This study was aimed at assessing the potential impacts of climate change on the depth and duration of soil frost under snow cover in forests growing at different geographical locations in Finland. Frost simulations using a process-based forest ecosystem model (FinnFor) were made for Scots pine Pinus sylvestris L. stands (height 17 m, stand density 1100 stems ha -1 ) growing on a moraine sandy soil. The climate change forecast used in the computations was based on the global ocean-atmosphere general circulation model HadCM2 that was dynamically downscaled to the regional level. The simulated climate warming during the winter months was about 4 to 5°C by the end of the 21st century. Frost simulations showed that the length of the soil frost period would lessen all over the country. Though winters will be warmer, the associated decrease in snow cover in southern Finland will increase the probability of frozen ground there in the middle of winter compared with the current climate. In central and northern Finland there will be so much snow, even in the future, that the maximum annual soil frost depth will decrease there.KEY WORDS: Climate change · Soil frost · Soil freezing · Snow cover · Hydraulic frost model · Scots pine Resale or republication not permitted without written consent of the publisherClim Res 17: [63][64][65][66][67][68][69][70][71][72] 2001 The modelling of frost in the soil profile under snowfree surfaces can be done with the help of the frost sum and soil properties (e.g. Saarelainen 1992, McCormick 1993, Venäläinen et al. 2001. The frost sum is the sum of below-0°C daily mean temperatures calculated from the beginning of the frost period. In Scandinavia the frost period typically starts in October and ends in May, in northern Lapland in June. If there is snow on the ground, the modelling of soil temperature becomes more complex. Models must include many variables describing both meteorological conditions, such as air temperature, short and long wave radiation, amount and type of snow, and soil characteristics, such as thermal conductivity and soil heat capacity (e.g. Bonan 1991, Cox et al. 1999. The influence of snow cover on temperature is illustrated in Fig. 1. The daily variation of air temperature in the case of late winter conditions can be more than 20°C, whereas at a depth of 80 cm below the snow surface the daily cycle is practically negligible.Jansson (1991) introduced a comprehensive soil model known as SOIL that includes the processes relevant for the calculation of soil temperature. Kellomäki & Väi-sänen (1997) have integrated this SOIL model into a process-based forest ecosystem model (FinnFor), which links ecosystem dynamics with climate through selected physiological processes. Peltola et al. (1999) used this model when they studied the consequences of climate warming on soil frost and on the windthrow risk for trees in different geographical locations in Finland. Peltola et al. (1999) used 2 options for climate warming: the increase of temperature was estimated to be...

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Forecasting of road surface and traffic conditions is an important aspect of traffic safety and winter road maintenance, especially in the harsh northern climate. The weather conditions can change quickly, for example, with the onset of snowfall or during rapid temperature variations. A prior knowledge of road weather is important from a public road safety standpoint. Proper consideration of upcoming weather events also helps the road maintenance authorities to attend the roads in an effective and economical manner. In Finland, the Finnish Meteorological Institute (FMI) is duty bound to issue warnings of hazardous traffic conditions to the general public. To strengthen these services towards more efficient estimation of rapidly varying conditions of the road surface at a national scale, a simulation model, RoadSurf, has been developed. As input, the model employs numerical weather forecasts, either directly or after modifications made by meteorologists, as well as observations from synoptic or road weather stations and radar precipitation measurement network. As output, the model produces not only road surface temperature, but also road surface condition classification and a traffic index describing the driving conditions in more general terms, as well as road surface friction. The model has been in operational use since 2000. In addition to the original goal of providing road weather forecasts for the national road network, the model has been used in several other applications, for example, in predicting pedestrian sidewalk conditions and in numerous intelligent traffic applications. The present study describes the road weather model RoadSurf and its main applications.

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