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1 MAY 2012
Impact of Antarctic Ozone Depletion and Recovery on Southern Hemisphere
Precipitation, Evaporation, and Extreme Changes
Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Quebec, Canada
(Manuscript received 3 July 2011, in final form 19 September 2011)
The possible impact of Antarctic ozone depletion and recovery on Southern Hemisphere (SH) mean and
extreme precipitation and evaporation is examined using multimodel output from the Climate Model Intercomparison Project 3 (CMIP3). By grouping models into four sets, those with and without ozone depletion
in twentieth-century climate simulations and those with and without ozone recovery in twenty-first-century
climate simulations, and comparing their multimodel-mean trends, it is shown that Antarctic ozone forcings
significantly modulate extratropical precipitation changes in austral summer. The impact on evaporation
trends is however minimal, especially in twentieth-century climate simulations. In general, ozone depletion
has increased (decreased) precipitation in high latitudes (midlatitudes), in agreement with the poleward
displacement of the westerly jet and associated storm tracks by Antarctic ozone depletion. Although weaker,
the opposite is also true for ozone recovery. These precipitation changes are primarily associated with changes
in light precipitation (1–10 mm day21). Contributions by very light precipitation (0.1–1 mm day21) and
moderate-to-heavy precipitation (.10 mm day21) are minor. Likewise, no systematic changes are found in
extreme precipitation events, although extreme surface wind events are highly sensitive to ozone forcings.
This result indicates that, while extratropical mean precipitation trends are significantly modulated by ozoneinduced large-scale circulation changes, extreme precipitation changes are likely more sensitive to thermodynamic processes near the surface than to dynamical processes in the free atmosphere.
1. Introduction
Southern Hemisphere (SH) climate changes over the
last few decades have been extensively documented in
recent studies. They include an expansion of the Hadley
cell (Seidel et al. 2008; Johanson and Fu 2009), a shift in
atmospheric mass from high to midlatitudes (Thompson
and Solomon 2002; Marshall 2003), a poleward displacement of the westerly jet and storm tracks (Thompson
and Solomon 2002; Marshall 2003; Fyfe 2003), an increase in surface wind speeds over the Southern Ocean
(Böning et al. 2008), anomalously dry conditions over
southern South America, New Zealand, and southern
Australia, and anomalously wet conditions over much
of Australia and South Africa (Gillett et al. 2006). A
freshening and warming of the Southern Ocean (Wong
et al. 1999; Gille 2002; Böning et al. 2008) and a significant
Corresponding author address: Seok-Woo Son, Department of
Atmospheric and Oceanic Sciences, McGill University, 805 Sherbrooke West, Montreal QC H3A 2K6, Canada.
E-mail: [email protected]
DOI: 10.1175/JCLI-D-11-00383.1
Ó 2012 American Meteorological Society
warming of the Antarctic Peninsula (Thompson and
Solomon 2002) have also been observed. While some of
these changes have been attributed to circulation changes
induced by the increase in anthropogenic greenhouse
gases (Fyfe et al. 1999; Kushner et al. 2001; Cai et al.
2003), such changes in austral summer have also been
influenced by Antarctic ozone depletion (Thompson and
Solomon 2002; Shindell and Schmidt 2004; Arblaster and
Meehl 2006; Perlwitz et al. 2008; Son et al. 2009, 2010;
McLandress et al. 2011; Polvani et al. 2011; Kang et al.
2012). It is known that both increasing greenhouse gases,
occurring year-round, and ozone depletion, which occurs
most significantly in late spring and summer, have driven
SH extratropical circulation changes in a similar way,
the cumulative effect resulting in more significant tropospheric climate change in austral summer than in other
seasons. In the future, the effects of these two forcings
are, however, predicted to oppose each other (Shindell
and Schmidt 2004; Perlwitz et al. 2008; McLandress et al.
2011) as Antarctic ozone concentrations are anticipated
to increase owing to implementation of the Montreal
Protocol (Austin et al. 2010).
