# Impacts of the cryosphere and atmosphere on observed microseisms generated
in the Southern Ocean

###### Abstract

The Southern Ocean (in the region 60-180°E) south of the Indian Ocean,
Australia and the West Pacific is noted for the frequent occurrence and
severity of its storms. These storms give rise to high-amplitude secondary
microseisms from sources including the deep ocean regions, and primary
microseisms where the swells impinge on submarine topographic features. A
better understanding of the varying microseism wavefield enables improvements
to seismic imaging, and development of proxy observables to complement sparse
in-situ wave observations and hindcast models of the global ocean wave
climate. We analyse 12-26 years of seismic data from 11 seismic stations
either on the East Antarctic coast or sited in the Indian Ocean, Australia and
New Zealand. The power spectral density of the seismic wavefield is calculated
to explore how the time-changing microseism intensity varies with: i) sea ice
coverage surrounding Antarctica; and ii) the Southern Annular Mode (SAM)
climate index. Variations in sea ice extent are found to be the dominant
control on the microseism intensity at Antarctic stations, which exhibit a
seasonal pattern phase-shifted by 4-5 months compared to stations in other
continents. Peaks in extremal intensity at East Antarctic stations occur in
March-April, with the highest peaks for secondary microseisms occurring during
negative SAM events. This relationship between microseism intensity and the
SAM index is opposite to that observed on the Antarctic Peninsula. This work
informs the complexity of microseism amplitudes in the Southern Hemisphere and
assists ongoing interdisciplinary investigations of interannual variability
and long-term trends.

JGR: Earth Surface

School of Natural Sciences, University of Tasmania, Private Bag 37, Hobart,
TAS 7001, Australia Institute for Marine and Antarctic Studies, University of
Tasmania, Private Bag 129, Hobart, TAS 7004, Australia Institute of Mine
Seismology, 50 Huntingfield Avenue, Huntingfield, TAS 7055, Australia CSIRO
Oceans and Atmosphere Flagship, Hobart, TAS 7004, Australia

R. J<EMAIL_ADDRESS>
Seasonal sea ice coverage is the dominant control on the microseism intensity
in East Antarctica

Microseism events with extremal intensity are most prevalent in East
Antarctica during March-April

East Antarctic extremal microseism events increase during the austral autumn
for a negative SAM climate index

##  1 Introduction

Microseisms are ubiquitously observed on seismometers around the world and
arise due to ocean swell and wave activity coupling with the solid earth.
Previously considered as ‘noise’ in the study of earthquakes and other seismic
events, the usefulness of seismic ambient wavefield [Nakata (2019)] is now
recognised since it provides a relatively high frequency energy source for
Earth imaging [Shapiro . (2005), Pilia . (2015), Shen . (2018)]. Seismic noise
caused by Earth surface or oceanic phenomena also have the potential to
provide an independent data stream to progress the understanding of the given
system and its changes. Examples of the use of ambient seismic noise include
volcano monitoring [Sens-Schönfelder Wegler (2011)], the study of stream flow
[Tsai . (2012)] and glacier activity [Winberry . (2013)], and very significant
progress in understanding and using the relationship between microseisms and
ocean waves [Ardhuin . (2012), Obrebski . (2012)].

Microseisms generated by ocean sources are described in terms of the observed
frequency [Ardhuin . (2019)]. The longest period signals ($\sim$50-500 s),
known as ‘hum’, are generated through the interaction of atmosphere, ocean and
sea floor [Rhie Romanowicz (2004), Traer . (2012)]. Primary microseisms
($\sim$10-30 s) are generated through interaction with the sea bed, notably at
a continental slope [Hasselmann (1963)]. Secondary microseisms ($\sim$1-10 s)
are generated through the interaction of two different wave groups in the deep
ocean [Longuet-Higgins (1950)]. The alternate name ‘double frequency’ may be
understood through the interaction of opposing wave groups of similar
frequency [Ardhuin . (2015)]. Recent work has further developed the
understanding of microseism generation mechanisms and also the way in which
energy from the water column is coupled to the solid-Earth [Gualtieri .
(2015), Ardhuin (2018)]. Microseism studies have been carried out using
observatory records from single three-component stations [Aster . (2010)] and
also using array recordings which afford the ability to locate multiple,
simultaneous sources of secondary microseisms [Gal . (2016), Gal . (2017)].
Extending the array analysis to three components where suitable recording
exists has helped to reveal the complexity of the ambient wavefield; e.g. the
previously undetected _Sn_ phase [Gal . (2016)].

Where seismic ambient signals are recorded over longer, preferably
multidecadal time frames, the ambient wavefield also provides a means of
investigating Earth system components that impact the generation of
microseisms [Stutzmann . (2009), Aster . (2010), Gal . (2015)]. Seismic
records are now being utilised to improve the homogeneity of ocean wave model
hindcasts [Stopa . (2019)], particularly for the Southern Hemisphere where
other observations which might be used for hindcast calibration are sparse
[Babanin . (2019)]. Cyclical features of global climate are of particular
interest, for example the Southern Annular Mode (SAM) climate index, or
Antarctic Oscillation (AO), which is associated with variability in wave power
[Bromirski . (2013), Bromirski Cayan (2015)]. The SAM concerns the north-south
movement of westerly winds that influence the strength and position of cold
fronts and mid-latitude systems. SAM indices are defined using either the
first empirical orthogonal function (EOF) of a Southern Hemisphere
extratropical climate variable (e.g. geopotential height and mean sea level
pressure), or the difference in zonal mean pressure between 40 and 65°S [Ho .
(2012)]. The positive mode is associated with the intensification and
contraction of the westerly wind belt towards Antarctica resulting in
increased polar storm activity [Hurrell van Loon (1994), Meehl . (1998),
Thompson Wallace (2000), Marshall . (2017)]. Meanwhile, the wind belt expands
towards the equator in the negative polarity [Marshall (2007), Marshall .
(2017)] leading to increased storm activity over the Australian continent
[Hemer . (2010), Marshall Thompson (2016), Wandres . (2018)]. The seasonal
variations in the SAM are summarised in Figure 1. The SAM has a relatively
short characteristic timescale of around 10 days, but the frequency of the
positive polarity also has increased slowly on decadal timescales due to
stratospheric ozone depletion [Thompson . (2011)]. The SAM is recognised to
influence global ocean surface waves [Hemer . (2010), Marshall . (2018)] and
surface temperatures [Marshall (2007)] which in turn affect sea ice and ice
shelves [Greene . (2017), Massom . (2018)].

