Assimilating satellite sea‐surface salinity data from SMOS, Aquarius and SMAP into a global ocean forecasting system

Abstract

Funding informationESA.Measuring Sea-Surface Salinity (SSS) from space is a relatively recent technique that relies on L-band radiometry that has evolved to a point where useful information is provided every few days. The impact of assimilating satellite SSS data is investigated using the global FOAM ocean forecasting system. This system assimilates daily satellite SSS products from the ESA Soil Moisture and Ocean Salinity (SMOS), NASA Aquarius and Soil Moisture Active Passive (SMAP) missions equatorward of 40 • N/S, in addition to other observing systems. The data are assimilated using a 3D-Var scheme that includes an observation bias correction scheme to estimate spatially and temporally varying bias estimates for each SSS satellite. The SSS assimilation is tested over 2 years and 3 months covering the 2015/2016 El Niño with assessment focussed on the tropical regions, particularly in the Pacific. Consistent reductions in root-mean-square errors are found in all tropical regions from each of the satellite SSS datasets. The largest improvements of up to about 8% are found in the tropical Pacific from assimilating both the SMOS and SMAP datasets together. The largest impact is in the intertropical convergence zone (ITCZ) in the central Pacific during 2015 and early 2016 where the surface salinity is reduced by about 0.03 pss on average, correcting for too little precipitation. A smaller magnitude, large-scale reduction in the SSS is also seen in the tropical Pacific that increases the modelled surface stratification and leads to reduced vertical mixing. Changes to the SSS in the ITCZ lead to changes in the meridional gradients of SSS that affects the sea surface height and surface currents, both of which are improved in the SMOS assimilation experiment compared to externally produced observation-based datasets. The results suggest that assimilation of SMOS and SMAP satellite data is now of an appropriate quality for operational implementation. 1Ocean Assimilation Model (FOAM) system (Blockley et al., 2014) produces daily analyses and 7-day forecasts of the three-dimensional ocean and sea-ice state, including temperature, salinity and velocity. The same system is also used to produce reanalyses over the satellite altimeter era in order to calibrate seasonal forecasts (MacLachlan et al., 2015).The observing systems routinely assimilated in FOAM include in situ and satellite sea-surface temperature (SST) This article is published with the permission of the Controller of HMSO and the Queen's Printer for Scotland.

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

“…On the other hand, the results from this paper demonstrate that SSS assimilation improves the near‐surface ocean state leading to improved coupled ENSO forecasts, thus reaffirming and extending our previous results (e.g., Hackert et al, ). Although there are currently very limited data available, the encouraging results of this study and of operational ocean‐only data assimilation experiments (e.g., Martin et al, ; Tranchant et al, ) lead us to expect that assimilation of satellite SSS into more realistic operational forecast systems will result in improved ENSO forecasts. Therefore, we reassert the major conclusions of Tranchant et al () and Martin (), namely, that SSS assimilation should be routinely included as an essential observed quantity for operational coupled modeling since SSS assimilation can offset shortcomings in atmospheric forcing fields of evaporation and precipitation.…”

“…During a simulation of the 2015 El Niño, Tranchant et al () found that patterns associated with SMOS SSS assimilation acted to enhance the propagation of tropical instability waves in the eastern Pacific and increase the acceleration of the warm/fresh pool migration to the east for the 2015 El Niño. On the other hand, Martin et al () found that the meridional SSS gradient is reduced near 5 °N by SMOS assimilation leading to SL changes and a reduction in tropical instability wave activity and a more zonal North Equatorial Counter Current. 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 (2015) 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 (2018) 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 (2018), Martin et al (2018), and Chakraborty et al (2015) have clear implications for ENSO prediction and these improvements help to justify the implementation of SSS assimilation into operational forecast systems.…”

The impact of Argo observations in a global weakly coupled ocean–atmosphere data assimilation and short‐range prediction system

“…However, work to assess the impact of assimilating satellite salinity observations has recently been carried out (Martin et al . ) with a view to future operational implementation. The greatest differences in the upper ocean build over the first 6 months, but it is unclear whether the full effects are realised even by the end of the 13‐month runs.…”

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

“…A common assumption is that errors are Gaussiandistributed and that the time evolution of the errors is linear. As such, there are common limitations to all currently used DA methods and a primary goal for improving the accuracy and applicability of DA in the coming decade will be to relax these limiting constraints (see Martin et al, 2019;Moore et al, 2019, this issue). This has relevance to future ocean-observing system design, as it may change requirements on the observing system either to test and design new methods or to take advantage of new capabilities afforded by the methods.…”