used globally, and the rating schemes and weights used in Mieczkowskis TCI are predominantly based on expert knowledge. In recent years, researchers have started to study the climatic preferences of tourists in more detail. There appear to be differences between the climatic preferences of tourists from different countries or cultures, but the results are not yet conclusive enough to form a foundation for a new generation of climate indices for tourism. Morgan et al (2000) developed a climate index that was specifically tailored to beach tourism: the beach climate index (BCI). In addition, they based the index on the actual preferences of beach visitors in Europe. 3.2 Beach Climate Index (BCI) Similar to Mieczkowski’s TCI, Morgan et al.’s beach climate index (BCI) is made up of smaller components (sub-indices) that, after weighting, add up

to a maximum score of 100 (ideal conditions). The weights are based on the importance that the beach users attached to each of the four components. Beach users expressed the importance values on a Likert scale between 1 (not important) and 9 (very important). The Likert scores for each component were added, and these aggregated scores were subsequently scaled so that they added up to 1. As table 4 shows, this procedure results in weights that differ substantially from those proposed by Mieczkowski (1985), with precipitation, wind, and sunshine becoming more relevant at the expense of thermal comfort. The resulting equation is as follows: BCI = 0.18 · TS + 029 · P + 026 · W + 027 · S In which BCI is the beach climate index, TS, P, W, and S are the components of thermal sensation, precipitation, wind, and sunshine, respectively. Each of the four components is itself represented by an index, with values ranging from 0 to 100. These values are the beach users’ evaluation of the

underlying weather conditions. To assess beach users’ preferred thermal sensation, Morgan et al. (2000) adopted an approach proposed by de Freitas (1990), who found that descriptors of subjective thermal sensation (“very hot,” “cool,” etc.) are strongly correlated to skin temperature Skin temperature in turn is a function of the effective air temperature, proportion of sunshine, and wind speed, in addition to several individual characteristics (Green, 1967 as cited by Morgan et al. 2000), which were set to representative values for North Europeans Page 9 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England Table 4: Relationship between thermal sensation, skin temperature (Ts), and the scoring system (adapted from Morgan et al., 2000) Temperatures below 210 and above 365"C were allotted 0 points reflecting very cold and extremely hot conditions. Source: Taken from Moreno and Amelung (2009) Thermal sensation Skin Temperature (Ts)

Index Score Cold Cool Neither cold nor warm Warm Hot Very hot 21.0–259 26.0–289 29.0–324 32.5–344 34.5–354 35.5–364 2 21 39 100 77 24 Respondents were asked to rank six thermal descriptors in order of preference: three, two, and one points were allotted to the first, second, and third most preferred thermal sensation, respectively. For each of the descriptors, scores were then aggregated and subsequently scaled, setting the value for the highest-scoring descriptor to 100. Morgan et al. (2000) adopted Mieckowski’s rating scheme for precipitation The maximum index value of 100 was assigned to instances of less than 15 mm/month, decreasing in linear fashion by 10 points for each additional 15 mm of precipitation. Amounts of precipitation exceeding 150 mm/month were given an index value of 0. Morgan et al’s (2000) scheme for wind contains three categories. Wind speeds of less than 4 m/s receive the maximum index value of 100, whereas wind speeds greater than 6 m/s

correspond to an index value of 0. A value of 50 is allotted to wind speeds of between 4 and 6 m/s In Morgan et al.’s scheme for sunshine, continuous sunshine was allocated the maximum index value of 100, falling in linear fashion to 0 for a situation of complete absence of sunshine. The final Beach Climate Index (BCI) can attain values ranging from 0 to 100. Morgan et al (2000) divides this range as suggested by Mieczkowski (1985), with values below 40 seen as unfavourable, the range between 40 and 60 as acceptable, values from 60 to 70 as good, between 70 and 80 as very good, and scores above 80 as excellent for beach tourism. Moreno and Amelung (2009) followed up on Morgan et al. (2000) by using the BCI to explore the consequences of climate change for coastal tourism in Europe. Their conclusion was that for decades to come, the Mediterranean climate will remain the most suitable climate in Europe for beach tourism, even though conditions in summer will deteriorate. This is an

important qualification of earlier findings (based on TCI) that in the second half of the century, northern European regions such as the south of the UK may be able to effectively compete with the Mediterranean on climate. Given the importance of beach tourism, BCI analyses may therefore provide insights that are complementary to the TCI results and also relevant to this particular region (SW) because much of the offer is based around sun, sea and sand. 3.3 Index Limitations While being widely used, the TCI presents a number of limitations, some of which apply to all climate indices. First of all, the TCI has been considered too coarse an indicator, as it is insensitive to the large variety of weather requirements that are posed by tourist activities. Mieczkowski (1985) explicitly mentioned the possibility to tailor the rating and weighting system to specific activities, but this flexibility has hardly been used so far. A second point of criticism is that the empirical validation of

the index is relatively weak. In particular, there is very little known about the influence on tourism of the physical and aesthetic components of the climate. To a large extent, Mieczkowski’s rating and weighting Page 10 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England scheme is based on his personal views, expert opinion, and existing biometeorological literature, the accuracy of which has so far not been tested extensively. Other, more subtle points of criticism are that the TCI does not take potential overriding effects of, for example, rain into account, and that it does not correct for potential cultural and geographical differences in climate preferences as suggested by de Freitas et al. (2008) This is not helped by the use of ‘subjective’ terms, for example what is ‘marginal’ or ‘acceptable’ for one person may not be for another. With the new UKCP09 datasets it may be possible to improve the tourism climate index

methodology by addressing some of its current limitations. In particular, the rich database of visitor data available for the Southwest may help test the value of the TCI as an explanatory variable for tourist visitation. Based on empirically established climate preferences, the BCI addresses a major point of criticism raised against the TCI. Climate preferences may differ between countries (eg as a result of habituation), but the BCI was based on climate preferences of tourists from northern Europe, so that this limitation is probably of limited importance for the SW study. Major remaining issues are the use of monthly (rather than daily) data, and the potential overriding capabilities of precipitation that are not taken into account. 4 Method The research method followed six distinct stages which are summarised below and detailed in the sub sections to follow: 4.1 Downloading the UKCP09 climate variables 4.2 Filling the gaps – missing climate variables from UKCP09 4.3 Creating

the 1970s baseline 4.4 Calculating the Climate Indices (TCI and BCI) 4.5 Identifying the 10%, 50% and 90% probabilities 4.6 Using a Geographic Information System to present the TCI results 4.7 Identify key climate variable thresholds and use the ‘threshold detector’ to indicate the likelihood of extreme weather events Three UKCP09 products were used as summarised in table 5: the historical climate information to create baseline index scores for the 1970s (1961-1990); sampled model outputs from the probabilistic climate projections to create the TCI scores for the 2020s (2011-2040) and 2050s (2041-2070); and the integrated weather generator and threshold detector to consider and assess the likelihood of extreme weather events. Table 5: UKCP09 products and why they were used. Products Historical climate information UK probabilistic projections over land Weather Generator and the Threshold Detector Why? To download historic data to create TCI scores for the 1970s and provide a

baseline to compare with the probabilistic projections To download probabilistic projections for a range of climate variables to create TCI scores for the 2020s and 2050s To consider and assess the likelihood of an extreme weather event (for example a heatwave or flood) 4.1 Downloading the UKCP09 climate variables The Tourism Climatic Index (TCI) consists of 5 sub-indices, which are based on 7 climate variables (monthly data): mean temperature, mean relative humidity, maximum temperature, Page 11 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England minimum relative humidity, amount of precipitation, hours of sunshine and wind speed. Three of the climate variables are not within the dataset and we have explained how we overcame this in section 4.2 Climate variables were downloaded for the 1970s using the historical climate information, and separately for the 2020s and 2050s using the probabilistic projections over land for the high emissions

scenario. The averaging period used was monthly and data was collected for all climate variables (except sea level pressure) for the 12 months for each year (to reflect public and school holidays as well as seasonal variations). Data was downloaded for each of the 46 SW 25km grid squares. Table 6 summarises what UKCP09 outputs were used, the selections made and the rationale behind each. Table 6: UKCP09 Outputs - Selections and Rationale Consideration Climate Variables Emissions Scenario Time Period Selection Those required by the indices High 1970s 2020s 2050s Temporal Averages Monthly Spatial Area South West - 46 25km grid squares Data and Output Type Absolute values Sampled data Probability 10% 50% 90% Rationale Used the variables needed for calculating the TCI and BCI scores To go with the largest projected change (according to current science) To create a baseline Regional policy link – current tourism strategy ‘Towards 2015’ and the regional SW Climate Change

