Model Configurations

In MITgcm.jl, a model configuration is represented as a MITgcm_config. Model parameters are handled as ordered dictionaries and stored as TOML files. Standard model configurations are readily supported.

Note

Compiling MITgcm requires a fortran compiler. Some configurations further require installing MPI and NetCDF libraries.

Tip

The ECCO-Docker image has MITgcm.jl pre-installed, as well as gfortran, MPI, and NetCDF allowing to run any MITgcm configuration. The ECCO-Binder instance is available to try functionalities in the cloud, freely.

MITgcm_config

The data structure that enables MITgcm.jl is called MITgcm_config.

MITgcm.MITgcm_config — Type

MITgcm_config()

Concrete type of AbstractModelConfig for MITgcm (as part of the ClimateModels.jl interface for MITgcm) which contains

    model :: String = "MITgcm"
    configuration :: String = ""
    inputs :: OrderedDict{Any,Any} = OrderedDict{Any,Any}()
    outputs :: OrderedDict{Any,Any} = OrderedDict{Any,Any}()
    channel :: Channel{Any} = Channel{Any}(10) 
    folder :: String = tempdir()
    ID :: UUID = UUIDs.uuid4()

and can be constructed using keywords as follows

unknown_config=MITgcm_config(configuration="unknown")

Model Parameters

Model parameters can be stored as a TOML file.

read_toml(fil)

OrderedCollections.OrderedDict{Any, Any} with 20 entries:
  :main           => OrderedDict{Any, Any}(:PARM01=>OrderedDict{Any, Any}(:tRef…
  :autodiff       => OrderedDict{Any, Any}(:AUTODIFF_PARM01=>OrderedDict{Any, A…
  :cal            => OrderedDict{Any, Any}(:CAL_NML=>OrderedDict{Any, Any}(:The…
  :cost           => OrderedDict{Any, Any}(:COST_NML=>OrderedDict{Any, Any}())
  :ctrl           => OrderedDict{Any, Any}(:ctrl_nml=>OrderedDict{Any, Any}(:do…
  :diagnostics    => OrderedDict{Any, Any}(:diagnostics_list=>OrderedDict{Any, …
  :ecco           => OrderedDict{Any, Any}(:ECCO_COST_NML=>OrderedDict{Any, Any…
  :exch2          => OrderedDict{Any, Any}(:W2_EXCH2_PARM01=>OrderedDict{Any, A…
  :exch2_192cores => OrderedDict{Any, Any}(:W2_EXCH2_PARM01=>OrderedDict{Any, A…
  :exf            => OrderedDict{Any, Any}(:EXF_NML_01=>OrderedDict{Any, Any}(:…
  :ggl90          => OrderedDict{Any, Any}(:GGL90_PARM01=>OrderedDict{Any, Any}…
  :gmredi         => OrderedDict{Any, Any}(:GM_PARM01=>OrderedDict{Any, Any}(:G…
  :optim          => OrderedDict{Any, Any}(:OPTIM=>OrderedDict{Any, Any}(:optim…
  :pkg            => OrderedDict{Any, Any}(:PACKAGES=>OrderedDict{Any, Any}(:us…
  :profiles       => OrderedDict{Any, Any}(:PROFILES_NML=>OrderedDict{Any, Any}…
  :salt_plume     => OrderedDict{Any, Any}(:SALT_PLUME_PARM01=>OrderedDict{Any,…
  :seaice         => OrderedDict{Any, Any}(:SEAICE_PARM01=>OrderedDict{Any, Any…
  :smooth         => OrderedDict{Any, Any}(:SMOOTH_NML=>OrderedDict{Any, Any}(S…
  :eedata         => OrderedDict{Any, Any}(:EEPARMS=>OrderedDict{Any, Any}(:use…
  :setup          => OrderedDict{Any, Any}(:main=>OrderedDict{Any, Any}(:catego…

ECCO solutions

Global ocean model configurations used in NASA's ECCO ocean state estimation program.

ECCO4 : Forget et al., 2015 (doi:10.5194/gmd-8-3071-2015)
OCCA2 : Forget, 2024 (under review).

Warning

Running these solutions requires at least 16 cores (preferably 96) and downloading forcing fields (96G at least). To try with a smaller model solution, see Verification Experiments.

