Journal of Geophysical Research: Biogeosciences

Forestation of boreal peatlands: Impacts of changing albedo and greenhouse gas fluxes on radiative forcing



[1] We estimated the magnitude of the radiative forcing (RF) due to changes in albedo following the forestation of peatlands, and calculated the net RF by taking into account the changes in both the albedo and the greenhouse gas (GHG) fluxes during one forest rotation. Data on radiation, tree biomass, and soil GHG fluxes were combined with models for canopy cover, tree carbon accumulation, and the RF due to increased atmospheric GHG concentrations for four typical site cases in Finland covering two soil nutrient levels in the south and north of the country. We also studied the observed long-term surface temperatures to detect any indications of drainage-induced effects. The magnitude of the albedo-induced RF was similar to that caused by the carbon sequestration of the growing trees. At three site cases out of four the drainage induced a cooling or negative RF, the tendency for cooling being higher at sites with a higher nutrient level. The differences in albedo-induced RF mainly arose from the spring season due to (1) the different snow cover duration in the south versus the north, and (2) the different albedos of drained and undrained snow covered peatlands. An increase in the maximum daily temperatures was observed in April in southern Finland, where the most intensive drainage practices have taken place, suggesting that forestry drainage has potentially affected the local climate. Our results show that the decreasing albedo resulting from peatland forestation contributes significantly to the RF, balancing out or even exceeding the cooling effect due to the changing GHG fluxes.

1. Introduction

[2] The northern boreal peatlands form an extensive biome covering some 3.5–5.8 million square kilometers [Gorham, 1991; Bleuten et al., 2006]. These ecosystems are created and develop in conditions with high water table levels and consequent low rates of oxygen diffusion into the surface peat soil. This slows down the decomposition of organic matter and leads to the accumulation of organic carbon (C) as peat, and to the production of methane (CH4) in the anoxic soil layers [Gorham, 1991]. In addition, small amounts of nitrous oxide (N2O) may be produced [Martikainen et al., 1993]. Thus, peatlands affect the composition of the atmosphere through the exchange of these greenhouse gases (GHGs). The GHGs absorb and reradiate infrared radiation and contribute to the radiation balance of the Earth, causing radiative forcing (RF). RF is a measure of the change in the net irradiance at the top of the troposphere, and is expressed as the rate of energy per unit area of the globe.

[3] Climate change, driven by the perturbation of the radiation balance, has already resulted in longer and drier growing seasons in northern latitudes [Keyser et al., 2000; Goetz et al., 2005]. The lowering of the water table level may induce biogeochemical changes, drastically altering the processes involved in carbon storage and GHG balances [Laine et al., 1995b]. The deepening of the oxic surface soil layer leads to changes in carbon transformation processes and increases peat decomposition rates [Riutta et al., 2007]. The CH4 emissions are then reduced, and the forested peatlands may become sinks for CH4 [Minkkinen and Laine, 2006]. The aerobic peat layers start to lose carbon in its oxidation to carbon dioxide (CO2), but sequestration in the new litter and humus layer and growing tree stands may compensate for these losses for some period of time [Minkkinen et al., 2002; Laine et al., 2006]. C losses and N2O fluxes from peat soil are highest at sites having a higher nutrient level, while at poor sites N2O fluxes are negligible [Martikainen et al., 1993] and the soil may even gain more C [Minkkinen et al., 1999; Laurila et al., 2007].

[4] Pristine peatlands are usually open habitats, with dominating field and bottom-layer vegetation, and often with sparse tree stands. Peatland drainage is a common management prescription to improve forest growth; as a result, over 10 million ha of peatlands have been drained for forestry in the Nordic countries and Russia [Paavilainen and Päivänen, 1995]. In Finland alone, about 5 million ha of peatland is under forestry production. A lowering in the water table level, induced either by climatic change or drainage by ditching, initiates a succession, which will lead to a decrease in the abundance and biomass of species adapted to wet conditions, such as many sedges and herbs, and to an increase in the cover of shrubs and trees [Laine et al., 1995a, 1995b; Laiho et al., 2003]. This kind of succession from an open or sparsely treed mire ecosystem toward a tree-covered forested peatland ecosystem is here called forestation; it may be caused by either natural or by anthropogenic actions. The term afforestation means the active regeneration of open habitats by planting or sowing.

[5] After drainage, the increased tree stand growth naturally results in an increased stand volume and biomass, and leads to canopy closure [e.g., Laine et al., 2006]. The increase is highest at originally wet, nutrient-rich sites where any predrainage tree stands are very sparse or missing [Keltikangas et al., 1986]. In addition to biogeochemical changes, the change in the vegetation cover from sedge and grass species to perennial shrubs and trees, especially conifers, induces biogeophysical changes, such as decreased surface reflectivity, i.e., albedo, defined as the ratio of reflected to incoming short-wave (SW) radiation. It is well documented that a change in the albedo impacts the radiation balance of the surface in question [e.g., Bala et al., 2007; Betts and Ball, 1997]. Since 1750, the surface albedo of the Earth has changed, mainly due to deforestation, causing a global average RF of about −0.2 W m−2 (i.e., cooling). This has had a moderate effect on the global net anthropogenic RF, the best estimate of which is about 1.6 W m−2 [Forster et al., 2007]. The surface albedo varies considerably with surface and vegetation types. In the case of open snow covered land, the albedo is typically within the range 0.5–0.8, while in summer open agricultural land, mire and forest have an albedo of about 0.1–0.2 [Berglund and Mace, 1972; Solantie, 1988; Betts and Ball, 1997; Kurbatova et al., 2002; Arneth et al., 2006].

[6] In addition to the impact on RF, the decrease in albedo affects the local surface temperatures in a more direct way: particularly in the daytime, temperatures are likely to be increased due to the absorption of short-wave radiation by the darker surface after the replacement of mire vegetation by tree cover. There is a range of other biogeophysical changes attributed to the forestation of open areas which influence the near-surface temperatures. For instance, the evaporation rate and aerodynamic roughness are higher in forests than open fields [Jackson et al., 2008; Pongratz et al., 2010]. The surface cooling associated with the enhanced evaporation may overwhelm the warming effect of the albedo decrease in the temperate zone, and particularly in the tropics [Bala et al., 2007; Juang et al., 2007; Bathiany et al., 2010]. However, when forestation occurs in northern latitudes, the albedo-induced warming typically exceeds the cooling due to the changes in water balance, resulting in increasing surface temperatures [Betts, 2001; Bala et al., 2007]. The surface emissivity is also increased, which alters the long-wave radiation balance and consequently the surface temperatures [Juang et al., 2007; Jackson et al., 2008]. In addition, heat transfer into the atmosphere affects the growth of the planetary boundary layer [Betts, 2006], which has an impact on the surface temperatures, which may increase more in a shallow boundary layer than in a deep one [e.g., Betts et al., 2007].

