Monitoring sea surface salinity (SSS) is important for understanding and forecasting the ocean circulation. It is even crucial in the context of the intensification of the water cycle. Until recently, SSS was one of the less observed essential ocean variables. Only sparse in situ observations, mostly closer to 5 m depth than the surface, were available to estimate the SSS. The recent satellite ESA Soil Moisture and Ocean Salinity (SMOS), NASA Aquarius SAC-D and Soil Moisture Active Passive (SMAP) missions have made it possible for the first time to measure SSS from space and can bring a valuable additional constraint to control the model salinity. Nevertheless, satellite SSS still contains some residual biases that must be removed prior to bias correction and data assimilation. One of the major challenges of this study is to estimate the SSS bias and a suitable observation error for the data assimilation system. It was made possible by modifying a 3D-Var bias correction scheme and by using the analysis of the residuals and errors with an adapted statistical technique.
This article presents the design and the analysis of an observing system
experiment (OSE) conducted with the 0.25
Finally, this study helped us to progress in the understanding of the biases and errors that can degrade the SMOS SSS data assimilation performance.
Recent progress in data treatment of sea surface salinity (SSS) from space makes possible its assimilation in ocean analysis systems (Boutin et al., 2017). Since the launch of the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission in 2009, then the launch of NASA's Aquarius in 2011 and Soil Moisture Active Passive (SMAP) in 2015, SSS observations from space are available and have been used in many studies (e.g., Tang et al., 2017; Vinogradova et al., 2014; Toyoda et al., 2015; Reul et al., 2013).
Here we present the impact of assimilating SSS observations from space into
the global 0.25
The most striking event in the global ocean for the year 2015 was the strong El Niño event. It is as strong as in 1997 (von Schuckmann et al., 2018). Because the maximum of the sea surface temperature (SST) anomalies stays off the eastern coast of South and Central America, it was more likely to be an El Niño Modoki (Ashok and Yamagata, 2009) or a central Pacific El Niño (Kao and Yu, 2009) than a classical eastern Pacific El Niño.
SSS anomalies (practical salinity scale, pss) in 2014
Warm anomalies began to build in the western Pacific in 2014 triggered by
westerly wind bursts but did not lead to the development of an El Niño
in the year. However, as suggested by McPhaden et al. (2015), the presence
of El Niño precursors in early 2014 helped the development of a strong
El Niño at the end of 2015. Anomalously eastward currents along the
Equator and in the North Equatorial Countercurrent (NECC) continued from 2014. This is associated with an
increase in precipitation and an eastward shift in fresh surface salinities.
A strong equatorial SSS anomaly in 2015 has been observed and described
(Hasson et al., 2018; Gasparin and Roemmich, 2016). The Pacific freshening
is due to an active ITCZ in 2015, but advection by anomalous eastward
currents also plays a role in the SSS changes. The difference between the two
annual SSS anomalies in 2014 and 2015 in our so-called Reference simulation
(hereafter REF) (see Sect. 3) is shown in Fig. 1. The
2015–2016 El Niño is also the first important climatic event fully
captured by the SMOS satellite where negative SSS anomalies have been
observed between 0 and 15
Data assimilation experiments conducted within the SMOS Niño 2015 project
(
Experiments conducted within the SMOS Niño15 project to test the impact of the satellite SSS data were carefully designed and analyzed to ensure robust conclusions on the impact of SSS measurements on ocean analysis. The system used for the OSE is based on the operational ocean monitoring and forecasting system operated at Mercator Ocean. The use of such system ensures that conclusions are relevant for operational applications.
To assess the benefit of assimilating SSS from satellite in a realistic context, all observations from the Global Ocean Observing System (GOOS) that are assimilated in real-time ocean analysis or reanalysis are also assimilated. SST, in situ temperature and salinity observations (from moorings, drifting platforms, ships), and along-track sea level anomalies are assimilated in the REF simulation and OSEs. OSEs conducted were designed to assess the impact of weekly SSS products as the system has a weekly assimilation cycle.
It is recommended to withhold part of the usually assimilated observations from the OSEs to have fully independent data to compare with; see Fujii et al. (2015). The tropical-atmosphere ocean (TAO) mooring salinity data were not assimilated and kept for verification. Although restricted to the few mooring points, those data are the only ones to provide long-term time series of daily temperature and salinity observations.