MITgcm.setup_ECCO4! — Function

setup_ECCO4!(config::MITgcm_config)

Setup method for ECCO4 and OCCA2 solutions.

using MITgcm

params=read_toml(:ECCO4)
folder=joinpath(pwd(),"tmp1")

MC=MITgcm_config(inputs=params,folder=folder)

#providing executable (optional)
#push!(MC.inputs[:setup][:main],(:exe => joinpath(pwd(),"mitgcmuv")))

#providing input folder (optional)
#push!(MC.inputs[:setup][:main],(:input_folder => joinpath(pwd(),"input_folder")))

#modifying run time options (optional)
#MC.inputs[:pkg][:PACKAGES][:useECCO]=false

setup(MC)

#modifying build options (optional)
#MC.inputs[:setup][:build][:options]=MITgcm.build_options_pleiades

build(MC)

launch(MC)

MITgcm.ECCO4_inputs.download_input_folder — Function

download_input_folder(config::MITgcm_config)

MITgcm.ECCO4_inputs.get_files — Function

get_files(list1::DataFrame,nam1::String,path1::String)

Create a list of Dataverse files from folder with specified name.

list1=ECCO4_inputs.get_list()
nam1="model initialization"
ECCO4_inputs.get_files(list1,nam1,tempname())

MITgcm.ECCO4_inputs.get_list — Function

get_list(; write_file=false)

Create a list of Dataverse folders for ECCOv4r2. If write_file=true then write to file joinpath(tempdir(),"Dataverse_list.csv")/

get_list(list1::DataFrame,name::String)

Create a list of Dataverse files from folder with specified name.

list1=ECCO4_inputs.get_list()
nam1="surface forcing fields"
list2=ECCO4_inputs.get_list(list1,nam1)

MITgcm.ECCO4_testreport.compute — Function

compute(pth0)

@everywhere begin
 using MITgcm
 using ECCO4_testreport.SharedArrays
end

report=ECCO4_testreport.compute("run")

MITgcm.ECCO4_testreport.compare — Function

compare(A::DataFrame,B::DataFrame)

using ClimateModels.DataFrames, ClimateModels.CSV
ref_file="test/testreport_baseline2.csv"
ref=CSV.read(ref_file,DataFrame)

using MITgcm
ECCO4_testreport.compare(report,ref)

compare(A::DataFrame,B::DataFrame,v::AbstractString)

Verification Experiments

The MITgcm/verification sub-folder of the MITgcm source code provides a suite of small model configurations, often used by model developers.

To use them, you first want to use MITgcm_download to get the source code.

using MITgcm
MITgcm_download()
MITgcm_path[1]

"/home/runner/.julia/scratchspaces/dce5fa8e-68ce-4431-a242-9469c69627a0/src/MITgcm_test"

To list of these model configurations is provided by verification_experiments.

ves=verification_experiments()
[ve.configuration for ve in ves]

8-element Vector{String}:
 "MLAdjust"
 "advect_cs"
 "advect_xy"
 "aim.5l_cs"
 "flt_example"
 "hs94.cs-32x32x5"
 "tutorial_global_oce_biogeo"
 "tutorial_held_suarez_cs"

To try one, you want to use MITgcm_config as follows.

MITgcm_config(configuration="MLAdjust")

  ID            = df335690-5fc4-42c4-9880-3681aa97fe49
  model         = MITgcm
  configuration = MLAdjust
  run folder    = /tmp/df335690-5fc4-42c4-9880-3681aa97fe49
  log subfolder = /tmp/df335690-5fc4-42c4-9880-3681aa97fe49/log

Functionalities

MITgcm.MITgcm_download — Function

MITgcm_download()

Download default, compact version of MITgcm from zenodo.

MITgcm.MITgcm_path — Constant

MITgcm_path

Path to a MITgcm folder. MITgcm_path[1] should generally be used. MITgcm_path[2] is mostly meant to facilitate comparisons between e.g. MITgcm releases when needed.

MITgcm.verification_experiments — Function

verification_experiments()

Get list of all most-standard configurations of MITgcm and return as an Array of MITgcm_config

exps=verification_experiments()

verification_experiments(nam::String)

Get one configurations of MITgcm and return as a MITgcm_config

advect_xy=verification_experiments("advect_xy")

MITgcm.setup_verification! — Function

setup_verification!(config::MITgcm_config)

Setup method for verification experiments.

MITgcm.testreport — Function

testreport(config::MITgcm_config,ext="")

Run the testreport script for one model config, with additional options (optional) speficied in ext

using MITgcm
testreport(MITgcm_config(configuration="front_relax"),"-norun")
#testreport(MITgcm_config(configuration="all"),"-norun")