[7] Considerable international and national efforts are being put into the mitigation of climate change. Therefore, it is important to assess how various human measures or natural changes contribute to the RF. The biogeophysical (e.g., change in the albedo) and biogeochemical (e.g., change in the typical carbon transformation processes) impacts on peatlands, induced either by climatic warming or land use change, alter the Earth's radiative balance, causing RF. However, the net RF related to the forestation of open or sparsely treed habitats, such as northern peatlands, has not been quantified. On the other hand, the land use change from open mire to forested peatland may bring about large local scale alterations in surface temperatures.

[8] Our general aims here are (1) to compare the relative contributions of changing albedo and GHG fluxes to the top-of-troposphere RF during the vegetation transition after forestation of a treeless and sparsely treed northern peatland habitat and (2) to study the effects of the peatland forestation on local surface temperatures in Finland. The particular hypotheses for the work are that (1) a physiognomic change in the vegetation cover from graminoids to shrubs and trees induces a change in the albedo and subsequently in global radiative forcing; (2) the impact of the changing albedo following forestation may be significant in comparison with that of the altered GHG balance; (3) the effect of peatland drainage and forestation on the local SW radiation balance can be seen in the long term surface temperatures in the areas with the most intensive drainage during the past decades, increasing especially the daytime temperatures in spring.

2. Material and Methods

2.1. Radiative Forcing due to Changes in Albedo and Greenhouse Gas Fluxes

[9] In this study, the net impact on the RF due to a change in albedo and GHG fluxes following the forestation of peatland is expressed as:

equation image
equation image
equation image

where RFΔalbLoc is the local radiative forcing caused by a change in the albedo of the forestation area, Aloc is the forestation area, Aglob is the surface area of the Earth ( = 5.1 × 1014 m2), and RFΔalb is the global RF due to the albedo change. RFΔghg is the radiative forcing due to the changes of GHG fluxes caused by the forestation, which is obtained as a sum of the changes in soil GHG fluxes (RFΔghgSoil) and changes in the CO2 sequestration in the trees (RFΔghgTree). The subscript Ch refers to the changed situation and Ref to the reference situation before the change. The albedo change takes place only in the forestation area and affects the local climate, but as the local radiation balance contributes to the global radiation balance, the albedo change also has global consequences. The GHG emissions, on the other hand, are diluted over the whole globe due to the long atmospheric lifetime of these gases. To make the RFs arising from the different effects comparable, all the calculations were made for one square meter of peatland (Aloc = 1 m2) and the local RF due to the albedo change was scaled by the Earth's surface area (equation (1)).

2.2. Sites and Data for the Calculation of Radiative Forcing

[10] The calculations were made for four site cases of forestry-drained peatlands in Finland, representing different climates and nutrient levels, with different tree stand successions: for one nutrient-poor and one nutrient-rich site both in southern and in northern Finland (hereafter referred to as south and north). Furthermore, every case had an original undrained mire as a reference case.

[11] In this study, different data and models were combined: global radiation and albedo data, simulated tree stand characteristics, biomasses and soil GHG fluxes, as well as models for estimating canopy cover, the accumulated carbon in trees from the biomass data, and the atmospheric concentration due to the gas fluxes, and subsequently radiative forcing.

2.2.1. Radiation Data

[12] Radiation data for the southern and northern Finland cases were taken from the Jokioinen (60°49′N, 23°30′E) and Sodankylä (67°21′N, 26°38′E) observatories, respectively, operated by the Finnish Meteorological Institute (FMI). Average daily global radiation was calculated from the data covering the years 1971–2000. At both sites, global radiation was measured with a Kipp&Zonen CM11 pyranometer.

2.2.2. Albedo Measurements

[13] Albedo was calculated as a ratio of the reflected and incoming SW radiation, as measured with a Kipp&Zonen CM7B pyranometer above the different ecosystems. Measurements were made at four sites: one open, i.e., a treeless site, and one forest in both the south and north. Measurements from the open sites were used directly to calculate the albedo for the undrained peatlands (both the nutrient-poor and nutrient-rich cases).

[14] In the absence of albedo data for an undrained peatland in the south, data were taken from a cultivated peatland located at Jokioinen (Table 1). There, the albedo was measured at a height of 5 m above the soil surface. The field was sown with barley (Hordeum vulgare L.) or with barley and undersown grass (mixture of Phleum pratense and Festuca pratensis) typically in May and harvested in in June (grass) and in August–September (grass and barley) [Lohila et al., 2004]. Since the summertime maximum albedo at the Jokioinen cropland exceeded 0.25, which is higher than the values typically measured at bogs [e.g., Berglund and Mace, 1972; Petzold and Rencz, 1975; Solantie, 1988; Kurbatova et al., 2002; Arneth et al., 2006], it was given a value of 0.15 between day of year (DOY) 126 and 304. Therefore, the Jokioinen data provided us with the seasonal dynamics and the albedo values outside this period.

Table 1. Characteristics of the Albedo Measurement Sites
  • a

    Estimated from the measured albedo; the snow has melted when the 10 day running average of albedo permanently reaches its summer level (average of July).

  • b

    In the radiative forcing calculations, a summer maximum of 0.15 was used for the open site in south; see text.

Site locationsouthsouthnorthnorth
Site coordinates60°54′N, 23°31′E60°39′N, 24°21′E69°08′N, 27°14′E67°21′N, 26°38′E
Height above sea level (m)104129155179
Time period from which data available2000–20032004–20071997–20072001–2007
Land use and site typeopen cultivated peatlanddrained nutrient-poor pine forestopen mesotrophic fenpine forest on mineral soil
Soil typepeat soilpeat soilpeat soilpodzol
Dominant vegetationbare soil, barley, grassScots pinesedges, mosses, shrubsScots pine
Tree density (ha−1)-1290-2100
Tree volume (m3 ha−1)-130-93
LAI (season maximum)
Vegetation height (m)0.515.30.412.7
Average time (DOY) of snowmelta96123138140
Mean albedo in July0.244 (0.15)b0.1240.1370.112

[15] For the undrained peatland case in the north, the albedo was obtained from a mesotrophic fen at Kaamanen (Table 1). The microtopography comprised hummocks and hollows, the height of the hummocks varying from 0.3 to 0.8 m and covering about 40% of the fen [Aurela et al., 2002]. At hollows, the vegetation consisted of sedges and some mosses, while the strings were dominated by different shrubs, e.g., Ledum palustre, Empetrum nigrum, Vaccinium uliginosum, Betula nana, Rubus chamaemorus, V. vitis-idaea and Salix spp. The albedo was measured at a height of 5 m.

