Real: Pliocene Equilibrium Reconstruction

%load_ext autoreload
%autoreload 2

import os
os.chdir('/glade/work/fengzhu/GitHub/cfr/docsrc/v2026/notebooks')
import pandas as pd
import xarray as xr
import numpy as np
import glob
from tqdm import tqdm
import cfr
cfr.use('v2026')
print(cfr.__version__)

2026.6.25

dirpath = '/lustre/desc1/p/uazn0050/fengzhu/PlioDA'
paths = sorted(glob.glob(os.path.join(dirpath, 'prior', f'*.nc')))
ds = {}
for path in tqdm(paths):
    name = os.path.splitext(os.path.basename(path))[0]
    ds[name] = xr.open_dataset(path)

100%|██████████| 70/70 [00:06<00:00, 10.93it/s]

df = pd.read_excel(os.path.join(dirpath, 'prior', 'TableS3_ModelPrior.xlsx'))
plio_run_names = df['PlioceneRunName'].values
pi_run_names = df['PlioceneRunName'].values

pm_list = []
for name in plio_run_names:
    # print(name)
    pm_list.append(cfr.PriorMember(ds[name]))

%%time
prior = cfr.Prior(pm_list)
prior.ds

CPU times: user 341 ms, sys: 1.68 s, total: 2.02 s
Wall time: 3.06 s

<xarray.Dataset> Size: 1GB
Dimensions:  (ens: 37, month: 12, lat: 180, lon: 360)
Coordinates:
  * month    (month) float64 96B 1.0 2.0 3.0 4.0 5.0 ... 8.0 9.0 10.0 11.0 12.0
  * lat      (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5
  * lon      (lon) float64 3kB 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5
Dimensions without coordinates: ens
Data variables:
    pr       (month, lat, lon, ens) float64 230MB 0.1576 0.1568 ... 0.6204
    tas      (month, lat, lon, ens) float64 230MB 249.2 248.9 ... 254.0 253.9
    tos      (month, lat, lon, ens) float64 230MB nan nan nan ... -1.725 -1.741
    sos      (month, lat, lon, ens) float64 230MB nan nan nan ... 30.96 31.19
    siconc   (month, lat, lon, ens) float64 230MB 0.0 nan 0.0 ... 98.84 99.38
    ev       (month, lat, lon, ens) float64 230MB 0.07027 0.0386 ... -0.01667

xarray.Dataset

• Dimensions:
  • ens: 37
  • month: 12
  • lat: 180
  • lon: 360
• Coordinates: (3)
  • month
    (month)
    float64
    1.0 2.0 3.0 4.0 ... 10.0 11.0 12.0

    Units :

    Calendar Month

    array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12.])

  • lat
    (lat)
    float64
    -89.5 -88.5 -87.5 ... 88.5 89.5

    Units :

    Decimal Degrees North

    array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
           -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,
           -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,
           -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,
           -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,
           -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,
           -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,
           -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,
            -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,
             0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
            10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,
            20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,
            30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,
            40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,
            50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,
            60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,
            70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])

  • lon
    (lon)
    float64
    0.5 1.5 2.5 ... 357.5 358.5 359.5

    Units :

    Decimal Degrees East

    Units_Range :

    0 - 360

    array([  0.5,   1.5,   2.5, ..., 357.5, 358.5, 359.5], shape=(360,))

• Data variables: (6)
  • pr
    (month, lat, lon, ens)
    float64
    0.1576 0.1568 ... 0.5632 0.6204

    Description :

    Total precipitation

    Units :

    mm / day

    array([[[[0.15757401, 0.15676474, 0.08633712, ...,        nan,
              0.19129158, 0.2125784 ],
             [0.1568893 , 0.15675853, 0.0857679 , ...,        nan,
              0.19126873, 0.21264495],
             [0.15646472, 0.15582468, 0.08522747, ...,        nan,
              0.19127996, 0.21277019],
             ...,
             [0.15939213, 0.15838555, 0.08792048, ...,        nan,
              0.19111883, 0.21233991],
             [0.1584577 , 0.15756059, 0.08743959, ...,        nan,
              0.19124337, 0.21244398],
             [0.15806546, 0.15690969, 0.08690366, ...,        nan,
              0.19129501, 0.21252094]],
    
            [[0.17850977, 0.18692915, 0.09281007, ...,        nan,
              0.16983851, 0.19294787],
             [0.17684652, 0.18953538, 0.09206353, ...,        nan,
              0.16959458, 0.19274478],
             [0.17599535, 0.19670524, 0.09126939, ...,        nan,
              0.16930361, 0.19263769],
    ...
             [0.58193317, 0.61491477, 1.21135599, ...,        nan,
              0.62374801, 0.6643234 ],
             [0.58261983, 0.61765746, 1.21318528, ...,        nan,
              0.62474729, 0.66588662],
             [0.58506725, 0.61828526, 1.21499042, ...,        nan,
              0.62573953, 0.66743949]],
    
            [[0.53260482, 0.59349435, 1.15685385, ...,        nan,
              0.56368825, 0.62126707],
             [0.53312319, 0.59359514, 1.15788054, ...,        nan,
              0.56413205, 0.62210567],
             [0.53319367, 0.59382913, 1.15875094, ...,        nan,
              0.56456736, 0.62287305],
             ...,
             [0.52831498, 0.59196176, 1.15416686, ...,        nan,
              0.56223511, 0.61870591],
             [0.52958823, 0.59328748, 1.15497313, ...,        nan,
              0.56270095, 0.61958461],
             [0.5313252 , 0.59352726, 1.15585856, ...,        nan,
              0.56320593, 0.62042326]]]], shape=(12, 180, 360, 37))

  • tas
    (month, lat, lon, ens)
    float64
    249.2 248.9 246.1 ... 254.0 253.9

    Description :

    Near surface (usually 2m) air temperature

    Units :

    Kelvin

    array([[[[249.18540955, 248.85432007, 246.13918092, ...,          nan,
              249.81728271, 249.20075911],
             [249.11569214, 248.87519141, 245.92750697, ...,          nan,
              249.78751143, 249.17093868],
             [249.0010376 , 248.91123413, 245.8439277 , ...,          nan,
              249.72088895, 249.10470848],
             ...,
             [249.23918152, 249.30074675, 246.1666756 , ...,          nan,
              249.84176064, 249.2258833 ],
             [249.05987549, 248.65549622, 246.17956618, ...,          nan,
              249.8357006 , 249.21959482],
             [249.13369751, 248.70552121, 246.23293573, ...,          nan,
              249.82929013, 249.2129697 ]],
    
