Anthropogenic and internal drivers of wind changes over the Amundsen Sea, West Antarctica, during the 20th and 21st centuries

Abstract

Ocean-driven ice loss from the West Antarctic Ice Sheet is a significant contributor to sea-level rise. Recent ocean variability in the Amundsen Sea is controlled by near-surface winds. We combine palaeoclimate reconstructions and climate model simulations to understand past and future influences on Amundsen Sea winds from anthropogenic forcing and internal climate variability. The reconstructions show strong historical wind trends. External forcing from greenhouse gases and stratospheric ozone depletion drove zonally uniform westerly wind trends centred over the deep Southern Ocean. Internally generated trends resemble a South Pacific Rossby wave train and were highly influential over the Amundsen Sea continental shelf. There was strong interannual and interdecadal variability over the Amundsen Sea, with periods of anticyclonic wind anomalies in the 1940s and 1990s, when rapid ice-sheet loss was initiated. Similar anticyclonic anomalies probably occurred prior to the 20th century but without causing the present ice loss. This suggests that ice loss may have been triggered naturally in the 1940s but failed to recover subsequently due to the increasing importance of anthropogenic forcing from greenhouse gases (since the 1960s) and ozone depletion (since the 1980s). Future projections also feature strong wind trends. Emissions mitigation influences wind trends over the deep Southern Ocean but has less influence on winds over the Amundsen Sea shelf, where internal variability creates a large and irreducible uncertainty. This suggests that strong emissions mitigation is needed to minimise ice loss this century but that the uncontrollable future influence of internal climate variability could be equally important.

Full text

The Cryosphere, 16, 5085–5105, 2022
https://doi.org/10.5194/tc-16-5085-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Anthropogenic and internal drivers of wind changes over the
Amundsen Sea, West Antarctica, during the 20th and 21st centuries
Paul R. Holland1, Gemma K. O’Connor2, Thomas J. Bracegirdle1, Pierre Dutrieux1, Kaitlin A. Naughten1,
Eric J. Steig2,3, David P. Schneider4,5, Adrian Jenkins6, and James A. Smith1
1British Antarctic Survey, High Cross, Madingley Road, Cambridge, CB3 0ET, UK
2Department of Earth and Space Sciences, University of Washington, Seattle, Washington, USA
3Department of Atmospheric Sciences, University of Washington, Seattle, Washington, USA
4National Center for Atmospheric Research, Boulder, Colorado, USA
5Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado, USA
6Department of Geography and Environmental Sciences, Northumbria University, Newcastle, UK
Correspondence: Paul R. Holland (p.holland@bas.ac.uk)
Received: 16 June 2022 – Discussion started: 12 July 2022
Revised: 27 October 2022 – Accepted: 5 December 2022 – Published: 22 December 2022
Abstract. Ocean-driven ice loss from the West Antarctic Ice
Sheet is a significant contributor to sea-level rise. Recent
ocean variability in the Amundsen Sea is controlled by near-
surface winds. We combine palaeoclimate reconstructions
and climate model simulations to understand past and future
influences on Amundsen Sea winds from anthropogenic forc-
ing and internal climate variability. The reconstructions show
strong historical wind trends. External forcing from green-
house gases and stratospheric ozone depletion drove zonally
uniform westerly wind trends centred over the deep South-
ern Ocean. Internally generated trends resemble a South Pa-
cific Rossby wave train and were highly influential over the
Amundsen Sea continental shelf. There was strong interan-
nual and interdecadal variability over the Amundsen Sea,
with periods of anticyclonic wind anomalies in the 1940s
and 1990s, when rapid ice-sheet loss was initiated. Similar
anticyclonic anomalies probably occurred prior to the 20th
century but without causing the present ice loss. This sug-
gests that ice loss may have been triggered naturally in the
1940s but failed to recover subsequently due to the increasing
importance of anthropogenic forcing from greenhouse gases
(since the 1960s) and ozone depletion (since the 1980s). Fu-
ture projections also feature strong wind trends. Emissions
mitigation influences wind trends over the deep Southern
Ocean but has less influence on winds over the Amundsen
Sea shelf, where internal variability creates a large and irre-
ducible uncertainty. This suggests that strong emissions miti-
gation is needed to minimise ice loss this century but that the
uncontrollable future influence of internal climate variability
could be equally important.
1 Introduction
The West Antarctic Ice Sheet (WAIS) is losing ice at an
increasing rate (Shepherd et al., 2019) and forms a ma-
jor source of uncertainty in projections of global sea-level
rise (IPCC, 2019). The most rapid ice loss is occurring in
the Amundsen Sea sector, where thinning and retreat of the
floating ice shelves is causing acceleration of their tributary
glaciers (De Rydt et al., 2021). The ice loss is caused by
changes in melting of ice shelves by the ocean (Shepherd et
al., 2004), but it is not clear why this melting has increased,
leading to the current ice-sheet mass imbalance.
Several lines of evidence document the history of ice loss
in this region. A synthesis of geological and geophysical
datasets implies that the ice-sheet geometry was broadly sta-
ble in this region for ∼10 000 years prior to the current re-
treat (Larter et al., 2014). Sediment records from beneath
Pine Island Glacier ice shelf show that its grounding line
started to retreat from a prominent seabed ridge in the 1940s,
with the ice shelf finally detaching from this ridge in the
1970s (Jenkins et al., 2010; Smith et al., 2017). Ice veloc-
ity records from satellite data suggest that Amundsen Sea
Published by Copernicus Publications on behalf of the European Geosciences Union.

5086 P. R. Holland et al.: Wind changes over the Amundsen Sea
glacier discharge was accelerating from the earliest obser-
vations in the 1970s (Mouginot et al., 2014), while satellite
altimetry and interferometry data show glacier thinning and
grounding-line retreat occurring since at least the early 1990s
(Rignot et al., 2014; Paolo et al., 2015; Konrad et al., 2017).
Glacier discharge and thinning have accelerated markedly in
recent decades (Mouginot et al., 2014; Paolo et al., 2015;
Konrad et al., 2017; Shepherd et al., 2019). Overall, this ev-
idence suggests that ice loss was triggered in the 1940s and
evolved slowly but continually thereafter, accelerating from
the 1990s onwards.
There are several possible explanations for this history of
ice loss. First, there is evidence that the 1940s ice retreat
was triggered by a period of strong climate variability asso-
ciated with the tropical Pacific (Schneider and Steig, 2008;
Steig et al., 2012, 2013; Jenkins et al., 2018; Holland et
al., 2019). If this is the sole cause of the changes, the cli-
matic anomaly must have been an extremely unusual event
because the ice sheet was previously stable for millennia in
this region (Larter et al., 2014). Second, it is likely that on-
going ice loss is sustained by a range of ice and ocean feed-
backs, including grounding-line retreat towards a deeper bed
(Favier et al., 2014), increasing ice damage (Lhermitte et
al., 2020), freshwater- and iceberg-induced ocean changes
(Bett et al., 2020), and increased access of warm water
into sub-ice-shelf cavities (De Rydt et al., 2014). However,
the WAIS cannot be experiencing a purely unstable, self-
sustaining retreat because the rate of ice discharge remains
responsive to ocean variability (Christianson et al., 2016;
Jenkins et al., 2018). Finally, an overall warming of the
Amundsen Sea over the 20th century may have caused an
increase in melting that sustains the current ice loss (Hol-
land et al., 2019; Naughten et al., 2022). Any such centennial
change could be caused by a combination of anthropogenic
forcing and internal climate variability. All of these different
processes may be contributing to the ongoing ice loss, but the
relative contributions of a historical trigger, ice–ocean feed-
backs, and changes in climatic forcing are not yet known.
The Amundsen Sea is subject to strong wind-driven vari-
ability – primarily linked to the tropical Pacific (Lachlan-
Cope and Connolley, 2006; Ding et al., 2011; Steig et
al., 2012) – that is clearly reflected in ice-shelf melting
(Dutrieux et al., 2014; Jenkins et al., 2018) and the ice-sheet
response (Christianson et al., 2016; Jenkins et al., 2018).
Variable winds modulate the supply of relatively warm Cir-
cumpolar Deep Water onto the shelf, driving decadal anoma-
lies in ocean thermocline depth and ice-shelf melting (Thoma
et al., 2008; Kimura et al., 2017). This strong decadal vari-
ability means that any trends caused by anthropogenic cli-
mate forcing may not be detectable in the short ocean and
ice-sheet observational records (commencing in the 1990s)
or atmospheric reanalysis data (reliable only since 1979).