Figure 1: Seasonal impacts of the Southern Annular Mode (SAM) on East
Antarctica and Australia. The solid white lines are an approximate location of
the circumpolar westerly winds. Regions shaded in red have warmer
temperatures, lower precipitation and reduced storm activity; regions in blue
have cooler temperatures, increased precipitation and heightened storm
activity. The peak season for these anomalies due to variations in the SAM is
stated in the overlaid text.

Although noted in passing by Aster+2008, the seasonal variability in
microseisms was first investigated at a single station (Dumont d’Urville) as
part of a global study undertaken by Stutzmann+2009-G3. Subsequently, the
microseism wavefield has been investigated in more detail as part of
continent-wide appraisals by both Grob+2011 and Anthony+2014-SRL. They
reported systematic noise level variations for stations with different
installation types and also noted the impact of sea ice in reducing winter
noise levels and hence phase-shifting the usual seasonal pattern seen at
seismic observatories globally. Gal+2015-JGR investigated patterns of
secondary microseism generation including the Southern Ocean using seismic
array techniques and found a strong frequency dependency, likely related to
water column depth, and preferred locations as observed by the Australian
seismic arrays. In a study focussed on the microseism variations observed from
the Antarctic Peninsula, Anthony+2017-JGR looked at the correlation of
microseism intensity with sea ice and atmospheric oscillations for the
Antarctic Peninsula and Weddell Sea areas of West Antarctica.

In this contribution, we investigate the influence of seasonal sea ice and
sub-seasonal atmosphere/ocean system oscillations on the amplitude of
microseisms recorded at permanent observatory stations in the Indian Ocean,
Australian and West Pacific sectors of the Southern Ocean. We aim to add to
the work of Anthony et al. 2017 noted above, extending the knowledge of
typical Southern Ocean microseism variations to eastern latitudes, and hence
inform the use of microseisms for Earth imaging and oceanographic applications
in the Southern Hemisphere.

##  2 Data

###  2.1 Seismic data

We consider seismic data recorded over a 12-26 year period through to 1
January 2018 at East Antarctic and Southern Ocean stations in the Australian
National Seismograph Network (AU), GEOSCOPE (G), and the IRIS/IDA (II) and
IRIS/USGS (IU) components of the Global Seismographic Network. Stations were
selected based on their locations in the Eastern Hemisphere at latitudes below
30°S, and greater than 10 years of data being available for analysis,
resulting in a total of 4 Antarctic stations and 7 Southern Ocean stations
(Figure 2 and Table 1). We use the vertical component, long-period $1\rm\,Hz$
sampling rate record (LHZ) at each station except for the two seismographs in
the Geoscience Australia network which use the broad-band $20\rm\,Hz$ vertical
record (BHZ).

Figure 2: Locations of stations used in this study: East Antarctica (red filled points) and Southern Ocean (blue unfilled points). The geographic definitions of segments within the Southern Ocean (Indian Ocean, Pacific Ocean and Ross Sea) are those used by the National Snow and Ice Data Centre and are shown in shades of grey. Table 1: Station information, channels and start time of seismic records used in this analysis. Location | Network | Code | Channel | Latitude | Longitude | Elevation | Start Date
---|---|---|---|---|---|---|---
|  |  |  | (degrees) | (degrees) | (m) |
Casey Station | IU | CASY | LHZ | -66.279 | 110.535 | 10 | 1996-02-19
Dumont d’Urville | G | DRV | LHZ | -66.665 | 140.002 | 40 | 1986-02-01
Mawson Station | AU | MAW | BHZ | -67.604 | 62.871 | 12 | 2003-01-31
Scott Base | IU | SBA | LHZ | -77.849 | 166.757 | 50 | 1998-10-28
Crozet Islands | G | CRZF | LHZ | -46.431 | 51.855 | 140 | 1986-02-01
Hobart | II | TAU | LHZ | -42.910 | 147.320 | 132 | 1994-01-17
Macquarie Island | AU | MCQ | BHZ | -54.499 | 158.956 | 14 | 2004-06-28
Narrogin | IU | NWAO | LHZ | -32.928 | 117.239 | 380 | 1991-11-25
Nouvelle-Amsterdam | G | AIS | LHZ | -37.796 | 77.569 | 35.9 | 1993-12-25
Port aux Français | G | PAF | LHZ | -49.351 | 70.211 | 17 | 1983-01-01
Wellington | IU | SNZO | LHZ | -41.309 | 174.704 | 120 | 1992-04-07

###  2.2 Microseism intensity

Time series and response files for each of the 11 stations were downloaded
from the online archives of the Incorporated Research Institutions for
Seismology (IRIS) data services. The continuous time series data were split
into three hour windows with 50% time overlap with each window then being
converted to power spectral density (PSD) using the freely available IRIS
Noise ToolKit, NTK IRIS DMC (2014); Hutko . (2017). The NTK PSD returns power
estimates in 1/8-octave period bins and $1\rm\,dB$ power intervals similar to
the commonly used PQLX algorithm McNamara Boaz (2011), and gives the user
freedom to choose the smoothing window. Following the method of
Anthony+2017-JGR, we specify a 1/4-octave smoothing window to minimise the
smearing of power across the period bins. This procedure yields 16 daily
measurements of the power spectrum at each station for a minimum of twelve
years up to 1 January 2018 (i.e. $>$75000 PSDs per station). The PSD
probability density function McNamara Buland (2004) is shown for CASY station
over the time period from 1 January 2006 to 1 January 2018 (Figure 3).