            [[249.5788269 , 249.66895528, 246.84787337, ...,          nan,
              250.33305216, 249.76455387],
             [249.60346985, 249.55814094, 246.75640974, ...,          nan,
              250.29262076, 249.72354455],
             [249.46238708, 249.15837253, 246.67086644, ...,          nan,
              250.23121267, 249.66263025],
    ...
             [250.43746948, 251.53524912, 269.29583803, ...,          nan,
              254.26118649, 254.05499402],
             [250.44926453, 251.53721368, 269.30208258, ...,          nan,
              254.27471853, 254.07002761],
             [250.46194458, 251.53899115, 269.30869779, ...,          nan,
              254.28928601, 254.08630827]],
    
            [[250.24884033, 252.05197265, 269.28723341, ...,          nan,
              254.03237944, 253.9101522 ],
             [250.25274658, 252.02469374, 269.28896576, ...,          nan,
              254.03675554, 253.91478209],
             [250.25665283, 251.99767137, 269.2907532 , ...,          nan,
              254.04118602, 253.91951021],
             ...,
             [250.23614502, 252.13442378, 269.28174901, ...,          nan,
              254.01794389, 253.89524189],
             [250.24057007, 252.1068948 , 269.28366741, ...,          nan,
              254.02302815, 253.90044088],
             [250.24481201, 252.07939794, 269.28548465, ...,          nan,
              254.02783996, 253.90540843]]]], shape=(12, 180, 360, 37))

  • tos
    (month, lat, lon, ens)
    float64
    nan nan nan ... -1.725 -1.741

    Description :

    Sea surface temperature

    Units :

    Celsius

    array([[[[        nan,         nan,         nan, ...,         nan,
                      nan,         nan],
             [        nan,         nan,         nan, ...,         nan,
                      nan,         nan],
             [        nan,         nan,         nan, ...,         nan,
                      nan,         nan],
             ...,
             [        nan,         nan,         nan, ...,         nan,
                      nan,         nan],
             [        nan,         nan,         nan, ...,         nan,
                      nan,         nan],
             [        nan,         nan,         nan, ...,         nan,
                      nan,         nan]],
    
            [[        nan,         nan,         nan, ...,         nan,
                      nan,         nan],
             [        nan,         nan,         nan, ...,         nan,
                      nan,         nan],
             [        nan,         nan,         nan, ...,         nan,
                      nan,         nan],
    ...
             [-1.79383303, -1.79784707, -1.354021  , ..., -1.73281012,
              -1.7282395 , -1.74640173],
             [-1.79369789, -1.79783003, -1.34988536, ..., -1.73236949,
              -1.72824998, -1.74643852],
             [-1.79369422, -1.79782424, -1.34905736, ..., -1.73192886,
              -1.72817229, -1.74636092]],
    
            [[-1.79419928, -1.79834167, -1.37659303, ..., -1.75047782,
              -1.72471827, -1.74089596],
             [-1.79419715, -1.79832385, -1.37611178, ..., -1.75043015,
              -1.72469171, -1.74087246],
             [-1.79419502, -1.79830602, -1.37563054, ..., -1.75038248,
              -1.72466514, -1.74084896],
             ...,
             [-1.7942236 , -1.79839515, -1.37875996, ..., -1.75062085,
              -1.72481212, -1.74097164],
             [-1.79420355, -1.79837733, -1.37755551, ..., -1.75057317,
              -1.72477141, -1.74094296],
             [-1.79420142, -1.7983595 , -1.37707427, ..., -1.7505255 ,
              -1.72474484, -1.74091946]]]], shape=(12, 180, 360, 37))

  • sos
    (month, lat, lon, ens)
    float64
    nan nan nan ... 29.37 30.96 31.19

    Description :

    Sea surface salinity

    Units :

    g / kg

    Units_Equivalent :

    psu

    array([[[[        nan,         nan,         nan, ...,         nan,
                      nan,         nan],
             [        nan,         nan,         nan, ...,         nan,
                      nan,         nan],
             [        nan,         nan,         nan, ...,         nan,
                      nan,         nan],
             ...,
             [        nan,         nan,         nan, ...,         nan,
                      nan,         nan],
             [        nan,         nan,         nan, ...,         nan,
                      nan,         nan],
             [        nan,         nan,         nan, ...,         nan,
                      nan,         nan]],
    
            [[        nan,         nan,         nan, ...,         nan,
                      nan,         nan],
             [        nan,         nan,         nan, ...,         nan,
                      nan,         nan],
             [        nan,         nan,         nan, ...,         nan,
                      nan,         nan],
    ...
             [30.56187062, 30.33411226, 29.84890909, ..., 29.43992336,
              31.03302654, 31.29376037],
             [30.56141946, 30.33485973, 29.8490399 , ..., 29.43793782,
              31.03342601, 31.29529036],
             [30.56091849, 30.33474993, 29.8483225 , ..., 29.43595228,
              31.03242184, 31.29371118]],
    
            [[30.55419477, 30.29161833, 29.82237004, ..., 29.36798919,
              30.95992692, 31.19301341],
             [30.55384429, 30.29151143, 29.82205222, ..., 29.36774533,
              30.95951331, 31.19261781],
             [30.5534938 , 30.29140453, 29.8217344 , ..., 29.36750146,
              30.95909971, 31.1922222 ],
             ...,
             [30.55535059, 30.29193904, 29.82337964, ..., 29.3687208 ,
              30.96125101, 31.19424364],
             [30.55489573, 30.29183214, 29.82300568, ..., 29.36847693,
              30.96075414, 31.19380463],
             [30.55454525, 30.29172523, 29.82268786, ..., 29.36823306,
              30.96034053, 31.19340902]]]], shape=(12, 180, 360, 37))

  • siconc
    (month, lat, lon, ens)
    float64
    0.0 nan 0.0 0.0 ... nan 98.84 99.38

    Description :

    Sea ice area percentage

    Units :

    Percent

    Units_Range :

