ArticlePublisher preview available

Natural variability of Southern Ocean convection as a driver of observed climate trends

DOI:10.1038/s41558-018-0350-3
Authors:
Liping Zhang at GFDL/NOAA
Liping Zhang
  • GFDL/NOAA
Thomas L. Delworth at National Oceanic and Atmospheric Administration
Thomas L. Delworth
William F. Cooke at National Oceanic and Atmospheric Administration
William F. Cooke
Xiaosong Yang at National Oceanic and Atmospheric Administration
Xiaosong Yang
Read publisher preview
Download citation
To read the full-text of this research, you can request a copy directly from the authors.

Abstract and Figures

Observed Southern Ocean surface cooling and sea-ice expansion over the past several decades are inconsistent with many historical simulations from climate models. Here we show that natural multidecadal variability involving Southern Ocean convection may have contributed strongly to the observed temperature and sea-ice trends. These observed trends are consistent with a particular phase of natural variability of the Southern Ocean as derived from climate model simulations. Ensembles of simulations are conducted starting from differing phases of this variability. The observed spatial pattern of trends is reproduced in simulations that start from an active phase of Southern Ocean convection. Simulations starting from a neutral phase do not reproduce the observed changes, similarly to the multimodel mean results of CMIP5 models. The long timescales associated with this natural variability show potential for skilful decadal prediction. © 2018, The Author(s), under exclusive licence to Springer Nature Limited.
Annual SST and sea-ice time series and trends a, Time series of SO area mean (50°–70°S) SST (K) anomalies over 1890–2012 and SIE (10¹² m²) anomalies over 1979–2012. These anomalies are with respect to their long-term mean values. The SST data are from Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) and Extended Reconstructed Sea Surface Temperature (ERSST) version 3. The sea-ice data are from HadISST and the National Snow and Ice Data Center (NSIDC). b, SST trend in HadISST over 1979–2012. c, SIC and SLP trends in NSIDC over 1979–2012. d,e, SST (d) and SIC/SLP (e) trends in ensemble mean results of SPEAR_AM2 historical run over 1979–2012. Units are K(30 yr)⁻¹ for the SST trend, 100%(30 yr)⁻¹ for the SIC trend and hPa(30 yr)⁻¹ for the SLP trend. Stippling on trends means that the trend is significant at the 95% level based on a two-sided Student t-test. Note that the trend pattern is not sensitive to the choice of ending year from 2010 to 2015 (Methods).
Annual SST and sea-ice time series and trends a, Time series of SO area mean (50°–70°S) SST (K) anomalies over 1890–2012 and SIE (10¹² m²) anomalies over 1979–2012. These anomalies are with respect to their long-term mean values. The SST data are from Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) and Extended Reconstructed Sea Surface Temperature (ERSST) version 3. The sea-ice data are from HadISST and the National Snow and Ice Data Center (NSIDC). b, SST trend in HadISST over 1979–2012. c, SIC and SLP trends in NSIDC over 1979–2012. d,e, SST (d) and SIC/SLP (e) trends in ensemble mean results of SPEAR_AM2 historical run over 1979–2012. Units are K(30 yr)⁻¹ for the SST trend, 100%(30 yr)⁻¹ for the SIC trend and hPa(30 yr)⁻¹ for the SLP trend. Stippling on trends means that the trend is significant at the 95% level based on a two-sided Student t-test. Note that the trend pattern is not sensitive to the choice of ending year from 2010 to 2015 (Methods).
… 
SO internal variability in the pre-industrial control run Analyses of output from a pre-industrial control simulation. a, Time series of the annual mean AABW cell index in a control run. The AABW index is defined each year as the absolute value of the minimum in the global overturning stream function in density space south of 60° S. Red (blue, green) dots show the periods used to initialize additional simulations (described in text) that are characterized by strong (weak, average) convective activity in the SO. The purple line overlying the AABW cell index time series denotes years when the Weddell Polynya appears in September, October or November. b, Composite of September MLD for active convection (red dots in a). c,d, Map of trends in annual mean SST (K(30 yr)⁻¹) (c) and SIC (100%(30 yr)⁻¹) and SLP (hPa(30 yr)⁻¹) (d) for the 30 years following a maximum in convective activity. Stippling on trend maps indicates that the trend is significant at the 95% level based on a two-sided Student t-test. For the trend patterns, data are 30 yr low-pass filtered before composite analysis.
SO internal variability in the pre-industrial control run Analyses of output from a pre-industrial control simulation. a, Time series of the annual mean AABW cell index in a control run. The AABW index is defined each year as the absolute value of the minimum in the global overturning stream function in density space south of 60° S. Red (blue, green) dots show the periods used to initialize additional simulations (described in text) that are characterized by strong (weak, average) convective activity in the SO. The purple line overlying the AABW cell index time series denotes years when the Weddell Polynya appears in September, October or November. b, Composite of September MLD for active convection (red dots in a). c,d, Map of trends in annual mean SST (K(30 yr)⁻¹) (c) and SIC (100%(30 yr)⁻¹) and SLP (hPa(30 yr)⁻¹) (d) for the 30 years following a maximum in convective activity. Stippling on trend maps indicates that the trend is significant at the 95% level based on a two-sided Student t-test. For the trend patterns, data are 30 yr low-pass filtered before composite analysis.
