To determine the contribution of the TAS to global sea level, we first estimate the global mean mass and steric sea level trends. Then the differences between the TAS trends and the global trends are computed. An estimate of the contribution of the differenced trends∆tr elativeis then computed using the ratio between the surface area AT AS of the TAS and the rest of the oceans Aglobal−AT AS, such that:
∆tglobal= AT AS
Aglobal−AT AS∆tr elative, (21)
is the effect of including the TAS into global ocean budgets. This approach is applied for both the mass and the steric components.
The global mean steric sea level and global mean mass time series are obtained from the NASA website [Llovel et al., 2014;Watkins et al., 2015]. The global mean steric sea level is computed from Scripps temperature and salinity grids [Roemmich and Gilson, 2009], in which the TAS region is completely excluded. A 3◦ ×3◦ mascon solution is used to construct the global mean mass time series, which includes several cells within the TAS region. Since the effect of the mass in the TAS on the global mean mass trend is only a fraction of the mass trend in the TAS itself, this will not significantly affect the outcome of the computation. The time series trends are computed between January 2005 - December 2010 for comparison with other studies, which results in 0.1 mm yr−1 for the
Table 6: Contribution to GMSL over January 2005 - December 2010 in mm yr−1. The first column contains the trends in the TAS. The second column contains the relative trends with respect to the global estimates. The last column shows the contribution to the global budget.
TAS only TAS-Global Global contr.
Sum 12.4 10.7 0.3
Steric 8.2 8.1 0.2
mass 4.2 2.6 0.1
steric trend and 1.6 mm yr−1 for the mass trend. The trends over the period January 2005 - December 2012 are comparable to the trend over the shorter period.
The TAS trends derived from the statisticaly-weighted time series are 8.2 mm yr−1 for the steric component, and 4.2 mm yr−1 for the mass component (Table 6, Col. 1). Tak-ing the difference between the TAS and global trends estimated above (Table 6, Col. 2), we compute that the TAS contribution is 0.2 mm yr−1 for global steric and 0.1 mm yr−1 for global mass. Because the ratio of the surface area of the TAS and the global ocean is small, the error bars on the contribution to the global estimates are small and therefore they are not provided in the table.
Based on the ORAS4 reanalyses,Dieng et al.[2015] estimated that the global mean steric sea level is underestimated by approximately 0.25 mm yr−1 over the period January 2005 - December 2013. Even though we computed the value over a slightly shorter pe-riod, our estimate of 0.2 mm yr−1 is close to this value. They did however not discus the effect of the 300 km cut-off from the coast in GRACE global mass, which was based on the time series ofJohnson and Chambers[2013]. A 300 km cut-off effectively removes the whole TAS contribution from the global solution. We estimate that excluding the TAS in global mass estimates causes underestimation of 0.1 mm yr−1. Combining both num-bers gives a trend of 0.3 mm yr−1, which is consistent with the 0.5±0.2 mm yr−1 com-puted byVon Schuckmann et al.[2014].
5 Conclusions
For the first time satellite altimetry, satellite gravimetry and reanalysis data are used to determine the steric and mass components of sea level variations in the TAS. To study the sea level variability in the TAS in more detail, time series of the sea level components are computed for four regions. The uncertainties of the time series of total sea level from altimetry, mass from GRACE and steric sea level from reanalyses, are used to derive a statistically optimal separation between the steric and mass components. The statistically weighted time series are used to study the interannual variability and trends in the TAS, as well as their contribution to global mean sea level.
The largest variability in steric sea level is found in the deeper areas as the South China Sea and the Banda-Celebes seas. The small water column of the Thailand-Java and Timor-Arafura regions is not able to expand as far as the other two. In constrast, the largest mass signals are present in the shallower regions. We argue that this is a dynami-cal response to larger steric sea level fluctuations in neighbouring areas.
Regression with the first two PCs of the equatorial Pacific wind stress and the DMI showed that the South China Sea largely is unaffected by ENSO and Indian Ocean dipole dynamics. The steric sea level in the Banda-Celebes and Timor-Arafura seas is strongly driven by PC1&2, which are used to represent wind forcing that is either in phase with
ENSO (PC1), or peaked around the southward shift of anomalous winds (PC2). The DMI has a small effect on the temperatures in Thailand-Java and causes a substantial fraction of (thermo-)steric variability in Timor-Arafura. Mass signals in the shallow areas are almost 180 degrees out-of-phase with PC1. PC2 shows a strong contribution, especially in the Timor-Arafura region. We believe that the shallow region mass changes, which dominate the total mass change in the TAS, are due to net transport from the neighboring WTPO to the shallow regions associated with variations in WTPO steric sea level, which are related to PC1&2.
