Cristian Salas 1 , Sebastian Vásquez 2 , Aquiles Sepúlveda 2 & Sergio Núñez 2

1. 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, Chile2Departamen of Geophysics, University of Concepción, Chile csalas@inpesca.cl 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: 1993-2014 • Domain: 7ºN - 44ºS y 69ºW - 129ºW • 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: 1993-2015 • Resolution spatial: 1° • Temporal resolution: each 10 days • 46 levels

5. External forcing: • 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 themodel • 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) (1994-2012). • 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 Vásquezet 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) (2011-2014). • 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 Vásquezet 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. . Satellite ROMS

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. Sp Sp S W Sp W S W S A A Summer Autumn Winter Spring A S 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!