Tree dehardening and budburst will occur earlier in a warmer climate, and this could lead to an increased risk of frost damage caused by temperature backlashes. By using a spring backlash index and a cold hardiness model, we assessed different aspects of risk for frost damage in Norway spruce forests during the present climate and for one future emission scenario. Uncertainties associated with climate modelling were quantified by using temperature data from three climate data sets: (1) E-Obs gridded observed climate data, (2) an ensemble of data from eight regional climate models (RCM) forced by ERA-40 reanalysis data, (3) an ensemble of regional climate scenarios produced by the regional climate model RCA3 driven at the boundary conditions by seven global climate models (GCM), all representing the SRES A1B emission scenario. The frost risk was analysed for three periods, 1961-1990, 2011-2040 and 2070-2097. The RCA3 GCM ensemble indicated that the risk for spring frost damage may increase in the boreo-nemoral forest zone of southern Scandinavia and the Baltic states/Belarus. This is due to an increased frequency of backlashes, lower freezing temperatures after the onset of the vegetation period and the last spring frost occurring when the trees are closer to budburst. The changes could be transient due to the fine balance between an increased risk of frost damage caused by dehardening during a period when freezing temperatures are common and a decreased risk caused by warmer temperatures. In the nemoral zone, the zone with highest risk for spring backlashes during the reference period (1961-1990), the spring frost severity may increase due to frost events occurring when the trees are closer to budburst. However, the risk in terms of frequency of backlashes and freezing temperature were projected to become lower already in the beginning of this century.

A model intercomparison between two atmospheric models, the non-hydrostatic Lokal Modell (LM) and the hydrostatic HIgh Resolution Limited Area Model (HIRLAM) is carried out for a one-week period, including a case of cyclogeneis leading to heavy precipitation over Northern Italy. The two models, very different in terms of data-assimilation and numerics, provide different results in terms of forecasts of surface fields. Opposite diurnal biases for the two models are found in terms of screen level temperatures. HIRLAM wind speed forecasts are too strong, while LM precipitation forecasts have larger extremes. The intercomparison exercise identifies some systematic differences in the weather products generated by the two systems and sheds some light on the biases of the two numerical weather prediction systems.

The propagation of spatio-temporal errors in precipitation estimates to runoff errors in the output from the conceptual hydrological HBV model was investigated. The study region was the Giman catchment in central Sweden, and the period year 2002. Five precipitation sources were considered: NWP model (H22), weather radar (RAD), precipitation gauges (PTH), and two versions of a mesoscale analysis system (M11, M22). To define the baseline estimates of precipitation and runoff, used to define seasonal precipitation and runoff biases, the mesoscale climate analysis M11 was used. The main precipitation biases were a systematic overestimation of precipitation by H22, in particular during winter and early spring, and a pronounced local overestimation by RAD during autumn, in the western part of the catchment. These overestimations in some cases exceeded 50% in terms of seasonal subcatchment relative accumulated volume bias, but generally the bias was within +/- 20%. The precipitation data from the different sources were used to drive the HBV model, set up and calibrated for two stations in Giman, both for continuous simulation during 2002 and for forecasting of the spring flood peak. In summer, autumn and winter all sources agreed well. In spring H22 overestimated the accumulated runoff volume by similar to 50% and peak discharge by almost 100%, owing to both overestimated snow depth and precipitation during the spring flood. PTH overestimated spring runoff volumes by similar to 15% owing to overestimated winter precipitation. The results demonstrate how biases in precipitation estimates may exhibit a substantial space-time variability, and may further become either magnified or reduced when applied for hydrological purposes, depending on both temporal and spatial variations in the catchment. Thus, the uncertainty in precipitation estimates should preferably be specified as a function of both time and space.

Assessing hydrological effects of global climate change at local scales is important for evaluating future hazards to society. However, applying climate model projections to local impact models can be difficult as outcomes can vary considerably between different climate models, and including results from many models is demanding. This study combines multiple climate model outputs with hydrological impact modelling through the use of response surfaces. Response surfaces represent the sensitivity of the impact model to incremental changes in climate variables and show probabilies for reaching a priori determined thresholds. Response surfaces were calculated using the HBV hydrological model for three basins in Sweden. An ensemble of future climate projections was then superimposed onto each response surface, producing a probability estimate for exceeding the threshold being evaluated. Site specific impacts thresholds were used where applicable. Probabilistic trends for future change in hazards or potential can be shown and evaluated. It is particularly useful for visualising the range of probable outcomes from climate models and can easily be updated with new results as they are made available.

As the risk of a forest fire is largely influenced by weather, evaluating its tendency under a changing climate becomes important for management and decision making. Currently, biases in climate models make it difficult to realistically estimate the future climate and consequent impact on fire risk. A distribution-based scaling (DBS) approach was developed as a post-processing tool that intends to correct systematic biases in climate modelling outputs. In this study, we used two projections, one driven by historical reanalysis (ERA40) and one from a global climate model (ECHAM5) for future projection, both having been dynamically down-scaled by a regional climate model (RCA3). The effects of the post-processing tool on relative humidity and wind speed were studied in addition to the primary variables precipitation and temperature. Finally, the Canadian Fire Weather Index system was used to evaluate the influence of changing meteorological conditions on the moisture content in fuel layers and the fire-spread risk. The forest fire risk results using DBS are proven to better reflect risk using observations than that using raw climate outputs. For future periods, southern Sweden is likely to have a higher fire risk than today, whereas northern Sweden will have a lower risk of forest fire.