While the surface climate impact of increasing greenhouse gases is relatively well understood, our understanding of stratospheric ozone-related climate change
at the surface, especially its mechanisms, is somewhat
limited. In particular the impact of stratospheric ozone
changes on the hydrological cycle in the SH is not well
understood. A series of recent studies have shown that
stratospheric ozone depletion has likely enhanced austral summer precipitation changes in the subtropics and
high latitudes but reduced them in midlatitudes, consistent with the poleward displacement of the westerly jet,
or equivalently the positive trend in the Southern Annular Mode (SAM) index (Son et al. 2009; McLandress
et al. 2011; Polvani et al. 2011; Kang et al. 2012). Although
they are crucial for understanding salinity changes in
the Southern Ocean, net hydrological changes, including
evaporation, are not yet well understood. In addition,
and arguably more importantly, potential changes in
extreme precipitation events are yet to be investigated.
It is known that individual precipitation events are
likely to get more intense as the climate warms (Emori
and Brown 2005; Sun et al. 2007; O’Gorman and
Schneider 2009). Previous studies suggest that it is predominantly thermodynamics that control changes in
extratropical extreme precipitation (Emori and Brown
2005; O’Gorman and Schneider 2009). Thus, it is questionable whether extreme precipitation events will respond to dynamical changes driven by the Antarctic
ozone hole.
The purpose of this study is to bridge the existing gap
in understanding the relative contributions of anthropogenic greenhouse gas emissions and stratospheric
ozone changes in forcing changes in the hydrological
cycle. Multimodel output from the Climate Model Intercomparison Project 3 (CMIP3) (Meehl et al. 2007) is
analyzed. By grouping models into those with prescribed
ozone depletion and recovery and those without it, we
show that Antarctic ozone forcings significantly affect
seasonal-mean precipitation trends in the extratropics
during austral summer but play a minimal role in evaporation and extreme precipitation trends.
Precipitation Climatology Project version-2 (GPCP)
precipitation (Adler et al. 2003) and Objectively Analyzed Air–Sea Heat Fluxes version-3 (OAFlux) global
ocean evaporation data (Yu et al. 2008). Those models
with significant biases are discarded, and 19 models are
selected for the analyses as described in Table 1.1
All CMIP3 models have prescribed stratospheric
ozone concentrations with a seasonal cycle. However,
not all models have incorporated stratospheric ozone
depletion in the latter part of the twentieth century and
recovery in the twenty-first century, as anthropogenic
ozone forcings were not mandated in the CMIP3 (Meehl
et al. 2007). Ten models prescribed ozone depletion and
ozone recovery, while nine models simply used climatological ozone fields.2 As such, models are grouped into
four sets: those with and without ozone depletion in
the twentieth century and those with and without ozone
recovery in the twenty-first century. For each group, the
multimodel-mean climatologies and trends are calculated for the fields of interest (precipitation and evaporation) over the twentieth and twenty-first centuries. As
in Son et al. (2009), climatologies and trends are first
calculated for each ensemble member and averaged
over all available ensemble members of a given model.
The ensemble average of each model is interpolated
onto a 48 latitude by 48 longitude grid and averaged over
all available models within a group. Hatching is used on
trend maps to denote where the multimodel mean trend
is greater than or equal to one standard deviation of
the trends of different models within that group. By
comparing the multimodel means of each group, the
impact of Antarctic ozone forcings on hydrological climate changes is systematically examined. Although this
approach does not necessarily reveal ozone-related surface climate changes, as each group comprises different
models, it is known that trend differences resulting from
different ozone forcings are likely larger than those associated with model-dependent internal variabilities
(Son et al. 2009).
Since daily data are archived only for selected decades
in the A1B runs, long-term trends are estimated in this
2. Data and methods
CMIP3 data from the twentieth-century climate simulations (20C3m) and twenty-first-century climate simulations with the special report on emissions scenarios
A1B forcing (A1B) are analyzed. From all available
models, the models that archived daily precipitation
are first selected. For those models, evaporation is calculated from surface latent heat flux as outlined in Yu
et al. (2008). Each model’s precipitation and evaporation climatologies are then compared with Global
Flexible Global Ocean–Atmosphere–Land System Model gridpoint version 1.0 (FGOALS1.0g) [Institute of Atmospheric Physics
(IAP), China] is discarded as twentieth-century precipitation in the
high-latitude region is found to be unreasonably higher than observations and all other models. Goddard Institute for Space Studies (GISS)
Model E-R (GISS-ER) (NASA, United States) is discarded as 1971–99
daily precipitation appears to be erroneous across the extent of the SH.
