https://github.com/markmbaum/geoclim.jl
Estimation of global silicate weathering for paleoclimate and exoplanet climate applications
https://github.com/markmbaum/geoclim.jl
climate exoplanets julia paleoclimate weathering
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Estimation of global silicate weathering for paleoclimate and exoplanet climate applications
- Host: GitHub
- URL: https://github.com/markmbaum/geoclim.jl
- Owner: markmbaum
- License: gpl-3.0
- Created: 2021-10-20T20:45:44.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2022-11-03T17:01:46.000Z (over 3 years ago)
- Last Synced: 2025-03-23T23:16:22.916Z (over 1 year ago)
- Topics: climate, exoplanets, julia, paleoclimate, weathering
- Language: Julia
- Homepage:
- Size: 2.83 MB
- Stars: 5
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# GEOCLIM.jl 🌋🌎🪨
[](https://github.com/markmbaum/GEOCLIM.jl/actions)
[](https://codecov.io/gh/markmbaum/GEOCLIM.jl)
[](https://doi.org/10.5281/zenodo.5784232)
## Global Silicate Weathering Estimation
This module replicates some features of the [GEOCLIM](https://geoclimmodel.wordpress.com/) model, originally written in Fortran, but now in Julia to make them easier to use. It also extends the original weathering equations, now including
* WHAK (named after [Walker, Hayes & Kasting](http://doi.wiley.com/10.1029/JC086iC10p09776))
1. Ignoring direct dependence on pCO2, as seen in [Goddéris et al.](https://doi.org/10.1038/ngeo2931), [Donnadieu et al.](https://doi.org/10.1029/2006GC001278), and elsewhere
2. Including direct pCO2 dependence, as seen in [Abbot et al.](https://doi.org/10.1088/0004-637X/756/2/178) and elsewhere
* MAC (named after [Maher & Chamberlain](https://doi.org/10.1126/science.1250770)) as seen in [Graham & Pierrehumbert](https://doi.org/10.3847/1538-4357/ab9362)
The module implements these formulations to estimate global silicate weathering rates from gridded climatology, typically taken from the results of a global climate model like [CCSM](https://www.cesm.ucar.edu/models/ccsm4.0/) or [FOAM](https://www.mcs.anl.gov/research/projects/foam/). It is intended to estimate weathering during periods of Earth history when the continental configuration was radically different, typically more than 100 million years ago. For more information about the original GEOCLIM, see the Methods/Supplement of [Goddéris et al.](https://doi.org/10.1038/ngeo2931)
The module also includes a number of handy functions for computing things based on the land-sea mask of the climatologies, like
* land/ocean fraction
* area-weighted average
* area-weighted sum
* mean distances to ocean
* land mass perimeter
The first three can also be restricted to latitude bands symmetric about the equator.
------
### Install
The module won't be put in Julia's general registry. If you want to easily install, add it with the package manager using the url.
```
julia> ] add https://github.com/markmbaum/GEOCLIM.jl
```
It's recommended that you do so in an [environment](https://pkgdocs.julialang.org/v1.2/environments/).
------
#### Weathering Functions
There are three weathering functions corresponding to the formulations listed above
1. `godderis(r, T, k, Eₐ, T₀)`
2. `whak(r, T, pCO2, k, Tₑ, T₀, pCO2₀, β=0.2)`
3. `mac(r, T, pCO2, Tₑ, T₀, pCO2₀;
n=0.316,
Λ=1.4e-3,
L=1.0,
ϕ=0.1,
ρ=12728.0,
k₀=8.7e-6,
𝐀=1e2,
X=0.36,
tₛ=1e5,
m=0.27,
μ=exp(2),
β=0.2)`
where `r` is runoff and `T` is temperature. You can find explanations of all the other arguments and their units in the [main source file](https://github.com/markmbaum/GEOCLIM.jl/blob/main/src/GEOCLIM.jl) or the referenced papers. These primary functions have no type restrictions.
------
#### Climatologies
The rest of the package is focused on two dimensional grids of results from GCM simulations and is structured around the `Climatology` type. Read GCM results into a `Climatology` by calling the constructor
```
Climatology(fnr, #runoff file name
vr, #runoff variable name
nullr, #runoff value interpreted as null
convr, #conversion factor applied to runoff values
fnT, #temperature file name
vT, #temperature variable name
fnf, #land fraction file name
vf; #land fraction variable name
fnlat="", #file where cell latitudes can be found (empty will use the runoff file)
latname="lat") #name of latitude vector
```
where the file names point to NetCDF files.
Then each of the weathering functions can be called on a `Climatology` by passing the struct instead of `r` and `T`, returning a global sum of weathering in each grid cell.
For example, to compute the global `mac` weathering estimate with a climatology variable called `clim`,
```
mac(clim, pCO2, Tₑ, T₀, pCO2₀)
```
The temperature and runoff are already in the struct, so you only need to provide the other weathering arguments.
------
#### Climatology Interpolation and Weathering Equilibria
Multiple climatologies can be linked into a `ClimatologyInterpolator` to easily perform cell-wise interpolation. The constructor is
```
ClimatologyInterpolator(𝒞::AbstractVector{Climatology}, x::AbstractVector{<:Real})
```
where `x` contains the independent variable you want to interpolate over. For example, if you have several simulations that are identical except for the atmospheric CO2 concentration, `x` could be `log10(pCO2)`.
Then you can find the root of some function applied to interpolated climatology by defining the function `f(C,x)`, where `C` is a climatology, and passing it to the `findequilibrium` function. For example, to find the CO2 concentration where global weathering balances some value, you could
```julia
#make an interpolator, assuming you already have three Climatology structs
logpCO2 = [1., 2., 3.]
I = ClimatologyInterpolator(C, logpCO2) #C is a Vector{Climatology}
#define the weathering operation using mac, where x is log10(pCO2)
w(x,c) = mac(c, exp10(x)*1e-6, 11.1, 288.15, 285e-6)
#find the log10(CO2) value where weathering balances 5e4 moles/second
findequilibrium(I, w, 5e4)
```