Oceanographic Data Analysis Laboratory Kathie Kelly Applied Physics Laboratory 515 Ben Hall IR Bldg class web site facultywashingtonedukellyaplclassesocean5692014 Applying Analysis Tools ID: 240221
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Oceanography 569Oceanographic Data Analysis Laboratory
Kathie KellyApplied Physics Laboratory515 Ben Hall IR Bldgclass web site: faculty.washington.edu/kellyapl/classes/ocean569_2014/Slide2
Applying Analysis Tools
practice exercise for projectSlide3
Sea Level Variability in the Eastern Mediterranean Sea
Data:
% sshm - weekly sea surface height anomaly (SSH)
% Qnet - daily net surface heat flux (OAFlux)
% alf _rho_cp - alpha/(rho*cp)
% where alpha is the coefficient of thermal expansion
% c
p
is the specific heat of sea water, rho is density of sea water
% txqs, tyqs - daily QuikSCAT wind stress components% coastline - lon, lat for graphics
Model:
%
thermal expansion from seasonal heating (vertical integral of temperature,
converted to SSH using the coefficient of thermal expansion)
%
% d(SSH)/dt = alf*Qnet/(rho*c_p)
%
Other candidate (for statistical comparison):
%
wind stressSlide4
Sea Level Variability in the Eastern Mediterranean Sea
Look at time-longitude
to determine obvious signals
Seasonal cycle apparent
No evidence of RW propagationSlide5
Sea Level Variability in the Eastern Mediterranean Sea
Run the heating model and compare with observed SSHSlide6
Sea Level Variability in the Eastern Mediterranean Sea
Run the heating model and compare with observed SSH
One outcome:
Assess skillSlide7
Sea Level Variability in the Eastern Mediterranean Sea
Compare winds with (nonseaonal) SSH
Match integral time scales of ssh and winds (approximately)Slide8
Sea Level Variability in the Eastern Mediterranean Sea
What analysis tools do we have for comparing data without a model?
Compare winds with (nonseaonal) SSHSlide9
Sea Level Variability in the Eastern Mediterranean Sea
Compare winds with (nonseaonal) SSH
SSH 1 and stress 1 correlated with zero lag:
Northward winds
high sea level
SSH 2 and stress 2 marginally correlated
Stress 1 leads stress 2:
Northward winds lead cyclone (eastward propagation of storms)
Overall: heating accounts for much of seasonal cycle
Rest is wind-driven, but mechanism unclearSlide10
Applying Analysis Tools
practice exercise for projectSlide11
ProjectsChose either project 1 or 2Use analysis tools from class (including models for the project)Write up results in a slide presentation (approximately 30 min)
Do NOT include your Matlab code – think of this as a seminar!Include in presentation: What is the science question?What is your analysis plan? Why?What special considerations are there? What tools did you use? models, statistical procedures, etcShow metrics: skill, significance tests, etcWhat did you conclude? Why?Show data examples, analysis outcomes, etcSlide12
Project 1: What Causes SST Variability in the Gulf Stream?
Data: SST, climatological MLD, Gulf Stream path, winds
One dimensional
mixed layer model:
dT/dt = (Q-qrad)/(rho*cp*h)
where qrad is the radiative flux leakage for the given climatological MLD h
1) Climatological analysis:
Do you see climatological errors? (errors that recur each year)
Is there a pattern to the errors?
How would you correct for an error in climatological Q/h?Slide13
Project 1: What Causes SST Variability in the Gulf Stream?
2) Analyze residual:
dT/dt(model) - dT/dt(obs)What are the errors in the heating model?Other candidates: Gulf Stream path changes Winds How do each of these affect SST?Slide14
Project 1: What Causes SST Variability in the Gulf Stream?
What are the missing terms in this mixed layer model?
Without writing a new model can you relate these candidate variables to the residual? (statistical relationships) What are some ways that wind can effect dT/dt?(Note: changes in wind speed are accounted for in Q)How does the change in Gulf Stream path affect dT/dt?Slide15
Project 2: What causes SSH anomalies in tropical Atlantic?
Candidates:
surface heating
(OAFlux/ISCCP net surface flux)
wind-forced Rossby waves
(QuikSCAT wind stress curl)Slide16
Project 2: What causes SSH anomalies in tropical Atlantic?
response to surface heating:
dη/dt = Qnet*α/(rho*c_p)
wind-forced Rossby waves:
dη/dt = c*dη/dx –r*η -C*curl
where
c
is the phase speed of the Rossby waves and r is a damping factor (eddy diffusion)
RW model needs an eastern boundary condition (because waves travel westward), so use SSH interpolated to the model t gridSlide17
Project 2: What causes SSH anomalies in tropical Atlantic?
More information:
RW model does not include thermosteric sea level, so remove heating response from SSH first
RW model has some fungible parameters, g’, r (damping), and phase speed, c.
Estimate phase speed, but adjust to reduce errors. Reduced gravity (g’) can be adjusted also.
The damping factor (alf/r) has been optimized so no need to change it.