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1 1 Supplementary Methods Downscaling of global climate model data Global Climate Model data were dynamically downscaled by the Regional Climate Model (RCM) CLM 1 ( meanwhile renamed to COSMO-CLM) to a 3800 km x 7100 km grid as shown in Supplementary Fig. 1. To keep consistency with previous studies which successfully did reproduce polar lows 2 4, we here used the same version, CLM Initial and boundary data as used for four IPCC-AR4 scenarios (C20, B1, A1B,A2) were provided by the ECHAM5/MPI-OM 5, 6 developed at the Max Planck Institute for Meteorology, Germany. During the simulation upper level large scale information of the ECHAM5/MPI- OM was constrained by means of spectral nudging 7. Thereby, wave numbers of zonal wind speed of up to 12 and meridional wind speed of up to 4 were chosen as being constraint (corresponding to a spatial scale of approximately 700 km and more). The constraint is implemented with a vertically growing strength (α = 0.5 ) at 850 hpa and above. Calculations were executed with an integration time step of 240 seconds on a rotated grid with the north pole located at 175 E and 21.3 N. This way ECHAM5/MPI-OM output data has been dynamically scaled down to a grid cell size of about 50x 50 km 2 (corresponding to 0.44 x 0.44 ). 1

2 The three hourly three dimensional output fields of Pressure, Wind and Temperature originating from these calculations are available at the World Data Center for Climate Tracking polar lows To count polar lows we use a method which we recently developed 3 and which we applied to Regional Climate Model (RCM) output data for past decades before 4. Full RCM output fields are composed of a superposition of large, medium and small scale information. The core of our method consists of a spatial two-dimensional discrete band pass filter to separate different scales of spatial data developed for RCM evaluation purposes 12. For consistency reasons we designed our filter the same way we did for our previous studies, i.e. relative to a 72 x 72 grid. Thus we approximated the response function κ(k ) = 0 for all k 6,κ(k ) = 1 for all k [8, 15], and κ(k ) = 0 for all k 18 with a footprint of grid points and the two-dimensional wave number k. That is, phenomena on scales larger than approximately 3516 km 600km 6 and smaller than approximately 3516 km 200km are filtered out, while scales between km and 450 km are to first order retained. All the positions at which the spatially band pass filtered maritime mean sea level pressure fields fall below 1 hpa are stored, when the gradient in a distance of 100 km is larger than 0.3 hpa. In a second step positions of consecutive (3 hours) minima are merged to tracks, if their distance is smaller than 200 km (assuming a travelling speed of less than 67 km h ). Finally, further criteria applying dynamical and technical constraints are requested, 2

3 again in the same configuration as used before 3. A strength of the minimum in the filtered mean sea level pressure field below a threshold of -2 hpa once along the track, wind speed above a threshold of 13.9 m s in at least 20% of the track s positions and vdt above a threshold of 43 K once along the track are demanded. Further, the first detected position has to be 1 farther north than the last and not more than 50% land grid boxes are allowed to neighbour the track. If this last criterion is fulfilled and the minimum in the filtered field is below -6 hpa, the other criteria are overridden. It has been shown 4 that the exact values chosen as thresholds influence the exact number of polar lows, but not the statistical properties. Calculation of vertical stability vdt Vertical stability in a projected 20th century and 21st century warmed up atmosphere has been calculated by a simplified measure, the vertical temperature difference between sea surface and at 500 hpa model levels (T 500 ) denoted vdt in the following. Model projections as used for IPCC-AR4 were provided by the World Climate Research Program s (WCRP s) Coupled Model Intercomparison Project phase 3 (CMIP3) multi-model dataset 13, referred to as CMIP3 data, were applied in this study. We have used data of those CMIP3 models for which monthly mean Surface Temperature (equal to Sea Surface Temperature over ice free ocean), Sea Ice concentration and T 500 are available for all the four scenarios (C20, B1, A1B and A2). Sea ice concentration available for the ocean grids only was bilinearly remapped onto the atmosphere grid of temperatures. Monthly vdt has been calculated excluding grid 3

