Transient Biological Pump¶

%load_ext autoreload
%autoreload 2

import os

from itertools import product

import pandas as pd
import numpy as np
import xarray as xr

import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import matplotlib.colors as colors
import cmocean

import cartopy
import cartopy.crs as ccrs

import xpersist as xp
cache_dir = '/glade/p/cgd/oce/projects/cesm2-marbl/xpersist_cache/3d_fields'
if (os.path.isdir(cache_dir)):
    xp.settings['cache_dir'] = cache_dir
os.makedirs(cache_dir, exist_ok=True)

os.environ['CESMDATAROOT'] = '/glade/scratch/mclong/inputdata'
import pop_tools


import climo_utils as cu
import utils
import discrete_obs 
import plot

import data_collections

ds_grid = pop_tools.get_grid('POP_gx1v7')
masked_area = ds_grid.TAREA.where(ds_grid.REGION_MASK > 0).fillna(0.).expand_dims('region')
masked_area.plot()

/glade/work/mclong/miniconda3/envs/cesm2-marbl/lib/python3.7/site-packages/numba/np/ufunc/parallel.py:365: NumbaWarning: The TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 6103. The TBB threading layer is disabled.
  warnings.warn(problem)

<matplotlib.collections.QuadMesh at 0x2b15cb9189d0>

../_images/transient-biological-pump_3_2.png

cluster, client = utils.get_ClusterClient()
cluster.scale(12) #adapt(minimum_jobs=0, maximum_jobs=24)
client

Client

Scheduler: tcp://10.12.206.51:32919
Dashboard: https://jupyterhub.hpc.ucar.edu/stable/user/mclong/proxy/8787/status

Cluster

Workers: 0
Cores: 0
Memory: 0 B

experiments = [
    'historical',
    'SSP1-2.6',
    'SSP2-4.5',
    'SSP3-7.0',
    'SSP5-8.5',
]

time_slice = {
    'historical': slice("1990-01-15", "2015-01-15"),
    'SSP1-2.6': slice("2086-01-15", "2101-01-15"),
    'SSP2-4.5': slice("2086-01-15", "2101-01-15"),
    'SSP3-7.0': slice("2086-01-15", "2101-01-15"),
    'SSP5-8.5': slice("2086-01-15", "2101-01-15"),
}

varlist = [
    'photoC_TOT_zint_100m',
    'photoC_diat_zint_100m',
    'POC_FLUX_100m', 
]

query = dict(
    experiment=experiments,
    stream='pop.h',
)

ts_int = data_collections.global_integral_timeseries_ann(query)
ts_avg = data_collections.global_mean_timeseries_ann(query)
epoch_mean = data_collections.epoch_mean(query)
epoch_mean

{ 'esm_collection': 'data/campaign-cesm2-cmip6-timeseries.json',
  'name': 'epoch_mean',
  'operator_kwargs': [ { },
                       { }],
  'operators': [ 'center_time',
                 '_mean_time_for_experiment'],
  'preprocess': None,
  'query': { 'experiment': [ 'historical',
                             'SSP1-2.6',
                             'SSP2-4.5',
                             'SSP3-7.0',
                             'SSP5-8.5'],
             'stream': 'pop.h'}}

nmolcm2s_to_molm2yr = 1e-9 * 1e4 * 86400 * 365.

dsets_epoch = epoch_mean.to_dataset_dict(varlist, clobber=False)

for key, ds in dsets_epoch.items():
    for v in varlist:
        if ds[v].attrs['units'] == 'mmol/m^3 cm/s':
            ds[v] = ds[v] * nmolcm2s_to_molm2yr
            ds[v].attrs['units'] = 'mol m$^{-2}$ yr$^{-1}$'        
    ds['NPP_diat_frac_100m'] = ds.photoC_diat_zint_100m / ds.photoC_TOT_zint_100m
    ds['ep_ratio_100m'] = ds.POC_FLUX_100m / ds.photoC_TOT_zint_100m

