Evaluating Arctic warming mechanisms in CMIP5 models
- 703 Downloads
Arctic warming is one of the most striking signals of global warming. The Arctic is one of the fastest warming regions on Earth and constitutes, thus, a good test bed to evaluate the ability of climate models to reproduce the physics and dynamics involved in Arctic warming. Different physical and dynamical mechanisms have been proposed to explain Arctic amplification. These mechanisms include the surface albedo feedback and poleward sensible and latent heat transport processes. During the winter season when Arctic amplification is most pronounced, the first mechanism relies on an enhancement in upward surface heat flux, while the second mechanism does not. In these mechanisms, it has been proposed that downward infrared radiation (IR) plays a role to a varying degree. Here, we show that the current generation of CMIP5 climate models all reproduce Arctic warming and there are high pattern correlations—typically greater than 0.9—between the surface air temperature (SAT) trend and the downward IR trend. However, we find that there are two groups of CMIP5 models: one with small pattern correlations between the Arctic SAT trend and the surface vertical heat flux trend (Group 1), and the other with large correlations (Group 2) between the same two variables. The Group 1 models exhibit higher pattern correlations between Arctic SAT and 500 hPa geopotential height trends, than do the Group 2 models. These findings suggest that Arctic warming in Group 1 models is more closely related to changes in the large-scale atmospheric circulation, whereas in Group 2, the albedo feedback effect plays a more important role. Interestingly, while Group 1 models have a warm or weak bias in their Arctic SAT, Group 2 models show large cold biases. This stark difference in model bias leads us to hypothesize that for a given model, the dominant Arctic warming mechanism and trend may be dependent on the bias of the model mean state.
KeywordsArctic amplification CMIP5 models Model bias
We would like to thank two anonymous reviewers for their helpful comments. We thank the Integrated Climate Data Center at CEN for making the ERA-40 data available and the Earth System Grid Federation for making the CMIP5 data available. We thank Silke Schubert for help with the ERA-40 data. CF acknowledges funding by the German Research Foundation (DFG) through the cluster of excellence CliSAP (EXC177). SL acknowledges NSF Grant AGS-1455577. SBF acknowledges NSF Grant AGS-1401220.
- Budyko MI, Izrael YA (1991) In: Budyko MI, Izrael YA (eds) Anthropogenic climate change, pp 277–318. Uni. Ariz. Press, TucsonGoogle Scholar
- Uppala SM, Kallberg PW, Simmons AJ, Andrae U, Da Costa Bechtold V, Fiorino M, Gibson JK, Haseler J, Hernandez A, Kelly GA, Li X, Onogi K, Saarinen S, Sokka N, Allen RP, Andersson E, Arpe K, Balmaseda MA, Beljaars ACM, Van De Berg L, Bidlot J, Bormann N, Caires S, Chevallier F, Dethof A, Dragosavac M, Fisher M, Fuentes M, Hagemann S, Holm E, Hoskins BJ, Isaksen K, Janssen PAE, Jenne R, McNally AP, Mahfouf J-F, Morcrette J-J, Rayner NA, Saunders RW, Simon P, Sterl A, Trenberth KE, Untch A, Vasiljevich D, Viterbo P, Woollen J (2005) The ERA-40 re-analysis. Q J R Meteorol Soc 131:2961–3012CrossRefGoogle Scholar