On the representation of Southern Ocean mass variability in GRACE ...
On the representation of Southern Ocean mass variability in GRACE-derived ocean bottom pressure anomalies Carmen Boening1 ([email protected]), V. Zlotnicki1, A. Fetter1, Jet Propulsion Laboratory, Pasadena, USA 2 Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, Germany
R. Timmermann2 , S. Danilov2, and J. Schroeter2
1
Introduction
Models and Data
The Gravity Recovery and Climate Experiment provides monthly estimates of the Earth’s gravity field. Temporal changes in the gravitational field are associated with mass movement. Over the ocean these fluctuations are primarily due to changes in atmosphere and ocean circulation associated with ocean bottom pressure (OBP). In this study the ability of GRACE to observe circulation variability in the Southern Ocean is investigated.
ECCO2 (configuration of the MITgcm [3]): Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2, [ 4]) • hydrostatic primitive equation finite-volume ocean model • free surface • dynamic-thermodynamic sea ice model with viscous-plastic rheology solved using a line successive-relaxation (LSR) algorithm • atmospheric forcing from ERA40 reanalysis; adjusted using a Green's function approach • cubed-sphere grid of global domain • 18 km horizontal resolution • 50 vertical z*-layers, shaved cells
OBP and ACC transport Studies utilizing ocean models [1] and altimetry [2] confirm that ocean bottom pressure anomalies around Antarctica are related to the variability of the ACC transport through Drake Passage. The correlation forms an annular band around the continent which is connected to geostrophic (f/H) contours (Fig. 1).
GRACE: JPL MASCON solution • degree/order 1-40 • 300 km Gauss filter • aliasing periods (161, 1362.7 days) removed
FESOM: Finite Element Sea Ice Ocean Model (FESOM; [5]) • hydrostatic primitive-equation Finite Element Ocean Model (FEOM; grown up from FENA model of [6]) • free surface • surface mass fluxes and SLP variations considered in pressure computation • dynamic-thermodynamic FiniteElement Sea-Ice Model (FESIM) with EVP rheology • atmospheric forcing from NCEP reanalysis • global domain • 1.5° horizontal resolution • 26 layers, shaved cells
PPN: G43A-0712
Atmospheric forcing of ACC transport variability
Dominant mode of OBP variability
The Southern Ocean circulation is driven by the prevailing wind system on time scales shorter than a few years. The resulting wind stress from the westerly winds forces the Antarctic Circumpolar Current. A major component of atmospheric variability is the Southern Annular Mode (SAM, [8]) which explains about 20-30% of the variance. This mode is an indicator for the strength of the westerlies and influences the short-term variability of the ACC [1].
Dominant signals in OBP are the annual and semiannual cycle. When removed, the largest part of OBP variability (1. EOF) is explained by a mode, which resembles the spatial representation of the SAM – OBP relation (Fig. 7). About 18% of the total variance can be explained by this mode.
Transport variability from GRACE GRACE-derived OBP features the characteristics of the OBP – Drake Passage transport variability relation (Fig. 3).
Fig. 7: First EOF of GRACE OBP. (annual, semi-annual cycle removed) Fig. 5: Correlation between SAM index and ECCO2 OBP.
The spatial pattern of the correlation between SAM index and ECCO2 OBP forms an annular band around Antarctica (Fig. 5). The monthly variability in OBP in this region is strongly connected to the changes in the westerlies that can be explained by changes in SAM.
The time series of the first principal component correlates well with the SAM index on an interannual time scale (Fig. 8). The correlation between the two time series filtered with a 5-months running mean is about 70% at a 99% significance level.
Fig. 1: Correlation between ECCO2 Drake Passage transport and ECCO2 OBP.
The Southern ACC boundary forms a northern boundary for the highly negative correlation along the coast. Averaging south of this front along its course thus provides a good proxy for the ACC transport variability (Fig. 2).
Fig. 3: Correlation between ECCO2 Drake Passage transport and GRACE OBP.
Averaging south of the Southern ACC front gives a similar estimate of transport variability. GRACE estimates show higher variability than the ocean models (Fig. 4).
Fig. 6: Correlation between SAM index and GRACE OBP.
Fig. 8: First PC of GRACE OBP and SAM index. (annual, semi-annual cycle removed) Filtered with a 5-months running mean.
This relation between the SAM index and monthly OBP anomalies is also well pronounced in the GRACE data (Fig. 6).
Conclusions
Fig. 2: Model-derived (ECCO2, FESOM) Drake Passage transport and OBP anomalies 1° south of the Southern ACC front ([5]).
Fig. 4: ECCO2, FESOM, and GRACE OBP anomalies averaged south of the Southern ACC front.