Climate model simulations suggest that a gradual west-
erly wind trend occurred over the Amundsen Sea shelf break
during the 20th century (Holland et al., 2019). Such westerly
wind trends are a very well established response of the South-
ern Hemisphere climate to anthropogenic forcings (Arblaster
and Meehl, 2006; Son et al., 2010; Thompson et al., 2011;
Gillett et al., 2013; Bracegirdle et al., 2014, 2020; Goyal et
al., 2021; Dalaiden et al., 2022). In ocean simulations, this
wind trend drives an increased prevalence of warm decadal
ocean anomalies and an increase in ice-shelf melting (Naugh-
ten et al., 2022), providing the first evidence that anthro-
pogenic forcings may have contributed to ice loss from the
WAIS. However, the importance of these externally forced
westerly wind trends has recently been challenged by palaeo-
climate reconstructions (Dalaiden et al., 2021; O’Connor et
al., 2021a). These reconstructions show that a local deepen-
ing of the Amundsen Sea Low dominates the wind trends,
leading to easterly trends over the Amundsen Sea shelf. The
larger-scale westerly trends are also found in the reconstruc-
tions but shifted further north. An analogous deepening of
the Amundsen Sea Low in recent decades (since 1979) is
thought to be largely driven by natural tropical Pacific vari-
ability (Raphael et al., 2016; Meehl et al., 2016; Purich et
al., 2016; Schneider and Deser, 2018). Therefore, the re-
constructions suggest that 20th-century wind trends over the
Amundsen Sea shelf, associated with the local Amundsen
Sea Low deepening pattern, were largely internally gener-
ated. Thus, the contribution of anthropogenic forcings to cen-
tennial wind changes in this region remains unclear.
It is crucial to quantify the contribution of anthropogeni-
cally forced climate change to past and future ice loss from
the WAIS. If the ice loss is dominated by internal climate
variability and ice–ocean feedbacks, it may be unavoidable
and/or irreversible on centennial timescales. If the ice loss
has a substantial anthropogenic component, it may respond
to future reductions in anthropogenic forcing. Quantifying
the interplay between these factors is therefore crucial to
adaptation and mitigation decisions. It is also important to
distinguish the influence of different anthropogenic forcings
(e.g. greenhouse gases (GHGs) versus ozone depletion), as
their mitigation is different. In this study, we combine palaeo-
climate reconstructions and climate model simulations to in-
vestigate historical wind trends over the Amundsen Sea and
their attribution to different forcings, as well as projected fu-
ture wind trends and their sources of uncertainty. Since wind-
forced ocean variability is known to influence ice loss in the
Amundsen Sea, understanding these wind changes is infor-
mative about the drivers of change in the WAIS.
2 Methods
2.1 Palaeoclimate reconstructions
Palaeoclimate reconstructions are used to understand histor-
ical changes in wind forcing over the Amundsen Sea, which
arise through the combined influence of external forcing and
internal variability. We use 1◦resolution annually resolved
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P. R. Holland et al.: Wind changes over the Amundsen Sea 5087
reconstructions of near-surface zonal winds and sea-level
pressure (SLP) spanning 1900 to 2005 from O’Connor et al.
(2021a). In addition, for this study, we extend the technique
to reconstruct near-surface meridional winds and sea-surface
temperatures (SSTs). The reconstructions are created using
an offline data assimilation method that combines the tempo-
ral variability from a sparse set of climate proxies with the
spatial covariance fields obtained from climate model sim-
ulations. Annually averaged anomaly states (relative to the
1961–1990 mean) are randomly drawn from climate model
output to form a “prior” ensemble, and this prior is used
for every year reconstructed (Hakim et al., 2016; Tardif et
al., 2019). This ensures that all temporal variance and trends
in the final reconstruction are generated from the proxy data
rather than the climate model. Proxy data used here include
the global PAGES2k proxy database (PAGES2k, 2017), sup-
plemented with additional ice-core accumulation and coral
records. This means that the reconstructions are well con-
strained over West Antarctica and the tropical Pacific, the
crucial regions of importance to this study.
O’Connor et al. (2021a) validate this reconstruction
against many other data sources, including other palaeocli-
mate reconstructions, modern atmospheric reanalyses (since
1979), longer-term reanalyses (since 1900), and various
Southern Annular Mode (SAM) indices. These datasets draw
on a variety of observations, including Antarctic station data,
but there are no long-term station data near the Amund-
sen Sea. Therefore, the most relevant validation compares
the reconstruction to modern reanalysis fields, which have
been well constrained over the Southern Ocean since 1979
following the onset of satellite infrared sounding (Hines et
al., 2000; Marshall, 2003; Marshall et al., 2022). This anal-
ysis shows that the reconstructions are most skilful over the
South Pacific, owing to the availability of proxy data used
in the reconstructions and the dominant climate modes in
this region (O’Connor et al., 2021a). The reconstructions are
also qualitatively in agreement with those of Dalaiden et al.
(2021), who use a different data assimilation method and a
proxy database focussed on the southern high latitudes. In
Sect. 3.1.3 below, we further validate the reconstructed zonal
winds over the Amundsen Sea against ERA5 reanalysis fields
and the SLP reconstruction of Fogt et al. (2019).
O’Connor et al. (2021a) pay particular attention to the in-
fluence of the climate simulations used as the prior, finding
that reconstructed trends are sensitive to the inclusion of an-
thropogenic forcing in the prior simulations. In this study, we
focus on reconstructions using the Community Earth System
Model 1 (CESM1) “Pacific Pacemaker” simulations as the
prior. This is an ensemble of historical simulations that not
only contain all anthropogenic forcings but also have east-
ern tropical Pacific SSTs constrained to follow observations
– the “pacemaker” (Schneider and Deser, 2018). We favour
these simulations because they should best represent both ex-
ternally forced and Pacific modes of variability. O’Connor et
al. (2021a) show that this prior creates reconstructions with
high skill throughout the South Pacific, and we find that the
reconstructions perform well when compared to ERA5 for
the time series of interest in this study (see Sect. 3.1.3).
We found that reconstructed near-surface wind trends near
the coastal regions of the Amundsen Sea are noisy and de-
viate significantly from geostrophic winds derived from the
reconstructed SLP. The derived geostrophic wind anoma-
lies are also better correlated with ERA5 reanalysis sur-
face wind anomalies. This is unsurprising because the re-
constructed SLP patterns show greater skill than near-surface
winds (O’Connor et al., 2021a) and should better reflect the
large-scale climate modes that are constrained by the re-
mote proxies. Near-surface winds are driven by the same
SLP of course but are also influenced by uncertain fea-
tures of the reconstructed near-surface atmospheric struc-
ture. Therefore, throughout this study we use geostrophic
wind anomalies from the reconstructed SLP. To make these
geostrophic winds most comparable to the climate model
winds considered next, a simple near-surface correction was
derived by comparing geostrophic and near-surface winds in
the CESM1 Large Ensemble, which is described below. The
correction consists of rotating the geostrophic winds by 10◦
clockwise and multiplying their amplitude by 0.9. This cor-
rection marginally improved the correlation of annual wind
anomalies to ERA5 reanalysis winds (see Sect. 3.1.3).
2.2 Climate model simulations
Climate simulations are used to understand the role of an-
thropogenic forcing in the past and future. By comparing
these simulations with the reconstructions, we are also able
to estimate the historical role of internal climate variability.
We consider near-surface winds, SLP, and SST from a total
of 180 simulations within 10 ensembles of simulations using
CESM1 under different forcings (Kay et al., 2015; England et
al., 2016; Sanderson et al., 2017, 2018; Schneider and Deser,
2018; Deser et al., 2020). The ensembles are fully described
in Table 1. Near-surface winds are output at the bottom pres-
sure level of the atmospheric model, whose height varies in
time and space but is around 50 m over our domain. The at-
mospheric model uses a 0.9◦latitude ×1.2◦longitude grid,
so all winds are binned onto a polar stereographic grid with
200 km resolution to clarify the plotting over the region of in-
terest. SLP and SST fields are considered on the native model
atmosphere and ocean grids, respectively.
Many features of this study are only possible because such
a wide range of simulations are available for CESM1. The
use of a single model is also necessary to ensure that the re-
sponses studied are driven only by the prescribed forcings.
However, this also means that our conclusions do not account
for model structural uncertainty. Fortunately, the CESM1
features a good representation of winds over the Amundsen
Sea (Holland et al., 2019) and contains a faithful represen-
tation of the Amundsen Sea Low (England et al., 2016) and
its crucial teleconnection to the tropical Pacific (Holland et
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5088 P. R. Holland et al.: Wind changes over the Amundsen Sea
Table 1. Description of the CESM1 model ensembles considered in this study. RCP denotes Representative Concentration Pathway.