[16] For the forested sites in the south, the data were obtained from the Kalevansuo forestry-drained peatland (originally dwarf shrub pine bog), drained about 40 years earlier (Table 1). Albedo measurements were conducted above the canopy at a height of 21.5 m above the ground. The tree stand composition was uneven and consisted mainly of Scots pine (Pinus sylvestris L.) with some small-sized Norway spruce (Picea abies L.) and Downy birch (B. pubescens). Forest floor vegetation consisted mainly of hummock dwarf shrubs (V. vitis-idaea, V. myrtillus, E. nigrum, V. uliginosum, L. palustre and B. nana), sedges like Eriophorum vaginatum and different mosses.

[17] The albedo data for forested peatlands in the north was taken from a Scots pine forest (P. sylvestris L.) located on fluvial sandy podzol at Sodankylä [Thum et al., 2007, Table 1]. The albedo was measured on a mast at a height of 48 m above the ground. The forest has naturally regenerated after forest fires. The trees were mostly 55–80 years old with a few much older trees. The sparse ground vegetation was mainly composed of lichens (e.g., Cladonia and Cladina spp.), mosses (e.g., Dicranum spp.) and ericaceous shrubs (E. nigrum L., Calluna vulgaris (L.) Hull).

[18] Due to the very low radiation levels, especially in the north where there is a period of darkness, the albedo cannot be measured in a reasonable way in midwinter. The radiation sensors may also be snow covered for part of the time, lowering the reliability of the winter measurements. For this reason, upper limits of 0.4 and 0.8 were defined for winter albedos in forests and open peatlands, respectively, based on both our own albedo measurements and on those of Solantie [1988]. For the nutrient-poor site cases, it was assumed that such sites already contained some trees, typically pines, in their pristine state. The upper (winter) albedo limit in the nutrient-poor cases before drainage was therefore set lower, at 0.55.

2.2.3. Forest Biomass Data

[19] The tree biomass data for drained sites were derived from simulations separately for each four site cases similarly to Minkkinen et al. [2001], using the MOTTI stand simulator [Hynynen et al., 2002; Salminen and Hynynen, 2001]. The data included basal area (m2 ha−1), mean height (m) and volume of the tree stand (m3 ha−1). The increase in these variables was simulated at 5 year intervals for a typical full rotation; this was taken as 85 and 100 years in the southern and northern cases, respectively, including thinnings, which for the nutrient-rich cases took place at ages of 40 and 65 years in the south and north, respectively. In the nutrient-poor cases, thinning was only conducted in the south at an age of 65 years. The carbon stocks in the stands were calculated from the simulated stem volume data separately for pine and spruce using models developed for forested peatlands by Minkkinen et al. [2001]. At undrained peatlands, the tree biomasses were assumed to stay unchanged.

2.2.4. Estimation of Canopy Cover

[20] Projected canopy cover, defined as “the proportion of the forest floor covered by the vertical projection of the tree crowns” [Jennings et al., 1999], was estimated, for albedo calculations, using empirical models and the simulated stand characteristics. The data consisted of 46 pine and 88 spruce-dominated sample plots in different parts of Finland, whose site nutrient levels were equivalent to those of our test sites. Canopy cover was measured with a Cajanus tube [Korhonen et al., 2006]. Nonlinear canopy cover models, having stand basal area and tree height as independent variables [Korhonen et al., 2007], were fitted separately for pine and spruce stands:

equation image
equation image

where CCp and CCs are the canopy cover percentages for pine and spruce, G is the basal area of the stand (m2 ha−1), H is the tree height (m), and N is a dummy variable for latitudes higher than 65°N. The standard errors for the pine and spruce models were 0.079 (r2 = 0.85) and 0.099 (r2 = 0.67), respectively.

[21] At high latitudes, the real shading effect of a canopy cannot be directly deduced from the canopy cover, which represents the proportion of vertical between-crown gaps. A better estimate of the probability of the collision between a photon and a needle can be obtained by using the angular gap fraction, which depends, for example, on the zenith angle, stand height and spacing [e.g., Davies, 1963]. Because of this, we estimated the relationship between the projected canopy cover and the fractional cover at zenith angles of 45–60°, which the sun typically reaches in Finland between April and August. We used measurement data from Hyytiälä in southern Finland (61°50′51″, 24°17′41″) [Suni et al., 2003] and Sodankylä in northern Finland (see section 2.2.2). The projected canopy cover measurements and the fractional cover data, estimated from hemispherical photographs, were obtained from 130 sample plots representing different species compositions and development stages. Based on these data, a nonlinear model was fitted as follows:

equation image

where FC45–60 is the fractional cover observed at zenith angles of 45–60° and CC is the measured projected canopy cover. The standard error of the model was 0.095 with r2 = 0.86.

2.2.5. Estimation of Albedo and Albedo-Induced RF Based on the Canopy Cover Data

[22] Albedo measurements from the open sites were used directly to calculate the albedo for undrained peatlands. Measurements from the forests were used to calculate the albedo for all the forested cases at the end of the rotation. The albedo for the time period between the drainage and the end of the rotation was estimated for each day of the year at 5 year intervals from the relationship between the measured albedo and the modeled fractional cover FC45–60 by assuming a linear relationship between these two. Using the daily albedo and daily average global radiation data, we calculated the annual average value of absorbed radiation (W m−2) during the whole tree stand rotation for both the undrained and forestry-drained cases at 5 year intervals. The difference between the latter two values equaled RFΔalbLoc.

[23] Calculating the albedo-induced RF from the data measured on the ground excludes the effects of the absorbing and reflecting properties of the atmosphere, particularly clouds. At the latitudes between 60° and 70°N, the RF observed during clear sky conditions at the top of the troposphere is approximately 20% lower than that observed on the ground. During cloudy conditions, the top-of-troposphere forcing drops on average to 50% of that observed below the clouds, mainly due to the back reflection by clouds (P. Räisänen, personal communication, 2010). Since quantifying the exact magnitude and dynamics of the relationship between the RFs observed at the tropopause and below the clouds is complicated, an average of these, 35%, was used. Consequently, the RF observed on the ground was multiplied by 0.65 to account for the atmospheric effects.

