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Role of SSS in MJO Predictability and Development of Sub-Monthly SSS Products
[28-Aug-2018] Zhu, J., Ren, L., Kumar, A., Murtugudde, R., and Xie, P.
Presented at the 2018 Ocean Salinity Science Team and Salinity Continuity Processing Meeting
In this presentation, we will give an update of our project supported by NASA's Ocean Salinity Science Team, by focusing on the first two tasks: (1) role of SSS in MJO predictability, and (2) development of sub-monthly SSS products.
For Task 1, based on the NCEP CFSv2, we have completed three sets of MJO prediction experiments. For each experiment, 45-day predictions are made every 5 days starting from November 1 of each of the 10 model years till the end of following March (totally, 31 cases for each winter), with 5 ensemble members applied by perturbing the atmospheric initial conditions. The three experiments, each having 10 years * 31 cases * 5 members (=1550) 45-day predictions, are referred to as CTL, noSST, and noSSS, respectively. In CTL, the forecast model is CFSv2 itself, and these runs are used for estimating the MJO potential predictability in CFSv2. In noSST (noSSS), the SST (SSS) feedback is damped by strongly nudging model SST (SSS) to its climatological states. The role of SSS in MJO predictability is assessed by comparing their MJO prediction skill in terms of the real-time multivariate MJO (RMM) indices. The assessment is supplemented by comparing intraseaonal SST evolutions among three experiments.
For Task 2 about the development of sub-monthly sea surface salinity (SSS) products, we developed the algorithm for blending the satellite and in situ measurements of SSS on a 5-days temporal and 1° latitude/longitude spatial resolution. The input data sets for this product are in situ SSS measurements aggregated and quality controlled by NOAA/NCEI, and passive microwave (PMW) retrievals from both the NASA Soil Moisture Active Passive (SMAP) and the European Space Agency's (ESA) Soil Moisture?Ocean Salinity (SMOS) satellites. This algorithm comprises two steps. First, the biases in the satellite retrievals are removed through probability distribution function (PDF). Then, the blended analysis is achieved through optimal interpolation (OI). So far, more than one year 5-day SSS data (starting from January 5, 2017 to current date with 4 days delay) has been processed.

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