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Validation et estimation des erreurs du MSL altimétrique

Le calcul et l'analyse de l'évolution du niveau moyen (Mean Sea Level, MSL) conduit légitimement à se poser la question de la fiabilité des altimètres pour mesurer une hausse de quelques millimètres par an sur une période de presque 20 ans. La question est d'autant plus pertinente que les sources d'erreurs concernant la stabilité à long terme sont potentiellement nombreuses. Elles sont principalement liées au vieillissement des instruments et à l'incertitude de leur étalonnage, à l'impact des événements liés à la vie du satellite (manoeuvres du satellite, incidents sur la plateforme ou des instruments,...), au calcul des solutions d'orbites et de toutes les corrections utilisées dans le calcul de la SSH déduites des modèles (marée, météo, ionosphère...). 

 

[L'intégralité de la page en version française sera disponible prochainement.] 

Error budget estimated from all the individual corrections

GMSL trend uncertainties 

Based on an assessment of all sources of errors affecting the altimetric system (Ablain et al. 2017), the GMSL trend uncertainty (90% confidence level) is estimated at ~0.4 mm/yr over the whole altimetry era (1993-2017). The main source of error is the wet tropospheric correction with a drift uncertainty in the order of 0.2 mm/yr (Legeais et al. 2018) over a 10-year period. To a lesser extent, the orbit error (Couhert et al. 2015; Escudier et al., 2017) and the instability of the altimeter parameters (range, sigma-0, significant wave height/SWH) (Ablain et al., 2012) also contribute to the GMSL trend uncertainty, at the order of 0.1 mm/yr. Furthermore, imperfect links between successive altimetry missions lead to an additional trend uncertainty of about 0.15 mm/yr over the 1993-2017 period (Zawadzki and Ablain, 2016).

Taking into account the temporal correlation of each source of error and their evolution in time (e.g. see next section describing larger errors on the first decade 1993-2002 of the altimetry record), (Ablain et al., 2018) allows estimating the GMSL trend uncertainty (within a 90% confidence interval) for any altimeter periods between 1993 and 2017 (see figure below).

GMSL trend uncertainties (mm/yr) estimated for any altimeter periods between 1993 and 2017. The confidence level is 90 % (1.65-sigma). On the Y-axis is represented the length of the window (in years) and on the X-axis the central date of the window used (in years).

Focus on TOPEX-A instrumental drift and its impact on the GMSL curve

The GMSL uncertainty is significantly larger during the first decade (1993-2002) of the record, due to larger errors affecting the T/P sea level measurements on climatic time scales, compared to subsequent missions. For instance, the T/P orbit solutions are more uncertain due to lower precision of the gravity field solutions estimated without GRACE data. Furthermore, the switch from TOPEX-A to TOPEX-B in February 1999 leads to a 3 mm bias uncertainty in the GMSL time series (Escudier et al., 2017). 

However, the most significant error affecting the first 6 years (January-1993 to February 1999) of the T/P GMSL measurements comes from an instrumental drift of TOPEX-A altimeter. This effect on the GMSL time series was recently highlighted via comparisons with tide gauges (Valladeau et al. 2012; Watson et al. 2015; Chen et al. 2017; Ablain et al. 2017), via a sea level budget approach (i.e., comparison with the sum of mass and steric components; Dieng et al., 2017) and via comparisons to Poseidon-1 measurements (Zawadzki, personal communication). In a recent study, Beckley et al. (2017) suggested that the corresponding error on the GMSL during the 1993-1998 time-span, results from incorrect onboard calibration parameters. 

All three approaches conclude that during the period January 1993 to February 1999, the altimetry-based GMSL slope was overestimated by ~1.5 mm/yr, with an uncertainty in the range of 0.5 to 1.0 mm/yr (Watson et al. 2015; Chen et al. 2017; Dieng et al. 2017). Beckley et al. (2017) proposed to not apply the suspicious onboard calibration correction on TOPEX-A measurements. The latter approach is similar to apply the TOPEX-A drift correction estimated by Dieng et al. (2017) and Ablain et al. (2017b). In the latter study, accurate comparisons between TOPEX A-based GMSL and tide gauge measurements lead to a drift correction of about -1.0 mm/yr between January 1993 and July 1995, and +3.0 mm/yr between August 1995 and February 1999, with an uncertainty of 1.0 mm/yr (68% confidence level). 

Evolution of the ensemble mean GMSL time series (average of the 6 GMSL products from AVISO/CNES, SL_cci/ESA, University of Colorado, CSIRO, NASA/GSFC, and NOAA). On the black, red and green curves, the TOPEX-A drift correction is applied, using the correction proposed respectively by Ablain et al.(2017b), Watson et al. (2015), Dieng et al. (2017) and Beckley at al. (2017). On the figure, the annual signal is removed, and a 6-month smoothing is applied; The GIA correction is also applied. Uncertainties (90% confidence interval) of correlated errors over a 1-year period are superimposed for each individual measurement (shaded area).