• Pressure anomalies averaged South of the SACC front are an excellent measure of Drake Passage transport variability according to two independent ocean models. • OBP South of SACC front computed from GRACE data and both models agree within 0.9-1 cm std dev. • Dominant mode of GRACE OBP variability (ann., semi-ann. cycle removed) resembles connection between SAM and OBP. • On interannual time scales (ann., semi-ann. cycle removed) SAM index and 1. principal component of GRACE OBP agree well.
References
Achknowledgements:
[1] Hughes, C. W., P. L. Woodworth, M. P. Meredith, V. Stepanov, T. Whitworth, and A. R. Pyne, Coherence of Antarctic sea levels, Southern Hemisphere Annular Mode, and flow through Drake Passage, Geophys. Res. Lett., 30(9), 1464, doi:10.1029/2003GL017240, 2003. [2] Woodworth, P. L., J. M. Vassie, C. W. Hughes, and M. P. Meredith (1996), A test of the ability of TOPEX/POSEIDON to monitor flows through the Drake Passage, J. Geophys. Res., 101(C5), 11,935–11,947. [3] J. Marshall, A. Adcroft, C. Hill, L. Perelman, and C. Heisey, 1997: A finite volume, incompressible Navier-Stokes model for studies of the ocean on parallel computers. J. Geophys. Res., 102, 5753–5766. [4] D. Menemenlis, J. Campin, P. Heimbach, C. Hill, T. Lee, A. Nguyen, M. Schodlock, and H. Zhang, 2008: ECCO2: High resolution global ocean and sea ice data synthesis. Mercator Ocean Quarterly Newsletter, 31, 13-21. [5] Timmermann, R, Danilov, S, Schröter, J, Böning, C, Sidorenko, D, Rollenhagen, K (2009): Ocean circulation and sea ice distribution in a finite element global sea ice - ocean model, Ocean Modelling, doi:10.1016/j.ocemod.2008.10.009 [6] Danilov, S., Kivman, G., Schröter, J.(2004).A finite element ocean model: principles and evaluation, Ocean Modelling, 6,125-150. [7] Orsi, A.H., T. Whitworth III, and W.D. Nowlin Jr., 1995: On the meridional extent and fronts of the Antarctic Circumpolar Current. Deep-Sea Research, 42, 641-673. [8] Marshall, G. J., 2003: Trends in the Southern Annular Mode from observations and reanalyses. J. Clim., 16, 4134-4143.
This work was performed in part at the Jet Propulsion Laboratory, and the California Institute of Technology. We would like to thank the German Space Operations Center (GSOC) of the German Aerospace Center (DLR) for providing continuously and nearly 100% of the raw telemetry data of the twin GRACE satellites.
R. Timmermann2 , S. Danilov2, and J. Schroeter2
1
Introduction
Models and Data
The Gravity Recovery and Climate Experiment provides monthly estimates of the Earth’s gravity field. Temporal changes in the gravitational field are associated with mass movement. Over the ocean these fluctuations are primarily due to changes in atmosphere and ocean circulation associated with ocean bottom pressure (OBP). In this study the ability of GRACE to observe circulation variability in the Southern Ocean is investigated.
ECCO2 (configuration of the MITgcm [3]): Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2, [ 4]) • hydrostatic primitive equation finite-volume ocean model • free surface • dynamic-thermodynamic sea ice model with viscous-plastic rheology solved using a line successive-relaxation (LSR) algorithm • atmospheric forcing from ERA40 reanalysis; adjusted using a Green's function approach • cubed-sphere grid of global domain • 18 km horizontal resolution • 50 vertical z*-layers, shaved cells
OBP and ACC transport Studies utilizing ocean models [1] and altimetry [2] confirm that ocean bottom pressure anomalies around Antarctica are related to the variability of the ACC transport through Drake Passage. The correlation forms an annular band around the continent which is connected to geostrophic (f/H) contours (Fig. 1).
GRACE: JPL MASCON solution • degree/order 1-40 • 300 km Gauss filter • aliasing periods (161, 1362.7 days) removed
FESOM: Finite Element Sea Ice Ocean Model (FESOM; [5]) • hydrostatic primitive-equation Finite Element Ocean Model (FEOM; grown up from FENA model of [6]) • free surface • surface mass fluxes and SLP variations considered in pressure computation • dynamic-thermodynamic FiniteElement Sea-Ice Model (FESIM) with EVP rheology • atmospheric forcing from NCEP reanalysis • global domain • 1.5° horizontal resolution • 26 layers, shaved cells
PPN: G43A-0712
Atmospheric forcing of ACC transport variability
Dominant mode of OBP variability
The Southern Ocean circulation is driven by the prevailing wind system on time scales shorter than a few years. The resulting wind stress from the westerly winds forces the Antarctic Circumpolar Current. A major component of atmospheric variability is the Southern Annular Mode (SAM, [8]) which explains about 20-30% of the variance. This mode is an indicator for the strength of the westerlies and influences the short-term variability of the ACC [1].