… 
Dependence of transient climate response on the initial state of SO convection a–d, The AABW cell time series (a), annual SST (b), SIC/SLP (c) and zonal mean subsurface temperature (d) trends over 1979–2012 in historical simulations initialized from a period with strong convection. e–h,i–l, The same as a–d but for simulations starting from states with weak and neutral convection, respectively. Units are Sv for the AABW cell, K(30 yr)⁻¹ for the SST and subsurface temperature trends, 100%(30 yr)⁻¹ for the SIC trend and hPa(30 yr)⁻¹ for the SLP trend. The shading in a,e,i denotes the ensemble spread (ensemble mean plus one standard deviation). Stippling on trends means that the trend is significant at the 95% level based on a two-sided Student t-test.
Dependence of transient climate response on the initial state of SO convection a–d, The AABW cell time series (a), annual SST (b), SIC/SLP (c) and zonal mean subsurface temperature (d) trends over 1979–2012 in historical simulations initialized from a period with strong convection. e–h,i–l, The same as a–d but for simulations starting from states with weak and neutral convection, respectively. Units are Sv for the AABW cell, K(30 yr)⁻¹ for the SST and subsurface temperature trends, 100%(30 yr)⁻¹ for the SIC trend and hPa(30 yr)⁻¹ for the SLP trend. The shading in a,e,i denotes the ensemble spread (ensemble mean plus one standard deviation). Stippling on trends means that the trend is significant at the 95% level based on a two-sided Student t-test.
… 
Figures - available from: Nature Climate Change
This content is subject to copyright. Terms and conditions apply.
  • A preview of this full-text is provided by Springer Nature.
  • Learn more
Preview content only
Content available from Nature Climate Change
This content is subject to copyright. Terms and conditions apply.
Articles
https://doi.org/10.1038/s41558-018-0350-3
1Atmospheric and Oceanic Science, Princeton University, Princeton, NJ, USA. 2NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA.
3University Corporation for Atmospheric Research, Boulder, CO, USA. *e-mail: Liping.Zhang@noaa.gov
While Arctic sea ice is rapidly decreasing in association
with increasing surface air temperature1, observations
clearly show an expansion of Southern Ocean (SO) sea-
ice extent (SIE)2 during the satellite era (1979–2012) (Fig. 1a). This
modest increase is consistent with the observed SO cooling trend
(Fig. 1a). The sea surface temperature (SST) and sea-ice concentra-
tion (SIC) trends are not homogeneous in space3,4, with opposing
signs in the Amundsen-Bellingshausen seas versus the Ross and
Weddell seas (Fig. 1b,c). Several mechanisms have been proposed
to explain these trends3–16. A leading idea involves surface wind
changes3–14 driven by a number of factors, including a positive trend
of the Southern Annular Mode in response to stratospheric ozone
depletion, or a deepened Amundsen Sea Low driven by remote
tropical Pacific or North Atlantic SST anomalies. In these studies,
the wind-driven surface heat flux, upper ocean dynamics and sea-
ice drift are key drivers for the observed sea-ice and SST dipoles.
Another explanation involves surface freshening15,16 caused by
anthropogenic warming, possibly via global water cycle amplifica-
tion and/or the melting of Antarctic glaciers and ice sheets. The sur-
face freshening enhances stratification and suppresses convective
mixing with the warmer water at depth, producing cold SST anom-
alies and thus inhibiting the melting of Antarctic sea ice. However,
other studies argue that wind anomalies, induced by ozone deple-
tion, favour an overall decrease rather than increase of Antarctic
sea ice17, and that the surface freshening caused by anthropogenic
warming is not large enough to trigger sea-ice expansion18,19.
In this study we examine the possibility that internal variabil-
ity involving deep ocean convection in the SO could be a major
contributor to the observed trends, probably in concert with other
previously identified factors. To explore this, we use simulations
with a newly developed coupled ocean–atmosphere sea-ice model
(SPEAR_AM2—see Methods for model details). When this model
is driven with estimates of changes in past radiative forcing, the
model simulation does not reproduce the observed SST and SIC
trends around the Antarctic. Instead, the model simulates a steady
warming and Antarctic sea-ice loss (Fig. 1d,e), as is commonly seen
in multimodel mean results of Coupled Model Intercomparison
Project Phase 5 (CMIP5) models20. One possible explanation for
the discrepancy between observations and model projections is that
natural variability may play a large role in the observed trends21,22.
Indeed, the observed sea-ice expansion is within the range of natu-
ral variability in the control run2,21,22.
