seager19

Model run results: https://wandb.ai/sdat2/seager19

Docker image for gfortran/gcc/cdf/conda: https://hub.docker.com/repository/docker/sdat2/seager19

Purpose

A repository to contain, analyze, and expand upon the model from:

Seager et al. 2019, Nature Climate Change, Strengthening Tropical Pacific Zonal Sea Surface Temperature Gradient Consistent with Rising Greenhouse Gases

https://doi.org/10.1038/s41558-019-0505-x

Summary of their paper

There is a west-east warm-to-cold contrast in the Pacific.
State-of-the-art GCMs predict weakening contrast with GHG concentration.
In observations, it has increased.
Their simple linear equatorial coupled model agrees with observations when forced with ECMWF reanalysis product.
When forced with CMIP5 ensemble relative humidity and windspeed their model can reproduce the bias, highlighting these fields as the mechanism through which it is created.

Citation

The citation for their paper is:

@article{seager2019strengthening,
  title={Strengthening tropical Pacific zonal sea surface temperature gradient consistent with rising greenhouse gases},
  author={Seager, Richard and Cane, Mark and Henderson, Naomi and Lee, Dong-Eun and Abernathey, Ryan and Zhang, Honghai},
  journal={Nature Climate Change},
  volume={9},
  number={7},
  pages={517--522},
  year={2019},
  url={https://doi.org/10.1038/s41558-019-0505-x},
  publisher={Nature Publishing Group}
}

Podcast

The paper is discussed in a podcast available at Deep Convection Season 1 Episode 5:

Specifically the time period 33:00-44:30.

Code

The code and data were taken from a Columbia University website:

This website is blocked from some IP addresses, such as the Cambridge VPN (probably my fault), and so you may need to try a different connection method if it doesn't load.

Setup

Get the Github repository

    git clone https://github.com/sdat2/seager19.git

    cd seager19

If you have root access and can install Linux packages

    # Enforcing these versions is necessary to be able to use 70s fortran.
    # If you would rather not change your version, you might be better off
    # Using the docker image instead.

    sudo apt-get install gfortran-4.8

    sudo apt-get install gcc-4.8

    sudo apt-get install --fix-missing libnetcdf-dev libnetcdff-dev

    sudo apt-get install cloc  # brew install cloc for mac

If you want to make the docker environment yourself

    docker build . -t sdat2/seager19:g4.8

    docker push sdat2/seager19:g4.8

If you need to install the singularity environment

    
    # on jasmin you would need to make a new tmp directory in your home directory
    # to be able to install the quite large singularity environment e.g:

    mkdir /home/users/sithom/tmp

    # and then call the command to create the singularity object

    TMPDIR=/home/users/sithom/tmp SINGULARITY_CACHEDIR=/home/users/sithom/tmp singularity pull docker://sdat2/seager19:g4.8

    # and then to launch the envirnonment

    singularity run seager19_g4.8.sif

Making the environment and testing it works (either in singularity or not)

    # in case conda isn't activated:

    conda init bash

    source ~/.bash_profile

    # activate the environment

    conda activate ./env/

    # test that the environment and package work before using:

    make test        # Also downloads the data if needed.

Add optional features

    make jupyter_pro  # adds timing etc.

    make vscode_pro  # sets up nice autodoc, pylint etc.

    make jupyter_dark  # dark mode for jupyter notebooks

    # and to reverse this:

    make jupyter_light

Examples of running the model

    # default run:

    python src/main.py name=cd_2.25 coup.c_d=2.25e-3

    # Sweep through different levels of Raleigh friction

    # values near the value in the paper
    python src/main.py atm.eps_days=1.05,0.95,0.85,0.75,0.65,0.55 -m

    python src/main.py atm.eps_days=1.7,1.8,1.85 -m 

    python src/main.py atm.eps_days=0.7,0.8 -m 


    # values from other papers quoted in the introduction to Romps (2014) "Raleigh Damping in the Free Troposphere"
    python src/main.py atm.eps_days=1.25,1.8,2,2.5,3,5,10 -m

    python src/main.py atm.eps_days=2.1,1.75,1.9 -m

    # Seager91, Matsuno66, Yu97, Gill80, Chang82, Sugiyama09, Wu00
    # breaks at 5, 10: Sugiyama09, Wu00. Breaks through tau.
    # looks pretty strange at 3: Chang82.

    python src/main.py atm.k_days=7,8,9,10 -m

    # uncoupled run without syncing:
    python src/main.py name=it_1 coup.iterations=1 coup.c_d=2.25e-3 wandb=false

To look at the commands to relicate the paper figures, see replicate.md.

