Extracting Hydrographic Sections
Description¶
Recipe showing how to extract the Overturning in the Subpolar North Atlantic (OSNAP) trans-basin hydrographic section using annual-mean outputs from the National Oceanography Centre Near-Present-Day global eORCA1 configuration of NEMO forced using ERA-5 climatologically adjusted atmospheric forcing from 1976-2024.
For more details on this model configuration and the available outputs, users can explore the Near-Present-Day documentation here.
Background¶
The Overturning in the Subpolar North Atlantic Program (OSNAP) is an international program designed to provide a continuous record of the full-water column, trans-basin fluxes of heat, mass and freshwater in the subpolar North Atlantic.
The OSNAP observing system comprises of two trans-basin arrays: extending from southern Labrador to the southwestern tip of Greenland across the Labrador Sea (OSNAP West), and from the southeastern tip of Greenland to Scotland (OSNAP East).
The diapycnal overturning stream function is used to characterise the strength and structure of the AMOC in density-space (e.g., $\sigma_{0}$ or $\sigma_{2}$) across OSNAP and can be defined at time $t$ as follows:
$$\Psi_{\sigma}(\sigma, t) = \int_{\sigma_{min}}^{\sigma} \int_{x_w}^{x_e} v(x, \sigma, t) \ dx \ d\sigma$$
where the velocity field $v(x, \sigma, t)$ normal to OSNAP is first integrated zonally between the western $x_w$ and eastern $x_e$ boundaries of the basin, before being accumulated from the sea surface $\sigma_{min}$ to a specified isopycnal $\sigma$.
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# -- Import required packages -- #
import gsw
import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
from nemo_cookbook import NEMODataTree
xr.set_options(display_style="text")
# -- Import required packages -- #
import gsw
import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
from nemo_cookbook import NEMODataTree
xr.set_options(display_style="text")
Out[1]:
<xarray.core.options.set_options at 0x3006b8050>
Using Dask¶
Optional: Connect Client to Dask Local Cluster to run analysis in parallel.
Note that, although using Dask is not strictly necessary for this simple example using eORCA1, if we wanted to generalise this recipe to eORCA025 or eORCA12 outputs, using Dask would be essential to avoid unnecessary slow calculations using only a single process.
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# -- Initialise Dask Local Cluster -- #
import dask
from dask.distributed import Client, LocalCluster
# Update temporary directory for Dask workers:
dask.config.set({'temporary_directory': '/dssgfs01/working/otooth/Diagnostics/nemo_cookbook/recipes/',
'local_directory': '/dssgfs01/working/otooth/Diagnostics/nemo_cookbook/recipes/'
})
# Create Local Cluster:
cluster = LocalCluster(n_workers=5, threads_per_worker=2, memory_limit='8GB')
client = Client(cluster)
client
# -- Initialise Dask Local Cluster -- #
import dask
from dask.distributed import Client, LocalCluster
# Update temporary directory for Dask workers:
dask.config.set({'temporary_directory': '/dssgfs01/working/otooth/Diagnostics/nemo_cookbook/recipes/',
'local_directory': '/dssgfs01/working/otooth/Diagnostics/nemo_cookbook/recipes/'
})
# Create Local Cluster:
cluster = LocalCluster(n_workers=5, threads_per_worker=2, memory_limit='8GB')
client = Client(cluster)
client
Accessing NEMO Model Data¶
Let's begin by loading the grid variables for our eORCA1 NEMO model from the JASMIN Object Store.
Alternatively, you can replace the domain_filepath below with a file path to your domain_cfg.nc file and read this with xarray's open_dataset() function.
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# Define directory path to ancillary files:
domain_url = "https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca1-jra55v1/domain_cfg"
# Open eORCA1 NEMO model domain_cfg:
ds_domain = xr.open_zarr(domain_url, consolidated=True, chunks={})
ds_domain
# Define directory path to ancillary files:
domain_url = "https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca1-jra55v1/domain_cfg"
# Open eORCA1 NEMO model domain_cfg:
ds_domain = xr.open_zarr(domain_url, consolidated=True, chunks={})
ds_domain
Out[3]:
<xarray.Dataset> Size: 709MB
Dimensions: (y: 331, x: 360, nav_lev: 75)
Coordinates:
* nav_lev (nav_lev) int64 600B 0 1 2 3 4 5 6 7 ... 68 69 70 71 72 73 74
* x (x) int64 3kB 0 1 2 3 4 5 6 7 ... 353 354 355 356 357 358 359
* y (y) int64 3kB 0 1 2 3 4 5 6 7 ... 324 325 326 327 328 329 330
Data variables: (12/49)
atlmsk (y, x) int8 119kB dask.array<chunksize=(331, 360), meta=np.ndarray>
bathy_metry (y, x) float32 477kB dask.array<chunksize=(331, 360), meta=np.ndarray>
bottom_level (y, x) int32 477kB dask.array<chunksize=(331, 360), meta=np.ndarray>
e1f (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
e1t (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
e1u (y, x) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
... ...
