Tutorial 1: Exploring Sea Surface Temperature¶

Description¶

• Jupyter Notebook demonstrating how to access & explore the eORCA1-JRA55v1 Near-Present-Day monthly mean sea surface temperature output (1976-2023).

In [1]:

# -- Importing Python libraries -- #
import pandas as pd
import xarray as xr

# -- Importing Python libraries -- # import pandas as pd import xarray as xr

Processing¶

• The easiest way to access Near-Present-Day outputs stored in the JASMIN Object Store is to the data catalog publicly available on GitHub.

• Below we will see how we can query the catalog to find variables and load them as an xarray Dataset using a read-only URL.

In [ ]:

# -- Accessing eORCA1-JRA55v1 SST data using data catalog -- #
# Defining the NPD data catalog URL:
catalog_url = "https://raw.githubusercontent.com/NOC-MSM/NOC_Near_Present_Day/main/jasmin_os/catalog/npd_jra55_v1_catalog.csv"

# Read the data catalog using Pandas:
catalog = pd.read_csv(catalog_url)
catalog

# -- Accessing eORCA1-JRA55v1 SST data using data catalog -- # # Defining the NPD data catalog URL: catalog_url = "https://raw.githubusercontent.com/NOC-MSM/NOC_Near_Present_Day/main/jasmin_os/catalog/npd_jra55_v1_catalog.csv" # Read the data catalog using Pandas: catalog = pd.read_csv(catalog_url) catalog

Out[ ]:

	variable	standard_name	long_name	units	dims	model	grid	freq	url
0	e3t	cell_thickness	t-cell thickness	m	('time_counter', 'deptht', 'y', 'x')	eORCA1	T	1m	https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca...
1	thetao_con	sea_water_conservative_temperature	sea_water_conservative_temperature	degC	('time_counter', 'deptht', 'y', 'x')	eORCA1	T	1m	https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca...
2	so_abs	sea_water_absolute_salinity	sea_water_absolute_salinity	g/kg	('time_counter', 'deptht', 'y', 'x')	eORCA1	T	1m	https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca...
3	zos	sea_surface_height_above_geoid	sea surface height above geoid	m	('time_counter', 'y', 'x')	eORCA1	T	1m	https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca...
4	zossq	square_of_sea_surface_height_above_geoid	square of sea surface height above geoid	m2	('time_counter', 'y', 'x')	eORCA1	T	1m	https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca...
...	...	...	...	...	...	...	...	...	...
389	avt_evd	enhanced_vertical_heat_diffusivity	convective enhancement of vertical diffusivity	m2/s	('time_counter', 'depthw', 'y', 'x')	eORCA025	W	1y	https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca...
390	diftrto	ocean_vertical_tracer_diffusivity_due_to_tides	vertical diffusivity due to tidal mixing	m2/s	('time_counter', 'depthw', 'y', 'x')	eORCA025	W	1y	https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca...
391	wmo	upward_ocean_mass_transport	vertical mass transport	kg/s	('time_counter', 'depthw', 'y', 'x')	eORCA025	W	1y	https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca...
392	wo	upward_sea_water_velocity	w	m/s	('time_counter', 'depthw', 'y', 'x')	eORCA025	W	1y	https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca...
393	w2o	square_of_upward_sea_water_velocity	ww	m/s	('time_counter', 'depthw', 'y', 'x')	eORCA025	W	1y	https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca...

394 rows × 9 columns

In [3]:

# -- Querying eORCA1-JRA55v1 SST Dataset -- #
# Querying the dataset to find monthly SST outputs from the eORCA1 model:
catalog.query("variable == 'tos_con' & freq == '1m' & model == 'eORCA1'")

# -- Querying eORCA1-JRA55v1 SST Dataset -- # # Querying the dataset to find monthly SST outputs from the eORCA1 model: catalog.query("variable == 'tos_con' & freq == '1m' & model == 'eORCA1'")

Out[3]:

	variable	standard_name	long_name	units	dims	model	grid	freq	url
23	tos_con	sea_surface_temperature	sea_surface_conservative_temperature	degC	('time_counter', 'y', 'x')	eORCA1	T	1m	https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca...