2.2.6. Soil Fluxes

[24] Annual soil CO2 balances for drained peatlands were estimated using the same soil respiration data [Minkkinen et al., 2007a] and models for litter production and decomposition as were used in the Finnish greenhouse gas inventory [Statistics Finland, 2009]. Litter and soil carbon dynamics were simulated for the four site cases with the Motti stand simulator [Salminen and Hynynen, 2001], while the average carbon store change for the rotation period was used as the soil CO2 emission factor. Methane emissions for drained peatlands were calculated for the four cases using the regression models by Minkkinen et al. [2007b], in which tree stand volume and site type were used as input data. The stand volumes for the different site types were derived from the Finnish national forest inventory, and area-weighted estimates for the four cases were then calculated. N2O fluxes were estimated based on chamber measurements at 68 forestry-drained peatland sites in Finland [Ojanen et al., 2010].

[25] The GHG balances for the undrained sites were obtained from the literature (Table 2). At both undrained and drained peatlands, the GHG balances were assumed to remain constant during the whole simulation period.

Table 2. Greenhouse Gas Fluxes From/Into the Soil in Each Site Case (g m−2 yr−1)a
South, nutrient-poor drained2440.1410.029
South, nutrient-rich drained465−0.2590.167
North, nutrient-poor drained2440.4870.029
North, nutrient-rich drained512−0.0960.167
Nutrient-poor undrainedb−70c20d0e
Nutrient-rich undrainedb−50c5d0e

2.2.7. Calculation of Atmospheric Concentrations and RF

[26] The RF due to GHG flux changes, RFΔghg, was calculated with a modified version of the REFUGE model [Monni et al., 2003]. In this model, RFΔghg is estimated by time-integrating the response function related to an instantaneous concentration pulse, taking into account the annual variation in the surface exchange fluxes and background concentrations of the long-lived GHGs considered. The concentration change Δχ due to net GHG flux ϕ can be expressed as

equation image

where k denotes the emission-concentration conversion factor resulting from the instantaneous and complete atmospheric mixing assumed in the model, and fa is an atmospheric lifetime function that indicates the airborne fraction of the pulse. The removal of CO2 from the atmosphere is modeled by a superposition of three relaxation timescales, while that of CH4 and N2O is approximated by a single exponential. For the present work, the lifetime functions were updated according to Forster et al. [2007]. The radiative forcing function for CO2 (RFc), CH4 (RFm) and N2O (RFn) are based on the “simplified expressions” of the Intergovernmental Panel on Climate Change (IPCC) [Ramaswamy et al., 2001; Forster et al., 2007] and include the indirect RF effects of CH4 and the spectral interactions between CH4 and N2O:

equation image
equation image
equation image

Where χc, χm and χn are the mixing ratios of CO2, CH4 and N2O, respectively; the subscript 0 denotes unperturbed concentrations; a = 5.35 W m−2, b = 0.0378 W m−2, c = 1.24 × 10−4 W m−2 and d = 0.12 W m−2 are constants; fo is a correction function for absorption overlap,

equation image

where e = 0.47 W m−2.

[27] In the present calculations, the RFΔghg of CO2 (RFΔghg,c) is determined as a marginal change with respect to a varying reference concentration χc,ref,

equation image

and correspondingly for CH4 (RFΔghg,m) and N2O (RFΔghg,n). The reference concentrations are assumed to follow the IPCC SRES A2 scenario until 2050 and thereafter remain constant [IPCC, 2001]. The total RF due to the three GHGs is then

equation image

2.2.8. Uncertainty Analysis

[28] As discussed by Minkkinen et al. [2002], the REFUGE model provides a suitable framework for estimating the radiative forcing for the coming 100 years. Even though the uncertainty of the modeled RF can be as high as 40% it is significantly reduced when the model is applied for a comparison of different land use scenarios, for example, like in this paper [Sinisalo, 1998]. Here we estimated the sensitivity of the RF calculations for some of the parameters, subjectively selected as representing the most critical ones, by changing slightly the initial “best estimate” values one at a time. Of the gas fluxes, the value of CH4 flux in a nutrient-rich, undrained mire was tested by increasing and decreasing the current emission rate of 20 g CH4 m−2 yr−1 by 50%. In addition, the CO2 balance of the forestry-drained nutrient-poor peat soil was changed from a source of 244 g m−2 yr−1 to a sink of −370 g m−2 yr−1. While the former value is based on a large-scale national data set on soil CO2 fluxes and simulations of litter production and decomposition, the latter value is derived from the only direct micrometeorological net ecosystem exchange measurement above such an ecosystem [Laurila et al., 2007]. In addition, the sensitivity of RF to the selected albedo limits was tested. We recalculated the RF for the cases in which (1) the upper limit of winter albedo in the forestry-drained case was lowered from 0.4 to 0.2 (motivated by the study of Viterbo and Betts [1999]), (2) the upper limit of winter albedo in the case of an undrained open mire was lowered from 0.8 to 0.4, and (3) the lower limit of summer albedo of an undrained mire was increased from 0.15 to 0.24, the latter value being the July average of the albedo measured at the Jokioinen grass field (Table 1).

2.3. Calculation of Trends in the Surface Temperatures

[29] To study whether the land use changes from open mire to peatland forest have increased the spring temperatures in Finland, the surface temperature trends for March, April and May were calculated from the monthly mean, minimum and maximum temperature records based on gridded climate data. The number of weather stations with homogenized temperature time series varied from about 50 in 1909 to 180 in the 1970s. By 2009, the amount of stations had been dropped to ca. 140. Trends for the southern and northern parts of Finland were studied separately; the country was divided by the latitude 65°N. The monthly mean temperature records were extracted from the monthly mean temperature grid values covering the 100 year period from 1909 to 2008 [Tietäväinen et al., 2010]. The monthly minimum and maximum temperature records were extracted from the daily grid values covering a 48 year period from 1961 to 2008 [Venäläinen et al., 2005]. The resolution of both of the gridded data sets was 10 km. The monthly average of the daily temperature range (DTR) was calculated as the difference between the monthly maximum and minimum temperatures.

[30] For mean temperatures, linear trends were calculated separately for the first 50 years (1909–1958) and the last 50 years (1959–2008), while for the maximum and minimum temperatures 48 years (1961–2008) were used. The significance of the trends was tested with the t test. To study the differences between the trends of the maximum and minimum temperatures, the daily temperature range trends were also calculated.

