Unique and independent historical observations, carried out in the central Arctic during the early twentieth century warming (ETCW) period, were used to evaluate the older (20CRv2) and newer (20CRv2c) versions of the 20th Century Reanalysis and the HIRHAM5 regional climate model. The latter can reduce several biases compared to its forcing data set (20CRv2) probably due to higher horizontal resolution and a more realistic cloud parameterization. However, low-level temperature and near-surface specific humidity agree best between 20CRv2c and the surface-based observations. This better performance results from more realistic lower boundary conditions for sea ice concentration and sea surface temperature, but it is limited mainly to polar night. Although sea level pressures are very similar, the vertical stratification and baroclinicity change in the transition from 20CRv2 to 20CRv2c. Compared to observed temperature profiles, the systematic cold bias above 400 hPa remains almost unchanged indicating an incorrect coupling between the planetary boundary layer and free troposphere. In addition to surface pressures, it is therefore recommended to assimilate available vertical profiles of temperature, humidity and wind speed. This might also reduce the large biases in 10 m wind speed, but the reliability of the sea ice data remains a great unknown.

At present. when high particulate matter (PM) concentrations in ambient air cause thousands o Ipremature deaths in Europe and global climate change is becoming the most critical issue in environmental protection, the state-of-the-science air quality and climate models constitute an essential research as well as decision support tools. Recently the great progress has been achieved in this research area. The present paper presents the goals and tools lor Air Quality (AQ) Modeling, and gives overview of' current challenges. including the meteurological. chemistry and climate modeling. The main emphasis is given to the regulatory and the Eulerian grid models. the latter arc currently operating as so called off-line or on-line modeling systems. The issues conncctccl with model implementation and validation is presented as well. finally, the conclusions arc drawn and rccornmcndations lor further development and integration ofAQ and climate modeling in Poland arc presented.