    0 - 100

    array([[[[0.00000000e+00,            nan, 0.00000000e+00, ...,
                         nan, 0.00000000e+00, 0.00000000e+00],
             [0.00000000e+00,            nan, 0.00000000e+00, ...,
                         nan, 0.00000000e+00, 0.00000000e+00],
             [0.00000000e+00,            nan, 0.00000000e+00, ...,
                         nan, 0.00000000e+00, 0.00000000e+00],
             ...,
             [0.00000000e+00,            nan, 0.00000000e+00, ...,
                         nan, 0.00000000e+00, 0.00000000e+00],
             [0.00000000e+00,            nan, 0.00000000e+00, ...,
                         nan, 0.00000000e+00, 0.00000000e+00],
             [0.00000000e+00,            nan, 0.00000000e+00, ...,
                         nan, 0.00000000e+00, 0.00000000e+00]],
    
            [[0.00000000e+00,            nan, 0.00000000e+00, ...,
                         nan, 0.00000000e+00, 0.00000000e+00],
             [0.00000000e+00,            nan, 0.00000000e+00, ...,
                         nan, 0.00000000e+00, 0.00000000e+00],
             [0.00000000e+00,            nan, 0.00000000e+00, ...,
                         nan, 0.00000000e+00, 0.00000000e+00],
    ...
                         nan, 9.86934499e+01, 9.92777108e+01],
             [9.79457021e+01, 9.83762778e+01, 9.61160178e+00, ...,
                         nan, 9.86880731e+01, 9.92753651e+01],
             [9.79414463e+01, 9.83701133e+01, 9.52274721e+00, ...,
                         nan, 9.86824098e+01, 9.92727640e+01]],
    
            [[9.80143905e+01, 9.87121198e+01, 1.00411379e+01, ...,
                         nan, 9.88343961e+01, 9.93780569e+01],
             [9.80134547e+01, 9.84732684e+01, 1.00107083e+01, ...,
                         nan, 9.88326094e+01, 9.93772023e+01],
             [9.80124176e+01, 9.84713605e+01, 9.97965609e+00, ...,
                         nan, 9.88307894e+01, 9.93763566e+01],
             ...,
             [9.80174899e+01, 9.84827979e+01, 1.01376914e+01, ...,
                         nan, 9.88403630e+01, 9.93810584e+01],
             [9.80163813e+01, 9.84800669e+01, 1.01041299e+01, ...,
                         nan, 9.88382442e+01, 9.93799720e+01],
             [9.80153501e+01, 9.87143804e+01, 1.00720451e+01, ...,
                         nan, 9.88362562e+01, 9.93789716e+01]]]],
          shape=(12, 180, 360, 37))

  • ev
    (month, lat, lon, ens)
    float64
    0.07027 0.0386 ... -0.01667

    Description :

    Total surface evaporation

    units :

    mm / day

    array([[[[ 7.02718571e-02,  3.85956801e-02, -8.44357722e-03, ...,
                          nan,  8.36388084e-03,  3.52631839e-03],
             [ 5.71399927e-02,  3.99768688e-02, -1.18658356e-02, ...,
                          nan,  8.47758480e-03,  3.64162836e-03],
             [ 4.93737459e-02,  4.42760214e-02, -1.30841918e-02, ...,
                          nan,  8.91992925e-03,  4.10920768e-03],
             ...,
             [ 7.82614201e-02,  6.96005374e-02, -8.51845369e-03, ...,
                          nan,  8.59419651e-03,  3.72893381e-03],
             [ 5.29201813e-02,  3.25551778e-02, -8.16681609e-03, ...,
                          nan,  8.47755939e-03,  3.64049263e-03],
             [ 6.52731806e-02,  3.23523842e-02, -7.05064042e-03, ...,
                          nan,  8.39644141e-03,  3.56377792e-03]],
    
            [[ 7.01852292e-02,  3.49565074e-02, -1.30864512e-02, ...,
                          nan,  1.84003016e-02,  1.45741326e-02],
             [ 7.67592862e-02,  3.36102545e-02, -1.40826507e-02, ...,
                          nan,  1.82303882e-02,  1.43941305e-02],
             [ 6.80581182e-02,  2.22014375e-02, -1.46430191e-02, ...,
                          nan,  1.82388750e-02,  1.44667816e-02],
    ...
                          nan,  1.53898116e-02, -1.07148335e-02],
             [ 5.54571711e-02,  3.78535390e-02,  1.06703734e+00, ...,
                          nan,  1.54368825e-02, -1.07711895e-02],
             [ 5.55120893e-02,  3.78696434e-02,  1.06849527e+00, ...,
                          nan,  1.54933922e-02, -1.08269573e-02]],
    
            [[ 5.39926663e-02,  1.21180244e-01,  1.04765618e+00, ...,
                          nan,  8.25873720e-03, -1.66254082e-02],
             [ 5.40312342e-02,  1.20609343e-01,  1.04815066e+00, ...,
                          nan,  8.32821787e-03, -1.65849313e-02],
             [ 5.40737584e-02,  1.20089471e-01,  1.04864919e+00, ...,
                          nan,  8.40125184e-03, -1.65428507e-02],
             ...,
             [ 5.38837910e-02,  1.23278365e-01,  1.04605198e+00, ...,
                          nan,  8.04484226e-03, -1.67475115e-02],
             [ 5.39178550e-02,  1.22483231e-01,  1.04661036e+00, ...,
                          nan,  8.11560079e-03, -1.67090536e-02],
             [ 5.39549701e-02,  1.21798299e-01,  1.04714572e+00, ...,
                          nan,  8.18806962e-03, -1.66670033e-02]]]],
          shape=(12, 180, 360, 37))

df = pd.read_csv(os.path.join(dirpath, 'proxy', 'df_proxy_midPliocene.csv'))
obs = cfr.Obs(df)
obs.setup()
obs.df