dsets_epoch

{'ocn.SSP5-8.5.pop.h': <xarray.Dataset>
 Dimensions:                 (lat_aux_grid: 395, member_id: 3, moc_comp: 3, moc_z: 61, nlat: 384, nlon: 320, transport_comp: 5, transport_reg: 2, z_t: 60, z_t_150m: 15, z_w: 60, z_w_bot: 60, z_w_top: 60)
 Coordinates:
     ULONG                   (nlat, nlon) float64 321.1 322.3 ... 319.6 320.0
     ULAT                    (nlat, nlon) float64 -78.95 -78.95 ... 72.41 72.41
     TLONG                   (nlat, nlon) float64 320.6 321.7 ... 319.4 319.8
     TLAT                    (nlat, nlon) float64 -79.22 -79.22 ... 72.19 72.19
   * member_id               (member_id) int64 4 10 11
   * lat_aux_grid            (lat_aux_grid) float32 -79.49 -78.95 ... 89.47 90.0
   * z_w_top                 (z_w_top) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05
   * moc_z                   (moc_z) float32 0.0 1e+03 2e+03 ... 5.25e+05 5.5e+05
   * z_t_150m                (z_t_150m) float32 500.0 1.5e+03 ... 1.45e+04
   * z_t                     (z_t) float32 500.0 1.5e+03 ... 5.125e+05 5.375e+05
   * z_w_bot                 (z_w_bot) float32 1e+03 2e+03 ... 5.25e+05 5.5e+05
   * z_w                     (z_w) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05
 Dimensions without coordinates: moc_comp, nlat, nlon, transport_comp, transport_reg
 Data variables: (12/58)
     photoC_TOT_zint_100m    (member_id, nlat, nlon) float64 0.0 0.0 ... 0.0 0.0
     nsurface_u              float64 8.297e+04
     DXU                     (nlat, nlon) float64 2.397e+06 ... 1.391e+06
     DYT                     (nlat, nlon) float64 5.94e+06 5.94e+06 ... 5.046e+06
     vonkar                  float64 0.4
     dz                      (z_t) float32 1e+03 1e+03 1e+03 ... 2.5e+04 2.5e+04
     ...                      ...
     HUW                     (nlat, nlon) float64 5.94e+06 5.94e+06 ... 5.046e+06
     salt_to_ppt             float64 1e+03
     photoC_diat_zint_100m   (member_id, nlat, nlon) float64 0.0 0.0 ... 0.0 0.0
     POC_FLUX_100m           (member_id, nlat, nlon) float64 0.0 0.0 ... 0.0 0.0
     NPP_diat_frac_100m      (member_id, nlat, nlon) float64 nan nan ... nan nan
     ep_ratio_100m           (member_id, nlat, nlon) float64 nan nan ... nan nan,
 'ocn.SSP3-7.0.pop.h': <xarray.Dataset>
 Dimensions:                 (lat_aux_grid: 395, member_id: 3, moc_comp: 3, moc_z: 61, nlat: 384, nlon: 320, transport_comp: 5, transport_reg: 2, z_t: 60, z_t_150m: 15, z_w: 60, z_w_bot: 60, z_w_top: 60)
 Coordinates:
     ULONG                   (nlat, nlon) float64 321.1 322.3 ... 319.6 320.0
     ULAT                    (nlat, nlon) float64 -78.95 -78.95 ... 72.41 72.41
     TLONG                   (nlat, nlon) float64 320.6 321.7 ... 319.4 319.8
     TLAT                    (nlat, nlon) float64 -79.22 -79.22 ... 72.19 72.19
   * member_id               (member_id) int64 4 10 11
   * lat_aux_grid            (lat_aux_grid) float32 -79.49 -78.95 ... 89.47 90.0
   * z_w_top                 (z_w_top) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05
   * moc_z                   (moc_z) float32 0.0 1e+03 2e+03 ... 5.25e+05 5.5e+05
   * z_t_150m                (z_t_150m) float32 500.0 1.5e+03 ... 1.45e+04
   * z_t                     (z_t) float32 500.0 1.5e+03 ... 5.125e+05 5.375e+05
   * z_w_bot                 (z_w_bot) float32 1e+03 2e+03 ... 5.25e+05 5.5e+05
   * z_w                     (z_w) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05
 Dimensions without coordinates: moc_comp, nlat, nlon, transport_comp, transport_reg
 Data variables: (12/58)
     photoC_TOT_zint_100m    (member_id, nlat, nlon) float64 0.0 0.0 ... 0.0 0.0
     nsurface_u              float64 8.297e+04