Comparaison avec les mesures in situ

Another approach for describing the GMSL error consists in comparing the sea level data with in situ data such as tide gauges or ARGO data (temperature and salinity profiles) (Ablain et al., 2009 (a), (b)). The stability of the altimetry system can thus be assessed by detecting drifts or leaps in the GMSL (SALP annual GMSL report 2017).

Comparison to tide gauges

The method to compare altimeter and tide gauges data (noted Alti-TG hereafter) is based on the main following steps: 

- The along-track altimeter data are averaged on 360x180 global grids (1°x1° cells). One grid is computed per mission cycle, except for comparisons to the PSMSL tide gauge network, where monthly grids are used.

- In a 300 km radius around each tide gauge, the best correlated grid element is chosen and a GMSL difference time series (Altimeter GMSL - Tide gauge GMSL) is computed.

- A mean global Alti-TG time series is computed from Alti-TG time series. The trend difference is computed from this global time series, which represents the estimated altimeter GMSL drift.

- Vertical Land Motion (VLM) correction: each trend difference is corrected for Glaciar Isostatic Ajustment (GIA) induced VLM, using Pr. Peltier’s ICE-5G(VM2) model (see Peltier 2004). Due to insufficient GPS data at the tide gauge locations over the whole GLOSS-CLIVAR or PSMSL networks, no correction is applied for the other VLM contributions.

The altimeter GMSL drift is estimated within a confidence level depending on different source of errors: vertical land motion at the tide gauges, high-frequency noise from altimeter and tide gauge data, collocation errors from both datasets, and the global alti-TG averaging scheme. Describing the temporal correlation of these errors and their evolution with time allows to provide the uncertainty of the GMSL altimeter drift over the entire altimetry period. It is estimated to 0.4 mm/yr between 1993 and 2017 at 90% confidence level (Ablain et al., 2018). Furthermore, the uncertainties have been also provided for any altimeter periods between 1993 and 2017 (see figure below).

Altimeter GMSL drift uncertainties (mm/yr) estimated from alti-TG comparison using the GLOSS/CLIVAR tide gauge network for any altimeter periods between 1993 and 2017. The confidence level is 90 % (1.65-sigma). On the Y-axis is represented the length of the window (in years) and on the X-axis the central date of the window used (in years).

The GMSL drifts and associated uncertainties (at the 90% confidence level), based on comparisons with GLOSS-CLIVAR and PSMSL data, are shown for each altimeter on the figure below. For most missions, no significant drift can be detected. Most notably, the drift for the reference GMSL time series provided on the AVISO website is estimated between -0.35 mm/yr and +0.45 mm/yr at the 90% confidence level, thus is not considered as significant.

It is worth noting that a significant drift is detected on the TOPEX-A –based GMSL. This drift is evaluated at 1.6 +/- 1.2 mm/yr (90% confidence level) when compared to data from the GLOSS-CLIVAR tide gauge network. Such a drift is well-known by the scientific community, and several corrections have been proposed. A significant drift, of -1.3 +/- 0.8 mm/yr, is also detected in the ERS-2 GMSL time series. Data from this altimeter are not used to compute the AVISO reference GMSL (based on only TOPEX and Jason missions). The ERS-1 altimeter may have been affected by a significant drift but the uncertainty from the comparison to tide gauges for this specific mission is too high to accurately estimate a drift. This high uncertainty is due to the short duration of the mission (only 3.5 years, with many data gaps) and very high altimeter noise levels.

At the moment, the Jason-3 and Sentinel-3A records are too short for a comparison with tide gauges and the detection of a drift.

GMSL drift on each altimeter estimated by comparison to tide gauges from the GLOSS-CLIVAR and PSMSL networks
Global MSL difference between the AVISO reference missions (Topex + Jason missions) and tide gauges from the GLOSS-CLIVAR network. This is obtained by the Alti-TG comparison process described previously

Erreurs locales des pentes du MSL

Concernant les erreurs locales des pentes du MSL, une même approche statistique a été appliquée afin de décrire localement les différentes sources d'incertitudes de l'altimétrie [Ablain et al., 2010]. Ces travaux ont mis en évidence le fort impact local de la variabilité interannuelle puisque celle-ci représente 80% de l'erreur formelle d'ajustement, ce qui démontre que la période altimétriques est encore trop courte pour estimer des pentes localement. Les erreurs restantes (20%) dues aux erreurs du système altimétrique varient en fonction de la latitude entre 1 et 2 mm/an principalement, à cause des incertitudes du calcul d'orbite et de la correction de troposphère humide respectivement aux hautes latitudes et dans la bande tropicale. Dans ce cas aussi, la description des erreurs locales devra être affinée pour prendre en compte notamment l'impact des champs de gravité variables dans le calcul d'orbite [Cerri et al., 2010] ou encore les incertitudes dans les zones proches des côtes ou des glaces liées à la dégradation de la mesure altimétrique.