	pid	lat	lon	depth	type	seasonality	time	value	R	psm_name	species	clean
0	DSDP516.Mg/Ca	-30.646322	324.994450	1313.0	Mg/Ca	1,2,3,4,5,6,12	3.25	1.153137	0.0169	MgCa	sacculifer	0.0
1	DSDP552.Mg/Ca	55.593510	345.982189	2301.0	Mg/Ca	1,2,3,4,5,6,7,8,9,10,11,12	3.25	0.899058	0.0169	MgCa	bulloides	0.0
2	DSDP590.Mg/Ca	-32.594207	162.898037	1299.0	Mg/Ca	1,2,3,4,5,12	3.25	1.157111	0.0169	MgCa	sacculifer	0.0
3	DSDP603.Mg/Ca	35.261855	290.479297	4640.0	Mg/Ca	1,2,3,4,5,6,7,8,9,10,11,12	3.25	1.381721	0.0169	MgCa	bulloides	0.0
4	DSDP609.Mg/Ca	49.422589	335.045676	3883.0	Mg/Ca	1,2,3,4,5,6,7,8,9,10,11,12	3.25	0.838967	0.0169	MgCa	bulloides	1.0
...	...	...	...	...	...	...	...	...	...	...	...	...
103	ODP959.UK37	3.053953	356.530460	2092.0	UK37	1,2,3,4,5,6,7,8,9,10,11,12	3.25	0.955809	0.0025	UK37	NaN	NaN
104	ODP978.UK37	35.655501	357.297471	1929.0	UK37	1,2,3,4,5,6,7,8,9,10,11,12	3.25	0.940120	0.0025	UK37	NaN	NaN
105	ODP982.UK37	57.058493	343.381278	1135.0	UK37	1,2,3,4,5,6,7,8,9,10,11,12	3.25	0.611470	0.0025	UK37	NaN	NaN
106	ODP999.UK37	12.533555	281.067292	2828.0	UK37	1,2,3,4,5,6,7,8,9,10,11,12	3.25	0.966871	0.0025	UK37	NaN	NaN
107	PuntaPiccola.UK37	37.115774	12.130472	NaN	UK37	1,2,3,4,5,6,7,8,9,10,11,12	3.25	0.898781	0.0025	UK37	NaN	NaN

108 rows × 12 columns

%%time

da_solver = cfr.enkf.Solver(prior, obs)
fwd_kws = {
    'MgCa': {
        'age': 3.25,
        'sw': 2,
        'H': 0.74,
    },
    'TEX86': {
        'mode': 'standard',
        'type': 'SST',
    },
    'UK37': {
        'order': 2,
    },
}
da_solver.prep(loc_radius=12000, startover=True, get_clim_kws={'mode': 'each_member'}, **fwd_kws)

>>> Proxy System Modeling: Y = H(X)

Downloading file 'omegaph/GLODAPv2.2016b.OmegaC.nc' from 'https://raw.githubusercontent.com/fzhu2e/PyBAYWATCH-data/main/omegaph/GLODAPv2.2016b.OmegaC.nc' to '/glade/u/home/fengzhu/.cache/pybaywatch'.

octave not found, please see README

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Downloading file 'omegaph/GLODAPv2.2016b.pHtsinsitutp.nc' from 'https://raw.githubusercontent.com/fzhu2e/PyBAYWATCH-data/main/omegaph/GLODAPv2.2016b.pHtsinsitutp.nc' to '/glade/u/home/fengzhu/.cache/pybaywatch'.
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Downloading file 'omegaph/gom.mat' from 'https://raw.githubusercontent.com/fzhu2e/PyBAYWATCH-data/main/omegaph/gom.mat' to '/glade/u/home/fengzhu/.cache/pybaywatch'.
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Downloading file 'params/species_model_params.mat' from 'https://raw.githubusercontent.com/fzhu2e/PyBAYWATCH-data/main/params/species_model_params.mat' to '/glade/u/home/fengzhu/.cache/pybaywatch'.
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Downloading file 'params/mgsw_iters_v2.mat' from 'https://raw.githubusercontent.com/fzhu2e/PyBAYWATCH-data/main/params/mgsw_iters_v2.mat' to '/glade/u/home/fengzhu/.cache/pybaywatch'.
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Downloading file 'ModelOutput/Output_SpatAg_SST/params_standard.mat' from 'https://raw.githubusercontent.com/fzhu2e/PyBAYWATCH-data/main/ModelOutput/Output_SpatAg_SST/params_standard.mat' to '/glade/u/home/fengzhu/.cache/pybaywatch'.
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Downloading file 'params/bayes_posterior_v2.mat' from 'https://raw.githubusercontent.com/fzhu2e/PyBAYWATCH-data/main/params/bayes_posterior_v2.mat' to '/glade/u/home/fengzhu/.cache/pybaywatch'.
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>>> Annualizing prior w/ season: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
>>> Computing the localization matrix

Processing variables: 100%|██████████| 6/6 [00:03<00:00,  1.62it/s]

CPU times: user 45.6 s, sys: 5.32 s, total: 50.9 s
Wall time: 56.8 s

%%time

da_solver.run(debug=True, method='EnSRF')

>>> DA update

Updating time slices: 100%|██████████| 1/1 [00:01<00:00,  1.17s/it]

CPU times: user 552 ms, sys: 352 ms, total: 903 ms
Wall time: 1.21 s

obs.clim['tos']

<xarray.DataArray 'tos' (site: 108, month: 12, ens: 37)> Size: 384kB
array([[[24.98964428, 25.52079057, 25.5379354 , ..., 26.22998639,
         24.60683721, 24.03946287],
        [26.14605525, 26.68145717, 26.81708031, ..., 27.50632178,
         26.10616433, 25.43292147],
        [26.16318037, 26.62394453, 26.8269065 , ..., 27.65054307,
         26.28907888, 25.64532733],
        ...,
        [20.51508323, 20.65533336, 20.56095535, ..., 22.0505918 ,
         19.97467467, 19.8853713 ],
        [21.47187967, 21.64690884, 21.5836901 , ..., 22.86629721,
         20.95866565, 20.73685943],
        [23.11565247, 23.50138813, 23.45586241, ..., 24.36300985,
         22.5273542 , 22.09731781]],

       [[13.15960312, 13.18999481, 14.73693834, ..., 12.59305271,
         13.26918689, 13.63496439],
        [12.8160487 , 12.85155492, 14.41613352, ..., 12.60536941,
         13.13885234, 13.44034377],
        [12.63055132, 12.73222439, 14.2329644 , ..., 12.66594088,
         13.09130877, 13.33864956],
...
        [28.81588589, 29.11561769, 29.82312085, ..., 28.60266947,
         30.38756534, 30.35128148],
        [28.29479923, 28.58438013, 29.45398586, ..., 28.24714082,
         30.01692973, 29.94374773],
        [27.15237667, 27.44973652, 28.46498738, ..., 27.63690315,
         28.71819758, 28.68152386]],

