Arctic sea level variability from high-resolution model simulations and implications for the Arctic observing system

Author:

Lyu GuokunORCID,Serra Nuno,Zhou Meng,Stammer Detlef

Abstract

Abstract. Two high-resolution model simulations are used to investigate the spatiotemporal variability of the Arctic Ocean sea level. The model simulations reveal barotropic sea level variability at periods of < 30 d, which is strongly captured by bottom pressure observations. The seasonal sea level variability is driven by volume exchanges with the Pacific and Atlantic oceans and the redistribution of the water by the wind. Halosteric effects due to river runoff and evaporation minus precipitation ice melting/formation also contribute in the marginal seas and seasonal sea ice extent regions. In the central Arctic Ocean, especially the Canadian Basin, the decadal halosteric effect dominates sea level variability. The study confirms that satellite altimetric observations and Gravity Recovery and Climate Experiment (GRACE) could infer the total freshwater content changes in the Canadian Basin at periods longer than 1 year, but they are unable to depict the seasonal and subseasonal freshwater content changes. The increasing number of profiles seems to capture freshwater content changes since 2007, encouraging further data synthesis work with a more complicated interpolation method. Further, in situ hydrographic observations should be enhanced to reveal the freshwater budget and close the gaps between satellite altimetry and GRACE, especially in the marginal seas.

Funder

Horizon 2020

National Natural Science Foundation of China

Publisher

Copernicus GmbH

Subject

Cell Biology,Developmental Biology,Embryology,Anatomy

Reference65 articles.

1. AMAP: AMAP Climate Change Update 2019: An Update to Key Findings of Snow, Water, Ice and Permafrost in the Arctic (SWIPA) 2017, AMAP – Arctic Monitoring and Assessment Programme, Oslo, Norway, 12 pp., 2019.

2. Armitage, T. W., Bacon, S., Ridout, A. L., Thomas, S. F., Aksenov, Y., and Wingham, D. J.: Arctic sea surface height variability and change from satellite radar altimetry and grace, 2003–2014, J. Geophys. Res.-Oceans, 121, 4303–4322, https://doi.org/10.1002/2015JC011579, 2016.

3. Behrendt, A., Sumata, H., Rabe, B., and Schauer, U.: A comprehensive, quality-controlled and up-to-date data set of temperature and salinity data for the Arctic Mediterranean Sea (Version 1.0), links to data files, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.872931, 2017.

4. Behrendt, A., Sumata, H., Rabe, B., and Schauer, U.: Udash – unified database for arctic and subarctic hydrography, Earth Syst. Sci. Data, 10, 1119–1138, https://doi.org/10.5194/essd-10-1119-2018, 2018.

5. Boyer, T., Levitus, S., Garcia, H., Locarnini, R. A., Stephens, C., and Antonov, J.: Objective analyses of annual, seasonal, and monthly temperature and salinity for the world ocean on a 0.25 grid, Int. J. Climatol., 25, 931–945, https://doi.org/10.1002/joc.1173, 2005.

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