Figure 3: PSD probability density function for Casey Station (CASY), East
Antarctica, shown as a time-averaged plot from 1 January 2006 to 1 January
2018. The colour bar shows the average number of counts per PSD in each period
bin, gridded in steps of $1\,\rm dB$ along the vertical axis. The 1st and 80th
percentiles of the probability density function are shown by the dashed and
dot-dashed black lines respectively. The grey dashed vertical lines show the
limits of the primary and secondary microseism bands. For comparision, we also
plot the Peterson+1993 New Low Noise Model and New High Noise Model curves in
purple (narrow dot-dashed lines). The solid red line shows the PSD of a
magnitude 5.8 earthquake that occurred approximately 100 kilometres south of
Casey Station at 20:35 UTC on 4 November 2007.

The broad power spectrum output by the NTK is subdivided for this analysis
into two bands containing the energy from the primary (11 - 30 s) and
secondary (5 - 10 s) microseisms. The intensity of the primary and secondary
microseisms at each time step is calculated by integrating the power spectral
density across each of these bands; here, we make use of the IRIS NTK
microseism energy bundle IRIS DMC (2015). Earthquakes are flagged and removed
following the method of Aster+2010-GRL; the process used to flag earthquakes
is detailed in Section 3.2. The microseism intensity then has a median filter
applied over a 7 day window with 50% overlap to ensure the effect of one-off
seismic events not directly removed are excluded from the analysis. We flag
and exclude data which lies greater than 11 dB outside the mean of the series;
this number was chosen by visual inspection to remove only clearly erroneous
points. The full time series of the primary and secondary microseism intensity
at each station is available in the supplement to the main text.

The annual periodicity and seasonality of the microseism intensity at each
station (Figure 4) is assessed by fitting a Fourier series to the most recent
twelve years of data (due to reliable measurements available at Macquarie
Island only after late-2005), following Aster+2008. The Fourier series is
fitted to the log-scale intensity assuming a fundamental mode of period one
year, a temporal shift in the origin of up to one period, and including both
odd and even terms up to sixth-order. The fraction of the variation in the
microseism data explained by a periodic annual cycle is measured as the
statistic $r^{2}=1-SSR/SST$, where $SSR$ is the sum of squared differences
between the measured microseism intensities and the Fourier series fit, and
$SST$ is the sum of squared differences between the measured intensities and
the mean intensity. The East Antarctic stations typically have $r^{2}\sim
73\%$ of their signal explained by the best-fit periodic annual cycle (median
for both primary and secondary bands; see Table 2) when applying a longer 14
day median filter, compared to around 50% for the Southern Ocean seismographs.
Diurnal cycles and short timescale variability that is not cyclical in nature
are largely excluded by our choice of the 14 day median filter; the $r^{2}$
statistic reduces appreciably if this source of variability is included though
remains unchanged for longer median filters. Departures from the best-fit
curves likely result from moderate to long timescale climate cycles leading to
variability in the signal between years (in either amplitude or time of year).

Figure 4: Fourier series fits to the annual cycle in the primary (top panel)
and secondary (bottom panel) microseism measured at the four East Antarctic
stations (solid red) and seven Southern Ocean stations (dashed blue) between 1
January 2006 and 1 January 2018. The intensity of the primary (and secondary)
microseism differs between stations; to enable easy comparison the annual
cycles from all stations are shifted to have a mean of zero decibels. The
unscaled annual cycles are shown in the supplementary material.

###  2.3 East Antarctic sea ice concentrations

Sea ice concentrations estimated from satellite microwave backscatter
observations were obtained for the Southern Hemisphere from the National Snow
and Ice Data Center, NSIDC Cavalieri . (1996, updated yearly). Daily
concentration measurements were retrieved for $25\times 25\rm\,km$ grid cells
at all southern latitudes below $39$°S for the twelve years prior to 1 January
2018 (or between 1 January 1992 and 1 January 2018 for DRV, see Section 3.1).
The polar stereographic projection used by the NSIDC has a nominal grid
resolution set at 70 degrees latitude to minimise distortion in the marginal
ice zone Snyder (1987); we apply a correction when determining the area of the
cells. We assume that a cell covered by at least 15% sea ice has a damping
effect on microseism activity from storms. However, some care must be taken
when interpreting these measurements as satellite sensors are known to
misreport summertime surface melt as open water leading to underestimates of
the concentration. Masks are applied to divide the sea ice concentration
observations into geographic regions (see Figure 2) and to remove ice-shelves
and continental Antarctica from our analysis Parkinson Cavalieri (2012). In
addition, the area of the Southern Pacific Ocean, Southern Indian Ocean and
the Ross Sea covered with at least a 15% sea ice concentration is calculated
for each day between 2006 and 2017. We apply a median filter with a 7 day
window and 50% overlap to the sea ice concentration values. This removes any
spurious values and provides consistency with the method used for the
microseism measurements.