       [[17.27940641, 18.46155153, 18.72935137, ..., 18.59167975,
         18.40038276, 18.32360715],
        [16.41073158, 17.60390502, 17.72796664, ..., 17.77028834,
         17.51002993, 17.34150959],
        [16.32921453, 17.52017026, 17.56754889, ..., 17.59238945,
         17.42921482, 17.18558818],
        ...,
        [24.45341295, 25.92965281, 25.81198479, ..., 24.59160362,
         25.74319989, 24.85062058],
        [21.50087597, 22.86761732, 23.12571876, ..., 22.41095442,
         22.88933679, 22.33521951],
        [18.99029965, 20.17563533, 20.60739092, ..., 20.17687656,
         20.19481303, 20.04177951]]], shape=(108, 12, 37))
Coordinates:
  * site     (site) object 864B 'DSDP516.Mg/Ca' ... 'PuntaPiccola.UK37'
  * month    (month) float64 96B 1.0 2.0 3.0 4.0 5.0 ... 8.0 9.0 10.0 11.0 12.0
  * ens      (ens) int64 296B 0 1 2 3 4 5 6 7 8 9 ... 28 29 30 31 32 33 34 35 36
    lat      (site, ens) float64 32kB -30.5 -30.5 -30.5 -30.5 ... 37.5 37.5 37.5
    lon      (site, ens) float64 32kB 324.5 324.5 324.5 324.5 ... 12.5 12.5 12.5
Attributes:
    Description:  Sea surface temperature
    Units:        Celsius

xarray.DataArray

'tos'

• site: 108
• month: 12
• ens: 37

• 24.99 25.52 25.54 26.58 26.75 24.66 ... 18.15 19.62 20.18 20.19 20.04

  array([[[24.98964428, 25.52079057, 25.5379354 , ..., 26.22998639,
           24.60683721, 24.03946287],
          [26.14605525, 26.68145717, 26.81708031, ..., 27.50632178,
           26.10616433, 25.43292147],
          [26.16318037, 26.62394453, 26.8269065 , ..., 27.65054307,
           26.28907888, 25.64532733],
          ...,
          [20.51508323, 20.65533336, 20.56095535, ..., 22.0505918 ,
           19.97467467, 19.8853713 ],
          [21.47187967, 21.64690884, 21.5836901 , ..., 22.86629721,
           20.95866565, 20.73685943],
          [23.11565247, 23.50138813, 23.45586241, ..., 24.36300985,
           22.5273542 , 22.09731781]],
  
         [[13.15960312, 13.18999481, 14.73693834, ..., 12.59305271,
           13.26918689, 13.63496439],
          [12.8160487 , 12.85155492, 14.41613352, ..., 12.60536941,
           13.13885234, 13.44034377],
          [12.63055132, 12.73222439, 14.2329644 , ..., 12.66594088,
           13.09130877, 13.33864956],
  ...
          [28.81588589, 29.11561769, 29.82312085, ..., 28.60266947,
           30.38756534, 30.35128148],
          [28.29479923, 28.58438013, 29.45398586, ..., 28.24714082,
           30.01692973, 29.94374773],
          [27.15237667, 27.44973652, 28.46498738, ..., 27.63690315,
           28.71819758, 28.68152386]],
  
         [[17.27940641, 18.46155153, 18.72935137, ..., 18.59167975,
           18.40038276, 18.32360715],
          [16.41073158, 17.60390502, 17.72796664, ..., 17.77028834,
           17.51002993, 17.34150959],
          [16.32921453, 17.52017026, 17.56754889, ..., 17.59238945,
           17.42921482, 17.18558818],
          ...,
          [24.45341295, 25.92965281, 25.81198479, ..., 24.59160362,
           25.74319989, 24.85062058],
          [21.50087597, 22.86761732, 23.12571876, ..., 22.41095442,
           22.88933679, 22.33521951],
          [18.99029965, 20.17563533, 20.60739092, ..., 20.17687656,
           20.19481303, 20.04177951]]], shape=(108, 12, 37))

• Coordinates: (5)
  • site
    (site)
    object
    'DSDP516.Mg/Ca' ... 'PuntaPiccol...

    array(['DSDP516.Mg/Ca', 'DSDP552.Mg/Ca', 'DSDP590.Mg/Ca', 'DSDP603.Mg/Ca',
           'DSDP609.Mg/Ca', 'DSDP610.Mg/Ca', 'IODPU1308.Mg/Ca', 'IODPU1313.Mg/Ca',
           'IODPU1488.Mg/Ca', 'ODP1143.Mg/Ca', 'ODP1241.Mg/Ca', 'ODP1264.Mg/Ca',
           'ODP625.Mg/Ca', 'ODP709.Mg/Ca', 'ODP763.Mg/Ca', 'ODP806.Mg/Ca',
           'ODP847.Mg/Ca', 'ODP982.Mg/Ca', 'ODP997.Mg/Ca', 'ODP999.Mg/Ca',
           'AND1B.TEX86', 'DSDP590.TEX86', 'DSDP608.TEX86', 'DSDP610.TEX86',
           'IODPU1338.TEX86', 'IODPU1425.TEX86', 'IODPU1459.TEX86',
           'IODPU1460.TEX86', 'IODPU1461.TEX86', 'IODPU1463.TEX86',
           'IODPU1478.TEX86', 'IODPU1488.TEX86', 'ODP1143.TEX86', 'ODP1172.TEX86',
           'ODP1241.TEX86', 'ODP806.TEX86', 'ODP850.TEX86', 'ODP882.TEX86',
           'ODP982.TEX86', 'PuntaPiccola.TEX86', 'DEQ161-592.UK37', 'DSDP36.UK37',
           'DSDP 475.UK37', 'DSDP590.UK37', 'DSDP593.UK37', 'DSDP594.UK37',
           'DSDP607.UK37', 'DSDP609.UK37', 'DSDP610.UK37', 'Eagles Roost.UK37',
           'FarmRoad.UK37', 'IODPU1308.UK37', 'IODPU1313.UK37', 'IODPU1337.UK37',
           'IODPU1338.UK37', 'IODPU1387.UK37', 'IODPU1417.UK37', 'IODPU1425.UK37',
           'IODPU1461.UK37', 'NansemondRiver.UK37', 'ODP1006.UK37', 'ODP1010.UK37',
           'ODP1012.UK37', 'ODP1014.UK37', 'ODP1018.UK37', 'ODP1021.UK37',
           'ODP1062.UK37', 'ODP1063.UK37', 'ODP1073.UK37', 'ODP1081.UK37',
           'ODP1082.UK37', 'ODP1084.UK37', 'ODP1085.UK37', 'ODP1087.UK37',
           'ODP1088.UK37', 'ODP1090.UK37', 'ODP1115.UK37', 'ODP1123.UK37',
           'ODP1125.UK37', 'ODP1143.UK37', 'ODP1146.UK37', 'ODP1147.UK37',
           'ODP1168.UK37', 'ODP1172.UK37', 'ODP1208.UK37', 'ODP1237.UK37',
           'ODP1239.UK37', 'ODP1241.UK37', 'ODP625.UK37', 'ODP642.UK37',
           'ODP662.UK37', 'ODP722.UK37', 'ODP806.UK37', 'ODP846.UK37',
           'ODP847.UK37', 'ODP850.UK37', 'ODP882.UK37', 'ODP883.UK37',
           'ODP887.UK37', 'ODP907.UK37', 'ODP925.UK37', 'ODP951.UK37',
           'ODP958.UK37', 'ODP959.UK37', 'ODP978.UK37', 'ODP982.UK37',
           'ODP999.UK37', 'PuntaPiccola.UK37'], dtype=object)

