We present the latest release of the global mean sea level (GMSL) record produced by the French space agency Centre National d’Etudes Spatiales (CNES) and distributed on the AVISO+ website. This dataset is based on reprocessed along-track data, so-called L2P 21, of the reference missions TOPEX/Poseidon (TP) and Jason-1, Jason-2 and Jason-3. The L2P 21 CNES/AVISO+ GMSL record covers the period January 1993 to December 2021 and is now delivered with an estimate of its measurement uncertainties following the method presented in

Since October 1992 and the launch of TOPEX/Poseidon, radar satellite altimetry has proved its capacity to monitor the small sea level variations induced by the natural climate variability and by the anthropogenic climate change

To ensure the best possible estimate of current sea level changes, space agencies regularly revisit and update the production of the sea level record from the satellite archive. In addition, since 2009, they have also been providing careful estimates of the associated measurement uncertainty to deliver to users information on the reliability and the accuracy of the sea level estimates.

This work presents the new release of the global mean sea level (GMSL) record and its associated measurement uncertainty from the AVISO+ project of the Centre National d'Etudes Spatiales (CNES). Note that the GMSL signal measured from space that we present here includes all sources of variability, including the forced response to anthropogenic emissions, the forced response to natural forcing (such as the solar activity) and the internal variability in the climate system. However, we do not intend to detect, separate or attribute the sea level signal to these different sources of variability. We only intend to provide the most accurate GMSL time series from satellite instruments along with the instrumental uncertainties. In this sense, the estimate of the 1993–2020 trend and acceleration we propose at the end of the article should not be interpreted as the forced response of GMSL to anthropogenic or natural forcing. They are only metrics of the lowest frequency in the space-based GMSL time series. The associated uncertainties are the measurement uncertainty only and are indicators of the typical level of instrumental uncertainty present in the CNES/AVISO+ GMSL product.

First we present the satellite altimetry data that are used (Sect.

Origins and references of the corrections contained in the L2P 21 along-track 1 Hz products. These products are the ones used to compute the current GMSL CNES/AVISO+ record. The terms in italics are the ones updated as compared to the previous version L2P 18.

The CNES/AVISO+ GMSL record is computed based on the Level-2+ (L2P) CNES/AVISO+ 1 Hz non-time-critical (NTC) along-track data of the altimetry reference missions TOPEX/Poseidon (TP), Jason-1 (J1), Jason-2 (J2) and Jason-3 (J3). The latest reprocessing of these products is used, i.e. version V03_00, hereinafter referred to as L2P 21. The L2P 21 products benefit from reprocessed data of individual missions and homogeneous state-of-the-art geophysical corrections to ensure accurate and stable climate data records

The main improvements brought by the L2P 21 standards, as compared to those of the previous version

A summary of the altimetry variables and geophysical corrections contained in the L2P 21 products of the four reference missions is presented in Table

The L2P 21 GMSL record has been computed following the AVISO+ method

The L2P 21 GMSL is currently built from four altimetry missions: TOPEX/Poseidon, Jason-1, Jason-2 and Jason-3. These missions flew successively on the same orbit since 1992 with calibration phases, called “tandem phases”, during which the successive satellites fly less than a minute apart over the same ground track. During tandem phases, consecutive missions observe precisely the same sea level such that the GMSL intermission offset (due to non-correlated instrumental differences) can be accurately estimated and corrected for. These tandem phases generally last from 6 to 12 months and are key to ensuring the long-term continuity and stability of the GMSL record

In practice, the global intermission offsets are computed as the mean difference in the respective GMSL values over a given sub-period (i.e. a given number of cycles) of the tandem phase. In the previous version of the CNES/AVISO+ GMSL record, only 9 cycles within each tandem phase (out of about 20) centred around the switching date from one mission to another were used to compute the intermission offsets. This was different from other groups who used the whole tandem phases

Altimetry missions used to establish the CNES/AVISO+ GMSL record. The periods covered by each mission in the GMSL record are provided in the second column, the corresponding cycles are given in column three, and the tandem-phase cycles used to compute the intermission offsets are given in the last three columns.

GMSL record of the reference missions with a focus on the respective tandem phases. Panel

Figure

We performed Shapiro–Wilk tests for the three GMSL difference time series over the tandem phases and confirm that their distribution is not clearly non-Gaussian (i.e.