Microseism intensity is expected to have an inverse relationship to sea ice
coverage due to the sea ice shielding the Antarctic continent from the
influence of ocean storms. The sea ice coverage in each region of the Southern
Ocean is normalised so that at the (average) summer minimum the sea ice
coverage is 0 and at the (average) winter maximum the sea ice coverage is 1;
this scaling is only applied to enable easy comparison between different
oceanic regions (raw measurements are shown in the supplementary material).
The average annual cycle of sea ice coverage in each region is defined for the
purpose of the current study by fitting a Fourier series to the data as for
the microseism intensity.

###  2.4 SAM index data

Daily values of the SAM index were retrieved from the National Weather Service
Climate Prediction Center Mo (2000). Their SAM index is defined as the leading
EOF of the mean monthly 700 hPa height anomalies in the Southern Hemisphere
poleward of 20 degrees; daily values are found by projecting short timescale
anomalies onto the fitted EOFs. When comparing to the microseism intensities,
we apply a median filter with a 7 day window and 50% overlap to the SAM index
to characterise the average behaviour of the SAM over the timescale of the
microseism measurements.

##  3 Methods

###  3.1 Correlation of sea ice concentration with microseism intensity and
SAM Index

The relationship between sea ice concentration in a given cell and the
microseism intensity as observed at the longest operating East Antarctic
seismograph, DRV, is assessed by examining their correlation over a 26 year
period. Individual years with extreme values in sea ice concentration are
expected to have extreme values in the microseism intensity. This analysis is
performed using the sea ice concentrations in each $25\times 25\rm\,km$ grid
cell in the Southern Pacific Ocean, Southern Indian Ocean and the Ross Sea
regions. Following the method of Anthony+2017-JGR, the median sea ice
concentration is calculated for each of the twelve 30.44 day months in the
calendar years from 1 January 1992 to 1 January 2018. Vectors of length $n=26$
years are created for each month for each sea ice grid cell. Similarly, the
median primary and secondary microseism intensity at station DRV over this 26
year period is grouped into vectors for the twelve 30.44 day long months. The
correlation between the sea ice concentration in each cell and the microseism
intensity at Dumont d’Urville is calculated by applying the Spearman rank
correlation coefficient to the vectors for each month (this statistic
considers only the relative order of extreme months). This correlation
analysis is repeated for each sea ice grid cell to produce a map of the
correlation for each month, as shown in Figure 5 for the primary and second
microseism intensity. Spearman rank coefficients with an absolute value
greater than 0.39 are statistically significant at the $2\sigma$ level for the
26 independent years of observations used in the analysis.

The correlation of sea ice concentration with the SAM index, associated with
the concentration of mid-latitude wind belts towards Antarctica in its
positive polarity, is also shown for the same range of years as the microseism
intensity given above. The median SAM index is calculated for each 30.44 day
long month and the correlation with each of the sea ice grid cells measured
(right-hand column of Figure 5). However, the characteristic timescale of the
SAM index is less than one month, and so many months may have periods of both
positive and negative SAM phase. The 26 years from 1992 to 2018 are ranked for
each calendar month based on the ratio of time spent in the positive polarity
to that in the negative SAM phase; i.e. the highest/lowest ranked years for a
given month will be those exclusively in the positive/negative phase, whilst
months with short timescale variations will have 50th percentile rankings. In
this manner, we use the Spearman rank coefficient to assess the relationship
between the average monthly sea ice concentration and variations in the SAM
index on both short and long-term timescales.

Figure 5: Correlation of monthly average sea ice concentration around East
Antarctica; with monthly primary microseism intensity for station DRV (left),
monthly secondary microseism intensity for DRV (centre), and SAM index
(right). Microseism values are calculated for 26 years of seismic records
between 1992 and 2018. Spearman rank correlation coefficients $|r|>0.39$ are
statistically significant at the $2\sigma$ level for our analysis over this
time frame. Correlations are shown as monthly averages for January, April,
July and October. The location of DRV is shown using a black filled circle;
the location of CASY is shown with an unfilled circle for comparison.

###  3.2 Correlation of extremal microseism events with SAM index

The index hour technique employed by Aster+2010-GRL is applied to the eleven
East Antarctic and Southern Ocean stations to identify extremal ocean wave or
microseism events, as described below. Firstly, earthquakes must be flagged as
these would otherwise comprise the majority of extremal events in our time
series. The microseism spectral amplitudes generated by ocean waves decline
rapidly at periods longer than 25 seconds Bromirski . (1999) whereas the
signature from earthquakes can be seen to longer periods (see Figure 3).
Earthquake dominated spectra are therefore removed from the time series at
each station based upon the spectrum at periods $>$30 seconds. PSDs with 95%
of period bins above 30 seconds exceeding the 80th time-averaged PSD
percentile are flagged and removed to produce an earthquake-culled data set
McNamara . (2009). These selection parameters are tailored to a region with
relatively low local seismicity.

Extremal ocean wave or microseism events are found in the earthquake-culled
time series where the 95th percentile in the integrated microseism power is
exceeded for three or more contiguous 3 hour PSD estimates (i.e. continuous 6
hours). The time and duration of these events is catalogued for each station
(i.e. number of index hours). The earthquake-culled time series is similarly
searched for availability of measurements comprising three or more contiguous
3 hour PSD estimates to provide a control level. The extremal microseism index
in a given interval (e.g. a month) is then defined as the fraction of 6 hour
windows that have extremal activity; i.e. the ratio of the duration of times
identified as extremal events to the duration of all times forming at least a
contiguous 6 hour block. The expected value of the extremal microseism index
is thus $<0.05$ (assuming the 95th percentile cut), though the mean annual
value of the index is normalised to unity in this work for clarity.

