Heavy snow loads in Finnish forests respond regionally asymmetrically to projected climate change

Abstract

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.

Effects of CMIP5 Projections on Volume Growth, Carbon Stock and Timber Yield in Managed Scots Pine, Norway Spruce and Silver Birch Stands under Southern and Northern Boreal Conditions

“…In this study, forest ecosystem model simulations were conducted for the current climate and changing climate, under two representative concentration pathways (RCP4.5 and RCP8.5). The representative set of individual GCMs of the CMIP5 database were selected for this study based on their skill at simulating the temperature and precipitation climatology under the current climate (1981-2010) (see, e.g., [15,23,24,44]). We used in this study also the multi-model mean monthly values for temperature and precipitation of 28 GCMs of CMIP5 database under the RCP4.5 and RCP8.5 forcing scenarios (see, e.g., [21,22]).…”

Effects of Climate Change on CO2 Concentration and Efflux in a Humic Boreal Lake: A Modeling Study

“…The same correction functions were used to correct both the calibration and scenario periods. The downscaled GCM data have been previously used in Lehtonen et al ().…”

Effects of using certain tree species in forest regeneration on regional wind damage risks in Finnish boreal forests under different CMIP5 projections

“…The 10 selected GCMs, which have a proven ability to relatively accurately simulate the temperature and precipitation of the current climate in northern Europe (Lehtonen et al 2016a(Lehtonen et al , 2016bRuosteenoja et al 2016), provided us with a good representation of the overall variability in the full ensemble of CMIP5 projections for monthly mean temperatures and precipitation for 2010-2099. Too high or low predicted values for daily mean temperatures and precipitation, in relation to the observed data, however, still needed to be bias-corrected, which was done using an empirical bias correction method called quantile mapping (see for more details, Räisänen and Räty 2013;Räty et al 2014).…”