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Ocean warming and accelerating Southern Ocean zonal flow

Nature Climate Change volume 11, pages 1090–1097 (2021)Cite this article

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Abstract

The Southern Ocean (>30° S) has taken up a large amount of anthropogenic heat north of the Subantarctic Front (SAF) of the Antarctic Circumpolar Current (ACC). Poor sampling before the 1990s and decadal variability have heretofore masked the ocean’s dynamic response to this warming. Here we use the lengthening satellite altimetry and Argo float records to show robust acceleration of zonally averaged Southern Ocean zonal flow at 48° S–58° S. This acceleration is reproduced in a hierarchy of climate models, including an ocean-eddy-resolving model. Anthropogenic ocean warming is the dominant driver, as large (small) heat gain in the downwelling (upwelling) regime north (south) of the SAF causes zonal acceleration on the northern flank of the ACC and adjacent subtropics due to increased baroclinicity; strengthened wind stress is of secondary importance. In Drake Passage, little warming occurs and the SAF velocity remains largely unchanged. Continued ocean warming could further accelerate Southern Ocean zonal flow.

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Fig. 1: Zonal mean sea-level, subsurface temperature and geostrophic velocity trends from observations.
Fig. 2: Time series of the observed zonal geostrophic velocity averaged between 48° S–58° S.
Fig. 3: Comparison between the observed and simulated time series and trends of zonal velocity averaged between 48° S–58° S.
Fig. 4: Zonal mean patterns of zonal velocity trend/change from observations and models.
Fig. 5: Spatial patterns of zonal velocity trend/change from observations and models.

Data Availability

Argo data are available at: http://www.argo.ucsd.edu. IAP data are available at: https://climatedataguide.ucar.edu/climate-data/ocean-temperature-analysis-and-heat-content-estimate-institute-atmospheric-physics. EN4 data are available at: https://www.metoffice.gov.uk/hadobs/en4/. WOA18 data are available at: https://www.nodc.noaa.gov/OC5/woa18/. Satellite altimetry data are available at: https://www.aviso.altimetry.fr/en/data.html. The CMIP6, CanESM5 large ensemble, CESM1-HR and CESM1-SR data are available on the Program for Climate Model Diagnostics and Intercomparison’s Earth System Grid (https://esgf-node.llnl.gov/search/cmip6/). The CESM LENS simulations are available on the Earth System Grid (www.earthsystemgrid.org). The CESM and MITgcm model data used in this study are available from the corresponding author upon request.

References

  1. Gille, S. T. Decadal-scale temperature trends in the Southern Hemisphere ocean. J. Clim. 21, 4749–4765 (2008).

    Article  Google Scholar 

  2. Böning, C. W., Dispert, A., Visbeck, M., Rintoul, S. R. & Schwarzkopf, F. U. The response of the Antarctic Circumpolar Current to recent climate change. Nat. Geosci. 1, 864–869 (2008).

    Article  Google Scholar 

  3. Roemmich, D. et al. Unabated planetary warming and its ocean structure since 2006. Nat. Clim. Change 5, 240–245 (2015).

    Article  Google Scholar 

  4. Durack, P. J. & Wijffels, S. E. Fifty-year trends in global ocean salinities and their relationship to broad-scale warming. J. Clim. 23, 4342–4362 (2010).

    Article  Google Scholar 

  5. Sabine, C. L. The oceanic sink for anthropogenic CO2. Science 305, 367–371 (2004).

    Article  CAS  Google Scholar 

  6. Talley, L. D. et al. Changes in ocean heat, carbon content, and ventilation: a review of the first decade of GO-SHIP global repeat hydrography. Annu. Rev. Mar. Sci. 8, 185–215 (2016).

    Article  CAS  Google Scholar 

  7. Waugh, D. W., Primeau, F., DeVries, T. & Holzer, M. Recent changes in the ventilation of the southern oceans. Science 339, 568–570 (2013).