The extremal primary and secondary microseism indices are further grouped
based upon the value of the Southern Annular Mode (SAM). The twelve year
period from 1 January 2006 through 1 January 2018 is subdivided into 30.44 day
long months and the median SAM index for each month calculated. Months with an
index below the 30th percentile are assumed to be predominantly in the
negative phase and those with an index above the 70th percentile are in the
positive phase. The mean extremal primary and secondary microseism indices are
thus calculated for each of the twelve 30.44 day long months comprising a
calendar year, but including only the months classified as in either the
positive or negative SAM phase (Figure 6).

Figure 6: Extremal microseism indices (see text for explanation), for 12 years
of observations, binned into 30.44 day months and grouped based upon the value
of the SAM in the corresponding 7 day window. Extremal microseism indices for
negative SAM values (below the 30th percentile) are shown as unfilled bars.
Microseism indices for positive SAM values (above the 70th percentile) are
shown as shaded bars. The extremal microseism indices in the top panel are
calculated using the primary microseism and normalised so that duration of all
storms in an average month is unity. The indices in the bottom panel are
calculated for the secondary microseism.

##  4 Results

###  4.1 Microseism intensity

The component of the microseism intensity explained by a periodic annual cycle
is fitted by a sixth-order Fourier series (as explained previously) for each
of the eleven East Antarctic and Southern Ocean stations. The Fourier fits to
the secondary microseism intensity are shown in Figure 4 and are grouped based
upon their geographic location (i.e. East Antarctica or Southern Ocean). The
secondary microseism intensity at all East Antarctic stations increases 6-10
decibels above the time-averaged intensity during March then slowly drops off
before bottoming at 4-6 decibels below the average during November. In
contrast, the seven oceanic stations peak in July with a smaller 2-3 decibels
above the time-averaged intensity and bottom out 2-3 decibels below the
average in December. The fluctuation from the mean value of the primary
microseism intensity follows a similar pattern to that of the secondary
microseism across all stations.

The East Antarctic stations also show a greater degree of annual periodicity
than those in the Southern Ocean based on the fraction of the microseism
intensity explained by the Fourier fits (Table 2). Specifically, a median of
74% of the variation in the East Antarctic stations is due to an annual cycle
compared to 47% at the oceanic stations, with the remainder resulting from
sub-seasonal and interannual variations (see Figures S1 and S2 in the
supporting information). The March peak in the microseism intensity at the
four Antarctic stations is confidently detected above short timescale signals
superimposed on the annual cycle in each of the 12 years examined in this work
(Figure S8); the amplitude of this peak shows only minor interannual
variations (e.g. no unusually quiet or noisy years). The July peak in oceanic
stations is similarly higher than the level in December each year (Figures S9
and S10). The primary microseism intensity shows a slightly greater degree of
annual periodicity than the secondary microseism for seven of the eleven
stations though the difference is not statistically significant.

Table 2: The fraction of the variation in the microseism intensity explained by a periodic annual cycle, as fitted using a high-order Fourier series. The first column is the name of the station, the second and fourth the day of the fitted annual cycle with maximum microseism intensity in the primary and secondary bands respectively, and the third and fifth columns are the corresponding $r^{2}$ statistics of the Fourier fits. Seismograph | Primary microseism | Secondary microseism
---|---|---
| peak day | $r^{2}$ | peak day | $r^{2}$
Casey Station | March 12 | 0.73 | March 29 | 0.70
Dumont d’Urville | February 24 | 0.67 | March 16 | 0.54
Mawson Station | March 10 | 0.80 | March 18 | 0.76
Scott Base | March 5 | 0.74 | March 12 | 0.79
Crozet Islands | July 12 | 0.51 | July 3 | 0.55
Hobart | July 20 | 0.43 | July 13 | 0.34
Macquarie Island | June 9 | 0.49 | July 6 | 0.46
Narrogin | July 18 | 0.43 | July 21 | 0.60
Nouvelle-Amsterdam | July 16 | 0.53 | July 3 | 0.49
Port aux Français | July 18 | 0.59 | July 7 | 0.63
Wellington | June 28 | 0.46 | July 5 | 0.36

The increase in microseism intensity during the southern winter at the seven
oceanic stations is explained by seasonal storm activity. The East Antarctic
stations meanwhile are shielded by sea ice throughout the winter which is
expected to dampen storm activity and thus the microseism intensity Grob .
(2011). The correlation between sea ice and microseism intensity is explored
further in the next section.

###  4.2 Sea ice extent and microseism intensity correlation

Sea ice is expected to impede the generation of both the primary and secondary
microseisms at Antarctic stations through the damping and continental shelf
shielding of ocean waves (preventing the formation of microseisms in proximity
to the coast). The annual periodicity in the microseism intensities at Casey
Station is compared to the annual cycle in the area of the South Pacific Ocean
exposed through ice melt in the top panel of Figure 7 (see Figure S3 in the
supporting information for fit to the raw data). The area of ocean covered by
sea ice is plotted on an inverted axis to more easily examine its correlation
with the microseism intensity, noting these variables are expected to be
negatively correlated for Antarctic stations. The annual cycle in the inverted
sea ice coverage has a comparable shape to the microseism intensity (in log
units/decibels), with the minimum ice coverage occurring in February and the
maximum coverage in October. The peak in the primary microseism intensity lags
the inverted sea ice coverage by approximately a month whilst the peak in the
secondary microseism lags the primary by a further month. A similar pattern is
observed at Dumont d’Urville, Mawson Station and Scott Base as shown in Figure
7.

Figure 7: Fourier series fits to the annual cycle in the primary (solid black)
and secondary (dashed black) microseism measured at the four Antarctic
stations between 1 January 2006 and 1 January 2018. The intensity of the
primary and secondary bands differ by a few orders of magnitude; to enable
easy comparison these are both shifted to have a mean of zero decibels. The
Fourier series fit to the sea ice observations in the region of the Southern
Ocean surrounding the relevant station are plotted in cyan for comparison.
Note that the sea ice coverage axis is reversed to explore the potential
causal relationship with microseism intensity.