Action Plan National policy link to achieve the 80% carbon emissions reduction The shoulder seasons may change as a result of climate change, so monthly data will provide a clearer picture than just looking at the seasons and allow the ‘shoulder months’ to be looked at in more detail. Covering the Regional Development Agency definition of the SW, incorporating: Gloucestershire, Bristol, Bath, Wiltshire, Somerset, Dorset, Bournemouth, Poole, Devon, Cornwall and the Isles of Scilly. Needed absolute values to calculate the tourism indices Needed sampled data to ensure coherence between the variables We chose to identify the 10%, 50% and 90% probability level. The 50% value is “as likely as not” to occur However the 10% and 90% probability level should not necessary be considered to be extreme values and will also be looked at. It is within the distribution possibility and the range should be illustrated and explored. 4.2 Filling the gaps – missing climate variables from UKCP09

Three of the climate variables needed for calculating the TCI equation were not available from the UKCP09 projections. These were: wind, minimum relative humidity, and hours of sunshine. Our methods for obtaining or calculating data for these variables are now outlined Page 12 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England 4.21 Wind The baseline was calculated after seeking advice from UKCIP. There is no UKCP09 historic or projection data for wind. However some historic wind speed data for the period 1969-1990 was obtained from the Met Office. In the absence of projections or an alternative approach for obtaining projections for future wind speed at present, the historic wind speed data were used to calculate baseline wind speed data for the 1970’s. Moreover this data were used as a control factor in the TCI score calculations for the 2020s and 2050s. The 30-year period from 1961-1990 is used to create the climatological averages for

the 1970s climate variables in UKCP09. Wind data was only available from 1969 and not 1961 which would have been needed for full consistency. As we didnt have this full data series for wind, we used the 1969-1990 data to create a 22 year average result and used this to represent the 30 year period for wind. For example, the wind value for January in the 1970s is the 22-year (1969-1990) average wind speed in January. The wind speed data was obtained from the Met Office as point data (centre of 5km grid squares) in a csv file format. These wind speed values were plotted in a geographical information system (GIS) as red dots and the values that fall within each 25km grid were then averaged (not weighted). Figure 2 shows an example of the csv file plotted as red dots and the UKCP09 grid in blue. Figure 2: Wind speed values plotted in GIS on the UKCP09 grid to calculate the averages 4.22 Minimum relative humidity Minimum relative humidity was itself not a variable in the UKCP09 datasets.

It is linearly dependent on the ratio of vapour pressure (e) and saturated vapour pressure at maximum temperature (eSatTmax). Saturated vapour pressure at maximum temperature is a function of maximum temperature: Page 13 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England Under the assumption that vapour pressure (e) is constant throughout the day, it can be calculated from mean relative humidity, which is in the dataset, and saturated vapour pressure at mean temperature: in which Crucial for the estimation of minimum relative humidity is the reliability of the vapour pressure estimate. Since vapour pressure was a variable in the baseline dataset (as opposed to the calculated datasets for future time-slices), estimated vapour pressure values could be validated against observed data. In figure 3, calculated vapour pressure values for each of the 46 cells in each of the 360 months in the 1961-1990 are plotted against the matching observed

vapour pressure data, revealing a very strong correlation: As a result, calculated minimum relative humidity values for future time-slices were used with confidence. Figure 3: calculated (horizontal axis) and observed values (vertical axis) of vapour pressure (in hPa) in the baseline dataset 4.23 Hours of sunshine Hours of sunshine are estimated from cloud cover data (which were given) and day length. Day length is determined by latitude and time of year, which are both known. From day length and cloud cover data, the number of hours with cloud cover could be calculated; the remaining hours with daylight are hours without cloud cover, or unclouded hours: with cloud cover in percents Unlike the calculated datasets for future time-slices, the baseline dataset included data on both cloud cover and hours of sunshine, so that the relationship between unclouded hours and hours of sunshine could be checked. For each of the 30x12x46 (years x months x cells) datapoints available for the SW

of England the calculated number of unclouded hours was Page 14 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England plotted against the observed number of sunshine hours (see Figure 4). Using the least squares linear regression method, the following equation resulted: The R2 and p values indicate very strong correlation. Figure 4: calculated number of unclouded hours per day (horizontal axis) and observed number of sunshine hours per day (vertical axis), according to the baseline dataset 4.3 Creating the 1970s baseline The 1970s baseline is the baseline provided for UKCP09 and is the baseline used to calculate the future climate change values from. It was decided to use the 1970s baseline for the UKCP09 project after user consultation who decided to stick with the same baseline used in 2002. This baseline also has a reliable set of observations to work from which was another reason it was used for UKCP09. The observed climate variable data

needed to create the 1970’s baseline TCI scores was obtained from the Met Office who host the free UKCP09 observed data: http://www.metofficegovuk/climatechange/science/monitoring/ukcp09/available/indexhtml The data received were the monthly long-term averages for the 1961-1990 climate period for each of the 25 x 25 km grid boxes. The data was for all of the monthly variables, apart from mean wind speed, days of sleet/snow falling, and days of snow lying, for which data start after 1961. The following 1961-1990 baseline average data sets were downloaded from the MetOffice website in one zip file containing 13 netCDF files (not all required): • • • • • • • AirFrost 1961-1990 LTA 25km.nc CloudCover 1961-1990 LTA 25km.nc GroundFrost 1961-1990 LTA 25km.nc MaxTemp 1961-1990 LTA 25km.nc MeanTemp 1961-1990 LTA 25km.nc MinTemp 1961-1990 LTA 25km.nc MSLPressure 1961-1990 LTA 25km.nc Page 15 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW

England • • • • • • RainDays1 1961-1990 LTA 25km.nc RainDay10 1961-1990 LTA 25km.nc Rainfall 1961-1990 LTA 25km.nc RelativeHumidity 1961-1990 LTA 25km.nc Sunshine 1961-1990 LTA 25km.nc VapourPressure 1961-1990 LTA 25km.nc The relevant variables were used along with the baseline wind data to calculate the 1970’s TCI baseline figures. 4.4 Calculating the Tourism Climate Index (TCI) The TCI sub-indices were calculated following the original paper by Mieczkowski (1985), as detailed in table 7. Effective temperature is observed (dry-bulb) temperature, corrected for relative humidity. Up to around 25 degrees Celsius, the impact of relative humidity is small, but at higher temperatures, the influence becomes increasingly larger. At higher temperatures, effective temperature is higher (lower) than observed temperature if relative humidity is higher (lower) than 50%. As an example of how to calculate a TCI value, suppose that for a given month in the dataset, values are as

follows: mean temperature is 18.5°C, maximum temperature is 25°C, mean relative humidity is 60%, minimum relative humidity is 45%, precipitation is 50 mm/month, there are 6.4 hours of sunshine per day, and wind speed is 15 km/h The first two sub-indices are comfort indices, combining temperature and humidity into effective temperature, a measure of perceived temperature. At high temperatures, perceived temperature is higher than real temperature if humidity is high, and lower than real temperature if humidity is low. In the example, both mean and maximum temperature are relatively low, so that effective mean and maximum temperature are equal to the observed mean and maximum temperature. The effective maximum temperature of 25° Celsius falls within the 20-27° range (Table 7, 2nd column), so the daytime comfort index attains the maximum value of 5.0 The effective mean temperature of 18.5° Celsius falls within the 18-19° range (2nd column), resulting in a daily comfort index of 4.0