4 boxes of more than 10% ice coverage or more than 10% land coverage. These monthly mean vdts form the average vdt, respectively. If several realisations of a model were provided, only one realisation was used (usually run1). If data was provided by two versions of a model, data from the newer version were used. A list of models used is shown in table1. 4

5 BCCR-BCM2.0 CCSM3.T47 CGCM3 CNRM-CM3 CSIRO-Mk3.5 ECHAM5/MPI-OM GFDL-CM2.1 GISS-ER INM-CM3.0 IPSL-CM4 MRI-CGCM2.3.2 UKMO-HadCM3 Norway USA Canada France Australia Germany USA USA Russia France Japan UK Table 1: Acronyms of Global Climate Models used for deriving vdt and their countries of origin. 5

6 2 Supplementary analysis Re-analysis-observation comparison Homogeneous observation based long-term climatologies of polar lows are not yet available. We here show that the numbers of the reanalysis based climatology 4 14, 15 show qualitative similarities to observation based ones (Supplementary Fig.2). Please note, that such observation based recording might be influenced by the subjective way of tracking or lacking availability of data. Supplementary Fig. 2(a) shows that a peak PLS of polar low frequency was not only simulated but also observed in satellite imagery from 1978 through A relatively large difference in absolute numbers may be linked to sparsity of satellite data at that time, which probably led to missing a number of polar lows. Supplementary Fig. 2(b) compares REA results with more recently recorded data. Due to a substantially improved data coverage, these results are expected to be much less fragmentary. For a comparison per PLS, the time period of two years is too short. Correlation of both time series is ρ = In this more recent period, the numbers of polar lows are in the same order of magnitude, 80 in REA and 90 in the observations over the two years. Vertical stability in different areas and during winter We trace back reduced frequency of polar lows to increased vertical stability conditions in a warmed up atmosphere, which we showed for the October-March period over the whole area in our main text. There, the average decrease of our vertical stability measure vdt (decreasing 6

7 vdt means increasing vertical stability) reads 2.23, 1.97 and 1.42 K between the future scenarios A2, A1B and B1 and C20, respectively. The results of increased vertical stability also hold for two sub regions including the maxima of polar low genesis, one in the northern North Atlantic and one in the region between Greenland and Iceland (Supplementary Fig. 3). In the North Atlantic region, which experiences a relatively moderate reduction of polar low frequency, the decrease of vdt is lower than average (mean vdt reads 1.59, 1.33 and 0.81 K, respectively). In the Greenland-Iceland region, the area showing the largest decrease in polar low frequency, the increase of vertical stability is more pronounced than average (mean vdt reads 2.51, 2.24 and 1.72 K, respectively) Figure 1: Simulation grid and area. 7

8 number of polar lows number of polar lows /Jan/ /Jan/ /Jan/ /Jan/01 polar low season /Jan 2004/Apr 2004/Jul 2004/Oct 2005/Jan 2005/Apr 2005/Jul 2005/Oct year/month (a) (b) Figure 2: Simulated and observed polar low frequency. (a) Number of detected polar lows per PLS from REA 4 (black) and observation based 14 (red), (b) Number of detected polar lows per month from REA 4 (black) and observation based 15 (red). From REA, only polar lows of the observed area and same wind speed threshholds as the obseravtions were considered, respectively. 8