Name Description Years nReference
LENS Large Ensemble: initial-condition ensemble of historical 1920–2100 40 Kay et al. (2015)
and projection simulations under all external forcings,
following RCP8.5 scenario for projections
PIctrl Pre-industrial control ensemble: 1760 years of control simulation 80 (see left) 22 Kay et al. (2015)
treated as ensemble of 22 non-overlapping 80-year simulations,
used for both historical and projection periods
PACE Pacific Pacemaker ensemble: same as LENS historical 1920–2013 20 Schneider and Deser (2018)
but with tropical Pacific SST anomalies
constrained to follow observations
MENS Medium Ensemble: same as LENS projections 2006–2080 15 Sanderson et al. (2018)
but following RCP4.5 forcing scenario
2.0degC Same as LENS projections but following a forcing 2006–2100 10 Sanderson et al. (2017)
scenario designed to keep global-mean temperatures
below 2 ◦C above pre-industrial levels
1.5degC Same as LENS projections but following a forcing 2006–2100 10 Sanderson et al. (2017)
scenario designed to keep global-mean temperatures
below 1.5 ◦C above pre-industrial levels
XOZO No-ozone-depletion ensemble: same as LENS but 1920–2005 8 England et al. (2016)
with stratospheric ozone fixed after 1955
XGHG No-GHG ensemble: same as LENS but with 1920–2080 20 Deser et al. (2020)
atmospheric GHGs fixed at 1920
XIND No-industrial-aerosol ensemble: same as LENS but 1920–2080 20 Deser et al. (2020)
with industrial aerosols fixed at 1920
XBMB No-biomass-burning ensemble: same as LENS but 1920–2029 15 Deser et al. (2020)
with biomass-burning aerosols fixed at 1920
al., 2019). It also produces a good general representation of
a wide range of other variables around Antarctica, including
SSTs, SLP, sea ice, ice-sheet surface mass balance (Agosta
et al., 2015; Lenaerts et al., 2016), and ocean conditions in
the Amundsen Sea (Barthel et al., 2020).
CESM1 does have some biases that are pertinent to the
present study. The westerly winds and absorbed shortwave
radiation over the Southern Ocean are both too strong (Kay et
al., 2016), and, like most climate models, historical CESM1
simulations feature unrealistic Antarctic sea-ice loss and
ocean surface warming trends from 1979 (Schneider and
Deser, 2018). These sea-surface trend biases do not seem to
heavily influence model winds, as the simulations accurately
represent pressure trends over the South Pacific (Schnei-
der and Deser, 2018; England et al., 2016) and wind trends
over the Amundsen Sea (Holland et al., 2019) over this time
period. On centennial timescales, CESM1 wind trends in
this region are representative of the wider Coupled Model
Intercomparison Project 5 (CMIP5) ensemble (Holland et
al., 2019), and we find that the ensemble of CESM1 histor-
ical trends comfortably includes the reconstructed historical
trends (see Sect. 3.1.2). Despite these encouraging results, it
remains possible that the model has an excessive wind re-
sponse to external forcing, and this should be kept in mind as
a source of uncertainty in using the model results to separate
the forced and internal components of the circulation history.
This study considers trends during the period 1920–2080,
for which most simulations are available (Table 1). This is di-
vided into two equal “historical” (1920–2000) and “future”
(2000–2080) periods, which are appropriate to climatic forc-
ing of the WAIS. The year 1920 pre-dates the recent ice-
sheet retreat (Smith et al., 2017), a modelled warming of
the Amundsen Sea (Naughten et al., 2022), and most anthro-
pogenic climate forcing (Kay et al., 2015). The WAIS was
rapidly losing mass well before 2000 (Mouginot et al., 2014;
Konrad et al., 2017). Therefore, any changes during 1920–
2000 are pertinent to the attribution of the ongoing ice loss.
The full consequences of ice-sheet melting will take cen-
turies to emerge, but climatic forcing in the decades prior to
2080 is the subject of immediate adaptation and mitigation
decisions.
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P. R. Holland et al.: Wind changes over the Amundsen Sea 5089
The central ensemble considered is the CESM1 “Large
Ensemble” (LENS) (Kay et al., 2015), a perturbed-initial-
condition ensemble from 1920–2100 using known exter-
nal forcings (GHGs, stratospheric ozone depletion, aerosols,
land-use change, solar, volcanic) up to 2005 and the strong
radiative forcing RCP8.5 scenario thereafter. All other en-
sembles use the same model as LENS but with different forc-
ings. By calculating the differences between ensembles with
and without particular forcings, we are able to isolate the lo-
cal climate “responses” to those forcings.
The Pacific Pacemaker (PACE) ensemble is used in the re-
construction prior. PACE uses the same external forcings as
LENS but is additionally constrained to follow the observed
history of SST anomalies in the central and eastern tropi-
cal Pacific Ocean (Schneider and Deser, 2018). In a previous
study (Holland et al., 2019), PACE was used to estimate the
role of internal variability associated with the tropical Pacific.
In the present study this function is performed by the palaeo-
climate reconstructions, which allow us to estimate the in-
fluence of variability from all sources, Pacific and otherwise.
This change in approach has important consequences for the
results, as discussed below.
A “pre-industrial control” ensemble (PIctrl) is used to as-
sess the significance of externally forced signals. PIctrl is
constructed from a long simulation without external forcings
(Kay et al., 2015), by splitting it into 22 non-overlapping 80-
year periods and then using the resulting ensemble for both
the historical and the future periods.
We consider four ensembles of projections under differ-
ent future forcing scenarios. LENS experiences an extreme
high-forcing scenario, RCP8.5 (van Vuuren et al., 2011).
The CESM1 “Medium Ensemble” (MENS) (Sanderson et
al., 2018) follows RCP4.5, an intermediate-forcing sce-
nario. The CESM1 “low-warming” ensembles (1.5degC and
2.0degC) are subjected to forcings designed never to exceed
global warming of 1.5 and 2 ◦C above pre-industrial levels,
targets that are associated with the Paris Agreement (Sander-
son et al., 2017). In CESM1, both scenarios require negative
net carbon emissions before 2100. Each member of these fu-
ture ensembles starts in 2005 and is a continuation of a his-
torical member from LENS, so we concatenate the appropri-
ate historical and future simulations in order to study future
trends from 2000.
Four further ensembles allow us to determine the individ-
ual influences of GHGs, ozone depletion, industrial aerosols,
and biomass-burning aerosols (England et al., 2016; Deser
et al., 2020). For each of these forcings, we consider an en-
semble of “leave-one-out” simulations in which the forcing
is held fixed while all other forcings evolve. Subtracting each
of these ensembles from LENS isolates the response to each
excluded forcing (Deser et al., 2020). For example, we intro-
duce a “no-greenhouse-gas” (XGHG) ensemble, which uses
the same forcings as LENS apart from GHG concentrations,
which are fixed at the 1920 level. We then derive the “GHG
response” by subtracting XGHG from LENS, yielding the
influence of GHGs that are increasing in LENS but fixed in
XGHG. This procedure is repeated for each of the forcings.
An advantage of this leave-one-out methodology is that the
derived difference includes nonlinear interactions between
the excluded forcing and all other forcings.
We consider a set of such climate responses, defined as the
difference in ensemble-mean winds between two ensembles
that have a difference in forcing. This has the advantage that
the significance of a response trend can be assessed by com-
paring the distributions of wind trends in the two ensembles.
Specifically, we conduct a two-sample ttest with unequal
sample size and variance to test the null hypothesis that the
ensembles have the same mean trend, and we treat the re-
sponse as significant if that hypothesis is rejected at the 95 %
confidence level (two-sided). For example, the GHG wind
trend response is significant if the distribution of LENS en-
semble trends differs from the distribution of XGHG ensem-
ble trends. We note that the number of simulations in each
ensemble varies (Table 1) and that this affects the signifi-
cance of the derived responses.
3 Results
3.1 Historical changes
3.1.1 Global patterns of change
Figure 1a shows a global map of the reconstructed his-
torical trends in SST and SLP. We re-iterate that this re-
construction is most skilful over the South Pacific, the fo-
cus of this study, and may be less trustworthy in other re-
gions (O’Connor et al., 2021a). The reconstruction shows
a widespread SST warming, particularly strong in the east-
ern and central tropical Pacific and a strengthening and
southward shift of the Southern Hemisphere westerly winds
(O’Connor et al., 2021a). Crucially, there is a substantial re-
gional deepening of SLP over the Amundsen Sea.
We interpret these features by separating their climatic
drivers using the climate model simulations. Figure 1b shows
the total externally forced changes (from GHGs, ozone
depletion, aerosols, land-use change, solar, and volcanic
sources) during this period, which is estimated as the ensem-
ble mean of the LENS trends. The LENS members contain
40 different random realisations of internal climate variabil-
ity, but they all have the same external forcing. Thus, if we
average over all members, the internal variability cancels out,
and the externally forced trends appear. This estimate agrees
with many previous studies that show historical external forc-
ing drove broad background warming over the South Pacific
(e.g. Deser et al., 2020; IPCC, 2019) and a zonally uniform
southward shift and acceleration of the westerlies (e.g. Ar-
blaster and Meehl, 2006; Goyal et al., 2021).