3. Results

3.1. Seasonal Variation in Albedo

[31] As indicated by rapidly declining albedo values, the end of the snowmelt on the open fen at Kaamanen took place in mid-May (Table 1 and Figure 1a). The albedo during the snow-free period ranged from 0.10 to 0.14. Snow usually appeared in mid-October with a consequent sharp increase in albedo. From mid-November until the end of February, an upper limit of 0.8 was applied, as explained above. On the cropland at Jokioinen, snow melted on average at the beginning of April (Figure 1a). A constant maximum value of 0.15 was assumed for DOY = 126–304. In winter, the albedo values fluctuated, due to variable snow cover, between 0.4 and 0.8.

Figure 1.

Ten day running means of average albedo at (a) open sites and (b) forest sites in the south and the north. Upper limits of 0.8 and 0.4 have been taken for the winter albedos at the open and forest sites, respectively. At the Jokioinen cropland, an upper limit of 0.15 has been taken for the albedo data for DOYs 127–300.

[32] In forests, winter albedos are typically lower than over open land due to the often bare, snowless branches of conifers which mask the snow covered soil. In the Sodankylä forest in the north, the measured albedo was higher than 0.4 most of the time before DOY 60 and after DOY 310, but these values were replaced with an upper limit of 0.4 (Figure 1b). From mid-March to mid-April, when there was still snow on the ground but the incoming radiation was already quite high, the albedo varied between 0.22 and 0.25. In the Kalevansuo forest in the south, the winter albedo varied between 0.15 and the upper limit of 0.4, peaking on days after fresh snow. During the growing season, the albedo fluctuated within 0.11–0.14 at Kalevansuo and within 0.11–0.12 at Sodankylä.

3.2. Development of Biomass, Canopy Cover, and Albedo

[33] The largest amount of tree biomass carbon, about 12 kg C m−2, at the end of the rotation was accumulated at the southern nutrient-rich site case, spruce being the dominant tree species (Figure 2a). At the other site cases, the amount of carbon accumulated was lower, ranging from 5.5 to 7 kg C m−2. The development of the canopy cover was most rapid in the southern nutrient-rich case, where the maximum canopy cover of 0.8 was already reached 40 years after the drainage, just before the thinning of the forest (Figure 2b). The canopy cover increase was slowest at the northern nutrient-poor site case. The maximum simulated canopy cover varied from 0.60 in the northern nutrient-poor case to 0.80 in the southern nutrient-rich case, and was always observed just before the first thinning (except at the poor site in north, where no thinnings took place). Before drainage, the canopy cover at the nutrient-rich site was 0, whereas at the nutrient-poor sites it was 0.36 and 0.27 in south and north, respectively, due to the existing small trees typical of such treed bogs.

Figure 2.

Development of (a) tree carbon store and (b) projected canopy cover at nutrient-rich (spruce) and nutrient-poor (pine) site cases in southern and northern Finland after drainage.

[34] Taking the northern site cases as an illustration, at the nutrient-rich site the decrease in albedo following forestation was most rapid during the first 20 years (Figure 3a), when also the canopy cover increased quickly. At the nutrient-poor site, the decrease in albedo was slow during the first 20 years, due to the existing sparse predrainage forest canopy, but was more rapid after that for the next 40 years (Figure 3b). At both sites, the largest difference in albedo was observed in spring. In summer, the drainage had only a minor effect on albedo.

Figure 3.

Albedo at 20 year intervals during one rotation period (100 years) in (a) nutrient-rich/north and (b) nutrient-poor/north site cases. Data for the years 0 and 100 are measured, while the other years' data have been estimated using the measured albedo and fractional canopy cover data.

3.3. Reflected and Absorbed Short-Wave Radiation

[35] The highest amount of SW radiation reflected back from an undrained nutrient-rich peatland was 70 and 120 W m−2 in the southern and northern cases, respectively, peaking in March in south and at the end of April in north (Figures 4a and 4b). For the rest of the year, the reflected radiation was quite similar in both undrained and forested peatland.

Figure 4.

(left) Daily means (1971–2000) of the incoming radiation (regular black line), reflected radiation at the undrained, open mire at the time of drainage (black bold line) and the reflected radiation at the forested peatland at the end of the rotation (gray bold line) at nutrient-rich site cases in (a) south and (b) north. (right) Differences in the daily mean absorbed radiation between the nutrient-rich undrained and forested peatland in (c) south and (d) north.

[36] Less SW radiation was absorbed in undrained (i.e., open) than forested peatland in spring due to the presence of snow cover (Figures 4c and 4d). The difference in the daily mean absorbed radiation between the undrained and forested peatlands was much larger in the north than in the south. For the nutrient-rich site cases, which had larger differences, the maximum difference in the north was 80–90 W m−2 at around DOY 110, whereas in the south it was 40–45 W m−2, and was observed at around DOY 70 (Figures 4c and 4d). At the nutrient-poor sites, the maximum difference in the north was 60–70 W m−2 at DOY 115–120 and in the south 30–40 W m−2 at around DOY 80 (data not shown).

3.4. RF of the Soil GHG Fluxes Following the Drainage

[37] At all site cases, drainage induced a positive RF due to increased emissions of CO2 and N2O and a negative RF due to decreased CH4 emissions (Figure 5). The cooling effect of the change in the CH4 fluxes was much higher at the nutrient-rich sites, but so too was the warming effect of the change in the CO2 fluxes. An increase in N2O fluxes caused a small warming effect at the poor sites, and a slightly larger one at the rich sites. When the RFs of all three gases were summed, the resulting RFΔghgSoil was negative at the nutrient-rich sites, but became positive at the poor sites after about 20 years (Figure 5).

Figure 5.

Radiative forcing of the soil greenhouse gas fluxes (RFΔghgSoil) during one rotation after the forestry drainage of peatland in the four different cases, calculated for a square meter.

3.5. RF Caused by the Changes in Albedo and the CO2 Fixed by Trees

[38] Next, we studied the RF caused by the tree growth only (RFΔghgTree+ RFΔalb), excluding the effect of the soil GHG fluxes. The greatest RFΔalb 85 or 100 years after the forestation was observed at the northern nutrient-rich site case and the smallest at the southern poor site (Figure 6). The greatest uptake of C by trees (i.e., smallest RFΔghgTree) took place at the southern nutrient-rich site and smallest at the northern poor site. At the southern rich site, where the absolute value of RFΔghgTree exceeded the RFΔalb after 15 years, the largest cooling effect by tree growth was observed. In the southern nutrient-poor case, the sum of RFΔghgTree and RFΔalb was always negative (i.e., cooling). In the north, the net effect of tree growth was at the nutrient-rich site positive the first 65 years, being thereafter a cooling one. At the northern nutrient-poor site case, RFΔalb and RFΔghgTree balanced each other out during the whole rotation period, the net effect being neither cooling nor warming.

Figure 6.