From Eqs. (

The obtained offset uncertainties are lower than the ones from the previous CNES/AVISO+ GMSL record, i.e. 0.5 mm at 1

The reference GMSL record available on the AVISO+ website is provided with different optional global corrections that the user may use according to its need.

First, we provide for the first time the empirical correction to account for the TP-A altimeter drift well documented by the community

For the rest of the paper, we use the reference L2P 21 GMSL record corrected for the TP-A altimeter drift as well as for the GIA correction. The resulting record covers the period from January 1993 to December 2021, hence providing a

The CNES/AVISO+ GMSL record is delivered with an updated estimate of its measurement uncertainties following the method developed in

The main differences between the L2P 21 and L2P 18 GMSL version come from the use of new geophysical corrections (i.e. DAC, internal tide, MSS) as well as the use of the WTC from the on-board radiometer instruments of the J2 and J3 missions (see Sect.

L2P 21 GMSL intermission offset uncertainties (1

However, the level of uncertainty for a few sources of uncertainties needs to be updated and adapted to the new L2P 21 GMSL record. This is the case of the correlated uncertainties at timescales of 2 months and 1 year, which are estimated empirically directly from a filtering of the GMSL time series

We also updated the uncertainty associated with the WTC over the Jason-3 period. Recent work by

L2P 21 GMSL uncertainty budget given at 1

Finally, we updated the uncertainties associated with the GMSL intermission offsets as presented in detail in Sect.

To estimate the sea level rise and acceleration of the L2P 21 GMSL record, we first apply a 2-month low-pass Lanczos filter to the time series. We then fit a quadratic regression model to the filtered time series following an ordinary least square (OLS) approach, as described in Sect. 6 in

We recall from

Error variance–covariance matrix of the L2P 21 GMSL obtained from the revised uncertainty budget presented in Table

This section presents the L2P 21 CNES/AVISO+ GMSL record, its trend and acceleration estimates along with their measurement uncertainties. The analysis is based on the data detailed in Sect.

Figure

In Fig.

Compared to the L2P 18 uncertainty envelope, Fig.

Comparison of the uncertainty envelopes (1.65

Figure

GMSL trends

GMSL accelerations

As shown in Fig.

Based on the approach described in Sect.

Figure

On the acceleration, we observe in Fig.

Accuracy and stability requirements of the GMSL record have been stated in the literature to allow scientific questions on climate change to be answered. The two intergovernmental organizations, the Global Climate Observing System (GCOS) and the Intergovernmental Panel on Climate Change (IPCC), have published their recommendation about the GMSL trend uncertainty stability: 0.3 m yr

To identify the limiting factors to the GMSL monitoring stability highlighted above, we here investigate the relative contribution of each uncertainty budget contributor to the total GMSL measurement uncertainty budget. The aim is to identify the main contributors and thus be able to suggest key topics of investigations to tame the measurement uncertainties and get closer to the stability requirements.

To do so, we derive for each contributor to the GMSL uncertainty budget presented in Sect.

Figure

Relative contribution of each uncertainty budget contributor to the GMSL trend uncertainty over periods of 10 years.

Relative contribution of each uncertainty budget contributor to the GMSL trend uncertainty over periods of 20 years.

Figure

Figures

Relative contribution of each uncertainty budget contributor to the GMSL acceleration uncertainty over periods of 10 years.

Relative contribution of each uncertainty budget contributor to the GMSL acceleration uncertainty over periods of 20 years.

As detailed in Sect.

At the beginning of the altimetry era, our analysis shows that significant improvements need to be achieved on the TP data quality. A new reprocessed dataset in GDR-F standards is ongoing and should be publicly released soon. Significant improvements are expected on the stability of the TP-A altimeter as well as on the offset estimation between the two altimeters. Despite the high expectation of the community for such reprocessing, the stability performances of the resulting GMSL will still not be better than it currently is with the last altimetry missions such as Jason-3. Indeed, the other three main uncertainty contributors will still limit the GMSL stability to about

As we note in Sect.

Finally, we found that a systematic limit to the GMSL stability measurements appears, which is the realizations of the ITRF. This might be the true limiting factor of the current observing system to the GMSL record stability. Improvements in the uncertainties in such a reference frame represent huge effort from many different scientific communities and governmental organizations. A first step toward this is the publication of a newer version than the ITRF2014 used in this paper: the ITRF2020. Using this new release might help to reduce the associated uncertainties as the time series is longer, seasonal signals are now considered in the local movements, and more data are used to constrain the realization (i.e. Galileo). Nonetheless, the expected uncertainty improvement of a few percent will make the uncertainties associated with the ITRF the major contributor to the GMSL stability uncertainties over periods longer than 20 years (see Meyssignac et al., 2022).

