Joint assimilation of Aquarius‐derived sea surface salinity and AVHRR‐derived sea surface temperature in an ocean general circulation model using SEEK filter: Implication for mixed layer depth and barrier layer thickness

Abstract

Sea surface salinity (SSS) from Aquarius mission and sea surface temperature (SST) from Advanced Very High Resolution Radiometer (AVHRR) for the years 2012–2014 are assimilated into the global Massachusetts Institute of Technology General Circulation Model (MITGCM). Investigation of the impact of assimilation of these two data sets on simulated mixed layer depth (MLD) and barrier layer thickness (BLT) forms the core of our study. The method of assimilation is the Singular Evolutive Extended Kalman (SEEK) filter. Several assimilation runs are performed. Single‐parameter assimilation, as well as joint assimilation, is conducted. To begin with, the model simulated SST and SSS are compared with independent Argo observations of these two parameters. Use of latitudinally varying error variances, which is a novel feature of our study, gives rise to the significant improvement in the simulation of SSS and SST. The best result occurs when joint assimilation is performed. Afterward, simulated MLD and BLT are compared with the same parameters derived from Argo observations forming an independent validation data set. Comparisons are performed both in temporal and spatial domains. Significant positive impact of assimilation is found in all the cases studied, and joint assimilation is found to outperform single‐parameter assimilation in each of the cases considered. It is found that simulations of MLD and BLT improve up to 24% and 29%, respectively, when a joint assimilation of SSS and SST is carried out.

Impact of Aquarius and SMAP Satellite Sea Surface Salinity Observations on Coupled El Niño/Southern Oscillation Forecasts

“…Density changes near the surface directly impact the MLD. Figure c shows that overall shoaling of the MLD is evident between 10 °S and ~5 °N and this feature is broadly consistent with the results of Chakraborty et al () that assimilated Aquarius SSS data. This MLD shoaling is coincident with thinning of the depth of the 20 °C isotherm (a proxy for the depth of the thermocline) in Figure d such that the equatorial waveguide is much shallower than both north and south of the equator west of 120 °W and is consistent with the meridional gradient pattern of the density plot (Figure b).…”

“…These variances are used to construct a diagonal matrix of model errors . Since the assimilation is limited to the tropical Indo‐Pacific region, a single value is chosen for the observational error corresponding to 3 cm (Busalacchi et al, ), 0.3 °C (Chakraborty et al, ), and 0.2 psu (Lagerloef et al, ) for SL, SST, and SSS, respectively. For T z and S z , the observational error is chosen as 0.75 °C and 0.31 psu, respectively.…”

“…Near the equator, SMOS SSS assimilation leads to shallower MLD across the entire Pacific and, the anomalous eastward currents of the 2015 El Niño are enhanced east of 150 °E. In a recent paper, Chakraborty et al () also found shallower values and an overall improvement in MLD for the tropical Pacific when assimilating Aquarius SSS data. In addition to SMOS, Martin et al () performed experiments using multiple SSS satellites (i.e., SMOS and Aquarius and SMOS and SMAP) that further improved the ocean analyses.…”

“…In addition to SMOS, Martin et al () performed experiments using multiple SSS satellites (i.e., SMOS and Aquarius and SMOS and SMAP) that further improved the ocean analyses. Thus, the results of Tranchant et al (), Martin et al (), and Chakraborty et al () have clear implications for ENSO prediction and these improvements help to justify the implementation of SSS assimilation into operational forecast systems.…”

Observational Needs for Improving Ocean and Coupled Reanalysis, S2S Prediction, and Decadal Prediction

“…Though, to date, the impacts shown have been somewhat minor. A number of other studies showed positive impacts due to the assimilation of SSS in controlled experiments, including improved upper ocean salinity (Vernieres et al, 2014), improved surface currents, mixed-layer depth, and barrier layer thickness (Chakraborty et al (2014(Chakraborty et al ( , 2015, and improved temporal variability of the vertical distribution of salinity in areas with large freshwater input (Seelanki et al, 2018). Still, the low temporal frequency of the data, large uncertainty estimates attributed to instantaneous observations, and large platform-specific biases (Bao et al, 2019), make the assimilation of SSS a continuing challenge.…”