A new detailed long-term hydrometeorological dataset: First results of extreme characteristics estimations for the Russian Arctic seas

Abstract

A detailed long-term hydrometeorological dataset for Russian Arctic seas has been created using hydrodynamic modelling via a regional nonhydrostatic atmospheric model, COSMO-CLM, for 1980-2016. This paper presents evaluation techniques of long-term experiments and a preliminary analysis of the dataset. The experiments are conducted for a model domain including the Barents, Kara, and Laptev Seas, with a 12 km grid. Many test experiments have been evaluated to determine the best model configuration, which includes a new model version 5.06, a "spectral nudging"technique, and an ERA-Interim reanalysis as forcing data. A reinitialization scheme of additional "assimilation"of soil properties reanalysis data is suggested to avoid possible errors, particularly due to soil draining in the model. The primary assessment has shown that a wind speed climatology based on COSMO-CLM experiments is very close to the ERA-Interim pattern, besides many details of wind speed distributions in different Arctic regions. At the same time, the high wind speed frequencies based on the COSMO-CLM data are increased compared to the ERA-Interim, especially over the Barents Sea, the Arctic islands (Novaya Zemlya), and some seacoasts and mainland areas. Regional details are manifested in a wind speed increase up to 0.5 - 1 m/s and marked well for large lakes, orography, as well as over the polar region. At the same time, there is a mesoscale wind speed decrease compared to the ERA-Interim data for the Pechora and Laptev Sea coasts and the New Siberian islands. Comparison of two periods (1980 - 1990 and 2010 - 2016) has shown that the spatial distributions of high wind speed frequencies are very similar, but there are some detailed differences. The wind speed frequencies above 17.2 and 20.8 m/s decreased in the last decade over Novaya Zemlya, southwest of Svalbard, Northern Atlantic, and the middle Siberia continent; at the same time, it increased between Franz Josef Land and Severnaya Zemlya and in the polar regions. The preliminary assessment of the results has revealed that the dataset is promising for analysis of regional wind speed regimes and estimation of severe wind speed risks. The next step of this work is to run simulations on a 3-km grid and collaborate with the scientific community to sufficiently use this dataset.