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Linking sea surface temperature, surface flux, and heat content in the North Atlantic: what can we learn about predictability? LuAnne Thompson School of.

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Presentation on theme: "Linking sea surface temperature, surface flux, and heat content in the North Atlantic: what can we learn about predictability? LuAnne Thompson School of."— Presentation transcript:

1 Linking sea surface temperature, surface flux, and heat content in the North Atlantic: what can we learn about predictability? LuAnne Thompson School of Oceanography University of Washington Kathryn Kelly (UW/APL), James Booth (NASA/GISS), Maylis Garcia (NESTA, Paris) NASA Ocean Surface Topography Science Team

2 Question: Can the heat content be used to predict surface heat flux in the North Atlantic? Where and when? How does it compare against SST? Answer Use sea level as a proxy for upper ocean heat content to find: Basin wide interannual relationships Basin wide seasonal relationships

3 Net surface flux from the atmosphere to the oceans Watt/m2: implied ocean heat transport convergence, maximum around 30-40N Out of the ocean Into the ocean

4 Role of heat transport: mean balance
Heat is lost from the Gulf Stream to the atmosphere (about 0.2 PWatts) Maximum ocean meridional heat transport about 1 PW at about 20-25N Heat released to atmosphere Heat transported by the Ocean

5 Heat Budget (temporally varying)
Heat storage rate in upper ocean Surface heat flux Heat Transport Convergence + = Heat exchanged with atmosphere UinTin UoutTout Tocean Heat transported by the Gulf Stream

6 Dong and Kelly (2004) and Kelly and Dong (2004) for Gulf Stream
Local regional heat budget on interannual time scales behave similarly to the mean Dong and Kelly (2004) and Kelly and Dong (2004) for Gulf Stream Heat Transport Convergence Heat stored in ocean Heat loss to atmosphere Tocean increases

7 Heat transport convergence interannual variations on regional scales much larger than the surface heat flux Ratio of interannual surface heat flux to heat storage rate from ECCO2 in North Atlantic (300 km Gaussian smoother)

8 Using observations to look at the relationship between the heat content and surface flux smooth both with 500 km Gaussian smoother Observational Analysis variables Source Comment Sea surface height (SSH) Monthly maps of sea level anomaly from Ssalto/Duacs 1/3° x 1/3°, Mercator grid, Aviso Used as proxy for upper ocean heat content Turbulent heat flux Qturb And net suface heat flux Qnet OAflux: Objectively Analyzed air-sea fluxes for the Global Oceans (Yu and Weller, 2007) ISCCP for radiative fluxes Fluxes are positive for warming the ocean.

9 Using sea level as a proxy for heat content
Using sea level as a proxy for heat content (see also Jayne et al 2003) Local sea level determined by thermosteric (thermal expansion), and halosteric (haline contraction). Thermosteric dominates in tropics and subtropics Willis and Roemmich (2004). Seasonal correlations between upper 700 m ocean heat content (mostly XBTs) and sea level,

10 Interannual relationships between SSH and Heat flux
Mode Water North GS Eastern Basin

11 Mode Water: sea level leads turbulent heat flux by about 5 months

12 Correlation Sea level leads Heat Flux Leads
Lag Correlations between sea level (SST) and surface flux in mode water: both lead the surface flux by a few months on interannual time scales. (mode water) On monthly time scales, SSH has longer persistence and shows more predictive skill than SST. Sea Level auto correlation Heat flux auto Correlation Lagged correlation: sea level heat flux Sea Level auto correlation Heat flux auto Correlation Lagged correlation: sea level heat flux Sea level leads Heat Flux Leads Correlation Sea level leads Heat Flux Leads SST auto correlation Lagged correlation: SSH heat flux Lagged correlation: SST heat flux SST auto correlation Heat flux auto correlation Lagged correlation: SST heat flux

13 First year Second year SSH SST
Correlation of low frequency turbulent heat flux: typically SSH leadsby 3-5 months SST has more skill than SSH up to one year ahead SSH has more skill more than a year ahead First year Second year SSH SST

14 Question: where does the 3-5 month lead come from
Question: where does the 3-5 month lead come from? Could be seasonality in the relationship? Investigate the seasonal relationships between heat content and surface flux Make 12 times series, one for each month, of anomalous heat content and surface flux Investigate heat content and surface flux persistence Investigate correlations between heat content and surface flux between different seasons

15 Mode Water North GS Eastern Basin
-Turbulent heat flux Watt m-2 December July March

16 Perform the lagged correlations between surface flux and heat content and look for the places where SSH predicts surface flux (warmer ocean predicts heat flux out of the ocean) Mode Water North GS Eastern Basin December July March

17 Perform the lagged correlations between surface flux and heat content and look for the places where SST predicts surface flux (warmer ocean predicts heat flux out of the ocean) Mode Water North GS Eastern Basin December July March

18 Look for longest run of significant correlations for each months at each location
Length of run Months of predicatiblity correlation

19 Correlation

20 Regions and numbers of months of predictability for turbulent heat flux from sea level (heat content) for each month of the year

21 Length of patch of correlation

22 Correlation higher for SSH than SST in some places

23 SSH generally has more predictability than SST

24 Length of patch of correlation: generally longer for SSH than for SST

25 Conclusions The 20 year satellite altimeter record allows investigation of the role of regional upper ocean heat content in ocean-atmosphere interactions. The heat content (and SST) in the ocean is correlated with surface flux on interannual time scales in the Gulf Stream, with SSH leading by 3-5 months with a warmer ocean fluxing heat from the ocean to the atmosphere Both SST and SSH have predictive skill throughout the year and throughout the basin SST and SSH predictive skill depends on season SST and SSH have different patterns of seasonal predictive skill The length of the runs of high correlations and the correlation coefficient are generally higher for SSH than for SST Most of the signal comes from latent heat flux, with the correlations also seen in net surface heat flux (not shown). Mode water and northern recirculation gyre show large correlations between SSH and surface flux with heat content in the mode water controlling December surface flux, and northern recirculation gyre controlling July surface flux

26 Implication for coupling between ocean and atmosphere: preliminary results for clouds
Minobe et al (2008, 2010) showed that in the climatological mean, south of the GS, the SST forces the formation of mid-level clouds in winter, while north of the Gulf Stream is a region of stratus formation in the summer. We find: January latent heat flux correlated with mid-level cloud fraction 0.75 (0.29) July Latent heat flux (-) correlated with low-level cloud fraction (0.29) Local forcing of deep heating in summer not obvious in SST, possibly in SSH X X

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