Références

  • (a) Ablain, M., Cazenave, A., Valladeau, G., Guinehut, S., 2009. A new assessment of the error budget of global mean sea level rate estimated by satellite altimetry over 1993-2008. Ocean Science, 5(2), 193-201. DOI: 10.5194/os-5-193-2009
  • (b) Ablain M., 2009. Quality assessment of tide gauge and altimeter measurements through SSH comparisons. Oral presentation in OSTST, Seattle 2009.
  • Ablain M., 2010. Quality assessment of tide gauge and altimeter measurements through SSH comparisons. Oral presentation in ESA Symposium, Bergen 2010.
  • Ablain, M., Philipps, S., Urvoy, M., Tran, N., Picot, N., 2012. Detection of long-term instabilities on altimeter backscatter coefficient thanks to wind speed data comparisons from altimeters and models. Marine Geodesy, 35(sup1), 258-275. DOI: 10.1080/01490419.2012.718675
  • Ablain, M., Legeais, J. F., Prandi, P., Marcos, M., Fenoglio-Marc, L., Dieng, H. B., Benveniste, J., Cazenave, A., 2017. Satellite altimetry-based sea level at global and regional scales. In: Cazenave A., Champollion N., Paul F., Benveniste J. (eds) Integrative Study of the Mean Sea Level and Its Components. Space Sciences Series of ISSI, vol 58. Springer, Cham. DOI: 10.1007/978-3-319-56490-6_2
  • Ablain M., R. Jugier, N. Picot., 2018. Estimation of any Altimeter Mean Seal Level Drifts between 1993 and 2017 by Comparison with Tide-gauges Measurements. Presented at International Review Workshop on Satellite Altimetry Cal/Val Activities and Applications 2018.
  • Beckley, B. D., Callahan, P. S., Hancock, D. W., Mitchum, G. T., Ray, R. D., 2017. On the "Cal-Mode" Correction to TOPEX Satellite Altimetry and Its Effect on the Global Mean Sea Level Time Series. Journal of Geophysical Research: Oceans, 122(11), 8371-8384. DOI: 10.1002/2017JC013090
  • Cerri L., Berthias J.P., Bertiger W., Haines B., Lemoine F.G., Mercier F., Ries J.C., Willis P., Zelensky N.P., Ziebart M., 2010. Precision Orbit Determination Standards for the Jason Series of Altimeter Missions. Marine Geodesy 2010.
  • Chen, X., Zhang, X., Church, J. A., Watson, C. S., King, M. A., Monselesan, D., Legresy, B., Harig, C., 2017. The increasing rate of global mean sea-level rise during 1993–2014. Nature Climate Change, 7, 492-495. DOI: 10.1038/nclimate3325
  • Couhert, A., Cerri, L., Legeais, J. F., Ablain, M., Zelensky, N. P., Haines, B. J., Lemoine, F.G., Bertiger, W.I., Desai, S.D., Otten, M., 2015. Towards the 1 mm/y stability of the radial orbit error at regional scales. Advances in Space Research, 55(1), 2-23. DOI: 10.1016/j.asr.2014.06.041
  • Dieng, H. B., Cazenave, A., Meyssignac, B., Ablain, M., 2017. New estimate of the current rate of sea level rise from a sea level budget approach. Geophysical Research Letters, 44(8), 3744-3751. DOI: 10.1002/2017GL073308
  • Escudier, P., Couhert, A., Mercier, F., Mallet, A., Thibaut, P., Tran, N., Amarouche, L., Picard, B., Carrère, L., Dibarboure, G., Ablain, M., Richard, J., Steunou, N., Dubois, P., Rio, M. H., and Dorandeu, J., 2017. Satellite radar altimetry: principle, geophysical correction and orbit, accuracy and precision, in: Satellite Altimetry Over Oceans and Land Surfaces, edited by: Stammer, D. and Cazenave, A., CRC Press, Taylor Francis, Boca Raton
  • Legeais, J.-F., Ablain, M., Zawadzki, L., Zuo, H., Johannessen, J. A., Scharffenberg, M. G., Fenoglio-Marc, L., Fernandes, M. J., Andersen, O. B., Rudenko, S., Cipollini, P., Quartly, G. D., Passaro, M., Cazenave, A., and Benveniste, J., 2018. An improved and homogeneous altimeter sea level record from the ESA Climate Change Initiative. Earth System Science Data, 10, 281-301. DOI: 10.5194/essd-10-281-2018
  • SALP annual GMSL report 2017. Validation of altimeter data by comparison with tide gauges measurements and with in-situ T/S ARGO profiles
  • Valladeau, G., Legeais, J. F., Ablain, M., Guinehut, S., Picot, N., 2012. Comparing altimetry with tide gauges and Argo profiling floats for data quality assessment and mean sea level studies. Marine Geodesy, 35(sup1), 42-60. DOI: 10.1080/01490419.2012.718226
  • Watson, C. S., White, N. J., Church, J. A., King, M. A., Burgette, R. J., Legresy, B., 2015. Unabated global mean sea-level rise over the satellite altimeter era. Nature Climate Change, 5(6), 565. DOI: 10.1038/nclimate2635
  • Zawadzki, L., Ablain, M., 2016. Accuracy of the mean sea level continuous record with future altimetric missions: Jason-3 vs. Sentinel-3a. Ocean Science, 12(1), 9-18. DOI: 10.5194/osd-12-1511-2015
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