the role of the southern ocean in sea level change · projections of ocean heat content and sea...

The role of the Southern Ocean in sea level change

CSHOR

Dr Xuebin Zhang | Principal Research ScientistDr Kewei Lyu | Postdoctoral Fellow

23 July 2020

CSHOR Sea-level Project Team (CSIRO, UNSW, UTAS, QNLM/OUC)

CSHOR sea level project2 |

CSIROXuebin ZhangKewei Lyu

UNSWJohn church Yuehua LiTrevor McDougall

UTASMatt KingSteven PhippsChristopher Watson

QNLM/OUCXianyao Chen and his group

Students: Jinping Wang, Shujing Zhang and Jingwei Zhang

Ocean and climate processes affecting global and regional sea level

CSHOR sea level project

IPCC AR5Fig. 13.1

Warming (cooling) of the ocean (thermal expansion/contraction)

Change in mass of glaciers and ice sheets (Barystatic)

Changes in liquid water storage on land (Barystatic)

“regional sea level change and coastal impacts” is one of seven Grand Challenges identified by the WCRP

3 |

Motivation for CSHOR sea-level projectThe Southern Ocean is a key area for improving understanding and projections of ocean heat content and sea level change because:

It is one of the key areas where heat enters the ocean, resulting in heat storage in the upper ocean and in the abyssal layers, and contributing to sea-level rise through thermal expansion (Purkey and Johnson, 2010; Roemmich et al. 2015; Wijffels et al. 2016).

Southern Ocean interacts with cryosphere in complicated ways, in particular a warming ocean is critical to the dynamic response of the Antarctic Ice Sheet (Church et al. 2013; DeConto and Pollard 2016).


The role of the Southern Ocean in sea level change


CSHOR sea level project can build up findings from other CSHOR projects to understand and project sea levels regionally and globally.

Achievements and highlights from FY 19-20

• Ocean heat uptake & redistribution in the Southern Hemisphere Oceans based on FAFMIP-type numerical perturbation experiments and decomposition using a theoretical framework (CSIRO, UNSW)

• Analysing CMIP6 sea level output and identifying the linkages between mean state biases and future projection uncertainties in different basins (CSIRO, UNSW)

• Producing high-resolution sea level fingerprints due to land ice mass changes based on NASA/JPL ISSM sea level module with high-resolution unstructured mesh grids (CSIRO, UTAS)

• Global and regional sea-level budget (UNSW, CSIRO)• Testing impacts of different vertical interpolation schemes on ocean heat content estimate (UNSW)• Uncertainties of Antarctic Ice Sheet (AIS) mass flux time series; AIS modelling for the future (UTAS)

Southern Ocean heat uptake and redistribution (CSIRO, UNSW)

All Heat

WaterWind

Ocean heat content changes with global mean removed

𝜃𝜃𝜃|𝑧𝑧 = �𝜃𝜃𝜃|𝑛𝑛 + −𝑁𝑁𝜃𝜕𝜕𝜃𝜃𝜕𝜕𝜕𝜕

heavespiciness

Pure warming or freshening

𝜌𝜌1 𝜌𝜌2 ?Pure warming – heat

Pure freshening - waterPure heave - wind

Theoretical ModellingComparison


How do ocean heat uptake and redistribution respond to atmospheric forcing (i.e., wind stress, heat and freshwater fluxes)? We first adopted a theoretical framework by Bindoff and McDougall, which proposed three physical processes: pure warming, pure freshening and pure heave in an isopycnal/diapycnal coordinate.

We then carried out FAFMIP-type perturbation experiments with some atmospheric forcing turned on/off to examine the impacts of individual atmospheric forcing, and compared findings between modelling and theoretical methods.

Both methods indicate key roles of wind and heat flux in the Southern Ocean warming.

Todd et al. (including Church, Lyu, Zhang) 2020, Journal of Advances in Modelling Earth Systems

Lyu, Zhang, Church, et al. 2020, Journal of Climate

Lyu, Zhang, Church, et al.2020, to be submitted toJournal of PhysicalOceanography.

Pure heave

(Bindoff and McDougall 1994)


Dynamic sea level projections: from CMIP5 to CMIP6

Grose et al. (including Lyuand Zhang) 2020: Insights from CMIP6 for Australia's future climate, Earth’s Future. (highlighted by AGU EOS Research spotlight )

• CMIP5 and CMIP6 ensembles show similar regional sea level distribution, for both present mean and future projection.