Accounting for interannual variations associated with the PC1, PC2, and DMI in-dices impacts the estimation of linear trends. Regressing PC2 and the DMI have over the whole TAS a slight positive effect on the mass and steric trends, while regressing PC1 reduces the trends. This leads to a reduction in mass and steric trends. The mass trends are also affected by the nodal cycle, because it causes a mass change with a mimimum in 2006 and a maximum in 2015. Trends are decreased with approximately 1.3 mm yr−1 when correcting for the nodal cycle.
The mass trend over the whole TAS is statistically equal to the trend estimated from mass redistribution fingerprints. However, within the four regions there are differences:
the deep regions have smaller mass trends than their fingerprint estimates, while in the Timor-Arafura region we find larger trends than its fingerprint. We argue that there is an internal redistribution of water within the TAS, where water flows from deep regions with a large steric response into the shallower regions.
Finally, we computed trends for the whole TAS and estimated the contribution to the global budgets. Omitting the TAS results in an underestimation of the mass and steric components with 0.1 and 0.2 mm yr−1, respectively. These numbers are in line with previ-ous studies.
Our approach for separating mass and steric contributions to sea level changes can be applied to other basins where Argo float coverage is limited, such as the Caribbean Sea, the Gulf of Mexico and the Yellow Sea. To investigate the drivers of mass and steric sea level variability, appropriate indices should be used.
Acknowledgments
This study is funded by the Netherlands Organisation for Scientific Research (NWO) through VIDI grant 864.12.012 (Multi-Scale Sea Level (MuSSeL)).
The RADS altimetry data are obtained from: http://rads.tudelft.nl/rads/rads.
shtml.
The ITSG-Grace2016 gravity fields are obtained from: https://www.tugraz.at/institute/
ifg/downloads/gravity-field-models/itsg-grace2016/.
All reanalysis data are found at: https://reanalyses.org/ocean/overview-current-reanalyses.
Scripps temperature and salinty grids interpolated from Argo float measurements: http:
//www.argo.ucsd.edu/Gridded_fields.html
ERA-Interim wind speed data are downloaded from: http://apps.ecmwf.int/datasets/
data/interim-full-moda/levtype=sfc/.
SSALTO/DUACS multimission altimetry grids are downloaded from: https://www.
aviso.altimetry.fr/en/data/product-information/information-about-mono-and-multi-mission-processing/
ssaltoduacs-multimission-altimeter-products.html.
Bibliography References
Ablain, M., Cazenave, A., Larnicol, G., Balmaseda, M., Cipollini, P., Faugère, Y., ... &
Benveniste, J. (2015). Improved sea level record over the satellite altimetry era (1993-2010) from the Climate Change Initiative Project. Ocean Science, 11, 67-82.
Andersen, O. B., Knudsen, P., & Stenseng, L. (2015). The DTU13 MSS (Mean Sea Sur-face) and MDT (Mean Dynamic Topography) from 20 Years of Satellite Altimetry. In International Association of Geodesy Symposia. 1-10. Springer. (International Associa-tion of Geodesy Symposia). DOI: 10.1007/1345_2015_182
Balmaseda, M. A., Mogensen, K., & Weaver, A. T. (2013). Evaluation of the ECMWF ocean reanalysis system ORAS4. Quarterly Journal of the Royal Meteorological Society, 139(674), 1132-1161.
Bamber, J., & Riva, R. (2010). The sea level fingerprint of recent ice mass fluxes. The Cryosphere 4, 621-627.
Behringer, D. W., & Xue, Y. (2004). Evaluation of the global ocean data assimilation sys-tem at NCEP: The Pacific Ocean. In Proc. Eighth Symp. on Integrated Observing and Assimilation Systems for Atmosphere, Oceans, and Land Surface.
Bingham, R. J., & C. W. Hughes (2012). Local diagnostics to estimate density-induced sea level variations over topography and along coastlines, Journal Geophysical Research, 117, C01013, doi:10.1029/2011JC007276.
