The Arctic summer atmosphere: an evaluation of reanalyses using ASCOS data

• Published in: Atmospheric Chemistry and Physics
• Main Authors: Wesslén, C., Tjernström, M., Bromwich, D. H., de Boer, G., Ekman, A. M. L., Bai, L.-S., Wang, S.-H.
• Format: Text
• Language: English
• Published: 2018
• Subjects: Arctic; Arctic Ocean; Longyearbyen; Climate change; oden
• Online Access: https://doi.org/10.5194/acp-14-2605-2014; https://www.atmos-chem-phys.net/14/2605/2014/

The Arctic has experienced large climate changes over recent decades, the largest for any region on Earth. To understand the underlying reasons for this climate sensitivity, reanalysis is an invaluable tool. The Arctic System Reanalysis (ASR) is a regional reanalysis, forced by ERA-Interim at the lateral boundaries and incorporating model physics adapted to Arctic conditions, developed to serve as a state-of-the-art, high-resolution synthesis tool for assessing Arctic climate variability and monitoring Arctic climate change. We use data from Arctic Summer Cloud-Ocean Study (ASCOS) to evaluate the performance of ASR and ERA-Interim for the Arctic Ocean. The ASCOS field experiment was deployed on the Swedish icebreaker Oden north of 87° N in the Atlantic sector of the Arctic during August and early September 2008. Data were collected during the transits from and to Longyearbyen and the 3-week ice drift with Oden moored to a drifting multiyear ice floe. These data are independent and detailed enough to evaluate process descriptions. The reanalyses captures basic meteorological variations coupled to the synoptic-scale systems, but have difficulties in estimating clouds and atmospheric moisture. While ERA-Interim has a systematic warm bias in the lowest troposphere, ASR has a cold bias of about the same magnitude on average. The results also indicate that more sophisticated descriptions of cloud microphysics in ASR did not significantly improve the modeling of cloud properties compared to ERA-Interim. This has consequences for the radiation balance, and hence the surface temperature, and illustrate how a modeling problem in one aspect of the atmosphere, here the clouds, feeds back to other parameters, especially near the surface and in the boundary layer.