The amount of precipitation of 50 mm/month is within the 45-60 mm/month range (3rd column), leading to a value of 3.5 for the precipitation index The average of 6.4 hours of sun per day falls within the 6-7 hour range (4th column), so that the sunshine index is 3.0 The last sub-index covers wind. The appreciation of wind depends on temperature In most circumstances, wind is a negative factor, in particular in very cold (wind chill, 8th column) and in very hot (hot climate, 7th column) conditions. Only in a warm climate can wind be a positive factor, but only up to a certain wind speed, beyond which it becomes a negative factor (trade wind, 6th column). In the remaining cases (normal, 5th column), less wind is preferred over more wind. At 185° Celsius, the wind regime is normal The wind speed of 15 km/h is within the 12.24 – 1979 km/h range, belonging to a wind index of 30 Using the equation from paragraph 3.1 (TCI = 8CID + 2CIA + 4R + 4S + 2W), the overall TCI score can now be

calculated: Page 16 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England Table 7: From data to sub-index scores of Mieczkowskis TCI Source: Amelung (2006) Effective Monthly Rating temperature precipitation (ºC) 5.0 20.27 (Mm) Amount of Wind chill sunshine Wind speed (Km/h) cooling 2 (Hrs/day) (Watts/m /hr) Normal Trade wind 12.241979 0.0149 >10 <2.88 15.0299 9.10 2.88575 30.0449 8.9 5.76903 Hot climate 19.20 4.5 & 27.28 18.19 4.0 & 9.041223 28.29 & <500 19.802429 17.18 3.5 & 45.0599 7.8 9.041223 60.0749 6.7 12.241979 29.30 15.17 3.0 & 5.76903 30.31 & 500.625 24.302879 10.15 2.5 & 75.0899 5.6 19.802429 90.01049 4.5 24.302879 2.88575 31.32 5.10 2.0 & <2.88 32.33 & <2.88 625.750 28.83852 0.5 1.5 & 105.01199 3.4 24.302879 2.88575 750.875 120.01349 2.3 28.803852 5.76903 875.1000 135.01499 1.2 9.041223

1000.1125 33.34 -5.0 1.0 & 34.35 0.5 35.36 0.25 4.5 1125.1250 0.0 -10.-5 -1.0 -15.-10 -2.0 -20.-15 -3.0 <-20 >150.0 <1 >38.52 >38.52 >12.24 >1250 Identifying the 10%, 50% and 90% probabilities TCI scores were calculated for the whole sample of 10,000 data points available for each combination of month, time period and 25km grid square. Where needed, TCI scores were averaged across months to allow for seasonal analysis. Subsequently, the 10,000 scores obtained were ranked according to magnitude. The median value (the 5,000th score in the ordered list), and the 10th and 90th percentiles (i.e the 1,000th and 9,000th score respectively) were saved for further analysis. As an example, in figure 5 all 10,000 TCI scores are plotted in order of magnitude for one of the grid cells (1466), for the month of July in the time slice of the 2020s.TCI scores range from just over 65 to just under 95 The median (sample 5000) is 80, the 10th (sample 1000)

and 90th (sample 9000) percentiles are 75 and just over 85. Page 17 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England Figure 5: 10,000 TCI scores are plotted in order of magnitude for one of the grid cells (1466), for the month of July in the time slice of the 2020s. It was expected that uncertainty would increase between the 2020s and the 2050s. To explore this, the standard deviation in TCI scores was calculated for each grid cell in each month and each time-slice. Table 8 shows the mean standard deviation for the Southwest Standard deviation is largest in the summer and shoulder months, in the 2020s as well as the 2050s. Standard deviation increases in all months except for September The largest increases are in October, May and April. Table 8: Standard deviation in TCI scores for the Southwest in the 2020s and 2050s for the 12 months of the year Page 18 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change

in SW England 4.6 Using a Geographic Information System to present the TCI results The results were presented using a Geographic Information System (GIS). This allowed a clear visual presentation of the TCI scores across the region and it aided comparison of the results between different grid squares, time series and probabilities. It also easily allowed the detailed score to be shown and could help with future mapping, prioritisation and flexible weighting of layers if we wanted to develop multiple overlays with tourism data and interpret these at different spatial scales. Figure 6 illustrates how the TCI scores for July (2020s, 10% probability) were joined by the grid value to a GIS (spatial) grid file which allows the data to be viewed by a majority of GIS applications. Figure 6: Illustrating how the TCI scores are joined to the grid value Using this method of visual representation allows for a simple first glance comparison of the TCI results. The GIS can also be used to develop

other layers, for example mapping sea level rise or different types of tourism data like the number of bed spaces or the value of tourism in an area. In addition GIS allows other data and climate variables to be overlaid to display areas that are most likely to experience the most impact from changes in climate and the TCI; for example, which campsites are most likely to be flooded by sea level rise or by using a tourism expenditure layer which areas will be most financially affected?. 4.7 Identify key climate variable thresholds and use the ‘threshold detector’ to indicate the likelihood of extreme weather events The UKCP09 regional projections show that extreme weather events are likely to increase for the South West and this will be important to investigate for tourism and the potential risks assessed. The tourism indices do not allow this level of detail to be considered but the application of two of the UKCP09 tools - the Weather Generator and the Threshold Detector - allows

users to investigate thresholds for extreme weather and use a smaller 5km grid square resolution to help identify the probability of these types of events occurring. The Weather Generator gives 100 – 1000 runs of daily data for each month over 30 year periods. It is modeled data however the weather generator is trained on observed data so Page 19 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England the baseline (although modelled) should be very similar. Data has to be downloaded from the Weather Generator to run a Threshold Detector job. Data was downloaded from the Weather Generator using the following selections: • High emissions scenario for the 2050s • 100 model variants (random sample) • Location - cluster of 5km grid squares (that overlay the 25km grid square 1690, Penzance) • For 30 years at a daily resolution • Downloaded as raw data in a CSV file Once the data was retrieved two threshold detector jobs were run using the

thresholds identified in table 8. Table 8: Extreme Weather and Identified Thresholds Extreme Weather Heatwave Heavy Rain 5 5.1 Threshold Max over 30oC by day, Min over 15 oC by night over 3 consecutive days (Predefined UKCP09 threshold) Precipitation greater than 25mm for more than one day (Met Office Definition) Results Tourism Climate Index The five TCI components are the weighted sub-indices of the TCI, and they add up to the overall TCI value: daytime comfort (comf day, combination of - monthly means of - Tmax and minimum relative humidity), daily comfort (comf 24, combination of - monthly means of - Tmean and mean relative humidity), precipitation, sunshine, and wind. Analyses on the level of the aggregate TCI hide the dynamics on the level of the individual components. By disaggregating and analysing the TCI components questions can be addressed such as what drives the changes in TCI scores, or whether changes in the individual variables are all pointing in the same

direction. Figure 7 shows an example of disaggregation for one of the cells (id 1623) in the 1970s (left) and the 2080s (right). TCI scores improve most between March and October In the shoulder months, e.g May, most of the increase comes from improvements in daytime comfort (comf day). In the peak months of July and August, daytime comfort was already at its maximum in the baseline period. In those months, daily comfort (comf 24), sunshine, and wind contribute most to the TCI increase. The contribution of wind may be surprising, given that wind speed is assumed constant (because no wind data were available from the projections). The role of wind, however, is not constant, as in Mieczkowskis scheme it depends on temperature. As summer temperature increases, the cooling effect of (modest amounts of) wind is appreciated more. The graphs also reveal improved wind performance in the winter months, caused by reduced wind chill. Page 20 Source: http://www.doksinet UKCP09 Case Study –

