A new assessment of global mean sea level from altimeters highlights a reduction of global trend from 2005 to 2008

A new assessment of global mean sea level from altimeters highlights a reduction of global trend from 2005 to 2008 M. Ablain, A. Cazenave, G. Valladeau, and S. Guinehut CLS, Ramonville Saint-Agne, France LEGOS, OMP, Toulouse, France Received: 20 October 2008 – Accepted: 27 October 2008 – Published: 7 January 2009 Correspondence to: M. Ablain (michael.ablain@cls.fr) Published by Copernicus Publications on behalf of the European Geosciences Union.

One of the most important indicators of global warming is the global Mean Sea Level (MSL) which integrates the response of many components of the climate system.Precise monitoring of MSL variations with global coverage is a major objective, not only for climate research but also for socio-economic purposes.Tide gauge records have shown that during the 20th century, global MSL has risen at an average rate of about 1.7 mm/yr (Church and White, 2006;Jevrejeva et al., 2008).Since 1993, altimeter measurements from TOPEX/Poseidon (T/P) and Jason-1 satellites provide precise MSL measurements with global coverage (e.g.Nerem and Mitchum, 2001;Cazenave and Nerem, 2004;Leuliette et al., 2004;Nerem et al., 2006).The most recently published study using altimeter data reports a global MSL rate of 3.36+/−0.41mm/yr over the 1993-2006 time span (Beckley et al., 2007).If the Global Isostatic Adjustment (GIA) correction (of about −0.3 mm/yr, Peltier, 2004) is accounted, this rate increases to 3.7 mm/yr.This is more than twice the rate of sea level rise of the 20th century.If Introduction

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Full real, such a result suggests that sea level rise is accelerating in response to increased warming.However differences in estimated MSL rates from different authors up to 0.5 mm/yr are commonly reported, mostly a result of differences in data processing and in applied geophysical corrections.
In the first part of this study, we propose a new calculation of the global MSL from January 1993 to June 2008 using new standards for the processing of the T/P and Jason-1 data.We investigate the stability of each geophysical correction applied to the sea surface height (SSH) measurements, in particular atmospheric corrections.
In the second part, we check if the reduced rate of rise as reported since 2005 from Jason-1 data is real or results from anomalies of the Jason-1 altimeter system.This question is legitimate as any calibration between Jason-1 and T/P data is not possible any more, the T/P mission having ended in November 2005.Note that because of abnormal trends detected on the global MSL deduced from Envisat and Geosat Followon altimeter systems, we cannot use these missions for the calibration of Jason-1 with a good confidence.For this purpose, we investigate all sources of errors potentially affecting altimetry-derived MSL.For instance we evaluate the impact on the MSL of using a wet troposphere correction deduced from either the onboard radiometer or a meteorological model.
Finally, an external calibration of the MSL is carried out through a comparison of tide gauge-based and altimeter MSL.This approach also allows detection of anomalies in altimetry measurements.

Altimeter MSL calculation over 1993-2008 period
Measuring MSL by satellite altimetry requires extreme stability of the system, in terms of orbit, instrumental and geophysical corrections.For that purpose, we need to use homogenous time series of T/P and Jason-1 SSH data.
The Jason-1 data used in this study are the Version B reprocessed Geophysical Data Records (GDRs) from cycles 1 to 232.SSHs are derived using the corrections Introduction

