Climate change induces multiple abiotic and biotic risks to forests and forestry. Risks in different spatial and temporal scales must be considered to ensure preconditions for sustainable multifunctional management of forests for different ecosystem services. For this purpose, the present review article summarizes the most recent findings on major abiotic and biotic risks to boreal forests in Finland under the current and changing climate, with the focus on windstorms, heavy snow loading, drought and forest fires and major insect pests and pathogens of trees. In general, the forest growth is projected to increase mainly in northern Finland. In the south, the growing conditions may become suboptimal, particularly for Norway spruce. Although the wind climate does not change remarkably, wind damage risk will increase especially in the south, because of the shortening of the soil frost period. The risk of snow damage is anticipated to increase in the north and decrease in the south. Increasing drought in summer will boost the risk of large‐scale forest fires. Also, the warmer climate increases the risk of bark beetle outbreaks and the wood decay by Heterobasidion root rot in coniferous forests. The probability of detrimental cascading events, such as those caused by a large‐scale wind damage followed by a widespread bark beetle outbreak, will increase remarkably in the future. Therefore, the simultaneous consideration of the biotic and abiotic risks is essential.

Surface weather patterns related to 35 major sudden stratospheric warmings (SSWs) in 1958–2010 are analyzed based on reanalysis data. Similar analyses are conducted with data from seven stratosphere‐resolving Earth system models. The analyses are carried out separately for displacement and splitting SSWs. On the basis of the observational analysis, it is shown that in northern Eurasia, the cold anomalies linked to the SSWs tend to be stronger and more widespread before the central date of the SSWs than during the first 2 months after the event central dates. This is particularly true for the displacement events. The cold anomalies preceding the SSWs are coupled to atmospheric blocking events which trigger the SSWs. While the role of SSWs as important predictors of cold air outbreaks in the Northern Hemisphere is well recognized, our results indicate that the impact of the preceding blocking on near‐surface temperatures is, in fact, widely more significant than the downward impact of the SSWs. Thus, stratosphere‐troposphere coupling provides only limited predictability for cold air outbreaks in Eurasia. The models reproduce qualitatively well the typical large‐scale surface weather patterns following the SSWs, but they largely miss the cooling preceding the SSWs over Europe and western Siberia. Hence, the strongest modeled temperature anomalies related to the SSWs occur after the events. Moreover, the model results indicate that the tropospheric response to SSWs is stronger following split events. At the same time, many models simulate too few splitting SSWs.

As both global (GCM) and regional (RCM) climate models have their own advantages, the most comprehensive picture of changes in precipitation and their uncertainty ranges can be achieved by comparing the results of both model categories. Here we have evaluated seasonal changes in indices representing excess or scarcity of precipitation in Europe on the basis of simulations performed with ten GCMs with a reasonably high spatial resolution and, for comparison, with five RCMs driven by one and the same GCM. We found no fundamental differences between the GCMs and RCMs in the projected tendency towards a more extreme precipitation climate, characterized by increases both in indices representing wet conditions and also dry conditions. Most evidently in the Northern European summer and Southern European winter, the differences in the responses of the various indices between the present sets of GCMs and RCMs could be explained by the dissimilar changes in seasonal mean precipitation. The scatter among the projected changes was in general much smaller for the RCM than for the GCM simulations. The projections of individual RCMs mainly fall within the interval determined by the GCM projections. This indicates that the GCM ensemble yielded more comprehensive estimates for the uncertainty ranges in the extreme precipitation indices.

Abstract. The target of this work was to assess the impact of projected climate change on forest-fire activity in Finland with special emphasis on large-scale fires. In addition, we were particularly interested to examine the intermodel variability of the projected change of fire danger. For this purpose, we utilized fire statistics covering the period 1996-2014 and consisting of almost 20 000 forest fires, as well as daily meteorological data from five global climate models under representative concentration pathway RCP4.5 and RCP8.5 scenarios. The model data were statistically downscaled onto a high-resolution grid using the quantilemapping method before performing the analysis. In examining the relationship between weather and fire danger, we applied the Canadian fire weather index (FWI) system. Our results suggest that the number of large forest fires may double or even triple during the present century. This would increase the risk that some of the fires could develop into real conflagrations which have become almost extinct in Finland due to active and efficient fire suppression. However, the results reveal substantial inter-model variability in the rate of the projected increase of forest-fire danger, emphasizing the large uncertainty related to the climate change signal in fire activity. We moreover showed that the majority of large fires in Finland occur within a relatively short period in May and June due to human activities and that FWI correlates poorer with the fire activity during this time of year than later in summer when lightning is a more important cause of fires.

Modelling crown snow loads in Finland: a comparison of two methodsLehtonen I., Hoppula P., Pirinen P., Gregow H. (2014). Modelling crown snow loads in Finland: a comparison of two methods. Silva Fennica vol. 48 no. 3 article id 1120. 30 p. Highlights• A new method to model crown snow loads is presented and compared with a previously published simpler method.• The heaviest crown snow loads in Finland are found to typically occur in the eastern parts of the country.• The relative importance of different snow load types varies between different regions of Finland. AbstractThe spatial occurrence of heavy crown snow loads in Finland between 1961 and 2010 is studied by using for the first time a model that classifies the snow load into four different types: rime, dry snow, wet snow and frozen snow. In producing this climatology, we used meteorological observations made at 29 locations across Finland. The model performance is evaluated against classified daily images of canopy snow cover and with the help of two short case studies. The results are further compared to those achieved with a simpler method used in previous studies. The heaviest crown snow loads are found to occur typically in eastern Finland. The new method reveals that this holds not only for the total snow loads but also for the different snow load types, although there are certain differences in their geographical occurrence. The greatest benefit achieved with the new method is the inclusion of rime accretion. The forests most prone to heavy riming are those located on tree-covered hills in northern Finland, but as the terrain elevation affects riming efficiency greatly, these small-scale variations in the snow load amounts could not be described in this study in great detail. Moreover, the results are more inaccurate in northern Finland where variations in the terrain elevation are greater than elsewhere. Otherwise, the largest uncertainties in this study are related to wind speed measurements and possibly partly because of that, we were not able to detect any significant trends in the crown snow-load amounts over the study period.