Other handy commands for development of repo

Make a notebook with helpful magic functions for dark mode

    make notebook name=your-notebook-name

New python script

    make py name=src/to-new.py

Update docs of the src directory

    make autodoc

Check where a variable is referenced in the ocean model

   grep -R "f1prime" ocean/SRC

Get CMIP5 multimodel means from Columbia

http://kage.ldeo.columbia.edu:81/SOURCES/.LDEO/.ClimateGroup/.PROJECTS/.IPCC/.CMIP5/.MultiModelMeans/.MMM-v2.3/.historical/.Surface/data.cdf

http://kage.ldeo.columbia.edu:81/SOURCES/.LDEO/.ClimateGroup/.PROJECTS/.IPCC/.CMIP5/.MultiModelMeans/.MMM-v2.3/.rcp85/.Surface/data.cdf

CMIP6/CMIP5 bias mechanism data

https://docs.google.com/spreadsheets/d/1QrCLil7uHMRJECOoSL18uk2mvBwMLxqesXbZpUWh3ko/edit?usp=sharing

Do the tropical cyclone genesis indices track this bias in nino3.4 as we would expect? I.e does the bias propagate into hazards with which there are teleconnections?

Calculate tropical cyclone indices on reanalysis, and try and find comparable results to check it has been correctly coded.
See if we can replicate the results from Carmargo Emanuel and Sobel, 2007 that there is a link with sheer.
Calculate on CMIP6 ensemble members in the most efficient manner.
Do the biases in CMIP6 ensemble members influence their trend in tropical cyclone genesis indices?

Some initial paper suggestions from Prof. Dan Chavas (Purdue University)

Nice overview of the genesis indices: https://onlinelibrary.wiley.com/doi/full/10.1002/wcc.373?casa_token=bEDmf7kYfGIAAAAA%3Al64qyjk7TIhh-YAEx3zwj7CCWk_LJh4JKaew8lFlix8v0Yz5jeCfoCat1oYexnILblokoqYUPsPpC5c
Hoogewind et al 2020: https://journals.ametsoc.org/view/journals/clim/33/5/jcli-d-18-0862.1.xml -- our paper using absolute vorticity and Ventilation Index; we looked at their joint distribution but you could just multiply them together.
Tang and Emanuel 2012: https://journals.ametsoc.org/view/journals/bams/93/12/bams-d-11-00165.1.xml
Camargo et al 2014: https://journals.ametsoc.org/view/journals/clim/27/24/jcli-d-13-00505.1.xml -- shows that mid-level saturation deficit may be superior to mid-level relative humidity

Question: Do the tropical cyclone genesis indices track this bias in nino3.4?
Hypothesis: The trend in the tropical cyclone genesis indices corresponds to that which would have been expected given the trend in nino3.4 and is statistically significant, and robust to the start year of the trend.
H0: There is no significant trend in the tropical cyclone genesis indices over the North Atlantic.

@article{camargo2016tropical,
  title={Tropical cyclones in climate models},
  author={Camargo, Suzana J and Wing, Allison A},
  journal={Wiley Interdisciplinary Reviews: Climate Change},
  volume={7},
  number={2},
  pages={211--237},
  year={2016},
  publisher={Wiley Online Library}
}

@article{camargo2007use,
  title={Use of a genesis potential index to diagnose ENSO effects on tropical cyclone genesis},
  author={Camargo, Suzana J and Emanuel, Kerry A and Sobel, Adam H},
  journal={Journal of Climate},
  volume={20},
  number={19},
  pages={4819--4834},
  year={2007}
}

@article{hoogewind2020exploring,
  title={Exploring controls on tropical cyclone count through the geography of environmental favorability},
  author={Hoogewind, Kimberly A and Chavas, Daniel R and Schenkel, Benjamin A and O’Neill, Morgan E},
  journal={Journal of Climate},
  volume={33},
  number={5},
  pages={1725--1745},
  year={2020},
  publisher={American Meteorological Society}
}

@article{tang2012ventilation,
  title={A ventilation index for tropical cyclones},
  author={Tang, Brian and Emanuel, Kerry},
  journal={Bulletin of the American Meteorological Society},
  volume={93},
  number={12},
  pages={1901--1912},
  year={2012},
  publisher={American Meteorological Society}
}

@article{camargo2014testing,
  title={Testing the performance of tropical cyclone genesis indices in future climates using the HiRAM model},
  author={Camargo, Suzana J and Tippett, Michael K and Sobel, Adam H and Vecchi, Gabriel A and Zhao, Ming},
  journal={Journal of Climate},
  volume={27},
  number={24},
  pages={9171--9196},
  year={2014}
}

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seager19's Introduction