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Interactive Discussion summarized in Table 1, except for dry troposphere and inverse barometer corrections which have been updated.For the latter, we use rectangular surface pressure grids from the European Center for Meteorological Weather Forecast (ECMWF) instead of the Gaussian grids provided in the standard GDRs.In theory, this modification should have no impact on the correction itself but in practice, jumps have been detected in the Gaussian pressure fields with significant effect on the MSL trend.A bias of 75.5 mm is then removed from Jason-1 SSHs to have Jason-1 and T/P SSH in a common datum.Thanks to the Jason-1 verification phase where both satellites were on the same orbit spaced out by 72 s, the SSH bias can be precisely determined, associated uncertainty being lower than 1 mm.
Concerning T/P, data have been reprocessed from the merged Geophysical Data Records (MGDRs) to be consistent with Jason-1 data (see Table 1).Some geophysical corrections have been updated: the GOT2000 model (Ray and Egbert, 2004) is used for ocean and loading tides; dry troposphere and combined atmospheric corrections are derived from rectangular ECMWF pressure grids and the MOG2D model (Carrere and Lyard, 2003).The wet troposphere correction is based on the TOPEX radiometer measurements, after removing a long-term drift (Scharroo et al., 2004) and correcting for the time dependent yaw state (Aviso T/P yearly report 2005).A non-parametric model is used for the sea state bias correction (SSB) with two distinct solutions for TOPEX-A and TOPEX-B (Gaspard, 2002).Subsequent SSH bias between TOPEX-A and TOPEX-B is then 1.17 cm, instead of 0.5 cm when using the 4-parameters SSB proposed with the merged GDRs.Finally, the standard orbit has been replaced by a new orbit generated by the Goddard Space Flight Center (GSFC).The new orbit is based on the GRACE gravity field model, CGM02C (Tapley et al., 2004) and is expressed in the ITRF2000 (Altamimi et al., 2002) reference frame throughout the period.
Using these new T/P and Jason-1 data (spurious measurements removed), global SSH grids are then computed for each cycle.To account for the heterogeneous data distribution with latitude and for data gaps, a 2 global MSL curve is further computed by geographically averaging data of each cycle (using a cosine latitude weighting function).

MSL evolution analysis
Figure 1 shows the global MSL curve between 1993 and 2008 after removing the annual and semi-annual cycles.A 60-day filtering is applied to the raw data (blue dots).
A 6-month smoothing is further performed (red curve).The mean rate of sea level rise estimated over 1993-2008 amounts to 3.11 mm/yr.Applying the GIA correction (−0.3 mm/yr) (Peltier, 2004) leads to a rate of rise of 3.4 mm/yr over the past 15 years.Although the global MSL evolves rather linearly (adjustment formal error is 0.02 mm/yr), inter-annual variations can nevertheless be observed, in particular during the 1997-1998 ENSO (El Nino Southern Oscillation) event.At the end of the time span (since 2005), the MSL curve appears relatively flat with a marked negative anomaly in mid-2007.
We have computed MSL rates using moving windows of 3-year and 5-year.These are shown in Fig. 2. Estimated MSL rates display two maxima, in 1997 and 2002.
Corresponding rates are in the range of 4-6 mm/yr when using the 3-year window.It is very likely that these two maxima reflect the influence of ENSO events on the MSL.The signature of the 1997-1998 ENSO is clearly visible on the MSL curve, as shown by Ngo-Duc et al. (2005) due to an excess of precipitation in tropical river basins.Another weaker ENSO event occurred in 2002-2004.It is likely that the secondary peak seen in Fig. 2 also reflects the sea level response to this ENSO event.Past decades sea level rates based on tide gauge records also show systematic high values during ENSO years (Landerer, 2008).
As expected, smaller rates are reported when using the 5-year window.In this case, inter-annual variations are partly smoothed.Quite low rates are observed during La Nina events (1999) and ( 2007), although no quantitative explanation has been given yet.Nevertheless, the recent reduction observed in sea level rate is likely real as it Introduction

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Full coincides with an exceptionally strong La Nina event (Kennedy, 2007).To check the robustness of the estimated smaller rate of sea level rise over the past few years, we next investigate whether it could be related to drifts or jumps in the Jason-1 altimetry system.

Uncertainties on altimeter measurements
In this section we discuss the main source of errors affecting Jason-1 SSH measurements.