Certain CMIP3 models prescribed ozone depletion in the
twentieth century but did not prescribe ozone recovery in the twentyfirst century; however, for other reasons, such models were not included in this study.
56, 40 km
T106 (1.18 3 1.18)
2.58 3 3.78
T42 (2.88 3 2.88)
IPSL, France
MRI, Japan
30, 0.4 hPa
19, 4 hPa
21, 10 hPa
4.08 3 5.08
T30 (3.98 3 3.98)
12, 10 hPa
19, 10 hPa
T63 (1.98 3 1.98)
T63 (1.98 3 1.98)
3.08 3 4.08
31, 1 hPa
45, 0.05 hPa
T47 (2.88 3 2.88)
Canadian Centre for Climate Modelling and Analysis
(CCCma), Canada
CCCma, Canada
Meteo-France/CNRM, France
Meteorological Institute of the University of Bonn
(MIUB), Germany, and Meteorological Research
Institute of the Korea Meteorological
Administration (KMA), South Korea
National Aeronautics and Space Administration
(NASA) GISS, United States
INM, Russia
31, 1 hPa
T63 (1.98 3 1.98)
16, 25 hPa
20, 30 km
26, 2.2 hPa
24, 3 hPa
19, 10 hPa
2.08 3 2.58
T106 (1.18 3 1.18)
T42 (2.88 3 2.88)
T42 (2.88 3 2.88)
18, 4.5 hPa
31, 10 hPa
24, 3 hPa
18, 4.5 hPa
26, 2.2 hPa
Vertical resolution
(levels, top)
T63 (1.98 3 1.98)
T63 (1.98 3 1.98)
2.08 3 2.58
T63 (1.98 3 1.98)
T85 (1.48 3 1.48)
Horizontal resolution
(lat 3 lon)
BCCR, Norway
Fixed ozone
Center for Climate System Research at University of
Tokyo (CCSR), National Institute for Environmental
Studies (NIES), and Frontier Research Center for
Global Change of Japan Agency for Marine-Earth
Science and Technology (FRCGC), Japan
NCAR, United States
CSIRO, Australia
MPI, Germany
National Oceanic and Atmospheric Administration
(NOAA)/GFDL, United States
NOAA/GFDL, United States
INGV, Italy
National Center for Atmospheric Research
(NCAR), United States
CSIRO, Australia
Varying ozone
Group, country
5 (1)
3 (1)
3 (0)
1 (0)
3 (2)
4 (3)
5 (1)
3 (1)
3 (1)
1 (0)
1 (0)
* Model documentation claims inclusion of ozone chemistry; however, analysis of Antarctic polar cap temperature by Son et al. (2008) found no ozone impact in either 20C3m or A1B simulations.
GISS Atmosphere–Ocean
Model (GISS-AOM)
Institute of Numerical Mathematics Coupled Model,
version 3.0 (INM-CM3.0)
L’Institut Pierre-Simon Laplace Coupled Model,
version 4 (IPSL CM4)
Meteorological Research Institute Coupled General
Circulation Model, version 2.3.2 (MRI CGCM2.3.2)
Bjerknes Center for Climate Research Bergen Climate
Model version 2.0 (BCCR-BCM2.0)
Coupled General Circulation Model, version 3.2
(CGCM3.1) (T47)
Centre National de Recherches Météorologiques Coupled
Global Climate Model, version 3 (CNRM-CM3)*
ECHAM4 and the global Hamburg Ocean Primitive
Equation (ECHO-G)
Parallel Climate Model version 1.1 (PCM1.1)
Commonwealth Scientific and Industrial Research
Organisation Mark version 3.0 (CSIRO Mk3.0)
CSIRO Mk3.5d
ECHAM5/Max Planck Institute Ocean Model (MPI-OM)
Geophysical Fluid Dynamics Laboratory Climate Model
version 2.0 (GFDL CM2.0)
Istituto Nazionale di Geofisica e Vulcanologia
(INGV)-Scale Interaction Experiment (SINTEX)-G
Model for Interdisciplinary Research on Climate 3.2
(high resolution version) [MIROC3.2(hires)]
Community Climate System Model, version 3.0 (CCSM3.0)
TABLE 1. Description of CMIP3 models used in this study. Details of each model are described in Randall et al. (2007). Resolutions refer to atmospheric resolution and horizontal
resolution is approximate for spectral models, where ‘‘T’’ refers to triangular truncation. The number of ensemble members refers to those used in precipitation analyses. Brackets
indicate where different ensemble members are used in evaporation analyses.
