Science Background

The Arctic is one of the most sensitive regions in the world to climate change. It is warming more rapidly than anywhere else on Earth – at about twice the global average rate (Solomon et al. 2007). This is a result of various feedback processes collectively referred to as polar amplification. At the same time, our ability to reproduce Arctic climate within numerical models, and to predict its future change suffer greater uncertainties than almost anywhere else in the world (Meehl et al. 2000).

One of the largest single sources of uncertainty within climate models is the representation of clouds (Solomon et al. 2007); this is particularly so in the Arctic where clouds are the dominant factor controlling the surface energy budget, affecting both the infra red (longwave, year-round) and solar (shortwave, summer only) radiation budgets.The fractions of solar radiation that are reflected by the cloud or pass through and reach the surface depend upon both the thickness of the cloud, and the number and size distribution of liquid water droplets and ice crystals in the cloud. For infra red radiation the cloud is also an emitter – radiating heat upwards and downwards – as well as an absorber of radiation from above and below. Uncertainty in predicting the surface energy budget results in problems predicting sea ice extent – the recent decrease of which is perhaps the most prominent manifestation of warming in the Arctic – since it is by far the largest factor controlling ice melt in summer, and freeze-up in winter (Steele et al. 2010).

The properties of cloud droplets and ice crystals depend upon the size, number, and chemistry of the aerosol particles on which they form – the cloud condensation nuclei (CCN) and ice nuclei (IN). There is some evidence that changes in aerosol have made a significant contribution to warming in the Arctic (Shindell & Faluvegi, 2009), probably through their influence on cloud radiative properties. Global models of aerosol processes struggle to reproduce the observed aerosol distributions and their seasonal cycles (Korhonen et al. 2008).

Low level clouds such as stratus and stratocumulus also have a strong influence on turbulent mixing within the lower atmosphere, affecting the surface heat and moisture exchange and the extent to which the surface and clouds are connected, and hence the transport of aerosol from surface sources up into cloud. The thermodynamic and turbulent structure of the lower atmosphere influence the cloud properties in turn – the two form a tightly coupled system, and errors in the representation of either within computer models will result in misrepresentation of the other.

Although climate models generally predict a strong warming in the Arctic, there are large differences between different models, and most if not all models fail to reproduce many details of even current climate. Clouds in particular are poorly represented – their large altitude, thermodynamic structure, microphysical and optical properties all suffer significant problems (Birch et al. 2009; Tjernström et al. 2008) biasing modelled radiative fluxes (Walsh et al. 2002; Tjernström et al. 2005) and hence the surface energy balance. The details of cloud droplet distributions, turbulent mixing, and other small-scale processes must be parameterized within climate models. The parameterizations used are usually derived from measurements obtained at much lower latitudes and are not necessarily appropriate for the Arctic. Most models apply the same parameterizations everywhere, and thus fail to account for potentially important differences between very different environments. For example, the ice nucleating aerosol found in the Arctic differ substantially from those found at lower latitudes (Prenni et al. 2007). The Arctic is a very clean environment, with often very low aerosol concentrations (Heintzenberg et al. 2006). This results in Arctic stratocumulus clouds having rather different drop size distributions, and thus radiative and turbulent properties, from typical mid-latitude marine stratocumulus. A failure to account for these differences in global models can have a dramatic impact on model’s representation of the cloud and lower atmosphere (Birch et al. 2012). In order to model the Arctic atmosphere accurately a more detailed treatment of aerosols and aerosol/cloud and cloud/boundary-layer interactions are required in future climate simulations.

References

Birch, C. E., I. M. Brooks, M. Tjernström, S. F. Milton, P. Earnshaw, S. Söderberg, P. O. G. Persson, 2009: The performance of a global and mesoscale model over the central Arctic Ocean during late summer. J. Geophys. Res. 114, D13104, doi:10.1029/2008JD010790.

Birch, C. E. I. M. Brooks, M. Tjernström, M. Shupe, S. F. Milton, P. Earnshaw, T. Mauritsen, J. Sedlar, P. Ola G. Persson, and C. Leck, 2012: Modelling atmospheric structure, cloud and their response to CCN in the central Arctic: ASCOS case studies. Atmos. Chem. Phys. 12, 3419–3435, doi: 10.5194/acp-12-3419-2012

Heintzenberg, J., C. Leck, W. Birmili, B. Wehner, and M. Tjernstroöm, 2006: Aerosol number-size distributions during clear and fog periods in the summer high Arctic: 1991, 1996, and 2001, Tellus, 58B, 41-50.

Korhonen, H; K. S. Carslaw, D. V. Spracklen, D. A. Ridley, J. Strom, 2008: A global model study of processes controlling aerosol size distributions in the Arctic spring and summer, J. Geophys. Res, 113. doi:10.1029/2007JD009114.

Meehl, G. A., G. J. Boer, C. Covey, M. Latif, and R. J. Stouffer, 2000: The coupled model intercomparison project. Bull. Amer. Meteorol. Soc. 81, 313-318.

Prenni, A.J., J.Y. Harrington, M. Tjernström, P.J. DeMott, A. Avramov, C.N. Long, S.M. Kreidenweis, P.Q. Olsson, and J. Verlinde, 2007: Can ice-nucleating aerosols affect Arctic seasonal climate? Bull. Amer. Meteorol. Soc., 88, 541-550, doi:10.1175/BAMS-88-4-541.

Shindell, D., and G. Faluvegi, 2009: Climate response to regional radiative forcing during the twentieth century, Nature Geosci. 2, 294-300.

Solomon, S., D. Qin, M. Manning, M. M., K. Averyt, M. M. B. Tignor, H. L. Miller, and Z. Chen (Eds.) 2007: Climate Change 2007: The Physical Science Basis, IPCC, Cambridge University Press, Cambridge, UK.

Steele, M., J. Zhang, and W. Ermold, 2010: Mechanisms of summertime upper Arctic Ocean warming and the effect on sea ice melt, J. Geophys. Res., 115, C11004, doi:10.1029/2009JC005849

Tjernström, M., M. Žagar, G. Svensson, J. J. Cassano, S. Pfeifer, A. Rinke, Kl. Wyser, K. Dethloff, C. Jones, T. Semmler, M. Shaw , 2005: Modeling the Arctic boundary layer: An evaluation of six ARCMIP regional-scale models using data from the SHEBA project, Boundary Layer Meteorol., 117, 337– 381.

Tjernström, M., J. Sedlar, and M. D. Shupe, 2008: How well do regional climate models  reproduce radiation and clouds in the Arctic?, J. App. Meteorol. Clim., 47, 2405–2422.

Walsh, J., V. Kattsov, W. Chapman, V. Govorkova, and T. Pavlova, 2002: Comparison of Arctic simulations by uncoupled and coupled global models. J. Climate. 15, 1429-1446.

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