  • month
    (month)
    float64
    1.0 2.0 3.0 4.0 ... 10.0 11.0 12.0

    Units :

    Calendar Month

    array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12.])

  • ens
    (ens)
    int64
    0 1 2 3 4 5 6 ... 31 32 33 34 35 36

    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
           18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
           36])

  • lat
    (site, ens)
    float64
    -30.5 -30.5 -30.5 ... 37.5 37.5

    Units :

    Decimal Degrees North

    array([[-30.5, -30.5, -30.5, ..., -30.5, -30.5, -30.5],
           [ 55.5,  55.5,  55.5, ...,  55.5,  55.5,  55.5],
           [-32.5, -32.5, -32.5, ..., -32.5, -32.5, -32.5],
           ...,
           [ 57.5,  57.5,  57.5, ...,  57.5,  57.5,  57.5],
           [ 12.5,  12.5,  12.5, ...,  12.5,  12.5,  12.5],
           [ 37.5,  37.5,  37.5, ...,  37.5,  37.5,  37.5]], shape=(108, 37))

  • lon
    (site, ens)
    float64
    324.5 324.5 324.5 ... 12.5 12.5

    Units :

    Decimal Degrees East

    Units_Range :

    0 - 360

    array([[324.5, 324.5, 324.5, ..., 324.5, 324.5, 324.5],
           [345.5, 345.5, 345.5, ..., 345.5, 345.5, 345.5],
           [162.5, 162.5, 162.5, ..., 162.5, 162.5, 162.5],
           ...,
           [343.5, 343.5, 343.5, ..., 343.5, 343.5, 343.5],
           [281.5, 281.5, 281.5, ..., 281.5, 281.5, 281.5],
           [ 12.5,  12.5,  12.5, ...,  12.5,  12.5,  12.5]], shape=(108, 37))

• Attributes: (2)

  Description :

  Sea surface temperature

  Units :

  Celsius

obs.records['AND1B.TEX86'].psm.clim

<xarray.Dataset> Size: 2kB
Dimensions:  (ens: 37)
Coordinates:
  * ens      (ens) int64 296B 0 1 2 3 4 5 6 7 8 9 ... 28 29 30 31 32 33 34 35 36
    lat      (ens) float64 296B -77.5 -77.5 -77.5 -77.5 ... -77.5 -76.5 -76.5
    lon      (ens) float64 296B 165.5 165.5 165.5 165.5 ... 171.5 165.5 165.5
    site     <U11 44B 'AND1B.TEX86'
Data variables:
    sos      (ens) float64 296B 34.71 34.08 34.33 33.85 ... 34.27 34.26 34.15
    tos      (ens) float64 296B -1.755 -1.106 -1.626 ... 2.654 -0.7089 -1.034

xarray.Dataset

• Dimensions:
  • ens: 37
• Coordinates: (4)
  • ens
    (ens)
    int64
    0 1 2 3 4 5 6 ... 31 32 33 34 35 36

    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
           18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
           36])

  • lat
    (ens)
    float64
    -77.5 -77.5 -77.5 ... -76.5 -76.5

    Units :

    Decimal Degrees North

    array([-77.5, -77.5, -77.5, -77.5, -77.5, -77.5, -77.5, -77.5, -76.5,
           -76.5, -77.5, -77.5, -77.5, -77.5, -77.5, -77.5, -77.5, -77.5,
           -77.5, -77.5, -77.5, -77.5, -76.5, -76.5, -76.5, -76.5, -77.5,
           -77.5, -77.5, -77.5, -77.5, -77.5, -77.5, -77.5, -77.5, -76.5,
           -76.5])

  • lon
    (ens)
    float64
    165.5 165.5 165.5 ... 165.5 165.5

    Units :

    Decimal Degrees East

    Units_Range :

    0 - 360

    array([165.5, 165.5, 165.5, 165.5, 167.5, 165.5, 165.5, 165.5, 165.5,
           165.5, 165.5, 165.5, 172.5, 165.5, 171.5, 171.5, 171.5, 171.5,
           171.5, 171.5, 171.5, 171.5, 165.5, 165.5, 165.5, 165.5, 171.5,
           171.5, 171.5, 171.5, 171.5, 171.5, 171.5, 171.5, 171.5, 165.5,
           165.5])

  • site
    ()
    <U11
    'AND1B.TEX86'

    array('AND1B.TEX86', dtype='<U11')

• Data variables: (2)
  • sos
    (ens)
    float64
    34.71 34.08 34.33 ... 34.26 34.15

    Description :

