Download Causes of exceptional atmospheric circulation changes in the

GEOPHYSICAL RESEARCH LETTERS, VOL. 31, L14205, doi:10.1029/2004GL019952, 2004
Causes of exceptional atmospheric circulation changes in the Southern
Hemisphere
Gareth J. Marshall,1 Peter A. Stott,2 John Turner,1 William M. Connolley,1 John C. King,1
and Thomas A. Lachlan-Cope1
Received 11 March 2004; revised 22 June 2004; accepted 7 July 2004; published 30 July 2004.
[1] We demonstrate that recent observed trends in the
annual and austral summer Southern Hemisphere Annular
Mode (SAM) are unlikely to be due to internal climate
variability, since they exceed any equivalent-length trends
in a millennial General Circulation Model (GCM) control
run with constant forcings. In contrast we show that
observed trends in the SAM are consistent with the
combined effects of anthropogenic and natural forcings in
GCM simulations. As these trends begin prior to
stratospheric ozone depletion we challenge the assertion
that this process is primarily responsible for changes in the
SAM. Moreover, anthropogenic forcings have a larger
effect on the austral summer SAM in combination with
INDEX
natural forcings than when acting in isolation.
TERMS: 1620 Global Change: Climate dynamics (3309); 3309
Meteorology and Atmospheric Dynamics: Climatology (1620);
3337 Meteorology and Atmospheric Dynamics: Numerical
modeling and data assimilation; 3349 Meteorology and
Atmospheric Dynamics: Polar meteorology. Citation: Marshall,
G. J., P. A. Stott, J. Turner, W. M. Connolley, J. C. King, and T. A.
Lachlan-Cope (2004), Causes of exceptional atmospheric
circulation changes in the Southern Hemisphere, Geophys. Res.
Lett., 31, L14205, doi:10.1029/2004GL019952.
result is a strengthening of the circumpolar vortex and
associated westerlies. Sensitivity experiments with climate
models have suggested Antarctic stratospheric ozone
depletion as a possible forcing mechanism [Sexton, 2001;
Gillet and Thompson, 2003; Gillet et al., 2003]. However,
General Circulation Model (GCM) predictions of future
climate with increased greenhouse gases also reveal a clear
shift in the SAM towards its positive phase [Fyfe et al., 1999;
Kushner et al., 2001; Stone et al., 2001; Cai et al., 2003].
Some workers have proposed that synergistic interactions
between these two mechanisms are responsible for the pronounced changes [Hartmann et al., 2000]. However, little has
been written regarding the possible influences of natural
forcings on the SAM.
[3] In this paper we estimate the significance of the
recent observed trends in the SAM by evaluating them
against those in a millennial length GCM control run with
constant forcings. Subsequently we investigate possible
forcing mechanisms by comparing the observed magnitude
of annual and seasonal trends in the SAM to those derived
from GCM simulations with varying external forcings.
2. Methodology
1. Introduction
[2] Southern Hemisphere (SH) extra-tropical atmospheric
variability is primarily driven via the SH Annular Mode
(SAM), a pattern of zonally symmetric, synchronous pressure
anomalies of opposite sign in Antarctica and mid-latitudes.
Changes in the SAM have been linked to large-scale
variability of the Southern Ocean [Hall and Visbeck, 2002;
Hughes et al., 2003] and Antarctic surface temperature
change [Kwok and Comiso, 2002; Thompson and Solomon,
2002; van den Broeke and van Lipzig, 2003], and can
therefore be considered a robust indication of broad-scale
extra-tropical SH climate change. Several, differently
defined indexes of the SAM [Limpasuvan and Hartmann,
1999] (also known as the High Latitude Mode [Rogers and
van Loon, 1982] or Antarctic Oscillation [Gong and Wang,
1999]) have revealed a positive trend over the last few
decades, such that pressures are now more likely to be higher
(lower) over SH mid-latitudes (Antarctica) [Thompson et al.,
2000; Thompson and Solomon, 2002; Marshall, 2003]. The
1
British Antarctic Survey, Natural Environment Research Council,
Cambridge, Cambridgeshire, UK.
2
Hadley Centre for Climate Prediction and Research, Met Office,
University of Reading, Reading, UK.
Copyright 2004 by the American Geophysical Union.
