The United Nations Framework Convention on Climate Change (UNFCC) declared in the Kyoto Protocol (Decision 11/CP.7) that changes in the carbon stocks of ecosystems, induced by LULUCF (land use, land-use change and forestry) activities can be included in the greenhouse gas emission budget of the signatory nations (UNFCC, 2001). Beside biogeochemical processes, LULUCF also influences biogeophysical processes (Betts, 2011; Bonan, 2008), but these effects are not yet considered in current climate policies.

Global climate models suggest that biogeochemical and biogeophysical effects vary greatly with latitude (Schaeffer et al., 2006; Bala et al., 2007; Bathiany et al., 2010; Davin and de Noblet-Ducoudre, 2010; Arora and Montenegro, 2011). In the tropics, biogeochemical and biogeophysical effects tend to act in the same direction, since tropical forests cool climate through both evaporative cooling and carbon sequestration (Costa and Foley, 2000; Gibbard et al., 2005). However, at middle and high latitudes, biogeophysical processes tend to counter the biogeochemical effect (Gibbard et al., 2005; Betts, 2000; Govindasamy et al., 2001), thus making the net LULUCF effect more challenging to assess. Indeed, forestation in boreal regions lowers the albedo and thus counterbalances the cooling effect of carbon storage.

Global climate models are important for understanding the climatic processes related to LULUCF and for quantifying the impacts on climate of forestation or deforestation over large areas. However, in highly heterogeneous landscapes, such as mid-latitude mountain ranges, global climate models are limited by their relatively coarse resolution, and the concept of radiative forcing (RF) (Myhre et al., 2013) can provide a useful alternative. The RF concept has already been employed to investigate the balance between biogeophysical (mainly albedo) and biogeochemical effects following forestation or deforestation. While some studies suggest that albedo RF can completely offset CO2 RF (Betts, 2000; Bernier et al., 2011; de Wit et al., 2013), others have found that the offset is rather small (e.g. Montenegro et al., 2009; Kirschbaum et al., 2011). The offset seems to vary widely depending on the regional characteristics of the determining variables: global radiation, snow cover, albedo change and carbon sequestration. These factors vary greatly, but little research has focused on how each of them influences RF (Kirschbaum et al., 2011), and only a few attempts have been made to quantify RF in a spatially explicit way (e.g. Betts, 2000; Montenegro et al., 2009). Betts (2000) estimated spatially explicit RF data on a resolution of 3.75∘ longitude by 2.5∘ latitude. Montenegro et al. (2009) performed their analysis on a resolution of 5 to 25 km. However, RF varies on much smaller scales. Moreover, decisions in regional planning are usually based on very local and regional information; therefore, it is crucial to quantify RF at finer resolutions.

Forest cover has increased in many temperate mountainous regions (Alewell and Bebi, 2011; MacDonald et al., 2000; Ramankutty et al., 2010; Kozak, 2003; Hagedorn et al., 2014) and analysis of LULUCF change in the Alps suggests that changes in forest cover near the treeline will further increase (Gehrig-Fasel et al., 2007). Some of the effects of this increase on various ecosystem services are already relatively well known (MacDonald et al., 2000; Laiolo et al., 2004; Bolliger et al., 2008) and have increasingly been considered in management strategies and subsidizing systems for agriculture and forestry (e.g. Gret-Regamey et al., 2013). However, few attempts to quantify RF in such regions have been made, even though snow cover and hence albedo in temperate mountains varies greatly. To optimize the effects of future land-use decisions, further research on the climatic impacts of forests is essential and should be included in spatially explicit valuation methods (Bebi et al., 2012).

Switzerland is a particular suitable study area for researching the effects of changes in forest cover, since many spatially explicit high-quality data sets are available and forest expansion is an ongoing dominant process of land-use change. The spatially explicit data series available are on land use/land cover (LULC) (1 ha raster), snow cover (1 km), global radiation (2.2 km), albedo change and carbon sequestration (both explicit for biogeographical regions) for the whole area of Switzerland. Forest cover is expanding by 4 % per decade at the country scale and by 8 % per decade in alpine areas (National Forest Inventory, NFI).