top_level (y, x) int32 477kB dask.array<chunksize=(331, 360), meta=np.ndarray>
umask (nav_lev, y, x) int8 9MB dask.array<chunksize=(75, 331, 360), meta=np.ndarray>
umaskutil (y, x) int8 119kB dask.array<chunksize=(331, 360), meta=np.ndarray>
vmask (nav_lev, y, x) int8 9MB dask.array<chunksize=(75, 331, 360), meta=np.ndarray>
vmaskutil (y, x) int8 119kB dask.array<chunksize=(331, 360), meta=np.ndarray>
wmask (nav_lev, y, x) int8 9MB dask.array<chunksize=(75, 331, 360), meta=np.ndarray>
Attributes:
CfgName: UNKNOWN
CfgIndex: -999
Iperio: 1
Jperio: 0
NFold: 1
NFtype: F
VertCoord: zps
IsfCav: 0
file_name: mesh_mask.nc
TimeStamp: 01/03/2025 22:19:49 -0000
Next, we need to open the conservative temperature and absolute salinity variables stored at T-points as a single dataset.
Here, for simplicity, we will only consider the time-period from 2014-2023
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# Define directory path to model output files:
gridT_url = "https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca1-jra55v1/T1y"
# Construct NEMO model T-grid dataset, including conservative temperature (deg C) and absolute salinity (g/kg) variables:
ds_gridT = xr.open_zarr(gridT_url, consolidated=True, chunks={}).sel(time_counter=slice("2014-01", "2023-12"))
# Calculate potential density anomaly referenced to the sea surface (kg/m3):
ds_gridT['sigma0'] = gsw.density.sigma0(CT=ds_gridT['thetao_con'], SA=ds_gridT['so_abs'])
ds_gridT['sigma0'].name = 'sigma0'
ds_gridT
# Define directory path to model output files:
gridT_url = "https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca1-jra55v1/T1y"
# Construct NEMO model T-grid dataset, including conservative temperature (deg C) and absolute salinity (g/kg) variables:
ds_gridT = xr.open_zarr(gridT_url, consolidated=True, chunks={}).sel(time_counter=slice("2014-01", "2023-12"))
# Calculate potential density anomaly referenced to the sea surface (kg/m3):
ds_gridT['sigma0'] = gsw.density.sigma0(CT=ds_gridT['thetao_con'], SA=ds_gridT['so_abs'])
ds_gridT['sigma0'].name = 'sigma0'
ds_gridT
Out[4]:
<xarray.Dataset> Size: 9GB
Dimensions: (time_counter: 10, y: 331, x: 360, deptht: 75,
axis_nbounds: 2)
Coordinates:
* deptht (deptht) float32 300B 0.5058 1.556 ... 5.902e+03
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] 80B dask.array<chunksize=(1,), meta=np.ndarray>
* time_counter (time_counter) datetime64[ns] 80B 2014-07-02T12:00...
Dimensions without coordinates: y, x, axis_nbounds
Data variables: (12/75)
berg_latent_heat_flux (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
deptht_bounds (deptht, axis_nbounds) float32 600B dask.array<chunksize=(25, 2), meta=np.ndarray>
e3t (time_counter, deptht, y, x) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>
empmr (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
evs (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
ficeberg (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
... ...
vohfcisf (time_counter, deptht, y, x) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>
vohflisf (time_counter, deptht, y, x) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>
vowflisf (time_counter, deptht, y, x) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>
zos (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
zossq (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
sigma0 (time_counter, deptht, y, x) float64 715MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>
Attributes:
name: OUTPUT/eORCA1_1y_grid_T
description: ocean T grid variables
title: ocean T grid variables
Conventions: CF-1.6
timeStamp: 2024-Dec-09 10:28:01 GMT
uuid: 89824b34-5d44-4531-8344-fe1ce39fbb7b
Next, we need to import the zonal & meridional velocities and vertical grid cell thicknesses stored at U- & V-points, respectively.
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# Define directory path to model output files:
gridU_url = "https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca1-jra55v1/U1y"
# Construct NEMO model U-grid dataset, including zonal velocities (m/s) and vertical grid cell thicknesses (m):
ds_gridU = xr.open_zarr(gridU_url, consolidated=True, chunks={}).sel(time_counter=slice("2014-01", "2023-12"))
ds_gridU
# Define directory path to model output files:
gridU_url = "https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca1-jra55v1/U1y"
# Construct NEMO model U-grid dataset, including zonal velocities (m/s) and vertical grid cell thicknesses (m):
ds_gridU = xr.open_zarr(gridU_url, consolidated=True, chunks={}).sel(time_counter=slice("2014-01", "2023-12"))
ds_gridU
Out[5]:
<xarray.Dataset> Size: 2GB
Dimensions: (depthu: 75, axis_nbounds: 2, time_counter: 10,
y: 331, x: 360)
Coordinates:
* depthu (depthu) float32 300B 0.5058 1.556 ... 5.902e+03
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] 80B dask.array<chunksize=(1,), meta=np.ndarray>
* time_counter (time_counter) datetime64[ns] 80B 2014-07-02T12:00:...