Tourism and Climate Change in SW England Figure 7: Disaggregation of TCI scores in their five components for cell 1623, in the baseline period of the 1970s (left) and in the 2050s (right), 50% probability level. 5.2 Presentation of results using GIS Overall a general observation from the resulting GIS maps is the notable difference between the North East (top half) which generally receives higher TCI scores to the South West (bottom half) of the region. In terms of specific destinations (25km grid squares) Bournemouth and Poole in Dorset (grid square 1700) seems to score highest across the region, with Dartmoor and Exmoor (grid squares 1655 and 1579) in Devon scoring the lowest. Looking at the monthly variation in TCI scores regardless of probability, February consistently has the lowest level of improvement with June, August and September having the highest improvement level. The main focus of this research was to investigate the 50% probability level for the high emissions

scenario, as this value is ‘as likely as not’ to occur. Figures 8 to 19 illustrate the GIS maps showing the TCI baseline for 1970 and 50% probability maps for the 2020’s and 2050’s for the four months of January, April, July and October, representing the four seasons. These maps illustrate how the TCI scores improve for the whole region for both the 2020’s and 2050’s. The only exception where there is a reduction in the TCI is for February as outlined in table 9 using grid square 1690 as an example. The TCI improvement is greatest for the shoulder months of June (by 15.6) and September (by 142) potentially extending the season. Table 9: TCI scores for Grid Square 1690 (extreme SW) for the 1970s and 50% probability level for 2020s and 2050s Month January February March April May June July August September October November December 1970s 29.1 33.7 40.7 51.2 55.3 63.2 72.2 69.7 57.3 43.4 35.7 31.2 2020s 29.6 32.7 42.6 51.5 59.6 70.4 76.7 77.3 63.5 46.4 36 32.1 2050s 30.3

32.6 42.8 53.8 63.1 78.8 81.7 82.4 71.5 48.6 36.4 32.2 Page 21 Change, 1970 to 2050 1.2 -1.1 2.1 2.6 7.8 15.6 9.5 12.7 14.2 5.2 0.7 1 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England Figure 8: TCI Jan 1970, 50% probability Figure 9: TCI Jan 2020, 50% probability Figure 10: TCI Jan 2050, 50% Probability Figure 11: TCI April 1970, 50% probability Figure 12: TCI April 2020, 50% probability Figure 13: TCI April 2050, 50% Probability Page 22 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England Figure 14: TCI July 1970, 50% probability Figure 15: TCI July 2020, 50% probability Figure 16: TCI July 2050, 50% Probability Figure 17: TCI Oct 1970, 50% probability Figure 18: TCI Oct 2020, 50% probability Figure 19: TCI Oct 2050, 50% Probability Page 23 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England 5.3 Difference in the 10%, 50% and 90%

probability levels It is important to investigate and understand the different probability levels. In general it is ‘very likely’ that the projection will exceed the 10% probability level, ‘as likely as not’ that the projection will be the 50% probability level and ‘very unlikely’ that the projection will exceed the 90% probability level, all according to the current science. Investigating the different probabilities for the TCI, show very different results. For example using the extreme SW grid cell (Id 1690) as an example the following graphs compare the 10%, 50% and 90% probability results by month for the 2020’s and 2050’s. Figures 20 and 21, show that the TCI score improves with the higher probability level. The improvements are greatest with the 90% scores and lowest with the 10% probability where in some cases it goes below the baseline results for the 1970’s. Figure 20: Grid 1690, TCI score 2020’s Figure 21: Grid 1690, TCI score 2050’s Figures 22 and 23

explores the TCI scores under the 10% probability further, showing a decline in the scores from the 1970’s baseline in both the 2020’s and 2050’s. In the TCI scores for the 2020’s there is a decline for 8 months of the year and suprisingly the largest decline of -5.3 is in July, by the 2050’s a decline is still seen but now in 2 months of the year, February and March. The increase observed in the remaining months is relatively small in comparison to the 50% and 90% probability scores. Figure 22: Grid 1690, TCI scores at 10% Figure 23: Grid 1690, TCI score variation Page 24 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England The 90% probability results are presented in Figure 24 which show a very different picture with no decline in TCI scores seen for either the 2020’s or 2050’s and the improvement in scores is also quite significant. Figure 24: Grid 1690, 90% probability results For example the 2050’s show a large proportion

of the region achieving the ‘ideal’ TCI score for the months of July and August as shown in figures 25 and 26. The months of June, August and September have the greatest degree in improvement from the 1970’s baseline (21.5, 198 and 207 respectively) amounting to a shift in at least two TCI categories Figure 25: TCI 90%, July 2050 Figure 26: TCI 90%, August 2050 Page 25 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England 5.4 Beach Climate Index The UKCP09 outputs were also applied to a beach-specific index known as the Beach Climate Index (BCI), developed by Morgan et al (2000) and applied to climate change by Moreno and Amelung (2009). This index gives some more insight into a specific type of tourism (i.e beach tourism), whereas the TCI relates to general tourism activities such as touring and sightseeing. Like TCI scores, BCI scores show the suitability of a given climate for beach tourism on a scale from 0 to 100. Naturally, BCI

scores are only meaningful for the coastal grid squares. For the South West of England, the BCI scores are lower than the TCI scores, because for beach tourism wind requirements are more stringent, precipitation is valued more negatively, and preferred temperatures are higher. Figure 27 displays the average BCI for the coastal grid cells of the South West in the three periods examined. The results in the baseline period are very poor, with average scores not exceeding 30, even in summer. Climate change may improve conditions for beach tourism substantially, with scores in August 2050 almost reaching 35. Nevertheless, scores between 30 and 40 are still qualified as unfavourable, so that the prospects of the South West of England becoming an internationally competitive beach destination within a few decades are dim. It is important to realise, however, that the scores obtained are based on monthly climate data. There may be important changes in the number of days that do attain

favourable conditions in terms of temperature, precipitation, sun and wind, but such changes cannot be detected. Nevertheless, an important feature of successful beach destinations is a climate that is not only attractive but also reliable. Figure 27: Average monthly Beach Climate Index scores for the coastal grid cells in the Southwest, 50% probability level for the 1970s, 2020s, and 2050s Figure 28 shows the BCI results for the Bournemouth grid cell (highest scoring on TCI), split up into its components for the 1970s, 2020s and 2050s. Whereas the summer scores for precipitation are quite good, and the scores for sunshine are reasonable, there is hardly any contribution from wind (wind speed is too high) and thermal comfort (not warm enough). Up to the 2050s, climate change does not produce sufficient increases in temperature to drastically alter this situation, at least not on the average monthly level. Page 26 Source: http://www.doksinet UKCP09 Case Study – Tourism and