We provide additional evidence supporting a strong role for
natural variability in the observed trends, probably involving
multidecadal modulation of SO convection and deep-water for-
mation. As shown below, various aspects of the observed changes
are consistent with a multidecadal weakening of Antarctic bottom
water (AABW) formation. In the 1970s, the open ocean Weddell
Polynya23,24 (Supplementary Fig. 1) was first seen by satellite. After
1976, no similar Weddell Polynya was observed until 2016. Ship-
based hydrographic observations show that the AABW has warmed
globally between the 1980s and 2000s25,26. Objectively analysed
ocean data also exhibit a warming temperature trend in the SO
subsurface and a cooling trend in the surface (Supplementary Fig. 2).
Both warming trends are consistent with weakened convection.
These observational results suggest a global-scale slowdown of the
bottom, southern limb of the meridional overturning circulation
(MOC) during 1979–201225. Meanwhile, multidecadal variabil-
ity of SO SST27,28 shown in reanalysis (Fig. 1a) and palaeoclimate
records29,30 highlights the low-frequency character of SO climate
and places the recent Antarctic sea-ice trends into a broader context.
SO internal variability in coupled model
In order to compare the observed changes with an estimate of
natural variability, we first examine SO natural climate variability
from an extended simulation of the SPEAR_AM2 model. We see
that the time series of the AABW cell, related to ocean convection
(Methods and Supplementary Figs. 3–5), has pronounced multi-
decadal to centennial-scale fluctuations that begin after an initial
1,000 year spin-up (Fig. 2a). When convection is strong, the mixed
layer depth (MLD) is largest in the open Weddell Sea, near the
Maud Rise (65° S, 0°) (Fig. 2b). This convection location resembles
the observed 1974–1976 Weddell Polynya (Supplementary Fig. 1).
Some MLD changes are also seen over the Antarctic continental
shelves, such as in the west Ross, Weddell seas and East Antarctic,
but the magnitude is much smaller than that in the open ocean
Natural variability of Southern Ocean convection
as a driver of observed climate trends
LipingZhang 1,2*, ThomasL.Delworth1,2, WilliamCooke2,3 and XiaosongYang2,3
Observed Southern Ocean surface cooling and sea-ice expansion over the past several decades are inconsistent with many his-
torical simulations from climate models. Here we show that natural multidecadal variability involving Southern Ocean convec-
tion may have contributed strongly to the observed temperature and sea-ice trends. These observed trends are consistent with
a particular phase of natural variability of the Southern Ocean as derived from climate model simulations. Ensembles of simula-
tions are conducted starting from differing phases of this variability. The observed spatial pattern of trends is reproduced in
simulations that start from an active phase of Southern Ocean convection. Simulations starting from a neutral phase do not
reproduce the observed changes, similarly to the multimodel mean results of CMIP5 models. The long timescales associated
with this natural variability show potential for skilful decadal prediction.
NATURE CLIMATE CHANGE | VOL 9 | JANUARY 2019 | 59–65 | www.nature.com/natureclimatechange 59
The Nature trademark is a registered trademark of Springer Nature Limited.
... resulted from anomalous multi-decadal variability or from aspects of the forced climate response not captured by models. Some studies suggest that these difference in Pacific and Southern Ocean trends could have resulted from internal atmosphere-ocean variability (Chung et al., 2019(Chung et al., , 2022Olonscheck et al., 2020;Watanabe et al., 2021;L. Zhang et al., 2019;Zhao & Allen, 2019), while others suggest they result in part from missing forcings or model biases in the pattern of response to forcing (Bintanja et al., 2013;Kohyama et al., 2017;Kostov et al., 2018;Schneider & Deser, 2018;Seager et al., 2019Seager et al., , 2022Suarez-Gutierrez et al., 2021;Wills et al., 2020). It is critical to dist ...
... It has also been suggested that observed trends in the Southern Ocean could result from an anomalous phase of Southern Ocean multi-decadal variability (e.g., L. Zhang et al. (2019)). This remains plausible, though its relevance for lower latitude SST trends depends on an active body of work to quantify the teleconnections from Southern Ocean SST changes (Dong et al., 2022;Hwang et al., 2017;Kang et al., 2019;Kim et al., 2022;X. ...
Systematic Climate Model Biases in the Large‐Scale Patterns of Recent Sea‐Surface Temperature and Sea‐Level Pressure Change
Article
Full-text available
  • Sep 2022
Observed surface temperature trends over recent decades are characterized by (a) intensified warming in the Indo‐Pacific Warm Pool and slight cooling in the eastern equatorial Pacific, consistent with Walker circulation strengthening, and (b) Southern Ocean cooling. In contrast, state‐of‐the‐art coupled climate models generally project enhanced warming in the eastern equatorial Pacific, Walker circulation weakening, and Southern Ocean warming. Here we investigate the ability of 16 climate model large ensembles to reproduce observed sea‐surface temperature and sea‐level pressure trends over 1979–2020 through a combination of externally forced climate change and internal variability. We find large‐scale differences between observed and modeled trends that are very unlikely (<5% probability) to occur due to internal variability as represented in models. Disparate trends in the ratio of Indo‐Pacific Warm Pool to tropical‐mean warming, which shows little multi‐decadal variability in models, hint that model biases in the response to historical forcing constitute part of the discrepancy.