Boening, C., J. K. Willis, F. W. Landerer, R. S. Nerem, & J. Fasullo (2012), The 2011 La Niña: So strong, the oceans fell, Geophysical Research Letters, 39, L19602, doi:10.1029/2012GL053055.
Broerse, T., R. Riva, W. Simons, R. Govers, & B. Vermeersen (2015), Postseismic GRACE and GPS observations indicate a rheology contrast above and below the Suma-tra slab, J. Geophys. Res. Solid Earth, 120, 5343âĂŞ5361, doi:10.1002/2015JB011951.
Cabanes, C., Grouazel, A., Schuckmann, K. V., Hamon, M., Turpin, V., Coatanoan, C., ... & Boyer Montégut, C. D. (2013). The CORA dataset: validation and diagnostics of in-situ ocean temperature and salinity measurements. Ocean Science, 9(1), 1-18.
Carton, J. A., & Giese, B. S. (2008). A reanalysis of ocean climate using Simple Ocean Data Assimilation (SODA). Monthly Weather Review, 136(8), 2999-3017.
Cazenave, A., & Le Cozannet, G. (2013). Sea level rise and its coastal impacts. Earth’s Future, 2, 15-34.
Chaussard, E., Amelung, F., Abidin, H., & Hong, S. H. (2013). Sinking cities in Indone-sia: ALOS PALSAR detects rapid subsidence due to groundwater and gas extraction.
Remote Sensing of Environment, 128, 150-161.
Cheng, M.K., Tapley, B.D., & Ries, C. (2013). Deceleration in the Earth’s oblateness.
Journal of Geophysical Research: Solid Earth, 118, 740-747, doi:10.1002/jgrb.50058.
Dieng, H. B., Cazenave, A., von Schuckmann, K., Ablain, M., & Meyssignac, B. (2015).
Sea level budget over 2005-2013: missing contributions and data errors. Ocean Science, 11(5), 789.
Dobslaw, H., Flechtner, F., Bergmann-Wolf, I., Dahle, C., Dill, R., Esselborn, S., &
Thomas, M. (2013). Simulating high-frequency atmosphere-ocean mass variability for dealiasing of satellite gravity observations: AOD1B RL05. Journal of Geophysical Re-search: Oceans, 118, 3704-3711, doi:10.1002/jgrc.20271.
Einarsson, I., Hoechner, A., Wang, R., & Kusche, J. (2010). Gravity changes due to the Sumatra-Andaman and Nias earthquakes as detected by the GRACE satellites: a reex-amination. Geophysical Journal International, 183(2), 733-747.
England, M. H., McGregor, S., Spence, P., Meehl, G. A., Timmermann, A., Cai, W., ... &
Santoso, A. (2014). Recent intensification of wind-driven circulation in the Pacific and the ongoing warming hiatus. Nature Climate Change, 4(3), 222-227.
Farrell, W. E., & Clark, J. A. (1976). On postglacial sea level. Geophysical Journal Inter-national, 46(3), 647-667.
Feng, M., Y. Li, & G. Meyers (2004). Multidecadal variations of Fremantle sea level:
Footprint of climate variability in the tropical Pacific, Geophysical Research Letters, 31, L16302, doi:10.1029/2004GL019947.
Feng, M., M. J. McPhaden, & T. Lee (2010). Decadal variability of the Pacific subtropical cells and their influence on the southeast Indian Ocean, Geophysical Research Letters, 37, L09606, doi:10.1029/2010GL042796.
Feng, W., Zhong, M., & Xu, H. (2012). Sea level variations in the South China Sea in-ferred from satellite gravity, altimetry, and oceanographic data. Science China Earth Sciences, 55(10), 1696-1701.
Fenoglio-Marc, L., Rietbroek, R., Grayek, S., Becker, M., Kusche, J., & Stanev, E. (2012).
Water mass variation in the Mediterranean and Black Seas. Journal of Geodynamics, 59, 168-182.
Ferry, N., Parent, L., Garric, G., Barnier, B., and Jourdain, N. C.: Mercator global Eddy permitting ocean reanalysis GLORYS1V1: Description and results, Mercator-Ocean Quarterly Newsletter, 36, 15-27, 2010.
Forget, G. A. E. L., Campin, J. M., Heimbach, P., Hill, C. N., Ponte, R. M., & Wunsch, C. (2015). ECCO version 4: an integrated framework for non-linear inverse modeling and global ocean state estimation. Geoscience Model Development, 8(10), 3071-3104.
