Parameters and instruments A. Proshutinsky, Woods Hole Oceanographic Institution
Lisan Yu Woods Hole Oceanographic Institution
description
Transcript of Lisan Yu Woods Hole Oceanographic Institution
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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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Objectively Analyzed air-sea Fluxes Project:OAFlux is a research project. Website:
http://oaflux.whoi.edu
• 1, 1958 onwards: available online•0.25, 1987 onwards: validation mode
•Net Heat flux•surface radiation
•Evaporation•Latent and •Sensible heat fluxes
•Wind and •Wind Stress
• 0.25 1987 onwards (12 sensor synthesis)
• 1983 onwards: under development
Online release in the coming fall
• 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.
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Spatial resolution matters for resolving atmosphere -ocean front interaction
OAFlux 0.25° versus 1°Gulf Stream
Kuroshio Extension
Agulhas Current
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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:(1) OAFlux 0.25° satellite-based vector wind
analysis (1987 – present) (Yu and Jin, 2012, JGR-Oceans)
(2) Jackson and Wick 0.25° satellite-derived qa/ta analysis (1999-2010) (Jackson and Wick, 2010) and GSSTFv3 qa (1987-1999) (Shie 2012).
(3) OISST 0.25° daily analysis (Reynolds et al., 2007).
What is in the OAFlux 0.25° heat flux analysis?
bias is latitude dependent
dry biased
wet biased
wet bias dominates the time series
Improve qa/ta estimates:
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What is in the OAFlux 0.25° vector wind analysis?
(1) 9 passive microwave radiometers• SSMI – F08, F10, F11, F13, F14, & F15;• SSMIS – F16 & F17;• AMSRE
(2) 1 passive polarimetric microwave radiometer• WindSat onboard Air Force Coriolis mission
(3) 2 Scatterometers• QuikSCAT – Ku band • ASCAT – C band
Sensor types
QuikSCAT period
A 12-sensor synthesis, daily, 0.25°, 1987-present
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Frontal-scale Air-sea 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”.
Winter-mean Latent and Sensible Heat Fluxes
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OAFlux vs Reanalyses
Buoy Perspective
LH (Buoy – Product)
OA-0.25 OA-1 ERAi MERRA CFSR NCEP
OA-0.25 OA-1 ERAi MERRA CFSR NCEP
SH (Buoy – Product)
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There are differences between products
Winter Mean LH+SH
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Frontal air-sea interaction SST modulation on surface heat fluxes
High correlations between SST and LH+SH during winter seasons are located NW of the GS north wall.
Think black:95% CI outlined
Thick black:Mean position of the 18C isotherm of SST
Correlation <SST, LH+SH> Winter (DJF), 1988-2010
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SST winter variability vs. SST-flux correlation
Location of maximum correlation is defined by the location of maximum SST variability.
SST STDWinter, 1988-2010
Corr <SST, LH+SH>Winter, 1988-2010
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Winter <SST, LH+SH> superimposed onto the GS topography
1988-2010
SST modulation on Heat Fluxes:Topographic effect
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Phase relationship between SST and fluxes
Problem in SST data
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.
Flux leads SSTSST leads Flux
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Wind stress is also modulated by SST
Corr <SST, LH+SH> Corr <SST, WindStress>
Reanalyzed fluxes have a weaker correlation with their own SST
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Winter versus Annual Mean
All months1988-2010
Winter (DJF)1988-2010
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Decadal changes in the GS region
winter (DJF), 1988-2012Response of heat fluxes to SST forcing
more heat release;cooling the sea surface
less heat release;warming the sea
surface
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Decadal changes in the GS region
winter (DJF), 1988 - 2012Response of wind stress to SST forcing
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Global connection: LH+SH
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Global connection: wind stresstrends and trend vectors
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On annual mean basis, the largest change in wind during the satellite era
(1987 onwards) is the southern hemisphere westerlyWind Speed (W)
Wind Stress ()
W2
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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)
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Poleward displacement of the ACC fronts from SSH
Annual Mean
SSH
ENSO signals included
ENSO signals filtered out
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
The ACC front positions
N. Lat (x=0)
SSH front
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24-year Mean Wind Stress Curl (1988-2011) (positive: counterclockwise)
Can the ACC fronts influence the winds?
Average of ~ 9000 daily means
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Stress curl bear the signature of ocean bathymetry
Drake Passage &South Georgia Ridge
Eltanin and Udintsev Fracture Zone
Eastern Indian Ridge
(Smith and Sandwell, 1994)
(curl negative: clockwise)
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Mean Stress Curl (positive: counterclockwise)
Magnitude of Mean SST Gradient (SST)
Magnitude of Mean SSH Gradient (SSH)
Influence of ocean topography on wind stress via SST
24-year average OAFlux
24-year average AVHRR SST
Mean Ocean Dynamic TopographyMaximenko and Niiler (2005)
|SSH|
|SST|
Curl
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Summary - 1
On flux data products:
Spatial resolution matters in resolving surface heat fluxes associated with ocean front/eddy variability.
The new high-resolution OAFlux analysis 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.
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Summary - 2
Perspective on the atmosphere-front interaction from the OAFlux 0.25° analysis: The GF and ACC regions
The GS region:1) 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.
2) 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:1) Wind stress curl shows the signature of ocean bathymetry.
2) Are SH westerly winds coupled with the ACC or a driver of the ACC?