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1 Variability of sea surface temperature diurnal warming Carol Anne Clayson Florida State University Geophysical Fluid Dynamics Institute SSTST Meeting.

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Presentation on theme: "1 Variability of sea surface temperature diurnal warming Carol Anne Clayson Florida State University Geophysical Fluid Dynamics Institute SSTST Meeting."— Presentation transcript:

1 1 Variability of sea surface temperature diurnal warming Carol Anne Clayson Florida State University Geophysical Fluid Dynamics Institute SSTST Meeting Seattle, WA 9 November 2010 Funded by NASA PO

2 Evolution of diurnal warm layers: typical diurnal ML example light winds (3.0 m/s) SST SST peak occurs about two hours after peak solar

3 Evolution of diurnal warm layers: typical diurnal ML example light winds (3.0 m/s) SST dSST = 1.3°C afternoon temperature profiles Day 1Day 4

4 Evolution of temperature profile over 6 hours Evolution of diurnal warm layers: stratified diurnal ML example Calm winds (0.5 m/s) SST SST peak occurs later TKE is minimal Development of temperature profile in model now dependent on background mixing parameters; absorption coefficient Increase of heat loss by 10 W m -2 can decrease the dSST by 0.2 o C due to increased mixing dSST = 3.4°C

5 Observations from TOGA COARE Soloviev and Lukas, 1997

6 Things we don’t know More rigorous validation, comparison of models needed Scaling of ε? As a function of u * ? What is profile of heat flux? At what depth does stratification impact mixing, at z/L? Length scales for anistropic turbulence? How to include in turbulence model? What affects the mixing length scale in this region? Can it be estimated solely by TKE and N, or do we need to include other parameters? Dissipation length scale?

7 dSST Algorithm PS = peak solar insolation (W/m 2 ) – ISCCP data P = daily averaged precipitation (mm/hr) -- GPCP U = 10 m daily averaged wind speed (m/s) -- SSM/I Wentz 11/20/9 9 11/22/9 9 11/21/9 9 11/23/9 9 22.29°C 4:10pm 20.94° C 7:40am Diurnal Warming (dSST) = 1.35°C 11/22/99 Clayson and Curry, 1996

8 Diurnal warming comparisons AMSR-E SeaFlux Collaborative work with others in GHRSST Diurnal Variability Working Group

9 Average dSST (1996- 2000) Larger average dSST values can be seen off of certain coastal regions and along the equator Maximum average dSST of 1°C observed directly off the western coast of the Galapagos Islands Influences from ITCZ and wind speed differences due to orography (gap flow, Central America) are noticeable Clayson and Weitlich, 2007 °C

10 Diurnal warming Peak diurnal warming of SST on March 18, 2000

11 Latent Heat Flux Difference (DV – PD), March 18, 2000 – 06ZLatent Heat Flux Percent Difference (DV – PD), March 18, 2000 – 06Z W/m 2 % Effects of diurnal SST

12 Sensible Heat Flux Difference (DV – PD), March 18, 2000 – 06ZSensible Heat Flux Percent Difference (DV – PD), March 18, 2000 – 06Z W/m 2 % Effects of diurnal SST

13 W/m 2 Annual Surface Energy Imbalance: 2000

14 Clear issues that need to be fixed Model underestimates at the very highest end (i.e., the maximum values peak at about 3 o C, not at 6 or 7 o C) Issues associated with parameterization: length of day simple sine curve used too linear initial temperature may need to be included heat flux loss not explicitly included uncertainties need to be estimated varying solar absorption profiles outgoing flux as an uncertainty?

15 Model improvements Ocean component: second moment turbulence closure model (Kantha and Clayson, 1994) – Modifications: inclusion of wave breaking turbulence, Langmuir circulation (Kantha and Clayson, 2004) – Also includes several possible skin models Multiple approaches analyzed... Finally: realization that dissipations in model are appropriate for thermocline, deep ocean, etc. At the surface layer: turbulence has shut down completely, only molecular processes are left

16 Model improvements However: vertical resolution is quite important! If too coarse: miss the peak values. If too high: numerical instability But: we don’t really have the measurements to back up what the real temperature profile should be (need also atmospheric parameters as well) So my solution: solve the heat conduction parabolic equation using Crank- Nicholson scheme with Neumann boundary conditions -- then see how coarse the resolution can be to match this

17 Some sample results Displacement of Peak SST

18 Some sample results Length of day

19 Some sample results Improved diurnal shape 6 AM 11 AM 4 PM 9 PM 2 AM 6 AM LOCAL TIME

20 Further work Comparison of new model with observations (but severely lacking in observations with all fluxes for esp. large events) New parameterization issues: length of day -- to be included simple sine curve used -- new curve developed too linear - use of neural net for nonlinearity initial temperature may need to be included -- small effect but possible to include heat flux loss not explicitly included -- will be included as a contributor to uncertainty uncertainties need to be estimated, e.g. varying solar absorption profiles outgoing flux


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