1 MAY 2012
FIG. 1. Multimodel-mean trends of (first row) precipitation, (second row) evaporation, and (third row) precipitation minus evaporation
in DJF. Trends are calculated by differencing 1961–70 from 1990–99 averages and 1990–99 from 2056–65 averages for twentieth- and
twenty-first-century trends, respectively. (left to right) Multimodel-mean trends are shown for models with and without ozone depletion
in the twentieth century and for models with and without ozone recovery in the twenty-first century, respectively. Cool colors denote an
increasing freshwater flux (increasing precipitation or decreasing evaporation) while warm colors denote a decreasing freshwater flux
(decreasing precipitation or increasing evaporation). Hatched areas denote where the multimodel-mean trend is greater than or equal to
one standard deviation. Contours show the climatology, with contour intervals of 100 mm season21 for all panels.
study using decadal differences. The twentieth-century
change, reflecting the impact of ozone depletion, is defined by the difference between 1990–99 and 1961–70
means. Likewise the twenty-first-century change, reflecting the impact of ozone recovery, is defined by the difference between 2056–65 and 1990–99 means. Since decadal
differences are qualitatively similar to the linear trends
computed from monthly-mean data over 1960–99 and
2000–79 (not shown), they are simply referred to as
‘‘trends’’ in this study. The possible changes in extreme precipitation events are examined by decomposing
seasonal-mean precipitation trends into three regimes
(Sun et al. 2007): very light (0.1–1 mm day21), light (1–
10 mm day21), and moderate-to-heavy (.10 mm day21)
precipitation changes. Five extreme precipitation indices
are also examined. They are the sum of precipitation on
all wet days divided by the number of wet days, the sum of
rainfall on days exceeding the 95th percentile threshold
as determined for the base period of 1961–90 (hereafter
95th percentile precipitation), the sum of rainfall on days
exceeding the 99th percentile threshold as determined for
the base period of 1961–90, seasonal maximum one-day
precipitation, and seasonal maximum five-day consecutive precipitation (ETCCDI/CRD 2009).
3. Results
Multimodel-mean trends of austral summer [December–
February (DJF)] precipitation and evaporation are presented in Fig. 1. Only the extratropics, poleward of 308S,
are shown, as tropical and subtropical trends are noisy
and largely insignificant. In the twentieth century the
1 MAY 2012
poleward displacement of storm tracks by increasing
greenhouse gases causes a dipolar trend in precipitation (Yin 2005). This is evident in the models
without ozone depletion; however, trends are only
weak. A similar pattern but with much stronger
magnitude is found in the models with prescribed
ozone depletion, indicating the combined effects of
increasing greenhouse gases and ozone depletion on
extratropical precipitation changes. The opposite is
generally true in the twenty-first century: models with
prescribed ozone recovery show relatively weaker
precipitation trends than models with fixed ozone forcing. As shown in previous studies (Son et al. 2009;
McLandress et al. 2011; Polvani et al. 2011), this sensitivity is observed only in austral summer.