Wet troposphere correction
One major source of error affecting the MSL estimate is the wet troposphere correction derived from microwave radiometers on-board altimetric satellites.Indeed, this correction is potentially contaminated by long-term instrumental drifts.Such drifts may result from internal temperature changes induced by yaw maneuvers or when the instrument is turned off.Calibrations with external measurements are periodically performed to detect drifts on the T/P radiometer (TMR) (Scharroo et al., 2004)   In addition, the physical content evolution of the wet troposphere correction appears to be a supplementary source of uncertainty to estimate its potential drift.The correction is firstly strongly correlated with ENSO oscillations, and in the meantime, the long term evolution of the correction is impacted by climate warming in relationship with the rise of vapor content around 0.041 kg/m 2 /yr since 1988 (DOE/Lawrence Livermore National Laboratory, 2007).The impact in the absolute wet troposphere correction is in order of +0.2 mm/yr.These physical oscillations and evolutions is a limiting factor in the accurate calibration of radiometer or model corrections.
Finally, from this analysis, we estimate the uncertainty of the MSL trend calculation due to the wet troposphere correction between 0.2 and 0.3 mm/yr over the whole altimeter period.

Dry troposphere and inverse barometer corrections
Another source of error is linked to the use of pressure fields provided by operational ECMWF model in Jason-1 and T/P products.Indeed, the dry troposphere and inverse Introduction

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Full barometer corrections are directly derived from these grids used to compute (time variable) surface pressure averaged over the oceanic domain.Although their good quality has already been demonstrated (Ponte et Dorandeu, 2003;Salstein, 2008), surface pressure grids may not be appropriate for long-term sea level estimates.We compared the Gaussian ECMWF surface pressure grids (as given in the Jason-1 GDRs) with NCEP reanalyses grids.Two jumps, in 2004 and 2006, of about 20 hPa have then been detected, one in the instantaneous ECMWF surface pressure grids, the other in the mean surface pressure grids.Corresponding effects on SSH andMSL trend (over 2002-2008) amount respectively to 2.5 mm and 0.2 mm/yr.Rectangular pressure grids (as given with the T/P M-GDRs) do not show such discontinuities and are thus preferred for the MSL calculation.We also compared time series of mean surface pressure averaged over the oceanic domain, from two sources: ECWMF rectangular grids and NCEP grids (see Fig. 4).Trends in time series are respectively 1.39 Pa/yr and 0.23 Pa/yr.Considering the uncertainty due to the potential heterogeneity between pressure and mean pressure fields, and the error on the global pressure trend, the impact on the global MSL trend, through inverse barometer and dry troposphere corrections, is in the order of 0.05 to 0.1 mm/yr.

Orbit calculation
Thanks to the T/P GSFC orbit update in the SSH calculation in order to be consistent with Jason-1 (see first section), T/P and Jason-1 SSH consistency have been significantly improved with the reduction of correlated geographically biases (Ablain et al., 2007).As far as the long term stability is concerned, the impact is weak on the global MSL trend (about +0.1 mm/yr over the T/P period) but much larger on regional slopes with opposite hemispheric differences close to 2 mm/yr.Despite this improvement, trend discrepancies remain between both hemispheres since MSL trend is about 2.5 mm/yr in the North and 3.5 mm/yr in the South.This 1 mm/yr trend differences cannot be explained by a physical process.Indeed, using the GSFC orbit computed with a new ITRF2005 reference frame (Altamimi et al., 2007) allows us to remove these Introduction

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Interactive Discussion hemispheric effects.The impact on global MSL is close to 0.1 mm/yr over the whole altimeter period.It is stronger on regional slopes with opposite hemispheric differences close to 1 mm/yr (Beckley et al., 2007).
In addition, other sources of trend discrepancies are observed between ascending and descending passes using GDR's orbit over the Jason-1 period with, respectively 2.9 mm/yr and 2.1 mm/yr as plotted in Fig. 5.These unexpected differences are reduced applying the ITRF2005 GSFC orbit close to 2.4 mm/yr and 2.3 mm/yr.However, some residual incoherent signals are still observed (see Fig. 5), since ascending and descending MSL curves are less well correlated than using GDR's orbit.In any case, orbit calculation is thus essential to explain and resolve these discrepancies.Assuming it is only a problem of orbit centring, the impact of this error on the global MSL trend is probably weak after averaging ascending and descending passes.Finally, a realistic error budget ranging from 0.1 to 0.15 mm/yr on the global MSL trend can be allocated to the orbit calculation.