Frederikse, T., Riva, R., Kleinherenbrink, M., Wada, Y., Broeke, M., & Marzeion, B.
(2016). Closing the sea level budget on a regional scale: Trends and variability on the Northwestern European continental shelf. Geophysical Research Letters, 43(20).
Grosso, P., Le Menn, M., De La, J. L. D. B., Wu, Z. Y., & Malardé, D. (2010). Practical versus absolute salinity measurements: New advances in high performance seawater salinity sensors. Deep Sea Research Part I: Oceanographic Research Papers, 57(1), 151-156.
Henry, O., Ablain, M., Meyssignac, B., Cazenave, A., Masters, D., Nerem, S., & Garric, G. (2014). Effect of the processing methodology on satellite altimetry-based global mean sea level rise over the Jason-1 operating period. Journal of Geodesy, 88(4), 351-361.
Hetland, E. A., & Hager, B. H. (2006). The effects of rheological layering on post-seismic deformation. Geophysical Journal International, 166(1), 277-292.
Johnson, G. C., & D. P. Chambers (2013), Ocean bottom pressure seasonal cycles and decadal trends from GRACE Release-05: Ocean circulation implications, Journal of Geophysical Research: Oceans, 118, 4228-4240, doi:10.1002/jgrc.20307.
Klees, R., Revtova, E. A., Gunter, B. C., Ditmar, P., Oudman, E., Winsemius, H. C., &
Savenije, H. H. G. (2008). The design of an optimal filter for monthly GRACE gravity models. Geophysical Journal International, 175(2), 417-432.
Kleinherenbrink, M., Riva, R., & Sun, Y. (2016). Sub-basin-scale sea level budgets from satellite altimetry, Argo floats and satellite gravimetry: a case study in the North At-lantic Ocean. Ocean Science, 12(6), 1179.
Klinger, B., Mayer-Gürr, T., Behzadpour, S., Ellmer, M., Kvas, A., Zehentner, N. (2016).
The new ITSG-Grace2016 release. EGU General Assembly 2016, Vienna.
Knudsen, P., Andersen, O. B., & Knudsen, T. (1996). ATSR sea surface temperature data in a global analysis with TOPEX/POSEIDON altimetry. Geophysical research letters, 23(8), 821-824.
Köhl, A. (2015). Evaluation of the GECCO2 ocean synthesis: transports of volume, heat and freshwater in the Atlantic. Quarterly Journal of the Royal Meteorological Society, 141(686), 166-181.
Landerer, F. W., J. H. Jungclaus, & J. Marotzke (2007). Ocean bottom pressure changes lead to a decreasing length-of-day in a warming climate, Geophysical Research Letters, 34, L06307, doi:10.1029/2006GL029106.
Lee, T., & M. J. McPhaden (2008). Decadal phase change in large-scale sea level and winds in the Indo-Pacific region at the end of the 20th century, Geophysical Research Letters, 35, L01605, doi:10.1029/2007GL032419.
Le Traon, P.Y., Nadal, F., & Ducet, N. (1998). An improved mapping method of multi-satellite altimeter data. Journal of atmospheric and oceanic technology, 15(2), 522-534.
Le Traon, P. Y., Dibarboure, G., & Ducet, N. (2001). Use of a high-resolution model to analyze the mapping capabilities of multiple-altimeter missions. Journal of Atmospheric and Oceanic Technology, 18(7), 1277-1288.
Leuliette, E. W., and Miller, L. (2009). Closing the sea level rise budget with altimetry, Argo, and GRACE, Geophysical Research Letters, 36, L04608, doi:10.1029/2008GL036010.
Leuliette, E. W., & Willis, J. K. (2011). Balancing the sea level budget. Oceanography, 24(2), 122-129, doi:10.5670/oceanog.2011.32.
Llovel, W., Willis, J. K., Landerer, F. W., & Fukumori, I. (2014). Deep-ocean contribu-tion to sea level and energy budget not detectable over the past decade. Nature Climate Change, 4(11), 1031-1035.
Masters, D., Nerem, R. S., Choe, C., Leuliette, E., Beckley, B., White, N., & Ablain, M.
(2012). Comparison of global mean sea level time series from TOPEX/Poseidon, Jason-1, and Jason-2. Marine Geodesy, 35(sup1), 20-41.