    Article  CAS  Google Scholar 

  8. Fyfe, J. C. Southern Ocean warming due to human influence. Geophys. Res. Lett. 33, L19701 (2006).

    Article  Google Scholar 

  9. Sigmond, M., Reader, M. C., Fyfe, J. C. & Gillett, N. P. Drivers of past and future Southern Ocean change: stratospheric ozone versus greenhouse gas impacts. Geophys. Res. Lett. 38, L12601 (2011).

    Article  Google Scholar 

  10. Swart, N. C., Gille, S. T., Fyfe, J. C. & Gillett, N. P. Recent Southern Ocean warming and freshening driven by greenhouse gas emissions and ozone depletion. Nat. Geosci. 11, 836–841 (2018).

    Article  CAS  Google Scholar 

  11. Shi, J.-R., Xie, S.-P. & Talley, L. D. Evolving relative importance of the Southern Ocean and North Atlantic in anthropogenic ocean heat uptake. J. Clim. 31, 7459–7479 (2018).

    Article  Google Scholar 

  12. Manabe, S., Bryan, K. & Spelman, M. J. Transient response of a global ocean–atmosphere model to a doubling of atmospheric carbon dioxide. J. Phys. Oceanogr. 20, 722–749 (1990).

    Article  Google Scholar 

  13. Armour, K. C., Marshall, J., Scott, J. R., Donohoe, A. & Newsom, E. R. Southern Ocean warming delayed by circumpolar upwelling and equatorward transport. Nat. Geosci. 9, 549–554 (2016).

    Article  CAS  Google Scholar 

  14. Shi, J.-R., Talley, L. D., Xie, S.-P., Liu, W. & Gille, S. T. Effects of buoyancy and wind forcing on Southern Ocean climate change. J. Clim. 33, 10003–10020 (2020).

    Article  Google Scholar 

  15. Rintoul, S. R. The global influence of localized dynamics in the Southern Ocean. Nature 558, 209–218 (2018).

    Article  CAS  Google Scholar 

  16. Hallberg, R. & Gnanadesikan, A. The role of eddies in determining the structure and response of the wind-driven Southern Hemisphere overturning: results from the Modeling Eddies in the Southern Ocean (MESO) project. J. Phys. Oceanogr. 36, 2232–2252 (2006).

    Article  Google Scholar 

  17. Meredith, M. P. & Hogg, A. M. Circumpolar response of Southern Ocean eddy activity to a change in the Southern Annular Mode. Geophys. Res. Lett. 33, L16608 (2006).

    Article  Google Scholar 

  18. Farneti, R., Delworth, T. L., Rosati, A. J., Griffies, S. M. & Zeng, F. The role of mesoscale eddies in the rectification of the Southern Ocean response to climate change. J. Phys. Oceanogr. 40, 1539–1557 (2010).

    Article  Google Scholar 

  19. Downes, S. M., Budnick, A. S., Sarmiento, J. L. & Farneti, R. Impacts of wind stress on the Antarctic Circumpolar Current fronts and associated subduction. Geophys. Res. Lett. 38, L11605 (2011).

    Article  Google Scholar 

  20. Meredith, M. P., Naveira Garabato, A. C., Hogg, A. M. & Farneti, R. Sensitivity of the overturning circulation in the Southern Ocean to decadal changes in wind forcing. J. Clim. 25, 99–110 (2012).

    Article  Google Scholar 

  21. Marshall, D. P., Ambaum, M. H. P., Maddison, J. R., Munday, D. R. & Novak, L. Eddy saturation and frictional control of the Antarctic Circumpolar Current. Geophys. Res. Lett. 44, 286–292 (2017).

    Article  Google Scholar 

  22. Hogg, A. M. et al. Recent trends in the Southern Ocean eddy field. J. Geophys. Res. Oceans 120, 257–267 (2015).

    Article  Google Scholar 

  23. Munday, D. R., Johnson, H. L. & Marshall, D. P. Eddy saturation of equilibrated circumpolar currents. J. Phys. Oceanogr. 43, 507–532 (2013).