0094-8276/04/2004GL019952$05.00
[4] We use an index of the SAM for 1958 – 2000 that has
been determined using a definition based upon mean sea
level pressure (MSLP) observations [Marshall, 2003]: note
that previous time-series of the SAM based on the National
Centers for Environmental Prediction (NCEP) reanalysis are
flawed because of a bias in geopotential height at high
southern latitudes prior to the assimilation of satellite
sounder data into this dataset commencing in the late
1970s [Hines et al., 2000]. Briefly, the zonal MSLP at SH
high latitudes (mean of 6 stations located at 65°S with a
good spread of longitudes) is normalized for each season or
annually (Dec – Nov) and subtracted from an equivalent
mid-latitude value (40°S) [after Gong and Wang, 1999].
Figure 1 shows the resultant individual seasonal values of
the SAM with the annual values superimposed. Since a
minimum in 1964 there have been statistically significant
(see Santer et al., 2000 for the methodology) positive trends
in the annual, autumn (MAM), and summer (DJF) SAM
indices.
[5] In order to estimate whether these observed trends in
the SAM lie outside the expected range of natural internal
climate variability, we compare them (from 1965 – 1997,
following the minimum in the annual data and up to the end
of model runs described later) to those of equivalent length
in a GCM control run. This has constant external forcings
(a seasonal and diurnal cycle) and therefore represents the
internal variability of the Earth’s climate system. The
L14205
1 of 4
L14205
MARSHALL ET AL.: SOUTHERN HEMISPHERE CIRCULATION CHANGES
L14205
Figure 1. Seasonal values of the SAM derived from
observations (bar chart) with annual values overlain as the
black line. The SAM index is a dimensionless quantity.
millennial control run utilized is obtained from the Hadley
Centre coupled model HadCM3 [Gordon et al., 2000;
Collins et al., 2001]. Prior to calculating the model’s
internal variability, we assess whether the model SAM is
realistic. Note that the model SAM was derived similarly to
the observations: an alternative method, in which we
calculate the SAM time-series as the first EOF of extratropical SH MSLP, has a correlation with the empirically
derived SAM time-series of 0.98. Thus, the latter effectively
captures almost all the variability of the SAM within the
model. Spectral analysis determines that neither the model
data nor the detrended observations contain any significant
periodicity. The variability of the SAM in the model is
marginally higher than in the detrended observations with
an independent estimate of external forcing (the ALL model
run: see next paragraph) removed from them, ranging from
0 – 4% seasonally and 3% annually. Hence the exaggeration
of internal SAM variability in the model is likely to be
underestimated and, as a consequence, so is the significance
of the observed trends as derived in Section 3. In addition,
the vertical and meridional structure of the model SAM—
as revealed by the first EOFs of MSLP, 500, 300 and
100 hPa—is similar in the model and European Centre for
Medium-range Weather Forecasts ERA-40 reanalysis data.
Thus, HadCM3 appears to provide a good facsimile of the
observed SAM. This has also been demonstrated with
regard to the North Atlantic Oscillation, an approximate
northern hemisphere analogue of the SAM [Collins et al.,
2001]. Frequency distributions of the trends in the control
run were calculated from 50% overlapping samples. This
takes account of the likely number of degrees of freedom in
the data, which was estimated to be 1.5 times the number of
non-overlapping segments [see Allen and Smith, 1996].
The distributions are approximately normal and thus the
associated confidence intervals may be calculated.
[6] Next we examine recent trends in the SAM derived
from three HadCM3 runs with varying external forcings
from 1860– 1997 [Stott et al., 2000; Tett et al., 2002]. The
first (ALL) is driven by changes in both natural and
anthropogenic forcings. Natural forcings are variability in
solar irradiance [Lean et al., 1995], and stratospheric aerosol
due to explosive volcanic eruptions [Sato et al., 1993].
Anthropogenic forcings incorporate both tropospheric forcings—such as well-mixed greenhouse gases, anthropogenic
sulfur emissions and their implied changes to cloud albedo
Figure 2. Annual and seasonal trends in the SAM during
1965– 1997 from observations (OBS: represented by a
cross) and the ensemble-mean of the all forcings (ALL),
natural forcings (NAT) and anthropogenic forcings (ANT)
HadCM3 runs (represented by circles). The 95% confidence
limits about zero shown in the observations column are the
uncertainties due to natural internal climate variability, as
simulated by a millennial-scale control run. 95% confidence
intervals for the ensemble-mean trends are similarly derived
(see text for details).