Dominant signals in OBP are the annual and semiannual cycle. When removed, the largest part of OBP variability (1. EOF) is explained by a mode, which resembles the spatial representation of the SAM – OBP relation (Fig. 7). About 18% of the total variance can be explained by this mode.
Transport variability from GRACE GRACE-derived OBP features the characteristics of the OBP – Drake Passage transport variability relation (Fig. 3).
Fig. 7: First EOF of GRACE OBP. (annual, semi-annual cycle removed) Fig. 5: Correlation between SAM index and ECCO2 OBP.
The spatial pattern of the correlation between SAM index and ECCO2 OBP forms an annular band around Antarctica (Fig. 5). The monthly variability in OBP in this region is strongly connected to the changes in the westerlies that can be explained by changes in SAM.
The time series of the first principal component correlates well with the SAM index on an interannual time scale (Fig. 8). The correlation between the two time series filtered with a 5-months running mean is about 70% at a 99% significance level.
Fig. 1: Correlation between ECCO2 Drake Passage transport and ECCO2 OBP.
The Southern ACC boundary forms a northern boundary for the highly negative correlation along the coast. Averaging south of this front along its course thus provides a good proxy for the ACC transport variability (Fig. 2).
Fig. 3: Correlation between ECCO2 Drake Passage transport and GRACE OBP.
Averaging south of the Southern ACC front gives a similar estimate of transport variability. GRACE estimates show higher variability than the ocean models (Fig. 4).
Fig. 6: Correlation between SAM index and GRACE OBP.
Fig. 8: First PC of GRACE OBP and SAM index. (annual, semi-annual cycle removed) Filtered with a 5-months running mean.
This relation between the SAM index and monthly OBP anomalies is also well pronounced in the GRACE data (Fig. 6).
Conclusions
Fig. 2: Model-derived (ECCO2, FESOM) Drake Passage transport and OBP anomalies 1° south of the Southern ACC front ([5]).
Fig. 4: ECCO2, FESOM, and GRACE OBP anomalies averaged south of the Southern ACC front.
• Pressure anomalies averaged South of the SACC front are an excellent measure of Drake Passage transport variability according to two independent ocean models. • OBP South of SACC front computed from GRACE data and both models agree within 0.9-1 cm std dev. • Dominant mode of GRACE OBP variability (ann., semi-ann. cycle removed) resembles connection between SAM and OBP. • On interannual time scales (ann., semi-ann. cycle removed) SAM index and 1. principal component of GRACE OBP agree well.
References
Achknowledgements:
[1] Hughes, C. W., P. L. Woodworth, M. P. Meredith, V. Stepanov, T. Whitworth, and A. R. Pyne, Coherence of Antarctic sea levels, Southern Hemisphere Annular Mode, and flow through Drake Passage, Geophys. Res. Lett., 30(9), 1464, doi:10.1029/2003GL017240, 2003. [2] Woodworth, P. L., J. M. Vassie, C. W. Hughes, and M. P. Meredith (1996), A test of the ability of TOPEX/POSEIDON to monitor flows through the Drake Passage, J. Geophys. Res., 101(C5), 11,935–11,947. [3] J. Marshall, A. Adcroft, C. Hill, L. Perelman, and C. Heisey, 1997: A finite volume, incompressible Navier-Stokes model for studies of the ocean on parallel computers. J. Geophys. Res., 102, 5753–5766. [4] D. Menemenlis, J. Campin, P. Heimbach, C. Hill, T. Lee, A. Nguyen, M. Schodlock, and H. Zhang, 2008: ECCO2: High resolution global ocean and sea ice data synthesis. Mercator Ocean Quarterly Newsletter, 31, 13-21. [5] Timmermann, R, Danilov, S, Schröter, J, Böning, C, Sidorenko, D, Rollenhagen, K (2009): Ocean circulation and sea ice distribution in a finite element global sea ice - ocean model, Ocean Modelling, doi:10.1016/j.ocemod.2008.10.009 [6] Danilov, S., Kivman, G., Schröter, J.(2004).A finite element ocean model: principles and evaluation, Ocean Modelling, 6,125-150. [7] Orsi, A.H., T. Whitworth III, and W.D. Nowlin Jr., 1995: On the meridional extent and fronts of the Antarctic Circumpolar Current. Deep-Sea Research, 42, 641-673. [8] Marshall, G. J., 2003: Trends in the Southern Annular Mode from observations and reanalyses. J. Clim., 16, 4134-4143.
This work was performed in part at the Jet Propulsion Laboratory, and the California Institute of Technology. We would like to thank the German Space Operations Center (GSOC) of the German Aerospace Center (DLR) for providing continuously and nearly 100% of the raw telemetry data of the twin GRACE satellites.
