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= Project work: source code, results =

[[/Gruppe1]] [[/Gruppe2]] [[/Gruppe3]] [[/Gruppe4]] [[/Gruppe5]]
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[[/Lesson1|Introduction and references for download]]
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== Scenario == == Scenario 1 ==
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== Source code == = Lesson 2: Radiative fluxes =
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[[/Gruppe1]] Radiative flux measurements are available from the [[http://science.larc.nasa.gov/ceres/index.html| Clouds and the Earth s Radiant Energy System (CERES) sensor]]. [[http://eosweb.larc.nasa.gov/PRODOCS/ceres/level3_syn-avg-zavg_table.html|Zonally averaged gridded products]] will be used to validate flux parameterizations.
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Execute with '''ipython -pylab''' and '''run model1.py''':
{{{#!python
from pylab import *
#model1.py
#LK 2010

# model setup time
iter=1000 # number of iterations
dt=60*60*24.0 # time step one day [s]

#model setup space
h_ml=10. # oceanic mixed layer depth [m]

# physical constants
T0=273.13 # zero degrees in [K]
sig=5.67e-8 # Stefan-Boltzmann constant [W/m^2/K^4]
# sea water
albedo_sw=0.06 # short wave oceanic albedo
eps_sw=0.97 # long wave oceanic emissivity
sig=5.67e-8 # Stefan-Boltzmann constant [W/m^2/K^4]
rho_sw=1024.0 # sea water density
Cp_sw=4000. # Oceanic heat capacity [J/Kg/K]

#Forcing data
I=400.0 # short wave insolation [W/m^2]

#Prognostic variables
T_ml=zeros(iter) # oceanic mixed layer temperature [K]
T_ml[0]=12.0 # initial temperature


#Model integration
for i in range(iter-1):
    Q1=(1-albedo_sw)*I # absorbed short wave radiation
    Q2=-eps_sw*sig*(T_ml[i]+T0)**4 # emitted long wave radiation
    
    T_ml[i+1]=T_ml[i]+(((Q1+Q2)*dt)/(h_ml*Cp_sw*rho_sw))

    if i==500: # Change forcing data after 600 days
        I=380.0

# Display results
figure(1)
plot(T_ml)
xlabel('Time [days]')
ylabel(r'Temperature $T_{ml}$ [$^\circ$ C]')
text(50,12.5,r'Initial temperature $T_{ml}=12^\circ$C')
text(50,12.7,r'Short wave insolation $I=400$ W/m$^2$')
text(500,14.0,r'Short wave insolation $I=380$ W/m$^2$')
savefig('lesson1_results1.png',dpi=75)

# Display 1/e time
T_ml_scaled=(T_ml[501:999]-T_ml[999])/(T_ml[501]-T_ml[999]) # Normalize to 0-1
figure(2)
plot(T_ml_scaled)
grid()
axis('tight')
yticks((1,1/e),('1','1/e'))
xlabel('Time after day 500 [days]')
ylabel(r'Normalized temperature $T_{ml}^*$')
savefig('lesson1_results2.png',dpi=75)
show()
}}}

== Simulation results ==
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[[/Lesson1|References for download]]

Sea ice 2

Lecture, exercises and practical by Jun.-Prof. Dr. Lars Kaleschke

  • Monday 13:30-15:00
  • Room ZMAW 022

Description of the course

The lecture will cover the thermodynamic coupling between the sea ice, the ocean, and the atmosphere. It is designed for master-level students with moderate knowledge in numerics, scientific programming, and sea ice physics. A conceptual model of the Arctic will be derived and simulation results will be analysed. For didactical reasons the model will be developed from scratch and kept as simple as possible, but complex enough to learn about the basic principles of the thermodynamic interaction between the ocean, the ice and the atmosphere for climatic, oceanographic and meteorological studies.

Acknowledgments

This lecture is based on content taken from a lecture Sea ice modeling by Aike Beckmann (Univ. Hamburg, Summer 2009) and a short course on Ice-Ocean Modeling and Data Assimilation which was conducted by Frank Kauker and Michael Karcher (Univ. Bremen, 6-7 December 2006).

Project work: source code, results

/Gruppe1 /Gruppe2 /Gruppe3 /Gruppe4 /Gruppe5

Lesson 1 - Ocean mixed layer and radiative forcing without sea ice and atmosphere

Scenario 1

mixed_layer.png

  • Ocean mixed layer forced by shortwave radiation only
  • No atmosphere
  • No exchange with deeper ocean layers, immediate mixing
  • Heat balance at the sea surface: Short wave incoming radiation + long wave outgoing radiation

    latex error! exitcode was 2 (signal 0), transscript follows:
    
    
  • Numerics: forward-in-time integration, finite differences
  • latex error! exitcode was 2 (signal 0), transscript follows:
    
    

Research questions

Compute the time evolution of the ocean mixed layer temperature T_ml(t) for different h_ml, initial temperatures T_ml(t=0), and short wave insolation Q_SW.

Estimate the typical time scale for stationarity and select appropriate time step delta_t for model integration.

Change insolation after the model has reached stationarity.

Lesson 2: Radiative fluxes

Radiative flux measurements are available from the Clouds and the Earth s Radiant Energy System (CERES) sensor. Zonally averaged gridded products will be used to validate flux parameterizations.

Literature

References for download

Maykut, G.A. & N. Untersteiner, 1971: Some results from a time-dependent thermodynamic model of sea ice. J. Geophys. Res.,76, 1550-1575.

Semtner, A., 1976: A model for the thermodynamic growth of sea ice in numerical investigations of climate, J. Phys. Oceanogr, 6, 379-389.

Hibler III, W.D., 1979: A dynamic-thermodynamic sea ice model. J. Phys. Oceanogr., 9, 815-846.

Parkinson, C.L. & W.M. Washington, 1979: A large-scale numerical model of sea ice., J. Geophys. Res., 84, 311-337.

LehreWiki: SeaIce2 (last edited 2010-06-21 15:10:36 by anonymous)