Climate Change in SW England Figure 28: BCI scores for the grid cell containing Bournemouth for the 50% probability level for the 1970s (left), 2020s (centre), and 2050s (right) Whilst the Beach Climate Index shows the South West as not making great strides to becoming a „beach‟ tourism destination this may show a different picture if we could look at smaller areas and some resorts may benefit. 5.5 Threshold Detector Two thresholds were run through the Threshold Detector, one for a heatwave and the other for heavy rain. The first was looking at the likelihood of a heatwave occurring using the predefined UKCP09 threshold (Max over 30C by day, Min over 15C by night over 3 consecutive days). The summary statistics are presented in Figure 29 The baseline statistics showed that a heatwave event has not occurred in Penzance for the baseline 30 year period from 1961-1990 and this is reflected in the minimum average number of event counts. The future results for the 2050s (2041-2060)

show the likelihood will increase, mainly affecting the month of July where the maximum average occurrence for a heatwave event could be as high as 1.7 each year Overall for the summer season (JunAug) the maximum average occurrence of a heatwave could be up to 19 each year from a baseline of zero. The mean average occurrence for the summer season is much lower at 01 per year, illustrating a potential of 3 heatwave events for the 2050s 30 year period. Figure 29: Heatwave threshold detector results for Penzance, Cornwall Page 27 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England The second threshold detector run was user defined looking at the likelihood of heavy rain occurring using the Met Office definition (Precipitation greater than 25mm for more than one day). The summary statistics are presented in Figure 30 Figure 30: Heavy rain threshold detector results for Penzance, Cornwall Reviewing the „maximum average‟ number of event counts

from the baseline to the 2050s shows significant variation for the months of January, February and November. The occurrence for heavy rain could be 4.5 times more likely to happen in January, 33 times more likely in February and 1.8 times in November The variation is less severe for the mean average results and they show the likelihood of this event going down slightly in the 2050‟s for the months of July and August because of the projection towards hotter and drier summers. Page 28 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England 5.6 Review of Tourism Data South West Tourism cannot accurately develop tourism data projections into the future because there have been changes in the survey methodology since 2000 that have led to inconsistencies. Ideally tourism projection data would be aligned with the future climate projections to help understand the likely impact and this is something that could be considered in the future if UK Tourism

statistics methodologies remain stable. In the meantime this section will take a brief look at some of the tourism data that exists to help explore what the implications of the projected changes to the TCI could be, considering visitor numbers and room occupancy, but also looking at visitor and community survey results. Visitor Numbers and Seasonality Looking back over the last 20 years (1989-2008) of domestic tourism data shows some variation with visitor numbers per year as represented in figure 31. Broadly speaking, between 1989 and 1993 trips were stable. From 1994, steady growth is evident throughout the late 1990’s and into the start of this decade, with trips to the region peaking in 2002 (26 million). Figure 31: Domestic trips to the South West from 1989-2008* Source: United Kingdom Tourism Survey, International Passenger Survey *Please note that estimates have been made to figures pre-2000 to adjust to the Government Office regional boundaries used from 2000 onwards. Met

Office records indicate that the best summers in recent years were in 2000, 2001, 2002, 2003 and 2005, and 1999 was the eclipse year which could have boosted visitor numbers. The domestic trips graph (figure 31), would suggest that there is some correlation between numbers of trips and the weather, however, there are undoubtedly other factors that need to be taken into account such as the economy for example. The South West Visitor Survey 2008 indicated that 85% of visitors to the South West were on a repeat visit, so it is also likely that large numbers of trips are booked on the basis of previous experiences on holiday in the region. Page 29 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England If we look at the individual months that domestic trips were taken over a ten year period as illustrated in table 10 the variation is slight with little change in the seasonality of tourism being evident. Table 10: Month of trip to the South West from

1999-2008 (Domestic visitors only) Occupancy Levels An improved tourism climate could mean an increase in visitor numbers, so do we have capacity in the South West to absorb more visitors? If we consider serviced accommodation capacity (most reliable data), this shows that across the SW region even at peak months such as August there is still capacity for more visitors within the current stock, with room occupancy peaking at 79% in August 2001 and 2003, and the lowest August capacity was 73% recorded in 2007 over a ten year period (1999 to 2008). Table 11 shows serviced room occupancy for the South West between 1999 and 2008 and clearly shows the capacity for more visitors at a regional level if there were an improvement in the TCI, particularly in the off peak periods. Part of the focus of the regional tourism strategy, Towards 2015, is to direct additional trips to the region into the off peak periods so the tourism industry becomes markedly less seasonal for businesses. Table 11:

South West Serviced Accommodation Room Occupancy (%) Source; South West Tourism Serviced Accommodation Occupancy Survey (1999-2007), BDRC Serviced Accommodation Occupancy Survey (2007 – 2008) However looking at the ‘average’ regional room capacity across the whole of the South West does not distinguish those honey-pot sites or other point destinations which are under extreme visitor pressure and arguably ‘at capacity’ in peak periods. Table 12 highlights the differentials between the regional room occupancy figures for six local authority districts. Page 30 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England Table 12: Serviced accommodation room occupancy in August between 2003 and 2008 for six destinations under more pressure Source: South West Tourism Serviced Accommodation Occupancy Survey (1999-2007), BDRC Serviced Accommodation Occupancy Survey (2007 – 2008) The data suggests that all of the districts, with the exception of

West Somerset, are under considerably more pressure than the region as a whole during the month of August. Whilst, the figures for West Somerset district consistently fall below the regional average this does highlight the need to drill down below district level wherever possible. Local knowledge tells us that if we were able to look at West Somerset in more detail and Minehead specifically (including Butlins Somerwest World) it is likely that this would paint a very different picture from the one displayed above. Another consideration is that capacity should not just be seen as available rooms or bed spaces, there are also community, infrastructure and natural resource capacities which can and are exceeded and would require further investigation to form a clearer and more complete picture. Role of Weather The weather and the overall health of the UK economy are key factors that affect visitor numbers. Industry surveys conducted by South West Tourism on a regular basis from 2003

onwards (Business Barometer and Business Snapshot) consistently show the weather amongst the top factors most likely to limit business activity in the tourism industry. Monitoring and planning for projected changes in weather and climate would seem to be important to the industry and in business planning. Figure 32 reflects recent non-visitor research and presents some quotes from the focus groups. The results also confirm the importance of favourable weather amongst both visitors and non-visitors when making their holiday plans. The lack of guaranteed sunshine is the greatest con when considering a holiday in the South West against a holiday in Europe, and is part of the value equation. Poor weather is also associated with greater costs whilst on holiday, for example entertainment. However, in the context of the UK, ‘sunny’ weather is identified as a strength amongst visitors to the region Page 31 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in

SW England Figure 32: Non-Visitor Study Focus Group Quotes . Last 10 years the weather‟s been so bad you just want to go away Whatever money I have nowadays I put towards a holiday with guaranteed sunshine rather than risk it on holidays in England We just work really hard, and I think when we want a holiday we want sunshine and just a chill-out week. Now the kids are older, we just fly abroad Source; Non-Visitor Study April 2009 – fionaandmichelle on behalf of South West Tourism To consider the impact of extreme weather events on tourism we can investigate the effect of previous incidents on occupancy levels. Two notable South West events identified earlier in this research were the August 2003 heatwave in Bournemouth and the July 2007 floods in Gloucestershire. Table 13 presents the serviced accommodation room occupancy figures over a six year period for Bournemouth and Gloucestershire for the corresponding month when the „extreme‟ weather incident took place. Comparison

over a number of years clearly shows a change in visitor numbers at the time of the incidents. Table 13: Serviced Accommodation Room Occupancy Figures from 2003 to 2008 Bournemouth - August Room occupancy Gloucestershire - July Room occupancy 2003 Heatwave 2004 2005 2006 2007 2008 87 75 82 78 69 72 2003 2004 2005 2006 2007 Floods 2008 64 72 61 64 57 69 Source; South West Tourism Serviced Accommodation Occupancy Survey (2003-2007), BDRC Serviced Accommodation Occupancy Survey (2008) In the case of Bournemouth, data for the months on either side of the August heatwave shows occupancy levels as normal in comparison to the rest of the year and the following years which would suggest that the August 2003 peak could be as a direct result of the heatwave. However, whilst Gloucestershire room occupancy levels for July 2007 (above) and also August appear to be abnormally low it is difficult to estimate how much can be attributed to the floods as generally speaking,

room occupancy was down across the county for the full year of 2007. Page 32 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England This perhaps highlights the limitations of the data being used in this instance. It is likely that the impact of the extreme weather event in Bournemouth is better highlighted than that in Gloucestershire by the use of serviced accommodation room occupancy data as accommodation stocks in Bournemouth are heavily weighted towards serviced accommodation. In contrast, Gloucestershire has far greater proportions of non-serviced accommodation such as self-catering units and camping and caravan pitches. Table 14 below shows the percentage of holiday home units occupied for Gloucestershire from 2006 to 2008. This clearly shows a drop in occupancy levels during July 2007 as the floods hit and rather interestingly also shows a good end to 2007. This is perhaps caused by an influx of trades people to clear up after the floods.