Mayer-Gürr, T., Zehentner, N., Klinger, B., & Kvas, A. (2014). ITSG-Grace2014: a new GRACE gravity field release computed in Graz. Procedings of GRACE Science Team Meet, Potsdam.
McGregor, S., Gupta, A. S., & England, M. H. (2012). Constraining wind stress products with sea surface height observations and implications for Pacific Ocean sea level trend attribution. Journal of Climate, 25(23), 8164-8176.
McGregor, S., Timmermann, A., Schneider, N., Stuecker, M. F., & England, M. H. (2012).
The Effect of the South Pacific Convergence Zone on the Termination of El Niño Events and the Meridional Asymmetry of ENSO*. Journal of Climate, 25(16), 5566-5586.
Merrifield, M. A. (2011). A shift in western tropical Pacific sea level trends during the 1990s. Journal of Climate, 24(15), 4126-4138.
Merrifield, M. A., & M. E. Maltrud (2011). Regional sea level trends due to a Pacific trade wind intensification, Geophysical Research Letters, 38, L21605, doi:10.1029/2011GL049576.
Merrifield, M. A., P. R. Thompson, & M. Lander (2012). Multidecadal sea level anoma-lies and trends in the western tropical Pacific, Geophysical Research Letters, 39, L13602, doi:10.1029/2012GL052032.
Meyers, G., McIntosh, P., Pigot, L., & Pook, M. (2007). The years of El NiÃśo, La NiÃśa, and interactions with the tropical Indian Ocean. Journal of Climate, 20(13), 2872-2880.
Milne, G. A., & Mitrovica, J. X. (1996). Postglacial sea-level change on a rotating Earth:
first results from a gravitationally self-consistent sea-level equation. Geophysical Journal International, 126(3), F13-F20.
Mitchum, G. T. (1998). Monitoring the stability of satellite altimeters with tide gauges.
Journal of Atmospheric and Oceanic Technology, 15(3), 721-730.
Mitchum, G. T. (2000). An improved calibration of satellite altimetric heights using tide gauge sea levels with adjustment for land motion. Marine Geodesy, 23(3), 145-166.
Nerem, R. S. (1995). Measuring global mean sea level variations using
TOPEX/POSEIDON altimeter data. Journal of Geophysical Research: Oceans, 100(C12), 25135-25151, doi:10.1029/95JC02303.
Pawlocwicz, P., McDougall, T. J., Feistel, R., & Tailleux, R. (2012). An historical per-spective on the development of the Thermodynamic Equation of Seawater-2010. Ocean Science, 8, 161-174.
Peltier, W. R., Argus, D. F., & Drummond, R. (2015). Space geodesy constrains ice age terminal deglaciation: The global ICE-6G_C (VM5a) model. Journal of Geophysical Research: Solid Earth, 120(1), 450-487.
Phien-Wej, N., Giao, P. H., & Nutalaya, P. (2006). Land subsidence in Bangkok, Thailand.
Engineering Geology, 82(4), 187-201.
Proudman, J. (1960). The condition that a long-period tide shall follow the equilibrium-law. Geophysical Journal International, 3(2), 244-249.
Pugh, D., & Woodworth, P. (2014). Sea-level science: understanding tides, surges, tsunamis and mean sea-level changes. Cambridge University Press.
Raucoules, D., Le Cozannet, G., Wöppelmann, G., De Michele, M., Gravelle, M., Daag, A., & Marcos, M. (2013). High nonlinear urban ground motion in Manila (Philippines) from 1993 to 2010 observed by DInSAR: implications for sea-level measurement. Re-mote Sensing of Environment, 139, 386-397.
Rietbroek, R., Brunnabend, S. E., Kusche, J., Schröter, J., & Dahle, C. (2016). Revisiting the contemporary sea-level budget on global and regional scales. Proceedings of the National Academy of Sciences, 113(6), 1504-1509.
Rodolfo, K. S., & Siringan, F. P. (2006). Global sea-level rise is recognised, but flood-ing from anthropogenic land subsidence is ignored around northern Manila Bay, Philip-pines. Disasters, 30(1), 118-139.
Roemmich, D., & Gilson, J. (2009). The 2004-2008 mean and annual cycle of tempera-ture, salinity, and steric height in the global ocean from the Argo Program. Progress in Oceanography, 82(2), 81-100.