• Dynamical downscaling based on high-resolution ocean models provides more regional details.

Linkages between model mean state biases and future projections in the combined CMIP5/6 ensemble (CSIRO, UNSW)


Kewei Lyu, Xuebin Zhang, and John A. Church 2020, Regional dynamic sea level simulated in the CMIP5 and CMIP6 models: mean biases, future projections, and their linkages, Journal of Climate

CMIP6

Contours: multi-model mean; Shading: composite differences

CMIP5Obs

• Models with equatorward bias in the westerly winds tend to project larger poleward shift of the westerly winds, larger wind stress curl forcing, and thus larger dynamic sea level changes in the Southern Ocean

• Most CMIP5 models have equatorward bias relative to observations, which means they might overestimate future projections in this region

Equatorward bias

Poleward bias

(Tamisiea et al., 2003)

Sea surface

Solid surface

Relative sea leveloceanland

Ice Sheet

Sea surface change

Solid surface change

Melting

falling rising Relative sea level change

A B

Sea-level fingerprint:Regional sea level distribution due to gravitational, rotational and elastic responses of the earth system to the melting of land ice

Sea-level fingerprint due to uniform GIS melting

Solving sea-level fingerprint using ISSM sea level module with unstructured high-resolution mesh grids (CSIRO, UTAS)


Change in ice thickness over the AIS from 2000 to 2100 under RCP8.5 (Golledge et al., 2019 )

100 km Typical resolution

from others

5 km25 km50 km 10 km Adopted in our study

ISSM mesh grids: high-resolution along the coast and at melting sources

With unstructured mesh grid, we adopted ISSM sea level module to produce high-resolution and more accurate sea level fingerprints, which can resolve melting resources like AIS at the resolution of 5~10 km, much better than previously available products of ~100 km resolution.

ISSM sea-level fingerprint due to AIS melting 2000-2100 under RCP8.5:10 km vs 100 km resolution of AIS

AIS fingerprint difference (10 km – 100 km)

AIS fingerprint (10 km)


Sea level fingerprints due to future projections of GIS and AIS melting (Golledge et al., 2019 )


2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100

Time

-4.5

-4

-3.5

-3

-2.5

-2

-1.5

-1

-0.5

0

0.5

Ice m

ass c

hang

e [Gt

]

10 4 Grounded-ice mass change from 2000 to 2100

RCP4.5: Greenland ice sheet

RCP8.5: Greenland ice sheet

RCP4.5: Antarctic ice sheet

RCP8.5: Antarctic ice sheet

GIS sea level fingerprint

AIS sea level fingerprint


Ice sheet modelling based on PISM (UTAS)

Boundary conditions (air temperature, precipitation, ocean temperature and salinity) simulated using CSIRO Mk3L climate system model

Projected future GMSL contribution of AIS melting up to 2500

• Church & White (2011) sea level reconstruction was updated using the latest observations, taking the time-evolving sea-level fingerprints and local vertical land motion (VLM) into account.

• The updated GMSL reconstruction agrees better with the sum of all contributors after correcting the local sea level observations with estimates of local VLM (not just GIA).

Sea-level budget (UNSW, CSIRO, QNLM) =?


Sea-level budget (historical 1958-2015)


Dynamic sea level plus global mean thermal expansion

Added mass into oceans (e.g., glaciers, GIS, AIS, Terrestrial water)

Glacial Isostatic Adjustment

Inverse barometer effect due to regional changes of surface air pressure

Sum of above components

Sea-level budget (at tide gauges; historical 1958-2015)


Sea level budgets at 170 tide gauge stations can be closed reasonably well, with difference of 0.3±1.3 mm/yr over 1958-2015 between direct TG observations and combined budget terms.

Testing impacts of new vertical interpolation on ocean heat content estimate (UNSW)


• Barker & McDougall (B&M) proposed new vertical interpolation method (Multiply-Rotated Piecewise Cubic Hermite Interpolating Polynomials) that better represent high resolution data, compared with exisitingReiniger and Ross and linear schemes (left).

• Based on mapping of one month of data (middle), B&M suggested linear interpolation had global heat content errors equivalent to 5 years of ocean heat uptake, and with large scale patterns.