Several studies (Reul et al., 2013, or Lee et al., 2012) show that SSS measured from space can bring new information. Recently, Toyoda et al. (2015) and Hackert et al. (2014) have shown the impact of assimilating Aquarius data in the Pacific region both in uncoupled and coupled ocean–atmosphere systems. In a recent paper, Chakraborty et al. (2014) show that the migration of the thermohaline fronts at the eastern edge of the western Pacific warm pool can be more realistic with the assimilation of Aquarius SSS. Data assimilation of Aquarius SSS can also help to better understand the variability of salinity structure in the Bay of Bengal (Seelanki et al., 2018). Finally, satellite SSS data assimilation is promising in an operational context both for ocean and seasonal forecasting.
Nevertheless, technical challenges are still open to assimilate SSS data efficiently in the context of global ocean analysis and forecasting. The assimilation of satellite SSS observations is challenging because of the various complex biases; see Köhl et al. (2014). The difference between the forecast and the satellite SSS can be 5 times larger than the misfit between the forecast and near-surface ARGO salinity. Since the signal-to-noise ratio is still not high today, retrieval algorithms must be improved. Careful analysis of the SSS data sets shows that a bias correction is needed before their assimilation as shown by Martin (2016). To have an optimal analysis, the hypothesis of unbiased errors has to be respected. This article details the bias correction scheme and the error estimation scheme used in the data assimilation system for those data. This is a necessary step to have a positive impact on SSS data assimilation.
The structure of this article is as follows: after a description of the OSE where the operational system, the bias correction, the SSS observation error and the presentation of the experimental design are described in Sect. 2, the effect of the SMOS SSS data assimilation is presented in Sect. 3, while discussions and conclusions are provided in Sect. 4.
The OSEs are conducted with the global 0.25
The Mercator Ocean real-time analysis and forecast is based on the version 3.1
of the NEMO ocean model (Madec, 2016), which uses a 0.25
The ocean model is forced by atmospheric fields from the European Centre for Medium-Range Weather Forecasts-Integrated Forecast System (ECMWF-IFS) at 3 h resolution to reproduce the diurnal cycle. Momentum and heat turbulent surface fluxes are computed by using (Large and Yeager, 2009) bulk formulae. Because there are large known biases in precipitation, a satellite-based large-scale correction of precipitation is applied to the precipitation fluxes. This correction has been inferred from the comparison between the remote-sensing system (RSS) passive microwave water cycle (PMWC) product (Hilburn, 2009) and the IFS ECMWF precipitation (Lellouche et al., 2013).
A monthly river runoff climatology is built with data on coastal runoff from 100 major rivers from Dai et al. (2009). This database uses new data, mostly from recent years, and streamflow simulated by the Community Land Model version 3 (Verstentein et al., 2004) to fill the gaps, in all land areas except Antarctica and Greenland. At high latitudes the effect of iceberg melting is also parameterized. The lack of interannual variability of the largest rivers is known to lead to large errors in the surface ocean salinity in the analysis and forecast. There is no SSS relaxation term for any climatology as is the case in operational conditions. More details concerning parameterization of the terms included in the momentum, heat and freshwater balances (i.e, advection, diffusion, mixing and surface fluxes) can be found in Lellouche et al. (2018).
All ocean observations assimilated in the real-time forecasting system are
assimilated in the same way in the OSEs presented here. Along-track sea level
anomaly (SLA) observations distributed by Copernicus Marine Environment
Monitoring Service (CMEMS) (
In this study, we assimilate an SMOS Level 3 (L3: provided on a grid, but
without infilling) SSS product at 0.25
The assimilation scheme implemented in the real-time Mercator Ocean systems is based on a reduced-order Kalman filter called SAM2 (Système d'Assimilation Mercator V2), and it is described in Lellouche et al. (2013, 2018).
As in the operational ocean forecasting system, we use a weekly assimilation cycle with an analysis date on the fourth day of the week.
The SAM2 system uses a background error covariance matrix with a reduced basis of a fixed collection of multivariate model anomalies. The model anomalies are computed from a previous simulation over an 8-year period with an in situ bias correction, detailed in Sect. 2.4. The forecast error covariances rely on a fixed basis, seasonally variable ensemble of anomalies calculated from this long experiment. A significant number of anomalies are kept from one analysis to the other, thus ensuring error covariance continuity. The aim is to obtain an ensemble of anomalies representative of the error covariance (Oke et al., 2008), which provide an estimate of the error in the ocean state at a given period of the year. The localization of the error covariance is performed assuming zero covariance beyond a distance defined as twice the local spatial correlation scale (Lellouche et al., 2013). These spatial correlation scales are also used to select the data around the analysis point. The model correction (analysis increment) is a linear combination of these anomalies. This correction is applied incrementally over the assimilation cycle temporal window using an incremental analysis update (Bloom et al., 1996; Benkiran and Greiner, 2008).