In contrast to the annular-like trends of precipitation,
evaporation shows relatively weak trends in the extratropics, which lack organization. The sensitivity of evaporation trends to ozone forcings is also weak, although
there is a hint that models with ozone depletion have
a weaker decreasing trend in high-latitude evaporation
than those without ozone depletion, presumably because
of the acceleration of surface westerlies by ozone depletion. This result, combined with the findings related
to precipitation trends, indicates that Antarctic ozone
forcings modulate long-term trends of surface freshwater
flux (or equivalently, precipitation minus evaporation)
by primarily affecting precipitation trends. Figure 1 (third
row) in particular suggests that Antarctic ozone depletion has likely contributed to the observed freshening
of the Southern Ocean (e.g., Böning et al. 2008); however,
its recovery would have little impact on Southern Ocean
freshening, as ozone-related precipitation changes in the
twenty-first century are partly cancelled by evaporation
Figure 2 presents the relative contributions of very
light, light, and moderate-to-heavy precipitation changes
to the DJF-mean precipitation changes shown in Fig. 1. It
is evident that mean precipitation trends are dominated
by light precipitation trends in the models (second row),
the precipitation regime that is somewhat overestimated
in the CMIP3 models (Dai 2006). Note that, although
very light precipitation events also show significant trends,
their cumulative impact is very weak (shading interval in
the first row is one-tenth of that in the second row). More
importantly, their sensitivity to ozone forcings is exactly
opposite to those of seasonal-mean precipitation trends
(cf. the first rows of Fig. 1 and Fig. 2). This is somewhat
surprising, but similar results—opposite trends between
very light and light precipitation events—are also found
in the precipitation response to anthropogenic warming (e.g., Sun et al. 2007). For instance, as shown in
the rightmost column of Fig. 2, very light and light
precipitation trends in the twenty-first-century with
fixed ozone but with increasing greenhouse gases show
opposite trends in the high latitudes. It appears that the
similar excitation of the SAM by both increasing
greenhouse gases and varying ozone affects the precipitation distribution. The mechanisms for this are,
however, unknown. The response may be accounted
for in terms of thermodynamic changes by increasing
greenhouse gases (i.e., a warm atmosphere can hold
more moisture, likely causing the probability distribution function of precipitation events to shift away from
very light to light precipitation events), but is more
perplexing in terms of ozone-forced dynamic changes.
Further studies are needed.
The trends in moderate-to-heavy precipitation events
(Fig. 2 third row), which are quantitatively similar to 95th
percentile precipitation trends (fourth row), are relatively
weak. Unlike trends in very light and light precipitation,
they show no dipole pattern: trends are predominantly
positive across the extent of the SH, particularly in the
twenty-first century, reflecting more intense and frequent extreme precipitation events with anthropogenic
warming. More importantly, no notable sensitivity to
ozone forcings is observed. Although not shown, a lack
of sensitivity is also found in the other extreme precipitation indices described in the data and methods
section. This suggests that Antarctic ozone forcings only
play a minimal, if any, role in extreme precipitation
changes. Here, it should be noted that extreme precipitation
events are sensitive to model resolution. Comparisons of
the MIROC3.2(hires) (T106) with MIROC3.2(medres)
(T42), and of CGCM3.1 (T63) with CGCM3.1 (T47), in
fact, show stronger trends in the higher-resolution models
(not shown). However, that each model group contains
models with a variety of resolutions and that multimodelmean trends in extreme precipitation are quantitatively
similar between models with time-varying ozone and
models with fixed ozone, although the former have generally higher resolution than the latter (Table 1), suggests
that the conclusion that Antarctic ozone forcings play
a minimal role in extreme precipitation changes holds.
The above results suggest that Antarctic ozone forcings affect hydrological climate changes in the SH extratropics by modifying dynamics that in turn modify light
precipitation trends. It may be questionable whether results are affected by model groups having different climate sensitivities: models with varying ozone are known
to have higher climate sensitivity to increasing greenhouse gases (Miller et al. 2006). However, that in the
twenty-first-century models with varying ozone exhibit
trends in mean precipitation consistent with ozone
recovery suggests that models with varying ozone are
not simply responding more strongly to increasing
FIG. 2. Multimodel-mean trends in (first row) very light (0.1–1 mm day21), (second row) light (1–10 mm day21), (third row) moderateto-heavy (.10 mm day21), and (fourth row) 95th percentile precipitation in DJF: other details as in Fig. 1. Note that the color scale is an
order of magnitude less for very light precipitation so that details can be seen. Contour intervals of climatologies are 20 mm season21 for
very light precipitation panels and 50 mm season21 for all other panels.
greenhouse gases, as the response to ozone recovery is
in the opposite direction. Nevertheless, results should be
treated with caution, as they are based solely on simple
multimodel averaging. One limitation of multimodel averaging, among others, is because individual models have
different climatologies (e.g., Barnes and Hartmann 2010).
