(1) Background: In Finland, slippery snowy or icy ground surface conditions can be quite hazardous to human health during wintertime. We focused on the impacts of the variability in weather conditions on tourists’ health via documented accidents during the winter season in the Sotkamo area. We attempted to estimate the slipping hazard in a specific context of space and time focusing on the weather and other possible parameters, responsible for fluctuations in the numbers of injuries/accidents; (2) Methods: We used statistical distributions with graphical illustrations to examine the distribution of visits to Kainuu Hospital by non-local patients and their characteristics/causes; graphs to illustrate the distribution of the different characteristics of weather conditions; questionnaires and interviews conducted among health care and safety personnel in Sotkamo and Kuusamo; (3) Results: There was a clear seasonal distribution in the numbers and types of extremity injuries of non-local patients. While the risk of slipping is emphasized, other factors leading to injuries are evaluated; and (4) Conclusions: The study highlighted the clear role of wintery weather conditions as a cause of extremity injuries even though other aspects must also be considered. Future scenarios, challenges and adaptive strategies are also discussed from the viewpoint of climate change.

Icy and snowy sidewalks are typical wintertime phenomena in Finland. Wintertime slipping injuries are common and lead to substantial economic costs to health care as well as losses to society due to long sick leaves. In Finland, almost every second person slips and falls outdoors annually, and around 70,000 persons are injured needing medical attention. Typically, the most slippery conditions are encountered when the daily average temperature is slightly below 0°C or temperature crosses 0°C and there is precipitation in some form. The Finnish Meteorological Institute (FMI) has developed a numerical weather model that simulates the level of slipperiness on the sidewalks. The model classifies the sidewalk slipperiness into three classes; normal, slippery and very slippery. The FMI issues warnings of hazardous sidewalk conditions to the general public. Pedestrians' road safety can be increased with sidewalk condition forecasts and warnings. When warned, people can choose proper footwear or use anti‐slip devices, change the route or mode of transport, postpone the journey or cancel it altogether. Precise and reliable weather and sidewalk condition forecasts enable targeted and more effective sidewalk maintenance activities that can improve the grip of sidewalks and thus reduce the risk of accidents and injuries. This study presents the sidewalk condition model RoadSurf‐Pedestrian, its physical principles and examples of model runs. There are some challenges in the modelling of the slipperiness but the model gives valuable information on the slipperiness for duty forecasters. Slipping injury statistics are also presented and used as verification data.

Road weather is a major concern for the public safety and health, industries and transport sectors. Half of the yearly 27,000 road and 50,000 pedestrian injuries in Finland, Norway and Sweden can be traced back to slippery road and walkway conditions. We simulated the climate change impacts on future roads and walkways for mid- and end-century in Finland, Norway and Sweden with the road weather model RoadSurf, driven by the regional climate model HCLIM38 with boundary data from two global climate models following the RCP8.5 scenario.Our simulations for mid-century suggest strong road surface temperature increases, especially in southern Finland (+ 5.1 °C) and Sweden (+ 7.1 °C). Snowy and icy road surface conditions decreased by 23 percentage points, causing 18.5 percentage points less difficult driving conditions during the cold season. Zero-degree-crossing days mostly decreased in autumn and spring by up to 7 days and increased in winter by up to 5 days. Sidewalks mostly showed a decrease in slipperiness, but a five percentage point increase of water above ice layers on the sidewalks in winter, suggesting the slip-season might become shorter, but more slippery.Our results are upper extreme estimates but can serve as a reference to help local decision-makers plan mitigation and adaptation measures ahead of time.

Pedestrians’ slipping injuries are a very typical problem in the Nordic countries, causing varying degrees of injuries and in the worst case, long sick leaves. There is a clear seasonal variation in the number of slips. Sidewalk slipperiness and the risk of slips is a complex combination of weather, winter maintenance activities, number of walkers, and the grip between shoes and surface, as well as human behavioral and physical factors. In this study, the effect of weather on pedestrians’ slipping injuries is studied. Daily weather observations are compared to the slip statistics that have been collected from commuting accident statistics in cases where the way of commuting has been walking. A total of 16 cities from Finland for 14 winters are included in this study. The results reveal that snow on the ground increases the slip risk more than three times compared to no-snow situations. Near zero temperatures and precipitation are very typical on days when slip injuries occur more than usual. However, there are also days when high amounts of slips cannot be explained with the weather. The study also shows that there are significant differences as to the number and timing of slips between different parts of the country.

In this paper, we evaluate the skill of the road weather model RoadSurf to reproduce present-day road weather conditions in Finland. RoadSurf was driven by meteorological input data from cycle 38 of the high-resolution regional climate model (RCM) HARMONIE-Climate (HCLIM38) with ALARO physics (HCLIM38-ALARO) and ERA-Interim forcing in the lateral boundaries. Simulated road surface temperatures and road surface conditions were compared to observations between 2002 and 2014 at 25 road weather stations located in different parts of Finland. The main characteristics of road weather conditions were accurately captured by RoadSurf in the study area. For example, the model simulated road surface temperatures with a mean monthly bias of −0.3 ∘C and mean absolute error of 0.9 ∘C. The RoadSurf's output bias most probably stemmed from the absence of road maintenance operations in the model, such as snow plowing and salting, and the biases in the input meteorological data. The biases in the input data were most evident in northern parts of Finland, where the regional climate model HCLIM38-ALARO overestimated precipitation and had a warm bias in near-surface air temperatures during the winter season. Moreover, the variability in the biases of air temperature was found to explain on average 57 % of the variability in the biases of road surface temperature. On the other hand, the absence of road maintenance operations in the model might have affected RoadSurf's ability to simulate road surface conditions: the model tended to overestimate icy and snowy road surfaces and underestimate the occurrence of water on the road. However, the overall good performance of RoadSurf implies that this approach can be used to study the impacts of climate change on road weather conditions in Finland by forcing RoadSurf with future climate projections from RCMs, such as HCLIM.

Winter storm severity, road surface conditions, traffic volume, and vehicle speed can influence the risk of winter-related vehicular crashes. The severity of a winter storm depends on many climatic factors, including precipitation type, intensity and duration of the storm, wind speed, air temperature, and pavement surface temperature. The rapid growth of snow and ice layer thicknesses on the road surface significantly reduce the grip between tires and the road surface, leading to hazardous travel conditions. Our investigation shows that vehicular crash rates in winter months have an inverse linear relationship with the 10th percentile grip statistics, which is a function of how quickly the road was plowed and salted and the bare pavement conditions regained. We found a logarithmic relationship between the ice and snow layer thicknesses and the grip loss. We developed a new model for forecasting ice layer thickness growth rate as a function of the relative humidity, dewpoint temperature, and pavement temperature. Real-time monitoring and forecasting the spatial and temporal variability of the grip on a road network can help road authorities to better optimize the salt application strategy for a given winter storm event and prioritize the timing and frequency for the deployment of their fleet of winter maintenance vehicles on different salt routes to minimize vehicular crash rates.