     DXU                     (nlat, nlon) float64 2.397e+06 ... 1.391e+06
     DYT                     (nlat, nlon) float64 5.94e+06 5.94e+06 ... 5.046e+06
     vonkar                  float64 0.4
     dz                      (z_t) float32 1e+03 1e+03 1e+03 ... 2.5e+04 2.5e+04
     ...                      ...
     HUW                     (nlat, nlon) float64 5.94e+06 5.94e+06 ... 5.046e+06
     salt_to_ppt             float64 1e+03
     photoC_diat_zint_100m   (member_id, nlat, nlon) float64 0.0 0.0 ... 0.0 0.0
     POC_FLUX_100m           (member_id, nlat, nlon) float64 0.0 0.0 ... 0.0 0.0
     NPP_diat_frac_100m      (member_id, nlat, nlon) float64 nan nan ... nan nan
     ep_ratio_100m           (member_id, nlat, nlon) float64 nan nan ... nan nan,
 'ocn.SSP2-4.5.pop.h': <xarray.Dataset>
 Dimensions:                 (lat_aux_grid: 395, member_id: 3, moc_comp: 3, moc_z: 61, nlat: 384, nlon: 320, transport_comp: 5, transport_reg: 2, z_t: 60, z_t_150m: 15, z_w: 60, z_w_bot: 60, z_w_top: 60)
 Coordinates:
     ULONG                   (nlat, nlon) float64 321.1 322.3 ... 319.6 320.0
     ULAT                    (nlat, nlon) float64 -78.95 -78.95 ... 72.41 72.41
     TLONG                   (nlat, nlon) float64 320.6 321.7 ... 319.4 319.8
     TLAT                    (nlat, nlon) float64 -79.22 -79.22 ... 72.19 72.19
   * member_id               (member_id) int64 4 10 11
   * lat_aux_grid            (lat_aux_grid) float32 -79.49 -78.95 ... 89.47 90.0
   * z_w_top                 (z_w_top) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05
   * moc_z                   (moc_z) float32 0.0 1e+03 2e+03 ... 5.25e+05 5.5e+05
   * z_t_150m                (z_t_150m) float32 500.0 1.5e+03 ... 1.45e+04
   * z_t                     (z_t) float32 500.0 1.5e+03 ... 5.125e+05 5.375e+05
   * z_w_bot                 (z_w_bot) float32 1e+03 2e+03 ... 5.25e+05 5.5e+05
   * z_w                     (z_w) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05
 Dimensions without coordinates: moc_comp, nlat, nlon, transport_comp, transport_reg
 Data variables: (12/58)
     photoC_TOT_zint_100m    (member_id, nlat, nlon) float64 0.0 0.0 ... 0.0 0.0
     nsurface_u              float64 8.297e+04
     DXU                     (nlat, nlon) float64 2.397e+06 ... 1.391e+06
     DYT                     (nlat, nlon) float64 5.94e+06 5.94e+06 ... 5.046e+06
     vonkar                  float64 0.4
     dz                      (z_t) float32 1e+03 1e+03 1e+03 ... 2.5e+04 2.5e+04
     ...                      ...
     HUW                     (nlat, nlon) float64 5.94e+06 5.94e+06 ... 5.046e+06
     salt_to_ppt             float64 1e+03
     photoC_diat_zint_100m   (member_id, nlat, nlon) float64 0.0 0.0 ... 0.0 0.0
     POC_FLUX_100m           (member_id, nlat, nlon) float64 0.0 0.0 ... 0.0 0.0
     NPP_diat_frac_100m      (member_id, nlat, nlon) float64 nan nan ... nan nan
     ep_ratio_100m           (member_id, nlat, nlon) float64 nan nan ... nan nan,
 'ocn.historical.pop.h': <xarray.Dataset>
 Dimensions:                 (lat_aux_grid: 395, member_id: 11, moc_comp: 3, moc_z: 61, nlat: 384, nlon: 320, transport_comp: 5, transport_reg: 2, z_t: 60, z_t_150m: 15, z_w: 60, z_w_bot: 60, z_w_top: 60)
 Coordinates:
     ULONG                   (nlat, nlon) float64 321.1 322.3 ... 319.6 320.0
     ULAT                    (nlat, nlon) float64 -78.95 -78.95 ... 72.41 72.41
     TLONG                   (nlat, nlon) float64 320.6 321.7 ... 319.4 319.8
     TLAT                    (nlat, nlon) float64 -79.22 -79.22 ... 72.19 72.19
   * member_id               (member_id) int64 1 2 3 4 5 6 7 8 9 10 11
   * lat_aux_grid            (lat_aux_grid) float32 -79.49 -78.95 ... 89.47 90.0