For instance, the DJF-mean climatological jet, defined by
the maximum westerly wind at 925 hPa, varies from 438
to 568S among models (not shown). Models with fixed
ozone forcing generally have a jet in lower latitudes
(46.28S 6 2.98) compared to those with ozone depletion (49.88S 6 2.78) [as expected, the latter is closer
to the real atmosphere (508S)]. Since storm tracks are
located on the poleward side of the westerly jet, the
simple multimodel averaging used in this study, which ignores individual model bias, could over- or underestimate
precipitation trends by averaging stormy regions with dry
1 MAY 2012
FIG. 3. (first row) Zonal-mean precipitation climatology and (second row) total, (third row) very light, (fourth row)
light, and (fifth row) moderate-to-heavy precipitation trends in DJF plotted as a function of jet-relative latitudes in
the SH. Zonal-mean values are shown for individual models (varying ozone models in blue and fixed ozone models in
red), multimodel averages (bold blue and red lines), and GPCP precipitation (bold black line). Both (left) twentiethcentury and (right) twenty-first-century simulations are shown.
Figure 3 shows seasonal-mean precipitation climatology and long-term trends for individual models and
multimodel means, shifted relative to the climatological
jet. Only zonally averaged fields are shown as precipitation trends are largely homogeneous in the zonal direction (Figs. 1 and 2). The CMIP3 models reproduce
DJF-mean precipitation reasonably well in the high latitudes (2308–08 relative to the jet). However, they generally underestimate it in midlatitudes (08–208 relative to
the jet) and overestimate it in the subtropics and tropics
(208–508 relative to the jet). Intermodel variation is
particularly large in the subtropics and tropics, making
any influence of ozone forcings difficult to distinguish. In
fact, although a recent study by Kang et al. (2012) has
shown that ozone depletion may have enhanced subtropical precipitation in the SH, no sensitivity of subtropical precipitation trends to ozone forcings is found
in multimodel-mean trends (second row). A noticeable
difference in subtropical precipitation trends is only
found in the twenty-first century (second row, right
column). This is, however, unlikely to be associated
with ozone recovery: it is, instead, thought to be a result
of the intensification of moderate-to-heavy (fifth row,
right column) tropical precipitation, caused by anthropogenic warming, among models with different tropical
TABLE 2. Summary of differences in percentage change between the models with time-varying ozone forcings and those with fixed
ozone forcing: percentage change is defined as a decadal change normalized by long-term climatology, calculated for the high-latitude
(averaged over 48–248 south of individual model’s climatological jet) and midlatitude (averaged over 128–08 north of individual
model’s climatological jet) regions. Only the values statistically significant at the 99% confidence level are shown. Significance tests
are based on a Monte Carlo approach. This approach selects one group of 10 models (eight for evaporation) and one group of 9 models
at random and calculates the percentage change difference between the means of the two groups. This is repeated 50 000 times to get
a statistical distribution. The actual difference between the mean of the varying ozone group and the fixed ozone group is then
compared with this statistical distribution at the 99% confidence level. Although not shown, overall results are qualitatively similar to
a two-sided Student’s t test at the 99% confidence level. Only results from DJF are shown, as no significant values are found in other
High latitudes
Twentieth century
Twenty-first century
Twentieth century
Twenty-first century
Very light
Moderate to heavy
Returning to extratropical precipitation trends, Fig. 3
confirms that ozone depletion tends to increase highlatitude precipitation trends but decrease midlatitude
precipitation trends by modulating light precipitation
events. The role of ozone recovery is the reverse: decreasing (increasing) high- (mid-) latitude precipitation
trends, relative to greenhouse-gas-induced trends alone.
