1. 2016 ROMS Asia-Pacific Workshop, Hobart, Australia
Spatio-temporal dynamics of the subtropical front in the southeastern Pacific: a modeling approach
Cristian Salas1, Sebastian Vásquez2, Aquiles Sepúlveda2 & Sergio Núñez2
1Instituto de Investigación Pesquera, Talcahuano, Chile 2Departamen of Geophysics, University of Concepción, Chile
2016 ROMS Asia-Pacific Workshop, Hobart, Australia

2. Outline
Hydrodynamic Model
Evaluation of the Hydrodynamic Model
Subtropical Front (Preliminary results)

3. Hydrodynamic Model
3D Model: ROMS_AGRIF v3.0 (Regional Ocean Modeling System) version IRD.
Interannual model:
Domain: 7ºN - 44ºS y 69ºW ºW
Spatial resolution: 10 km (~1/10°) and 610X587 grid points.
Temporal resolution: daily
Sigma levels: 32 σ leves

4. Atmospheric Forcing Boundary conditions NCEP-DOE Reanalysis 2
(Wind, Solar radiation, heat flux, etc.)
Characteristics:
Period: From 01/01/1948 until now.
Spatial resolution: 1.9°
Temporal resolution: each 6 hours
28 sigma levels
ECCO KalmanFilter/kf080
Ocean General Circulation Model (OGCM) combined with data in situ.
Characteristics:
Period:
Resolution spatial: 1°
Temporal resolution: each 10 days
46 levels

5. External forcing: Model outputs: Bathymetry: ETOPO2, resolution: 4 km
Tides: Global model TPXO7 (TOPEX/Poseidon)
Model outputs:
Temperature (T)
Salinity (S)
Currents (U y V)
Sea level (z)

6. Validation of the model
Using different data we validated and evaluated the model:
Satellite information, PATHFINDER y AQUARIUS
CTD data, oceanographic cruise WOCE
El Niño signal 3.4
Drifters from Global Drifter Program

7. Sea Surface Temperature (SST) evaluation.
Seasonal cycle of Sea Surface Temperature anomaly from PATHFINDER data and from ROMS outputs.
Season: summer (Dic-Feb), autumn (Mar-May), winter (Jun-Aug) and spring (Sep-Nov) ( ).
The model showed a good performance to reproduce the principal seasonal features in all the domain.

8. Sea Surface Temperature (SST) evaluation
The first two modes of variability for the principal components (PC) of SST explained a large percentage of the variance.
We will analyze the first two modes.
The percentage of the variance was: 55.8% for the model 38.9% for the satellite.

9. Sea Surface Temperature (SST) evaluation
The spatial and temporal modes of variability of the SST was calculated based on EOFs, and then compared to the Pathfinder dataset from 1994 to 2012.
The first PC of SST is consistent with observations in their spatial patterns.
The first mode of variability of the temporal PC for SST showed a high correlation level between the modeled and observed (0.93)
The methodology was based in EOF from Vásquez, 2012 and Vásquez et al,. 2012

10. Sea Surface Temperature (SST) evaluation
The second mode of variability was analyzed
The PC of SST is consistent with observations in their spatial patterns.
The second mode of variability of the temporal PC for SST showed a high correlation level between the model and observation satellites (0.84)

11. Sea Surface Salinity (SSS) evaluation
Seasonal cycle of Sea Surface Salinity anomaly from AQUARIUS data and from ROMS model outputs.
Season: summer (Dic-Feb), autumn (Mar-May), winter (Jun-Aug) and spring (Sep-Nov) ( ).
The model showed a good performance to reproduce the principal seasonal features in all the domain.

