Abstract's details

Sea level rise uncertainties: insights from a metrological approach

Emma Woolliams (National Physical Laboratory, United Kingdom)


Michaël Ablain (Magellium, France); Anne Barnoud (Magellium, France); Benoit Meyssignac (LEGOS, France); Adrien Guérou (CLS, France); Salvatore Dinardo (CLS, France); Ngan Tran (CLS, France); Sébastien Figerou (CLS, France); Hannah Cheales (NPL, United Kingdom); Sajedeh Behnia (NPL, United Kingdom); Jonathan Mittaz (Reading University, United Kingdom); Craig Donlon (ESA-ESTEC, Netherlands); Robert Cullen (ESA-ESTEC, Netherlands)

Event: 2022 Ocean Surface Topography Science Team Meeting

Session: Quantifying Errors and Uncertainties in Altimetry data

Presentation type: Type Oral

Contribution: PDF file


The altimetry data record (1993-2022) has enabled the global mean sea level (GMSL) rise to be quantified. Not only is the sea level rising, but it is accelerating (Dieng et al., 2017; Nerem et al., 2018; Ablain et al. 2019) and understanding this acceleration is necessary both to support society’s response to climate change and to inform climate research on the sea level budget, greenhouse gas forcing and the Earth energy imbalance. Here we describe work on quantifying the uncertainty on the sea level climate data record, and identifying the uncertainty needed to provide new answers to the climate research questions.

Work by Ablain et al. (2019) and updated in Guérou et al. (2022) at a global scale, has quantified GMSL rise as +3.3 mm/yr (90% confidence uncertainties 0.3 mm/yr) over 1993-2021. Its acceleration has been quantified at 0.12 ± 0.05 mm/yr². At local scales, (Prandi et al. 2021), the sea level is rising almost everywhere over the globe, at rates ranging between 0 and 6 mm/yr, with uncertainties ranging from 0.8 to 1.2 mm/yr depending on the location. The local sea level accelerations are ranging between -1 mm/yr² and +1 mm/yr² with uncertainties between 0.057 and 0.12 mm/yr² (Prandi et al., 2021).

Here we present more recent work refining these uncertainty estimates through a metrological approach that propagates uncertainties, and error covariance structures, through the full processing chain of the altimeter measurement from the raw waveform to GMSL, using methods described in Mittaz et al. (2019). We review the assumptions in the derivation of the retracking model from the radar equation, consider error correlation structures in the corrections, and provide fresh consideration of the error covariance matrix, building on the earlier research.

The work was developed in the ASELSU project funded by ESA. The project has also reviewed the climate science research questions to understand the need for improved altimeter observations. There are three major science questions that require accuracies greater than those currently achieved. These questions are the closure of the sea level budget, the detection and attribution of the signal in sea level that is forced by greenhouse gas emissions (GHG) and the estimate of the Earth energy imbalance (EEI).

Dieng et al. 2017: https://doi.org/10.1002/joc.4996.
Nerem et al. 2018: https://doi.org/10.1073/pnas.1717312115
Ablain et al 2019: https://doi.org/10.5194/essd-2019-10
Guérou et al. 2022: https://doi.org/10.5194/egusphere-2022-330
Prandi et al. 2021: https://doi.org/10.1038/s41597-020-00786-7
Mittaz et al. 2019: https://doi.org/10.1088/1681-7575/ab1705


Oral presentation show times:

Room Start Date End Date
Sala Grande Thu, Nov 03 2022,10:00 Thu, Nov 03 2022,10:15
Emma Woolliams
National Physical Laboratory
United Kingdom