Dimensions without coordinates: axis_nbounds, y, x
Data variables: (12/14)
depthu_bounds (depthu, axis_nbounds) float32 600B dask.array<chunksize=(25, 2), meta=np.ndarray>
e3u (time_counter, depthu, y, x) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>
hfx (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
hfx_adv (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
hfx_diff (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
sozosatr (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
... ...
time_counter_bounds (time_counter, axis_nbounds) datetime64[ns] 160B dask.array<chunksize=(1, 2), meta=np.ndarray>
u2o (time_counter, depthu, y, x) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>
umo (time_counter, depthu, y, x) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>
umo_vint (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
uo (time_counter, depthu, y, x) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>
uo_eiv (time_counter, depthu, y, x) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>
Attributes:
name: OUTPUT/eORCA1_1y_grid_U
description: ocean U grid variables
title: ocean U grid variables
Conventions: CF-1.6
timeStamp: 2024-Dec-09 10:18:04 GMT
uuid: 8156ec68-1106-4f56-9f8c-d8798e1bd32f
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# Define directory path to model output files:
gridV_url = "https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca1-jra55v1/V1y"
# Construct NEMO model V-grid dataset, including meridional velocities (m/s) and vertical grid cell thicknesses (m):
ds_gridV = xr.open_zarr(gridV_url, consolidated=True, chunks={}).sel(time_counter=slice("2014-01", "2023-12"))
ds_gridV
# Define directory path to model output files:
gridV_url = "https://noc-msm-o.s3-ext.jc.rl.ac.uk/npd-eorca1-jra55v1/V1y"
# Construct NEMO model V-grid dataset, including meridional velocities (m/s) and vertical grid cell thicknesses (m):
ds_gridV = xr.open_zarr(gridV_url, consolidated=True, chunks={}).sel(time_counter=slice("2014-01", "2023-12"))
ds_gridV
Out[6]:
<xarray.Dataset> Size: 2GB
Dimensions: (depthv: 75, axis_nbounds: 2, time_counter: 10,
y: 331, x: 360)
Coordinates:
* depthv (depthv) float32 300B 0.5058 1.556 ... 5.902e+03
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] 80B dask.array<chunksize=(1,), meta=np.ndarray>
* time_counter (time_counter) datetime64[ns] 80B 2014-07-02T12:00:...
Dimensions without coordinates: axis_nbounds, y, x
Data variables: (12/13)
depthv_bounds (depthv, axis_nbounds) float32 600B dask.array<chunksize=(25, 2), meta=np.ndarray>
e3v (time_counter, depthv, y, x) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>
hfy (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
hfy_adv (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
hfy_diff (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
somesatr (time_counter, y, x) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
... ...
time_centered_bounds (time_counter, axis_nbounds) datetime64[ns] 160B dask.array<chunksize=(1, 2), meta=np.ndarray>
time_counter_bounds (time_counter, axis_nbounds) datetime64[ns] 160B dask.array<chunksize=(1, 2), meta=np.ndarray>
v2o (time_counter, depthv, y, x) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>
vmo (time_counter, depthv, y, x) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>
vo (time_counter, depthv, y, x) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>
vo_eiv (time_counter, depthv, y, x) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>
Attributes:
name: OUTPUT/eORCA1_1y_grid_V
description: ocean V grid variables
title: ocean V grid variables
Conventions: CF-1.6
timeStamp: 2024-Dec-09 10:18:04 GMT
uuid: b77cb501-a564-4451-8908-d66fe201e54a
Creating a NEMODataTree¶
Next, let's create a NEMODataTree to store our domain and T, U & V-grid variables for the eORCA1 model.
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# Define dictionary of grid datasets defining eORCA1 parent model domain with no child/grand-child nests:
datasets = {"parent":
{"domain": ds_domain,
"gridT": ds_gridT,
"gridU": ds_gridU,
"gridV": ds_gridV
}
}
# Initialise a new NEMODataTree whose parent domain is zonally periodic & north-folding on F-points:
nemo = NEMODataTree.from_datasets(datasets=datasets, iperio=True, nftype="F", read_mask=True)
nemo
# Define dictionary of grid datasets defining eORCA1 parent model domain with no child/grand-child nests:
datasets = {"parent":
{"domain": ds_domain,
"gridT": ds_gridT,
"gridU": ds_gridU,
"gridV": ds_gridV
}
}
# Initialise a new NEMODataTree whose parent domain is zonally periodic & north-folding on F-points:
nemo = NEMODataTree.from_datasets(datasets=datasets, iperio=True, nftype="F", read_mask=True)
nemo
Out[7]:
<xarray.DataTree 'NEMO model'>
Group: /
│ Dimensions: (time_counter: 10, axis_nbounds: 2)
│ Coordinates:
│ time_centered (time_counter) datetime64[ns] 80B dask.array<chunksize=(1,), meta=np.ndarray>
│ * time_counter (time_counter) datetime64[ns] 80B 2014-07-02T12:00:...