This sensitivity is statistically significant at the 99% confidence level, as summarized in Table 2. Note that, while
the midlatitude mean precipitation trend difference in
the twentieth century is not significant, the decrease in
midlatitude light precipitation by ozone depletion is
statistically significant. Note also that, although the
sensitivity of high-latitude precipitation to ozone
forcings is not significant in the twenty-first century
in Table 2, it becomes significant if linear trends of
monthly-mean precipitation, which are generally stronger than decadal differences, are used. The identical
analyses are further performed for other seasons and
no significant difference in multimodel-mean trends between the models with and without time-varying ozone
forcings is found.
4. Discussion
The multimodel analyses, based on CMIP3 models,
show that Antarctic ozone forcings significantly affect
austral summer precipitation trends in the SH extratropics. Their effects are primarily realized by changes in
light precipitation events (1–10 mm day21), with negligible changes in extreme precipitation events attributable to ozone forcings. This lack of sensitivity of extreme
precipitation events to ozone forcings is somewhat
contradictory to the atmospheric circulation changes
by ozone forcings: ozone depletion is known to strengthen
extratropical westerlies or, equivalently, to favor the positive polarity of the SAM (Son et al. 2008, 2010;
McLandress et al. 2011; Polvani et al. 2011). As shown
in Fig. 4, trends of both seasonal-mean and frequency
of occurrence of 95th percentile surface westerlies are
strengthened by ozone depletion (second row). While
mean tropospheric circulation changes are consistent
with mean precipitation changes, no link is established between extreme events (cf. first and second
rows). In other words, strengthening of the 95th percentile wind by ozone depletion does not lead to
strengthening of 95th percentile precipitation. This
result suggests that thermodynamic effects are likely
more important than dynamic effects in the extratropical extreme precipitation changes as discussed
by Emori and Brown (2005) and O’Gorman and
Schneider (2009). In fact, surface air temperature
trends, which control water vapor content in the atmosphere, show a negligible sensitivity to ozone forcings (third row).
As previously stated, the findings of this study are
based solely on multimodel averaging and thus should
be treated with care. Although multimodel averaging
can reduce model biases, it does not allow direct attribution of SH surface climate changes to Antarctic ozone
forcings. This approach may also underestimate surface
climate responses to ozone forcings by averaging models
with realistic climatologies and trends with those without them. More quantitative studies using climate model
sensitivity tests (e.g., McLandress et al. 2011; Polvani
et al. 2011) are needed for better quantifying stratospheric ozone-related hydrological climate changes in
the SH.
1 MAY 2012
FIG. 4. (first row) Zonal-mean precipitation, (second row) surface zonal wind and (third row) surface air temperature trends in DJF plotted as a function of jet-relative latitudes in the SH: (left) mean trends and (right) frequency of occurrence of 95th percentile events are shown. Only twentieth-century simulations are presented. Note
that the frequency trends on the right are different from 95th percentile precipitation trends, as the latter is a cumulative quantity. Owing to surface wind data nonavailability, CCSM3.0, PCM1.1 and INM-CM3.0 are not included in
these panels. The same color convention as in Fig. 3 is used.
Acknowledgments. We thank Jacques Derome for
helpful discussion in the course of this research and the
anonymous reviewers for helpful comments that improved this manuscript. We also thank the Program
for Climate Model Diagnosis and Intercomparison
(PCMDI) for collecting and archiving the IPCC/AR4
model data, the JSC/CLIVAR Working Groups on Coupled Modeling (WGCM) and their Coupled Model Intercomparison Project (CMIP) and Climate Simulation
Panel for organizing the model data analysis activity,
and the IPCC WG1 TSU for technical support. The
IPCC Data Archive at Lawrence Livermore National
Laboratory is supported by the Office of Sciences, U.S.
Department of Energy. This study is funded by the Korea
Polar Research Institute (KOPRI) grant under Project
PE 11010. The work by A.P. is in part supported by
the Global Environmental and Climate Change Centre,
Montreal, Quebec, Canada. A.P. is also grateful of the
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