The Isfjorden region at the west coast of Spitsbergen is the most easily accessible area in the Svalbard Archipelago, making it a perfect outdoor laboratory for Arctic research. Due to its location in the high Arctic together with its complex terrain, the climatic conditions vary substantially both in time and space. Based on a new high-resolution reanalysis, we present climatologies of five major atmospheric variables over the Isfjorden region during 2011–2021 with special focus on local effects. For example, we find that topographic channeling effects often lead to differences in near-surface wind speeds of several m s⁻¹ over small horizontal distances. During winter, the fjord acts as a heat and moisture island, ultimately impacting the adjacent low-elevation land areas. These land–sea gradients reverse during summer. High mountain areas surrounding the fjord experience substantially different climatic conditions, with for example, seasonal precipitation doubling from sea level to approximately 700 m. The spatial variability over the Isfjorden region is in general found to be smaller than its temporal counterpart but larger than the diurnal cycle. Besides these findings, this study furthermore demonstrates the importance of high-resolution regional atmospheric reanalyses compared to global products for the characterization of the local micro-climate over Arctic fjords and the interaction with surrounding land areas.

https://doi.org/10.1029/2022JD038011

High-accuracy reanalysis products are beneficial to the retrieval and modeling of zenith tropospheric delay (ZTD) in GNSS positioning and the study of GNSS Meteorology. The performance of meteorological parameters and ZTD derived from Copernicus Arctic Regional ReAnalysis (CARRA), ERA5 and ERA5-Land over the Greenland covering the year of 2020 are comprehensively compared and analyzed in this study. Firstly, the accuracies of the atmospheric pressure, temperature and water vapor pressure retrieved from three reanalysis data are compared with the observations obtained from 6 radiosondes and 30 automatic weather stations. The results show that the accuracy of temperature data from ERA5 pressure-level in the height range of 23–30 km is higher than that of CARRA pressure-level. The atmospheric pressure, temperature and water vapor pressure from CARRA single-level are superior to those of ERA5 single-level and ERA5-Land. Secondly, the ZTD derived from CARRA, ERA5 and ERA5-Land are compared with GNSS final ZTD data from 37 UNAVCO stations. The Root Mean Square error (RMS) values of ERA5 pressure-level ZTD is 6.7 mm, showing slightly better accuracy than CARRA pressure-level ZTD of 10.7 mm. The RMS values of ZTD derived from CARRA single-level, ERA5 single-level and ERA5-Land based on the Saastamoinen model are 11.9 mm, 13.4 mm and 22.7 mm, respectively, which indicates that CARRA single-level ZTD shows best consistency with GNSS ZTD among three single-level reanalysis data. Moreover, the reanalysis-derived ZTD show obviously poorer accuracy in warm-season than that in cold-season.

https://doi.org/10.1016/j.asr.2023.09.002

The northern Barents Sea is a cold, seasonally ice-covered Arctic shelf sea region that has experienced major warming and sea ice loss in recent decades. Here, a 2-year observational record from two ocean moorings provides new knowledge about the seasonal hydrographic variability in the region and about the ocean exchange across its northern margin. The combined records of temperature, salinity, and currents show the advection of warmer and saltier waters of Atlantic origin into the Barents Sea from the north. The source of these warmer water masses is the Atlantic Water boundary current that flows along the continental slope north of Svalbard. Time-varying southward inflow through cross-shelf troughs was the main driver of the seasonal cycle in ocean temperature at the moorings. Inflows were intensified in autumn and early winter, in some cases occurring below the sea ice cover and halocline water. On shorter timescales, subtidal current variability was correlated with the large-scale meridional atmospheric pressure gradient, suggesting wind-driven modulation of the inflow. The mooring records also show that import of sea ice into the Barents Sea has a lasting impact on the upper ocean, where salinity and stratification are strongly affected by the amount of sea ice that has melted in the area. A fresh layer separated the ocean surface from the warm mid-depth waters following large sea ice imports in 2019, whereas diluted Atlantic Water was found close to the surface during episodes in autumn 2018 following a long ice-free period. Thus, the advective imports of ocean water and sea ice from surrounding areas are both key drivers of ocean variability in the region.

https://doi.org/10.5194/os-18-1389-2022

Seasonal snow cover has important climatic and ecological implications for the ice-free regions of coastal Greenland. Here we present, for the first time, a dataset of quality-controlled snow depth measurements from nine locations in coastal Greenland with varying periods between 1997 and 2021. Using a simple modelling approach (∆snow) we estimate snow water equivalent values solely based on the daily time series of snow depth. Snow pit measurements from two locations enable us to evaluate the ∆snow model. As there is very little in-situ data available for Greenland, we then test the performance of the regional atmospheric climate model (RACMO2.3p2, 5.5 km spatial resolution) and reanalysis product (CARRA, 2.5 km spatial resolution) at the nine locations with snow observations. Using the combined information from all three data sources, we study spatio-temporal characteristics of the seasonal snow cover in coastal Greenland by the example of six ecologically relevant snow indicators (maximum snow water equivalent, melt onset, melt duration, snow cover duration, snow cover onset, snow cover end). In particular, we evaluate the ability of RACMO2.3p2 and CARRA to simulate these snow indicators at the nine different locations, perform a time series analysis of the indicators and assess their spatial variability. The different locations have considerable spatial and temporal variability in snow cover characteristics and seasonal maximum snow water equivalent (amount of liquid water stored in the snowpack)values range from less than 50 mm w.e. to greater than 600 mm w.e. The correlation coefficients between maximum snow water equivalent output from ∆snow and CARRA/RACMO are 0.73 and 0.48 respectively. Correlation coefficients are highest for maximum snow water equivalent and snow cover duration, and model and reanalysis output underestimate snow cover onset. We find little evidence of statistically significant (p < 0.05) trends at varied periods between 1997 and 2021 except for the earlier onset of snow melt in Zackenberg (−8 days/decade, p = 0.02, based upon RACMO output). While we stress the need for context-specific validation, this study suggests that in most cases snow depth or snow water equivalent output from CARRA can describe spatial-temporal characteristics of seasonal snow cover, particularly changes in melt onset and snow cover end.

https://doi.org/10.5194/egusphere-2024-1999