... The decades-long overall increasing trend in the Antarctic sea ice has been attributed to internal variability of the climate system. In particularly, the trends have been linked to changes in surface winds (Holland and Kwok, 2012;Holland et al., 2017a) to various ocean processes including Southern Ocean convection (Zhang et al., 2019), ice shelf melt (Bintanja et al., 2013;2015), and ice-ocean feedbacks (Zhang, 2007;Goosse and Zunz, 2014), and to well-known modes of climate system variability (Raphael and Hobbs, 2014) related to ocean-atmosphere interactions including the El Niño-Southern Oscillation (ENSO) (Stammerjohn et al., 2008), the Southern Annular Mode (SAM) (Holland et al., 2017b), the Amundsen Sea Low (ASL) (Fogt et al., 2012;Turner et al., 2015;Holland et al., 2018), the Zonal Wave Three (ZW3) (Raphael, 2007), the Interdecadal Pacific Oscillation (IPO) (Lopez et al., 2016;Meehl et al., 2016), the Atlantic Multidecadal Oscillation (AMO) (Yu et al., 2017), and the South Pacific Oscillation (Yu et al., 2021a). External factors, such as increases in the greenhouse gas emissions and ozone depletion, have also been found to influence Antarctic sea ice trends by modifying high-latitude climate variability modes, particularly the SAM (Thompson and Solomon, 2002;Gillett et al., 2013;Fogt and Zbacnik, 2014;Christidis and Stott, 2015). ...
A regional and seasonal approach to explain the observed trends in the Antarctic sea ice in recent decades
Article
  • Jan 2023
In contrast to the declining sea ice extent across most of the Arctic in the past several decades, sea ice extent in the Antarctic has experienced opposite regional trends with regions of sea ice expansion exceeding those of contraction. Various mechanisms have been put forward to explain the Antarctic sea ice trends, but few have been successful at explaining a large portion of the observed trends. Dividing the Southern Ocean into the Pacific, Atlantic, and Indian Ocean sectors and examining the trends over 1979–2018 separately for each sector and for each season of the year, we are able to identify modes of variability that statistically explain more than 42% of the trends in all three sectors and all four seasons. In certain sector and season, up to 94% of the trends can be explained. The leading modes of variability that explain a substantial portion of the trends appear to be related to several known climate variability modes including the Southern Annular Mode (SAM), the Pacific South American (PSA) 1 and 2 and the Zonal Wavenumber 2 to 6 patterns. In the Atlantic and Indian Ocean sectors, the changes in the occurrences of the main contributing modes to sea ice trends are dominated by local sea‐surface temperature (SST) variations, while in the Pacific sector, they are related to changes in global SST. This article is protected by copyright. All rights reserved.
... Some studies argued that the representation of SO convection has far-reaching impacts on the regional climate, including the LLC coverage (Gjermundsen et al., 2021;Winton et al., 2010;Zhang et al., 2019). We speculate that the time scales of cloud feedback acting on GMST increase are different from that of deep ocean circulation in climate model simulations under gradually changing forcing. ...
Southern Ocean Control of 2°C Global Warming in Climate Models
Article
Full-text available
  • Jan 2023
Plain Language Summary In December 2015 at Paris, United Nations agreed to hold the increase in the global average temperature to “well below” 2°C above pre‐industrial levels and pursuing efforts to limit 1.5°C above pre‐industrial levels. It naturally leads to a question as to when these targets will reach. However, under a business‐as‐usual scenario, the time to reach these targets varies widely among climate models. Using Coupled Model Intercomparison Project Phase 5 and 6, we show that a 2°C global warming is closely related to the late 19th century condition of Southern Ocean (SO) state such that the initially cold SO climate models actually produced fast warming rate and vice versa. This is because these initially cold SO climate models that mostly accompany more low‐level cloudiness and Antarctic sea ice concentration, could actually absorb more downward shortwave radiation by reducing cloudiness and sea‐ice during a warming progress compared to initially warm SO models. Finally, our results demonstrate that climate models that correctly simulate initial SO state can improve 2°C warming projections with reduced uncertainties.
... This leads to dramatic seasonal change of sea-ice extent from a minimum of nearly 3.0 × 10 6 km 2 in late summer to a maximum of nearly 20.0 × 10 6 km 2 (Fig. 1) in early spring (Gloersen et al. 1992). In addition to the seasonal change, sea-ice area in the Antarctic also changes over a range of timescales, from daily (e.g., Kimura and Wakatsuchi 2011), to intraseasonal (e.g., Baba et al. 2006), interannual (e.g., Comiso et al. 2017), and multi-decadal timescales (e.g., Turner et al. 2016;Goosse and Zunz 2014;Zhang et al. 2019). ...