   * z_w_top                 (z_w_top) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05
   * moc_z                   (moc_z) float32 0.0 1e+03 2e+03 ... 5.25e+05 5.5e+05
   * z_t_150m                (z_t_150m) float32 500.0 1.5e+03 ... 1.45e+04
   * z_t                     (z_t) float32 500.0 1.5e+03 ... 5.125e+05 5.375e+05
   * z_w_bot                 (z_w_bot) float32 1e+03 2e+03 ... 5.25e+05 5.5e+05
   * z_w                     (z_w) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05
 Dimensions without coordinates: moc_comp, nlat, nlon, transport_comp, transport_reg
 Data variables: (12/58)
     photoC_TOT_zint_100m    (member_id, nlat, nlon) float64 0.0 0.0 ... 0.0 0.0
     nsurface_u              float64 8.297e+04
     DXU                     (nlat, nlon) float64 2.397e+06 ... 1.391e+06
     DYT                     (nlat, nlon) float64 5.94e+06 5.94e+06 ... 5.046e+06
     vonkar                  float64 0.4
     dz                      (z_t) float32 1e+03 1e+03 1e+03 ... 2.5e+04 2.5e+04
     ...                      ...
     HUW                     (nlat, nlon) float64 5.94e+06 5.94e+06 ... 5.046e+06
     salt_to_ppt             float64 1e+03
     photoC_diat_zint_100m   (member_id, nlat, nlon) float64 0.0 0.0 ... 0.0 0.0
     POC_FLUX_100m           (member_id, nlat, nlon) float64 0.0 0.0 ... 0.0 0.0
     NPP_diat_frac_100m      (member_id, nlat, nlon) float64 nan nan ... nan nan
     ep_ratio_100m           (member_id, nlat, nlon) float64 nan nan ... nan nan,
 'ocn.SSP1-2.6.pop.h': <xarray.Dataset>
 Dimensions:                 (lat_aux_grid: 395, member_id: 3, moc_comp: 3, moc_z: 61, nlat: 384, nlon: 320, transport_comp: 5, transport_reg: 2, z_t: 60, z_t_150m: 15, z_w: 60, z_w_bot: 60, z_w_top: 60)
 Coordinates:
     ULONG                   (nlat, nlon) float64 321.1 322.3 ... 319.6 320.0
     ULAT                    (nlat, nlon) float64 -78.95 -78.95 ... 72.41 72.41
     TLONG                   (nlat, nlon) float64 320.6 321.7 ... 319.4 319.8
     TLAT                    (nlat, nlon) float64 -79.22 -79.22 ... 72.19 72.19
   * member_id               (member_id) int64 4 10 11
   * lat_aux_grid            (lat_aux_grid) float32 -79.49 -78.95 ... 89.47 90.0
   * z_w_top                 (z_w_top) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05
   * moc_z                   (moc_z) float32 0.0 1e+03 2e+03 ... 5.25e+05 5.5e+05
   * z_t_150m                (z_t_150m) float32 500.0 1.5e+03 ... 1.45e+04
   * z_t                     (z_t) float32 500.0 1.5e+03 ... 5.125e+05 5.375e+05
   * z_w_bot                 (z_w_bot) float32 1e+03 2e+03 ... 5.25e+05 5.5e+05
   * z_w                     (z_w) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05
 Dimensions without coordinates: moc_comp, nlat, nlon, transport_comp, transport_reg
 Data variables: (12/58)
     photoC_TOT_zint_100m    (member_id, nlat, nlon) float64 0.0 0.0 ... 0.0 0.0
     nsurface_u              float64 8.297e+04
     DXU                     (nlat, nlon) float64 2.397e+06 ... 1.391e+06
     DYT                     (nlat, nlon) float64 5.94e+06 5.94e+06 ... 5.046e+06
     vonkar                  float64 0.4
     dz                      (z_t) float32 1e+03 1e+03 1e+03 ... 2.5e+04 2.5e+04
     ...                      ...
     HUW                     (nlat, nlon) float64 5.94e+06 5.94e+06 ... 5.046e+06
     salt_to_ppt             float64 1e+03
     photoC_diat_zint_100m   (member_id, nlat, nlon) float64 0.0 0.0 ... 0.0 0.0
     POC_FLUX_100m           (member_id, nlat, nlon) float64 0.0 0.0 ... 0.0 0.0
     NPP_diat_frac_100m      (member_id, nlat, nlon) float64 nan nan ... nan nan
     ep_ratio_100m           (member_id, nlat, nlon) float64 nan nan ... nan nan}

