On the relative importance of Argo, SST and altimetry for an ocean reanalysis

Transcription

1 Document prepared on February 20, 2007 for the Argo Steering Team Meeting (AST-8), Paris March 7-9 On the relative importance of Argo, SST and altimetry for an ocean reanalysis Peter R. Oke and Andreas Schiller Bluelink 1 is an Australian partnership between CSIRO, the Bureau of Meteorology and the Royal Australian Navy. The Bluelink ocean model is the Ocean Forecasting Australia Model (OFAM), a global configuration of MOM4.0d with 1/10 o resolution around Australia ( o E, south of 30 o N). We assimilate observations into OFAM using the Bluelink Ocean Data Assimilation System (BODAS), an ensemble Optimal Interpolation (EnOI; Oke et al. 2002; 2006) system that has been developed under Bluelink. BODAS is an economical version of a simplified ensemble Kalman filter (using a stationary ensemble). We use BODAS to assimilate observations of sea-level anomaly (SLA), sea-surface temperature (SST) and in situ temperature (T) and salinity (S) from various sources including Argo and SOOP-XBT. T and S profiles are sourced from the USGODAE and Coriolis web-servers and from the Enact data-base (Ingleby and Huddleston 2006). Under Bluelink we have performed a series of ocean reanalyses that we refer to as the Bluelink ReANalyses (BRAN). Results from the first BRAN experiment (BRAN1.0), a 14-year reanalysis that assimilated SLA and in situ T and S (no satellite SST), are described by Oke et al. (2005). The most recent BRAN experiment (BRAN1.5) is a 3.5-year reanalysis that assimilates SLA, SST and T and S profiles. BRAN1.5 SST is typically within 0.7 o of observed SST, with anomaly correlations of around 0.7; and BRAN1.5 sea-level anomalies are typically within 10 cm of independent (with-held from assimilation) along-track altimetry, with anomaly correlations of about 0.7. In addition to BRAN1.5, we have performed a series of Observing System Evaluations (OSEs). These OSEs include no Argo 2, no Altimetry and no SST, where each component of the global ocean observing system (GOOS) is systematically withheld. We have also performed experiments with only 1, or 2 altimeters. Each OSE is a 6-month integration covering the period January to June Comparisons with independent 3 AMSRE-SST and along-track altimetry (Topex/Poseidon was with-held for validation) observations indicate that the first month or two of each OSE involves an adjustment, when the skill of the reanalysis equilibrates (Figure 1). The comparisons in Figure 1 lead us to the following conclusions: 1. Assimilation of SST is essential to obtain skill in SST, as we might expect. 2. The neglect of either Argo observations or SST has minimal impact on the skill of the SLA fields, provided altimetry is assimilated. We note that the difference between the OSE with no assimilation and with no altimetry indicates that the assimilation of Argo and SST significantly improves the reanalysed SLA field when altimetry is with-held, reducing the SLA errors by about 4 cm Here we include XBT profiles in with Argo. 3 Only every 7 th AMSRE-SST field is assimilated. 1

2 Figure 1 is restricted to an assessment of the OSEs on SST and SLA 4. To assess the relative importance of each component of the GOOS on the sub-surface T and S fields, we make the assumption that BRAN1.5, with all data types assimilated, is the closest to the true ocean state. That is, we regard it as the best available measure of the true ocean state. We therefore regard the difference between BRAN1.5 and the OSEs to be an indicator of the error introduced by with-holding the component of the GOOS. The metric we have used here is the depth averaged RMS difference between each OSE and BRAN1.5 over the upper ocean (between the surface and 1000 m depth) and between March and June 2006 (the last 4 months of each OSE), based on daily mean fields. We only show results for the region of the model that has resolution higher than 1 o. The depth averages of the RMS difference for T and S over the upper ocean are shown in Figure 2 and 3 respectively. Clearly, a longer integration of each OSE is desirable since it would probably eliminate any eddy-scale features from these comparisons. However, the model-assimilation system is too computationally expensive to reasonably permit this at the present time. The results (Figure 2 and 3) indicate that for this application: 1. All three components of the GOOS, namely SST, Argo and altimetry, have an important and unique contribution to the reanalysis of upper-ocean T and S fields. 2. Argo appears to be the most important component of the GOOS in the tropical Indian Ocean for constraining upper-ocean T; and also appears to be the most important component in the Kuroshio extension and in the Arabian Sea for constraining both the upper-ocean T and S fields. Clearly the relative importance of Argo is closely linked to the distribution of floats (Figure 2d). 3. Altimetry appears to be the most important component of the GOOS in the Tasman Sea for constraining the upper-ocean T and S fields. 4. Both altimetry and Argo appear to be equally important for constraining S off north-western Australia and off the Java-Sumatra coast in BRAN SST appears to be the most important component of the GOOS in the Indonesian Seas and in the shallow coastal waters around Australia (e.g., Gulf of Carpentaria, Great Barrier Reef, Great Australian Bight). These regions are where altimetry and Argo do not return reliable (or any) observations. These conclusions should be regarded as preliminary. A detailed analysis of these OSEs is ongoing. This study must also be accompanied by a number of caveats. The impact of each data type in a reanalysis depends on a number of factors that include the method of assimilation and the error estimates ascribed to each observation type. It is possible that with additional tuning of error estimates, the impact of with-holding a component of the GOOS may change significantly. The assimilation system developed under Bluelink is necessarily sub-optimal. It has been developed with through-put and robustness in mind. As a result we expect that observations are typically under-fitted. That is, we expect that with more tuning and optimisation, each data type could be used more effectively. This may modify the relative importance of each of the data types addressed in this study. 4 Comparisons with independent in situ T and S profiles are ongoing. 2

3 References Ingleby, B., and M. Huddleston (2006), Quality control of ocean temperature and salinity profiles - historical and real-time data. To appear in Journal of Marine Systems. Oke, P. R., J. S. Allen, R. N. Miller, G. D. Egbert and P. Michael Kosro, 2002: Assimilation of surface velocity data into a primitive equation coastal ocean model. Journal of Geophysical Research, 107(C9), Oke, P. R., A. Schiller, D. A. Griffin, G. B. Brassington 2005: Ensemble data assimilation for an eddy-resolving ocean model. Quarterly Journal of the Royal Meteorological Society, 131, Oke, P. R., P. Sakov, and S. P. Corney 2006: Impacts of localisation in the EnKF and EnOI: Experiments with a small model. Ocean Dynamics, 57, Figure 1: Time series of RMS model-observation difference for AMSRE-SST (top) and alongtrack altimetric sea-level anomaly (bottom) in the Australian region ( o E, 60 o S-10 o N) during 2003 for BRAN1.5 (with Argo, altimetry and SST assimilated) and each of the OSEs. 3

4 Figure 2: Depth average ( m) of the RMS difference between potential temperature in each OSE and in BRAN1.5 for the period March-June 2003 (left); and the zonal average (right). Panel (d) also shows the distribution of Argo floats at the start of May

5 Figure 3: As for Figure 2, except for salinity. 5