The Importance of Representing Mixed-Phase Clouds for Simulating Distinctive Atmospheric States in the Arctic
2014 (English)In: Journal of Climate, ISSN 0894-8755, E-ISSN 1520-0442, Vol. 27, no 1, 265-272 p.Article in journal (Refereed) Published
Observations from the Surface Heat Budget of the Arctic Ocean experiment (SHEBA) suggest that the Arctic Basin is characterized by two distinctly different preferred atmospheric states during wintertime. These states appear as two peaks in the frequency distribution of surface downwelling longwave radiation (LWD), representing radiatively clear and opaque conditions. Here, the authors have investigated the occurrence and representation of these states in the widely used ECMWF Interim Re-Analysis (ERA-Interim) dataset. An interannually recurring bimodal distribution of LWD values is not a clearly observable feature in the reanalysis data. However, large differences in the simulated liquid water content of clouds in ERA-Interim compared to observations are identified and these are linked to the lack of a radiatively opaque peak in the reanalysis. Using a single-column model, dynamically controlled by data from ERA-Interim, the authors show that, by tuning the glaciation speed of supercooled liquid clouds, it is possible to reach a very good agreement between the model and observations from the SHEBA campaign in terms of LWD. The results suggest that the presence of two preferred Arctic states, as observed during SHEBA, is a recurring feature of the Arctic climate system during winter [December–March (DJFM)]. The mean increase in LWD during the Arctic winter compared to ERA-Interim is 15 W m−2. This has a substantial bearing on climate model evaluation in the Arctic as it indicates the importance of representing Arctic states in climate models and reanalysis data and that doing so could have a significant impact on winter ice thickness and surface temperatures in the Arctic.
Place, publisher, year, edition, pages
2014. Vol. 27, no 1, 265-272 p.
Meteorology and Atmospheric Sciences
IdentifiersURN: urn:nbn:se:su:diva-99962DOI: 10.1175/JCLI-D-13-00271.1ISI: 000329276000017OAI: oai:DiVA.org:su-99962DiVA: diva2:689866
FunderSwedish e‐Science Research Center
Swedish e-Science Research Centre (SERC2014-01-222014-01-222014-01-31Bibliographically approved