We isolate the contribution of individual external forc-
ings using the set of leave-one-out ensembles, as described
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5090 P. R. Holland et al.: Wind changes over the Amundsen Sea
Figure 1. Global trends in sea-surface temperature (colour) and sea-level pressure (contours) over the historical period. For SLP, positive and
negative contours are plotted in grey and black, respectively, and the zero contour is omitted. (a) Trends in the palaeoclimate reconstruction.
(b) Trends caused by external forcing. (c) Trends associated with internal variability (panel aminus panel b). (d, f) Trends caused by
greenhouse-gas forcing and ozone depletion, respectively. (e) Trends associated with a hypothetical trend of −1◦C per century in the
Interdecadal Pacific Oscillation index.
above. The GHG response in Fig. 1d is derived by subtracting
the XGHG ensemble-mean trends from the LENS ensemble-
mean trends, since the only difference between these en-
sembles is the increasing GHGs in LENS. The historical in-
crease in atmospheric GHG concentrations causes a strong
SST warming (Fig. 1d), which exceeds the total externally
forced warming (Fig. 1b) because aerosol forcing drove cool-
ing during this period (not shown), particularly in the North-
ern Hemisphere (Deser et al., 2020). GHG forcing drove over
half of the historical changes in the westerly winds (Arblaster
and Meehl, 2006; Gillett et al., 2013; Dalaiden et al., 2022).
Similarly deriving an “ozone response” in Fig. 1f by subtract-
ing XOZO from LENS, we see that ozone depletion drove
no strong SST trends but made a substantial contribution to
the wind trends (Thompson et al., 2011; Son et al., 2010).
The ozone wind response is slightly weaker than the GHG
wind response because ozone depletion is only influential
in summer months and our chosen historical trend period
includes many decades before the onset of rapid ozone de-
pletion. Aerosols drove no substantial Southern Hemisphere
wind trends over this period and are not considered further.
By subtracting the externally forced changes in Fig. 1b
from the total reconstructed trends in Fig. 1a, we may es-
timate the part of the trends caused by internally generated
variability (Fig. 1c). This approach requires that the recon-
struction is consistent with the LENS simulations. We are
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P. R. Holland et al.: Wind changes over the Amundsen Sea 5091
confident this is the case because the reconstructed trends
sit comfortably within the LENS ensemble (Sect. 3.1.2), and
the reconstruction uses CESM1 simulations as its prior en-
semble (Sect. 2.1). Figure 1c reveals a coherent pattern of
alternating SLP anomalies over the South Pacific that feature
a prominent trend towards low pressure over the Amundsen
Sea. These SLP trends are also supported by SST trends,
with warm anomalies wherever the associated wind trend
is southward and cold anomalies wherever it is northward
(Ciasto and Thompson, 2008). This demonstrates that the re-
constructed historical deepening of the Amundsen Sea Low
(Dalaiden et al., 2021; O’Connor et al., 2021a) is primarily
internally generated. External forcing drives a strong annu-
lar SLP trend but makes a much smaller contribution to the
local trend anomaly pattern, i.e. the deviation from zonal-
mean trends (Fig. 1b). The pattern of alternating SLP anoma-
lies in Fig. 1c is suggestive of a Rossby wave train, a well-
established mechanism for the poleward propagation of at-
mospheric anomalies over the South Pacific (Karoly, 1989;
Lachlan-Cope and Connolley, 2006; Ding et al., 2011; Steig
et al., 2012; Holland et al., 2019). However, the anomaly pat-
tern also contains features that are unusual.
It is well known that Pacific modes of variability – e.g. the
El Niño–Southern Oscillation (ENSO) and Interdecadal Pa-
cific Oscillation (IPO) – induce a global-scale atmospheric
response that is highly influential over the Amundsen Sea
(Karoly, 1989; Lachlan-Cope and Connolley, 2006; Ding et
al., 2011; Steig et al., 2012; Dutrieux et al., 2014; Jenkins et
al., 2018; Holland et al., 2019). Figure 1e illustrates the spa-
tial structure of the IPO response. This panel is constructed
from the regression of monthly ERA5 reanalysis SLP and
SST fields (Hersbach, 2020) onto the IPO tripole index (Hen-
ley et al., 2015) during the period 1979–2020. To compare
this panel with the others, it is plotted as the response of SLP
and SST to a hypothetical trend of −1◦C per century in the
IPO index. (For example, the hypothetical SLP trend (hPa
per century) is calculated by multiplying the SLP regression
onto the IPO index (hPa◦C−1) by the hypothetical trend in
the IPO index (−1◦C per century).) The illustrative value of
−1◦C per century was arbitrarily chosen to produce a deep-
ening pressure over the Amundsen Sea (Fig. 1e) that is com-
parable to the internally generated trend in the reconstruction
(Fig. 1c).
Comparing Fig. 1c and e suggests that the reconstructed
internally generated trends cannot be simply related to the
classical IPO and ENSO modes of Pacific variability. Trends
associated with these modes typically feature a zonal dipole
of SST and SLP anomalies over the tropical Pacific, and a
Rossby wave train radiating southwards from the western
tropical Pacific (Fig. 1e). Instead, the internally generated
trends appear to originate in the subtropical South Pacific
at approximately 30◦S (Fig. 1c). Crucially, the deepening
pressure trend over the Amundsen Sea in the reconstruction
occurs alongside a warming of the eastern tropical Pacific
(Fig. 1c), not the cooling expected from IPO or ENSO vari-
ability (Fig. 1e). We conclude that the internally generated
trends are not associated with these classical modes but do
follow a similar (Rossby wave) propagation. Similar results
are obtained with an alternative palaeoclimate reconstruc-
tion (Quentin Dalaiden, personal communication, 2022). It is
not necessarily surprising that this centennial variability has
a different pattern to interannual (ENSO) and interdecadal
(IPO) variability. Little is known about variability on these
centennial timescales. It is important to note that this discus-
sion refers only to 80-year trends, which arise through the
residual of many shorter IPO and/or ENSO anomalies of op-
posing sign. This aspect is revisited in Sect. 3.1.3 below.
3.1.2 Winds over the Amundsen Sea
Figure 2 has the same six panels as Fig. 1 but with the anal-
ysis repeated for near-surface wind trends rather than SLP
and SST and focussing on the Pacific sector of the Southern
Ocean. The magenta boxes show three areas of the Amund-
sen Sea that illustrate wind changes of potential relevance to
the WAIS – shelf sea, shelf break, and deep ocean. The wind
trends reflect the SLP trends in Fig. 1. Figure 2a shows the
reconstructed historical wind trends, with vectors coloured
black if either the zonal or the meridional wind trend is
significant relative to the interannual variability in the re-
construction. The reconstruction features significant westerly
wind trends over almost the entire Southern Ocean, apart
from the Amundsen Sea shelf. A cyclonic pattern of wind
trends is centred on the Amundsen Sea shelf break, arising
from the deepening pressure trend in Fig. 1a. This means that
the westerly trends to the north transition to easterly trends
near the Amundsen Sea coast.
Figure 2b, d, and f show the externally forced wind trends
derived from the climate model simulations. These figures
are derived in the same way as their counterparts in Fig. 1.
Wind trend vectors are coloured black if either the zonal
or the meridional wind trend is significant according to a
two-sample ttest that compares the distribution of trends
within two ensembles. For example, in Fig. 2b the externally
forced wind trends are estimated as the LENS ensemble-
mean trends. At each location, these are considered sig-
nificant if the distribution of trends in the LENS ensem-
ble has a significantly different mean from the distribu-
tion of trends in the PIctrl ensemble under the ttest. Fig-
ure 2d shows the GHG-induced trends derived by subtracting
XGHG ensemble-mean trends from LENS ensemble-mean
trends, with vectors shown as significant if the distributions
of trends in LENS and XGHG have a different mean under
the ttest. Figure 2f is the same for ozone.