Our study design is twofold: first, we use the spatially explicit data sets to show the pattern of RF assuming that each location in Switzerland is facing a transition from agriculturally used open land to forest. This is not related to any particular or realistic scenario; however, the spatial pattern of RF can be of high interest for any land-use policies steering forest-cover change towards desired futures. In Switzerland agricultural subsidies directly influence farmers decisions on whether to keep managing or abandon their land. The latter will usually result in forest expansion. Second, we include the type and location of five different land-use transitions to calculate RF in Switzerland between 1985 and 1997. In summary, we estimate (i) to which extent albedo RF offsets CO2 RF in different parts of temperate mountainous regions, (ii) how each input parameter influences RF and (iii) what the inclusion of albedo change implies for the greenhouse gas inventory in Switzerland.

In the forest, average C stocks in biomass ranged from 95 to 170 tC ha-1 (NFI). In the soil (mineral soil 0–100 cm + organic layer) the average across Switzerland was 143 tc ha-1 (Nussbaum et al., 2012). In the intensively used open land, the average value for biomass was 4.34 tc ha-1 (Heldstab et al., 2012) and 91 tc ha-1 in the soils. In comparison, the C stocks of the extensively used open land amounted to 7 tc ha-1 in the biomass and 63 tc ha-1 in the soil. Minimum and maximum carbon sequestration in Switzerland of transitions from intensively or extensively used open land to closed forest thus ranged from 143 to 241 tc ha-1 (Fig. 3e). This corresponded to a CO2 RF of -16 to -27 W m-2 (Fig. 4b).

The albedos of snow-covered closed forests ranged from 0.168 to 0.267, while those of snow-free closed forests ranged from 0.101 to 0.139. Similarly, the albedos of snow-covered open forests ranged from 0.217 to 0.307 and those of snow-free open forests from 0.117 to 0.141 (Fig. 5). The average albedo of intensively used open land was 0.170/0.475 (snow free/snow covered) while that of extensively used open land was 0.154/0.549 (snow free/snow covered). The albedo differences between transitions from intensively or extensively used open land to closed forest thus ranged from 0.208 to 0.375 (snow covered) and from 0.023 to 0.066 (snow free) (Fig. 3c, d). Albedo change in Switzerland caused albedo RF ranging from 2 to 21 W m-2 (Fig. 4a).

The net RF at different locations in the study area ranged from -24 to 2 W m-2 and the offset of CO2 RF caused by albedo RF differed between 11 and 109 %. The differences were particularly marked on an elevational gradient and between the five biogeographical regions. Below an elevation of 600 m a.s.l., the albedo RF offset CO2 RF by 15 % on average. Between 600 and 1200 m, the offset was 22 % and above 1200 m 54 %. The highest RF was observed in high snow-rich alpine regions. In the Central Alps, 13 % of all possible LULC areas had an offset of more than 80 %. In the southern Prealps 7 % of all possible LULC areas had an offset higher than 80 %. In the Central Alps, as well as in the southern Prealps, we found areas where net RF was positive. However, these areas amounted to less than 0.2 % of both regions. We only found positive RF at elevations above 1850 m. The albedo RF was the lowest in the Plateau region and at low elevations of the Jura. Albedo RF accounted, on average, for less than 14 % of the CO2 RF in these regions. The lowest net RF (average -20 W m-2) was found in the northern Prealps below 1200 m. Above 1200 m the net RF in the northern Prealps was lower than in southern Prealps and Central Alps, although the snow cover in the northern Prealps was persistent. However, the effect of a persistent snow cover in the northern Prealps was outweighed by the low global radiation, the low treeline and the high carbon sequestration in this alpine region (see Fig. 3).

The net RF forcing of forests across the mountainous terrain in Switzerland strongly depended on the elevation (Fig. 5). The albedo RF increased with altitude, with several factors contributing to this increase (Fig. 3). First, global radiation increased with altitude, reaching maxima in continental parts of the Alps. Second, albedo (snow free, snow covered) of forests was in general lower above 1200m a.s.l. (where coniferous species dominate). Third, both snow cover and snow-cover period strongly increased with altitude. The carbon sequestration potential of forests decreased with altitude (due to unfavorable climatic conditions towards treeline).