2 of 4
L14205
MARSHALL ET AL.: SOUTHERN HEMISPHERE CIRCULATION CHANGES
L14205
Figure 3. Summer (DJF) values of the SAM in the all forcings (ALL) run (black) and observations (red). SAM values are
based on normalized data for 1958– 1997. The full line represents the mean and the dotted lines the four ensemble members
of the HadCM3 ALL run. The linear trends from the two datasets for 1965 – 1997 are also shown.
and tropospheric ozone—and stratospheric ozone depletion.
Note that the prescribed ozone losses are up to twice
observed estimates in some seasons [Randel and Wu,
1999]. The other two runs examined, NAT and ANT, are
driven solely by the natural and anthropogenic forcings,
respectively. Each of these runs comprises four ensemble
members, with identical initial conditions for a given
ensemble member in ALL, NAT and ANT. These conditions
are taken from states in the control run separated by
100 years. SAM trends are calculated from the mean of
the ensemble members for the period 1965 –1997, the period
of recent increase in the observed SAM that overlaps with
the model data. The internal variability of the mean is
estimated by dividing the standard deviation of the control
run by the square root of the number of ensemble members
[see Karoly et al., 2003]; therefore, with four ensemble
members, the confidence intervals of the runs with external
forcings are half that of the control run.
3. Results
[7] The observed (OBS) trends for 1965 – 1997 and the
control run-derived 95% confidence intervals (centered on
zero) are shown in Figure 2. This reveals that, based on the
model’s estimate of the natural variability of the SAM, the
recent observed annual and summer trends are statistically
significant at <5% level (actually both <1% while in autumn
the trend is significant at <10%). We note that the estimate
of internal SAM variability in the model is likely to be
underestimated [see Mitchell et al., 2001, Figure 12.2
caption] and, as a consequence, the significance level of
the observed trends will be somewhat conservative. Further
evidence that the observed trends in the SAM lie outside the
range of internal climate variability is indicated by the
largest trends of similar length (33 years) in the control
run, calculated using a moving window, having a magnitude
of only 87% and 71% of those observed annually and in
summer, respectively.
[8] Equivalent mean trend and associated 95% confidence
intervals in the SAM from 1965– 1997 derived from the
three runs with varying external forcings are also illustrated
in Figure 2. Both annual and seasonal SAM trends in the
ALL mean are consistent with those observed, increasing
our confidence that the model is able to accurately represent
the SAM during this period. The summer SAM in the ALL
run is illustrated in Figure 3, together with observations and
the 1965– 1997 trend in both datasets. We see that the rapid
decline in the mid-1960s is identifiable in the model mean,
and while values before this are clearly not as high as
observed, they are above average compared to the earlier
model data. The subsequent recent trend in the SAM in the
ALL mean is seen to be of greater magnitude than any
previously. Thus the model simulation provides further
evidence that the observed trend is exceptional. We note
that the overstated ozone losses may exaggerate the ALL
trend following the establishment of the ozone hole in about
1980, but the fact that the model data show good agreement
with observations both before and after this date suggests
these effects are slight relative to the magnitude of the trend
itself.
[9] The annual, autumn and summer SAM trends in the
ALL run are significantly different from zero at the 5% level
(cf. Figure 2). In contrast, none of the trends derived from
the NAT mean are significant, and are therefore consistent
with those in the observations and ALL run only in winter
and spring. Most of the SAM trends in the ANT mean are
also not statistically significant, and many of these are
closer to the NAT mean than the observed trend. The one
exception is in summer, when the ANT trend is significantly
different from zero (although only half the magnitude of
that in the observations and ALL) but not statistically
different from the summer NAT trend. Thus, it seems that
the effects of anthropogenic forcing are concentrated in
austral summer.
