1. The 21 st century changes in the Arctic sea ice cover as a function of its present state: what can we learn from CMIP5 models ? T. Fichefet, F. Massonnet, G. Philippon-Berthier, C. Bitz, M. Holland, H. Goosse, P. -Y. Barriat 1.… cannot be analyzed on common time windows, 2.… cannot be constrained by current trends (not robust), 3.… have similar long-term slopes  importance of the mean state, 4.… are shifted in time but show resembling patterns. Take home messages CMIP5 projections of summer Arctic sea ice 2. What will the September 2060 ice extent be? CMIP5 setup 18 Earth System and General Circulation Models. 2 Representative Concentration Pathways (RCP4.5/8.5, Moss et al., 2010 ). Arctic sea ice only, work on each model grid. « SSIE »=September sea ice extent. Multi-model mean NSIDC (Fetterer et al., 2002) Arctic Sept. ice extent - RCP4.5Arctic Sept. ice extent - RCP8.5 1. 1979-2010 mean and trends In 2060 (an example date), some models are already at near ice-free conditions, other are still losing ice. It does not make sense to compare models at that time and, even worse, to look at the multi-model mean! For these reasons, there are no systematic and clear relationships between initial state (extent, thickness,…) and future changes of September extent, because the models are not synchronized. Instead of looking at changes over a certain time period, we propose to look at the time taken to reach a certain threshold extent. Reasonable simulated mean extent in summer. Underestimation of the trend over 1979-2010 (OK over 1979-2006, improvement w.r.t. CMIP3 on that period (Stroeve et al., 2007)). Members clustered for the mean, scattered for the trends. member mean of members member mean of members Obs. ± 2σ uncertainty interval Multi-model mean 3. How long does it take to drop below 1 million km² ? The time taken to reach a given extent is a linear function of the initial summer extent. Yet apparently trivial, this approach has the advantage to compare models when they have similar mean states, and not at a given time. Year at which September extent < 1 million km² The relationships are valid for the two RCPs, other initial variables, and other thresholds. September sea ice extent is dropping below a given threshold earlier in the 21st century when: the initial SSIE is lower, the initial sea ice cover is thinner on average, the extent covered by thin ice (<0.5m) is larger, the amplitude of the seasonal cycle of sea ice extent is larger. RCP8.5 Fig. 4: Corrrelations (y-axis), across the CMIP5 models, between the year of low ice extent (YLE), i.e. the year at which SSIE drops below a given extent (θ, x- axis), and the initial mean state over 1979-2010: SSIE (thick+bullet lines), annual mean ice thickness (thick dashed), September thin ice (<0.5m) extent (thin), and amplitude of the seasonal cycle of ice extent (thin dashed). The procedure is repeated for RCP4.5 and RCP8.5. 1979-2010 mean September sea ice extent (10 6 km²) 4. Why should one trust models with a good mean state?Additional info References -F. Fetterer, K. Knowles, W. Meier, M. Savoie, Sea ice index (electronic reference) (2002) http://nsidc.org/data/docs/noaa/g02135_seaice_index/ http://nsidc.org/data/docs/noaa/g02135_seaice_index/ -R. J. Moss et al., The next generation of scenarios for climate change research and assessment, Nature, 2010. -J. Stroeve, M. M. Holland, W. meier, T. Scambos, M. Serreze, Arctic sea ice decline: faster than forecast, GRL, 2007 -J. E. Kay, M. M. Holland, A. Jahn, Interannual to multidecadal Arctic sea ice extent trends in a warming world, GRL, 2011. Affiliations TF, FM, GPB, HG, PYB: Georges Lemaître Centre for Earth and Climate Research, Earth and Life Institute, Université catholique de Louvain, Belgium CB: Department of Atmospheric Sciences, University of Washington, Washington MH: National Center for Atmospheric Research, Colorado Contacts francois.massonnet@uclouvain.be www.climate.be/u/fmasson thierry.fichefet@uclouvain.be Because the CMIP5 models have a similar long-term trend of SSIE (see frames 2 and 3), but scattered short-term trends (frame 1), it is likely that trends of SSIE in each model must at some point adjust to the common long-term trend. This is achieved by a rapid loss of ice, at a timing peculiar to each model (indicated by the vertical dashed lines, Figure on the left), but at a common, critical SSIE (≈ 3 million km²). Given that the time to reach this critical extent is a function of the initial mean state (frame 3), we recommend evaluating models on their mean state. According to the 6 models selected on the left, SSIE permanently drops below 1 million km² between 2049 (earlier model) and 2077 (later model), under RCP8.5. Note: for RCP4.5, similar conclusions hold for models with few ice now (the others don’t reach 3 million km² before 2100). -2 0 1 -2 0 1 Trend over the previous 32 years (10 6 km²/10y) 21001900 20002100 1900 200021001900 2000 Running trends of models with SSIE or amplitude of seasonal cycle extent in the range of observations Running trends of other models -2 0 1 -2 0 1 -2 0 1 -2 0 1 On the contrary, the 1979-2010 trends should not be used to constrain projections: for current, observed mean SSIE, these are too volatile statistics (see the members of the same models, frame 1), consistently with Kay et al., 2011. If the observed trends happen to be at the tail of the distribution due to the high natural variability, then we will reject good models for wrong reasons. Figure: Running trends of CMIP5 models SSIE under historical + RCP8.5 forcings. EC-Earth CCSM4 HadGEM2-CCIPSL-CM5A-MR MPI-ESM-LR IPSL-CM5A-LR bcc-csm1-1 CSIRO-Mk6-3-0 MRI-CGCM3 MIROC5 GISS-E2-R CNRM-CM5 NorESM1-M GFDL-ESM2M CanESM2 inmcm4 HadGEM2-ES Inter-model (x) correlation: 0.82 -2 0 1 Trend over the previous 32 years (10 6 km²/10y)