12. Sea Surface Salinity (SSS) evaluation
We analyze the first mode of variability for the Principal Components (PC) of SSS.
The percentage of the variance: was 22.8% for the model and 27.5% for the satellite data

13. Sea Surface Salinity (SSS) evaluation
The spatial and temporal modes of variability of the SSS were calculated based on EOFs, and then compared to the AQUARIUS dataset from 2011 to 2014.
The first PC of SSS is consistent with the observations in their spatial patterns.
The first mode of variability of the temporal PC for SSS showed a high correlation level between the model and the satellite data (0.63)
The methodology was based in EOF, from Vásquez, 2012 and Vásquez et al,. 2012

14. Model validation using CTD data
Longitudinal transect from the south of Peru
We realized a comparative analysis of model vertical structure.
We used the CTD data from the World Ocean Circulation Experiment (WOCE)
Chile: January 17, 2010 – February 09, 2010
Peru: April 24, 2009 – May 05, 2009
Longitudinal transect from central Chile

15. Model validation using CTD data
Temperature from the longitudinal transect of the south of Peru
We will analyze the vertical structure for ROMS and WOCE temperature. Latitude 16°45'S.

16. Model validation using CTD data
Salinity from the longitudinal transect of the south of Peru
The vertical structure analysis for ROMS and WOCE salinity, Latitude 16°45'S.

17. Model validation using CTD data
Temperature from the longitudinal transect of central Chile
The vertical structure analysis for ROMS and WOCE temperature, Latitude 32°30'S.

18. Model validation using CTD data
Salinity from the longitudinal transect of central Chile
The vertical structure analysis for ROMS and WOCE salinity, Latitude 32°30'S.

19. Model validation using CTD data
TS diagram from the longitudinal transect of the south of Peru
WOCE
ROMS
TS diagram from the longitudinal transect of central Chile
WOCE
ROMS

20. Model validation using El Niño 3.4 signal
Relationship between depth anomaly of the isotherm (20°C) and El Niño 3.4 signal in the equatorial region (105°W,5°S)
The 20°C ROMS isotherm depth [m] vs El Niño 3.4

21. Model validation using El Niño 3.4 signal
From an annual perspective we have analyzed the meridional extension of thermal anomalies for the first slice of 50 km wide from the shore. .
ROMS
Satellite
Warmer in 98 and coolder in 2007

22. Model validation using drifters to 25 m depth
Surface currents were mainly oriented eastwardly with a progressive velocity increment (from south of 36°S and west of 80°W) toward the higher latitudes, reaching maximum velocities around 45°S (~23 cm/s).
A secondary maximum is observed at 38°S and seems to be associated with the STF, with an average velocities around 18 cm/s.
Drifter ROMS model
Drifter Data

23. Spatial and temporal variability of the Subtropical Front (STF)2
Southeastern
Pacific Ocean
(SEP) 1 3
The regional circulation in the SEP is characterizes by the anticyclonic subtropical gyre.
This ocean current system it has:
The Chile Peru Current (CPC) flowing equatorward along the coast (Humbold Current).
The westward South Equatorial Current (SEC) north of 25°S
The eastward South Pacific Current (SPC) from 30–40°S. Considered an extension of the East Australian Current and is related to the Subtropical Front (STF) that separates relatively warm and salty Subtropical water from colder and fresher Subantarctic water. The STF surface velocities are characterized by a predominant flow towards the coast and high mesoscale activity, which could promote ocean–coast exchange

24. The STF generally coincides with a Sea Surface Salinity (SSS) in the range of 34.3 – 34.8 PSU (Stramma et al., 1995, Chaigneau & Pizarro, 2005)
From the monthly average ROMS outputs, we analyzed the seasonal variability of the STF mean position

25. S
Summer
Autumn
Winter
Spring A W Sp

26. We see strong variability interannual in longitude 80°W than in longitude 100°W.
In spring-summer the STF moves north and in autumn-winter moves south.
The yellow boxes remark La Niña 2007 and El Niño 2008 event, the brown boxes remark El Niño 1998 event.
During El Niño the position of the STF moves further south while during La Niña event the STF moves further north.

27. Biophysical model: The spatial distribution of successful jack mackerel individuals during the transportation process after 120 days from the spawning area of ​​Peru.

28. Thank you! Gracias!