    Article  Google Scholar 

  24. Roemmich, D. et al. Decadal spinup of the South Pacific subtropical gyre. J. Phys. Oceanogr. 37, 162–173 (2007).

    Article  Google Scholar 

  25. Fine, R. A., Peacock, S., Maltrud, M. E. & Bryan, F. O. A new look at ocean ventilation time scales and their uncertainties. J. Geophys. Res. Oceans 122, 3771–3798 (2017).

    Article  Google Scholar 

  26. Hu, S. et al. Deep-reaching acceleration of global mean ocean circulation over the past two decades. Sci. Adv. 6, eaax7727 (2020).

    Article  Google Scholar 

  27. Gent, P. R., Large, W. G. & Bryan, F. O. What sets the mean transport through Drake Passage? J. Geophys. Res. Oceans 106, 2693–2712 (2001).

    Article  Google Scholar 

  28. Borowski, D., Gerdes, R. & Olbers, D. Thermohaline and wind forcing of a circumpolar channel with blocked geostrophic contours. J. Phys. Oceanogr. 32, 2520–2540 (2002).

    Article  Google Scholar 

  29. Hogg, A. M. C. An Antarctic Circumpolar Current driven by surface buoyancy forcing. Geophys. Res. Lett. 37, L23601 (2010).

    Article  Google Scholar 

  30. Wang, G., Xie, S. P., Huang, R. X. & Chen, C. Robust warming pattern of global subtropical oceans and its mechanism. J. Clim. 28, 8574–8584 (2015).

    Article  Google Scholar 

  31. Hogg, A. M. C. & Gayen, B. Ocean gyres driven by surface buoyancy forcing. Geophys. Res. Lett. 47, e2020GL088539 (2020).

    Article  Google Scholar 

  32. Gent, P. R. & Mcwilliams, J. C. Isopycnal mixing in ocean circulation models. J. Phys. Oceanogr. 20, 150–155 (1990).

    Article  Google Scholar 

  33. Gent, P. R. & Danabasoglu, G. Response to increasing Southern Hemisphere winds in CCSM4. J. Clim. 24, 4992–4998 (2011).

    Article  Google Scholar 

  34. Liu, W. et al. Southern Ocean heat uptake, redistribution, and storage in a warming climate: the role of meridional overturning circulation. J. Clim. 31, 4727–4743 (2018).

    Article  Google Scholar 

  35. Roemmich, D. & Gilson, J. The 2004–2008 mean and annual cycle of temperature, salinity, and steric height in the global ocean from the Argo program. Prog. Oceanogr. 82, 81–100 (2009).

    Article  Google Scholar 

  36. Deser, C., Phillips, A., Bourdette, V. & Teng, H. Uncertainty in climate change projections: the role of internal variability. Clim. Dyn. 38, 527–546 (2012).

    Article  Google Scholar 

  37. Kay, J. E. et al. The Community Earth System Model (CESM) large ensemble project: a community resource for studying climate change in the presence of internal climate variability. Bull. Am. Meteorol. Soc. 96, 1333–1349 (2015).

    Article  Google Scholar 

  38. Swart, N. C. et al. The Canadian Earth System Model version 5 (CanESM5.0.3). Geosci. Model Dev. Discuss. 12, 4823–4873 (2019).

    Article  CAS  Google Scholar 

  39. Marshall, G. J. Trends in the Southern Annular Mode from observations and reanalyses. J. Clim. 16, 4134–4143 (2003).

    Article  Google Scholar 

  40. McDonagh, E. L. et al. Decadal changes in the South Indian Ocean thermocline. J. Clim. 18, 1575–1590 (2005).

    Article  Google Scholar 

  41. Chidichimo, M. P., Donohue, K. A., Watts, D. R. & Tracey, K. L. Baroclinic transport time series of the Antarctic Circumpolar Current measured in Drake Passage. J. Phys. Oceanogr. 44, 1829–1853 (2014).

    Article  Google Scholar 

  42. Donohue, K. A., Tracey, K. L., Watts, D. R., Chidichimo, M. P. & Chereskin, T. K. Mean Antarctic Circumpolar Current transport measured in Drake Passage. Geophys. Res. Lett. 43, 11760–11767 (2016).

