Deposition of black carbon (BC) aerosol in the Arctic lowers snow albedo, thus contributing to warming in the region. However, the processes and impacts associated with BC deposition are poorly ...understood because of the scarcity and uncertainties of measurements of BC in snow with adequate spatiotemporal resolution. We sampled snowpack at two sites (11 m and 300 m above sea level) at Ny‐Ålesund, Spitsbergen, in April 2013. We also collected falling snow near the surface with a windsock from September 2012 to April 2013. The size distribution of BC in snowpack and falling snow was measured using a single‐particle soot photometer combined with a characterized nebulizer. The BC size distributions did not show significant variations with depth in the snowpack, suggesting stable size distributions in falling snow. The BC number and mass concentrations (CNBC and CMBC) at the two sites agreed to within 19% and 10%, respectively, despite the sites' different snow water equivalent (SWE) loadings. This indicates the small influence of the amount of SWE (or precipitation) on these quantities. Average CNBC and CMBC in snowpack and falling snow at nearly the same locations agreed to within 5% and 16%, after small corrections for artifacts associated with the sampling of the falling snow. This comparison shows that the dry deposition was a small contributor to the total BC deposition. CMBC were highest (2.4 ± 3.0 μg L−1) in December–February and lowest (1.2 ± 1.2 μg L−1) in September–November.
Key Points
The concentrations of black carbon measured in snowpack in Spitsbergen were somewhat insensitive to the snow water equivalent
Concentrations of black carbon in falling snow and ambient air in midwinter were higher than those in fall on average
The BC concentrations in snowpack measured by this study were lower than those previously reported by factors of 2–6
Studies from recent years involving development and application of statistical downscaling models for Scandinavia (mainly Norway and Sweden) are reviewed. In most of the studies linear techniques ...were applied. Local temperature and/or precipitation were predictands in a majority of the studies. Large-scale temperature fields, either from 2 m or 850 hPa, were found to be the best predictors for local temperature, while a combination of atmospheric circulation indices and tropospheric humidity information were the best predictors for local precipitation. Statistically downscaled temperature scenarios for Scandinavia differ depending on climate model, emission scenario and downscaling strategy. There are nevertheless several common features in the temperature scenarios. The warming rates during the 21st century are projected to increase with distance from the coast and with latitude. In most of Scandinavia higher warming rates are projected in winter than in summer. For precipitation, the spread between different scenarios is larger than for temperature. A substantial part of the projected precipitation change is connected to projected changes in atmospheric circulation, which differ considerably from one model integration to another. A tendency for increased large-scale humidity over Scandinavia still implies that projections for the 21st century typically indicate increased annual precipitation. This tendency is most significant during winter. In northern Scandinavia the projections tend to show increased precipitation also during summer, but several scenarios show reduced summer precipitation in parts of southern Scandinavia. Comparisons with results from global and regional climate models indicate that both regional modeling and statistical downscaling add value to the results from the global models.
This paper addresses the role of virtual reality in addressing the specific challenge of the increasing complexity and decreasing usability when dealing with the level of detail required to model a ...zero emission neighbourhood (ZEN).1 In such neighbourhoods, there is a need to handle both 'top down' neighbourhood level data with 'bottom up' building and material level data. This can quickly become overwhelming particularly when dealing with non expert users such as planners, architects, researchers and citizens who play a key part in the design process of future ZENs. Visualisation is an invaluable means to communicate complex data in an interactive way that makes it easier for diverse stakeholders to engage in decision making early and throughout the design process. The main purpose of this work has been to make ZEN key performance indicators (KPIs) more easily comprehensible to a diverse set of stakeholders who need to be involved in the early design phase. The paper investigates how existing extended reality (XR) technologies, such as virtual reality, can be integrated with an existing dynamic LCA method in order to provide visualise feedback on KPIs in early phase design of sustainable neighbourhoods. This existing method provides a dynamic link between the REVIT Bim and the ZEB Tool using a Dynamo plugin.2 The results presented in this paper demonstrate how virtual reality can help to improve stakeholder participation in the early design phase and more easily integrate science-based knowledge on GHG emissions and other KPIs into the further development of the user-centered architectural and urban ZEN toolbox for the design and planning, operation and monitoring of ZENs. 3
A scenario from the coupled atmosphere–ocean climate model ECHAM4/OPYC3 was downscaled by empirical and dynamical methods to show projected changes in temperature (T) and precipitation (R) in Norway ...under global warming. In the empirical models, large-scaleTwas applied as a predictor forT. ForR, bothTand sea-level pressure (SLP) were applied as predictors during most of the year, while only SLP was applied during summer. The dynamical model, HIRHAM, is a regional climate model based upon HIRLAM, but with physical parametrisations from ECHAM4/OPYC3. Both approaches project from 1980–1999 to 2030–2049 an increase in annual mean temperatures of between 1 and 2.5°C in various parts of the country. The projected warming is at a minimum along the southern coast, while greater warming is projected inland and in the north. Though the differences between the approaches are not statistically significant, empirical downscaling systematically leads to a larger projected increase in annual mean temperature than dynamical downscaling does. The difference is at a maximum during winter and/or spring at localities exposed to temperature inversions. Empirical downscaling projects larger winter warming in inland valleys than at more freely exposed localities, and thus implies a reduced intensity or frequency of winter inversions. It is argued that less favourable conditions for ground inversions are consistent with the future projection of increased winter wind speeds and reduced snow-cover. For precipitation, both downscaling approaches project a statistically significant increase in the west during autumn, and in the south during winter. The only significant difference between the results is that dynamical downscaling projects increased summer precipitation in the southwest, while the empirically downscaled scenario shows no significant change. For summer precipitation the present empirical models do not include any predictor carrying the 'climate change signal', and thus the results from the dynamical downscaling are probably more realistic concerning summer precipitation.