Table 14: Self-catering Holiday Homes Unit Occupancy % from 2006 to 2008 % 2006 2007 2008 January February 31 40 34 47 46 21 March 42 65 42 April 71 62 44 May 79 77 62 June 83 75 61 July 87 66 93 August September October November December 87 82 50 31 40 72 63 80 64 64 93 86 75 43 54 This highlights the need for the thorough investigation of all available data. It would also clearly be an improvement if data was captured at a local level at the time of an extreme weather event. This would help to portray a robust, immediate picture of the scale of the impact which can otherwise be lost in other data collection methods that are carried out over longer periods of time and at different geographical levels. Community and Environmental Effects The observed changes in the TCI show improvements over time and could lead to an increase in visitor numbers across the year, in both peak and shoulder periods. However there are months in the year where this could be readily absorbed and

welcomed by businesses and communities, such as Autumn and Winter, but the spring and summer months would find it harder to accommodate, especially in the public and school holiday periods. The 2006 Community Attitudes Survey asked residents‟ their opinions on a number of tourism related issues and the impact that it had on their local area. These findings begin to highlight some of the issues that increased visitor numbers could lead to. Figure 33 shows that those who live in coastal, resort or honeypot sites are more likely to feel where they live received more visitors than it could cope with, but overall more than a fifth of respondents in each sub-group felt that their area received more visitors than it could cope with. Page 33 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England Figure 33: Residents‟ opinions on the number of visitors they received during the summer months 29% RESORTS 47% 22% HISTORIC 59% 31% COASTAL Has

about the right number of visitors Would benefit from more visitors 13% 55% Receives more visitors than it can cope with 6% 15% 60% 26% HONEYPOT 4% 14% 53% 24% COUNTRY 20% 15% 4% Dont know 17% RANDOM 0% 59% 20% 40% 19% 60% 5% 80% 100% Source: South West Tourism Community Attitudes Survey 2006 Differences in opinion are clear between the resident groups with regards to the effect of tourism on the condition of the natural environment. Figure 34 shows that in all of the groups a negative effect is seen ranging from 14% (Historic and Random) to 25% (Resort) of the samples feeling the effect was bad or very bad. However respondents are more likely to feel that tourism has a good or very good effect ranging from 38% (Resorts) to 59% (Random) of the samples. When considering these results, what we are not sure of is what the respondent perceives the „natural environment‟ to be. Figure 34: Residents‟ opinions on the effect of tourism on the condition of the

natural environment RESORTS 24% HISTORIC 13% COASTAL COUNTRY 33% 35% 18% 3% HONEYPOT 15% 0% 10% 8% 37% 25% 30% 4% 4% 34% 35% 20% 5% 6% 36% 34% 14% 2% 5% 41% 37% 18% RANDOM 36% 5% 5% 54% 40% 50% 60% 70% 6% 5% 3% 80% 90% Very bad Bad effect No effect Good effect Very good effect Dont know 100% Source: South West Tourism Community Attitudes Survey 2006 However when residents were asked more specifically about associated problems, table 15 clearly illustrates that tourism has a range of negative impacts on local communities and their environment. Traffic and congestion is the most problematic issue across all of the samples, ranging from 54% (Country) to 70% (Resort) that considered this to be a problem where they lived. This is clearly already an issue of significant concern which is directly linked to the need to reduce emissions to help mitigate the impacts of climate change. Page 34 Source: http://www.doksinet UKCP09 Case Study –

Tourism and Climate Change in SW England The Resort sample consistently score highest in experiencing tourism associated problems, followed by the Coastal and then the Honeypot samples. Across most of the samples, litter is seen to be the next key problem followed by noise, then irresponsible behaviour. It can be expected that if an increase in the TCI leads to more visitors, all of these associated problems could be exacerbated if remedial and management measures are not put in place. Table 15: Residents‟ opinions on tourism associated problems where they live RANDOM HONEYPOT COUNTRY COASTAL HISTORIC RESORTS A lot A little Not at all Dont know Noise generated by tourists 3% 19% 76% 2% 7% 24% 67% 2% 6% 17% 75% 2% 6% 28% 64% 2% 4% 19% 74% 3% 12% 31% 55% 3% A lot A little Not at all Dont know Litter dropped by tourists 4% 27% 68% 1% 7% 27% 65% 2% 4% 21% 73% 2% 5% 28% 65% 2% 5% 21% 73% 2% 14% 36% 49% 2% A lot A little Not at all Dont know Traffic fumes/ congestion

generated by tourists 9% 33% 56% 2% 14% 34% 50% 2% 11% 26% 61% 3% 16% 41% 43% 1% 9% 29% 58% 4% 20% 41% 38% 2% A lot A little Not at all Dont know 22% 42% 35% 1% 28% 34% 36% 2% 21% 33% 42% 3% 26% 37% 37% 2% 28% 34% 35% 3% 38% 32% 29% 1% Irresponsible behaviour of tourists Source: South West Tourism Community Attitudes Survey 2006 Whilst this chapter provides useful detail of the insights that can be gained through analysis of existing tourism data in relation to the impact of the weather on tourists‟ behaviours, it also clearly highlights the need and scope for improved data collection and investigation in this area. Page 35 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England Tourism Data Development The correlation between the weather and tourists’ behaviours should be explored more thoroughly and the subject should be integrated into all existing and future tourism research projects where the opportunity exists, to enable a

better understanding of the likely impacts of increases in the TCI. In the first instance, this can be implemented easily at a regional level but more localised data will generally lead to a better understanding of the impacts. A program to enlist the engagement of tourism organisations at a more localised level should be put into place to ensure that standardised information is gathered across the region for this purpose. As already highlighted in this chapter, current data collection sources are probably not detailed enough in their outputs both in terms of the timescales or geographical levels that analysis is available at, to provide detailed insights into the impacts of extreme weather events. Many of which are relatively localised and occur over a relatively short period of time. The most beneficial and reliable way to gather this data would be to have the ability to react to extreme weather events, during any given year, and carry out immediate research in the areas concerned.

It is understood that the national occupancy surveys will in the future have the ability to analyse data for individual days. Whilst this will be a great improvement on the current format, this will only provide part of the picture and is still likely to fall short in some areas meaning that further data collection will still be required. Planning is underway at South West Tourism to develop methodologies and a best practice tool kit to enable destinations to plan ahead for the potential impact of increases in the TCI and extreme weather events upon their local area. This project will aim to build upon existing tourism research and research from other sources to build a forecasting model to look at the impacts in terms of visitor numbers in the first instance. It will then go on to look at the implications of the changes in visitor numbers on key areas to include the local economy, infrastructure, accommodation capacities, leisure facilities and provide guidance for planning issues.

Page 36 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England Conclusions This study has revealed some interesting findings although the results have to be considered with some caution. In part due to the inherent problems with modelling climate, using indices, averaging and the lack of UKCP09 projection data for some of the variables such as wind which will have limited this analysis and are areas for future improvement. However, despite these limitations the results are in line with the overall UK projections for the region and present key areas for further investigation if the tourism industry is to be proactive in its response and ensure it is resilient for the future. The initial TCI results indicate that the climate for tourism activity will not change much in the winter but the shoulder season both sides shows an improvement, and in the height of the summer could become excellent and even ideal especially under the 90% probability by 2050.