Others potential errors
Other factors can also impact the global MSL trend as the SSH bias applied to link each MSL time series together.In order to estimate the impact of the SSH bias uncertainty, we compared the MSL trend derived from Jason-1 and T/P considering three altimeter subsets: TOPEX-A, TOPEX-B and Jason-1.We applied appropriate bias to sea surface heights (TOPEX-A/TOPEX-B=11.7 mm, TOPEX-B/Jason-1=75 mm) and we estimated an error from each subset.A realistic error between 1 and 2 mm for TOPEX-A/TOPEX-B bias is applied taking into account the uncertainty to estimate the SSH bias without overlapping between both datasets and a strong decrease of the MSL evolution in relationship with "La Nin ã" 1999.The error is reduced between 0.5 and 1 mm for TOPEX/Jason-1 thanks to the Cal/Val phase allowing an accurate cross-calibration between both missions.Uncertainty associated to each bias is large enough to significantly affect the global MSL trend.Considering extreme bias errors, we find an MSL trend ranging from 2.8 to 3.3 mm/yr (see Fig. 6), highlighting an error Introduction

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Full Another source of potential errors concerns altimeter instrumental ageing.Altimeter parameters are precisely monitored over all the mission life-time to detect instrumental anomalies.However, after analyzing altimeter parameters impacting directly the MSL calculation, potential drifts in the altimeter wind speed (derived from Sigma0 parameter) have been detected.Comparisons with models (NCEP reanalysis and ECMWF model) and cross-calibration between altimeter missions allow us to highlight it (see Fig. 7).Over the Jason-1 period, the Jason-1 and ECMWF altimeter wind speeds show a significant trend, respectively around 5.2 and 5.9 cm s −1 /yr.In the meantime, the Envisat altimeter mission provides a wind speed trend weaker of about 1.6 m s −1 /yr, which is relatively close to the NCEP reanalysis (3 cm s −1 /yr).Considering now the T/P period (not plotted here), the wind speed trend deduced from T/P measurements is around 1 cm s −1 /yr and 2.5 cm s −1 /yr for the NCEP reanalysis.As the real evolution of wind speed is unknown, this analysis just highlights the long-term trend discrepancies between each wind speed derived from altimeters and models.An uncertainty varying between 2 and 4 cm s −1 /yr can then be considered, impacting the MSL evolution from 0.05 to 0.10 mm/yr over the entire period through the sea state bias correction.

Total error budget
Considering independently all the potential errors of altimeter data described previously and reported in Table 2, the global MSL trend is then majorized by an upper bound limit error of 0.9 mm/yr.A more realistic error can be calculated using an inverse method (Bretherton et al., 1976).Thanks to this appropriate mathematic formalism, we are able to describe the error differently according to the period (TOPEX and Jason-1 periods can be separate) or the nature of the error (jump or drift for instance).Finally, assuming the maxima uncertainties, we obtained a statistical error of 0.6 mm/yr in confidence interval of 90%.Introduction

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Full Screen / Esc Printer-friendly Version Interactive Discussion If we focus on the 2005-2008 period, only the error related to pressure fields and especially wet troposphere correction could specifically impact this period.Other errors have a behavior very homogenous over all the altimeter period (orbit, wind speed), or do not impact the end of the period (TOPEX/TOPEX-B and TOPEX/Jason-1 subsets for instance).Then this can not explained a change in the MSL trend.We have already shown that the incoherency between mean pressure and pressure field has a weak impact.Over a short period of 3 years, it is limited to 0.2 mm/yr.Concerning the wet troposphere correction, stronger uncertainties have been described (0.5 mm/yr between ECMWF and radiometer correction).But, in the meantime, AMR (Envisat) and JMR (Jason-1) corrections show a very good consistency and a similar trend over this period.Finally, on no account, altimetric errors can explain the slowing down MSL evolution at the end of the period.