Von Schuckmann, K., Sallée, J. B., Chambers, D., Le Traon, P. Y., Cabanes, C., Gaillard, F., ... & Hamon, M. (2014). Consistency of the current global ocean observing systems from an Argo perspective. Ocean Science, 10(3), 923-949.
Scharroo, R., Leuliette, E. W., Lillibridge, J. L., Byrne, D., Naeije, M. C., & Mitchum, G.
T. (2012). RADS: Consistent multi-mission products. In Proceedings of Symposium on 20 Years of Progress in Radar Altimetry (Vol. 20).
Strassburg, M. W., Hamlington, B. D., Leben, R. R., Manurung, P., Lumban Gaol, J., Nababan, B., ... & Kim, K. Y. (2015). Sea level trends in Southeast Asian seas. Climate of the Past, 11(5), 743-750.
Swenson, S., Chambers, D., & Wahr, J. (2008). Estimating geocenter variations from a combination of GRACE and ocean model output. Journal of Geophysical Research:
Solid Earth, 113, B08410, doi:10.1029/2007JB005338.
Swenson, S., & Wahr, J. (2002). Methods for inferring regional surface-mass anoma-lies from Gravity Recovery and Climate Experiment (GRACE) measurements of time-variable gravity. Journal of Geophysical Research: Solid Earth, 107(B9), 2193, doi:10.1029/2001JB000576.
IOC, SCOR (2010). IAPSO: The international thermodynamic equation of seawater-2010:
Calculation and use of thermodynamic properties, Intergovernmental Oceanographic Commission, Manuals and Guides No. 56. UNESCO, 3212(30), 7.
Tapley, B. D., Bettadpur, S., Watkins, M., & Reigber, C. (2004). The gravity recovery and climate experiment: Mission overview and early results. Geophysical Research Letters, 31, L09607, doi:10.1029/2004GL019920.
Tamisiea, M. E., Hill, E. M., Ponte, R. M., Davis, J. L., Velicogna, I., & Vinogradova, N.
T. (2010). Impact of self-attraction and loading on the annual cycle in sea level. Journal of Geophysical Research: Oceans, 115, C07004, doi:10.1029/2009JC005687.
Tangdamrongsub, N., Ditmar, P. G., Steele-Dunne, S. C., Gunter, B. C., & Sutanudjaja, E. H. (2016). Assessing total water storage and identifying flood events over Tonlé Sap basin in Cambodia using GRACE and MODIS satellite observations combined with hydrological models. Remote Sensing of Environment, 181, 162-173.
Vinogradov, S. V., Ponte, R. M., Heimbach, P., & Wunsch, C. (2008), The mean seasonal cycle in sea level estimated from a data-constrained general circulation model, Journal of Geophysical Research, 113, C03032, doi:10.1029/2007JC004496.
Wada, Y., Van Beek, L. P. H., & Bierkens, M. F. (2011). Modelling global water stress of the recent past: on the relative importance of trends in water demand and climate variability. Hydrology and Earth System Sciences, 15(12), 3785-3808.
Wahr, J., Molenaar, M., & Bryan, F. (1998). Time variability of the Earth’s gravity field:
Hydrological and oceanic effects and their possible detection using GRACE. Journal of Geophysical Research: Solid Earth, 103(B12), 30205-30229.
Watkins, MM, DN Wiese, D-N Yuan, C Boening, & FW Landerer (2015), Improved methods for observing Earth’s time variable mass distribution with GRACE using spher-ical cap mascons. Journal of Geophysspher-ical Research Solid Earth, 120, 2648-2671. doi:
10.1002/2014JB011547.
Widlansky, M. J., Timmermann, A., McGregor, S., Stuecker, M. F., & Cai, W. (2014). An interhemispheric tropical sea level seesaw due to El Niño Taimasa. Journal of Climate, 27(3), 1070-1081.
Wijffels, S., & Meyers, G. (2004). An intersection of oceanic waveguides: Variability in the Indonesian Throughflow region. Journal of Physical Oceanography, 34(5), 1232-1253.
Willis, J. K., Chambers, D. P., & Nerem, R. S. (2008). Assessing the globally averaged sea level budget on seasonal to interannual timescales. Journal of Geophysical Research:
Oceans, 113, C06015, doi:10.1029/2007JC004517.
Oceans, 113, C06015, doi:10.1029/2007JC004517.