Example usage

To use malta in a project:

A simple run of constant emissons of 10 Gg of CFC-11 with zero initial conditions from 2010-2020 inclusive could be:

import numpy as np
from malta import model

start_year = 2010
end_year = 2021
species = "CFC11"

# Set up and run model
years = np.array([str(yr) for yr in range(start_year,end_year)])
emistot = np.repeat(10, len(years))
emissions = model.create_emissions(species, emistot)
sink = model.create_sink(species)
ds_out = model.run_model(years, emissions, sink) 

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
Cell In[1], line 2
      1 import numpy as np
----> 2 from malta import model
      4 start_year = 2010
      5 end_year = 2021

File ~/checkouts/readthedocs.org/user_builds/malta/envs/latest/lib/python3.9/site-packages/malta/model.py:5
      3 import numpy as np
      4 from malta import transport
----> 5 from malta import utils
      6 import xarray as xr
      7 import pandas as pd

File ~/checkouts/readthedocs.org/user_builds/malta/envs/latest/lib/python3.9/site-packages/malta/utils.py:2
      1 # Auxillary routines for 2D model
----> 2 import xarray as xr
      3 import json
      4 import os

File ~/checkouts/readthedocs.org/user_builds/malta/envs/latest/lib/python3.9/site-packages/xarray/__init__.py:3
      1 from importlib.metadata import version as _version
----> 3 from xarray import testing, tutorial
      4 from xarray.backends.api import (
      5     load_dataarray,
      6     load_dataset,
   (...)
     10     save_mfdataset,
     11 )
     12 from xarray.backends.zarr import open_zarr

File ~/checkouts/readthedocs.org/user_builds/malta/envs/latest/lib/python3.9/site-packages/xarray/testing.py:11
      8 import pandas as pd
     10 from xarray.core import duck_array_ops, formatting, utils
---> 11 from xarray.core.coordinates import Coordinates
     12 from xarray.core.dataarray import DataArray
     13 from xarray.core.dataset import Dataset

File ~/checkouts/readthedocs.org/user_builds/malta/envs/latest/lib/python3.9/site-packages/xarray/core/coordinates.py:16
     13 import pandas as pd
     15 from xarray.core import formatting
---> 16 from xarray.core.alignment import Aligner
     17 from xarray.core.indexes import (
     18     Index,
     19     Indexes,
   (...)
     23     create_default_index_implicit,
     24 )
     25 from xarray.core.merge import merge_coordinates_without_align, merge_coords

File ~/checkouts/readthedocs.org/user_builds/malta/envs/latest/lib/python3.9/site-packages/xarray/core/alignment.py:24
     22 from xarray.core.types import T_Alignable
     23 from xarray.core.utils import is_dict_like, is_full_slice
---> 24 from xarray.core.variable import Variable, as_compatible_data, calculate_dimensions
     26 if TYPE_CHECKING:
     27     from xarray.core.dataarray import DataArray

File ~/checkouts/readthedocs.org/user_builds/malta/envs/latest/lib/python3.9/site-packages/xarray/core/variable.py:48
     30 from xarray.core.pycompat import (
     31     integer_types,
     32     is_0d_dask_array,
   (...)
     35     to_numpy,
     36 )
     37 from xarray.core.utils import (
     38     OrderedSet,
     39     _default,
   (...)
     46     maybe_coerce_to_str,
     47 )
---> 48 from xarray.namedarray.core import NamedArray
     50 NON_NUMPY_SUPPORTED_ARRAY_TYPES = (
     51     indexing.ExplicitlyIndexed,
     52     pd.Index,
     53 )
     54 # https://github.com/python/mypy/issues/224

File ~/checkouts/readthedocs.org/user_builds/malta/envs/latest/lib/python3.9/site-packages/xarray/namedarray/core.py:24
     22 from xarray.core import dtypes, formatting, formatting_html
     23 from xarray.namedarray._aggregations import NamedArrayAggregations
---> 24 from xarray.namedarray._typing import (
     25     _arrayfunction_or_api,
     26     _chunkedarray,
     27     _DType,
     28     _DType_co,
     29     _ScalarType_co,
     30     _ShapeType_co,
     31 )
     32 from xarray.namedarray.utils import (
     33     _default,
     34     is_duck_dask_array,
     35     to_0d_object_array,
     36 )
     38 if TYPE_CHECKING:

File ~/checkouts/readthedocs.org/user_builds/malta/envs/latest/lib/python3.9/site-packages/xarray/namedarray/_typing.py:28
     24 _T = TypeVar("_T")
     25 _T_co = TypeVar("_T_co", covariant=True)
---> 28 _DType = TypeVar("_DType", bound=np.dtype[Any])
     29 _DType_co = TypeVar("_DType_co", covariant=True, bound=np.dtype[Any])
     30 # A subset of `npt.DTypeLike` that can be parametrized w.r.t. `np.generic`

TypeError: 'numpy._DTypeMeta' object is not subscriptable

It is then simple to plot the outputs using the simple plotting tools for xarray’s datasets:

ds_out.burden.plot()

Or, for example, the global mean mole fraction at the surface could be plot using the ‘weighted’ functionality:

ds_out[species][:,0,:].weighted(np.cos(np.deg2rad(ds_out.lat))).mean("lat").plot()

To change how the emissions are distributed in space, we can allocate this by, for example:

emissions = model.create_emissions(species, emistot, distribute="gdp")

which distributes the 10 Gg of emissions by the latitudinal dependence on gdp.

Initial conditions (of dimensions altitude x latitude) can be passed to the model using the ics keyword, for example using the final mole fraction from the previous run:

ics = ds_out[f"{species}_end"][-1,:,:]
ds_out = model.run_model(years, emissions, sink, ics=ics) 
ds_out[species][:,0,:].weighted(np.cos(np.deg2rad(ds_out.lat))).mean("lat").plot()

By far the slowest part of transport is the convection scheme as it involves solving a matrix.
If you want to speed up the run, there is little loss of accurace for many long-lived substances (lifetime of a year or more) by turning off the convective scheme by setting ics=False:

ds_out = model.run_model(years, emissions, sink, ics=ics, convection=False) 
ds_out[species][:,0,:].weighted(np.cos(np.deg2rad(ds_out.lat))).mean("lat").plot()