The fraction of the annual cycle in the primary and secondary microseisms at
each station explained by seasonal sea ice melt, and the time lag between
these processes, is investigated by fitting the inverted sea ice coverage
Fourier series to the microseism intensity Fourier series. The amplitude,
phase (or time lag) and vertical offset of the sea ice Fourier series are free
parameters in these fits. The time lags that best align the annual cycles in
the primary and secondary microseism intensities to the cycles in the
(inverted) sea ice coverage at the four East Antarctic stations are shown in
Table 3; the $r^{2}$ statistic of the fits is also provided. The primary
microseism lags the annual sea ice melt cycle by between 10 and 27 days whilst
the secondary microseism lags the ice melt by between 21 and 56 days.
Variation in time lags between stations is due to their disparate geographic
locations; e.g. the sea ice may impinge on the continental shelf which is
responsible for generating the primary microseism at different times near each
station. The sea ice melt cycle leads the annual cycle in the primary and
secondary microseism intensity at greater than the $15\sigma$ level at each
East Antarctic station. The annual cycle in the primary microseism intensity
further leads the cycle in the secondary microseism, significant at least at
the $5\sigma$ level. These results support the hypothesis that increased sea
ice coverage leads to suppressed microseism intensities in coastal East
Antarctica; i.e. as expected, the annual cycles in sea ice extent and
microseism intensity are inversely related, with a fitted annual phase shift
of between 125 and 170 degrees (4-5 months). Moreover, sea ice coverage
explains between 78 and 93% of the variation in the annual cycle in the
primary microseism intensity and between 85 and 95% of the variation for the
secondary microseism.

Maximum microseism intensities lagging the summer sea ice minimum may be
explained by strengthening ocean waves increasing storm activity in the
austral autumn. Meanwhile, the lag between the winter sea ice maximum coverage
and minimum microseism intensity is likely due to a general decline in
southern ocean storminess from October to December Young (1999). The relative
importance of the sea ice and ocean wave state controls on the microseism
intensity in East Antarctica are compared. We assume that the average Fourier
series for the microseism intensity (separately for the primary and secondary
bands) across the seven oceanic stations is representative of the microseism
signal generated by the ocean wave climate. This proxy for the annual cycle in
the ocean wave climate is fitted to the microseism intensities at the East
Antarctic stations as before, though assuming zero phase shift. The wave
climate proxy explains 13% of the variation in the annual cycle of the
secondary microseism intensity at Casey Station, with much smaller fractions
for the primary microseism and at the other stations. The seasonal variation
in sea ice extent is therefore the dominant control on the microseism
intensity in East Antarctica.

Table 3: Time lag and correlation between measurements of the primary and secondary microseism compared to the fractional sea ice coverage for the four East Antarctic stations. The first column is the name of the seismograph, the second the corresponding region of ocean, the third and fifth the delay between the sea ice observations and the primary and secondary microseisms respectively (the inverted sea ice coverage leads the increase in microseism intensity), and the fourth and sixth are the $r^{2}$ statistic of the correlation. Seismograph | Ocean | Primary microseism | Secondary microseism
---|---|---|---
|  | delay (days) | $r^{2}$ | delay (days) | $r^{2}$
Casey Station | Pacific Ocean | $26.9\pm 0.6$ | 0.82 | $56.3\pm 0.4$ | 0.91
Dumont d’Urville | Pacific Ocean | $9.7\pm 0.5$ | 0.87 | $41.2\pm 0.5$ | 0.85
Mawson Station | Indian Ocean | $16.4\pm 0.7$ | 0.78 | $20.9\pm 0.5$ | 0.92
Scott Base | Ross Sea | $15.8\pm 0.3$ | 0.93 | $27.6\pm 0.3$ | 0.95

###  4.3 Seasonality of extremal microseism events

The seasonal variation in the extremal microseism index is examined for the
four East Antarctic and seven Southern Ocean stations in months where the
Southern Annular Mode (SAM) is classified as either the positive or negative
polarity. The seasonality of the secondary microseism index for the Antarctic
stations is shown in the histograms in the bottom panel of Figure 6 (red
bars). The overall pattern seen in the secondary extremal microseism index for
East Antarctic stations is driven by the same mechanisms as the microseism
intensity previously examined; here we are interested in the difference
between the positive and negative phases. In general, the extremal microseism
index is higher from March to April during the negative SAM phase but higher
from June through December during the positive phase. Weak seasonal variations
with the SAM are seen in the extremal primary microseism index at the East
Antarctic stations, as shown in the top panel of Figure 6, however the
patterns are not consistent between stations. The number of extremal events in
the primary band at Mawson and Scott Base also shows minimal variation
throughout the year in stark contrast to the strong annual cycle seen in their
microseism intensity. Meanwhile, the extremal microseism indices at the
oceanic stations show only a weak relationship with the SAM in both the
primary and secondary bands (see Figure 6). The secondary microseism index at
NWAO (Narrogin, Western Australia) is however notably higher during the
negative polarity from June to September. The significance of these findings
and a comparison with previous studies in this field is made in the discussion
(Section 5).

The correlation between the extremal microseism index at the four East
Antarctic stations on interannual timescales is examined in Figure 8. The
number of extremal events measured at Casey Station and Dumont d’Urville peaks
between 2005 and 2009, and is at a minimum between 2011 and 2013. By contrast,
the number of extremal events at Mawson Station peaks between 2012 and 2014,
whilst Scott Base has a constant level across all years.