    Article  Google Scholar 

  43. Hughes, C. W., Williams, J., Coward, A. C. & de Cuevas, B. A. Antarctic circumpolar transport and the southern mode: a model investigation of interannual to decadal timescales. Ocean Sci. 10, 215–225 (2014).

    Article  Google Scholar 

  44. Killworth, P. D. & Hughes, C. W. The Antarctic Circumpolar Current as a free equivalent-barotropic jet. J. Mar. Res. 60, 19–45 (2002).

    Article  Google Scholar 

  45. Koenig, Z., Provost, C., Ferrari, R., Sennechael, N. & Rio, M.-H. Volume transport of the Antarctic Circumpolar Current: production and validation of a 20 year long time series obtained from in situ and satellite observations. J. Geophys. Res. Oceans 119, 5407–5433 (2014).

    Article  Google Scholar 

  46. Orsi, A. H., Whitworth, T. & Nowlin, W. D. On the meridional extent and fronts of the Antarctic Circumpolar Current. Deep Sea Res. I 42, 641–673 (1995).

    Article  Google Scholar 

  47. Fasullo, J. T. & Nerem, R. S. Altimeter-era emergence of the patterns of forced sea-level rise in climate models and implications for the future. Proc. Natl Acad. Sci. USA 115, 12944–12949 (2018).

    Article  CAS  Google Scholar 

  48. Cheng, L., Wang, G., Abraham, J. & Huang, G. Decadal ocean heat redistribution since the late 1990s and its association with key climate modes. Climate 6, 91 (2018).

    Article  Google Scholar 

  49. Cheng, L. & Zhu, J. Benefits of CMIP5 multimodel ensemble in reconstructing historical ocean subsurface temperature variations. J. Clim. 29, 5393–5416 (2016).

    Article  Google Scholar 

  50. Good, S. A., Martin, M. J. & Rayner, N. A. EN4: quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates. J. Geophys. Res. Oceans 118, 6704–6716 (2013).

    Article  Google Scholar 

  51. Gouretski, V. & Reseghetti, F. On depth and temperature biases in bathythermograph data: development of a new correction scheme based on analysis of a global ocean database. Deep Sea Res. I 57, 812–833 (2010).

    Article  Google Scholar 

  52. Locarnini, R. A. et al. World Ocean Atlas 2018 Volume 1: Temperature NOAA Atlas NESDIS 81 (Ocean Climate Laboratory National Centers for Environmental Information, 2019).

  53. Eyring, V. et al. Overview of the Coupled Model Intercomparison Project phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).

    Article  Google Scholar 

  54. O’Neill, B. C. et al. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9, 3461–3482 (2016).

    Article  Google Scholar 

  55. Haarsma, R. J. et al. High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6. Geosci. Model Dev. 9, 4185–4208 (2016).

    Article  Google Scholar 

  56. Liu, W., Lu, J. & Xie, S.-P. Understanding the Indian Ocean response to double CO2 forcing in a coupled model. Ocean Dyn. 65, 1037–1046 (2015).

    Article  Google Scholar 

  57. Forget, G. et al. ECCO version 4: an integrated framework for non-linear inverse modeling and global ocean state estimation. Geosci. Model Dev. Discuss. 8, 3653–3743 (2015).

    Google Scholar 

  58. Redi, M. H. Oceanic isopycnal mixing by coordinate rotation. J. Phys. Oceanogr. 12, 1154–1158 (1982).

    Article  Google Scholar 

  59. Gaspar, P., Grégoris, Y. & Lefevre, J.-M. A simple eddy kinetic energy model for simulations of the oceanic vertical mixing: tests at Station Papa and long-term upper ocean study site. J. Geophys. Res. Oceans 95, 16179–16193 (1990).

    Article  Google Scholar 

  60. Peng, Q., Xie, S. P., Wang, D., Zheng, X. T. & Zhang, H. Coupled ocean–atmosphere dynamics of the 2017 extreme coastal El Niño. Nat. Commun. 10, 298 (2019).