│ Dimensions without coordinates: axis_nbounds
│ Data variables:
│ time_centered_bounds (time_counter, axis_nbounds) datetime64[ns] 160B dask.array<chunksize=(1, 2), meta=np.ndarray>
│ time_counter_bounds (time_counter, axis_nbounds) datetime64[ns] 160B dask.array<chunksize=(1, 2), meta=np.ndarray>
│ Attributes:
│ nftype: F
│ iperio: True
├── Group: /gridT
│ Dimensions: (time_counter: 10, j: 331, i: 360, k: 75,
│ axis_nbounds: 2)
│ Coordinates:
│ * deptht (k) float32 300B 0.5058 1.556 ... 5.698e+03 5.902e+03
│ time_centered (time_counter) datetime64[ns] 80B dask.array<chunksize=(1,), meta=np.ndarray>
│ gphit (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
│ glamt (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
│ * k (k) int64 600B 1 2 3 4 5 6 7 ... 69 70 71 72 73 74 75
│ * j (j) int64 3kB 1 2 3 4 5 6 ... 326 327 328 329 330 331
│ * i (i) int64 3kB 1 2 3 4 5 6 ... 355 356 357 358 359 360
│ Dimensions without coordinates: axis_nbounds
│ Data variables: (12/81)
│ berg_latent_heat_flux (time_counter, j, i) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
│ deptht_bounds (k, axis_nbounds) float32 600B dask.array<chunksize=(25, 2), meta=np.ndarray>
│ e3t (time_counter, k, j, i) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>
│ empmr (time_counter, j, i) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
│ evs (time_counter, j, i) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
│ ficeberg (time_counter, j, i) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
│ ... ...
│ e1t (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
│ e2t (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
│ top_level (j, i) int32 477kB dask.array<chunksize=(331, 360), meta=np.ndarray>
│ bottom_level (j, i) int32 477kB dask.array<chunksize=(331, 360), meta=np.ndarray>
│ tmask (k, j, i) int8 9MB dask.array<chunksize=(75, 331, 360), meta=np.ndarray>
│ tmaskutil (j, i) int8 119kB dask.array<chunksize=(331, 360), meta=np.ndarray>
│ Attributes:
│ nftype: F
│ iperio: True
├── Group: /gridU
│ Dimensions: (k: 75, axis_nbounds: 2, time_counter: 10, j: 331,
│ i: 360)
│ Coordinates:
│ * depthu (k) float32 300B 0.5058 1.556 ... 5.698e+03 5.902e+03
│ time_centered (time_counter) datetime64[ns] 80B dask.array<chunksize=(1,), meta=np.ndarray>
│ gphiu (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
│ glamu (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
│ * k (k) int64 600B 1 2 3 4 5 6 7 ... 69 70 71 72 73 74 75
│ * j (j) int64 3kB 1 2 3 4 5 6 ... 326 327 328 329 330 331
│ * i (i) float64 3kB 1.5 2.5 3.5 4.5 ... 358.5 359.5 360.5
│ Dimensions without coordinates: axis_nbounds
│ Data variables: (12/18)
│ depthu_bounds (k, axis_nbounds) float32 600B dask.array<chunksize=(25, 2), meta=np.ndarray>
│ e3u (time_counter, k, j, i) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>
│ hfx (time_counter, j, i) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
│ hfx_adv (time_counter, j, i) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
│ hfx_diff (time_counter, j, i) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
│ sozosatr (time_counter, j, i) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
│ ... ...