Radiative forcing depended not only on location but also on the type of LULC transition. LULC transitions from open land to open forest had a higher offset (approx. 80 %) than transitions from already established open forest to closed forest (approx. 40 %). Transitions from open forest to closed forest were generally associated with relatively high change in carbon stocks (around 70 tc ha-1), and relatively small albedo change (for snow-covered albedo < 0.06 and for snow-free albedo < 0.01).

Between 1985 and 1997, all five types of LULC transitions (Table A4) took place on an area of 24 000 ha. More than 70 % of them were situated above 1000 m. Above 1000 m, the most frequent transition was the one from open to closed forest (50 %), followed by transitions from extensively used open land to open forest (20 %) and from extensively used open land to closed forest (13 %). Land-use change occurred especially in high altitude regions where albedo RF strongly offsets CO2 RF (Fig. 5). At the same time, the most frequent transition was the one from open forest to closed forest, where albedo RF had the least influence on net RF. In summary, the CO2 RF for all land-use transitions that were part of the forest expansion and succession between 1985 and 1997 in Switzerland was reduced by approximately 40 %, if the albedo RF is taken into account.

The net RF varied greatly on small spatial scales. In our study area, the parameter that had the most significant influence on the spatial variability of RF was snow cover, followed by carbon sequestration, difference in snow-covered albedo, global radiation and difference in snow-free albedo (Fig. 6a). Thus, if the average snow cover (in days year-1) and carbon sequestration at a certain location were known, the net RF and the offset of CO2 RF through albedo RF could be estimated well without including all factors in an explicit calculation. We found that the offset of CO2 RF can only be higher than 50 % if the snow cover lasted over 120 days year-1. An increase in snow cover caused an exponential increase in albedo RF. Because, first, persistent snow cover was found in regions with high global radiation and second because the longer snow covered the surface, the longer it would be present during the days with high global radiation in March, April and May.

The uncertainty of net radiative forcing was mainly attributed to uncertainties in carbon sequestration, followed by snow-covered albedo difference, global radiation and snow-free albedo difference. The main effects of uncertainty related to each input factor were much more relevant than the contribution of interactions between all input factors (Fig. 6a).

Our detailed assessment of radiative forcing across Switzerland shows that the albedo RF increases with increasing altitude, which offsets the CO2 RF at high elevations with long snow-covered periods, high global radiation and low carbon sequestration. The altitudinal RF gradient in Switzerland is very strong in comparison to the latitudinal gradient in boreal regions (Betts, 2000; Montenegro et al., 2009). The persistence of snow cover increases with increasing elevation and increasing latitude. However, while persistence of snow cover and global radiation are usually positively correlated in mountainous regions, causing high RF, they are negatively correlated in boreal regions. The strong altitudinal RF gradient found in this study is likely to be even more pronounced if altitudinal changes in forest structure and its influence on albedo and carbon sequestration are included on higher resolution because forests with very low carbon stocks can also have a low albedo (de Wit et al., 2014).

However, despite the general increase of RF with elevation, each biogeographical region has its specific characteristics. While the Jura and Plateau (under 600 m a.s.l.) are characterized, respectively, by albedos of 0.136 and 0.139 (snow free) and 0.272 and 0.276 (snow covered), the albedo in the southern Prealps under 600 m is comparably low (0.112 and 0.185, respectively). This regional difference may be related to the different forest types and soil characteristics. Whereas beech dominates in the Jura and the Plateau, many stands in the southern Prealps are dominated by chestnut. Moreover, forest soils in the lowlands of the southern Prealps contain particularly high fractions of black fire derived carbon (Eckmeier et al., 2010). The darker soil colour may lower the albedo in addition to differences in the canopy.