Processes governing seasonal and interannual change of the Antarctic sea-ice area
Article
Full-text available
  • Dec 2022
Seasonal and interannual variabilities of sea-ice area in the Southern Ocean were examined using daily sea-ice concentration and ice velocity products for 2003–2019, derived from Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and AMSR2 data. This study quantified the contributions to changes in the sea-ice area due to sea-ice transport and local processes, including ice formation/melting and ice deformation. Regional differences in the processes of seasonal advance and retreat of sea ice were elucidated. In most regions, sea-ice area increases mainly due to new ice formation in the marginal ice zone during autumn and winter. However, in the Amundsen–Bellingshausen seas, ice melting occurs in the marginal ice zone, even during winter, and expansion of the ice cover is attributable mainly to off-ice transport. With regard to interannual variability, the maximum ice area for each year is highly correlated with increase of ice area attributable to the ice formation in the marginal ice zone. Revealed processes that controls sea-ice changes could help improve our understanding of the sea-ice response to climate change.
... Moreover, the trend for SIE expansion accelerated after 2000 by almost a factor of five relative to the trend for 1979 to 1999 14 . Several hypotheses have been proposed to explain this observed positive trend, including surface wind anomalies induced by the Southern Annual mode (SAM) or tropical SST anomalies [15][16][17][18][19][20][21][22] , a freshening of the Southern Ocean (SO) [23][24][25][26][27][28] , internal variability [29][30][31] , the role of mesoscale eddies 32 , and successful simulations of sea ice drift velocity 33 and SO SST 34 . The accelerating rate of sea ice expansion during the post-2000 period is suggested to be related to the negative phase of the interdecadal Pacific oscillation (IPO) that can induce atmosphere teleconnection patterns and thus alter the surface wind anomalies over the SO 14 . ...
The relative role of the subsurface Southern Ocean in driving negative Antarctic Sea ice extent anomalies in 2016–2021
Article
Full-text available
  • Nov 2022
The low Antarctic sea ice extent following its dramatic decline in late 2016 has persisted over a multiyear period. However, it remains unclear to what extent this low sea ice extent can be attributed to changing ocean conditions. Here, we investigate the causes of this period of low Antarctic sea ice extent using a coupled climate model partially constrained by observations. We find that the subsurface Southern Ocean played a smaller role than the atmosphere in the extreme sea ice extent low in 2016, but was critical for the persistence of negative anomalies over 2016–2021. Prior to 2016, the subsurface Southern Ocean warmed in response to enhanced westerly winds. Decadal hindcasts show that subsurface warming has persisted and gradually destabilized the ocean from below, reducing sea ice extent over several years. The simultaneous variations in the atmosphere and ocean after 2016 have further amplified the decline in Antarctic sea ice extent. Warming of the subsurface Southern Ocean played only a minor role in shrinking Antarctic Sea ice extent in 2016, but was critical for the persistence of negative anomalies between 2016 and 2021, according to coupled climate model simulations partially constrained by observations.
... Some studies argued that the representation of SO convection has far-reaching impacts on the regional climate, including the LLC coverage (Winton et al., 2010;Zhang et al., 2019;Gjermundsen et al., 2021). We speculate that the time scales of cloud feedback acting on GMST increase are different from that of deep ocean circulation in climate model simulations under gradually changing forcing. ...
... Multi-annual to decadal changes in climate may also occur in the absence of external drivers through natural internal variability (Cassou et al., 2018), especially in the Atlantic (Knight et al., 2005;Delworth et al., 2007), Pacific (Power et al., 2021), and Southern oceans (Zhang L. et al., 2019), but also the atmosphere (Dimdore-Miles et al., 2022). Moreover, many multi-annual to decadal changes in climate may occur through a combination of external forcing and internal variability (e.g., Huang et al., 2020;Bonnet et al., 2021;Wu et al., 2021) complicating the separation of forcing's from the natural variability in observed changes. ...
Attribution of multi-annual to decadal changes in the climate system: The Large Ensemble Single Forcing Model Intercomparison Project (LESFMIP)
Article
Full-text available
  • Sep 2022
Multi-annual to decadal changes in climate are accompanied by changes in extreme events that cause major impacts on society and severe challenges for adaptation. Early warnings of such changes are now potentially possible through operational decadal predictions. However, improved understanding of the causes of regional changes in climate on these timescales is needed both to attribute recent events and to gain further confidence in forecasts. Here we document the Large Ensemble Single Forcing Model Intercomparison Project that will address this need through coordinated model experiments enabling the impacts of different external drivers to be isolated. We highlight the need to account for model errors and propose an attribution approach that exploits differences between models to diagnose the real-world situation and overcomes potential errors in atmospheric circulation changes. The experiments and analysis proposed here will provide substantial improvements to our ability to understand near-term changes in climate and will support the World Climate Research Program Lighthouse Activity on Explaining and Predicting Earth System Change.
... While Arctic sea ice is rapidly reducing in association with increasing surface air temperature (SAT) (Stroeve et al 2007), observations clearly show a gradually but uneven increasing trend of Antarctic sea ice extent before 2014 (Holland 2014), followed by a precipitous decline (Turner et al 2017, Parkinson 2019. Antarctic sea ice changes are affected by several factors, including but not limited to anthropogenic forcing Solomon 2002, Arblaster andMeehl 2006), atmospheric circulation and oceanic heat flux changes (Hobbs et al 2016), and internal multidecadal variability of the Antarctic-Southern Ocean system (Lecomte et al 2017, Meehl et al 2019, Zhang et al 2019. From the perspective of atmospheric circulation, previous studies have focused on the tropicalpolar teleconnections driven by tropical ocean variability (Yuan et al 2018. ...