Observations from the Norwegian Arctic show positive trends in annual mean temperatures from 1912 to the 1930s and from the 1960s to 1996. Between these periods there was a negative trend, and there ...is no statistically significant trend in the record as a whole. The present temperature is approximately the same as in the 1920s, and lower than during the 1930s and 1950s. Spring is the only season which shows a statistically significant warming from 1912 to 1996. Annual precipitation, on the other hand, has increased in the Norwegian Arctic. At Spitsbergen the measurements show a statistically significant increase in annual and in spring, summer and autumn precipitation. Monthly values of mean sea level pressure of 4 grid points were used to develop models for monthly mean temperature and monthly precipitation at Spitsbergen. During the period 1912 to 1993 the temperature model accounts for 30 to 45% of the variance in the seasonal mean temperatures. The correlation between observed and modelled values is at a minimum in the summer and at a maximum in the autumn. The precipitation model accounts for 15 to 35% of the variance in seasonal precipitation sums. The correlation between observed and modelled values is lowest in winter, when the problems with drifting and blowing snow are greatest. Even though the observed and modelled seasonal values in most cases are better correlated for temperature than for precipitation, the precipitation model accounts for more of the decadal scale variability and long-term trends. The precipitation model reproduces the observed positive precipitation trends on both a seasonal and annual basis. Concerning decadal scale variability, most of the main observed features are also modelled satisfactorily. It is concluded that the major observed features concerning decadal scale variability and trends in precipitation at Spitsbergen are connected to variability in the atmospheric circulation pattern. The temperature model reproduces reasonably well the observed positive trend during the last 3 decades of the series. The very low temperature before 1920 and the high values in the 1930s and the 1950s, on the other hand, are not modelled satisfactorily. Thus, while the temperature increase of the later decades may mainly be explained as a result of changes in advection, the temperature increases in the Norwegian Arctic from the beginning of the measurements to the 1930s cannot be explained in this way.
A study of the long-term changes of various climatic extremes was made jointly by a number of European countries. It was found that the changes in maximum and minimum temperatures follow, in broad ...terms, the corresponding well-documented mean temperature changes.
Observational series and downscaled scenarios of air temperature are used to describe long-term variations 1900-2050 in different climatic indices that are important for the living conditions in the ...Nordic Arctic (Northern Fennoscandia, Svalbard, Faeroe Islands, and the Greenland-Iceland-Norwegian Sea regions). In addition to air temperature; indices illustrating vegetation conditions (growing season), energy consumption (heating season), and frost conditions (freezing season) are studied. The analyses are based on smoothed daily temperature series deduced from monthly averages for 27 Nordic climate stations, and are focusing on conditions in the climatological 30-yr reference periods 1901-1930, 1931-1960, and 1961-1990, and the scenario period 2021-2050. Also values for two recent time periods (1976-2000 and 1990-2002) are included. The results show substantial variations in growing, heating and freezing indices in the Nordic Arctic during the 20th century. Compared to the period 1961-1990, the growing season has increased during the recent decades in large parts of the region. Projections up to 2050 indicate that the growing season may increase by 3 to 4 wk at most of the stations in the region. The heating season has been reduced during the latest decades, and the projections indicate a further reduction during the next 50 yr.
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