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Lisan Yu Woods Hole Oceanographic Institution

Air-sea Interaction in the vicinity of ocean fronts - Perspective from OAFlux high-resolution analysis. Lisan Yu Woods Hole Oceanographic Institution. Acknowledgement: Dr. Xiangze Jin (WHOI).

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Lisan Yu Woods Hole Oceanographic Institution

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  1. Air-sea Interaction in the vicinity of ocean fronts - Perspective from OAFlux high-resolution analysis Lisan Yu Woods Hole Oceanographic Institution Acknowledgement: Dr. Xiangze Jin (WHOI)

  2. Objectively Analyzed air-sea Fluxes Project:OAFlux is a research project. Website: http://oaflux.whoi.edu • OAFlux synthesis takes into account of data errors in constructing air-sea fluxes of heat, moisture, and • momentum (least-squares estimation based on the Gauss-Markov theorem) • Global 1°-gridded flux analysis was released in 2008 and is currently maintained with 2-3 updates/year. • Efforts in recent years have been on high-resolution flux analysis for front-scale air-sea interaction. • Matured datasets are distributed freely online. • Evaporation • Latent and • Sensible heat fluxes • 1, 1958 onwards: available online • 0.25, 1987 onwards: validation mode • 0.25 1987 onwards (12 sensor synthesis) • Wind and • Wind Stress Online release in the coming fall • Net Heat flux • surface radiation • 1983 onwards: under development

  3. OAFlux 0.25° versus 1° Gulf Stream Spatial resolution matters for resolving atmosphere -ocean front interaction Kuroshio Extension Agulhas Current

  4. What is in the OAFlux 0.25° heat flux analysis? qa/ta derived from satellite sensors need to be bias corrected and gap filled before used in OAFlux synthesis. • The development of OAFlux 0.25° LH/SH analysis benefits from three recent products: • OAFlux 0.25° satellite-based vector wind analysis (1987 – present) (Yu and Jin, 2012, JGR-Oceans) • Jackson and Wick 0.25° satellite-derived qa/ta analysis (1999-2010) (Jackson and Wick, 2010) and GSSTFv3 qa (1987-1999) (Shie 2012). • OISST 0.25° daily analysis (Reynolds et al., 2007). Improve qa/ta estimates: bias is latitude dependent dry biased wet biased wet bias dominates the time series

  5. What is in the OAFlux 0.25° vector wind analysis? • A 12-sensor synthesis, daily, 0.25°, 1987-present QuikSCAT period (1) 9 passive microwave radiometers • SSMI – F08, F10, F11, F13, F14, & F15; • SSMIS – F16 & F17; • AMSRE • (2) 1 passivepolarimetric microwave radiometer • WindSat onboard Air Force Coriolis mission • (3) 2 Scatterometers • QuikSCAT – Ku band • ASCAT – C band Sensor types

  6. Frontal-scale Air-sea fluxes Winter-mean Latent and Sensible Heat Fluxes • Latest atmospheric reanalyses have improved spatial resolution • - ERAinterim (0.7°), • - MERRA (2/3°x1/3°), • - CFSR (0.3°). • - By comparison, NCEP/NCAR (1.875°) • Large differences exist between products. • Questions: • What can we learn from these products on the characteristics of frontal –scale atmosphere-ocean interaction? • What new insights can we obtain from OAFlux-0.25°? Part of research is in collaboration with NCEP/CFSR funded by NOAA MAPP on “Research to advance climate reanalysis”.

  7. OAFluxvsReanalysesBuoy Perspective LH (Buoy – Product) OA-0.25 OA-1 ERAi MERRA CFSR NCEP SH (Buoy – Product) OA-0.25 OA-1 ERAi MERRA CFSR NCEP

  8. There are differences between products Winter Mean LH+SH

  9. Correlation <SST, LH+SH> Winter (DJF), 1988-2010 Frontal air-sea interactionSST modulation on surface heat fluxes Thick black: Mean position of the 18C isotherm of SST Think black: 95% CI outlined High correlations between SST and LH+SH during winter seasons are located NW of the GS north wall.