Radiative forcing caused by the changes in albedo (RFΔalb) and tree growth (RFΔghgTree) together with their sum following drainage, calculated for a square meter and scaled by the Earth's surface area.

3.6. Net RF Caused by the Changes in Albedo and GHG Fluxes Following the Forestry Drainage

[39] The effect of the change in soil and tree GHG fluxes (RFΔghgTree+ RFΔghgSoil) after the drainage was cooling at all the site cases (Figure 7). When all the RF factors considered here were summed, the lowest RFnet (i.e., cooling) was observed at the southern nutrient-rich site case, being about −3.8 × 10−14 W m−2 at the end of the rotation (Figure 7). At the northern rich and southern poor sites, the RFnet was also negative during the whole rotation period, but less cooling was observed at these site cases than at the southern rich site. The only site case where warming effect, about 1.0 × 10−14 W m−2 at the end of the rotation, was observed was the northern nutrient-poor site.

Figure 7.

Radiative forcing caused by the combined changes in soil GHG fluxes and tree CO2 uptake (RFΔghg) and by the changes in albedo (RFΔalb), as well as the total radiative forcing as the sum of these, following drainage, calculated for a square meter and scaled by the Earth's surface area.

[40] The cumulative RF over the whole rotation period was negative at all sites except at the northern poor site (Figure 8). In general the tendency for cooling was higher at the southern and the nutrient-rich site cases.

Figure 8.

Cumulative radiative forcing for the different site cases integrated over one rotation (85 years in southern, 100 years in northern site cases), indicating net cooling (negative values) or warming (positive values).

3.7. Sensitivity of Radiative Forcing Calculation

[41] Of the selected variables, the RF was most sensitive to the changes in the CH4 and CO2 fluxes. Assuming a 50% smaller (greater) CH4 emission (i.e., ±10 g m−2 yr−1) for the undrained nutrient-rich mires (the reference situation), the RF would be in both the southern and northern site cases about 2 × 10−14 W m−2 higher (lower) than the “best estimates” (cases 1 and 2 in Figure 9). In the north, the sign of the RF would change to positive.

Figure 9.

Sensitivity of RF for some selected parameter values. Best estimate of RF is shown with the solid bold line. The dashed lines indicate RF after adjusting one of the parameters at a time as follows: case 1, undrained mire, CH4 flux 10 (original value 20) g m−2 yr−1; case 2, undrained mire, CH4 flux 30 (20) g m−2 yr−1; case 3, open mire, high winter albedo 0.4 (0.8); case 4, undrained mire, summer albedo 0.24 (0.15); case 5, drained peatland, soil CO2 balance −370 (244) g m−2 yr−1; case 6, drained peatland, high winter albedo 0.2 (0.4).

[42] Assuming a soil CO2 balance of −370 g m−2 yr−1 for the forestry-drained nutrient-poor peatland would decrease the RF by as much as 3.0−3.7 × 10−14 W m−2 and change the sign of RF from positive to negative in the north (case 5).

[43] On the other hand, changing the values of albedo within the given limits would cause smaller deviations from the “best estimate” of RF. For instance, changing the upper limit of winter albedo from 0.8 to 0.4 would result in a reduction of about 0.3 and 0.8 × 10−14 W m−2 in the RF at the end of the rotation in the southern and northern nutrient-rich sites, respectively, as compared to the “best estimate” (case 3). On the other hand, approximately 1 × 10−14 W m−2 less cooling or more warming would result, depending on the site case, if the summer albedo at the open, undrained mire (the reference situation) was increased from 0.15 to 0.24 (case 4). Lowering the limit of the maximum winter albedo in the forested sites from 0.4 to 0.2 would not have significant impact (case 6 in Figure 9) In general, the sensitivity of RF to changes in albedo was greater in the north, due to the longer duration of snow cover there.

3.8. Long-Term Trends in the Surface Temperatures

[44] In the early half of the 20th century (1909–1958), both increasing and decreasing mean temperature trends were found, but these were not statistically significant (at the 5% level) (Table 3). In the latter half of the 20th century (1959–2008), there has been a significant increase in the mean temperatures in April in southern (p = 0.001) and northern (p = 0.01) Finland. In March, the increase was not significant because of the larger variability.

Table 3. Temperature Trends (K/decade) for the Maximum, Mean, and Minimum Temperature Records and for the Daily Temperature Range in Southern (<65°) and Northern (>65°) Finlanda
  • a

    DTR, daily temperature range.

  • b

    Statistically significant trends (p value maximum 0.05) according to the t test are indicated in boldface.

1909–1958, Mean
1959–2008, Mean
1961–2008, Maximum/Minimum/DTR
South0.42 / 0.73 / −0.310.64 / 0.37 / 0.260.20 / 0.14 / 0.06
North0.47 / 1.00 / −0.540.45 / 0.62 / −0.180.19 / 0.23 / −0.04

[45] Both the maximum and minimum temperatures have increased significantly in March and April since 1961, the p values ranging from 0.0002 in the case of the April maximum temperatures in the south to 0.03 in the March minimum temperatures in the south and in the March maximum temperatures in the north. In March the daily minima have increased faster than the maxima, but the narrowing of the daily temperature range is significant only in the north, the p value being 0.006. In April, there is a statistically significant increasing trend in the DTR in the south (p value 0.009), showing that the increase in the daily maxima has been substantially larger than that in the minima. In the north the daily minima have also continued to rise faster than the maxima in April, but the decreasing trend in the DTR is smaller than in March, and is not statistically significant.

4. Discussion

4.1. Effects of Forestry Drainage on the Radiative Forcing

[46] In this paper we have estimated the impact of peatland forestation on the radiative forcing by considering the changes in albedo and net GHG fluxes for some typical sites in Finland. We have demonstrated that the positive (i.e., warming) RF due to the albedo change following the forestry drainage of peatlands is of the same magnitude as the negative RF caused by the altered GHG fluxes. Our results indicated that in three site cases out of four, the forestry drainage have induced negative RF, and in one case positive. Earlier simulations have suggested that forestry drainage in Finland has caused a negative RF due to the postdrainage changes in GHG fluxes, i.e., decreased CH4 emissions and increased CO2 fixation in trees and soil organic matter [Laine et al. 1996; Minkkinen et al., 2002]. In these studies, however, the soil CO2 fluxes were estimated in a different way, and the effect of albedo was not taken into account at all.