Influence of tropical Atlantic meridional dipole of sea surface temperature anomalies on Antarctic autumn sea ice
Article
Full-text available
Antarctic sea ice plays an important role in polar ecosystems and global climate, while its variability is affected by many factors. Teleconnections between the tropical and high latitudes have profound impacts on Antarctic climate changes through the stationary Rossby wave mechanism. Recent studies have connected long-term Antarctic sea ice changes to multidecadal variabilities of the tropical ocean, including the Atlantic Multidecadal Oscillation and the Interdecadal Pacific Oscillation. On interannual timescales, whether an impact exists from teleconnection of the tropical Atlantic is not clear. Here we find an impact of sea surface temperature (SST) variability of the tropical Atlantic meridional dipole mode on Antarctic sea ice that is most prominent in austral autumn. The meridional dipole SST anomalies in the tropical Atlantic force deep convection anomalies locally and over the tropical Pacific, generating stationary Rossby wave trains propagating eastward and poleward, which induce atmospheric circulation anomalies affecting sea ice. Specifically, convective anomalies over the equatorial Atlantic and Pacific are opposite-signed, accompanied by anomalous wave sources over the subtropical Southern Hemisphere. The planetary-scale atmospheric response has significant impacts on sea ice concentration anomalies in the Ross Sea, near the Antarctic Peninsula, and east of the Weddell Sea.
Wave-triggered breakup in the marginal ice zone generates lognormal floe size distributions: a simulation study
Article
Full-text available
Fragmentation of the sea ice cover by ocean waves is an important mechanism impacting ice evolution. Fractured ice is more sensitive to melt, leading to a local reduction in ice concentration, facilitating wave propagation. A positive feedback loop, accelerating sea ice retreat, is then introduced. Despite recent efforts to incorporate this process and the resulting floe size distribution (FSD) into the sea ice components of global climate models (GCMs), the physics governing ice breakup under wave action remains poorly understood and its parametrisation highly simplified. We propose a two-dimensional numerical model of wave-induced sea ice breakup to estimate the FSD resulting from repeated fracture events. This model, based on linear water wave theory and visco-elastic sea ice rheology, solves for the scattering of an incoming time-harmonic wave by the ice cover and derives the corresponding strain field. Fracture occurs when the strain exceeds an empirical threshold. The geometry is then updated for the next iteration of the breakup procedure. The resulting FSD is analysed for both monochromatic and polychromatic forcings. For the latter results, FSDs obtained for discrete frequencies are combined following a prescribed wave spectrum. We find that under realistic wave forcing, lognormal FSDs emerge consistently in a large variety of model configurations. Care is taken to evaluate the statistical significance of this finding. This result contrasts with the power law FSD behaviour often assumed by modellers. We discuss the properties of these modelled distributions with respect to the ice rheological properties and the forcing waves. The projected output can be used to improve empirical parametrisations used to couple sea ice and ocean wave GCM components.
Uncertainty in Pre-industrial Global Ocean Initialization Can Yield Irreducible Uncertainty in Southern Ocean Surface Climate
Article
  • Oct 2022
How do ocean initial states impact historical and future climate projections in Earth system models? To answer this question, we use the 50-member Canadian Earth System Model (CanESM2) large ensemble, in which individual ensemble members are initialized using a combination of different oceanic initial states and atmospheric micro-perturbations. We show that global ocean heat content anomalies associated with the different ocean initial states, particularly differences in deep ocean heat content due to ocean drift, persist from initialization at year 1950 through the end of the simulations at year 2100. We also find that these anomalies most readily impact surface climate over the Southern Ocean. Differences in ocean initial states affect Southern Ocean surface climate because persistent deep ocean temperature anomalies upwell along sloping isopycnal surfaces that delineate neighboring branches of the Upper and Lower Cells of the Global Meridional Overturning Circulation. As a result, up to a quarter of the ensemble variance in Southern Ocean turbulent heat fluxes, heat uptake, and surface temperature trends can be traced to variance in the ocean initial state, notably deep ocean temperature differences of order 0.1K due to model drift. Such a discernible impact of varying ocean initial conditions on ensemble variance over the Southern Ocean is evident throughout the full 150 simulation years of the ensemble, even though upper ocean temperature anomalies due to varying ocean initial conditions rapidly dissipate over the first two decades of model integration over much of the rest of the globe.
Vertical ocean heat redistribution sustaining sea-ice concentration trends in the Ross Sea
Article
Full-text available
  • Dec 2017
Several processes have been hypothesized to explain the slight overall expansion of Antarctic sea ice over the satellite observation era, including externally forced changes in local winds or in the Southern Ocean’s hydrological cycle, as well as internal climate variability. Here, we show the critical influence of an ocean–sea-ice feedback. Once initiated by an external perturbation, it may be sufficient to sustain the observed sea-ice expansion in the Ross Sea, the region with the largest and most significant expansion. We quantify the heat trapped at the base of the ocean mixed layer and demonstrate that it is of the same order of magnitude as the latent heat storage due to the long-term changes in sea-ice volume. The evidence thus suggests that the recent ice coverage increase in the Ross Sea could have been achieved through a reorganization of energy within the near-surface ice-ocean system.