Overall it appears that seasonality could be reduced and the ‘holiday’ season widened. This in turn could mean more visitors, not only throughout the season but at peak times. This finding is supported by some research undertaken by Amelung et al (2007) where they identified that UK tourism activities are likely to be extended throughout a longer more favourable summer period, and could lead to a large increase in visitation from destinations that become uncomfortably hot. This is not all good news however and the tourism industry needs to consider these results with care. Rather than a reduction in seasonality this could lead to intensified concentrations of tourist activity in peak season when some areas and infrastructure are already struggling to cope. The South West already has sites, destinations and transport infrastructure which are under severe stress and pressure from tourists and where these issues exist, careful management will be required. The generic UKCP09 warmer

drier summer projections appear to be supported by the initial results from this study and paint an optimistic picture for the SW of England in terms of longer and more reliable summer tourist seasons. This could lull people and businesses (especially tourism) into a false sense of security as these climate projections are built on averages, and look either at seasons or months per year. Despite their appeal these averaged seasonal or monthly pictures could be misleading and can mask the likelihood of erratic and extreme weather patterns or spikes. The UKCP09 regional projections and the results from this study also show that extreme weather events are likely to increase for the SW, so the likelihood of erratic and extreme weather also needs to be investigated and considered. For example extreme weather events which can happen within a 24 hour period such as the Gloucestershire and Boscastle floods or Bournemouth’s heatwave would not show up using monthly or seasonal averages. These

extreme weather events will directly and indirectly affect visitor numbers and consumer spending and choice. Prediction of the future is a risky business and there is a danger that because the changes expected are perceived to be minor and taking place over a period of decades there is a danger that the response by individual businesses to respond and adapt could be limited. The prudent business will aim to reduce risks and stay well informed about the latest research, understanding the uncertainties involved but not allowing that to restrict action. Changes in the climate will occur and the tourism sector in the South West will be affected by it in some way or another and survival depends on the capacity to adapt to these changes. This can be done by reacting to changes as they arise, or a more proactive approach by trying to pre-empt and plan for any negative changes that may occur in the foreseeable future. Page 37 Source: http://www.doksinet UKCP09 Case Study – Tourism and

Climate Change in SW England The general findings reflect a longer season with better conditions in the high season but investments depend on timing and detail and large uncertainties remain there. We know very little about the vulnerability of the tourism sector (let alone the individual segments): how much change can be handled before the system breaks down (or an equivalent question on the positive side)? How important is climate relative to other factors in the case of the South West? What change in the climate do potential tourists perceive, and how are these related to real/actual changes? In addition: climate variability is large, so it is impossible to say with any level of confidence when to invest in new opportunities that may arise with climate change. One element in a proactive approach is to examine ones own vulnerability to climate change by exploring how broad or narrow the range of favourable climate conditions is, and how close to this ranges edges current conditions

are and will impact on business (i.e how much climate change is needed to be negatively or positively affected). This approach, which starts from the peculiarities of the own organisation, reduces the dependence on the uncertain projections from climate models, and leads more naturally to strategies for vulnerability reduction. The projected changes in the TCI and the increased frequency of extreme and erratic weather will directly and indirectly affect tourism, and this will need adequate consideration in both regional and local development and planning and for the review of the regional tourism strategy ‘Towards 2015’. The impacts will present both challenges and opportunities in varying degrees across the region depending on the business location, type and its vulnerability. The impacts can be direct and indirect and can be split into four broad categories to be explored as summarised earlier in table 1. South West Tourism is working with the Climate SouthWest Tourism Sector

Group to interpret and understand the implications of these findings and how this should inform the evidence-base and review process of ‘Towards 2015’. It is clear that tourism could be affected in a variety of ways and at different levels. From a strategic perspective the findings from this study will need to inform the consultation events and be used alongside the other sets of regional data and evidence that will inform the review of the regional strategy. It is difficult for many to plan beyond their current operating year, let alone looking into the future of 2020 or 2050. One of the major challenges will be to link the projected climate change impacts to tourism decision-making processes, and to consider the most appropriate way for the region and destinations to respond in the next 50 years. The results of this research will inform further discussions where we will bring together Destination Management Organisations and regional stakeholders to discuss and debate the impacts

of these findings on the future of tourism, alongside other prominent challenges like reducing the environmental impact of tourism, the credit crunch, security issues, changing markets and peak oil. To help the sector respond South West Tourism will work with regional partners, Climate SouthWest, DMO’s and businesses to investigate and respond to the challenge. The following section considers some of the next steps, including areas for improvement with the study and future areas for investigation. Page 38 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England 6 Next Steps and Recommendations This research has started to explore how changes in climate and weather may affect tourism in the South West using the latest UKCP09 projections. It provides a first step in the examination of impacts and vulnerabilities for tourism in the South West of England. Further work needs to be done which in turn will inform the development and implementation of

adaptation strategies, and the building of resilience and adaptive capacity within the tourism sector. It must however be recognised that this is a cyclical process which should and can always be improved upon, but we hope in the interim it will inform further work with UKCP09 data across the country. The results provide the foundation but there is plenty of room for data improvement, further investigation and analysis in the following areas: 7.1 Explore and develop the tourism indices (TCI and BCI) further for the South West. 7.2 Ideally there would be wind speed projections that could be used within the indices but this appears unlikely from UKCP09. This is important as wind appears to have quite a big influence on the results (see section 5.1) and this study used baseline data for the future due to the lack of projected data. 7.3 For minimum relative humidity, we could compare our derived ‘relative humidity’ with the Regional Climate Model (RCM) data released as a

by-product of UKCP09 and includes 11 runs of the Hadley centre regional climate model. The difference between this and the UKCP09 probabilistic projections is that a) UKCP09 is probabilistic and b) UKCP09 includes single runs of other climate models from around the world, therefore incorporating more uncertainty. The advantage of the RCM is that it has relative humidity in it. The reason it was not used in this study is that it is not in a user friendly format, is difficult to use, is daily so quite data intensive, and is only available for the medium emission scenario. 7.4 Undertake further analysis and investigation with the ‘threshold detector’ to explore the results across the region and to cover different destinations. Could also look at a range of different thresholds. The higher resolution at a 5km grid and the daily results provide the chance to explore the daily TCI potentially and to explore the likelihood of extreme weather events alongside the Tourism Indices to

present a fuller picture for destination planning. 7.5 Investigate a different framework for analysing data through weather ‘types’ such as that developed by Besancenot (1989). Instead of putting a single value on weather conditions, this approach classifies weather conditions on a given day as 1 of 9 weather types: e.g "very nice and sunny", "rainy" or "nice weather, partly clouded" This may be more realistic for tourism purposes as different types of tourists/tourist activities require different kinds of weather. When defining weather types, Besancenot tried to mimic the intuitive weather categories that tourists use. With Besancenots approach, the changes in the probability of the various weather types can be analysed, to assess the changes in suitability for different kinds of tourism activities. 7.6 Investigate other studies that are being conducted internationally, regionally and on a destination basis, for example the climate modelling and

vulnerability profiling being undertaken under the CARIBSAVE Climate Change Risk Atlas. This research uses up to 18 Coupled Global Climate Models overlaid with Regional Climate Modelling provided by using PRECIS. The work examines and provides outputs on a range of Page 39 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England climate variables that specifically impact tourism in destinations and then links these variables to key sectors that are integral to the tourism product and supply chain. 7.7 Study the climate preferences of tourists visiting the SW and further explore the correlation between the weather and tourists’ behaviours. This could be done through visitor research how weather influences trip choice and spend, for example will this and last summer’s (2008 and 2009) weather have affected the local tourism industry, this year and as a consequence next year. Some research on this issue is available on tourists from Canada,