[47] When considering only the GHG fluxes, we obtained results partly similar to Minkkinen et al. [2002]: cooling was observed for all site cases (dashed line in Figure 7). Even without including the tree CO2 uptake in the calculations, drainage exerted a negative RF at the nutrient-rich sites (Figure 5). However, according to the soil CO2 balance simulations used in this study, the soil was a net CO2 source (Table 2), whereas in the study of Minkkinen et al. [2002] the majority of drained peatlands were estimated to be CO2 sinks. Controversy still exists regarding the CO2 balance of drained peat soils: while the results used here, obtained by combining modeled litter production and decay and measured soil respiration, suggest a CO2 source, there is new evidence from micrometeorological measurements that the soils of the drained nutrient-poor sites would act as net CO2 sinks, despite the increased peat oxidation [Laurila et al., 2007]. Using these new data would turn the nutrient-poor site cases toward more cooling (Figure 9). Hence, there is a need to obtain more data on the net CO2 balance and on the C cycle components of different peatland types.

[48] Our results also suggest that even the mere forestation of open areas in the boreal zone (without the impact of soil GHG fluxes) may, in some cases, induce a positive RF (Figure 6). This observation is consistent with Betts [2000], who concluded in his simulation study that forestation in the boreal zone may actually accelerate rather than mitigate climate change. However, Betts [2000] did not take into account the changes in the soil fluxes of other GHGs like CH4 and N2O, which are potentially important when considering the land use change on peatlands.

[49] The conclusion that the changes in soil GHG fluxes produced a negative RF at nutrient-rich sites and positive at poorer sites was mainly due to the postdrainage cessation of the CH4 emissions, which were originally higher at the nutrient-rich sites. A steep decrease in the RF was observed at the nutrient-rich sites after the water-level drawdown and the rapid extinction of the emissions of CH4 (Figure 5). Due to its short residence time in the atmosphere (pulse-decay lifetime of 12 years), the RF caused by CH4 quickly settles to a new, lower level. CO2 has a much longer atmospheric residence time, and the positive RF consequently increases throughout the rotation period and eventually exceeds the negative RF due to the elimination of CH4 emissions. At site cases with a lower nutrient level, the original CH4 emissions were smaller, and the warming effect due to soil CO2 emissions already exceeded the cooling effect of CH4 some 20 years after the drainage. Despite the effectiveness of N2O as a GHG, its tiny emissions into the atmosphere only resulted in a marginal positive contribution to the RF due to soil GHG fluxes.

[50] Due to the dominant role of CH4 in calculations spanning up to 100 years, the results of this study are highly sensitive to the CH4 flux rates specified for undrained peatland. For example, if a CH4 flux rate of only half of the original were used, the RF at the nutrient-rich site case in south at the end of the rotation would have been only about 50% of the RF obtained with higher CH4 emissions, and would have changed the sign of the RF in the north (Figure 9). This highlights the role of high-quality flux data also from undrained peatlands to reduce the uncertainties in the RF calculations.

[51] As can be deduced from the ratio of the albedo-induced RF to the RF due to the C fixation in trees (Figure 6), the warming effect of tree biomass increment was greater in the north than in the south. This is mainly explained by the fact that in the north, the difference in the absorbed energy between the undrained and forested peatland was larger than in the south. The role of the albedo is relatively more important in the north, where the duration of snow cover is longer, particularly in spring. In the north, in the period of the highest difference in albedos between the open mire and forest (approximately DOY 60–130), the daily mean incoming radiation is already high, up to 180 W m−2, and the difference in absorbed radiation between a forest and an open mire is significant. The importance of the autumn season was much smaller: by the time snow typically appeared, the amount of incoming radiation was already small, the daily mean being less than 30 W m−2. For this reason, and as observed in earlier studies [e.g., Betts and Ball, 1997; Viterbo and Betts, 1999; Bathiany et al., 2010], the spring is the crucial season for the albedo effects, explaining why the albedo-induced warming in the north was similar to or higher than in the south, despite the smaller tree biomass and canopy cover there.

[52] The global contribution of the peatland forestation in Finland can be roughly estimated by multiplying the instantaneous RFs by the total area of forestry-drained peatlands. With an area of 4.8 million ha (see section 4.2), and applying the RF of the southern nutrient-rich peatland (in Figure 6), the magnitude of the net radiative forcing 85 years after the forestation would be −1.8 mW m−2, having a negligible contribution to the global net anthropogenic RF since 1750, which is about 1.6 W m−2. Furthermore, forestation of all boreal peatlands (3.5 million km2) would result in a negative RF of −0.13 W m−2 (assuming the RF of nutrient-rich site case in southern Finland). Although the sign of the RF could be positive or negative, depending on the peatland type and fertility, soil GHG fluxes, tree growth, etc., this coarse extrapolation exercise shows that such a radical land use change would not dramatically affect the global RF.

[53] It should also be remembered that the carbon stock that accumulates in trees has a temporary character. At the end of the rotation, the forest is cut and, in the case of managed forests, the accumulated carbon is mostly released relatively rapidly back into the atmosphere. In this paper, the release of the carbon accumulated in trees is not accounted for. Including it in the calculations would probably result in all the site cases having a positive net RF. Boreal forests also affect the climate system by enhancing the production of aerosols, which have a cooling effect on climate; this is, however, difficult to quantify [e.g., Kurtén et al., 2003].

[54] The results obtained here are valid only for the current climate. If climate warming proceeds as predicted, winters will get milder in the boreal zone and subsequently the snow covered period will become shorter [Jylhä et al., 2008], thus reducing the difference in springtime albedo between the open and forested peatland areas. Hence, in a warmer climate, the warming effect caused by the decreasing albedo following forestation would be smaller, suggesting that the effect of the forestry drainage of mires in the future is likely to shift more toward cooling. On the other hand, restoring the forestry-drained areas to their original state by means of rewetting would probably no longer have a cooling effect of similar magnitude due to the shortening of the snow cover period in the warmer climate.

4.2. Effects of Forestry Drainage on the Regional Surface Temperatures

[55] While the effects of the RF arising from the concentration increase of well mixed GHGs are global, those induced by albedo changes are felt locally in surface temperatures. In the northern latitudes, the albedo-induced effects on the surface temperatures are estimated to be more important than the impact of the other biogeophysical changes, such as the altered water balance [e.g., Bala et al., 2007]. Owing to the greater contribution of the biogeophysical, mainly albedo, effects on RF as compared to the biogeochemical effects, the forestation of open areas in the northern latitudes typically leads to warming, whereas in temperate and tropical regions, cooling is more probably observed [Bala et al., 2007; Jackson et al., 2008; Bathiany et al., 2010; Juang et al., 2007].