    Article  Google Scholar 

  61. Peng, Q. et al. Eastern Pacific wind effect on the evolution of El Niño: implications for ENSO diversity. J. Clim. 33, 3197–3212 (2020).

    Article  Google Scholar 

Download references

Acknowledgements

We thank S. T. Gille, S. Sun and Y. Lu for enlightening discussions. J.-R.S. is supported by the US National Science Foundation (AGS-1637450) and the Southern Ocean Carbon and Climate Observations and Modeling project under National Science Foundation Award PLR-1425989. L.D.T. is also supported by the Southern Ocean Carbon and Climate Observations and Modeling project. Q.P. is supported by the National Natural Science Foundation of China (42005035). W.L. is supported by the Regents’ Faculty Fellowship, Alfred P. Sloan Foundation Research Fellowship and US National Science Foundation (OCE-2123422).

Author information

Authors and Affiliations

  1. Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA

    Jia-Rui Shi, Lynne D. Talley, Shang-Ping Xie & Qihua Peng

  2. Woods Hole Oceanographic Institution, Woods Hole, MA, USA

    Jia-Rui Shi

  3. State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China

    Qihua Peng

  4. Department of Earth and Planetary Sciences, University of California Riverside, Riverside, CA, USA

    Wei Liu

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Contributions

J.-R.S. designed the research, analysed the data and wrote the paper. L.D.T. and S.-P.X. designed the research and wrote the paper. Q.P. and W.L. performed the experiment.

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Correspondence to Jia-Rui Shi.

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Peer review information Nature Climate Change thanks Andrew Hogg and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Absolute geostrophic velocity trend from observations.

(a) Ug from AVISO, same with Fig. 1c. (b) Zonal absolute geostrophic velocity (Ug) trend applying the surface AVISO-based Ug in (a) as a reference velocity. Gray contours are climatological absolute Ug, with solid contours representing eastward flow and dashed contours representing westward flow.

Extended Data Fig. 2 Zonal mean patterns of potential temperature trend/change from observations and models.

Potential temperature trend from 1979 to 2019 from (a) IAP (observations), (b) CMIP6 MMM, (c) CESM1-SR, and (d) CESM1-HR. (e) Zonal mean potential temperature change from the CESM1_∆Buoy experiment relative to the control run. (f) Zonal mean potential temperature change from the MITgcm_∆SST experiment relative to the control run. Green contours are the climatological Ug or U (in cm/s) from the corresponding cases.

Extended Data Fig. 3 Time series of upper 100 m and 2,000m zonal velocity.

(a, b), Time series of upper 100 m zonal velocity averaged between 48˚S-58˚S relative to the average of 1955–2004 from CMIP6 and LENS simulations, respectively. CMIP6 multi-model mean (MMM) is the black curve, with superimposed observation-based products: IAP (brown), EN4 (green), and Argo (red; since 2005). The velocities from observation-based products apply the surface altimetry-based Ug as a reference velocity. (c, d), Same with (a, b), but for the 2,000 m zonal velocity.

Extended Data Fig. 4 Changes of zonal mean zonal velocity and transport from CMIP6.

(a) Transport changes of zonal mean zonal velocity between 1995–2035 and 1955–1995 from CMIP6 MMM. Total transport change is shown as black curve, upper 2,000 m transport is shown as blue curve, and baroclinic transport with no motion at 2,000 m is shown as red curve. (b) Full-depth zonal velocity change between 1995–2035 and 1955–1995 from CMIP6 MMM. Gray contours are climatological zonal velocity, with solid contours representing eastward flow and dashed contours representing westward flow.

Extended Data Fig. 5 Scatter plot of zonal velocity trend against temperature trend and against wind trend.