Estimation of altimeter MSL drift
The analysis of potential drifts in altimeter measurements points out uncertainties, in particular for the wet troposphere correction.A relevant way of checking the reliability of the global MSL over all the period and especially for the 3 last years is to compare altimeter data with independent in-situ datasets such as tide gauge measurements.Several studies have been already performed over the whole altimeter period (Mitchum 1998 and2002;Chambers 1998) showing the good consistency between altimeter and tide gauge measurements.More recent results (Beckley et al., 2007), obtained with regard to a 64-site tide gauge network, provide an estimation of the drift derived from in-situ and altimeter measurements of 0.04 mm/year for T/P between 1993 and 2002 and 0.69 mm/year for Jason-1 from 2002 to 2006 included.We present here a new assessment of these long-term comparisons until 2008 in agreement with our MSL calculation and using an extended in-situ network.After analyzing the reliable in-situ time series (no jump or abnormal strong drifts), 134 tide gauges have been selected from the University of Hawai Sea Level Center.Some of them do not cover the whole altimeter period, but they are calibrated together in order to be used.Thanks to this higher number of tide gauges, the coastal sampling is improved (especially along african coasts), allowing to better take into account the regional MSL trend variability.In addition, the consistency between altimeter and insitu measurements is increased, improving the capacity to detect a change between altimeter and in-situ data as described further.
In addition, vertical land movements impacting tide gauges (post-glacial rebound, plate tectonics, water land storage. ..) and not observed by altimetric measurements have also been taken into account.Such a correction has been estimated from the global GPS station network of the ULR analysis centre consortium (W öppelmann et al., 2007).After selecting the closest GPS stations from the given tide gauges and removing those separated by a fault, a correction around 0.3 mm/yr is then deduced.Applying it, the altimeter drift estimation is now very weak close to +0.01 mm/yr for Jason-1 and +0.45 mm/yr for TOPEX (see Fig. 8).Merging both altimeter missions over the whole period, the drift becomes close to +0.3 mm/yr.

Accuracy of the method
These comparisons don't highlight any anomaly on altimeter measurements, especially at the end of the period since no change is observed from Jason-1 and tide-gauge comparisons.This result seems to prove the reliability of Jason-1 altimeter data between 2005 and 2007.However the accuracy of the method is a limiting factor to detect altimeter drift or jump.The relatively strong adjustment formal error, 0.27 mm/yr for Jason-1 and 0.11 mm/yr for T/P, points out the sensitivity of the drift calculation, though it has been reduced (by 0.15 mm/yr) thanks to the higher number of tide gauges.It mainly ensues from the consistency between altimeter and in-situ measurements (the standard deviation of differences is about 7 cm).It takes into account the error of in-situ and altimeter MSL, but also the colocation error which depends on the distance between Introduction

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Full the tide gauge and the closest altimeter measurements.The use of long data time series lead to reduce the impact of this uncertainty on the trend estimation.In addition, the correction of vertical movements directly impacts the long-term drift estimation.The uncertainty of this correction remains relatively significant since the colocation between tide gauges and GPS stations is possible only for about 60 sites.The accuracy close to 0.2 mm/yr could be refined with an extended GPS station network.Finally, an important limitation of the method is the capability to detect an altimeter change in open ocean because of the coastal sampling.This is in particular true for the wet troposphere correction whose regional trends are strong and variables in wet tropical areas but not well displayed on tide gauge sites.On balance, the method is able to assess the long term drift of the global altimeter MSL.Taking into account the error related to the adjustment formal error and the uncertainty of the vertical land movement correction, the accuracy of the method is close to 0.5 mm/yr over all the altimetric period.Finally, the altimeter drift estimation derived from Jason-1 and T/P data is around 0.3+/−0.5 mm/yr.