In [4]:

# -- Load eORCA1-JRA55v1 SST Dataset -- #
# Use the catalog URL to load the dataset using xarray:
url = catalog.query("variable == 'tos_con' & freq == '1m' & model == 'eORCA1'")["url"].iloc[0]
ds = xr.open_zarr(url)
ds

# -- Load eORCA1-JRA55v1 SST Dataset -- # # Use the catalog URL to load the dataset using xarray: url = catalog.query("variable == 'tos_con' & freq == '1m' & model == 'eORCA1'")["url"].iloc[0] ds = xr.open_zarr(url) ds

Out[4]:

<xarray.Dataset> Size: 277MB
Dimensions:        (y: 331, x: 360, time_counter: 577)
Coordinates:
    nav_lat        (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
    nav_lon        (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
    time_centered  (time_counter) datetime64[ns] 5kB dask.array<chunksize=(1,), meta=np.ndarray>
  * time_counter   (time_counter) datetime64[ns] 5kB 1976-01-16T12:00:00 ... ...
Dimensions without coordinates: y, x
Data variables:
    tos_con        (time_counter, y, x) float32 275MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

xarray.Dataset

• Dimensions:
  • y: 331
  • x: 360
  • time_counter: 577
• Coordinates: (4)
  • nav_lat
    (y, x)
    float64
    dask.array<chunksize=(331, 360), meta=np.ndarray>

    Array Chunk Bytes 0.91 MiB 0.91 MiB Shape (331, 360) (331, 360) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • nav_lon
    (y, x)
    float64
    dask.array<chunksize=(331, 360), meta=np.ndarray>

    Array Chunk Bytes 0.91 MiB 0.91 MiB Shape (331, 360) (331, 360) Dask graph 1 chunks in 2 graph layers Data type float64 numpy.ndarray

  • time_centered
    (time_counter)
    datetime64[ns]
    dask.array<chunksize=(1,), meta=np.ndarray>

    bounds :

    time_centered_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    1900-01-01 00:00:00

    Array Chunk Bytes 4.51 kiB 8 B Shape (577,) (1,) Dask graph 577 chunks in 2 graph layers Data type datetime64[ns] numpy.ndarray

  • time_counter
    (time_counter)
    datetime64[ns]
    1976-01-16T12:00:00 ... 2024-01-...

    axis :

    T

    bounds :

    time_counter_bounds

    long_name :

    Time axis

    standard_name :

    time

    time_origin :

    1900-01-01 00:00:00

    array(['1976-01-16T12:00:00.000000000', '1976-02-15T12:00:00.000000000',
           '1976-03-16T12:00:00.000000000', ..., '2023-11-16T00:00:00.000000000',
           '2023-12-16T12:00:00.000000000', '2024-01-16T12:00:00.000000000'],
          shape=(577,), dtype='datetime64[ns]')

• Data variables: (1)
  • tos_con
    (time_counter, y, x)
    float32
    dask.array<chunksize=(1, 331, 360), meta=np.ndarray>

    cell_methods :

    time: mean (interval: 3600 s)

    interval_operation :

    3600 s

    interval_write :

    1 month

    long_name :

    sea_surface_conservative_temperature

    online_operation :

    average

    standard_name :

    sea_surface_temperature

    units :

    degC

    Array Chunk Bytes 262.28 MiB 465.47 kiB Shape (577, 331, 360) (1, 331, 360) Dask graph 577 chunks in 2 graph layers Data type float32 numpy.ndarray

• Indexes: (1)
  • time_counter
    PandasIndex

    PandasIndex(DatetimeIndex(['1976-01-16 12:00:00', '1976-02-15 12:00:00',
                   '1976-03-16 12:00:00', '1976-04-16 00:00:00',
                   '1976-05-16 12:00:00', '1976-06-16 00:00:00',
                   '1976-07-16 12:00:00', '1976-08-16 12:00:00',
                   '1976-09-16 00:00:00', '1976-10-16 12:00:00',
                   ...
                   '2023-04-16 00:00:00', '2023-05-16 12:00:00',
                   '2023-06-16 00:00:00', '2023-07-16 12:00:00',
                   '2023-08-16 12:00:00', '2023-09-16 00:00:00',
                   '2023-10-16 12:00:00', '2023-11-16 00:00:00',
                   '2023-12-16 12:00:00', '2024-01-16 12:00:00'],
                  dtype='datetime64[ns]', name='time_counter', length=577, freq=None))

• Attributes: (0)

Visualise¶

• You'll notice that our SST output is returned as a lazy dataset comprised of dask arrays, meaning it is not yet loaded into memory.

• Only once we have performed operations on out outputs and call .load() or .plot() will we execute the resulting task graph on our local machine and load the final result into memory.

• Below we will compute a seasonal climatology of SST for the whole Near-Present-Day simulation (1976-2023) and plot this as four pcolormesh subplots.

In [5]:

# -- Plot Seasonal Climatology of SST -- #
ds.tos_con.groupby("time_counter.season").mean().plot(col="season", col_wrap=2)

# -- Plot Seasonal Climatology of SST -- # ds.tos_con.groupby("time_counter.season").mean().plot(col="season", col_wrap=2)

Out[5]:

<xarray.plot.facetgrid.FacetGrid at 0x145df5faacf0>