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Recently, I’ve been trying to remove model drift from my high-resolution CCSM 20th century climate change simulation. The model drift is estimated using the two companion control runs, HRC08 and HRC09. All three runs were spun up from a similar initial condition, with CO2 held fixed in year 2000 level. Following Gupta et al., [2013]1, we tried to identify the model drift by fitting a cubic polynomial to the full record of control runs (nearly 70 years).


Testing the new features of Hugo-Academic theme…


It’s been a while since I started using git. The version control system helps to track changes in computer files and coordinating work on those files among multiple people. Composing a manuscript is nothing unlike developing a program. If multiple authors work on the same manuscript, git can be very helpful to avoid conflicts. If you are the only author, git can help you to document what has been changed since the last commit or put your advisor’s comment in another branch.


One of the many advantages of Python is its abundant and often powerful Libraries. For my research, besides plotting maps, I often play with time series. When it comes to manipulating and plotting time series, no other tools can beat python pandas.


Recently, I’m looking into the Meridional Heat Transport across the 35$^{\circ}$S in our high-resolution CCSM simulation following Dong et al. 2011. To calculate the heat transport, the following formula is used:

\begin{equation} \int \rho C_{p}v\theta dx dz \end{equation}

The tricky part is whether one should use temporal and spatial-varying values of $C_{p}$ (seawater heat capacity) and $\rho$ (seawater density). Since density and heat capacity are not among the standard outputs, I went on looking for some standard libraries to calculate them using the fields I have: temperature, pressure, and salinity. Soon, I found the Python version of Gibbs SeaWater Oceanographic Package.


Recent Talks

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Agulhas leakage variability in a coupled climate system: controls and responses
Wed, Apr 19, 2017 12:00 AM
1-hour seminar
Wed, Apr 5, 2017 12:00 AM
Assessing the skill of 30-day climate model output for Lagrangian analyses of Agulhas Leakage
Thu, Sep 24, 2015 12:00 AM


Climate changes in the high-resolution 20th century simulation

We noticed that Agulhas leakage is indeed higher in the year 2000 CO2 level control runs, but no significant increasing trend in the climate change simulation. It might be due to the equilibrium timescale of westerlies to changing CO2, or an internal bias of our high-res CCSM.

Large-scale forcing dominates interannual variability of Agulhas leakage

The interannual variability of Agulhas leakage is accessed in an ocean eddy resolving coupled simulation to test the hypothesis that, on such timescales large-scale forcing dominates leakage variability, regardless of eddy structures.

Quantifying Agulhas leakage in a high-resolution coupled climate model

The senstivity of leakage estimates to the model output velocity frequency needs to be addressed before we can apply it to investigate its link to climate variability.


I am a teaching assistant for the following courses at University of Miami:

I have completed following courses:

  • MPO 503 Physical Oceanography
  • MPO 511 Geophysical Fluid Dynamics I
  • MPO 551 Intro to Atmospheric Science
  • MPO 611 Geophysical Fluid Dynamics II
  • MPO 612 Large-scale Ocean Circulation
  • MPO 665 General Circulation of Atmosphere
  • MPO 583 Special Topics in Climate study
  • MPO 668 ENSO Dynamic
  • MPO 524 Applied Data Analysis
  • MPO 675 Mesoscale Oceanography
  • MGG 676 Paleoclimatology

Course Projects:

Seminar Talks:


  • NASA Summer School on Satellite Observations and Climate Models, JPL/Caltech, 2017
  • METEOR 1002 cruise from Namibia to Mauritius, 2013
  • Community Earth System Model Tutorial, NCAR, Boulder, CO, 2013