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Impact of stratospheric variability on tropospheric climate change

Abstract

An improved stratospheric representation has been included in simulations with the Hadley Centre HadGEM1 coupled ocean atmosphere model with natural and anthropogenic forcings for the period 1979–2003. An improved stratospheric ozone dataset is employed that includes natural variations in ozone as well as the usual anthropogenic trends. In addition, in a second set of simulations the quasi biennial oscillation (QBO) of stratospheric equatorial zonal wind is also imposed using a relaxation towards ERA-40 zonal wind values. The resulting impact on tropospheric variability and trends is described. We show that the modelled cooling rate at the tropopause is enhanced by the improved ozone dataset and this improvement is even more marked when the QBO is also included. The same applies to warming trends in the upper tropical troposphere which are slightly reduced. Our stratospheric improvements produce a significant increase of internal variability but no change in the positive trend of annual mean global mean near-surface temperature. Warming rates are increased significantly over a large portion of the Arctic Ocean. The improved stratospheric representation, especially the QBO relaxation, causes a substantial reduction in near-surface temperature and precipitation response to the El Chichón eruption, especially in the tropical region. The winter increase in the phase of the northern annular mode observed in the aftermath of the two major recent volcanic eruptions is partly captured, especially after the El Chichón eruption. The positive trend in the southern annular mode (SAM) is increased and becomes statistically significant which demonstrates that the observed increase in the SAM is largely subject to internal variability in the stratosphere. The possible inclusion in simulations for future assessments of full ozone chemistry and a gravity wave scheme to internally generate a QBO is discussed.

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Notes

  1. Time and resources allowed to run three simulations at the time and three is generally recognised to be a reasonable minimum number of ensemble members. In terms of maximising the signal to noise ratio, the more members the better but the standard deviation reduces with \( 1/\sqrt n \) which suggests diminishing returns as ensemble size is increased.

  2. In this paper, averages over the “tropical region” refer to area-mean averages over those grid points located between the tropics (about 23.5°S–23.5°N), while the “equatorial region” implies a latitude belt of half the width of the tropical one and centred at the equator. Each extra-tropical region refers to grid points located poleward of the tropics while the Arctic and Antarctic regions refer to grid points located poleward of the circles.

  3. Unless otherwise specified, the “near-surface” temperature refers to the air temperature 1.5 m above the surface.

  4. In Sects. 3.3 and 3.4 we rarely mention the near-surface implications of the baseline+ozone simulations because the global mean near-surface temperature in their first ensemble member shows a drifts towards colder temperatures in the second half of the modelled time period when it stays well outside the range of variability of the remaining two ensemble members (not shown). This first ensemble member may suffer from its initial ocean state having been sampled relatively early into the control simulation with pre-industrial atmospheric composition (see Dall’Amico et al. 2009, their Sect. 2.1).

  5. Note that if one considers spatial domains smaller than the global, variances can be expected to grow in both sets of simulations and their ratio can be expected to approach one. Further, the total variance is the sum of the variance at all frequencies: intra-seasonal variability adds to the variance in the baseline+ozone+QBO as well as in the baseline simulations. The ratio of these variances can be expected to approach 1 if, instead of annual means, means over shorter periods of time are considered (e.g. de-seasonalised 3-monthly means or seasonal means).

  6. We considered the 50°N–60°N zonal wind at 50, 100, 200 and 500 hPa.

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Acknowledgments

Funding was provided by the UK National Environment Research Council. Peter Stott and Adam Scaife were supported by the Joint DECC, Defra and MoD Integrated Climate Programme—DECC/Defra (GA01101), MoD (CBC/2B/0417_Annex C5). We wish to thank Keith Shine, Terry Davies, Jason Lowe, Gareth Jones, Scott Osprey, Warwick Norton, Jonathan Gregory, Oliver Browne, Gareth Marshall, Michael Ponater, Robert Sausen and Veronika Eyring for their help in acquiring and processing data, their illuminating suggestions and their support. We also wish to thank all those people at the UK Met Office and various UK Universities who contributed throughout the years to the development of the Hadley Centre Global Environmental Model and ancillary datasets.

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Dall’Amico, M., Stott, P.A., Scaife, A.A. et al. Impact of stratospheric variability on tropospheric climate change. Clim Dyn 34, 399–417 (2010). https://doi.org/10.1007/s00382-009-0580-1

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Keywords

  • Simulations of recent climate with natural and anthropogenic forcings assessed by the IPCC 2007 AR4
  • Observed ozone distributions
  • Quasi-biennial oscillation (QBO) of stratospheric equatorial zonal wind
  • Variability and trends at the tropopause and in the troposphere
  • Response to the volcanic eruptions of El Chichón and Mt. Pinatubo