##  5 Discussion

The microseism study in this work examines the correlation between sea ice
coverage, and the SAM index, with the microseism intensity across East
Antarctica and the surrounding Southern Ocean. Similar research has previously
been undertaken by Anthony+2017-JGR focused on the northern tip of the
Antarctic Peninsula and the Drake Passage, using measurements at Palmer
Station (PMSA) and East Falkland Island (EFI) respectively. This previous work
found that the primary and secondary microseism intensity at Palmer Station
peaks in May, falls until September, rises to a small peak in November, before
reaching a minimum (of similar level to September) around January. By
contrast, the intensities in East Antarctic peak two months earlier in March
and fall steadily until November before increasing again. The shifted peak in
the microseism intensity results from the earlier annual sea ice expansion
around East Antarctica (e.g. these stations are between 2 and 15° further
south). The increasing storm activity into the austral autumn/winter is thus
detected at Palmer Station for longer before being suppressed by the growing
sea ice. Similarly, the increase in microseism intensity (after the minimum)
occurs two months later in East Antarctica than at Palmer Station as the sea
ice retreat occurs earlier along the northern tip of the Antarctic Peninsula.
The annual pattern in the microseism intensities measured at East Falkland
Island is similar to the oceanic stations of our current investigation.
Anthony+2017-JGR suggest that the primary microseism at EFI results from
Rayleigh waves generated off the west coast of the Antarctic Peninsula and
therefore remain somewhat linked to seasonal variations in sea ice extent (the
primary microseism intensity between May and September is suppressed); however
our oceanic stations show no clear signature of the annual sea ice cycle due
to their greater distance from the Antarctic continent. Grob+2011 also
conducted a initial analysis of the relationship between sea ice extent and
microseism intensity at coastal stations across the Antarctic continent
obtaining similar results on both the Antarctic Peninsula and East Antarctica.

The secondary microseism signal at East Antarctic stations is found to lag the
primary microseism by between 4.5 and 32 days, with the greatest time delays
at Casey Station and Dumont d’Urville. The time lag between the annual cycles
in the two microseism bands is significant at the $5\sigma$ level. The large
time lag is indicative of different source generation regions for the primary
and secondary microseisms. Primary microseisms generated through the
interaction of ocean waves on the continental shelf are expected to be quickly
impeded as sea ice forms. The concentration of near-coastal sea ice
surrounding Dumont d’Urville has a significant negative correlation with the
primary microseism intensity well into the austral winter (Figure 5); this is
consistent with the findings of Anthony+2017-JGR at Palmer station. By
contrast, secondary microseisms generated through the interaction of opposing
wave groups in the deep ocean are much less sensitive to near-coastal sea ice;
the secondary microseism shows minimal correlation with the sea ice
concentration beyond the austral autumn. These findings may aid efforts to
reconstruct the spatial and temporal distribution of sea ice around Antarctica
based on the microseism amplitudes Cannata . (2019).

The duration of extremal secondary microseism events measured at the East
Antarctic stations is higher in March and April during a negative SAM phase
than for the positive polarity. Specifically, at Casey Stations during these
two months, the extremal index is between 45% and 110% greater during the
negative SAM polarity when measured using either the primary or secondary
microseism. At Scott Base, the duration of extremal events measured in March
using the secondary microseism are 140% greater during a negative phase than
for the positive SAM polarity. This trend is also seen in Dumont d’Urville,
though to a lesser degree, whilst Mawson Station (MAW) does not show a
consistent pattern for the two microseism bands. However, the spectral
amplitudes at MAW are also approximately an order of magnitude lower than the
other stations, which Grob+2011 suggest may indicate an incorrect instrument
response. By contrast, the comparable study of extreme microseism events
recorded on the Antarctic peninsula by Anthony+2017-JGR found increased storm
activity in the Drake Passage during the positive SAM polarity leads to
greatly increased microseism intensity levels (primarily from October to
January). During the austral winter, we similarly find an increased number of
extremal microseism events in the positive SAM phase.

These measurements of relatively subdued East Antarctic microseismic activity
in the austral autumn during positive SAM events are in contrast to the
expected intensification of the polar vortex and westerly circumpolar winds,
and the corresponding increase in Antarctic storm activity Hurrell van Loon
(1994); Meehl . (1998); Thompson Wallace (2000); Marshall . (2017). This
finding may be explained by the different growth rate of the sea ice during
the austral autumn for the two SAM polarities. The sea ice concentration
around East Antarctica is strongly correlated with the SAM index during April
(Figure 5). The reduced sea ice concentration is likely explained by the
warmer East Antarctic temperatures during the negative SAM phase Marshall
(2007). During years with a negative SAM polarity in much of the austral
autumn, warmer surface temperatures are expected to lead to slower or delayed
growth of the sea ice around East Antarctica. The delayed growth of the East
Antarctic sea ice thus exposes the coastal East Antarctic stations directly to
any storm activity, even though these storms may be weaker or less frequent
than during the positive polarity. By contrast, in the Antarctic Peninsula
Marshall (2007) the surface temperature is higher for the positive SAM phase;
greater storm activity should be detected during the positive polarity as the
sea ice retreat is faster or occurs earlier. Meanwhile, during the austral
winter the growth of new sea ice shifts deeper into the Southern Ocean; the
concentration of sea ice formed closer to the continent in previous months
becomes independent of the SAM index due to its short characteristic
timescale. The sea ice extent is therefore expected to become independent of
the SAM index on the order of one characteristic timescale after the summer
minimum (i.e. approximately one month; on longer timescales sea ice growth
will have occurred under both polarities).