4. Conclusions
[10] Our results imply that a non-linear combination of
anthropogenic and natural forcings is responsible for the
observed changes in the SAM since the mid-1960s. Previous studies have focused attention only on the former. The
anthropogenic forcing component is clearly most prominent
3 of 4
L14205
MARSHALL ET AL.: SOUTHERN HEMISPHERE CIRCULATION CHANGES
in summer, the season with the largest trend—suggesting
agreement with those studies proposing ozone depletion as
the principal cause of changes in the SAM [Sexton, 2001;
Thompson and Solomon, 2002; Gillet and Thompson,
2003]. Nevertheless, observations and both the ALL
(cf. Figure 3) and ANT runs reveal that the recent summer
trend in the SAM begins at least a decade before any ozone
loss. Furthermore, Kushner et al. [2001] show that the
largest seasonal response to greenhouse gas increases in
another GCM is also in summer. Therefore we conclude that
increases in greenhouse gases are an important component
of the forcing and disagree with the assertion that ozone
depletion alone is primarily responsible for the observed
shift in the SAM [Gillet and Thompson, 2003]. Finally, the
requirement to include natural forcings in the model in order
to reproduce observed SAM trends points to a complex
combination of physical processes being responsible
[Hartmann et al., 2000; Karoly, 2003]. Partitioning the
contributions of the various forcing factors influencing the
SAM, both anthropogenic and natural, and understanding
how they interact will allow us to better predict its role in
determining future climate change in the SH.
[11] Acknowledgments. PAS is funded by the Department of
Environment, Food and Rural Affairs under contract PECD 7/12/37. We
thank Paul Kushner and an anonymous reviewer for their comments.
References
Allen, M. R., and L. A. Smith (1996), Monte Carlo SSA: Detecting
irregular oscillations in the presence of colored noise, J. Clim., 9,
3373 – 3404.
Cai, W., P. H. Whetton, and D. J. Karoly (2003), The response of the
Antarctic oscillation to increasing and stabilized atmospheric CO2,
J. Clim., 16, 1525 – 1538.
Collins, M., S. F. B. Tett, and C. Cooper (2001), The internal climate
variability of HadCM3, a version of the Hadley Centre coupled model
without flux adjustments, Clim. Dyn., 17, 61 – 81.
Fyfe, J. C., G. J. Boer, and G. M. Flato (1999), The Arctic and Antarctic
oscillations and their projected changes under global warming, Geophys.
Res. Lett., 26, 1601 – 1604.
Gillet, N. P., and D. W. J. Thompson (2003), Simulation of recent Southern
Hemisphere climate change, Science, 302, 273 – 275.
Gillet, N. P., M. R. Allen, and K. D. Williams (2003), Modelling the atmospheric response to doubled CO2 and depleted stratospheric ozone using a
stratosphere-resolving coupled GCM, Q. J. R. Meteorol. Soc., 129, 947 –
966.
Gong, D., and S. Wang (1999), Definition of Antarctic oscillation index,
Geophys Res. Lett., 26, 459 – 462.
Gordon, C., C. Cooper, C. A. Senior, H. Banks, J. M. Gregory, T. C. Johns,
J. F. B. Mitchell, and R. A. Wood (2000), The simulation of SST, sea ice
extents and ocean heat transports in a version of the Hadley Centre
coupled model without flux adjustments, Clim. Dyn., 16, 147 – 168.
Hall, A., and M. Visbeck (2002), Synchronous variability in the Southern
Hemisphere atmosphere, sea ice, and ocean resulting from the annular
mode, J. Clim., 15, 3043 – 3057.
Hartmann, D. L., J. M. Wallace, V. Limpasuvan, D. W. J. Thompson, and
J. R. Holton (2000), Can ozone depletion and global warming interact to
produce rapid climate change?, Proc. Natl. Acad. Sci. U. S. A., 97, 1412 –
1417.
Hines, K. M., D. H. Bromwich, and G. J. Marshall (2000), Artificial surface
pressure trends in the NCEP-NCAR reanalysis over the Southern Ocean
and Antarctica, J. Clim., 13, 3940 – 3952.
L14205
Hughes, C. W., P. L. Woodworth, M. P. Meredith, V. Stepanov,
T. Whitworth, and A. R. Pyne (2003), Coherence of Antarctic sea
levels, Southern Hemisphere Annular Mode, and flow through Drake
Passage, Geophys. Res. Lett., 30(9), 1464, doi:10.1029/2003GL017240.
Karoly, D. J. (2003), Ozone and climate change, Science, 302, 236 – 237.
Karoly, D. J., K. Braganza, P. A. Stott, J. M. Arblaster, G. A. Meehl, A. J.