The observation errors specified in the assimilation scheme are assumed to be uncorrelated with each other. Observation errors include representativity errors specified as a fixed error map and an instrumental error. Representativity errors for in situ observations were calculated a posteriori from a reanalysis over the period 2008–2012. The applied statistic method (Desroziers et al., 2005) consists of the computation of a ratio, which is a function of observation errors, innovations and residuals. These estimated errors are constant throughout the year.
Representativity error of in situ SSS (
Instrumental errors used for the current operational system.
The instrumental errors of SLA, SST and in situ measurements are summarized in Table 1. Figure 2a shows the representativity error used for the in situ SSS and an example of the resulting salinity error (Fig. 2b) for in situ data for the week of 20–27 January 2016. The SSS error from space is estimated during the bias correction scheme procedure (see Sect. 2.5) and then used in SAM2.
Biases between model and data exist for subsurface quantities such as temperature and salinity. As with the time-varying error components, such biases can often be related to systematic errors in the forcing (Leeuwenburgh, 2007).
As written in Lellouche et al. (2013), a 3D-Var bias correction is applied for large-scale 3-D temperature and salinity fields. The aim of this bias correction is to correct the large-scale, slowly evolving errors of the model, whereas the SAM assimilation scheme is used to correct the smaller scales of the model forecast error.
This is applied separately to the model's prognostic
Because temperature and salinity biases are not necessarily correlated at
large scales, these two variables are processed separately. Spatial
correlations in
Earlier attempts to assimilate SSS data have shown the importance of using
unbiased satellite SSS data while implementing rigorous quality control in
an upstream process (Tranchant et al., 2015). In this study, the bias
control of satellite SSS has been modeled by modifying the current
Example of model salinity bias (
To get an optimal set of parameters (weights, spatial scales and errors),
several estimations were performed with data withdrawing. Figure 3a and c
show examples of the model salinity bias
Example of the final product of Desroziers ratios
The Desroziers diagnostic (Desroziers et al., 2005) is commonly used for
estimating observation error statistics and is used here to adapt the
observation error from the background and analysis residuals calculated in
the bias correction (see also Lellouche et al., 2018). Following Desroziers
et al. (2005), the observation error of the bias
Example of SSS error (Eq. 6) of SMOS over the tropical Pacific used in the data assimilation system for the week of 20–27 January 2016.
Finally, for each weekly analysis, the total observation error of satellite
SSS (SMOS) (Fig. 5) prescribed in the data assimilation scheme is the
maximum of the above observation error estimated during the bias correction
process and the measurements error (
Experiment descriptions.
Two parallel simulations were produced: the REF experiment and the SMOS
experiment (hereafter SMOSexp); see Table 2. The only difference is the
assimilation of the SSS SMOS observations. Both experiments begin in January
2014 from the same initial conditions coming from a previous reanalysis
using only the bias correction of
The comparison between the two simulations highlights the impact of the SSS data assimilation on the ocean circulation and the comparison to the other observations (independent or not) will allow us to verify the coherency between the different observation networks and the way they are assimilated.
Different diagnostics are now used to assess the impact of SSS data assimilation on the analyzed model fields. First the analysis from the REF and SMOSexp simulations is evaluated against the assimilated observations. Then, the 3-D fields of the simulations with and without SSS data assimilated are compared and the changes in the surface and subsurface fields are analyzed. Finally, TAO/TRITON (TRIangle Trancs Ocean buoy Network) array salinity observations which are deliberately withheld and delayed-time ThermoSalinoGraph (TSG) which are not assimilated in the analysis of all experiments are used to conduct an independent analysis–observation comparison. Our analysis focuses on the tropical Pacific region during the 2015 El Niño event.
The REF and SMOSexp simulations differ only by assimilating satellite SSS data (Table 2). We first check the success of the assimilation procedure in reducing the misfit from the assimilated SSS observations within the prescribed error bar. We then look at the root mean square (rms) of in situ salinity observation innovations near 6 m depth in both simulations. The forecasted field is mostly independent of the reference data because those data have not been assimilated yet and the model forecast ranges from 1 to 7 days.