│ uo (time_counter, k, j, i) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>
│ uo_eiv (time_counter, k, j, i) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>
│ e1u (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
│ e2u (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
│ umask (k, j, i) int8 9MB dask.array<chunksize=(75, 331, 360), meta=np.ndarray>
│ umaskutil (j, i) int8 119kB dask.array<chunksize=(331, 360), meta=np.ndarray>
│ Attributes:
│ name: OUTPUT/eORCA1_1y_grid_U
│ description: ocean U grid variables
│ title: ocean U grid variables
│ Conventions: CF-1.6
│ timeStamp: 2024-Dec-09 10:18:04 GMT
│ uuid: 8156ec68-1106-4f56-9f8c-d8798e1bd32f
│ nftype: F
│ iperio: True
├── Group: /gridV
│ Dimensions: (k: 75, axis_nbounds: 2, time_counter: 10, j: 331,
│ i: 360)
│ Coordinates:
│ * depthv (k) float32 300B 0.5058 1.556 ... 5.698e+03 5.902e+03
│ time_centered (time_counter) datetime64[ns] 80B dask.array<chunksize=(1,), meta=np.ndarray>
│ gphiv (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
│ glamv (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
│ * k (k) int64 600B 1 2 3 4 5 6 7 ... 69 70 71 72 73 74 75
│ * j (j) float64 3kB 1.5 2.5 3.5 4.5 ... 329.5 330.5 331.5
│ * i (i) int64 3kB 1 2 3 4 5 6 ... 355 356 357 358 359 360
│ Dimensions without coordinates: axis_nbounds
│ Data variables: (12/17)
│ depthv_bounds (k, axis_nbounds) float32 600B dask.array<chunksize=(25, 2), meta=np.ndarray>
│ e3v (time_counter, k, j, i) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>
│ hfy (time_counter, j, i) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
│ hfy_adv (time_counter, j, i) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
│ hfy_diff (time_counter, j, i) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
│ somesatr (time_counter, j, i) float32 5MB dask.array<chunksize=(1, 331, 360), meta=np.ndarray>
│ ... ...
│ vo (time_counter, k, j, i) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>
│ vo_eiv (time_counter, k, j, i) float32 357MB dask.array<chunksize=(1, 25, 331, 360), meta=np.ndarray>
│ e1v (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
│ e2v (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
│ vmask (k, j, i) int8 9MB dask.array<chunksize=(75, 331, 360), meta=np.ndarray>
│ vmaskutil (j, i) int8 119kB dask.array<chunksize=(331, 360), meta=np.ndarray>
│ Attributes:
│ name: OUTPUT/eORCA1_1y_grid_V
│ description: ocean V grid variables
│ title: ocean V grid variables
│ Conventions: CF-1.6
│ timeStamp: 2024-Dec-09 10:18:04 GMT
│ uuid: b77cb501-a564-4451-8908-d66fe201e54a
│ nftype: F
│ iperio: True
├── Group: /gridW
│ Dimensions: (j: 331, i: 360, k: 75)
│ Coordinates:
│ gphit (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
│ glamt (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
│ * k (k) float64 600B 0.5 1.5 2.5 3.5 4.5 ... 71.5 72.5 73.5 74.5
│ * j (j) int64 3kB 1 2 3 4 5 6 7 8 ... 325 326 327 328 329 330 331
│ * i (i) int64 3kB 1 2 3 4 5 6 7 8 ... 354 355 356 357 358 359 360
│ Data variables:
│ e1t (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
│ e2t (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
│ wmask (k, j, i) int8 9MB dask.array<chunksize=(75, 331, 360), meta=np.ndarray>
│ wmaskutil (j, i) int8 119kB dask.array<chunksize=(331, 360), meta=np.ndarray>
│ Attributes:
│ nftype: F
│ iperio: True
└── Group: /gridF
Dimensions: (j: 331, i: 360, k: 75)
Coordinates:
gphif (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
glamf (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
* k (k) int64 600B 1 2 3 4 5 6 7 8 9 ... 68 69 70 71 72 73 74 75
* j (j) float64 3kB 1.5 2.5 3.5 4.5 ... 328.5 329.5 330.5 331.5
* i (i) float64 3kB 1.5 2.5 3.5 4.5 ... 357.5 358.5 359.5 360.5
Data variables:
e1f (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
e2f (j, i) float64 953kB dask.array<chunksize=(331, 360), meta=np.ndarray>
fmask (k, j, i) int8 9MB dask.array<chunksize=(75, 331, 360), meta=np.ndarray>
fmaskutil (j, i) int8 119kB dask.array<chunksize=(331, 360), meta=np.ndarray>
Attributes:
nftype: F
iperio: True
Preparing OSNAP Coordinates¶
Next, we will prepare geographical (lat, lon) coordinates defining the Overturning in the Subpolar North Atlantic (OSNAP) array from the JASMIN Object Store.
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# Define S3 URL to OSNAP gridded observational data in JASMIN Object Store:
url = "https://noc-msm-o.s3-ext.jc.rl.ac.uk/ocean-obs/OSNAP/OSNAP_Gridded_TSV_201408_202006_2023"
ds_osnap = xr.open_zarr(url, consolidated=True, chunks={})
# Define OSNAP section coordinates (adding final land point -> UK):
lon_osnap = np.concatenate([ds_osnap['LONGITUDE'].values, np.array([-4.0])])
lat_osnap = np.concatenate([ds_osnap['LATITUDE'].values, np.array([56.0])])
# Define S3 URL to OSNAP gridded observational data in JASMIN Object Store:
url = "https://noc-msm-o.s3-ext.jc.rl.ac.uk/ocean-obs/OSNAP/OSNAP_Gridded_TSV_201408_202006_2023"
ds_osnap = xr.open_zarr(url, consolidated=True, chunks={})
# Define OSNAP section coordinates (adding final land point -> UK):
lon_osnap = np.concatenate([ds_osnap['LONGITUDE'].values, np.array([-4.0])])
lat_osnap = np.concatenate([ds_osnap['LATITUDE'].values, np.array([56.0])])
Extracting the OSNAP array as a continuous hydrographic section¶
Now, let's use the extract_section() function to extract the OSNAP array from our NEMO model output.
