Weather from ozone hole variations
Seok-Woo Son1, Ariaan Purich2, Harry Hendon3 and Baek-Min Kim4
1Seoul National University, South Korea; 2CSIRO Marine and Atmospheric Research, Australia; 3Bureau of Meteorology, Australia; 4Korea Polar Research Institute, South Korea
CSIRO MARINE AND ATMOSPHERIC RESEARCH
Southern Hemisphere (SH) climate change in the past has been attributed to both greenhouse gas
increase and Antarctic ozone depletion [1,2]. Recent studies have shown that the latter has played at
least a comparable role to the former in austral summer [1,3,4]. We show that the Antarctic ozone
hole has affected not only the long-term trend but also the inter-annual variability of SH surface
climate. A significant negative correlation is found between September Antarctic ozone
concentration and the October Southern Annular Mode (SAM) index, resulting in systematic
variations in precipitation and surface temperature throughout the SH.
Ozone hole and atmospheric circulation
Ozone correlations with surface climate
An O3 index is defined by integrating Bodeker Scientific total column ozone
poleward of 63°S and relating it with meteorological fields over 1979–2010.
The September O3 index and its slowly-varying component (Fig. 1a) show
strong inter-annual variations and a distinct declining trend. High ozone years
are typically associated with stronger wave forcing in the lower stratosphere in
late winter, which cause warmer temperatures in the Antarctic stratosphere
and prevent the formation of conditions crucial for spring ozone depletion [5].
In Fig. 1b, the inter-annual variation is isolated by removing the long-term
trend, and referred to as the September O3-dt index.-
The inter-annual relationship between the September O3-dt index and
October Climate Research Unit (CRU) station precipitation and temperature
(Fig. 3a,b) are consistent with SAM index associations: high September O3-dt,
leading to low October SAM, is associated with reduced precipitation and
increased temperature in subtropical Australia [6–8], reduced temperature in
Patagonia and on the Antarctic Peninsula, and increased temperatures in
eastern Antarctica [6,9].
A time lag exists in
the maximum ozoneSAM correlation: the
September
O3-dt
index leads the
October
Marshall
SAM index with a
correlation of -0.66.
Figure 1: Time series of (a)
September O3 and (b) September
O3-dt and October SAM indices.
In
(a),
the
slowly-varying
component (dashed line) is
defined as the 20-year low-pass
filtered O3 index.
The September O3 index is correlated with ERA-Interim polar-cap geopotential
height anomalies, Z’ (Fig. 2a): significant correlations reach the surface in
October and January. By removing the long-term trend from the O3 index (Fig.
2b), surface correlations are increased in October, but essentially removed in
January, suggesting that the October relationship is largely due to inter-annual
variations in O3, whereas the January correlation is primarily due to the longterm trend.
September O3-dt is
highly
correlated
with July-August 10
hPa Z', confirming
that spring ozone is
controlled largely by
late-winter
lower
stratospheric wave
activities [5].
Figure 3: Lag-correlation maps between the September O3-dt index and October meteorological fields. (a) CRU
precipitation; (b) CRU daily-mean temperature; (c) Australian precipitation; (d) Australian daily-maximum temperature; and
(e) Australian daily-minimum temperature. For CRU data, only stations with data availability of at least 75 % of the analysis
period are used. The correlation coefficients that are significant at the 95 % confidence level are shown by filled circles in
(a)–(b), and hatched in (c)–(e).
A significant relationship is observed in the Australian regional analyses of
precipitation and daily-maximum temperature (Fig. 3c,d), but not in dailyminimum temperature (Fig. 3e). The similarity between Fig. 3c,d suggests
daytime temperature is modulated by cloud cover change associated with
precipitation: a high SAM index (low O3) is typically associated with
anomalous surface easterlies in the midlatitudes that enhance moisture
transport from the ocean to eastern Australia [7].
Conclusions
These findings suggest that the seasonal forecast of the SH extratropics
could be improved by considering Antarctic lower-stratospheric variability
on an inter-annual timescale. Although this might be computationally
expensive, its benefit could be as good as that of tropical sea surface
temperature variabilities, such as El Niño-Southern Oscillation and the Indian
Ocean Dipole. These results have important implications for SH water
resource management and agriculture, and provide additional evidence of the
stratospheric influence on surface weather and climate [10].
Figure 2: Lag-correlation of polarcap geopotential height anomaly
Z’ with (a) September O3 and (b)
September O3-dt indices. The
contour interval is 0.1, and
correlations that are significant at
the 95 % and 99 % confidence
levels are bound by yellow and
green contour lines, respectively.
FOR FURTHER INFORMATION
REFERENCES
REFERENCES
ACKNOWLEDGEMENTS
Ariaan Purich
e [email protected]
w www.csiro.au/cmar
[1] Son et al. (2010). J. Geophys. Res., 115.
[2] Thompson et al. (2011). Nature Geosci.
[3] McLandress et al. (2010). J. Climate, 24.
[4] Polvani et al. (2011). J. Climate, 24.
[5] Salby et al. (2011). Geophys. Res. Lett., 38.
[6] Gillett et al. (2006). Geophys. Res. Lett., 33.
[7] Hendon et al. (2007). J. Climate, 20.
[8] Risbey et al. (2009). Mon. Wea. Rev., 137.
[9] Thompson and Solomon (2002), Science, 296.
[10] Baldwin et al. (2003). Science, 301.
We thank Gareth Marshall (British Antarctic Survey, UK) for
the use of his SAM index, Greg Bodeker (Bodeker Scientific,
NZ) for providing us with total column ozone data, and David
Lister (University of East Anglia, UK) for providing us with CRU
station data. This study is funded by the Korea Polar Research
Institute (KOPRI) grant under project PE 12010.