Global warming leads to a prolongation and intensification of the thermal growing season. In this study, we present projections for the growing season length and growing degree day sum (GDD) in Europe by the end of the 21st century using two threshold temperatures, 5 and 10 ∘ C. The analysis was based on simulations performed with 22-23 CMIP5 global models under the RCP4.5 and RCP8.5 scenarios. Systematic errors in the temporal mean and variability of modelled temperatures were eliminated, and the data were downscaled spatially by employing a bias-correction method. To determine the onset, termination and GDD of the growing season, two methods have been used. The previously developed Fourier method is suited for exploring long-term means, while the novel temperature deviation integral method is applicable to inter-annual variations.According to the multi-model mean of the RCP8.5 simulations in the late 21st century, for the majority of Europe the growing season is prolonged by 1.5-2 months, the GDD above 5 ∘ C increasing by 60-100%. Responses to RCP4.5 are qualitatively similar but smaller. A decomposition of the uncertainty variance reveals that in the near-term future the contribution of internal variability is pronounced, but by the end of the century inter-model differences dominate.In studying growing-season conditions on an annual basis, we found that in coming decades years with a GDD below the recent past mean become very uncommon. In the majority of years, GDD will exceed the 10-year or even the 20-or 50-year return level derived from recent past data.

Abstract. We built an automatic chamber system to measure greenhouse gas (GHG) exchange in forested peatland ecosystems. We aimed to build a system robust enough which would work throughout the year and could measure through a changing snowpack in addition to producing annual GHG fluxes by integrating the measurements without the need of using models. The system worked rather well throughout the year, but it was not service free. Gap filling of data was still necessary. We observed problems in carbon dioxide (CO2) respiration flux estimation during calm summer nights, when a CO2 concentration gradient from soil/moss system to atmosphere builds up. Chambers greatly overestimated the night-time respiration. This was due to the disturbance caused by the chamber to the soil-moss CO2 gradient and consequent initial pulse of CO2 to the chamber headspace. We tested different flux calculation and measurement methods to solve this problem. The estimated flux was strongly dependent on (1) the starting point of the fit after closing the chamber, (2) the length of the fit, (3) the type of the fit (linear and polynomial), (4) the speed of the fan mixing the air inside the chamber, and (5) atmospheric turbulence (friction velocity, u*). The best fitting method (the most robust, least random variation) for respiration measurements on our sites was linear fitting with the period of 120–240 s after chamber closure. Furthermore, the fan should be adjusted to spin at minimum speed to avoid the pulse-effect, but it should be kept on to ensure mixing. If night-time problems cannot be solved, emissions can be estimated using daytime data from opaque chambers.

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.

Droughts can have an impact on forest functioning and production, and even lead to tree mortality. However, drought is an elusive phenomenon that is difficult to quantify and define universally. In this study, we assessed the performance of a set of indicators that have been used to describe drought conditions in the summer months (June, July, August) over a 30-year period (1981-2010) in Finland. Those indicators include the Standardized Precipitation Index (SPI), the Standardized Precipitation-Evapotranspiration Index (SPEI), the Soil Moisture Index (SMI), and the Soil Moisture Anomaly (SMA). Herein, regional soil moisture was produced by the land surface model JSBACH of the Max Planck Institute for Meteorology Earth System Model (MPI-ESM). Results show that the buffering effect of soil moisture and the associated soil moisture memory can impact on the onset and duration of drought as indicated by the SMI and SMA, while the SPI and SPEI are directly controlled by meteorological conditions. In particular, we investigated whether the SMI, SMA and SPEI are able to indicate the Extreme Drought affecting Forest health (EDF), which we defined according to the extreme drought that caused severe forest damages in Finland in 2006. The EDF thresholds for the aforementioned indicators are suggested, based on the reported statistics of forest damages in Finland in 2006. SMI was found to be the best indicator in capturing the spatial extent of forest damage induced by the extreme drought in 2006. In addition, through the application of the EDF thresholds over the summer months of the 30-year study period, the SPEI and SMA tended to show more frequent EDF events and a higher fraction of influenced area than SMI. This is because the SPEI and SMA are standardized indicators that show the degree of anomalies from statistical means over the aggregation period of climate conditions and soil moisture, respectively. However, in boreal forests in Finland, the high initial soil moisture or existence of peat often prevent the EDFs indicated by the SPEI and SMA to produce very low soil moisture that could be indicated as EDFs by the SMI. Therefore, we consider SMI is more appropriate for indicating EDFs in boreal forests. The selected EDF thresholds for those indicators could be calibrated when there are more forest health observation data available. Furthermore, in the context of future climate scenarios, assessments of EDF risks in northern areas should, in addition to climate data, rely on a land surface model capable of reliable prediction of soil moisture. © 2016 Author(s)

Abstract. This study examined the impacts of projected climate change on heavy snow loads on Finnish forests, where snow-induced forest damage occurs frequently. For snowload calculations, we used daily data from five global climate models under representative concentration pathway (RCP) scenarios RCP4.5 and RCP8.5, statistically downscaled onto a high-resolution grid using a quantile-mapping method. Our results suggest that projected climate warming results in regionally asymmetric response on heavy snow loads in Finnish forests. In eastern and northern Finland, the annual maximum snow loads on tree crowns were projected to increase during the present century, as opposed to southern and western parts of the country. The change was rather similar both for heavy rime loads and wet snow loads, as well as for frozen snow loads. Only the heaviest dry snow loads were projected to decrease over almost the whole of Finland. Our results are aligned with previous snowfall projections, typically indicating increasing heavy snowfalls over the areas with mean temperature below −8 • C. In spite of some uncertainties related to our results, we conclude that the risk for snow-induced forest damage is likely to increase in the future in the eastern and northern parts of Finland, i.e. in the areas experiencing the coldest winters in the country. The increase is partly due to the increase in wet snow hazards but also due to more favourable conditions for rime accumulation in a future climate that is more humid but still cold enough.