• B&M interpolation gives global integrals of heat content increase from the 1960s to 2006-2018 (255 ZJ) about 13% larger than linear interpolation( Barker & McDougall, 2020)

Linear

B&M

R&R

Publications (15 published, 4 submitted during FY 19-20)Published1. Carson, M., Lyu, K., Richter, K., Becker, M., Domingues, C. M., Han, W., & Zanna, L. (2019). Climate Model

Uncertainty and Trend Detection in Regional Sea Level Projections: A Review. Surveys in Geophysics. doi.org/10.1007/s10712-019-09559-3.

2. Grose, M., S. Narsey, F. Delage, A. Dowdy, M. Bador, G. Boschat, C. Chung, J. Kajtar, S. Rauniyar, M. Freund, K. Lyu, H. Rashid, X. Zhang, S. Wales, C. Trenham, N. Holbrook, T. Cowan, L. Alexander, J. Arblaster and S. Power (2020). Insights from CMIP6 for Australia's future climate, Earth’s Future, 8, e2019EF001469. doi.org/10.1029/2019EF001469.

3. Han, W., Stammer, D., Thompson, P., Ezer, T., Palanisamy, H., Zhang, X., Domingue, C. M., Zhang, L. and Yuan, D. (2019). Impacts of Basin-Scale Climate Modes on Coastal Sea Level: a Review. Surveys in Geophysics. 40, 1493–1541. doi.org/10.1007/s10712-019-09562-8.

4. King, M. A., and C. S. Watson (2020). Antarctic Surface Mass Balance: natural variability, noise and detecting new trends. Geophysical Research Letters, 47, e2020GL087493. doi.org/10.1029/2020GL087493.

5. Li, Z., N. J. Holbrook, X. Zhang, E. C. J. Oliver, and E. A. Cougnon (2020). Remote Forcing of Tasman Sea Marine Heatwaves. Journal of Climate, 33, 5337–5354. doi.org/10.1175/JCLI-D-19-0641.1.

6. Lyu, K., Zhang, X., Church, J. A., & Wu, Q. (2020a). Processes Responsible for the Southern Hemisphere Ocean Heat Uptake and Redistribution under Anthropogenic Warming. Journal of Climate, 33(9), 3787-3807. doi:10.1175/jcli-d-19-0478.1.

7. Lyu, K., X. Zhang, and J. A. Church (2020b). Regional Dynamic Sea Level Simulated in the CMIP5 and CMIP6 Models: Mean Biases, Future Projections, and Their Linkages. Journal of Climate, 33, 6377–6398. doi.org/10.1175/JCLI-D-19-1029.1.

8. Meyssignac, Benoit, et al. (2019). Measuring Global Ocean Heat Content to estimate the Earth Energy Imbalance. Frontiers in Marine Science, 6(432). https://doi.org/10.3389/fmars.2019.00432.

9. Palmer, M. D., Durack, P. J., Chidichimo, M. P., Church, J. A., Cravatte, S., Hill, K., . . . Wijffels, S. (2019). Adequacy of the Ocean Observation System for Quantifying Regional Heat and Freshwater Storage and Change. Frontiers in Marine Science, 6(416). doi:10.3389/fmars.2019.00416

10. Ponte, R. M., Carson, M., Cirano, M., Domingues, C. M., Jevrejeva, S., Marcos, M., . . . Zhang, X. (2019). Towards Comprehensive Observing and Modeling Systems for Monitoring and Predicting Regional to Coastal Sea Level. Frontiers in Marine Science, 6(437). doi:10.3389/fmars.2019.00437 (M #1)

11. Richter, Kristin; Meyssignac, Benoit; Slangen, Aimee; Melet, Angélique; Church, John; Fettweis, Xavier; Marzeion, Ben; Agosta, Cecile; Ligtenberg, Stefan; Spada, Giorgio; Palmer, Matthew; Roberts, Christopher; Champollion, Nicolas (2020). Detecting a forced signal in satellite-era sea level change. Environmental Research Letters, https://doi.org/10.1088/1748-9326/ab986e.

12. Stammer, D., van de Wal, R. S. W., Nicholls, R. J., Church, J. A., Le Cozannet, G., Lowe, J. A., . . . Hinkel, J. (2019). Framework for High-End Estimates of Sea Level Rise for Stakeholder Applications. Earth's Future, 7(8), 923-938. doi:10.1029/2019ef001163.