We have presented the latest release of the CNES/AVISO+ GMSL record based on the reprocessed CNES L2P 21 1 Hz along-track data of the reference missions, TOPEX/Poseidon, Jason-1, Jason-2 and Jason-3. This dataset covers the period January 1993 to December 2021, and it is now provided with an estimate of its measurement uncertainties, available online, as well as an empirical correction of the TP-A altimeter drift as proposed in

The GMSL measurement uncertainties, based on an updated version of the uncertainty budget, are reduced as compared to the previous CNES/AVISO+ record. This is mostly due to improved instrumental standards and geophysical corrections proposed in the input data products. A few improvements in the method have been presented, such as a new statistical method to estimate the GMSL intermission offsets and its related uncertainties. We showed that the intermission offset uncertainties are reduced when using as many tandem-phase measurements as possible. We also updated the uncertainties associated with the WTC of the Jason-3 radiometer that is suspected to show higher instability than the other radiometers on board the altimetry missions. This impacts the stability of the GMSL measurements at the end of the data record. We recalled that the variance–covariance matrix and the derived uncertainties only represent the instrumental uncertainties and are only indicators of the typical level of instrumental uncertainty present in the CNES/AVISO+ GMSL product.

A major result of this paper is the quantification of the respective contribution to the GMSL measurement uncertainties in the individual uncertainty contributors. We have highlighted the results for different timescales and found that the stability of the GMSL record is limited by four major contributors: the correlated errors at short timescales (2 months and 1 year), the WTC from radiometers, the TOPEX/Poseidon data quality and the ITRF realizations. Whereas two of these sources of uncertainties are well identified and will certainly be relatively easily addressed (i.e. the TP data quality and the WTC stability), the two others clearly set the current limitations of the altimetry observing system (i.e. the ITRF realizations) as well as of our knowledge of the description of its uncertainties (i.e. mixed origins in the description of the annual correlated uncertainties). Our results challenge the altimetry observing system as it is designed today and highlight clear topics of research to be explored in the future to help the altimetry community to improve the GMSL measurement accuracy and stability.

We present here the relative contribution to the GMSL trend variance of each uncertainty budget contributor. The uncertainty contributors are detailed in Table

Variance contribution to the GMSL trend variance of the high-frequency errors correlated at 2 months.

Variance contribution to the GMSL trend variance of the high-frequency errors correlated at 1 year.

Variance contribution to the GMSL trend variance of the WTC from radiometers.

Variance contribution to the GMSL trend variance of the orbit solutions.

Variance contribution to the GMSL trend variance of the intermission offsets.

Variance contribution to the GMSL trend variance of TP data quality.

Variance contribution to the GMSL trend variance of the ITRF drift uncertainty.

Variance contribution to the GMSL trend variance of the GIA drift uncertainty.

We present here the relative contribution to the GMSL acceleration variance of each uncertainty budget contributor. The uncertainty contributors are detailed in Table

Variance contribution to the GMSL acceleration variance of the high-frequency errors correlated at 2 months.

Variance contribution to the GMSL acceleration variance of the high-frequency errors correlated at 1 year.

Variance contribution to the GMSL acceleration variance of the WTC from radiometers.

Variance contribution to the GMSL acceleration variance of the orbit solutions.

Variance contribution to the GMSL acceleration variance of the intermission offsets.

Variance contribution to the GMSL acceleration variance of TP data quality.

Variance contribution to the GMSL acceleration variance of the ITRF drift uncertainty.

Variance contribution to the GMSL acceleration variance of the GIA drift uncertainty.

The GMSL dataset described in this paper is available to download at the following address:

AG led the study, performed the computations and wrote the manuscript with some input from the authors. BM wrote the introduction of the manuscript and contributed to the discussion. AR gave strong support to establishing the statistical approach used in Sect.

The contact author has declared that none of the authors has any competing interests.

Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This research has been supported by the Centre National d'Etudes Spatiales (grant no. SALP).

This paper was edited by Jochen Wollschlaeger and reviewed by Thomas Frederikse and Huseyin Baki Iz.