(a) Scatter plot of trend (1979–2014) of upper 100 m zonal velocity relative to 2,000 m depth versus trend of temperature difference between 45˚S and 60˚S, along with the linear relationship for the CMIP6 models: BBC-CSM2-MR, BCC-ESM1, CAMS-CSM1-0, CanESM5, CAS-ESM2-0, CESM2, CESM2-FV2, CESM2-WACCM, CESM2-WACCM-FV2, CMCC-CM2-HR4, CMCC-CM2-SR5, EC-Earth3, EC-Earth3-Veg, FGOALS-g3, FIO-ESM-2-0, GFDL-CM4, GISS-E2-1-G, IPSL-CM6A-LR, MCM-UA-1-0, MIROC6, MPI-ESM1-1-2-HAM. MPI-ESM1-2-LR, MRI-ESM2-0, NESM3, SAM0-UNICON, and TaiESM1. Each red triangle indicates the result of each CMIP6 model. The correlation coefficient is 0.71 across models. The black triangle represents the trend from the IAP product. (b) Scatter plot of velocity trend versus SAM. Each blue square indicates the result from each CMIP6 model. The correlation coefficient is 0.16 across models. The black square represents the SAM trend from ERA5 (observations).

Extended Data Fig. 6 Wind-change-induced temperature and zonal velocity changes.

(a, c) Zonal mean potential temperature change and (b, d) U change induced by wind stress change (∆Wind) from CESM1_∆Wind (a, b) relative to CESM1_∆Buoy and MITgcm_∆Wind (c, d) relative to MITgcm_CTL (Methods). Gray contours are the climatological zonal velocity U (in m/s) from the corresponding cases. Upper 100 m U change driven by wind stress change from (e) CESM1 and (f) MITgcm. Mean zonal velocities of 6 cm/s and 12 cm/s are shown as thin and thick green contours.

Extended Data Fig. 7 Baroclinic transport change from CMIP6 ensemble mean and the position of mean ‘SAF’.

Upper 2,000 m baroclinic transport change (shadings) between 1998–2018 and 1940–1960 from CMIP6 ensemble mean. Black curve is the mean ‘SAF’ during 1940–1960 and cyan is the mean ‘SAF’ during 1998–2018. Red curve is the 1993–2019 mean ‘SAF’ based on the sea surface height from satellite observations.

Extended Data Fig. 8 Streamwise mean of upper 2,000 m baroclinic transport change and zonal velocity change.

(a) upper 2,000 m baroclinic transport change and (b) zonal geostrophic velocity change between 1998–2018 and 1940–1960 from IAP data. The ‘SAF’ is defined as the observed 1993–2019 mean sea surface height passing through the Drake Passage at the point 67.5˚W, 57.5˚S. (c)-(d), same with (a)-(b), but the simulated velocity/transport change and the ‘SAF’ are based on CMIP6 ensemble mean. Gray curves in (b) and (d) are the streamwise mean climatological zonal velocity.

Extended Data Fig. 9 Zonal velocity, potential density and potential temperature changes from observed datasets.

Upper 100 m zonal geostrophic velocity, Ug, trend (1993–2019) from the IAP, EN4 and change from WOA18 (1985–2017 mean minus 1955–1984 mean) (top row). Corresponding trend/change of upper 2,000 m averaged potential density and potential temperature are shown in middle row and bottom row, respectively. Black contours indicate Subantarctic Front and Southern ACC Front. Stippling indicates regions exceeding 90% statistical significance computed from the two-tailed t-test.

Extended Data Fig. 10 Spatial patterns of temperature trend/change from Argo and model simulations.

(a) Upper 2,000 m potential temperature trend from Argo observations (2005–2019). Black contours indicate the Subantarctic Front (SAF) and Southern ACC Front (SACCF). (b, c) Upper 2,000 m potential temperature trend from CESM1-SR (b) and CESM1-HR (c). (d, e) Upper 2,000 m potential temperature change from the CESM1_∆Buoy experiment (d) and the MITgcm_∆SST experiment (e) relative to the corresponding control runs. Stippling indicates regions exceeding 90% statistical significance computed from the two-tailed t test.

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Shi, JR., Talley, L.D., Xie, SP. et al. Ocean warming and accelerating Southern Ocean zonal flow. Nat. Clim. Chang. 11, 1090–1097 (2021). https://doi.org/10.1038/s41558-021-01212-5

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