Physical controls of Southern Ocean deep-convection variability in CMIP5 models and the Kiel Climate Model: Southern Ocean Deep Convection
Article
Full-text available
Global climate models exhibit large biases in the Southern Ocean. For example, in models Antarctic bottom water is formed mostly through open-ocean deep convection rather than through shelf convection. Still, the timescale, region, and intensity of deep-convection variability vary widely among models. We investigate the physical controls of this variability in the Atlantic sector of the Southern Ocean, where most of the models simulate recurring deep-convection events. We analyzed output from 11 exemplary CMIP5 models and four versions of the Kiel Climate Model. Of the several potential physical control parameters that we tested, the ones shared by all these models are as follows: Stratification in the convection region influences the timescale of the deep-convection variability; i.e., models with a strong (weak) stratification vary on long (short) timescales. Also, sea ice volume affects the modeled mean state in the Southern Ocean: Large (small) sea ice volume is associated with a nonconvective (convective) predominant regime.
Diagnosis of Decadal Predictability of Southern Ocean Sea Surface Temperature in the GFDL CM2.1 Model
Article
Full-text available
  • Jun 2017
The average predictability time (APT) method is used to identify the most predictable components of decadal sea surface temperature (SST) variations over the Southern Ocean (SO) in a 4000 year unforced control run of the GFDL CM2.1 model. The most predictable component shows significant predictive skill for periods as long as 20 years. The physical pattern of this variability has a uniform sign of SST anomalies over the SO, with maximum values over the Amundsen-Bellingshausen-Weddell Seas. Spectral analysis of the associated APT time series shows a broad peak on time scales of 70-120 years. This most predictable pattern is closely related to the mature phase of a mode of internal variability in the SO that is associated with fluctuations of deep ocean convection. The second most predictable component of SO SST is characterized by a dipole structure, with SST anomalies of one sign over the Weddell Sea and SST anomalies of the opposite sign over the Amundsen-Bellingshausen Seas. This component has significant predictive skill for periods as long as 6 years. This dipole mode is associated with a transition between phases of the dominant pattern of SO internal variability. The long time scales associated with variations in SO deep convection provide the source of the predictive skill of SO SST on decadal scales. These analyses suggest that if we could adequately initialize the SO deep convection in a numerical forecast model, the future evolution of SO SST and its associated climate impacts is potentially predictable.
Estimating Decadal Predictability for the Southern Ocean Using the GFDL CM2.1 Model
Article
Full-text available
  • Mar 2017
This study explores the potential predictability of the Southern Ocean (SO) climate on decadal timescales as represented in the GFDL CM2.1 model using prognostic methods. We conduct perfect model predictability experiments starting from ten different initial states, and show potentially predictable variations of Antarctic bottom water formation (AABW) rates on time scales as long as twenty years. The associated Weddell Sea (WS) subsurface temperatures and Antarctic sea ice have comparable potential predictability as the AABW cell. The predictability of sea surface temperature (SST) variations over the WS and the SO is somewhat smaller, with predictable scales out to a decade. This reduced predictability is likely associated with stronger damping from air-sea interaction. As a complement to our perfect predictability study, we also make hindcasts of SO decadal variability using the GFDL CM2.1 decadal prediction system. Significant predictive skill for SO SST on multi-year time scales is found in th...
Wind-driven ocean dynamics impact on the contrasting sea-ice trends around West Antarctica
Article
Full-text available
  • Mar 2017
Since late 1978, Antarctic sea-ice extent in the East Pacific has retreated persistently over the Amundsen and Bellingshausen Seas in warm seasons, but expanded over the Ross and Amundsen Seas in cold seasons, while almost opposite seasonal trends have occurred in the Atlantic over the Weddell Sea. By using a surface-forced ocean and sea-ice coupled model, we show that regional wind-driven ocean dynamics played a key role in driving these trends. In the East Pacific, the strengthening Southern Hemisphere (SH) westerlies in the region enhanced the Ekman upwelling of warm upper Circumpolar Deep Water and increased the northward Ekman transport of cold Antarctic surface water. The associated surface ocean warming south of 68°S and the cooling north of 68°S directly contributed to the retreat of sea ice in warm seasons and the expansion in cold seasons, respectively. In the Atlantic, the poleward shifting SH westerlies in the region strengthened the northern branch of the Weddell Gyre, which in turn increased the meridional thermal gradient across it as constrained by the thermal wind balance. Ocean heat budget analysis further suggests that the strengthened northern branch of the Weddell Gyre acted as a barrier against the poleward ocean heat transport, and thus produced anomalous heat divergence within the Weddell Gyre and anomalous heat convergence north of the gyre. The associated cooling within the Weddell Gyre and the warming north of the gyre contributed to the expansion of sea ice in warm seasons and the retreat in cold seasons, respectively.
