Multi-Variate Salinity Assimilation Pre- and Post-Argo Robin Wedd, Oscar Alves and Yonghong Yin Centre for Australian Weather and Climate Research, Australian Bureau of Meteorology The POAMA Ensemble Ocean Data Assimilation System (PEODAS) is the next generation ocean data assimilation system developed for operational implementation at the Australian Bureau of Meteorology. It combines the Predictive Ocean Atmosphere Model for Australia (POAMA; Alves et al, 2003) with an extension of the Bluelink Ocean Data Assimilation System (BODAS; Oke et al. 2008). BODAS is an ensemble optimal interpolation system. A dynamic 110 member ensemble of perturbed model runs is used to approximate the background error covariances (BECs) required for the assimilation of data with an additional central run. The BECs created are dynamic, anisotropic, inhomogeneous and state- dependent. In contrast to ensemble Kalman filter systems, only the central run undergoes analysis in PEODAS, and the ensemble members are compressed around the analysis. This makes PEODAS computationally inexpensive, while retaining the advantages of an ensemble-based system: dynamic state-dependent BECs, a spread of model states for probabilistic forecasts, and easy scalability to match the available resources. Perturbation of the model ensemble is achieved using surface forcing field errors calculated from an EOF analysis of the differences between the NCEP and ECMWF reanalysis products in a similar fashion to that described by Alves and Robert (2005). Figure 5 shows the mean state of average salinity to 300m for the reanalysis period in FULL_S, and the divergences of the other re-analyses from FULL_S. UPDATE_S is generally within 0.1 psu, the exceptions being a tendency to underestimate the magnitude of the salinity maximum in the south Pacific and the salinity minimum off the coast of Mexico. Both NO_S and CONTROL also have problems in these areas. The south Pacific salinity field is poorly simulated in the NO_S run, with a 3.5 psu divergence from the FULL_S run to the north east of the salinity maximum. CONTROL is again able to produce salinity fields closer to the full assimilation than NO_S, providing more evidence that assimilating temperature data without balancing the associated salinity fields results in a degradation of the simulated salinity field. The Argo array of temperature and salinity profiling floats began deployment in 2000 (Gould, 2005). There are currently over 3300 floats in operation. Observational salinity data has increased rapidly in both volume and ocean coverage since the projects inception. This increase in data allows us to examine the accuracy of modelled profiles when little or no salinity data is available, as in the pre-Argo period. The influence of salinity variability in the western equatorial Pacific on the onset of El Nino oscillations has been recognised (Maes and Picaut, 2005), and the accuracy of salinity profiles is an important factor in assessing the results of re-analyses and forecasts. The experiments described here were designed to test the ability of multi-variate ensemble-based covariances to balance salinity fields when no salinity data is available for assimilation. The results show that with no data assimilation areas of extreme salinity are not well maintained by the POAMA model. Assimilating only temperature data and making no salinity corrections is shown to yield salinity fields which are even less accurate than assimilating no data at all. Assimilating temperature data and making salinity corrections based on the cross-covariance information from the PEODAS ensemble gives salinity fields which are close to those yielded after full salinity data assimilation. These results suggest that the use of some form of salinity correction is essential for accurate salinity field modelling in the pre-Argo period, and demonstrate that the technique of correction via multi-variate ensemble-based covariance statistics provides accurate salinity fields and is suitable for use in historical reanalysis experiments. Description of Re-Analyses Conclusions PEODAS Salinity Data Pre- and Post-Argo Figure 1: Available raw global observations in the Enact EN3 data set (Ingelby et al. 2007) Figure 2: a) The locations of salinity observations in the Enact EN3 data set in b) The locations of salinity observations in the Enact EN3 data set in a) b) Alves, O., C. Robert, 2005: "Tropical Pacific Ocean model error covariances from Mont Carlo simulations" Quart. J. Roy. Meteor. Soc., 131, Ingelby, B., M. Huddelson, "Quality control of ocean temperature and salinity profiles — Historical and real-time data", Journal of Marine Systems, 65, Results Figure 5: Average salinity to 300m mean state ( ) deviation from FULL_S in the central Pacific. Contours are every 0.05 psu. Figure 4: Salinity mean state ( ) deviation from FULL_S with depth in the equatorial Pacific. Contours are every 0.05 psu. Figure 3: Salinity observations minus analysis for the TOGA-TAO mooring at 156°E 5°S. Contours are every 0.2 psu. Full Assimilation (FULL_S): Both salinity and temperature data are assimilated. No Salinity Update (NO_S): Temperature data are assimilated. No salinity assimilation or correction is applied No Salinity Assimilation (UPDATE_S): Temperature data are assimilated, but not salinity. The salinity state of the model is updated according to the multivariate covariance structure of the BECs. No Assimilation (CONTROL): A forced model integration over the same period as the re-analysis runs is used as a control run. No data are assimilated. Gould, J. 2005: "From swallow floats to Argo—The development of neutrally buoyant floats" Deep Sea Res., Part II, 52, 529–543. Maes, C., J. Picaut, 2005: "Importance of the Salinity Barrier Layer for the Build-up of El Nino", Journal of Climate, 18, Figure 3 compares the evolution of salinity fields in each re-analysis and that observed by a TOGA-TAO (McPhaden, M.J. 1995) mooring at 156°E 5°S. No data is available for the mooring from January to March The FULL_S run models the salinity profile accurately: deviations are below 0.2 psu for the majority of the period compared. UPDATE_S also shows good agreement with the observations, though with more deviation evident in the surface and sub-surface salinity maximum. NO_S shows widespread differences of greater than 0.4 psu fresher than the observation field, particularly in the sub-surface region ranging from 50m to 250m in depth, with areas up to 0.8 psu fresher evident. CONTROL is also generally fresher than the observations, with large differences at the surface after Sub-surface differences are also visible, though of a lesser magnitude to those of the NO_S run. Figure 4 shows the salinity mean state with depth along the equator for FULL_S over the reanalysis period, and the differences of each of the other reanalyses with FULL_S. The salinity fields of the FULL_S and UPDATE_S runs generally agree to within 0.1 psu, with the exception of two points at the eastern and western equatorial surface. These regions are also too fresh in the CONTROL and NO_S runs, and are believed to be due to the model not capturing the high fresh water flux of the regions correctly. The sub-surface maximum between 50m and 200m depth, with peak values reaching 35.5 psu in FULL_S, is not well maintained by the CONTROL run. NO_S shows a breakdown of the sub-surface maximum to an even greater extent than CONTROL, and additional over- estimation of deep water salinity. This is believed to be due to the lack of salinity corrections generating dynamical imbalances and mixing highly saline mid-depth waters with the fresher water below. In contrast, with no assimilation at all, CONTROL maintains a reasonable sub-surface structure, but is too fresh in both the west Pacific barrier layer and the sub-surface salinity maximum. Four re-analysis runs were conducted over the period 2000 to All assimilated data are taken from the Enact EN3 data set (Ingleby and Huddleston, 2006). Current increments are generated based on the background error covariances for all runs.