The extremal microseism indices at Narrogin in Western Australia are also
increased during the negative SAM polarity, though in the austral
winter/spring during July, August and September. Specifically, the number of
extremal index is 130 to 340% higher during September for the negative SAM
phase, and up to 180% higher in July and August (Figure 6). These measurements
are consistent with the accepted interpretation that, during the negative SAM
polarity, the belt of strong westerly winds expands towards the equator
leading to increased storm activity over the Australian continent Hemer .
(2010); Marshall Thompson (2016); Wandres . (2018). Port aux Français
similarly has an increased secondary microseism extremal index over the
austral winter during the negative SAM polarity, whilst the winter extremal
index at Macquarie Island is instead higher in the positive phase due to its
more southern location (i.e. wind belt moves north of MCQ in the negative
phase). The extremal index at the other oceanic stations shows no clear
relationship with the SAM, perhaps due to the influence of local climate
drivers.

Figure 8: Extremal microseism index measured at the four East Antarctic
seismographs for their operational history (or after 1995). The black solid
line is the one-year moving average of the primary microseism index and the
dashed line is the moving average for the secondary microseism. The El Niño-
Southern Oscillation index is plotted on the secondary axis in purple for
comparison.

We examined the correlation of the extremal activity with other climate
indices (that have longer characteristic timescales than SAM) to explain the
interannual variability. Specifically, we find no correlation between the
microseism index at East Antarctic stations and the El Niño-Southern
Oscillation index (shown in Figure 8) or other widely-used Southern Hemisphere
climate indices. Interannual variations in the extremal microseism index
(associated with storm activity) may be explained by a combination of several
climate indices, including more localised climate variability such as the
Pacific South American teleconnection patterns Marshall Thompson (2016);
Marshall . (2017).

The improved knowledge of the annual cycle of ambient microseism levels will
aid the planning of Earth imaging studies in Antarctica. Researchers
collecting data for ambient seismic tomography, for example, can ensure that
instruments are deployed during months of strong signal levels and understand
departures from the omni-directional wavefield assumption. Further, as the
impacts of global change appear to be taking place in East Antarctica faster
than previously anticipated Witze (2018); Fox (2019), and since both sea ice
loss and ocean swell can play a part in ice shelf disintegration Massom .
(2018), microseism intensity may prove useful in understanding rapid changes
in these interacting Earth systems. Recent use of microseism intensity for the
detection of long-term climate trends by the oceanography community Stopa .
(2019) may be further developed using the relationships we have identified in
this contribution.

##  6 Conclusions

In this contribution, we have analysed 12-26 years of microseism observations
from 11 seismographs on the East Antarctic coast, in Australia, New Zealand
and the Southern Indian Ocean, to characterise the variability of recorded
primary and secondary microseism intensities. The relationship between
satellite observations of sea ice extent and microseism occurrence, and the
influence of the Southern Annular Mode (SAM) index were also investigated.

Annual variations in the sea ice extent are the dominant control on the
microseism intensity measured at East Antarctic stations, explaining a median
of $\sim$73% of both the primary and secondary band microseism signal. By
contrast, the Southern Ocean wave climate explains at most 13% of the
microseism signal in coastal East Antarctica. Consistent with previous
studies, the microseism intensity cycle in Antarctica shows a seasonal pattern
phase-shifted by 4-5 months compared to seismic stations from other Southern
Hemisphere continents, with higher levels observed in the austral summer and
autumn months.

We observe a time lag of approximately one month between the minimum sea ice
coverage and the maximum primary microseism intensity for East Antarctic
stations. The reforming sea ice quickly curtails the production of primary
microseisms with an inferred source region close to the continental shelf. The
lag of a further month between the primary and secondary microseism intensity
peaks suggests a deeper ocean source for the secondary microseisms.

Peaks in the number of extremal microseism events for the area of the Southern
Ocean investigated in this contribution occur during March and April, with the
peaks at Casey Station and Scott Base between 45 and 140% higher during these
months for a negative SAM polarity. Previous work Anthony . (2017) found the
opposite relationship is observed on the Antarctic Peninsula where an
increased number of extremal events are detected during the positive SAM
phase. This could be explained by the warmer surface temperatures present
around coastal East Antarctic during the negative polarity leading to a
weakened or delayed growth of sea ice in the early austral autumn; by
contrast, the surface temperatures are colder along the Antarctic Peninsula
during the negative phase. The storm activity in Western Australia is also
strongly anti-correlated with the SAM index; the number of extremal events in
September increases by between 130 to 340% during the negative polarity.

We have found microseism intensity at stations in the Southern Ocean exhibit a
region-specific response to variations in the SAM climate index, in contrast
to the consistent annual pattern seen across coastal Antarctica due to
seasonal sea ice growth. Understanding such controls on microseism intensity
will prove valuable in the use of microseisms as a proxy observable for the
ocean wave state to study the impacts of global change on East Antarctica. In
particular, both sea ice and ocean swell have been observed to foreshadow ice
shelf disintegration. Constraints from seismology, together with in-situ ocean
wave observations, can assist in the calibration of satellite data to
investigate both interannual variability in the Southern Ocean wave state and
any underlying climate trends from ocean wave hindcast reanalyses.

###### Acknowledgements.

This research was supported by the Australian Research Council through the
Discovery Program (Project DP150101005) and the Special Research Initiative
for Antarctic Gateway Partnership (Project SR140300001). We thank Rob Anthony
and an anonymous reviewer for their helpful and constructive feedback that has
improved this manuscript. The facilities of the IRIS Data Management Center
were used for access to waveforms and related metadata, used in this study.
IRIS Data Services are funded through the Seismological Facilities for the
Advancement of Geoscience and EarthScope (SAGE) Proposal of the National
Science Foundation under Cooperative Agreement EAR-1261681. This work included
data from the AU, G, II, and IU seismic networks obtained from the IRIS data
centre; https://doi.org/10.18715/GEOSCOPE.G, https://doi.org/10.7914/SN/II,
and https://doi.org/10.7914/SN/IU.

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