    Sea surface salinity

    Units :

    g / kg

    Units_Equivalent :

    psu

    array([34.70877841, 34.08478722, 34.3315691 , 33.84909859, 32.52622446,
           34.38041763, 32.66093469, 32.83334716, 34.23905706, 34.49033451,
           32.85973327, 33.81252222, 34.34856272, 33.70267398, 34.02659603,
           33.59452361, 32.67585822, 34.10121604, 33.85626292, 33.29874275,
           33.9173229 , 33.50323712, 34.27252668, 34.02969915, 34.27735562,
           33.88709319, 33.9408543 , 34.0006449 , 34.06223899, 34.06016814,
           34.08589581, 33.88025254, 33.45537672, 34.34426064, 34.26896906,
           34.26388339, 34.15180794])

  • tos
    (ens)
    float64
    -1.755 -1.106 ... -0.7089 -1.034

    Description :

    Sea surface temperature

    Units :

    Celsius

    array([-1.75457253, -1.10599004, -1.62552317, -1.4836717 ,  1.84770944,
           -1.83796226, -1.15521316, -0.18549225,  0.28388026, -0.12505616,
           -0.1746568 , -1.82021391, -1.54917904, -1.55725115, -1.33370263,
           -0.85280264,  0.05080001, -1.36477264, -1.08039331, -0.29911046,
           -1.12263704, -0.4644581 , -0.58174113,  0.05816216, -0.47269077,
            0.39905269, -1.08984547, -1.09639717, -1.12085733, -1.13738467,
           -1.1627627 , -0.93760438, -0.58462777,  2.52682331,  2.65374934,
           -0.70885199, -1.03383969])

da_solver.obs.df

	pid	lat	lon	depth	type	seasonality	time	value	R	psm_name	species	clean	pseudo	Ym
0	DSDP516.Mg/Ca	-30.646322	324.994450	1313.0	Mg/Ca	1,2,3,4,5,6,12	3.25	1.153137	0.0169	MgCa	sacculifer	0.0	[1.0475985789968556, 1.0840266910980385, 1.084...	1.063031
1	DSDP552.Mg/Ca	55.593510	345.982189	2301.0	Mg/Ca	1,2,3,4,5,6,7,8,9,10,11,12	3.25	0.899058	0.0169	MgCa	bulloides	0.0	[0.8790122833288491, 0.9304375599758773, 0.997...	0.785512
2	DSDP590.Mg/Ca	-32.594207	162.898037	1299.0	Mg/Ca	1,2,3,4,5,12	3.25	1.157111	0.0169	MgCa	sacculifer	0.0	[1.0078668290061659, 1.0693127594208578, 1.064...	1.056172
3	DSDP603.Mg/Ca	35.261855	290.479297	4640.0	Mg/Ca	1,2,3,4,5,6,7,8,9,10,11,12	3.25	1.381721	0.0169	MgCa	bulloides	0.0	[1.3526891904768947, 1.43754172903691, 1.41165...	1.330346
4	DSDP609.Mg/Ca	49.422589	335.045676	3883.0	Mg/Ca	1,2,3,4,5,6,7,8,9,10,11,12	3.25	0.838967	0.0169	MgCa	bulloides	1.0	[0.6706339455958514, 0.8230265774912799, 0.756...	0.528415
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
103	ODP959.UK37	3.053953	356.530460	2092.0	UK37	1,2,3,4,5,6,7,8,9,10,11,12	3.25	0.955809	0.0025	UK37	NaN	NaN	[0.9748423127669279, 0.9783112988235072, 0.982...	0.973247
104	ODP978.UK37	35.655501	357.297471	1929.0	UK37	1,2,3,4,5,6,7,8,9,10,11,12	3.25	0.940120	0.0025	UK37	NaN	NaN	[0.7704574251030296, 0.7984664965587505, 0.822...	0.753707
105	ODP982.UK37	57.058493	343.381278	1135.0	UK37	1,2,3,4,5,6,7,8,9,10,11,12	3.25	0.611470	0.0025	UK37	NaN	NaN	[0.551810302904041, 0.5562428402868642, 0.6039...	0.492307
106	ODP999.UK37	12.533555	281.067292	2828.0	UK37	1,2,3,4,5,6,7,8,9,10,11,12	3.25	0.966871	0.0025	UK37	NaN	NaN	[0.9369960402682951, 0.940948717815467, 0.9622...	0.946704
107	PuntaPiccola.UK37	37.115774	12.130472	NaN	UK37	1,2,3,4,5,6,7,8,9,10,11,12	3.25	0.898781	0.0025	UK37	NaN	NaN	[0.7789384208648916, 0.8155885819627052, 0.827...	0.760645

108 rows × 14 columns

print('Prior')
gmst = da_solver.prior.ds['tas'].mean('ens').x.geo_mean().values - 273.15
print(f'GMST = {gmst:.2f}')
gmsst = da_solver.prior.ds['tos'].mean('ens').x.geo_mean().values
print(f'GMSST = {gmsst:.2f}')

print('Posterior')
gmst = da_solver.post['tas'].isel(time=0).mean('ens').x.geo_mean().values - 273.15
print(f'GMST = {gmst:.2f}')
gmsst = da_solver.post['tos'].isel(time=0).mean('ens').x.geo_mean().values
print(f'GMSST = {gmsst:.2f}')

Prior
GMST = 16.00
GMSST = 19.84
Posterior
GMST = 17.29
GMSST = 20.67

ds_Jess = xr.open_dataset(os.path.join(dirpath, 'recons', 'pliomip-cloud_Loc24_locFixed.nc'))
ds_Jess

<xarray.Dataset> Size: 22MB
Dimensions:        (time: 3, lat: 180, lon: 360, month: 12)
Coordinates:
  * time           (time) float64 24B 0.0 3.25 4.75
  * lat            (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5
  * lon            (lon) float64 3kB 0.5 1.5 2.5 3.5 ... 356.5 357.5 358.5 359.5
  * month          (month) float64 96B 1.0 2.0 3.0 4.0 ... 9.0 10.0 11.0 12.0
Data variables: (12/14)
    pr_annual      (time, lat, lon) float64 2MB ...
    pr_DJF         (time, lat, lon) float64 2MB ...
    pr_JJA         (time, lat, lon) float64 2MB ...
    ev_annual      (time, lat, lon) float64 2MB ...
    ev_DJF         (time, lat, lon) float64 2MB ...
    ev_JJA         (time, lat, lon) float64 2MB ...
    ...             ...
    tas_JJA        (time, lat, lon) float64 2MB ...
    siconc_annual  (time, lat, lon) float64 2MB ...
    siconc_DJF     (time, lat, lon) float64 2MB ...
    siconc_JJA     (time, lat, lon) float64 2MB ...
    tos_annual     (time, lat, lon) float64 2MB ...
    sos_annual     (time, lat, lon) float64 2MB ...

