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FORCeS eScience course 2021 - “Tools in Climate Science: Linking Observations with Modelling”

My Final report

Anne Fouilloux

annefou-at-uio.no

Assistant: XXX

Final Report

Abstract

Write a short abstract here. 1/2 page max should be sufficient.

Acknowledgements

The computing resources used for this course have been kindly provided by Galaxy europe in the framework of the EOSC-Nordic and RELIANCE projects. The storage resources were provided by UNINETT Sigma2 - the Norwegian National Infrastructure for High Performance Computing and Data Storage in Norway as part of NS1000K project.

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Introduction

Introduce your topic and how you are planning to tackle it. This section is about 1 page maximum.

You can add formula in a markdown cell:

\[ \int_0^\infty \frac{x^3}{e^x-1}\,dx = \frac{\pi^4}{15} \]

You can also have formula \(x^2 + y^2\) embedded in your text.

Using markdown to write text

• You can use markdown

• or MyST markdown works in Jupyter Notebooks as well. For more information about MyST markdown, check out the MyST guide in Jupyter Book, or see the MyST markdown documentation.

Note

Here is a note. It will render “properly” when generating your pages with jupyter-book using:

   jupyter-book build eScienceCourse_2021_AnneFouilloux/ --builder singlehtml

Method

Import Python packages

import xarray as xr
xr.set_options(display_style='html')
import intake
import cftime
from holoviews import opts
from bokeh.plotting import output_notebook
output_notebook()
import hvplot.xarray
import cmaps

Loading BokehJS ...

Select and Read datasets

• Here you should explain which datasets you have been using, giving as much information as possible.

Open CMIP6 online catalog

cat_url = "https://storage.googleapis.com/cmip6/pangeo-cmip6.json"
col = intake.open_esm_datastore(cat_url)

Search and select corresponding data

cat = col.search(source_id=['CESM2'], experiment_id=['historical'], table_id=['Amon'], variable_id=['tas'], member_id=['r1i1p1f1'])
cat.df

	activity_id	institution_id	source_id	experiment_id	member_id	table_id	variable_id	grid_label	zstore	dcpp_init_year	version
0	CMIP	NCAR	CESM2	historical	r1i1p1f1	Amon	tas	gn	gs://cmip6/CMIP6/CMIP/NCAR/CESM2/historical/r1...	NaN	20190308

Create dictionary from the list of datasets we found

• This step may take several minutes so be patient!

dset_dict = cat.to_dataset_dict(zarr_kwargs={'use_cftime':True})

--> The keys in the returned dictionary of datasets are constructed as follows:
	'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'

100.00% [1/1 00:00<00:00]

Open dataset

• Use xarray python package to analyze netCDF dataset

• open_dataset allows to get all the metadata without loading data into memory.

• with xarray, we only load into memory what is needed.

dset = dset_dict['CMIP.NCAR.CESM2.historical.Amon.gn']
dset

<xarray.Dataset>
Dimensions:    (lat: 192, lon: 288, member_id: 1, nbnd: 2, time: 1980)
Coordinates:
  * lat        (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
    lat_bnds   (lat, nbnd) float32 dask.array<chunksize=(192, 2), meta=np.ndarray>
  * lon        (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
    lon_bnds   (lon, nbnd) float32 dask.array<chunksize=(288, 2), meta=np.ndarray>
  * time       (time) object 1850-01-15 12:00:00 ... 2014-12-15 12:00:00
    time_bnds  (time, nbnd) object dask.array<chunksize=(1980, 2), meta=np.ndarray>
  * member_id  (member_id) <U8 'r1i1p1f1'
Dimensions without coordinates: nbnd
Data variables:
    tas        (member_id, time, lat, lon) float32 dask.array<chunksize=(1, 600, 192, 288), meta=np.ndarray>
Attributes: (12/48)
    Conventions:             CF-1.7 CMIP-6.2
    activity_id:             CMIP
    branch_method:           standard
    branch_time_in_child:    674885.0
    branch_time_in_parent:   219000.0
    case_id:                 15
    ...                      ...
    variable_id:             tas
    variant_info:            CMIP6 20th century experiments (1850-2014) with ...
    variant_label:           r1i1p1f1
    status:                  2019-10-25;created;by nhn2@columbia.edu
    intake_esm_varname:      ['tas']
    intake_esm_dataset_key:  CMIP.NCAR.CESM2.historical.Amon.gn

Analysis Method

• explain here your method with references whenever necessary.

Mean over all dates

dmean = dset.mean("time", keep_attrs=True).squeeze()

Results

Visualization

• you can add metadata to your cells such as hide-input so that the code will only be shown when clicking on ‘+’

start_date = str(dset.time.min().dt.strftime("%b %Y").values)
end_date =  str(dset.time.max().dt.strftime("%b %Y").values)
p = dmean.tas.hvplot(
    cmap=cmaps.BlueYellowRed,
    geo=True,
    coastline='10m',
    frame_width=400,
    clabel='K',
    clim=(220,310) 
)
p.opts(opts.Overlay( title="Average " + dmean.tas.attrs['title'] +  ' (' + start_date + ' - ' + end_date + ')'))

There is a lot more that you can do with outputs (such as including interactive outputs) with your book. For more information about this, see the Jupyter Book documentation

Discussion

In this section, you should discuss your results (about 1 page).

You can create a citation by adding new entries in the file references.bib and cite for instance:

{cite}`perez2011python`

Which would show the citation [PGH11].

References

To add your bibliography add in a markdown cell:

```{bibliography}
```

PGH11

Fernando Perez, Brian E Granger, and John D Hunter. Python: an ecosystem for scientific computing. Computing in Science & Engineering, 13(2):13–21, 2011.

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