We need to provide the names of the zonal & meridional velocity variables (uv_vars=["uo", "vo"]) and any scalar variables (e.g. potential density vars=sigma0) to extract along the secton.
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# Extract the OSNAP section from the NEMO model data:
ds_osnap = nemo.extract_section(lon_section=lon_osnap,
lat_section=lat_osnap,
uv_vars=["uo", "vo"],
vars=["sigma0"],
dom=".",
)
ds_osnap
# Extract the OSNAP section from the NEMO model data:
ds_osnap = nemo.extract_section(lon_section=lon_osnap,
lat_section=lat_osnap,
uv_vars=["uo", "vo"],
vars=["sigma0"],
dom=".",
)
ds_osnap
Out[9]:
<xarray.Dataset> Size: 1MB
Dimensions: (bdy: 68, time_counter: 10, k: 75)
Coordinates:
* time_counter (time_counter) datetime64[ns] 80B 2014-07-02T12:00:00 ... 2...
* k (k) int64 600B 1 2 3 4 5 6 7 8 9 ... 68 69 70 71 72 73 74 75
* bdy (bdy) int64 544B 0 1 2 3 4 5 6 7 8 ... 60 61 62 63 64 65 66 67
glamb (bdy) float64 544B -56.74 -55.72 -54.7 ... -5.497 -4.411
gphib (bdy) float64 544B 51.89 51.96 52.03 ... 56.5 56.16 56.08
depthb (k, bdy) float64 41kB 0.5058 0.5058 ... 5.902e+03 5.902e+03
Data variables:
i_bdy (bdy) float64 544B 232.0 233.0 234.0 ... 279.5 280.0 281.0
j_bdy (bdy) float64 544B 263.5 263.5 263.5 ... 270.0 269.5 269.5
flux_type (bdy) <U1 272B 'V' 'V' 'V' 'U' 'V' 'V' ... 'V' 'V' 'U' 'V' 'V'
flux_dir (bdy) int64 544B 1 1 1 -1 1 1 1 1 -1 1 ... 1 1 1 1 1 1 1 1 1 1
velocity (time_counter, k, bdy) float64 408kB dask.array<chunksize=(10, 75, 68), meta=np.ndarray>
bmask (k, bdy) float64 41kB dask.array<chunksize=(75, 68), meta=np.ndarray>
e1b (bdy) float64 544B dask.array<chunksize=(68,), meta=np.ndarray>
e3b (time_counter, k, bdy) float64 408kB dask.array<chunksize=(10, 75, 68), meta=np.ndarray>
sigma0 (time_counter, k, bdy) float64 408kB dask.array<chunksize=(10, 75, 68), meta=np.ndarray>
Visualising properties along the OSNAP array¶
Next, let's plot the time-mean potential density along the OSNAP array:
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# -- Plot the time-mean velocity along the OSNAP section -- #
plt.figure(figsize=(10, 4))
# Plot time-mean velocity across OSNAP section:
ds_osnap['velocity'].mean(dim='time_counter').plot(y='depthb', yincrease=False, cbar_kwargs={"label": "Velocity [ms$^{-1}$]"})
# Plot OSNAP West - East reference line:
plt.axvline(x=ds_osnap['bdy'].where(ds_osnap['glamb'] <= -44).max(), color='k', lw=2, ls='--')
# Axes labels:
plt.title("eORCA1 JRA55v1: Velocity across OSNAP Section (2014-2023)", fontdict={'size':12, 'weight':'bold'})
plt.xlabel("Boundary Index", fontdict={'size':11, 'weight':'bold'})
plt.ylabel("Depth [m]", fontdict={'size':11, 'weight':'bold'})
plt.ylim([4000, 0])
# -- Plot the time-mean velocity along the OSNAP section -- #
plt.figure(figsize=(10, 4))
# Plot time-mean velocity across OSNAP section:
ds_osnap['velocity'].mean(dim='time_counter').plot(y='depthb', yincrease=False, cbar_kwargs={"label": "Velocity [ms$^{-1}$]"})
# Plot OSNAP West - East reference line:
plt.axvline(x=ds_osnap['bdy'].where(ds_osnap['glamb'] <= -44).max(), color='k', lw=2, ls='--')
# Axes labels:
plt.title("eORCA1 JRA55v1: Velocity across OSNAP Section (2014-2023)", fontdict={'size':12, 'weight':'bold'})
plt.xlabel("Boundary Index", fontdict={'size':11, 'weight':'bold'})
plt.ylabel("Depth [m]", fontdict={'size':11, 'weight':'bold'})
plt.ylim([4000, 0])