Figure 2b, d, and f show that the historical westerly wind
trends are clearly associated with radiative forcing from
GHGs and ozone depletion (Arblaster and Meehl, 2006; Son
et al., 2010; Thompson et al., 2011; Gillett et al., 2013;
Dalaiden et al., 2022). In a feature that may be important to
climatic forcing of the WAIS, these anthropogenic changes
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5092 P. R. Holland et al.: Wind changes over the Amundsen Sea
Figure 2. Historical trends in near-surface winds over West Antarctica and the South Pacific. The panels are the same as Fig. 1. Black vectors
have a zonal or meridional wind trend significant at the 95 % confidence level. The significance test is different for the different panels (see
main text). The green contour is the 1000 m isobath at the continental shelf break. Magenta boxes show three selected regions of interest,
including (south to north) the Amundsen Sea shelf, shelf break, and deep ocean. These are the locations of time series in Figs. 4–6.
are strongest over the deep ocean but not significant over
the Amundsen Sea shelf. The shelf break sits in a transition
zone, where externally forced trends are significant overall
(Fig. 2b), but neither GHG nor ozone responses are signifi-
cant individually (Fig. 2d and f). This spatial pattern of re-
sponse to external forcings is also found in the wider ensem-
ble of climate models (Bracegirdle et al., 2014). The broader
westerly trends are driven by meridional gradients in atmo-
spheric warming (Harvey et al., 2014; Shaw, 2019), but local
controls on wind trends over the Amundsen Sea are complex
and require further study.
Over the time period considered, internal variability in-
duces wind trends of magnitude comparable to the exter-
nally forced trends (Fig. 2c). As expected from the deep-
ening SLP trends described above, these internally gener-
ated wind trends have a cyclonic orientation, centred over
the deep ocean north of the Amundsen Sea. As a result, in-
ternal variability is associated with an easterly trend over the
Amundsen Sea, in direct opposition to the externally forced
westerly trend. The internally generated trends are strongest
on the shelf and weaken to the north, in contrast to the exter-
nally forced trends. Considering this region alone, the inter-
nally generated trends closely resemble those expected from
the hypothetical negative trend in the IPO index (Fig. 2e).
As mentioned above, it is important to understand the level
of consistency between the palaeoclimate reconstruction and
climate model simulations, since the role of internal variabil-
ity is derived by taking their difference. Since the reconstruc-
tions use CESM1 simulations as their prior ensemble, in prin-
ciple there is no structural difference between the two. This
can be illustrated by comparing the reconstruction to individ-
ual simulations from the LENS ensemble. The LENS ensem-
ble members all have the same externally forced trends but
have 40 different realisations of the trends generated by in-
ternal variability. Figure 3 shows the reconstruction (top row)
alongside two ensemble members chosen manually to illus-
trate the range of internal variability (other rows). For each of
these sources, both the total historical trends (left panels) and
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P. R. Holland et al.: Wind changes over the Amundsen Sea 5093
the internally generated part only (right panels) are shown.
The internally generated trends are calculated, as before, by
subtracting the externally forced trends (the LENS ensem-
ble mean) from the total trends. The LENS ensemble mem-
bers produce a wide variety of internally generated trends.
By chance, LENS member 9 has a realisation of internal
variability that produces cyclonic trends, with a similar in-
fluence over the Amundsen Sea to the reconstruction. This
is not unusual within the ensemble and illustrates the con-
sistency between the reconstruction and LENS. By contrast,
LENS member 18 exhibits anticyclonic internally generated
trends. This highlights the importance of the reconstruction
to this study. Without the reconstruction, we would have to
treat all LENS members as being equally plausible estimates
of the real historical trends and would not be able to constrain
the important role of internal climate variability.
3.1.3 Temporal evolution of the anomalies
So far, we have only considered 80-year historical wind
trends, but there is substantial variability in the winds on
shorter timescales, and this is known to influence WAIS ice
loss (Christianson et al., 2016; Jenkins et al., 2018). Figure 4
shows time series of zonal wind anomalies within the three
magenta regions shown in Fig. 2 during 1900–2020, the full
time period of the palaeoclimate reconstruction and ERA5
reanalysis. The regions extend zonally from 102–115◦W
and meridionally from 73–75◦S (shelf), 70.2–71.8◦S (shelf
break), and 61–63◦S (deep ocean).
The statistics shown in each panel quantify the interan-
nual correlation in zonal wind anomalies between the recon-
struction and ERA5 during their period of overlap, 1979–
2005, showing that the reconstruction skill is higher over the
deep ocean and decreases towards the south (O’Connor et
al., 2021a). This validation may be extended further back in
time using zonal winds derived from the interpolated station-
based SLP reconstructions of Fogt et al. (2019). These sea-
sonal reconstructions are averaged to produce annual-mean
fields, and geostrophic winds are converted to near-surface
winds using the simple correction described in Sect. 2.1.
These derived winds are correlated to ERA5 only over the
deep-ocean region (r=0.44, p=0.01, 1979–2013). In this
region, zonal wind anomalies derived from O’Connor et al.
(2021a) and Fogt et al. (2019) are correlated to each other
with r=0.49, p < 0.01, during 1957–2005. This agreement
is encouraging given the complete independence of these
datasets and the remoteness of their observational constraints
from the Amundsen Sea.
Consistent with the cyclonic trends in Fig. 2a, over the
1920–2000 period the reconstruction features westerly trends
in the deep ocean, no trend at the shelf break, and easterly
trends over the shelf (Fig. 4). However, there is substantial
variability in all three regions on both interannual and inter-
decadal timescales. Several large multi-annual anomalies are
visible in the record, which overall appears to be dominated
by interdecadal variability. Shelf-break winds contain vari-
ability that reverses on a timescale of approximately 50 years
(Fig. 4b). We are not aware of any previous studies docu-
menting this slow wind variability in this region, which may
be crucial to the WAIS.
In principle, this wind variability arises through a com-
bination of external forcing and internal climate variabil-
ity. In common with the previous sections, the individual
contributions can be separated by combining reconstructions
and simulations. Figure 5 shows time series of the separate
contributions to the zonal wind anomalies, for the three se-
lected regions, over the historical and future time periods.
The two columns both show the same annual time series
in thin lines but then emphasise either interdecadal evolu-
tion (left) or centennial trends (right) in thick lines. The to-
tal externally forced zonal wind anomalies for each location
are the LENS ensemble mean. Historical GHG-forced and
ozone-forced anomalies are the LENS ensemble mean minus
the XGHG and XOZO ensemble means, respectively. The in-
ternally generated anomalies are the reconstructed anomalies
minus the externally forced anomalies (the LENS ensemble
mean).
Significant externally generated westerly wind changes
occur over the deep ocean and shelf break during the
historical period (Fig. 5a–d, black lines). Over the deep
ocean, GHG-forced changes are established from approxi-
mately 1960 onwards, while ozone-induced changes increase
rapidly after approximately 1980 (Fig. 5a, blue and green
lines). This leads to significant historical GHG- and ozone-
forced trends (Fig. 5b). As noted above, the shelf-break re-
gion sits in a transition zone where the forced responses are
much weaker (Fig. 5c). While the overall externally forced
trend is significant, neither the GHG-forced nor the ozone-
forced trends are significant individually (Fig. 5d). There are
no significant externally forced changes over the shelf region
(Fig. 5e and f). All of these results are consistent with the
spatial patterns in Fig. 2, since the derivation of the trends
and significance test are identical.
The internally generated variability is substantial in all re-
gions and shows many important features (Fig. 5, red lines).
Annual anomalies (thin red lines) are large in amplitude rel-
ative to the externally forced changes. A particularly large
westerly wind anomaly occurred over the shelf and shelf
break in 1940 (Fig. 5c and e), which is clearly part of an anti-
cyclonic feature as it is accompanied by an easterly anomaly
over the deep ocean to the north (Fig. 5a). The exceptional
strength of this climatic anomaly is well known from ice
cores (Schneider and Steig, 2008; Steig et al., 2013), which
are constraining the reconstruction here. Interdecadal anoma-
lies (thick red lines, left column) also show substantial vari-
ability. This variability broadly shows a 50-year reversal,
with opposing easterly and westerly anomalies between the
deep ocean and shelf break. Internally generated easterly
trends (right column) are also large and play an important
role in counteracting the externally forced westerly trends.
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5094 P. R. Holland et al.: Wind changes over the Amundsen Sea
Figure 3. Comparison between palaeoclimate reconstruction and selected LENS ensemble members. (a) Reconstructed historical wind
trends. (c, e) Wind trends in selected illustrative ensemble members. (b, d, f) Deviation of each of these fields from the externally forced
trends in the LENS ensemble mean, illustrating the role of internal climate variability in each field.
At the shelf break the externally forced trend is cancelled
out completely by the internally generated trend, while in the
deep ocean approximately half of the externally forced trend
is cancelled out. On the shelf, all variability and trends are
internally generated.
