Modelling the vertical structure of the atmospheric boundary layer over Arctic fjords in Svalbard

• Published in: Quarterly Journal of the Royal Meteorological Society
• Main Authors: Kilpeläinen, T., Vihma, T., Manninen, M., Sjöblom, A., Jakobson, E., Palo, T., Maturilli, M.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2012
• Subjects: Sea ice; Svalbard
• Online Access: http://dx.doi.org/10.1002/qj.1914; https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Fqj.1914; https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/qj.1914

Abstract The vertical structure of the atmospheric boundary layer (ABL), simulated with the mesoscale model Weather Research and Forecasting (WRF) as well as with its polar optimized version Polar WRF, was compared to tethered balloon soundings and mast observations taken in March and April 2009 from two Arctic fjords in Svalbard. From twelve short (48 h) simulations, the Quasi‐Normal Scale Elimination scheme for the ABL and the NOAH land surface scheme for the surface were found to perform best and were selected for one long (16 day) simulation. The differences in performance of the standard WRF and Polar WRF were marginal. A warm bias, especially near the surface, was found in the modelled temperature profiles related to underestimated temperature inversion strength and depth. The modelled humidity inversions were generally deeper but weaker than the observed, and often occurred independently of temperature inversions. The largest errors in temperature and humidity occurred under high pressure conditions. Multiple temperature and humidity inversions were usually not captured by WRF. Compared to the compact sea ice east of Svalbard, the modelled temperature and humidity inversions were weaker and less frequent over the fjords. The biases in modelled wind speed profiles were closely related to low‐level jets (LLJs); the modelled LLJs were stronger and deeper, and typically located at higher altitudes than the observed LLJs. Errors in the near‐surface variables were notably reduced by applying post‐processing equations based on other modelled variables. Copyright © 2012 Royal Meteorological Society