Abstract

Abstract. Sea level is an essential climate variable (ECV) that has a direct effect on many people through inundations of coastal areas, and it is also a clear indicator of climate changes due to external forcing factors and internal climate variability. Regional patterns of sea level change inform us on ocean circulation variations in response to natural climate modes such as El Niño and the Pacific Decadal Oscillation, and anthropogenic forcing. Comparing numerical climate models to a consistent set of observations enables us to assess the performance of these models and help us to understand and predict these phenomena, and thereby alleviate some of the environmental conditions associated with them. All such studies rely on the existence of long-term consistent high-accuracy datasets of sea level. The Climate Change Initiative (CCI) of the European Space Agency was established in 2010 to provide improved time series of some ECVs, including sea level, with the purpose of providing such data openly to all to enable the widest possible utilisation of such data. Now in its second phase, the Sea Level CCI project (SL_cci) merges data from nine different altimeter missions in a clear, consistent and well-documented manner, selecting the most appropriate satellite orbits and geophysical corrections in order to further reduce the error budget. This paper summarises the corrections required, the provenance of corrections and the evaluation of options that have been adopted for the recently released v2.0 dataset (https://doi.org/10.5270/esa-sea_level_cci-1993_2015-v_2.0-201612). This information enables scientists and other users to clearly understand which corrections have been applied and their effects on the sea level dataset. The overall result of these changes is that the rate of rise of global mean sea level (GMSL) still equates to ∼ 3.2 mm yr−1 during 1992–2015, but there is now greater confidence in this result as the errors associated with several of the corrections have been reduced. Compared with v1.1 of the SL_cci dataset, the new rate of change is 0.2 mm yr−1 less during 1993 to 2001 and 0.2 mm yr−1 higher during 2002 to 2014. Application of new correction models brought a reduction of altimeter crossover variances for most corrections.

Highlights

  • Sea level is widely recognised as an essential climate variable (ECV) that has a significant impact on mankind

  • Where the mean sea surface (MSS) is the sum of the geoid and the mean dynamic topography (MDT), which corresponds to the topographic variations associated with the mean circulation of the ocean

  • During phase 2 of the European Space Agency (ESA) Sea Level Climate Change Initiative (CCI) project, the consortium has reappraised all the corrections to be used in the production of the v2.0 dataset

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Summary

Introduction

Sea level is widely recognised as an essential climate variable (ECV) that has a significant impact on mankind. The land masses are still undergoing a delayed response to the removal of their burden from the last ice age (a phenomenon known as “glacial isostatic adjustment”) Together these effects and sea level rise amplify the vulnerability of coastal regions, producing major societal impacts. Where the mean sea surface (MSS) is the sum of the geoid (the geopotential surface indicating the level that would be recorded for a motionless ocean) and the mean dynamic topography (MDT), which corresponds to the topographic variations associated with the mean circulation of the ocean Values for these corrections are supplied in the geophysical data records (GDRs) provided by the space and meteorological agencies; there is a need to review whether new ones are more accurate, and to establish a consistent selection across all missions used. “Altimetry comparison with in situ data”, which computes differences between altimeter SLA data and those from in situ sea level measurements, e.g. tide gauges or Argo-based steric sea level data (Legeais et al, 2016a); this third approach allows for the detection of potential drifts or jumps in the long-term sea level time series

Orbits and range
Modelled orbits
Precise determination of the altimeter range
Corrections to atmospheric propagation
Dry tropospheric correction
Wet tropospheric correction
Ionospheric correction
Corrections for sea state bias
Corrections for short-term atmospheric and oceanographic phenomena
Atmospheric pressure correction
Reference surfaces
Editing and gridding
Conclusions
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