xarray.Dataset

• Dimensions:
  • time: 3
  • lat: 180
  • lon: 360
  • month: 12
• Coordinates: (4)
  • time
    (time)
    float64
    0.0 3.25 4.75

    Units :

    Ma

    array([0.  , 3.25, 4.75])

  • lat
    (lat)
    float64
    -89.5 -88.5 -87.5 ... 88.5 89.5

    Units :

    Decimal degrees north

    array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,
           -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,
           -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,
           -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,
           -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,
           -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,
           -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,
           -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,
            -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,
             0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,
            10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,
            20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,
            30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,
            40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,
            50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,
            60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,
            70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,
            80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])

  • lon
    (lon)
    float64
    0.5 1.5 2.5 ... 357.5 358.5 359.5

    Units :

    Decimal degrees east

    Range :

    0-360

    array([  0.5,   1.5,   2.5, ..., 357.5, 358.5, 359.5], shape=(360,))

  • month
    (month)
    float64
    1.0 2.0 3.0 4.0 ... 10.0 11.0 12.0

    Units :

    Month of year

    array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12.])

• Data variables: (14)
  • pr_annual
    (time, lat, lon)
    float64
    ...

    Description :

    Annual precipitation (total)

    Units :

    mm/day

    [194400 values with dtype=float64]

  • pr_DJF
    (time, lat, lon)
    float64
    ...

    Description :

    Winter (DJF) precipitation (total)

    Units :

    mm/day

    [194400 values with dtype=float64]

  • pr_JJA
    (time, lat, lon)
    float64
    ...

    Description :

    Summer (JJA) precipitation (total)

    Units :

    mm/day

    [194400 values with dtype=float64]

  • ev_annual
    (time, lat, lon)
    float64
    ...

    Description :

    Annual evaporation

    Units :

    mm/day

    [194400 values with dtype=float64]

  • ev_DJF
    (time, lat, lon)
    float64
    ...

    Description :

    Winter (DJF) evaporation

    Units :

    mm/day

    [194400 values with dtype=float64]

  • ev_JJA
    (time, lat, lon)
    float64
    ...

    Description :

    Summer (JJA) evaporation

    Units :

    mm/day

    [194400 values with dtype=float64]

  • tas_annual
    (time, lat, lon)
    float64
    ...

    Description :

    Annual near surface (usually 2m) air temperature

    Units :

    Kelvin

    [194400 values with dtype=float64]

  • tas_DJF
    (time, lat, lon)
    float64
    ...

    Description :

    Winter (DJF) near surface (usually 2m) air temperature

    Units :

    Kelvin

    [194400 values with dtype=float64]

  • tas_JJA
    (time, lat, lon)
    float64
    ...

    Description :

    Summer (JJA) near surface (usually 2m) air temperature

    Units :

    Kelvin

    [194400 values with dtype=float64]

  • siconc_annual
    (time, lat, lon)
    float64
    ...

    Description :

    Annual sea ice area percentage

    Units :

    %

    [194400 values with dtype=float64]

  • siconc_DJF
    (time, lat, lon)
    float64
    ...

    Description :

    Winter (DJF) sea ice area percentage

    Units :

    %

    [194400 values with dtype=float64]

  • siconc_JJA
    (time, lat, lon)
    float64
    ...

    Description :

    Summer (JJA) sea ice area percentage

    Units :

    %

    [194400 values with dtype=float64]

  • tos_annual
    (time, lat, lon)
    float64
    ...

    Description :

    Annual sea surface temperature

    Units :

    Celsius

    [194400 values with dtype=float64]

  • sos_annual
    (time, lat, lon)
    float64
    ...

    Description :

    Annual sea surface salinity

    Units :

    psu

    [194400 values with dtype=float64]

%%time
da_Feng = da_solver.post['tos'].mean('ens')
da_Jess = ds_Jess['tos_annual'].isel(time=1)

fig, ax = da_Feng.x.plot(
    levels=np.linspace(0, 40, 21),
    cbar_kwargs={'ticks': np.linspace(0, 40, 11)},
    cmap='RdBu_r',
    title='Posterior (Feng)',
    df_sites=obs.df[['lat', 'lon', 'type']],
    site_marker_dict={
        'Mg/Ca': 'o',
        'UK37': 's',
        'TEX86': '^',
    },
    count_site_num=True,
    lgd_kws={'bbox_to_anchor': (0, -0.2)},
)

fig, ax = da_Jess.x.plot(
    levels=np.linspace(0, 40, 21),
    cbar_kwargs={'ticks': np.linspace(0, 40, 11)},
    cmap='RdBu_r',
    title='Posterior (Jess)',
    df_sites=df[['lat', 'lon', 'type']],
    site_marker_dict={
        'Mg/Ca': 'o',
        'UK37': 's',
        'TEX86': '^',
    },
    count_site_num=True,
    lgd_kws={'bbox_to_anchor': (0, -0.2)},
)


fig, ax = (da_Feng - da_Jess).x.plot(
    levels=np.linspace(-0.2, 0.2, 11),
    cbar_kwargs={'ticks': np.linspace(-0.2, 0.2, 11)},
    cmap='RdBu_r',
    title='Diff (tos)',
    df_sites=df[['lat', 'lon', 'type']],
    site_marker_dict={
        'Mg/Ca': 'o',
        'UK37': 's',
        'TEX86': '^',
    },
    count_site_num=True,
    lgd_kws={'bbox_to_anchor': (0, -0.2)},
)

CPU times: user 998 ms, sys: 0 ns, total: 998 ms
Wall time: 996 ms

np.max(np.abs(da_Feng - da_Jess))

<xarray.DataArray ()> Size: 8B
array(0.14067962)
Attributes:
    Units:    Celsius

xarray.DataArray

• 0.1407

  array(0.14067962)

• Attributes: (1)

  Units :

  Celsius