Biogeophysical impacts of peatland forestation on regional climate changes in Finland

Abstract

Abstract. Land cover changes can impact the climate by influencing the surface energy and water balance. Naturally treeless or sparsely treed peatlands were extensively drained to stimulate forest growth in Finland over the second half of 20th century. The aim of this study is to investigate the biogeophysical effects of peatland forestation on regional climate in Finland. Two sets of 18-year climate simulations were done with the regional climate model REMO by using land cover data based on pre-drainage (1920s) and postdrainage (2000s) Finnish national forest inventories. In the most intensive peatland forestation area, located in the middle west of Finland, the results show a warming in April of up to 0.43 K in monthly-averaged daily mean 2 m air temperature, whereas a slight cooling from May to October of less than 0.1 K in general is found. Consequently, snow clearance days over that area are advanced up to 5 days in the mean of 15 years. No clear signal is found for precipitation. Through analysing the simulated temperature and energy balance terms, as well as snow depth over five selected subregions, a positive feedback induced by peatland forestation is found between decreased surface albedo and increased surface air temperature in the snow-melting period. Our modelled results show good qualitative agreements with the observational data. In general, decreased surface albedo in the snow-melting period and increased evapotranspiration in the growing period are the most important biogeophysical aspects induced by peatland forestation that cause changes in climate. The results from this study can be further integrally analysed with biogeochemical effects of peatland forestation to provide background information for adapting future forest management to mitigate climate warming effects. Moreover, they provide insights about the impacts of projected forestation of tundra at high latitudes due to climate change.

The regional climate model REMO (v2015) coupled with the 1-D freshwater lake model FLake (v1): Fenno-Scandinavian climate and lakes

“…Moreover, the forest fraction of the grid cell linearly influences the albedo maximum and minimum values so that they reach values of α sn max = 0.4 and α sn min = 0.3 at a forest fraction of unity (details in Kotlarski, 2007). However, these values of forested α sn max and α sn min are slightly higher than those shown in the literature (e.g., Roesch et al, 2001, and references therein), and therefore in the present work we have reduced both α sn max and α sn min to 0.25 for completely forest-covered grid boxes, following Gao et al (2014). The temperature-dependent α sn takes into account that the snow albedo is lower when snow is wet, i.e., is near the freezing point.…”

“…In the warmstart method, the model was run for the whole time period with default initial values (for REMO and REMO-FLake separately) and then the model state in July 2014 was used as the initial state for the actual and analyzed simulations (starting in July). More details about the method for the same domain can be found in Gao et al (2014) and Gao et al (2015).…”

Rewetting Offers Rapid Climate Benefits for Tropical and Agricultural Peatlands But Not for Forestry‐Drained Peatlands

“…Thus, abandonment may combine the tree biomass CO 2 sink during the first decades ( Figure 5) with preserving most of the peat. However, keeping the tree stand may warm the climate locally, as forest albedo is lower than that of open mire (Gao et al, 2014;Lohila et al, 2010). Yet, part of this warming may be offset by the higher formation of aerosols and clouds, as trees are important sources of volatile organic compounds (Teuling et al, 2017;Tunved et al, 2006).…”

Reconstructing European forest management from 1600 to 2010

“…Finding comprehensive data to parameterize and validate historical litter-based activities is not currently possible. To our knowledge, there exist two quantitative studies on traditional non-timber forest uses (Gimmi et al, 2012(Gimmi et al, , 2008. The quantitative studies were localized in Swiss valleys and showed that the model results were sensitive to data such as litter demand for livestock and straw production rate, data which are impossible to find for all of Europe for different times between 1600 and 2010.…”