Climate Dynamics

, Volume 43, Issue 1–2, pp 289–303 | Cite as

Mixed-phase clouds cause climate model biases in Arctic wintertime temperature inversions



Temperature inversions are a common feature of the Arctic wintertime boundary layer. They have important impacts on both radiative and turbulent heat fluxes and partly determine local climate-change feedbacks. Understanding the spread in inversion strength modelled by current global climate models is therefore an important step in better understanding Arctic climate and its present and future changes. Here, we show how the formation of Arctic air masses leads to the emergence of a cloudy and a clear state of the Arctic winter boundary layer. In the cloudy state, cloud liquid water is present, little to no surface radiative cooling occurs and inversions are elevated and relatively weak, whereas surface radiative cooling leads to strong surface-based temperature inversions in the clear state. Comparing model output to observations, we find that most climate models lack a realistic representation of the cloudy state. An idealised single-column model experiment of the formation of Arctic air reveals that this bias is linked to inadequate mixed-phase cloud microphysics, whereas turbulent and conductive heat fluxes control the strength of inversions within the clear state.


Arctic Boundary layer Turbulence Temperature inversion 



We are grateful to Tiina Kippeläinen for the inspiration to parts of this study, Anthony del Genio for information on the GISS model, Tongwen Wu for information on the BCC-CSM-1-1 model, Suvarchal Kumar Cheedela for developing and helping with the single-column version of ECHAM6 and Bjorn Stevens for helpful comments and discussions. Comments by Dirk Notz helped to improve the clarity of the manuscript. We are grateful to two anonymous reviewers for concise and helpful comments that enabled us to substantially improve the present paper. We thank the investigators involved in the collection and processing of SHEBA and ARM observations for making those datasets available. ERA40 and ERA-interim reanalyses data have been obtained from the ECMWF data server. The HadCRUT3v dataset has been provided by the Climatic Research Unit at the University of East Anglia. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP5, and we thank the climate modeling groups (listed in Table 1) for producing and making available their model output. Plots in this paper have been generated using NCL (UCAR/NCAR/CISL/VETS 2012) provided by NCAR. Brian Medeiros acknowledges support by the Office of Science (BER), U.S. Department of Energy. NCAR is sponsored by the National Science Foundation.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Felix Pithan
    • 1
    • 2
  • Brian Medeiros
    • 3
  • Thorsten Mauritsen
    • 1
  1. 1.Max-Planck-Institute for MeteorologyHamburgGermany
  2. 2.International Max Planck Research School on Earth System ModellingHamburgGermany
  3. 3.National Center for Atmospheric ResearchBoulderUSA

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