Out[10]:
(4000.0, 0.0)
Calculating Meridional Overturning Stream Functions along the OSNAP array¶
Finally, let's calculate the meridional overturning stream function in potential density coordinates along the OSNAP array using the .compute_binned_statistic() function:
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from nemo_cookbook.stats import compute_binned_statistic
# Calculate volume transport in Sv:
ds_osnap['volume_transport'] = 1E-6 * (ds_osnap['velocity'] * ds_osnap['e1b'] * ds_osnap['e3b'])
# Define potential density bins:
sigma0_bins = np.arange(21, 28.2, 0.01)
# Compute Total OSNAP diapycnal overturning stream function:
ds_osnap['moc_total'] = compute_binned_statistic(vars=[ds_osnap['sigma0']],
values=ds_osnap['volume_transport'],
keep_dims=['time_counter'],
bins=[sigma0_bins],
statistic='nansum',
mask=None
).cumsum(dim='sigma0_bins')
from nemo_cookbook.stats import compute_binned_statistic
# Calculate volume transport in Sv:
ds_osnap['volume_transport'] = 1E-6 * (ds_osnap['velocity'] * ds_osnap['e1b'] * ds_osnap['e3b'])
# Define potential density bins:
sigma0_bins = np.arange(21, 28.2, 0.01)
# Compute Total OSNAP diapycnal overturning stream function:
ds_osnap['moc_total'] = compute_binned_statistic(vars=[ds_osnap['sigma0']],
values=ds_osnap['volume_transport'],
keep_dims=['time_counter'],
bins=[sigma0_bins],
statistic='nansum',
mask=None
).cumsum(dim='sigma0_bins')
Next, let's calculate the meridional overturning stream functions for the OSNAP East and OSNAP West arrays separately.
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# Determine index to split OSNAP West & OSNAP East sections:
station_OWest_OEast = ds_osnap['bdy'].where(ds_osnap['glamb'] <= -44).max()
# OSNAP East diapycnal overturning stream function:
mask_OEast = ds_osnap['bdy'] >= station_OWest_OEast
ds_osnap['moc_east'] = compute_binned_statistic(vars=[ds_osnap['sigma0']],
values=ds_osnap['volume_transport'],
keep_dims=['time_counter'],
bins=[sigma0_bins],
statistic='nansum',
mask=mask_OEast
).cumsum(dim='sigma0_bins')
# OSNAP West diapycnal overturning stream function:
mask_OWest = ds_osnap['bdy'] < station_OWest_OEast
ds_osnap['moc_west'] = compute_binned_statistic(vars=[ds_osnap['sigma0']],
values=ds_osnap['volume_transport'],
keep_dims=['time_counter'],
bins=[sigma0_bins],
statistic='nansum',
mask=mask_OWest
).cumsum(dim='sigma0_bins')
ds_osnap
# Determine index to split OSNAP West & OSNAP East sections:
station_OWest_OEast = ds_osnap['bdy'].where(ds_osnap['glamb'] <= -44).max()
# OSNAP East diapycnal overturning stream function:
mask_OEast = ds_osnap['bdy'] >= station_OWest_OEast
ds_osnap['moc_east'] = compute_binned_statistic(vars=[ds_osnap['sigma0']],
values=ds_osnap['volume_transport'],
keep_dims=['time_counter'],
bins=[sigma0_bins],
statistic='nansum',
mask=mask_OEast
).cumsum(dim='sigma0_bins')
# OSNAP West diapycnal overturning stream function:
mask_OWest = ds_osnap['bdy'] < station_OWest_OEast
ds_osnap['moc_west'] = compute_binned_statistic(vars=[ds_osnap['sigma0']],
values=ds_osnap['volume_transport'],
keep_dims=['time_counter'],
bins=[sigma0_bins],
statistic='nansum',
mask=mask_OWest
).cumsum(dim='sigma0_bins')
ds_osnap
Out[12]:
<xarray.Dataset> Size: 2MB
Dimensions: (bdy: 68, time_counter: 10, k: 75, sigma0_bins: 719)
Coordinates:
* time_counter (time_counter) datetime64[ns] 80B 2014-07-02T12:00:00 ....
