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Carbon storage and catchment hydrology are influenced both by land use changes and climatic changes, but there are few studies addressing both responses under both driving forces. We investigated the relative importance of climate change vs. land use change for four Alpine catchments using the LPJ-GUESS model. Two scenarios of grassland management were calibrated based on the more detailed model PROGRASS. The simulations until 2100 show that only reforestation could lead to an increase of carbon storage under climatic change, whereby a cessation of carbon accumulation occurred in all catchments after 2050. The initial increase in carbon storage was attributable mainly to forest re-growth on abandoned land, whereas the stagnation and decline in the second half of the century was mainly driven by climate change. If land was used more intensively, i.e. as grassland, litter input to the soil decreased due to harvesting, resulting in a decline of soil carbon storage (1.2−2.9 kg C m) that was larger than the climate-induced change (0.8-1.4 kg C m). Land use change influenced transpiration both directly and in interaction with climate change. The response of forested catchments diverged with climatic change (11-40 mm increase in AET), reflecting the differences in forest age, topography and water holding capacity within and between catchments. For grass-dominated catchments, however, transpiration responded in a similar manner to climate change (light management: 23-32 mm AET decrease, heavy management: 29-44 mm AET decrease), likely because grassroots are concentrated in the uppermost soil layers. Both the water and the carbon cycle were more strongly influenced by land use compared to climatic changes, as land use had not only a direct effect on carbon storage and transpiration, but also an indirect effect by modifying the climate change response of transpiration and carbon flux in the catchments. For the carbon cycle, climate change led to a cessation of the catchment response (sink/source strength is limited), whereas for the water cycle, the effect of land use change remains evident throughout the simulation period (changes in evapotranspiration do not attenuate). Thus we conclude that management will have a large potential to influence the carbon and water cycle, which needs to be considered in management planning as well as in climate and hydrological modelling. [ABSTRACT FROM AUTHOR]

This paper discusses the effects of vegetation cover and soil parameters on the climate change projections of a regional climate model over the Arctic domain. Different setups of the land surface model of the regional climate model HIRHAM were realized to analyze differences in the atmospheric circulation caused by (1) the incorporation of freezing/thawing of soil moisture, (2) the consideration of top organic soil horizons typical for the Arctic and (3) a vegetation shift due to a changing climate. The largest direct thermal effect in 2 m air temperature was found for the vegetation shift, which ranged between −1.5 K and 3 K. The inclusion of a freeze/thaw scheme for soil moisture shows equally large sensitivities in spring over cool areas with high soil moisture content. Although the sensitivity signal in 2 m air temperature for the experiments differs in amplitude, all experiments show changes in mean sea level pressure (mslp) and geopotential height (z) throughout the troposphere of similar magnitude (mslp: −2 hPa to 1.5 hPa, z: −15 gpm to 5 gpm). This points to the importance of dynamical feedbacks within the atmosphere-land system. Land and soil processes have a distinct remote influence on large scale atmospheric circulation patterns in addition to their direct, regional effects. The assessment of induced uncertainties due to the changed implementations of land surface processes discussed in this study demonstrates the need to take all those processes for future Arctic climate projections into account, and demonstrates a clear need to include similar implementations in regional and global climate models. [ABSTRACT FROM AUTHOR]

The dynamic vegetation model (LPJ-GUESS) is used to project transient impacts of changes in climate on vegetation of the Barents Region. We incorporate additional plant functional types, i.e. shrubs and defined different types of open ground vegetation, to improve the representation of arctic vegetation in the global model. We use future climate projections as well as control climate data for 1981-2000 from a regional climate model (REMO) that assumes a development of atmospheric CO2-concentration according to the B2-SRES scenario [IPCC, Climate Change 2001: The scientific basis. Contribution working group I to the Third assessment report of the IPCC. Cambridge University Press, Cambridge (2001)]. The model showed a generally good fit with observed data, both qualitatively when model outputs were compared to vegetation maps and quantitatively when compared with observations of biomass, NPP and LAI. The main discrepancy between the model output and observed vegetation is the overestimation of forest abundance for the northern parts of the Kola Peninsula that cannot be explained by climatic factors alone. Over the next hundred years, the model predicted an increase in boreal needle leaved evergreen forest, as extensions northwards and upwards in mountain areas, and as an increase in biomass, NPP and LAI. The model also projected that shadeintolerant broadleaved summergreen trees will be found further north and higher up in the mountain areas. Surprisingly, shrublands will decrease in extent as they are replaced by forest at their southern margins and restricted to areas high up in the mountains and to areas in northern Russia. Open ground vegetation will largely disappear in the Scandinavian mountains. Also counter-intuitively, tundra will increase in abundance due to the occupation of previously unvegetated areas in the northern part of the Barents Region. Spring greening will occur earlier and LAI will increase. Consequently, albedo will decrease both in summer and winter time, particularly in the Scandinavian mountains (by up to 18%). Although this positive feedback to climate could be offset to some extent by increased CO2 drawdown from vegetation, increasing soil respiration results in NEE close to zero, so we cannot conclude to what extent or whether the Barents Region will become a source or a sink of CO2. [ABSTRACT FROM AUTHOR]

The EU project BALANCE (Global Change Vulnerabilities in the Barents region: Linking Arctic Natural Resources, Climate Change and Economies) aims to assess vulnerability to climate change in the Barents Sea Region. As a prerequisite the potential impact of climate change on selected ecosystems of the study area has to be quantified, which is the subject of the present paper. A set of ecosystem models was run to generate baseline and future scenarios for 1990, 2020, 2050 and 2080. The models are based on data from the Regional Climate Model (REMO), driven by a GCM which in turn is forced by the IPCC-B2 scenario. The climate change is documented by means of the Köppen climate classification. Since the multitude of models requires the effect of climate change on individual terrestrial and marine systems to be integrated, the paper concentrates on a standardised visualisation of potential impacts by use of a Geographical Information System for the timeslices 2050 and 2080. The resulting maps show that both terrestrial and marine ecosystems of the Barents region will undergo significant changes until both 2050 and 2080. [ABSTRACT FROM AUTHOR]

Climate change is expected to alter the distribution of habitats and thus the distribution of species connected with these habitats in the terrestrial Barents Sea region. It was hypothesised that wild species connected with the tundra and open-land biome may be particularly at risk as forest area expands. Fourteen species of birds were identified as useful indicators for the biodiversity dependent upon this biome. By bringing together species distribution information with the LPJ-GUESS vegetation model, and with estimates of future wild and domestic reindeer density, potential impacts on these species between the present time and 2080 were assessed. Over this period there was a net loss of open land within the current breeding range of most bird species. Grazing reindeer were modelled as increasing the amount of open land retained for nine of the tundra bird species. [ABSTRACT FROM AUTHOR]

In the context of the EU-Project BALANCE (http://balance-eu.info) the regional climate model REMO was used for extensive calculations of the Barents Sea climate to investigate the vulnerability of this region to climate change. The regional climate model REMO simulated the climate change of the Barents Sea Region between 1961 and 2100 (Control and Climate Change run, CCC-Run). REMO on ~50 km horizontal resolution was driven by the transient ECHAM4/OPYC3 IPCC SRES B2 scenario. The output of the CCC-Run was applied to drive the dynamic vegetation model LPJ-GUESS. The results of the vegetation model were used to repeat the CCC-Run with dynamic vegetation fields. The feedback effect of the modified vegetation on the climate change signal is investigated and discussed with focus on precipitation, temperature and snow cover. The effect of the offline coupled vegetation feedback run is much lower than the greenhouse gas effect. [ABSTRACT FROM AUTHOR]