Given the known importance of Pacific variability to this
region, Fig. 6 examines the extent to which this internal vari-
ability is related to the IPO tripole index. This index is based
on the temperature difference between tropical and subtropi-
cal Pacific SSTs (Henley et al., 2015) from the HadISST1.1
dataset (Rayner et al., 2003). The IPO emerges only on long
timescales (Newman et al., 2016), so this index is usually
calculated from monthly SSTs and then subjected to a 13-
year filter to yield the IPO. In Fig. 6, we plot the standard-
ised annual-mean and 13-year-mean time series of both the
IPO index and the reconstructed internal variability in zonal
winds. The statistics of the annual-mean and 13-year-mean
correlations are shown in each panel. For the deep-ocean re-
gion the correlations are negative, so the IPO index is re-
versed in Fig. 6a for illustrative purposes.
Annual anomalies in reconstructed internal variability are
significantly correlated to the annual-mean values of the IPO
index in all regions (Fig. 6). While the correlation coeffi-
cients are modest, this significance is remarkable considering
the independent origin of these datasets. The IPO index is ob-
tained from optimal interpolation of historical ocean temper-
ature measurements (Rayner et al., 2003), while the internal
variability in winds is derived by combining climate model
simulations and a reconstruction that uses proxy records from
ice cores, tree rings, and coral records. The most extreme an-
nual events in the record, around 1940 and 2000, seem very
well explained by the tropical Pacific. The reversing sign of
the correlations between shelf and deep ocean illustrates the
fact that a positive ENSO or IPO anomaly is associated with
anticyclonic wind anomalies associated with a local high-
pressure anomaly (Holland et al., 2019).
Unfortunately, there are insufficient data to assess the link-
age on longer timescales. The 13-year-mean internally gen-
erated wind anomalies are not significantly correlated to the
IPO (Fig. 6), which is unsurprising given that there are only
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P. R. Holland et al.: Wind changes over the Amundsen Sea 5095
Figure 4. Historical evolution of zonal wind anomalies during
1900–2020. Time series are calculated in the three regions high-
lighted in Fig. 1 from the palaeoclimate reconstruction and ERA5
reanalysis. Thin lines show annual anomalies, while thick lines
show the 13-year running mean, selected because it is character-
istic of the Interdecadal Pacific Oscillation. The 1920–2000 trend
lines are solid if the trend is significant at the 95 % confidence level
and dashed otherwise. Anomalies are plotted relative to mean values
over 1979–2005, the period of overlap between the reconstruction
and ERA5. Statistics are given for the correlation between datasets
during this overlap.
∼70 years of data, and there appears to be a ∼50-year re-
versal within the records. The change in sign between shelf
and deep-ocean correlations appears to persist on 13-year
timescales, suggesting the local pressure anomaly pattern.
On centennial timescales this reversal disappears, with all in-
ternally generated trends being easterly (Figs. 2c and 5b, d,
and f). We know very little about the characteristics of in-
ternal variability on centennial timescales. As noted above,
the internally generated centennial trends do not follow the
IPO pattern around the tropics (Fig. 1), but that does not
preclude the sub-centennial variability from being strongly
influenced by the IPO. Similar results are obtained with an
alternative palaeoclimate reconstruction (Quentin Dalaiden,
personal communication, 2022).
Holland et al. (2019) found a significant historical west-
erly wind trend at the shelf break, but the same shelf-break
box is found to have no zonal wind trend in the present study
(Fig. 4b). These studies consider the same externally forced
trends (from LENS) but differ in their consideration of inter-
nally generated trends. Holland et al. (2019) used the Pacific
Pacemaker (PACE) ensemble mean to estimate the role of
internal variability associated with the tropical Pacific and
were unable to constrain variability from other sources. The
present study uses the palaeoclimate reconstruction to es-
timate the role of variability from all sources, Pacific and
otherwise. In the PACE ensemble mean the Pacific variabil-
ity enhances the externally forced westerly trend (Holland
et al., 2019), while in the reconstruction the internal vari-
ability cancels out the externally forced trend (Fig. 5d). On
further investigation, we found that the internally generated
trends in the reconstruction sit within the PACE ensemble
spread but do not follow the PACE ensemble mean. We con-
clude that additional non-tropical-Pacific variability is cap-
tured in the reconstruction that modifies the mean tropical
Pacific trend captured by PACE. This is supported by the
fact that the reconstructed trends deviate significantly from
a classical Pacific pattern (Fig. 1). This is a further illustra-
tion of the value in using the palaeoclimate reconstruction
to estimate the real historical trajectory of internal variabil-
ity. While the details of this study differ from Holland et al.
(2019) within the shelf-break box, the present results sup-
port the overall conclusion of that study that the region is
impacted by westerly wind trends with a substantial anthro-
pogenic component, modulated by internal variability on all
timescales.
In summary, the palaeoclimate reconstruction indicates
that zonal winds are subject to strong variability on all
timescales. Centennial trends feature a cyclonic pattern that
switches from westerly over the deep ocean to easterly on
the shelf (Fig. 4). The variability is strong on interannual
timescales and also expressed on interdecadal timescales,
with a ∼50-year reversal. Wind anomalies are driven by both
external forcing and internal variability (Fig. 5). The exter-
nally forced part has distinct GHG and ozone signatures. The
internally generated component is partly related to the trop-
ical Pacific, which induces variability on interannual and in-
terdecadal timescales (Fig. 6).
3.2 Future changes
We next consider the future period for which consistent pro-
jections are available, 2001–2080. These analyses are nec-
essarily different as we cannot use the palaeoclimate recon-
structions. We first consider externally forced changes. Fig-
ure 7a shows the externally forced wind trends following
RCP8.5 (i.e. the LENS ensemble mean), and Fig. 7b–d show
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5096 P. R. Holland et al.: Wind changes over the Amundsen Sea
Figure 5. Historical and future influences on zonal wind anomalies. Panels show the internally generated, total externally forced, and GHG-
and ozone-forced contributions to wind changes in the three regions shown in Fig. 2. Future changes are shown under the strong-forcing
RCP8.5 scenario. All panels show annual-mean time series (thin lines). Panels (a),(c), and (e) also show the same time series after a 13-year
running mean (thick lines) to highlight the interdecadal evolution of the responses. Panels (b),(d), and (f) also show historical and future
trends, plotted solid if the trend is significant at the 95 % confidence level (see text) and dashed otherwise. Ozone is plotted as the ozone
response before 2000 and the non-GHG response afterwards. Internally generated anomalies are plotted relative to 1979–2005, as in Fig. 4.
the other scenarios. Historical and future responses are di-
rectly comparable in Figs. 2 and 7 as the plotting is identical.
High-forcing (RCP8.5) and intermediate-forcing
(RCP4.5) scenarios (Fig. 7a and b) feature strong ex-
ternally forced westerly wind trends over most of the
Southern Ocean (Bracegirdle et al., 2020; Goyal et al., 2021)
but much weaker trends over the Amundsen Sea shelf
(Bracegirdle et al., 2014). Weaker-forcing scenarios are
cast in the context of the Paris Agreement, which commits
nations to “holding the increase in the global average tem-
perature to well below 2 ◦C above pre-industrial levels and
pursuing efforts to limit the temperature increase to 1.5 ◦C”.
The 2.0degC scenario substantially reduces future wind
trends in this region (Fig. 7c), while 1.5degC eliminates the
westerly trends entirely (Fig. 7d). Stabilising global warming
removes the Equator-to-pole gradient in temperature trends
that drives the westerly wind trends. We speculate that the
small remaining wind trends are likely to be very sensitive
to the forcing pathway by which temperatures are stabilised.
The 1.5degC scenario involves aggressive emissions miti-
gation and a rapid transition to negative carbon emissions
(Sanderson et al., 2017). Historical GHG emissions commit
Earth’s climate to warming and wind changes for most of
the future period studied, so extreme emissions changes are
required to reverse these by 2080. Even then, the 1.5degC
scenario merely prevents any future trends and does not
reverse the historical changes.
As with the historical changes, we may combine ensem-
bles to derive the role of individual external forcings. Fig-
ure 7e shows the GHG-forced changes in the RCP8.5 sce-
nario (i.e. LENS minus XGHG). This future GHG response
is approximately twice as strong as the historical GHG re-
sponse (Fig. 2d), with a similar pattern. We cannot directly
determine future ozone-forced changes because the fixed-
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P. R. Holland et al.: Wind changes over the Amundsen Sea 5097
Figure 6. Evolution of internally generated zonal wind anomalies
and their relationship to the Interdecadal Pacific Oscillation. Pan-
els show the internally generated anomalies and IPO index in the
three regions shown in Fig. 2. Thin lines show annual anomalies,
while thick lines show a 13-year running mean. All time series are
standardised by the 13-year-running-mean data. The IPO index is
inverted in panel (a) for illustrative purposes because its correlation
to wind anomalies is negative in the deep-ocean region. Statistics
show the correlation between the two time series on annual and 13-
year timescales.
ozone ensemble XOZO terminates in 2005 (Table 1). How-
ever, Fig. 7f shows the combined response to all non-GHG
external forcings (XGHG minus PIctrl), which we are con-
fident is dominated by the ozone response. For the histor-
ical period the non-GHG response closely resembles the
ozone response, and in future scenarios the influence of ex-
ternal forcings other than GHGs and ozone (e.g. aerosols)
is even weaker (van Vuuren et al., 2011). The future non-
GHG response (Fig. 7f) shows a weakening of the west-
erlies, as expected from the prescribed recovery of strato-
spheric ozone (Son et al., 2010; Sigmond et al., 2011; Barnes
et al., 2014) and in almost exact opposition to the historical
ozone-induced changes (Fig. 2f). Thus the models yield the
expected result that the future westerly wind trends are driven
by GHGs but are partially compensated for by ozone recov-
ery (Thompson et al., 2011). The influence of ozone recov-
ery is expected to be very similar between forcing scenarios
(Keeble et al., 2021), so the difference between scenarios in
Fig. 7a–d is determined by the extent to which increasing
GHGs outweigh this recovery.