13. Todd, A., L. Zanna, M. Couldrey, J. Gregory, Q. Wu, J. Church, R. Farneti, R. Navarro-Labastida, K. Lyu, O. Saenko, D.Yang and X. Zhang (2020). Ocean-only FAFMIP: understanding regional patterns of ocean heat content and dynamic sea level change. Journal of Advances in Modelling Earth Systems, doi: 10.1029/2019MS002027.

14. Van de Wal, R.S.W., X. Zhang, S. Minobe, S. Jevrejeva, R.E.M. Riva, C. Little, K. Richter and M. Palmer (2019). Uncertainties in long-term process-based coastal sea-level projections. Surveys in Geophysics, 40, 1655-1671. https://doi.org/10.1007/s10712-019-09575-3.

Book Chapter:15. Holbrook, N.J., D.C. Claar, A.J. Hobday, K.L. McInnes, E.C. Oliver, A. Sen Gupta, M.J. Widlansky, and X. Zhang,

Chapter 18: Ocean Extremes and Habitat Impacts. AGU Monograph: ENSO in a Changing Climate, In press.

Submitted16. Li, S., W. Liu, K. Lyu and X. Zhang (2020). The effect of stratospheric ozone depletion on Southern Ocean heat

uptake and storage. Climate Dynamics, submitted 17. Niphadkar, P., M. Bowen, X. Zhang (2020). Sea level around the New Zealand coast: trends, variability and future

projections. Frontiers in Marine Science, submitted.18. Wang, J., J.A. Church, X. Zhang and X. Chen, Comparing global and regional sea level changes between

observations and IPCC projections, Nature Communications, submitted. 19. Wu, Q., X. Zhang, J.A. Church, J. Hu, J. Gregory (2020). Evolving patterns of sterodynamic sea-level rise under

mitigation scenarios and insights from linear system theory. Climate Dynamics, submitted.

In preparation• Jin, Y., X. Zhang, J. A. Church and X. Bao, Projected sea level changes in the China marginal seas based on

dynamical downscaling. Journal of Climate, to be submitted soon.• Lyu, K., X. Zhang, J. A. Church, et al., Drivers of the Southern Ocean heat and salt redistribution: revisiting a

theoretical framework based on model perturbation experiments. Journal of Physical Oceanography, in preparation.

• X. Zhang, K. Lyu and J. Church: Projected changes of ocean gyre circulation and associated sea level changes based on CMIP5/6 models, Geophysical Research Letters, in preparation.


• Outreach: convening sessions and giving talks at national and international conferences (e.g., AMOS, AOGS, COSIMA); being interviewed by leading national media (ABC Radio and ABC Science News).

• Capacity building: one postdoc, two PhD students, one honours student. (one more PhD student hopefully starting soon)

• Collaboration: intra- and inter-CSHOR project collaborationclosely interacting with three international groups: FAFMIP modelling,

International Space Science Institute (ISSI) regional sea level working group, and CLIVAR Pacific Panel, and with Australian modelling community via Consortium for Ocean-Sea Ice Modelling in Australia (COSIMA).

closely collaborating with Scripps Institution of Oceanography (US) and University of Auckland (NZ) on SO modelling, dynamical downscaling and sea level studies

CSHOR sea level project

Outreach, capacity building and collaboration

20 |

Outlook for FY 20-21

Staffing arrangement for postdoc Kewei Lyu – University hiring Research activities:

More ocean model experiments, e.g., testing the effects of freshwater/melting on ocean circulation and sea level in the Southern Ocean

Analysis of sea level output from available CMIP6 model experiments Refined regional sea level fingerprints associated with melting of land ice, in particular AIS using high-

resolution unstructured mesh grid based on updated AIS simulations Analysis of GRACE and GRACE-Follow On time series to produce estimates of Antarctic and Greenland ice mass

change (UTAS) Better regional sea level budget closure (UNSW) Working towards next-generation of total sea level projection based on updated & refined components

Better understanding and projecting regional and global sea levels

Climate Change in the Oeans | Xuebin Zhang22 |

IPCC AR5 Fig. 13.1

Reconstruction Observation Projection

Global mean sea level rising rate: 1901 to 2010 1.7±0.2 mm yr-1

1971 to 2010 2.0±0.3 mm yr-1

Satellite 1993-2010 3.2 ±0.4 mm yr-1

the role of the southern ocean in sea level change · projections of ocean heat content and sea...

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