Broccoli, and K. W. Dixon (2003), Detection of a human influence on
North American climate, Science, 302, 1200 – 1203.
Kushner, P. J., I. M. Held, and T. L. Delworth (2001), Southern Hemisphere
atmospheric circulation response to global warming, J. Clim., 14, 2238 –
2249.
Kwok, R., and J. Comiso (2002), Spatial patterns of variability in Antarctic
surface temperature: Connections to the Southern Hemisphere Annular
Mode and the Southern Oscillation, Geophys Res. Lett., 29(14), 1705,
doi:10.1029/2002GL015415.
Lean, J., J. Beer, and R. Bradley (1995), Reconstruction of solar irradiance
since 1610: Implications for climate change, Geophys. Res. Lett., 22,
3195 – 3198.
Limpasuvan, V., and D. L. Hartmann (1999), Eddies and the annular modes
of climate variability, Geophys. Res. Lett., 26, 3133 – 3136.
Marshall, G. J. (2003), Trends in the Southern Annular Mode from observations and reanalyses, J. Clim., 16, 4134 – 4143.
Mitchell, J. F. B., et al. (2001), Detection of climate change and attribution
of causes, in Climate Change 2001: The Scientific Basis: Contribution of
Working Group I to the Third Assessment Report of the Intergovernmental
Panel on Climate Change, edited by J. T. Houghton et al., pp. 695 – 738,
Cambridge Univ. Press, New York.
Randel, W. J., and F. Wu (1999), A stratospheric ozone trends data set for
global modelling studies, Geophys Res. Lett., 26, 3089 – 3092.
Rogers, J. C., and H. van Loon (1982), Spatial variability of sea level
pressure and 500 mb height anomalies over the Southern Hemisphere,
Mon. Weather Rev., 110, 1375 – 1392.
Santer, B. D., T. M. L. Wigley, J. S. Boyle, D. J. Gaffen, J. J. Hnilo,
D. Nychka, D. E. Parker, and K. E. Taylor (2000), Statistical significance
of trends and trend differences in layer-average temperature time series,
J. Geophys. Res., 105, 7337 – 7356.
Sato, M., J. E. Hansen, M. P. McCormick, and J. B. Pollack (1993), Stratospheric aerosol optical depths 1850 – 1990, J. Geophys. Res., 98, 22,987 –
22,994.
Sexton, D. M. H. (2001), The effect of stratospheric ozone depletion on
the phase of the Antarctic Oscillation, Geophys. Res. Lett., 28, 3697 –
3700.
Stone, D. A., A. J. Weaver, and R. J. Stouffer (2001), Projection of climate
change onto modes of atmospheric variability, J. Clim., 14, 3551 – 3565.
Stott, P. A., S. F. B. Tett, G. S. Jones, M. R. Allen, J. F. B. Mitchell, and
G. J. Jenkins (2000), External control of twentieth century temperature by
natural and anthropogenic forcings, Science, 290, 2133 – 2137.
Tett, S. F. B., et al. (2002), Estimation of natural and anthropogenic contributions to twentieth century temperature change, J. Geophys. Res.,
107(D16), 4306, doi:10.1029/2000JD000028.
Thompson, D. W. J., and S. Solomon (2002), Interpretation of recent Southern Hemisphere climate change, Science, 296, 895 – 899.
Thompson, D. W. J., J. M. Wallace, and G. C. Hegerl (2000), Annular
modes in the extratropical circulation. Part II: Trends, J. Clim., 13,
1018 – 1036.
van den Broeke, M. R., and N. P. M. van Lipzig (2003), Response of
wintertime Antarctic temperatures to the Antarctic Oscillation: Results
of a regional climate model, in Antarctic Peninsula Climate Variability:
Historical and Paleoenvironmental Perspectives, Antarct. Res. Ser.,
vol. 79, edited by E. Domack et al., pp. 43 – 58, AGU, Washington,
D. C.
W. M. Connolley, J. C. King, T. A. Lachlan-Cope, G. J. Marshall, and
J. Turner, British Antarctic Survey, Natural Environment Research Council,
Cambridge, Cambridgeshire CB3 OET, UK. ([email protected])
P. A. Stott, Hadley Centre for Climate Prediction and Research, Met
Office, University of Reading, Reading, UK.
4 of 4