The future evolution of the different components of the
externally forced wind changes is shown in Fig. 5, which
again shows results for RCP8.5. In this strong-forcing sce-
nario, future externally forced trends are of similar magni-
tude to historical externally forced trends in all regions. Over
the deep ocean (Fig. 5b), an increase in the GHG-forced
westerly wind trend is compensated for by a negative west-
erly trend from stratospheric ozone recovery (represented
by the non-GHG response). Over the shelf break (Fig. 5d),
the ozone-forced trend remains insignificant, but the GHG-
forced trend becomes significant in the future period. The re-
covery of ozone-forced wind trends is focussed earlier in the
future period (Keeble et al., 2021), so the overall externally
forced trends are weaker during this period and stronger after
(Fig. 5a).
Internal variability was an important contributor to wind
trends over the Amundsen Sea in the past (Figs. 2 and 5) and
could also be important in the future. The evidence suggests
that tropical Pacific variability is primarily natural in origin
(Stevenson et al., 2012; Cai et al., 2015; Yeh et al., 2018) and
cannot be predicted on timescales longer than a few years
(Lou et al., 2019). Furthermore, centennial variability in this
region does not even appear to follow the recognised modes
of tropical Pacific variability (Fig. 1). Therefore, the role of
internal variability creates a substantial and potentially irre-
ducible uncertainty in any projection of future wind changes
over the Amundsen Sea, particularly in the shelf region.
One way to quantify the influence of internal variability on
future wind trends is by considering the ensemble spread in
the projections. Figure 8a shows the LENS ensemble-mean
trends (i.e. the RCP8.5 externally forced trends in Fig. 7a).
Figure 8b quantifies the associated ensemble spread, plot-
ted as the wind trends associated with +1 standard devia-
tion in both zonal and meridional wind trends. This panel
shows that there is substantial intra-ensemble variance in the
trends, which is evenly spatially distributed and has no ob-
vious preferential wind direction. Figure 8c and e show the
future wind trends in two LENS members, chosen manu-
ally to illustrate the range of intra-ensemble variance. Both
members have strong westerly wind trends overall but dif-
fer in their trends over the Amundsen Sea shelf. Figure 8d
and f isolate the differing contribution of internal variabil-
ity in these two members, by subtracting the LENS ensem-
ble mean (the externally forced trends) from both. The in-
ternally generated trends have a centre of action north of the
Amundsen Sea shelf in both cases, but the trends have either
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5098 P. R. Holland et al.: Wind changes over the Amundsen Sea
Figure 7. Projected wind trends over West Antarctica and their attribution. Panels (a)–(d) show externally forced wind trends in four different
forcing scenarios, progressing from the strongest anthropogenic forcing (RCP8.5) to an aggressive emissions mitigation scenario (1.5degC).
Panels (e) and (f) show the individual contributions of greenhouse gases and ozone depletion in the RCP8.5 scenario.
a cyclonic or an anticyclonic orientation depending upon the
particular trajectory of internal variability manifested in each
simulation.
Another way to illustrate the potential role of future inter-
nal variability is to simply imagine that the historical vari-
ability could be repeated or exactly reversed. The advantage
of this approach is that our knowledge of the historical vari-
ability is constrained by observations. Comparing the histor-
ical internally generated trends (Fig. 2c) to the LENS ensem-
ble spread of future internally generated trends (Fig. 8b, d,
and f) shows that this is a representative illustration. Fol-
lowing this rationale, Fig. 9 illustrates possible future wind
trends that combine forcing-scenario uncertainty with the
historically based estimate of future internal variability. This
is illustrated simply by adding or subtracting the historical
internally generated trends (Fig. 2c) to or from the exter-
nally forced trends in the 1.5degC and RCP8.5 forcing sce-
narios (Fig. 7d and a). It is clear that external forcing and
its scenario uncertainty control westerly trends over the deep
ocean, while internal variability and its irreducible uncer-
tainty control wind trends over the Amundsen Sea shelf and
shelf break.
4 Discussion
Recent wind anomalies are known to have driven variabil-
ity in Amundsen Sea ocean conditions and ice-shelf melt-
ing that have influenced ice loss from the WAIS (Thoma et
al., 2008; Dutrieux et al., 2014; Christianson et al., 2016;
Jenkins et al., 2018). Reconstructed winds show that even
larger anomalies occurred in the past (Fig. 4), so it is rea-
sonable to assume that these anomalies also induced an ice-
sheet response. The reconstructions also reveal interdecadal
(∼50 year) variability with an amplitude approximately half
that of the annual anomalies. Since ice sheets respond more
fully to slower variability (Williams et al., 2012; Snow et
al., 2017; Hoffman et al., 2019), it is very likely that these
interdecadal anomalies have influenced the WAIS.
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P. R. Holland et al.: Wind changes over the Amundsen Sea 5099
Figure 8. Influence of internal variability on projected wind trends over West Antarctica. (a) LENS ensemble-mean wind trends (the RCP8.5
externally forced trend in Fig. 7a). (b) LENS intra-ensemble standard deviation in wind trends, plotted as vectors illustrating one positive
standard deviation in both zonal and meridional trends. (c, e) Wind trends in selected illustrative LENS members. (d, f) Deviation of the
trends in these selected members from the ensemble-mean trend, illustrating the role of internal climate variability.
On centennial timescales, the reconstruction shows that
strong westerly wind trends were prevalent over most of the
Southern Ocean (Fig. 2). These overall trends were driven
by external forcing but partly compensated for by internally
generated trends. Over the Amundsen Sea shelf break these
contributions cancelled out, leaving no significant overall
trend, in contrast to earlier results based on climate mod-
els alone (Holland et al., 2019). Over the Amundsen Sea
shelf, externally forced trends were absent entirely and an
internally generated easterly trend occurred. Where present,
the compensating externally forced and internally generated
trends are both of similar magnitude to the recent wind vari-
ability that we know to be influential on WAIS ice loss. This
implies that both the trends and their compensation have been
important to the historical evolution of the ice loss.
This evidence suggests a possible narrative for the histor-
ical changes in the WAIS. Sediment records show that the
ungrounding of Pine Island Glacier commenced in the 1940s
(Smith et al., 2017), when the reconstruction shows extreme
annual wind events within a multi-decadal period of anoma-
lously anticyclonic winds (Fig. 4). These anomalies were all
internally generated (Fig. 5) and partly associated with trop-
ical Pacific variability (Fig. 6). By the mid-1970s the ice
shelf was fully ungrounded (Jenkins et al., 2010; Smith et
al., 2017), following a period of retreat that was probably en-
hanced by ice and ocean feedbacks (De Rydt et al., 2014;
De Rydt and Gudmundsson, 2016). The triggering internal
variability had reversed by the 1960s, but by then the GHG-
forced wind anomalies had appeared (Fig. 5). Perhaps, with-
out external forcing, the reversed internal variability might
have allowed the ice to re-advance and fully re-ground on
the ridge. In the 1980s the external forcing increased further
with ever-increasing GHGs and the onset of ozone depletion,
and the internally generated anomalies became anticyclonic
again. The wind changes resulting from these anthropogenic
and natural drivers were of approximately equal magnitude,
suggesting that a combination of these factors drove the
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5100 P. R. Holland et al.: Wind changes over the Amundsen Sea
Figure 9. Illustrative wind trend projections under different combinations of the external-forcing scenario and a single realisation of internal
variability. The columns denote wind trends projected under weak (a, c) and strong (b, d) external forcing, while the rows denote the influence
of either subtracting (a, b) or adding (c, d) the wind trends associated with historical internal variability, which is used for illustrative purposes.
The projected externally forced wind trends are shown in Fig. 7a and d, while the historical internally generated wind trends are shown in
Fig. 2c.
current period of rapid, accelerating ice loss (Mouginot et
al., 2014; Konrad et al., 2017; Shepherd et al., 2019).
