THE ÅS TEMPERATURE SERIES IN SOUTHERN NORWAY–HOMOGENEITY TESTING AND CLIMATE ANALYSIS

Homogeneity is important when analyzing climatic long-term time series. This is to ensure that the variability in the time series is not affected by changes such as station relocations, instrumentation changes and changes in the surroundings. The subject of this study is a long-term temperature series from the Norwegian University of Life Sciences at Ås in Southern Norway, located in a rural area about 30 km south of Oslo. Different methods for calculation of monthly mean temperature were studied and new monthly means were calculated before the homogeneity testing was performed. The statistical method used for the testing was the Standard Normal Homogeneity Test (SNHT) by Hans Alexandersson. Five breaks caused by relocations and changes in instrumentation were identified. The seasonal adjustments of the breaks lay between –0.4°C and +0.5°C. Comparison with two other homogenized temperature series in the Oslo fjord region showed similar linear trends, which suggests that the long-term linear temperature trends in the Oslo fjord region are not much affected by spatial climate variation

Arctic rock coast responses under a changing climate

It has been widely reported that Arctic sea ice has decreased in both extent and thickness, coupled with steadily rising mean annual temperatures. These trends have been particularly severe along the rock coast of southern Svalbard. Concerns have been raised over the potential for higher energy storms and longer ice-free open water seasons to increase the exposure of Arctic coasts, and consequently the concentration of infrastructure critical to Arctic community survival, to enhanced rates of erosion. Here we present and apply innovative remote sensing, monitoring and process analyses to assess the impact of recent coastal climatic changes. High resolution analyses demonstrate that the small scale (<0.001 m3) changes that are rarely considered quantitatively exhibit geomorphic responses distinct from those of larger, more readily detected cliff failures. We monitor temperature depth profiles in both the shore platform and the cliff face to show rock sensitivity over time to both global and local influences. The results demonstrate the efficacy of thermal processes on Arctic rock cliffs relative to platforms, and may hold implications for understanding strandflat development rates. New three-dimensional thermography (thermal mapping) and process zone characterisation has been used to spatially assess the sensitivity of Arctic rock coast responses to contemporary processes on deglaciating coasts. Through the spatial and temporal analyses of key geomorphic behaviour zones and comparison over a range of sites, the complex and changing interplay between subaerial weathering and cryogenic and intertidal processes has been highlighted. These data challenge long standing assumptions over the future of Arctic rock coasts and identify new, focused lines of enquiry on the decline in cryogenic processes and understanding the sensitivity of Arctic rock coasts to climatic changes

Air temperature variations and gradients along the coast and fjords of western Spitsbergen

Daily temperature measurements from six meteorological stations along the coast and fjords of western Spitsbergen have been digitized and quality controlled in a Norwegian, Russian and Polish collaboration. Complete daily data series have been reconstructed back to 1948 for all of the stations. One of the station’s monthly temperature series has previously been extended back to 1898 and is included in this study. The long-term series show large temperature variability on western Spitsbergen with colder periods in the 1910s and 1960s and warmer periods in the 1930s, 1950s and in the 21st century. The most recent years are the warmest ones in the instrumental records. There is a positive and statistically significant trend in the annual times series for all of the stations; however, the strongest warming is seen in winter and spring. For the period 1979-2015, the linear trends range from 1.0 to 1.38°C/decade for the annual series and from 2.0 to 2.38°C/decade in winter. Threshold statistics demonstrate a decrease in the number of cold days per year and an increase in the number of warm days. A decreasing inter-annual variability is observed. In winter, spring and autumn, the stations in the northernmost areas of west Spitsbergen and in the innermost parts of Isfjorden are the coldest ones. In summer, however, the southernmost station is the coldest one

Measured and modeled historical precipitation trends for Svalbard

Abstract Precipitation plays an important role in the Arctic hydrological cycle, affecting different areas like the surface energy budget and the mass balance of glaciers. Thus, accurate measurements of precipitation are crucial for physical process studies, but gauge measurements in the Arctic are sparse and subject to relocations and several gauge issues. From Svalbard, we analyze precipitation trends at six weather stations for the last 50–100 years by combining different observation series and adjusting for inhomogeneities. For the past 50 years, the measured annual precipitation has increased by 30%–45%. However, precipitation measurements in the cold and windy climate are strongly influenced by gauge undercatch. Correcting for undercatch reduces the trend values by 10% points, since the fraction of solid precipitation has decreased and undercatch is larger for solid precipitation. Thus, precipitation corrected for undercatch should be used to study “true” precipitation trends in the Arctic. Precipitation over Svalbard has been modeled by downscaling reanalysis data to a spatial resolution of 1 km. In general, the modeled annual precipitation is higher (13%–175%) than the measured values and mainly higher than the precipitation corrected for undercatch. Although the model resolves orographic effects on a regional scale, the downscaling is not able to reproduce local orographic enhancement for onshore winds, nor local effects of rain shadow. The downscaled dataset explains approximately 60% of the interannual precipitation variability. The model-based trends during 1979–2018 are positive, but weaker (~4% decade −1 ) than the observed (~8% decade −1 ) trends