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Deconstructing the climate change response of the Northern Hemisphere wintertime storm tracks

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Abstract

There are large uncertainties in the circulation response of the atmosphere to climate change. One manifestation of this is the substantial spread in projections for the extratropical storm tracks made by different state-of-the-art climate models. In this study we perform a series of sensitivity experiments, with the atmosphere component of a single climate model, in order to identify the causes of the differences between storm track responses in different models. In particular, the Northern Hemisphere wintertime storm tracks in the CMIP3 multi-model ensemble are considered. A number of potential physical drivers of storm track change are identified and their influence on the storm tracks is assessed. The experimental design aims to perturb the different physical drivers independently, by magnitudes representative of the range of values present in the CMIP3 model runs, and this is achieved via perturbations to the sea surface temperature and the sea-ice concentration forcing fields. We ask the question: can the spread of projections for the extratropical storm tracks present in the CMIP3 models be accounted for in a simple way by any of the identified drivers? The results suggest that, whilst the changes in the upper-tropospheric equator-to-pole temperature difference have an influence on the storm track response to climate change, the large spread of projections for the extratropical storm track present in the northern North Atlantic in particular is more strongly associated with changes in the lower-tropospheric equator-to-pole temperature difference.

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Acknowledgments

BJH was supported by the Natural Environment Research Councils Project Testing and Evaluating Model Predictions of European Storms (TEMPEST) during the course of this work. The authors acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.

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Authors and Affiliations

  1. Department of Meteorology, National Centre for Atmospheric Science, University of Reading, Reading, UK

    B. J. Harvey & L. C. Shaffrey

  2. Department of Physics, Atmospheric, Oceanic and Planetary Physics, University of Oxford, Oxford, UK

    T. J. Woollings

Authors
  1. B. J. Harvey
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  2. L. C. Shaffrey
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  3. T. J. Woollings
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Corresponding author

Correspondence to B. J. Harvey.

Appendices

Appendix 1: List of CMIP3 models used in this study

Data from the following 21 models are used in this study. One run per model is used, and this is the run denoted ‘run1’ in almost all cases. It is noted in the main text if particular fields are unavailable for any of the models.

BCCR-BCM2.0 (Bjerknes Centre for Climate Research), CGCM3.1(T47) and CGCM3.1(T63) (Canadian Centre for Climate Modelling & Analysis), CNRM-CM3 (Mètèo-France/Centre National de Recherches Mètèorologiques), CSIRO-Mk3.0 and CSIRO-Mk3.5 (CSIRO Atmospheric Research), ECHAM5/MPI-OM (Max Planck Institute for Meteorology), ECHO-G (Meteorological Institute of the University of Bonn, Meteorological Research Institute of KMA, and Model and Data group), GFDL-CM2.0 and GFDL-CM2.1 (US Dept. of Commerce/NOAA/Geophysical Fluid Dynamics Laboratory), GISS-AOM and GISS-ER (NASA/Goddard Institute for Space Studies), INGV-SXG (Instituto Nazionale di Geofisica e Vulcanologia), INM-CM3.0 (Institute for Numerical Mathematics), IPSL-CM4 (Institut Pierre Simon Laplace), MIROC3.2(hires) and MIROC3.2(medres) [Center for Climate System Research (The University of Tokyo), National Institute for Environmental Studies, and Frontier Research Center for Global Change (JAMSTEC)], MRI-CGCM2.3.2 (Meteorological Research Institute), NCAR CCSM3 (National Center for Atmospheric Research), UKMO-HadCM3 and UKMO-HadGEM1 (Hadley Centre for Climate Prediction and Research/Met Office).

Appendix 2: Design of the sea-ice concentration fields

As noted in Sect. 2.2, due to the large range of 20C3M ice extents in the CMIP3 models (Stroeve et al. 2007), simply taking the multi-model mean of the SIC values (as was done for the SSTs) produces an ice distribution with an unrealistically smooth ice edge region. To avoid this problem artificial SIC distributions have been created to mimic the typical ice distribution of the CMIP3 models, whilst retaining a realistic ice-edge structure. Here we describe the algorithm for constructing these SIC fields.

Four ice distributions are used in this study, one representing the 20C3M ice distribution (used in CON-20C), one representing the mean SRESA1B response (used in CON-A1B), and two capturing the spread of the responses between the models (used in ARC±). For each month of the CON-20C experiment, a multi-model mean ice edge position is defined as the 50 % contour of the multi-model mean SIC distribution. The artificial SIC distribution is then constructed to equal 100 % poleward of this ice edge, linearly reducing over a distance of 5° to 0 % equatorward of the ice edge. The width of this ice edge region was chosen by a consideration of typical ice edge regions in the CMIP3 20C3M wintertime ice distributions.

The remaining three ice distributions are then constructed by retreating the ice edge in CON-20C towards the pole. The distance retreated, a function of longitudinal grid point, is first calculated separately for each CMIP3 model. The distances retreated for CON-A1B, ARC+ and ARC− are then taken as the median and standard deviation of the retreats in the models. This process is illustrated in Fig. 8 which shows, for the January values, the number of degrees latitude retreated by each the ice edge in each model. Also shown, in the lower panel, is the resulting ice edge positions of the four ice distributions. Once the position of the ice edge is found, artificial SIC distributions are then constructed, as above, to equal 100 % poleward of this ice edge, linearly reducing over a distance of 5° to 0 % equatorward of the ice edge.

Fig. 8
figure 8

The large panel shows the distance of ice edge retreat, as a function of longitude, for the monthly-mean January ice distributions of each CMIP3 model. The retreat is defined as the difference in ice edge position between years 1961–2000 from 20C3M and years 2061–2100 from SRESA1B and is measured in units of degrees of latitude. The models are ordered by the total area of monthly mean January ice in the 20C3M period, as indicated on the left-side axis; also indicated (right-side axis) is the change in January ice area between the 20C3M and SRESA1B periods. The lower panel indicates the constructed January ice edge positions for the CON-20C, CON-A1B, ARC− and ARC+ experiments, as indicated

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Harvey, B.J., Shaffrey, L.C. & Woollings, T.J. Deconstructing the climate change response of the Northern Hemisphere wintertime storm tracks. Clim Dyn 45, 2847–2860 (2015). https://doi.org/10.1007/s00382-015-2510-8

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