RMSE of SSS with respect to SMOS data (solid lines) and RMSE of
salinity near 6 m depth with respect to in situ salinity data (dashed
lines) in the 1–6-day forecast fields in REF (black lines) and SMOSexp (red
line) in the global domain
Figure 6 shows the time series of root-mean-square errors (RMSEs) of the model near-surface salinity at 6 m depth with respect to in situ observations (dotted lines) and of the model SSS (0.5 m depth) with respect to the bias-corrected SMOS SSS (solid lines) for both simulations (REF in black, SMOSexp in red). As expected, the SMOS SSS data assimilation clearly leads to a significant reduction in the innovations of the SMOS data (solid lines). When the SSS SMOS is assimilated, the time series of RMSE for the global, the tropical Pacific and the central Pacific (Niño3.4) domains present the same reduction with a higher variability for the smallest domain (Niño3.4). The global RMSE to SMOS data are around 0.28 pss (practical salinity scale) in the reference simulation and reduced to 0.21 pss when debiased SMOS data are assimilated, corresponding to an error reduction of 24 %. This shows that the combination of bias correction and data assimilation perform well.
Percentage of RMSE difference in SSS for SMOS and for in situ salinity at 6 m depth in different regions. The average number of SSS data assimilated per week is also indicated.
Nevertheless, the essential issue is the salinity RMSE compared to in situ salinity observations (dotted lines). This error is slightly reduced from 0.20 to 0.19 pss in the global domain (5 %), but this reduction can reach 10 % in the northern tropical Pacific where the salinity anomaly is the strongest; see Table 3. This larger decrease in the near-surface salinity RMSE is consistent with that observed for the SSS SMOS RMSE (30 %). In addition, the reduction of the near-surface salinity RMSE is more important in the western part of the equatorial Pacific (Niño4). This shows that the assimilation of SMOS SSS observations does not introduce overall incoherent information and can even reduce the misfit with the in situ salinity observations. It also confirms that SSS errors estimated in the bias correction procedure and used in the assimilation scheme are well tuned and the data bring coherent information. Consequently, salinity large-scale biases are removed well. From Table 3, it should be mentioned that the number of in situ salinity observation per week is very small compared to the SMOS observations and is maybe not always sufficient to ensure robust statistics in small regions.
Mean difference
Time series and maps of the misfits between observation and model forecasts are complementary in the analysis of the temporal and spatial variability of the model–observation differences. Figure 7 shows the mean and root-mean-square differences of monthly mean SSS in the analysis fields in REF and SMOSexp compared to the original (non-debiased) SMOS data over the year 2015 for the tropical Pacific Ocean.
The mean SSS bias in REF exhibits large-scale patterns, coinciding with the 2015 SSS anomaly for the open ocean (Fig. 1). A large bias is also found in the Indonesian archipelago. In contrast, the bias is effectively reduced in SMOSexp as are the root-mean-square differences, which are reduced to less than 0.2 pss (black isohaline) in most of the tropical Pacific Ocean.
Average salinity RMSE (pss) compared to all in situ measurements
The mean RMSE and the percentage of RMSE difference in the salinity profiles (mainly from Argo floats) are computed over the entire period and the global domain (Fig. 8). There is a slight decrease in the first 30 m below the surface when SSS data are assimilated additionally to in situ salinity data. It shows that the additional information brought by the SSS is in agreement with the salinity in situ observations close to the surface. It can even help improve the global salinity representation in the first 30 m by better constraining the model forecast with the satellite SSS.
In situ temperature innovations in the global domain as well as in the tropical Pacific region do not show significant changes. The same is found for SLA (CMEMS/DUACS, Data Unification and Altimeter Combination System, along track) and SST innovations (OSTIA L4). SSS data assimilation has a quite neutral impact on the innovations associated with those observations.
Mean October 2015 SSS estimation from the REF experiment
We now look at the changes in the analyzed surface and subsurface fields due to the SSS data assimilation by comparing the 3-D analysis of the REF and SMOSexp experiments. At a basin scale, the REF simulation already agrees well with the 2015 mean deduced from the “unbiased” CATDS SMOS observations (Fig. 9). SMOS data assimilation induced changes on the order of 0.2 pss. It tends to weaken the salinity negative anomaly represented in the REF simulation within the ITCZ and SPCZ regions. This is in agreement with Kidd et al. (2013), who show an overestimation of the ECMWF precipitation in the tropics compared to satellite observations. Elsewhere, the SMOS data assimilation increases the salinity. Large changes also occurred in the coastal zones (Indonesian archipelago and Central America coast), even if the specified error in SSS data was larger in those regions than in the open ocean.