  10. SST winter variability vs. SST-flux correlation SST STD Winter, 1988-2010 Corr <SST, LH+SH> Winter, 1988-2010 Location of maximum correlation is defined by the location of maximum SST variability.

  11. Winter <SST, LH+SH> superimposed onto the GS topography SST modulation on Heat Fluxes:Topographic effect 1988-2010

  12. Phase relationship between SST and fluxesProblem in SST data SST leads Flux Flux leads SST SST (from OISST ¼-deg) does not have daily variability; the time series is dictated by weekly variability. Perhaps the weekly 1-deg was used a background. The lack of daily variability in SST data hampers the determination of the phase relationship.

  13. Wind stress is also modulated by SST Corr <SST, WindStress> Corr <SST, LH+SH> Reanalyzed fluxes have a weaker correlation with their own SST

  14. Winter versus Annual Mean All months 1988-2010 Winter (DJF) 1988-2010

  15. Decadal changes in the GS regionwinter (DJF), 1988-2012 Response of heat fluxes to SST forcing less heat release; warming the sea surface more heat release; cooling the sea surface  

  16. Decadal changes in the GS regionwinter (DJF), 1988 - 2012 Response of wind stress to SST forcing

  17. Global connection: LH+SH

  18. Global connection: wind stresstrends and trend vectors

  19. On annual mean basis, the largest change in wind during the satellite era (1987 onwards) is the southern hemisphere westerly Wind Speed (W) Wind Stress ()   W2

  20. Shift in the SH westerly band Trends in N. Lat(x=0) (°Latitude per 10 yrs) Linear trends in x (10-2 Nm-2 per 10 yrs) (background colors) Trends in S. Lat(x=0): (°Latitude per 10 yrs)

  21. Poleward displacement of the ACC fronts from SSH The ACC front positions • Sokolov & Rintoul (2009): • Each of the ACC fronts has shifted to the south by about 60km, 1992-2007 • Rate of change = • 0.55°/16yrs  0.34°/10 yrs SSH N. Lat (x=0) SSH front ENSO signals included Annual Mean ENSO signals filtered out

  22. Can the ACC fronts influence the winds? 24-year Mean Wind Stress Curl (1988-2011) (positive: counterclockwise) Average of ~ 9000 daily means

  23. Stress curl bear the signature of ocean bathymetry (Smith and Sandwell, 1994) Eltaninand Udintsev Fracture Zone Drake Passage & South Georgia Ridge Eastern Indian Ridge (curl negative: clockwise)

  24. Influence of ocean topography on wind stress via SST 24-year average OAFlux Mean Stress Curl (positive: counterclockwise) Curl 24-year average AVHRR SST Magnitude of Mean SST Gradient (SST) |SST| Mean Ocean Dynamic Topography Maximenko and Niiler (2005) |SSH| Magnitude of Mean SSH Gradient (SSH)

  25. Summary - 1 • On flux data products: • Spatial resolution matters in resolving surface heat fluxes associated with ocean front/eddy variability. • The new high-resolution OAFluxanalysis do show improved accuracy and improved physical representation for frontal-scale air-sea interaction in the vicinity of ocean fronts/eddies. • CFSR produces better surface fluxes over the ocean front regions among all reanalyses, perhaps due to the semi coupled nature of the system.

  26. Summary - 2 • Perspective on the atmosphere-front interaction from the OAFlux 0.25° analysis: The GF and ACC regions • The GS region: • Influence of SST on surface flux variability in winter seasons is maximum in the area confined between the shelfbreak and the north wall of the GS, the area that features the largest SST variability in winter. • Significant changes in surface heat and momentum fluxes have been observed in the GS region during the satellite era of past 25 years. The changes are related to both local feedback and large-scale circulation pattern change. • The ACC region: • Wind stress curl shows the signature of ocean bathymetry. • Are SH westerly winds coupled with the ACC or a driver of the ACC?

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