EQUIX

EQUIX#

import cf_xarray
import dcpy
import distributed
import hvplot.xarray
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pump
import xgcm
from dcpy.oceans import read_osu_microstructure_mat
from IPython.display import Image
from scipy.io import loadmat

import eddydiff
import eddydiff as ed
import xarray as xr

xr.set_options(keep_attrs=True)

plt.rcParams["figure.dpi"] = 140
plt.rcParams["savefig.dpi"] = 200
plt.style.use("ggplot")

Todo#

[x] See if IOP variance terms match chameleon
[x] is there a consolidated TAO data file
[x] are they in the PMEL TAO files?
[ ] use 10 min averages to calculate KT, Jq
[ ] What is happening with the top few bins
[x] Take out Chameleon MLD properly not just slice it out
[x] filter χpod bad estimates using dT/dz - already done
[x] filter out binned estimates using count
[ ] pick bins that don’t outcrop?
[ ] try chameleon dTdz using xgcm transform
[ ] look at T-S diagram north, south of the equator

Data file notes#

indus/chipod/eq08_long/processed/chi_analysis/deglitched/

allchi_eq08_long.mat EQUIX EOP APL mooring; renamed to equix_eop.mat
mean_chi_*.mat seem to be IOP files per χpod

EOP units: 204 205 304 312 314 315 328

mean_chi vs summary files

summary files: T1, T2, AZ etc.
mean_chi files: T1, T2, eps, chi, KT, Jq

Image("../images/equix-chipods.png")

_images/3c74b0bb1cf781750424ebb3fdfa9bd9f5920b0ca3438b5bd09886df3b0ee150.png

Image("../images/equix-tao-chipods.png")

_images/135188b978ac6ae019e04a798c6cb37470ec8a9ff241528bf70148e60c62b9ed.png

Read climatologies#

argograd = xr.open_zarr(
    "../datasets/argo_monthly_iso_gradients.zarr", decode_times=False
)
argograd.Smean.attrs = {"standard_name": "sea_water_salinity"}
argograd.Tmean.attrs = {"standard_name": "sea_water_potential_temperature"}
argograd["temp"] = dcpy.eos.temp(argograd.Smean, argograd.Tmean, argograd.pres, 0)
argograd = argograd.cf.guess_coord_axis()
argograd.pres.attrs.update({"positive": "down", "standard_name": "sea_water_pressure"})
argograd = argograd.cf.add_bounds("pres")
argo = (
    argograd.sel(lon=220, method="nearest")
    .sel(lat=slice(-3, 8), pres=slice(300))
    .mean("time")
)
argo["pden"] = ed.jmd95.dens(argo.Smean, argo.Tmean, 0)
argo["gamma_n"] = dcpy.oceans.neutral_density(argo)
argo

<xarray.Dataset>
Dimensions:      (bounds: 2, lat: 11, pres: 25)
Coordinates:
  * lat          (lat) float32 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5
    lon          float32 220.5
  * pres         (pres) float64 2.5 10.0 20.0 30.0 ... 240.0 260.0 280.0 300.0
    pres_bounds  (bounds, pres) float32 -1.25 6.25 15.0 ... 270.0 290.0 310.0
Dimensions without coordinates: bounds
Data variables:
    Smean        (pres, lat) float32 dask.array<chunksize=(25, 11), meta=np.ndarray>
    Tmean        (pres, lat) float32 dask.array<chunksize=(25, 11), meta=np.ndarray>
    dSdia        (pres, lat) float64 dask.array<chunksize=(25, 11), meta=np.ndarray>
    dSdz         (pres, lat) float32 dask.array<chunksize=(25, 11), meta=np.ndarray>
    dSiso        (pres, lat) float64 dask.array<chunksize=(25, 11), meta=np.ndarray>
    dTdia        (pres, lat) float64 dask.array<chunksize=(25, 11), meta=np.ndarray>
    dTdz         (pres, lat) float32 dask.array<chunksize=(25, 11), meta=np.ndarray>
    dTiso        (pres, lat) float64 dask.array<chunksize=(25, 11), meta=np.ndarray>
    ρmean        (pres, lat) float32 dask.array<chunksize=(25, 11), meta=np.ndarray>
    temp         (pres, lat) float32 dask.array<chunksize=(25, 11), meta=np.ndarray>
    pden         (pres, lat) float32 dask.array<chunksize=(25, 11), meta=np.ndarray>
    gamma_n      (lat, pres) float32 dask.array<chunksize=(11, 25), meta=np.ndarray>
Attributes:
    dataset:  argo
    name:     Mean fields and isopycnal, diapycnal gradients from Argo

xarray.Dataset

Dimensions:
- bounds: 2
- lat: 11
- pres: 25

Coordinates: (4)

lat
(lat)
float32
-2.5 -1.5 -0.5 0.5 ... 5.5 6.5 7.5
units :
degrees_north
standard_name :
latitude
axis :
Y
point_spacing :
even
```
array([-2.5, -1.5, -0.5,  0.5,  1.5,  2.5,  3.5,  4.5,  5.5,  6.5,  7.5],
      dtype=float32)
```
lon
()
float32
220.5
units :
degrees_east
standard_name :
longitude
axis :
X
modulo :
360.0
point_spacing :
even
```
array(220.5, dtype=float32)
```

pres

(pres)

float64

2.5 10.0 20.0 ... 260.0 280.0 300.0

axis :: Z
point_spacing :: uneven
positive :: down
units :: dbar
standard_name :: sea_water_pressure
bounds :: pres_bounds

array([  2.5,  10. ,  20. ,  30. ,  40. ,  50. ,  60. ,  70. ,  80. ,  90. ,
       100. , 110. , 120. , 130. , 140. , 150. , 160. , 170. , 182.5, 200. ,
       220. , 240. , 260. , 280. , 300. ])

pres_bounds

(bounds, pres)

float32

-1.25 6.25 15.0 ... 290.0 310.0

axis :: Z
point_spacing :: uneven
positive :: down
units :: dbar
standard_name :: sea_water_pressure

array([[ -1.25,   6.25,  15.  ,  25.  ,  35.  ,  45.  ,  55.  ,  65.  ,
         75.  ,  85.  ,  95.  , 105.  , 115.  , 125.  , 135.  , 145.  ,
        155.  , 165.  , 176.25, 191.25, 210.  , 230.  , 250.  , 270.  ,
        290.  ],
       [  6.25,  13.75,  25.  ,  35.  ,  45.  ,  55.  ,  65.  ,  75.  ,
         85.  ,  95.  , 105.  , 115.  , 125.  , 135.  , 145.  , 155.  ,
        165.  , 175.  , 188.75, 208.75, 230.  , 250.  , 270.  , 290.  ,
        310.  ]], dtype=float32)

Data variables: (12)

Smean

(pres, lat)

float32