Vertical section along the Equator of the mean model salinity difference between the SMOSexp and REF experiments for the year 2015.
The associated vertical salinity changes brought by SMOS SSS data assimilation at the Equator are represented in Fig. 10. The largest high-salinity anomaly is found in the first 50 m depth and along the coastal bathymetry; elsewhere changes are very small (less than 0.05 pss). Overall, at the Equator (excepted in coastal areas), the data assimilation of SMOS SSS leads to fresher waters in the east and saltier waters in the west for the year 2015.
The highest variability of the surface salinity at a monthly scale during the year 2015 is found within the ITCZ, SPCZ and in the eastern Pacific fresh pool in both simulations and SMOS observations (not shown). SMOS assimilation decreases the intensity of the variability of the SSS, in agreement with the observed variability. In summary, the SSS assimilation acts to counteract the precipitation excess, with a visible result in the salinity both in terms of time mean but also in terms of variability.
Hovmöller diagram of SSS at 5
During the El Niño 2015 event, a strong salinity anomaly pattern developed in
the tropical Pacific (Gasparin et Roemmich, 2016); see also Fig. 1. This
anomaly corresponds to the ITCZ and SPCZ areas. Figure 11 shows the
time–longitude evolution of the SSS at 5
While the impact of SSS assimilation is neutral on the other variables (temperature and sea surface height, SSH) in terms of data assimilation statistics (RMSE averaged in different areas), it is not the case when we look at the time evolution of model fields.
Hovmöller diagram of differences in SSS
SST differences at 5
Hovmöller diagram of barrier layer thickness (BLT) at 5
Another effect of SSS changes can be viewed on barrier layers which are
quasi-permanent in the tropical Pacific. Barrier layer thickness (BLT) can
influence the air–sea interaction, ocean heat budget, climate change and
onset of El Niño–Southern Oscillation (ENSO) events (Maes et al., 2002, 2004). The barrier layer acts as a
barrier to turbulent mixing of cooler thermocline waters into the mixed layer and
thereby plays an important role in the ocean surface layer heat budget (Lukas
and Lindstrom, 1991). The Hovmöller diagram of BLT at 5
Hovmöller diagram of 28–40-day (33 days) band-passed SSH anomalies
at 4
From Figs. 12a and 13, we show that the eastern and central Pacific
are saltier in the SMOSexp experiment, which induces a decrease in the
stratification and then a decreased BLT. A decrease in the stratification by
SSS data assimilation can increase the convective mixing, on the one hand, and the TIWs can be
modified by this change in stratification, on the other hand. From a long-term TAO mooring record at 0
We now compare the analyzed fields to independent observations, i.e., withheld from all assimilation experiments. This will allow verification that the changes in the physical fields induced by the SMOS data assimilation are in agreement with external sources of information. For this purpose, the TAO mooring (salinity) observations and the reprocessed TSG data from the French SSS Observation Service were withheld from all experiments. This is therefore a fully independent validation.
Time evolution of the hourly TAO observed salinity (black), the
hourly model REF (green), SMOSexp (red) simulations and the assimilated SMOS
data (magenta) at three different TAO moorings locations: cold tongue
TAO moorings deliver high-frequency measurements at fixed locations. Such
platforms allow us to look at high-frequency variability that is not
captured by drifting platforms. The hourly analyzed salinity is collocated
at the TAO mooring positions for the REF and SMOSexp simulations. Figure 15
shows the time evolution of TAO salinity observations (valid at 1 m depth)
at three mooring locations in the equatorial Pacific (warm pool, cold tongue
and salt front) compared to the model (analysis) for the REF and SMOSexp OSE
experiments at the first level (
Difference in model salinity RMSE (pss) at 1 m depth calculated against the 1 m depth TAO mooring salinity values (SMOSexp – REF) calculated over the period 1 January 2014 to 16 March 2016 (negative/positive difference implies a reduction/increase in RMSE by the SMOS assimilation). Moorings are only included if they have more than 1 week of measurements during the period.
These three examples show a positive impact, but it is also interesting to
have a global view of all TAO moorings over the 2015/2016 El Niño event.
