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Northern hemisphere winter atmospheric climate: modes of natural variability and climate change

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Abstract

Under anthropogenic climate change it is possible that the increased radiative forcing and associated changes in mean climate may affect the “dynamical equilibrium” of the climate system; leading to a change in the relative dominance of different modes of natural variability, the characteristics of their patterns or their behavior in the time domain. Here we use multi-century integrations of version three of the Hadley Centre atmosphere model coupled to a mixed layer ocean to examine potential changes in atmosphere-surface ocean modes of variability. After first evaluating the simulated modes of Northern Hemisphere winter surface temperature and geopotential height against observations, we examine their behavior under an idealized equilibrium doubling of atmospheric CO2. We find no significant changes in the order of dominance, the spatial patterns or the associated time series of the modes. Having established that the dynamic equilibrium is preserved in the model on doubling of CO2, we go on to examine the temperature pattern of mean climate change in terms of the modes of variability; the motivation being that the pattern of change might be explicable in terms of changes in the amount of time the system resides in a particular mode. In addition, if the two are closely related, we might be able to assess the relative credibility of different spatial patterns of climate change from different models (or model versions) by assessing their representation of variability. Significant shifts do appear to occur in the mean position of residence when examining a truncated set of the leading order modes. However, on examining the complete spectrum of modes, it is found that the mean climate change pattern is close to orthogonal to all of the modes and the large shifts are a manifestation of this orthogonality. The results suggest that care should be exercised in using a truncated set of variability EOFs to evaluate climate change signals.

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Notes

  1. The temperature anomaly T is expressed as a vector. Each element is a temperature at each spatial point on the model grid.

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Acknowledgments

SK was supported by a University of Reading Research Endowment Trust Fund PhD studentship. MC was supported by the UK Department of the Environment, Food and Rural Affairs under Contract PECD/7/12/37, by the European Community ENSEMBLES (GOCE-CT-2003-505539) and DYNAMITE (GOCE-003903) projects under the Sixth Framework Programme. Access to the ECMWF ERA-40 dataset kindly supplied by the British Atmospheric Data Centre.

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Authors and Affiliations

  1. Department of Meteorology, University of Reading, Earley Gate, Reading, Berkshire, RG6 6BB, UK

    Sarah P. E. Keeley & Alan J. Thorpe

  2. Met Office Hadley Centre, FitzRoy Road, Exeter, EX1 3PB, UK

    Matthew Collins

Authors
  1. Sarah P. E. Keeley
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  2. Matthew Collins
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  3. Alan J. Thorpe
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Correspondence to Sarah P. E. Keeley.

Appendix

Appendix

To determine the impact of not including a dynamical ocean we compare the modes of MSLP from a 600-year control run of HadCM3. HadCM3 has a dynamic ocean model coupled to HadAM3; the same atmosphere model used in HadSM3, with the same atmospheric resolution. By comparing the EOFs produced it gives us some idea about how the dynamic ocean is affecting the modes of variability.

EOFs of MSLP were produced from a 600-year sample from a long equilibrium run of HadCM3 and are shown in Fig. 10; the first three EOFs are well separated. Each of the first six EOFs is similar to the corresponding EOF from HadSM3 (Fig. 3) with each EOF having correlation coefficients over 0.7. By including the dynamic ocean the fraction of variance explained by each mode is slightly altered. This may be due to the fact that the ocean adds some long-term memory to the system and therefore affects the low frequency variability of the system. The results show that the inclusion of the dynamic ocean model does not alter the spatial patterns of variability produced for the control climate. The dynamic model does increase the importance of the leading two modes for explaining the variance in the climate system. The top two modes of HadCM3 explain 45.6% of the variance compared to 35.2% for the top two modes of HadSM3.

Fig. 10
figure 10

As for Fig. 3 except for Coupled Atmosphere–Ocean GCM HadCM3

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We also consider the modes of SAT variability within HadCM3; the results are shown in Fig. 11; with the first three EOFs being well separated. The first six EOFs for HadCM3 are also similar to those found in HadSM3 (see Fig. 1) but with different ordering, with EOFs (as defined by HadSM3) 4 and 5 being more dominant in HadCM3 analysis than EOFs 2 and 3. The SAT EOFs of HadSM3 are slightly closer in ordering to the EOFs of the ERA-40 data set. This is likely to be because the SSTs in HadSM3 are constrained to be close to climatological values because of the heat fluxes; HadCM3 runs without flux adjustments to the surface and is known to develop biases in ocean temperature (see McAvaney et al. 2001). We therefore expect the EOFs of the temperature field of HadSM3 are closer to observations.

Fig. 11
figure 11

As for Fig. 1 except for HadCM3

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Keeley, S.P.E., Collins, M. & Thorpe, A.J. Northern hemisphere winter atmospheric climate: modes of natural variability and climate change. Clim Dyn 31, 195–211 (2008). https://doi.org/10.1007/s00382-007-0346-6

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  • DOI: https://doi.org/10.1007/s00382-007-0346-6

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