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The conventional climate datasets based on observations only are a widely used source of information for climate and hydrology. On the Norwegian mainland, the seNorge datasets of daily mean temperature and total precipitation amount constitute a valuable meteorological input for snow- and hydrological simulations which are routinely conducted over such a complex and heterogeneous terrain. A new seNorge version (seNorge2) has been released recently and to support operational applications for civil protection purposes, it must be updated daily and presented on a high-resolution grid (1 km of grid spacing). The archive goes back to 1957. The seNorge2 statistical interpolation schemes can provide high-resolution fields for applications requiring long-term datasets at regional or national level, where the challenge is to simulate small-scale processes often taking place in complex terrain. The statistical schemes build upon classical spatial interpolation methods, such as Optimal Interpolation and successive-correction schemes, and introduce original approaches. For both temperature and precipitation, the spatial interpolation exploits the concept of (spatial) scale-separation and the first-guess field is derived from the observed data. Furthermore, the geographical coordinates and the elevation are used as complementary information. The evaluation of the seNorge2 products is presented both from a general point of view, through systematic cross-validations, and specifically as the meteorological input in the operational model chains used for snow- and hydrological simulations. The seNorge snow model is used for simulation of snow fields and the DDD (Distance Distribution Dynamics) rainfall-runoff model is the hydrological model used. The evaluation points out important information for the future seNorge2 developments: the daily mean temperature fields constitute an accurate and precise dataset, on average the predicted temperature is an unbiased estimate of the actual temperature and its precision (at grid points) varies between 0.8 °C and 2.4 °C; the daily precipitation fields provide a reasonable estimate of the actual precipitation, the cross-validation shows that on average the precision of the estimates (at grid points) is about ±20 %, though a systematic underestimation of precipitation occurs in data-sparse areas and for intense precipitation. Both the seNorge snow and the DDD models have been able to make profitable use of seNorge2, partly because of the automatic calibration procedure they incorporate for precipitation. The dataset described in this article is available for public download at http://doi.org/10.5281/zenodo.845733. [ABSTRACT FROM AUTHOR]

Observed trends in annual maximum snow depth (SD) in Norway are analyzed and examined in the context of changes in winter climate from 1961 until today. Trends are evaluated for the 50-year period (1961-2010) and for three 30-year periods (1961-1990,1971-2000,1981-2010). The analyzed dataset is the most extensive and geographically representative for the country so far, and the analysis gives an up-to-date picture of the recent development in snow accumulation, in regions characterized by colder winter climate long-term trends are found to be positive in general, while short-term trends shift from strongly positive in the first period to predominantly negative in the last period. Variation in SD is here mainly linked to variation in precipitation. In regions of warmer winter climate variation in SD is dominated by temperature, and long-term trends are mainly negative. Short-term trends start out weak overall in the first period but become strongly negative most places in the last period. It is likely that, although more and more regions in Norway will experience declining maximum annual SD in a projected wetter and warmer future climate, some inland and higher mountain regions may still accumulate more snow in the coming decades. [ABSTRACT FROM AUTHOR]

Freshwater ice plays an important role in physical, biological, and chemical processes affecting cold-region lakes. To examine the potential magnitude of climate-change-related impacts in ice-cover characteristics, particularly its formation, duration, breakup, thickness, and structural composition over North America, this study employs a one-dimensional, process-based, lake simulation model, 'MyLake' (Multi-year simulation model for Lake thermo- and phytoplankton dynamics). The model is first calibrated and validated for Baker Lake, Nunavut, Canada, and then used to simulate patterns of ice conditions using a set of hypothetical lakes of varying depth (5, 20, and 40 m) located at a 2° latitude/longitude grid pattern for the major cold-region portion of North America between 40 and 75° latitude. The model is driven by gridded atmospheric forcing data from the North American Regional Reanalysis (NARR) and the Canadian Regional Climate Model (CRCM) with projections of future climate corresponding to the SRES A2 emissions scenario. The NARR-based simulation results for the period 1979-2006 are consistent with observations of lake-ice thickness and phenology obtained from the Canadian Ice Database. The CRCM-based lake-ice simulation results for the baseline (1961-1990) and future (2041-2070) time periods indicate that projected air-temperature warming will advance break-up by 10-20 days and delay freeze-up by 5-15 days, thereby reducing lake-ice duration by about 15-35 days. Lake depth is also found to have significant influence on lake-ice freeze-up dates and maximum thicknesses but not on the break-up dates. Such changes are accompanied by a reduction in ice thickness of 10-30 cm. Cover composition is also altered as a result of changes in ice thickness and snow loading resulting in somewhat greater thicknesses of white-ice (1-5 cm) over most areas except towards the east and west coasts, as well as more southerly latitudes where it slightly decreased. Implications of such cover changes are also discussed. Copyright © 2011 Royal Meteorological Society and Crown in the right of Canada. [ABSTRACT FROM AUTHOR]

Multimedia environmental fate models are useful tools to investigate the long-term impacts of remediation measures designed to alleviate potential ecological and human health concerns in contaminated areas. Estimating and communicating the uncertainties associated with the model simulations is a critical task for demonstrating the transparency and reliability of the results. The Extended Fourier Amplitude Sensitivity Test (Extended FAST) method for sensitivity analysis and Bayesian Markov chain Monte Carlo (MCMC) method for uncertainty analysis and model calibration have several advantages over methods typically applied for multimedia environmental fate models. Most importantly, the simulation results and their uncertainties can be anchored to the available observations and their uncertainties. We apply these techniques for simulating the historical fate of polychiorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) in the Grenland fjords, Norway, and for predicting the effects of different contaminated sediment remediation (capping) scenarios on the future levels of PCDD/Fs in cod and crab therein. The remediation scenario simulations show that a significant remediation effect can first be seen when significant portions of the contaminated sediment areas are cleaned up, and that increase in capping area leads to both earlier achievement of good fjord status and narrower uncertainty in the predicted timing for this. [ABSTRACT FROM AUTHOR]