As in Martin et al. (2019), Fig. 16 shows the differences in root mean square difference (RMSD) from
hourly TAO mooring salinity values at 1 m depth calculated over the period
1 January 2014 to 16 March 2016. The impact of the SMOS assimilation is
contrasted by showing negative (positive) values, which indicates that it
reduces (increases) the RMSD. The impact is positive and more significant in
the western tropical Pacific near the dateline and in the western Pacific up
to 5
Ship route of the
Post-processed TSG observations from the French SSS Observation Service
(SSS-OS; (
Figure 17b and c (zoom) show the comparison between the
TSG salinity observations (in red) along the
The L3 SMOS CATDS data used in this study are regarded as an unbiased product. Yet, they still contain some residual biases that must be removed prior to bias correction and data assimilation. One of the major challenges of this study was to estimate the residual SSS bias and a suitable observation error for the data assimilation system. It was made possible by using a 3D-Var bias correction scheme and an analysis of the residuals and errors with a statistical technique (Desroziers et al., 2005). The “debiased” data could then be assimilated by the SAM2 assimilation scheme which relies on the unbiased hypothesis. The bias estimated by the ocean forecasting system can also be used to correct the L3 SMOS CATDS data for other purposes.
The system was carefully tuned and tested to efficiently assimilate the new SSS observations before running the longer simulations that are analyzed here. The proper specification of the observation operator and error covariance matrix were also based on discussions with the data provider. This study helped us to progress in the understanding of the biases and errors that can degrade the SMOS SSS performance.
Nevertheless, there is still room for improvement. For instance, we used a zonal error as input to the error estimation with the Desroziers technique (Desroziers et al., 2005). It could be beneficial to take into account the smaller scales linked to a shallow stratification that arises with strong precipitations and/or river runoff.
The SMOS data need accurate in situ data (not only at the surface) to
correct their own biases and estimate a suitable error (including
data or system representativity). When enough accurate SMOS data are available,
they really act as a gap-filler. There is a clear impact on the scale of about
1–2
Globally, the SSS data assimilation slightly improves the simulation compared
to a simulation assimilating only observations of in situ, SST and SLA data.
It highlights that no incoherent information was brought by the SSS data
compared to the other assimilated observations. When looking at the impact of
the SMOS SSS assimilation, we found a positive impact in salinity with
respect to in situ data over the top 30 m. The RMSE of in situ surface salinity is reduced in all regions of the tropical Pacific
and is very often close to 0.15 pss. The improvement varies depending on the
region and can reach 10 % in the north tropical Pacific where the SSS
anomaly is the strongest. Comparisons to independent TAO/TRITON data
corroborate the fact that the impact of SMOS SSS assimilation is larger in
the ITCZ and SPCZ regions. This also reflects that the overestimation of
There is little impact on the SST. For instance, the area of the SST warmer
than 28.5
The next step will be to assimilate SSS from space at higher latitudes where low SST degrades the brightness temperature sensitivity to SSS (Sabia et al., 2014). A longer ocean reanalysis with continuously improved SSS SMOS (available for over 9 years) and SMAP (available since 2015) data could bring new information on the water cycle.
The focus of this study was on the tropical Pacific. But the system is global, and, in spite of RFI pollution near some coasts, we found clear improvements near the Amazon and the Rio de la Plata plumes. So, the benefit from assimilating SMOS SSS is not restricted to the equatorial band. Its positive impact near the midlatitude major rivers is a chance to better monitor the strengthening of the water cycle (Durack, 2015).
Sea surface salinity data derived from voluntary observing
ships were collected, validated, archived and made freely available by the
French Sea Surface Salinity Observation Service
(
BT, ER and EG designed and wrote the paper. BT performed the numerical simulations. EG and OL worked on the bias correction scheme.
The authors declare that they have no conflict of interest.
This article is part of the special issue “The Copernicus Marine Environment Monitoring Service (CMEMS): scientific advances”. It is not associated with a conference.
We gratefully acknowledge funding from ESA as part of the SMOS-Niño15 project, coordinated by Craig Donlon. We also thank the providers of the data sets used here. Jacqueline Boutin (LOCEAN/CATDS) provided the SMOS data and provided useful input to understand the nature of the SMOS bias estimates. Thanks to the GTMBA Project Office of NOAA/PMEL for providing TAO/TRITON mooring data. We would also like to acknowledge Matthew Martin (MetOffice) for his careful reading of the paper and his comments, which were very helpful. We would also like to acknowledge the contribution of reviewers, whose suggestions improved this paper significantly.
This paper was edited by Ananda Pascual and reviewed by Yosuke Fujii and one anonymous referee.