Sweden, New Zealand, the Netherlands and other countries. In the SW, climate profiles could be established for visitors engaging in a range of popular tourist activities (beach, camping, hiking, boating etc.) so that the impact of climate change on tourism in the SW can be more accurately explored, and specific opportunities and vulnerabilities can be identified. 7.8 Investigate a program to enlist the engagement of tourism organisations at a more localised level should be put into place to ensure that standardised information is gathered across the region. This will hopefully ensure more detailed outputs and information both in terms of the timescales and geographical levels, to provide detailed insights into the likely impacts of changes to the TCI and extreme weather events. 7.9 After extreme weather events occur in the South West ideally immediate research will be carried out in the areas concerned to determine the impact on tourism. 7.10 Develop methodologies and a best

practice tool kit to enable destinations to plan ahead for the potential impact of increases in the TCI and extreme weather events upon their local area. This project will aim to build upon existing tourism research and research from other sources to build a forecasting model to look at the impacts in terms of visitor numbers in the first instance. It will then go on to look at the implications of the changes in visitor numbers on key areas to include the local economy, infrastructure, accommodation capacities, leisure facilities and provide guidance for planning issues. 7.11 Work with the Environment Agency and destinations to map projected sea level rise and identify vulnerable locations and destinations. 7.12 Overlay some of the key findings with tourism data sets to identify any correlations, for example linking the climate information with data on visitation, seasonality and the value of visitor spend in the off-peak periods. 7.13 Produce some clear information and interpret

the results for use by Destinations and explore what else is needed by decision makers at this level. 7.14 Consider the results alongside historical evidence and tourism forecast modeling and scenario planning to enable a more comprehensive picture. Page 40 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England 8 Links and further information UK Climate Projections 2009 – The latest climate projections provide users with probabilistic data for a range of variables though a variety of tools, reports and pre-prepared maps and graphs. For more information visit http://ukclimateprojectionsdefragovuk South West Climate Change Impacts Partnership - To raise awareness of the impacts of climate change, inform and advise on the challenges and opportunities of climate change in SW England, and develop practical adaptation responses. http://www.oursouthwestcom/climate South West Climate Change Action Plan - The plan draws together issues from across

the region to ensure that there is a shared vision on tackling climate change, access to a common evidence base and a jointly agreed set of priorities for taking the issues forward. http://www.swcouncilsgovuk/nqcontentcfm?a id=3580&tt=swra Climate Change Act 2008 - An Act to set a target for the year 2050 for the reduction of targeted greenhouse gas emissions and to provide for a system and powers to enable this target to be reached. http://www.opsigovuk/acts/acts2008/ukpga 20080027 en 1 Met Office - Climate change science, information and guides. http://www.metofficegovuk/climatechange/ and for observational baseline data sets: http://www.metofficegovuk/climatechange/science/monitoring/ukcp09/ Adaptation Checklist for Tourism Businesses (2007) – To explain to tourism business owners how climate change affects their business. www.oursouthwestcom/climate/registry/tourism-leaflet-2007pdf Shifting Shores (2008) - National Trust published research into the long-term future of the SW

coastline, and the impact that climate change (through sea level rise, coastal flooding and increased erosion) is predicted to have on this coast over the next century. http://www.nationaltrustorguk/main/w-global/w-news/w-latest news/w-news-shifting-shoresreport/ Davos Declaration - Climate Change and Tourism Responding to Global Challenges (2007). 2nd International Conference on Climate Change and tourism. UNEP The declaration is available at: www.unwtoorg/pdf/pr071046pdf Pitt Review: Independent Review into the floods of 2007 - A report produced following an independent enquiry which examined the emergency response to the 2007 flooding and investigated how we can reduce the risk and impact of floods in the future. http://www.cabinetofficegovuk/~/media/assets/wwwcabinetofficegovuk/flooding review/pit t review full%20pdf.ashx Flooding: Minimising the Risk – The Environment Agency has produced a pack which gives practical advice on minimising the impact of flooding and keeping your

visitors safe in the event of a flood. To receive a pack, please email janefletcher-peters@environmentagencygovuk Tourism Case Studies - Climate SouthWest Tourism Adaptation Case Studies on Kitley House Hotel and The National Trust are available to download at: www.oursouthwestcom/climate/case-studies Page 41 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England A Changing Climate for Business: Business Planning for the Impacts of Climate Change (2005) - A UKCIP report containing key messages and suggestions of initial adaptation actions for businesses and organisations. Includes the Business Areas Climate Impacts Assessment Tool (BACLIAT). http://www.ukciporguk/indexphp?option=com content&task=view&id=82&Itemid=374 Climate Change and Small Businesses (2008) - The results of a study, commissioned by Climate South East, investigating the attitudes of directors and managers of SMEs towards climate change. It provides an indication of

small business perceptions of climate change impacts and current levels of response. http://www.climatesoutheastorguk/images/uploads/Climate Change and Small Businesse s Final.pdf Preparing for Climate Change: A Practical Guide for Small Businesses - Sets out the evidence of climate change and the impacts of severe weather events. Provides advice on what businesses can do to reduce the risks, including building resilience, developing a business continuity plan and ensuring adequate insurance cover. http://www.axacouk/aboutus/corporate publications/climatechange docs/AXA%20Preparin g%20for%20climate%20change.pdf Climate Change and Tourism - Responding to Global Challenges: http://www.unepfr/scp/publications/detailsasp?id=WEB/0142/PAExperts in Climate Change and Tourism: www.e-clatorg Page 42 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England 9 References Amelung, B. (2006) Global (environmental) Change and Tourism: Issues of Scale and

Distribution. PhD thesis Faculty of Liberal Arts and Sciences Maastricht, Universiteit Maastricht. Amelung, B., S Nicholls, and D Viner (2007) ‘Implications of global climate change for tourism flows and seasonality.’ Journal of Travel Research 45(3): 285-296 Amelung, B. and D Viner (2006) ‘Mediterranean tourism: exploring the future with the Tourism Climatic Index.’ Journal of Sustainable Tourism 14(4): 349-366 Besancenot, J.-P (1989)Climat et Tourisme Paris: Masson de Freitas, C. R, D Scott, and G McBoyle 2008 ‘A second generation climate index for tourism (CIT): Specification and verification’. International Journal of Biometeorology 52: 399–407. Mieczkowski, Z. (1985) The tourism climatic index: a method of evaluating world climates for tourism. Can Geogr 29(3): 220-233 Morgan, R., E Gatell, R Junyent, A Micallef, E Ozhan, and A T Williams (2000) ‘An improved user-based beach climate index’. Journal of Coastal Conservation 6:41–50 Moreno, A., B Amelung 2009

‘Climate change and tourist comfort on Europes beaches in Summer: a reassessment’. Coastal Management 37(6): 550-568 Nicholls, S. and B Amelung (2008) ‘Climate change and tourism in Northwestern Europe: impacts and adaptation’. Tourism Analysis 13(1): 21-31 Platt, R. and S Retallack (2009) Consumer Power: how the public thinks lower-carbon behaviour could be made mainstream. IPPR: London Scott, D., McBoyle, G and Schwartzentruber, M (2004b) ‘Climate change and the distribution of climatic resources for tourism in North America’. Climate Research 27(2): 105117 Simpson, M.C, Gossling, S, Scott, D, Hall, CM and Gladin, E (2008) Climate Change Adaptation and Mitigation in the Tourism Sector: Frameworks, Tools and Practices. UNEP, University of Oxford, UNWTO, WMO: Paris, France South West Tourism (2009) Survey of Non-Visitor and Lapsed Visitors to the South West. South West Tourism: Exeter South West Tourism (2007) Value of Tourism. South West Tourism: Exeter South West Tourism

(2005). ‘Towards 2015’: Shaping Tomorrow’s Tourism South West Tourism: Exeter SWCCIP (2003) ‘Warming to the idea’. The SW Region Climate Change Impact Scoping Study. Meeting the Challenge of Climate Change in the South West UKTS (2007), UK Tourism Statistics, Visit Britain Stern, N. (2007) The Economics of Climate Change Page 43 Source: http://www.doksinet UKCP09 Case Study – Tourism and Climate Change in SW England www.swtourismorguk Page 44