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Observations and Modeling of Rain-Induced Near Surface Salinity Anomalies William Asher, Kyla Drushka, Andrew Jessup Ruth Branch, and Dan Clark Applied.

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Presentation on theme: "Observations and Modeling of Rain-Induced Near Surface Salinity Anomalies William Asher, Kyla Drushka, Andrew Jessup Ruth Branch, and Dan Clark Applied."— Presentation transcript:

1 Observations and Modeling of Rain-Induced Near Surface Salinity Anomalies William Asher, Kyla Drushka, Andrew Jessup Ruth Branch, and Dan Clark Applied Physics Laboratory University of Washington Seattle, Washington NASA Aquarius Mission: Grant NNX09AU73G

2 Why are salinity gradients at the ocean surface important? Mis-match between SMOS/Aquarius with respect to measurement depths ARGO float salinity profile Microwave penetration depth Ebuchi and Abe, IGARSS, 2014

3 How do salinity gradients form? Measure  S as a function of U, R etc.

4 Can their presence be predicted from surface meteorological measurements? Predict  S from remote measurements of U, R etc.

5 Measuring salinity gradients: The Surface Salinity Profiler (SSP) Towed from ship Follows surface at tow speeds of 2 m/s Rides outboard of wake Instruments mounted on rigid keel Instrumented with: 0.05 m Seabird 49 CTD 0.20 m Seabird 49 CTD 1.00 m Seabird 49 CTD 2.00 m Seabird 19 CTD

6 Measuring salinity gradients in the equatorial Pacific Ocean using the SSP SSP deployed from the R/V Kilo Moana Cruise conducted December 6-16, 2011 Sailed from Apia, Western Samoa to Honolulu, Hawaii Deployed the SSP a total of eight times Sampled 3 rain events

7 SSP Observation of Rain, Equatorial Pacific R/V Kilo Moana, December 13, N, W Deployment #6

8 SSP Observation of Rain, Equatorial Pacific R/V Kilo Moana, December 13, N, W Deployment #6

9 SSP Observation of Rain, Equatorial Pacific R/V Kilo Moana, December 13, N, W Deployment #6

10 SSP Observation of Rain, Equatorial Pacific Deployment #4

11 SSP Observation of Rain, Equatorial Pacific Deployment #4

12 Salinity gradients and rain rate  S = S 0.3 – S 0.1

13 Correlation of density gradient with rain rate  S = S 0.3 – S 0.1  =  0.3 –  0.1 Unstable stratification Stable stratification

14 Salinity anomaly vs. wind speed  S = S 0.3 – S 0.1

15 Salinity anomaly vs. wind speed  S = S 0.3 – S 0.1 Asher et al. (2014), J. Geophys. Res., doi: /2014JC009954

16 Comparing SSP data with GOTM: Salinity SSP Data KM-11 Deployment #6 GOTM v4.2 (devel.) k-  turbulence model

17 Comparing SSP data with GOTM: Salinity Details at 5 – 10 cm GOTM data from 10 cm SSP data from 10 cm GOTM data from 5 cm SSP data from 10 cm

18 Comparing SSP data with GOTM: Temperature GOTM v4.2 (devel.) k-  turbulence model SSP Data KM-11 Deployment #6

19 Comparing SSP data with GOTM: Temperature Details at 5 – 10 cm

20 Comparing SSP data with GOTM: Salinity Scaled rain rate SSP Data KM-11 Deployment #6 GOTM v4.2 (devel.) k-  turbulence model Rain rate corrected for ship speed

21 Comparing SSP data with GOTM: Temperature Scaled rain rate GOTM v4.2 (devel.) k-  turbulence model Rain rate corrected for ship speed SSP Data KM-11 Deployment #6

22 Comparing SSP data with GOTM: Scaled rain rate Temperature Salinity

23 1.Salinity anomaly magnitudes in the top meter of the ocean surface depend on both rain rate and wind speed 2.Existing stratification has an affect on the salinity anomaly 3.A 1-d vertical turbulence model reproduces the general features of the salinity anomaly, but … 4.There are significant differences for the temperature anomaly 5.It is not clear if the differences between the model and the measurements are due to problems in the model or caused by incorrect model input data Conclusions: Modeling results

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