Our spatially highly resolved estimates of RF are in agreement with the results of Betts (2000) and Montenegro et al. (2009) which are based on much coarser resolutions. In Betts (2000), the two pixels encompassing Switzerland show a net carbon sequestration of 100–150 and 150–200 tc ha-1 associated with reforestation of pasture, while in Montenegro et al. (2009), the pixels indicate a drawdown ranging between 100–150 tc ha-1 (maximum scenario) and 0–20 tc ha-1 (minimum scenario). However, many pixels are missing in alpine regions. In our study, we found similar values for the drawdown with values ranging from -10 to 160 tc ha-1 (minus 10 indicates emission instead of drawdown).

Small-scale variability, especially in topographically complex areas, was not captured in these former studies. Here, we used data on global radiation and snow cover on a resolution of 2.2 km (MeteoSwiss) and 1 km (Huesler et al., 2012). To calculate differences in carbon sequestration and albedo, we relied on a biogeographical categorization and altitudinal stratification, which are based on major differences in vegetation and other ecological factors (Gutersohn, 1973; Wohlgemuth, 1996). Although the spatial resolution was high in our study, it should be refined further to allow, for instance, a comparison of RF on northern and southern slopes and a better capture of forest types and structures near the treeline.

The global sensitivity analysis with FAST showed that interactions between the input parameters were small in comparison to the main effects of each parameter. Thus, we verified the results of our global sensitivity analysis in a local analysis by estimating the partial derivates for each factor. Both analyses showed good agreement since not only the interactions but also non-linear effects had little influence. The amount of RF at a specific location is essentially influenced by carbon sequestration and snow cover (Fig. 6a). These two factors are good indicators for estimating the amount of RF. Global radiation and snow-covered albedo are also important, but they influence RF five times less than carbon sequestration and even seven times less than snow cover. The factors with the most potential for improving our results are better estimates of carbon sequestration (Fig. 6b), followed by reducing albedo uncertainty and the global radiation. The average parameter for atmospheric absorption (0.23) could be replaced by a spatially explicit parameter. Including a spatially explicit parameter for atmospheric absorption would probably increase the elevation gradient of RF, because atmospheric absorption should be higher in low elevations than in high elevations. According to our sensitivity analysis and Bright and Kvalevag (2013) improving data on atmospheric absorption will have a relatively small influence on the results.

The sensitivity analysis with FAST is based on uncertainty estimates because exact values are not available. For example, MODIS values can be assigned to an average uncertainty of 10 % (O'Halloran et al., 2012, Strahler et al., 1999). This uncertainty is likely to be much higher in topographically complex areas than in even terrain as the algorithm used to produce albedo values only indirectly accounts for topography. Moreover, the uncertainties associated with the different input parameters do not always refer to the same statistical measures and are thus not completely consistent (references of uncertainties are listed in the Table A3).

Regarding the different time horizons, RF increased by 17 % for a time horizon of 1000 years compared with a horizon of 100 years. Larger time horizons increased RF since CO2 RF constantly decreased due to interactions of atmospheric CO2 with the carbon cycle, while albedo RF became constant after forests reached a steady state. Since we used a time horizon of 100 years, we rather underestimated albedo RF. This apparently goes against the findings of Schaeffer et al. (2006) and Kirschbaum et al. (2011) who both argue that CO2 RF becomes more dominant for larger time horizons. However, they consider relatively short time periods (including rotations) where carbon sequestration does not end before the forests are removed. We think it is also necessary to include large time horizons when estimating RF, but it is of course an oversimplification to assume a fixed state after transition, since forests are frequently disturbed (e.g. O'Halloran et al., 2012). Over longer periods snow cover will be less persistent in the Alps, which will potentially decrease the albedo RF (de Wit et al., 2013; Pitman et al., 2011).

The maps of the possible carbon sequestration and albedo of forests in each biogeographical region (Fig. 3) reflect mainly ecosystem characteristics. However, forests in Switzerland have long been under permanent anthropogenic influence and are thus not in a naturally balanced equilibrium, i.e. at the end of succession. For example, forests at high elevations in the Jura are often used for pasturing. Hence, they are less dense and sequester less carbon than they would if left to develop as undisturbed forests. The relationship between albedo RF, CO2 RF and net RF will thus always depend on the actual and previous forest and land management. Our analysis of transitions from extensively used open land to closed forest and open forest corroborates this conclusion. The results indicated that the climatic benefit will be smaller if forests are kept in an open structure (e.g. due to pasturing) than when the canopy closes during succession. These findings are in line with former studies that estimate the effects of succession and forest structure on RF and show that changes in carbon stocks and changes in albedo are not linearly related (Kirschbaum et al., 2011; Bernier et al., 2011).