9 static stability in IPCC AR4 scenarios: C20 A1B B1 A2 vdt [K] vdt [K] models: mpi_echam5 csiro_mk3_5 ukmo_hadcm3 cccma_cgcm3_1 cnrm_cm3 inmcm3_0 mri_cgcm2 bccr_bcm2.0 gfdl_cm2_1 giss_model_e_r ipsl_cm4 ncar_ccsm (a) Northern North Atlantic 30.0 (b) Greenland Iceland Sea Figure 3: Projected vertical stability in two sub regions. 30 years ( for C20 and for B1, A1B, A2) mean vertical stability as given by the temperature difference between sea surface and air at 500 hpa level. (a) for an area in the northern North Atlantic (bordered by 65 N, 80 N, 20 E and 20 W ) and (b) for an area between Iceland and Greenland (bordered 55 N, 70 N, 50 E and 15 E). Stability is calculated over ice free ocean grid cells from October to March. 9

10 number of polar lows per polar low season vdt [K] number of polar lows per polar low season vdt [K] left hand side: mean and std deviation of number of polar lows (PL) per PL season right hand side: vertical stability origin of data: RCM driven by C20 A1B REA B1 A (a) Whole region (b) Northern North Atlantic (c) Greenland Iceland Sea Figure 4: Biases in the number of polar lows and in projected vertical stability between REA and IPCC driven RCM-simulations. Average number and standard deviation of polar lows per polar low season and 30 years mean vertical stability ( for C20 and REA and for B1, A1B, A2, respectively) as given by the temperature difference between sea surface and air at 500 hpa level over ice free ocean (SST > 1.7 C). (a) for the whole simulation area, (b) for an area in the northern North Atlantic (bordered by 65 N, 80 N, 20 E and 20 W ) and (c) for an area between Iceland and Greenland (bordered 55 N, 70 N, 50 E and 15 E). Vertical stability is calculated over ice free ocean grid cells from October to March. 10

11 1. Rockel, B., Will, A. & Hense, A. The Regional Climate Model COSMO-CLM (CCLM). Meteorologische Zeitschrift 17, (2008). 2. Zahn, M., von Storch, H. & Bakan, S. Climate mode simulation of North Atlantic polar lows in a limited area model. Tellus A 60, (2008). 3. Zahn, M. & von Storch, H. Tracking polar lows in CLM. Meteorologische Zeitschrift 17, (2008). 4. Zahn, M., & von Storch, H. A long-term climatology of North Atlantic polar lows. Geophys. Res. Lett. 35 (2008). 5. Roeckner et al. The atmospheric general circulation model ECHAM 5. PART I: Model description. MPI-Report No 349 (2003). 6. Marsland, S. J., Haak, H., Jungclaus, J. H., Latif, M. & Röske, F. The maxplanck-institute global ocean/sea ice model with orthogonal cur vilinear coordinates. Ocean Modelling 5, (2003). 7. von Storch, H., Langenberg, H. & Feser, F. A spectral nudging technique for dynamical downscaling purposes. Mon. Wea. Rev. 128, (2000). 8. Zahn, M. Climate Simulation with CLM, Scenario 20C run no.1, North Atlantic region.. available online-doi: /WDCC/CLM 20C ZS (2010). 9. Zahn, M. Climate Simulation with CLM, Scenario B1 run no.1, North Atlantic region. available online-doi: /WDCC/CLM B1 ZS (2010). 11

12 10. Zahn, M. Climate Simulation with CLM, Scenario A1B run no.1, North Atlantic region. available online-doi: /WDCC/CLM A1B ZS (2010). 11. Zahn, M. Climate Simulation with CLM, Scenario A2 run no.1, North Atlantic region. available online-doi: /WDCC/CLM A2 ZS (2010). 12. Feser, F. & von Storch, H. A spatial two-dimensional discrete filter for limited area model evaluation purposes. Mon. Wea. Rev. 133, (2005). 13. Meehl, G. A. et al. The WCRP CMIP3 multimodel dataset: A new era in climate change research. Bulletin of the American Meteorological Society 88, (2007). 14. Wilhelmsen, K. Climatological study of gale-producing polar lows near Norway. Tellus 37A, (1985). 15. Blechschmidt, A.-M. A 2-year climatology of polar low events over the nordic seas from satellite remote sensing. Geophys. Res. Lett. 35 (2008). 12

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