While much remains uncertain, this narrative can simulta-
neously satisfy lines of evidence from palaeoclimate proxies,
Amundsen Sea sediment records, ocean observations, cli-
mate model simulations, and the satellite record of ice loss.
Importantly, it also resolves an apparent paradox. The WAIS
is thought to have been broadly stable for ∼10 000 years in
this region (Larter et al., 2014) before the current retreat was
initiated in the 1940s (Smith et al., 2017) by internal climate
variability (Figs. 4 and 5). However, by definition it is very
unlikely that this natural variability was unprecedented in the
previous millennia, so why did it trigger an exceptional ice
retreat? One way to resolve this apparent paradox is if the
initial retreat was natural but the subsequent failure of the ice
sheet to re-advance was influenced by anthropogenic forcing.
Perhaps the ice sheet experienced several short-lived retreats
during the last few millennia, from which it recovered, but
it failed to recover from the 1940s retreat because external
forcing over-rode the subsequent reversal of the natural cli-
matic variability (Figs. 4 and 5). We emphasise that this is
not the only possible resolution of this “paradox”, however.
For example, if some slow background change had occurred
over the millennia, such as a reduction in surface accumu-
lation, this could have gradually reduced the stability of the
glaciers in the region. The 1940s variability may then have
been able to destabilise the glaciers simply because they were
more vulnerable.
Turning to the future changes, 2000–2080, we again find
that both external forcing and internal variability are of com-
parable importance. Remarkably, the choice of emissions
scenario is important to wind trends everywhere apart from
the Amundsen Sea shelf. On the shelf, future wind trends
are dictated solely by internal variability, while over the
deep ocean the external forcing plays a larger role. Under an
aggressive emissions mitigation scenario that limits global
warming to 1.5 ◦C, there are no externally forced zonal
wind trends during the future period. Under high-emissions
(RCP8.5) or even intermediate-emissions (RCP4.5) scenar-
ios, future externally forced zonal wind trends approximately
equal the historical externally forced trends. Thus, future
forcing will range between maintaining the existing exter-
nally forced changes (1.5degC scenario) and approximately
doubling them (RCP8.5). To the extent that ice loss from the
WAIS is driven by externally forced wind trends, that part of
the ice loss cannot be reversed before 2080 and will probably
increase.
Internally generated trends provided an important com-
pensation of the externally forced trends during the histor-
ical period and could be similarly influential in the future.
If externally forced trends were eliminated in the future, in-
ternally generated trends could dictate the future trajectory
of the WAIS (Fig. 9). On the other hand, if higher-emissions
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P. R. Holland et al.: Wind changes over the Amundsen Sea 5101
scenarios are followed, internal variability may do little more
than influence the timing and rate of the ongoing ice loss.
Minimising future wind-driven WAIS ice loss will require
strong emissions mitigation, but the uncontrollable future in-
fluence of internal climate variability could be equally im-
portant.
There are, of course, many caveats to these findings. This
study considers a single palaeoclimate reconstruction and cli-
mate model and is therefore subject to the structural uncer-
tainty inherent in these sources (see Sect. 2). The study does
not investigate seasonal variations due to the annual reso-
lution of the reconstruction. This is an important limitation
because there are strong seasonal variations in the impacts
of both external forcing (e.g. ozone depletion; Thompson et
al., 2011) and internal variability (e.g. the tropical Pacific
teleconnection; Ding et al., 2012). The study also only con-
siders wind forcing of changes in the ocean, but of course
thermodynamic forcings may also be important, and ice-
sheet dynamics and ice–ocean feedbacks certainly modulate
the ice response to any climatic forcing. Finally, we require
further information about the oceanographic implications of
the wind changes described in this study. Based on our con-
clusions, anthropogenic forcings are expected to be most in-
fluential over the deep ocean and in summer, while inter-
nal variability should be more influential on the shelf and
in winter. Therefore, to derive the relative influence of ex-
ternal forcing, we urgently need information on the regional
and seasonal influence of winds on ocean properties and ice
melting. Despite all these important limitations on the inter-
pretation of our results, we emphasise that all of the wind
changes discussed are comparable in magnitude to the re-
cent variability, which we know to have been influential over
WAIS ice loss.
5 Conclusions
Over recent decades, wind-driven variability in the Amund-
sen Sea has regulated ocean melting of the WAIS. This study
combines palaeoclimate reconstructions and climate model
simulations to understand wind changes over the Amundsen
Sea during the 20th and 21st centuries.
By combining these two sources of information, we are
able to separate natural, internally generated wind changes
from anthropogenic, externally forced changes. Both are im-
portant. Firstly, internal variability on interannual and inter-
decadal timescales has a magnitude comparable to centennial
trends. Secondly, the centennial trends themselves are gener-
ated by comparable contributions from external forcing and
internal variability. To the extent that wind-driven changes
control WAIS ice loss, both external forcing and internal vari-
ability are important contributors.
Historical wind trends (1920–2000) have two components:
acceleration of the westerlies over the deep ocean, forced by
GHGs and ozone depletion, and a cyclonic trend pattern cen-
tred over the Amundsen Sea that is largely internally gen-
erated. Over most of the region, internally generated east-
erly wind trends compensate for externally forced westerly
wind trends. Historical winds also exhibit strong variability,
linked to the tropical Pacific, including both strong interan-
nual anomalies and interdecadal variability that reverses on a
timescale of approximately 50 years.
This evidence suggests a possible narrative for historical
ice loss from the WAIS. Ice retreat was triggered in the 1940s
by internal variability. This variability had reversed by the
1960s, but by then GHG-forced wind changes had started
to increase. Perhaps without external forcing, the reversed
internal variability may have allowed the ice sheet to re-
advance. In the 1980s the external forcing increased further
with the onset of ozone depletion, and the internal variability
changed sign again. These changes drove the current period
of rapid, accelerating ice loss. There remain many uncertain-
ties with this narrative, but it resolves an apparent paradox
whereby the present ice loss appears to have been triggered
naturally but involves a retreat unprecedented in millennia.
Perhaps short-lived retreats have occurred several times in
the past, but the ice always re-advanced in the absence of
anthropogenic forcing.
During the future period (2000–2080), westerly wind
trends driven by GHGs are partially offset by stratospheric
ozone recovery. Wind trends are responsive to forcing sce-
nario, but only extreme emissions mitigation consistent with
a Paris Agreement target of 1.5 ◦C warming above pre-
industrial levels is able to prevent further wind trends during
this period (let alone offset historical trends). The choice of
anthropogenic forcing scenario is most influential on west-
erly trends over the deep ocean, while the irreducible un-
certainty associated with internally generated variability is
strongest on the Amundsen Sea shelf. Minimal future wind-
driven WAIS ice loss will require strong emissions mitigation
and a favourable evolution of natural climate variability.
Data availability. The CESM1 simulations are available at the
NCAR Climate Data Gateway (https://www.earthsystemgrid.
org/, NCAR, 2021), as detailed in the references in Ta-
ble 1. The palaeoclimate reconstructions generated in the
study by O’Connor et al. (2021a) are archived at Zenodo
(https://doi.org/10.5281/zenodo.5507607, O’Connor et al., 2021b).
Author contributions. PRH and GKO’C conceived the study and
led the data processing. All authors discussed the results and impli-
cations and collaborated on writing the manuscript at all stages.
Competing interests. The contact author has declared that none of
the authors has any competing interests.
https://doi.org/10.5194/tc-16-5085-2022 The Cryosphere, 16, 5085–5105, 2022

5102 P. R. Holland et al.: Wind changes over the Amundsen Sea
Disclaimer. Publisher’s note: Copernicus Publications remains
neutral with regard to jurisdictional claims in published maps and
institutional affiliations.
Acknowledgements. We are grateful to the originators of the many
open-access datasets synthesised in this study, particularly the
PAGES2k palaeoclimate proxy database and the CESM1 climate
model simulations. We thank all the scientists, software engineers,
and administrators who contributed to the development of CESM1.
Financial support. This publication was supported by PROTECT.
This project has received funding from the European Union’s Hori-
zon 2020 research and innovation programme under grant agree-
ment no. 869304, PROTECT contribution number 52. Gemma K.
O’Connor was supported by the NSF Graduate Research Fel-
lowship Program. Kaitlin A. Naughten was supported by award
NE/S011994/1. David P. Schneider was partially supported by NSF
grant 1952199 and partially supported by the National Center for
Atmospheric Research (NCAR), which is a major facility sponsored
by the NSF under cooperative agreement no. 1852977. CESM1 is
primarily supported by the National Science Foundation (NSF).
Review statement. This paper was edited by Michiel van den
Broeke and reviewed by two anonymous referees.
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