* k (k) int64 600B 1 2 3 4 5 6 7 8 ... 68 69 70 71 72 73 74 75
* bdy (bdy) int64 544B 0 1 2 3 4 5 6 7 ... 61 62 63 64 65 66 67
glamb (bdy) float64 544B -56.74 -55.72 -54.7 ... -5.497 -4.411
gphib (bdy) float64 544B 51.89 51.96 52.03 ... 56.5 56.16 56.08
depthb (k, bdy) float64 41kB 0.5058 0.5058 ... 5.902e+03
* sigma0_bins (sigma0_bins) float64 6kB 21.01 21.02 ... 28.18 28.19
Data variables: (12/13)
i_bdy (bdy) float64 544B 232.0 233.0 234.0 ... 279.5 280.0 281.0
j_bdy (bdy) float64 544B 263.5 263.5 263.5 ... 270.0 269.5 269.5
flux_type (bdy) <U1 272B 'V' 'V' 'V' 'U' 'V' ... 'V' 'V' 'U' 'V' 'V'
flux_dir (bdy) int64 544B 1 1 1 -1 1 1 1 1 -1 ... 1 1 1 1 1 1 1 1 1
velocity (time_counter, k, bdy) float64 408kB dask.array<chunksize=(10, 75, 68), meta=np.ndarray>
bmask (k, bdy) float64 41kB dask.array<chunksize=(75, 68), meta=np.ndarray>
... ...
e3b (time_counter, k, bdy) float64 408kB dask.array<chunksize=(10, 75, 68), meta=np.ndarray>
sigma0 (time_counter, k, bdy) float64 408kB dask.array<chunksize=(10, 75, 68), meta=np.ndarray>
volume_transport (time_counter, k, bdy) float64 408kB dask.array<chunksize=(10, 75, 68), meta=np.ndarray>
moc_total (time_counter, sigma0_bins) float64 58kB dask.array<chunksize=(10, 719), meta=np.ndarray>
moc_east (time_counter, sigma0_bins) float64 58kB dask.array<chunksize=(10, 719), meta=np.ndarray>
moc_west (time_counter, sigma0_bins) float64 58kB dask.array<chunksize=(10, 719), meta=np.ndarray>
Notice that the resulting Dataset includes dask arrays, so we haven't actually computed the diapycnal overturning yet. To do this, we need to call the .compute() method:
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ds_osnap = ds_osnap.compute()
ds_osnap = ds_osnap.compute()
OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.
Visualising the time-mean diapycnal overturning stream functions¶
Finally, let's visualise the results by plotting the time-mean OSNAP overturning stream functions in potential density-coordinates:
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# Plot time-mean diapycnal overturning stream functions along the OSNAP section:
plt.figure(figsize=(4, 7))
plt.grid(True, lw=1, color='0.5', alpha=0.3)
# Plot Osnap Total, OSNAP East & OSNAP West diapycnal overturning stream functions:
ds_osnap['moc_total'].mean(dim='time_counter').plot(yincrease=False, y='sigma0_bins', label='Total', lw=3)
ds_osnap['moc_east'].mean(dim='time_counter').plot(yincrease=False, y='sigma0_bins', label='OEast', lw=2)
ds_osnap['moc_west'].mean(dim='time_counter').plot(yincrease=False, y='sigma0_bins', label='OWest', lw=2)
# Axes labels:
plt.title("eORCA1 JRA55v1:\nOSNAP AMOC$_{\\sigma_{0}}$ (2014-2023)", fontdict={"size": 12, "weight": "bold"})
plt.xlabel("$\\Psi_{\\sigma_0}$ (Sv)", fontdict={"size": 10, "weight": "bold"})
plt.ylabel("Potential Density $\\sigma_0$ (kg m$^{-3}$)", fontdict={"size": 10, "weight": "bold"})
plt.ylim([28.2, 24.5])
plt.legend()
# Plot time-mean diapycnal overturning stream functions along the OSNAP section:
plt.figure(figsize=(4, 7))
plt.grid(True, lw=1, color='0.5', alpha=0.3)
# Plot Osnap Total, OSNAP East & OSNAP West diapycnal overturning stream functions:
ds_osnap['moc_total'].mean(dim='time_counter').plot(yincrease=False, y='sigma0_bins', label='Total', lw=3)
ds_osnap['moc_east'].mean(dim='time_counter').plot(yincrease=False, y='sigma0_bins', label='OEast', lw=2)
ds_osnap['moc_west'].mean(dim='time_counter').plot(yincrease=False, y='sigma0_bins', label='OWest', lw=2)
# Axes labels:
plt.title("eORCA1 JRA55v1:\nOSNAP AMOC$_{\\sigma_{0}}$ (2014-2023)", fontdict={"size": 12, "weight": "bold"})
plt.xlabel("$\\Psi_{\\sigma_0}$ (Sv)", fontdict={"size": 10, "weight": "bold"})
plt.ylabel("Potential Density $\\sigma_0$ (kg m$^{-3}$)", fontdict={"size": 10, "weight": "bold"})
plt.ylim([28.2, 24.5])
plt.legend()
Out[14]:
<matplotlib.legend.Legend at 0x126a8e660>
