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Can In-Situ Floats and Satellite Altimeters Detect the Impact of Global Warming on Atlantic Ocean Overturning?. Josh K. Willis Jet Propulsion Laboratory. Physical Oceanography. This research powered by. Argo. Subsurface velocity level of known motion.
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Can In-Situ Floats and Satellite Altimeters Detect the Impact of Global Warming on Atlantic Ocean Overturning? Josh K. Willis Jet Propulsion Laboratory Physical Oceanography This research powered by
Subsurface velocity level of known motion Profile data provide geostrophic shear Argo Floats Limited by 2000 m isobath
Profile data ~27,000 profiles excluding those from WHOI floats with pressure errors
Argo Trajectories Density is always computed first! Displacements from different depths are combined using vertical shear to estimate 1000 db velocity
AVISO SSH Data Snapshots of SSH from AVISO
Combining profile data with Altimetry Regression Coefficient between SSH and density 500 m
Float Displacements and Altimetry “Pseudo-displacements” computed from AVISO Regression btwn SSH and displacements
2004 thru 2006 Time Mean • Willis and Fu, JGR, 2008 • Combined Altimeter data with • Argo profiles => 3-D density • Argo displacements =>1000 db dynamic height • 3-D Geostrophic velocity using density & 1000 db dynamic height as reference level Willis and Fu, JGR, 2008
2004 thru 2006 Time Mean Density Willis and Fu, JGR, 2008
2004 thru 2006 Time Mean Circulation @ 1000 db Willis and Fu, JGR, 2008
2004 thru 2006 Time Mean SSH Can we integrate, west to east? Geostrophic Velocity
Northward flow of surface water Difference dynamic height at 2000 m isobath NADW Return flow Boundary Current Separated 2004 thru 2006 Time Mean Steep Topography
Error ~ e12 + e22 Transport Errors No Argo data in the shallow regions
Errors Due to Lack of Shallow Data 0 – 1000 m Northward Transport from 18-km ECCO run with 2000 m isobath mask At these lats, lack of deep data doesn’t cause big errors RMS error in 3-month ave: 1.1 Sv
Temporal Variability • Again altimeter & Argo data => (Altimeter * regression coeff.) provide initial guess • 3-month time averages of 3-D dynamic height • Error bars => both mapping error & 1.1 Sv error due to lack of shallow data Willis and Fu, JGR, 2008
Data distribution near 40° N 3-month periods June, 2004 June, 2002 June, 2008 June, 2006
Trend: ~ 2.4 Sv increase Time Series at 41°N Mean: 15.5 Sv RMS: 2.4 Sv
Summary • AMOC at 41°N ~ 16 Sv • 2.4 Sv RMS variability • ~3 Sv error bar • Variability Mostly Ekman • No significant trend, 2002 to present • SSH => 2.4 Sv increase over 16 years • Argo and Altimetry complement other types of AMOC observations
Time Series of Density Maps June, 2002 June, 2004 0/1000 db steric height from density June, 2006
Time Series of 1000 db Velocity June, 2002 June, 2004 June, 2008 June, 2006
Time Series of Velocity at 39°N June, 2002 June, 2004 June, 2006 June, 2008
Mapping Density Covariance function for density Noise, length scale from fit
Mapping Displacements Covariance Functions Noise: .0013 (m/s)2 ~3.6 cm/s >> 0.2 cm/s
Float Displacements and Altimetry Regression Correlation
Computing subsurface displacements x x RMS error: 0.2 cm/s Park et al., JTECH, 2005
Computing Transport Errors 3-Year Mean Difference between independent estimates Same, normalized by (1-skill)
0 by definition for time mean Variance reduction in density from using a x SSH Mapping Density ρestimate = { ρprofile – α × SSH } + α × SSH
Mapping Density Covariance function for density 2-scale covariance function: <ρiρj> = ½ (1 + r + 1/6 r2 – 1/6 r3) exp(–r) + ½ exp(-R2) + n δij, r = sqrt[ (xi – xj)2 / lx2 + (yi – yj)2 / ly2 ], R = sqrt[ (xi – xj)2 / Lx2 + (yi – yj)2 / Ly2], From Rio and Hernandez, JGR-Oceans, 2004
Mapping Displacements 2-scale covariance function: <pipj> = [½ (1 + r + 1/6 r2 – 1/6 r3) exp(–r) + ½ exp(-R2)] × exp(-Ω)+ n δij, Ω = (ζi – ζj)2 / (ζi2 + ζj2) <uu>, <uv>, and <vv> derived from: As in Lavender et al., DSR-I, 2008