Our results should be valuable for future studies on the climatic impacts of LULUCF, especially for comparing and evaluating the results from climate models since our results are mainly based on satellite and field data. They should also be valuable for assessing the ecosystem (climate) service of forests in temperate mountains, i.e. for addressing the question of whether forest expansion in these regions is beneficial for climate or not. However, the interpretation of RF values has to be done carefully. First, the concept of radiative forcing has been developed to compare the impact of different forcing agents on the global mean temperature (Hansen et al., 2005). When applied at the regional and local scales one should keep in mind that the comparison of different forcing agents is far from being straightforward. For instance, the impact of albedo will remain mostly local while those from CO2 will be globally distributed and therefore diluted. Furthermore, the climate sensitivities of CO2 RF and albedo RF may differ (Davin et al., 2007).

Finally, it is important to note that our study does not account for all possible effects of forests on climate such as changes in evapotranspiration, surface roughness, and impacts on aerosols and other gases than CO2. The uptake of CH4 during forest expansion in the Alps increased according to Hiltbrunner et al. (2012), which adds to a negative CO2 RF. This effect is, however, one magnitude smaller than the effect of CO2 sequestration. The effect of a changing aerosol concentration, providing cooling (through cloud formation), may be very important (Spracklen et al., 2008). Changes in evapotranspiration and surface roughness, due to forest expansion, have a cooling impact in many geographical contexts (Bonan, 2008; Luyssaert et al., 2014), but we did not include the impacts of these changes in our study. In general, the influence of evapotranspiration and surface roughness will be low in those areas where snow cover plays an important role (Bonan, 2008; Bathiany et al., 2010; Gibbard et al., 2005; Lee et al., 2011). Hence, these effects will be more important at low elevations (e.g. on the Swiss Plateau) than at high elevations in the Alps. The gap between the benefits of forest expansion at low and high elevations may thus become even wider if evapotranspiration and surface roughness are also integrated.

In the temperate mountainous regions of Switzerland, the net RF associated with changes in forest cover varies greatly on small spatial scales. At low elevations, with low to moderate snow cover, RF is strongly negative due to a dominance of CO2 RF. At high elevations in continental regions with persistent snow cover, a very high global radiation, low carbon sequestration and low albedos of mostly evergreen tree species, RF can be positive. As a consequence, both clearly negative and positive values of RF can be found within a horizontal distance of 5 km in alpine valleys. Therefore, the climatic benefits of changes in forest cover can only be properly assessed using data at a high spatial resolution.

Our results indicate that it is very important to include albedo RF when estimating the impact on climate of changes in forest cover. Maps of RF, such as those produced in this study, indicate where climatic benefits from changes in forest cover can be expected and where not. In the Swiss Alps, the relevance of albedo RF is especially high because most transitions from open land to forest occur in regions where albedo RF causes a strong offset of CO2 RF. Practitioners and politicians who need information about ecosystem services on local and regional scales should take into account that RF in the Swiss Alps mainly depends on the persistence of snow cover and the potential for carbon sequestration. Moreover, late successional forest-cover changes from open forests to closed forests are more beneficial for climate than early successional changes.

Our results could be improved if changes in evapotranspiration, surface roughness, aerosols and gases other than CO2 were included. To determine the impacts of RF better, however, further advances in climate modelling are necessary. A promising approach could thus be the coupling of regional climate models with global climate models. Regional models are able to simulate all the effects of changes in land use on climate (including evapotranspiration, surface roughness and so on) on a small scale. Coupling regional models with global models allows the integration of feedbacks with the global circulation. This could help to close the gap between RF and temperature changes, and thus answer the question about where temperature changes caused by RF can be expected and how much change is likely.