Conclusions
On the one hand, thanks to the analysis of each error budget, we show that the global MSL trend is 3.11+/−0.6mm/yr with a confidence interval of 90%.On the other hand, the altimeter MSL drift derived from altimeter and tide gauge comparisons is on the same order close to +0.3+/−0.5 mm/yr.The good consistency of these both independent approaches demonstrates the reliability of T/P and Jason-1 altimeter data to compute the global MSL trend from 1993 to 2008.The capability to observe interannual variations related to ENSO oscillations is possible thanks to the good accuracy of altimeter MSL, as underlined here for the 1999 "La Nina" event.Indeed, we have demonstrated that the weak MSL trend observed for the 3 last years (1 mm/yr) cannot result in the altimeter MSL drift error.Besides, preliminary MSL analyses (not described here) using Jason Though the MSL trend error is already in agreement with scientific objectives, it could probably be significantly reduced applying homogenous SSH calculation for T/P and Jason-1, and very soon Jason-2.The use of similar orbits or similar retracking algorithms for T/P and Jason-1 data would reduce the correlated geophysical biases.
The accuracy of TOPEX-A/ TOPEX-B and TOPEX/Jason-1 SSH biases could be then improved.In addition, the use of pressure fields derived from models with a stable configuration (without jump) would reduce or even remove the drift uncertainty linked to the dry troposphere and dynamical atmosphere corrections.In the same idea, a more stable wet troposphere correction derived from operational model will be useful to better calibrate corrections derived from radiometer.Introduction

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Full and Jason-1 radiometer (JMR)(Desai et al., 2004; Jason-1 GDR-B release).Though meteorological models do not represent necessarily the truth in term of stability, they provide a good estimate of the radiometer drift error through altimetry missions and model cross-calibration.Here we use outputs from three meteorological models: the ECMWF operational model from 2002 onwards, the ERA40 reanalysis(Uppala et al., 2005) from 1992 to 2002, and the NCEP reanalysis(Kalnay, 1997)  over the same period.Operational model data regularly show jumps, thus are inadequate for long-term comparison with TMR data, at least before 2002.Fortunately, more coherent ERA40 and NCEP reanalyses allow us to assess the reliability of TMR correction.In addition, the Envisat radiometer (MWR) and model cross-calibration provides a complementary comparison to study the longterm JMR stability.Figure3shows the wet troposphere correction daily differences for −0.25 mm/yr on the global MSL trend in the worse case.The impact of the SSH bias uncertainty is depending on the period.
-1 data since June 2008, indicate an acceleration of the MSL relationship with the end of the 2007-2008 "La Ni ña" event.

Fig. 1 .
Fig. 1.Altimeter MSL from Jason-1 and T/P over the 1993-2007 period without GIA correction applied.Annual and semi annual signals have been adjusted and a 60-day low-pass filter has been applied.Red curve is smoothed over a semi-annual period.
MWR minus ECMWF.TMR and model cross-calibrations (cases 1 and 2) highlight a negative slope between 0.2 and 0.3 mm/yr from 1992 to 2002.The trend obtained with NCEP reanalysis is more accurate than with ERA40 reanalysis, since significant inter-annual signals are observed with this last one.Concerning case 3 using JMR and ECMWF model from 2002 to 2008, the global trend is very small, on the order of 0.1 mm/yr.However, the end of the curve beyond 2006 displays a significant increase close to 1 mm/yr.In the meantime, Envisat and ECMWF comparison (case 4) highlights a similar slope from 2006 onwards, whereas between 2002 and 2005 both radiometers do not show a very good agreement.The cross-calibration of both radiometer corrections, independently calibrated, is here useful to detect a drift in the ECMWF model probably related to several model changes.These evolutions(February and September 2006, June and November 2007, March 2008)can generate small jumps close to 1 mm signing as a drift over a period of 2 or 3 years.