[56] The drainage areas of originally treeless and sparsely treed peatlands, where the albedo change following forestation is highest, have increased in Finland from less than 0.5 million ha in 1959 to more than 2.2 million ha today [Keltikangas et al., 1986]. The total area of drained peatlands has increased from 0.92 million ha in the early 1950s [Hökkä et al., 2002] to 4.8 million ha by 2007; simultaneously, the tree stand volumes in peatland forests have increased from ca. 250 million m3 to over 500 million m3 in 2007 [Finnish Forest Research Institute, 2008]. The proportion of the total peatland area that is drained is largest between latitudes 62° to 65°. Of the total drained area, about 2.7 million ha is located south of 65° [Hökkä et al., 2002]; this is more than 10% of the total land area. Considering that the maximum difference of absorbed solar radiation in open mire versus closed forest is as much as 40–80 W m−2 (Figure 4), it seems quite possible that forestation has caused an increase in daytime maximum temperatures, in particular, during the past 50 years. In northern Finland, the forested area has been much smaller, and we would thus expect the influence on surface temperatures to be smaller than in southern Finland.

[57] We observed an increase in the maximum daily temperatures in April in southern Finland during 1961–2008. The rise was larger in the maximum (i.e., daytime) than in the minimum or mean temperatures, suggesting that the rise might be attributed to changes in the surface radiative properties after forestry drainage. These changes, mainly in albedo, induce a positive RF. In March and May, the rise in the maximum daily temperatures was less than that in the corresponding minima, suggesting that the increasing trends observed are probably more due to global warming than to the local biogeophysical impacts. The effects of drainage on surface temperatures are likely to be observed particularly in daytime temperatures, when the absorption of SW radiation by the darker surface occurs. The increase in nighttime temperatures and the decrease in the diurnal temperature range, on the other hand, may be interpreted as being due to global warming [Cubash et al., 2001; Dai et al., 1999]. Thus, an increase in the diurnal temperature range could be due to warming attributable to, e.g., the change in the local SW energy balance following the albedo change. Although not addressed in this work, the long-wave radiation components should be also accounted for when estimating the impacts of land use on local surface temperatures. For example, DTR is closely coupled to the diurnal cycle of the LW radiation flux and boundary layer dynamics [e.g., Betts, 2006].

[58] In addition, there are other mechanisms, not taken into account in this paper, by which the land use change from open to forested peatland modifies the surface temperatures and also RF. One of the most important factors is the increased evapotranspiration which leads to cooling. Also, higher rates of evapotranspiration increase the cloudiness and hence atmospheric albedo, leading to a more negative RF [e.g., Betts et al., 2007]. On the other hand, as a strong greenhouse gas, the increasing water vapor concentration affects the surface radiative balance, but this does not contribute to the planetary RF [Montenegro et al., 2009].

[59] The maximum difference for absorbed SW radiation in southern Finland (measured at the beginning of the 2000s) was observed in our data in March (Figure 4c), indicating that the snowmelt has taken place shortly after that. However, the typical long-term (1971–2000) snowmelt at Jokioinen, for example, has taken place in mid-April [Drebs et al., 2002], and in 1961–1990 even later [Finnish Meteorological Institute, 1991], suggesting that the greatest effect of forestation on long-term daytime temperatures (Table 3) should indeed be observed in the April records.

[60] In contrast to the observation of increased surface temperatures in springtime, drainage of peatlands has been shown to decrease the local nighttime minimum temperatures during the growing season, at least for the first 15 years after the drainage [Solantie, 1994]. This is due to the changes in the soil heat conductivity: the dry peat effectively insulates the lower soil layers and atmosphere. Therefore, during calm and clear summer nights, the heat flux from a drained peat soil cannot compensate the radiative cooling of the surface, causing a drop in the minimum temperature [Venäläinen et al., 1999]. On the other hand, the decreasing latent heat flux from the drier peat may slightly increase the growing season maximum temperatures after the drainage [Solantie, 1998]. After some decades, the increasing latent heat flux by the growing trees again decreases the maximum temperatures. These effects, albeit having a potentially large impact on local surface temperatures, do not, however, contribute to RF, but are defined as “noninitial radiative effects” [Forster et al., 2007].

5. Conclusions

[61] Despite the significant positive contribution of the lower albedo to the radiative forcing, the forestation caused a negative net radiative forcing (i.e., cooling) at three out of the four site cases considered here. Thus, the cooling impact of the C uptake from the atmosphere into tree biomass and the diminishing CH4 emissions outweighed the warming impact of the decreasing albedo and the increasing soil CO2 and N2O emissions after the drainage. Largest cooling effect was observed at the southern nutrient-rich site case, whereas warming was observed at the northern nutrient-poor site case. The warming effect was most remarkable in spring, when highest differences in albedo between the undrained and forested peatland occurred. Our results thus show that, when assessing the climate effect of a land use change including either forestation or deforestation, it is necessary to take into account not only GHGs, but also, at least as importantly, the effect of a changing albedo.

[62] The largest uncertainties in the RF calculations were attributed to the determination of CH4 fluxes in the reference situation and of soil CO2 balance in the changed situation. Although the best currently available data were used, the outcome of the calculations would be different if, for example, the results of the first direct CO2 balance study [Laurila et al., 2007] can be generalized to larger areas. Selection of the lower and upper limits for the measured albedo also creates uncertainty in the results; however they do not seem to be as critical as the GHG fluxes.

[63] We also found indications that the drainage of peatlands has increased daytime temperatures in the intensively drained areas in April. This is most probably due to increased absorption of the short-wave radiation in the darker surface with lower albedo after forestation, but other biogeophysical changes, not addressed in this work, also have their impact on the surface temperatures. Although our observations do not constitute proof that the albedo change has influenced surface temperatures in Finland, the result agrees with earlier studies which suggest that forestation of open areas is likely to result in net warming in the boreal zone [Betts, 2001; Bala et al., 2007; Bathiany et al., 2010]. Furthermore, we are not able to infer from the records how much of the warming is attributable to the global increase of GHG concentrations and how much to the local albedo change. In order to investigate these issues further, we are planning to perform in the future regional climate model simulations. These results provide a future framework for studying the effect of peatland forestation using regional climate model simulations.


[64] The financial support by the European Commission through the projects NitroEurope IP (017841) and GHG Europe (244122), and by the Academy of Finland Centre of Excellence program (project 1118615) are gratefully acknowledged. We are grateful to Pentti Pirinen for the radiation data and to Mika Aurela for the albedo data for the Kaamanen and Sodankylä sites. We would like to thank Alan K. Betts, an anonymous referee, an associate editor, and the editor, Dennis Baldocchi, for their constructive comments on the manuscript.