Conditions leading to the unprecedented low Antarctic sea ice extent during the 2016 austral spring season
Article
  • Aug 2017
The 2016 austral spring was characterized by the lowest anomalous Southern Hemisphere (SH) sea ice extent seen in the observational record (1979-present) and coincided with anomalously warm surface waters surrounding most of Antarctica. Two distinct processes contributed to this event: Firstly, the extreme El Niño event peaking in December-February (DJF) 2015/16 contributed to pronounced extra-tropical SH sea-surface temperature and sea ice extent anomalies in the eastern Ross, Amundsen, and Bellingshausen Seas that persisted in part until the following 2016 austral spring. Secondly, internal unforced atmospheric variability of the Southern Annular Mode promoted the exceptional low sea ice extent in November-December 2016. These results suggest that a combination of tropically-forced and internal SH atmospheric variability contributed to the unprecedented sea ice decline during the 2016 austral spring, on top of the slow background changes expected from greenhouse gas and ozone forcing.
Global Atmospheric Teleconnections and Multidecadal Climate Oscillations Driven by Southern Ocean Convection
Article
  • Jul 2017
A 1000-yr control simulation in a low-resolution coupled atmosphere-ocean model from the Geophysical Fluid Dynamics Laboratory (GFDL) family of climate models shows a natural, highly regular multidecadal oscillation between periods of Southern Ocean (SO) open-ocean convection and nonconvective periods. It is shown here that convective periods are associated with warming of the SO sea surface temperatures (SSTs), and more broadly of the Southern Hemisphere (SH) SSTs and atmospheric temperatures. This SO warming results in a decrease in the meridional gradient of SSTs in the SH, changing the large-scale pressure patterns, reducing the midlatitude baroclinicity and thus the magnitude of the southern Ferrel and Hadley cells, and weakening the SO westerly winds and the SH tropical trade winds. The rearrangement of the atmospheric circulation is consistent with the global energy balance. During convective decades, the increase in incoming top-of-the-atmosphere radiation in the SH is balanced by an increase in the Northern Hemisphere (NH) outgoing radiation. The energy supplying this increase is carried by enhanced atmospheric transport across the equator, as the intertropical convergence zone and associated wind patterns shift southward, toward the anomalously warmer SH. While the critical role of the SO for climate on long, paleoclimate time scales is now beyond debate, the strength and global scale of the teleconnections observed here also suggest an important role for the SO in global climate dynamics on the shorter interannual and multidecadal time scales.
Preconditioning of the Weddell Sea Polynya by the Ocean Mesoscale and Dense Water Overflows
Article
  • Jun 2017
The Weddell Sea polynya is a large opening in the open-ocean sea ice cover associated with intense deep convection in the ocean. A necessary condition to form and maintain a polynya is the presence of a strong subsurface heat reservoir. This study investigates the processes that control the stratification and hence the buildup of the subsurface heat reservoir in the Weddell Sea. To do so, a climate model run for 200 years under preindustrial forcing with two eddying resolutions in the ocean (0.25° CM2.5 and 0.10° CM2.6) is investigated. Over the course of the simulation, CM2.6 develops two polynyas in the Weddell Sea, while CM2.5 exhibits quasi-continuous deep convection but no polynyas, exemplifying that deep convection is not a sufficient condition for a polynya to occur. CM2.5 features a weaker subsurface heat reservoir than CM2.6 owing to weak stratification associated with episodes of gravitational instability and enhanced vertical mixing of heat, resulting in an erosion of the reservoir. In contrast, in CM2.6, the water column is more stably stratified, allowing the subsurface heat reservoir to build up. The enhanced stratification in CM2.6 arises from its refined horizontal grid spacing and resolution of topography, which allows, in particular, a better representation of the restratifying effect by transient mesoscale eddies and of the overflows of dense waters along the continental slope.
Important role for ocean warming and increased ice-shelf melt in Antarctic sea-ice expansion
Article
  • Mar 2013
Changes in sea ice significantly modulate climate change because of its high reflective and strong insulating nature. In contrast to Arctic sea ice, sea ice surrounding Antarctica has expanded1, with record extent2 in 2010. This ice expansion has previously been attributed to dynamical atmospheric changes that induce atmospheric cooling3. Here we show that accelerated basal melting of Antarctic ice shelves is likely to have contributed significantly to sea-ice expansion. Specifically, we present observations indicating that melt water from Antarctica’s ice shelves accumulates in a cool and fresh surface layer that shields the surface ocean from the warmer deeper waters that are melting the ice shelves. Simulating these processes in a coupled climate model we find that cool and fresh surface water from ice-shelf melt indeed leads to expanding sea ice in austral autumn and winter. This powerful negative feedback counteracts Southern Hemispheric atmospheric warming. Although changes in atmospheric dynamics most likely govern regional sea-ice trends4, our analyses indicate that the overall sea-ice trend is dominated by increased ice-shelf melt. We suggest that cool sea surface temperatures around Antarctica could offset projected snowfall increases in Antarctica, with implications for estimates of future sea-level rise.