dsets_ts = ts_int.to_dataset_dict(varlist, clobber=False)

for key, ds in dsets_ts.items():
    ds['NPP_diat_frac_100m'] = ds.photoC_diat_zint_100m / ds.photoC_TOT_zint_100m
    ds['ep_ratio_100m'] = ds.POC_FLUX_100m / ds.photoC_TOT_zint_100m

dsets_ts

{'ocn.SSP5-8.5.pop.h': <xarray.Dataset>
 Dimensions:                 (lat_aux_grid: 395, member_id: 3, moc_comp: 3, moc_z: 61, nlat: 384, nlon: 320, time: 86, transport_comp: 5, transport_reg: 2, z_t: 60, z_t_150m: 15, z_w: 60, z_w_bot: 60, z_w_top: 60)
 Coordinates: (12/13)
   * member_id               (member_id) int64 4 10 11
   * lat_aux_grid            (lat_aux_grid) float32 -79.49 -78.95 ... 89.47 90.0
     TLONG                   (nlat, nlon) float64 320.6 321.7 ... 319.4 319.8
   * z_w_top                 (z_w_top) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05
     TLAT                    (nlat, nlon) float64 -79.22 -79.22 ... 72.19 72.19
   * moc_z                   (moc_z) float32 0.0 1e+03 2e+03 ... 5.25e+05 5.5e+05
     ...                      ...
   * z_t                     (z_t) float32 500.0 1.5e+03 ... 5.125e+05 5.375e+05
     ULONG                   (nlat, nlon) float64 321.1 322.3 ... 319.6 320.0
   * z_w_bot                 (z_w_bot) float32 1e+03 2e+03 ... 5.25e+05 5.5e+05
   * z_w                     (z_w) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05
     ULAT                    (nlat, nlon) float64 -78.95 -78.95 ... 72.41 72.41
   * time                    (time) int64 2015 2016 2017 2018 ... 2098 2099 2100
 Dimensions without coordinates: moc_comp, nlat, nlon, transport_comp, transport_reg
 Data variables: (12/58)
     nsurface_u              float64 8.297e+04
     DXU                     (nlat, nlon) float64 2.397e+06 ... 1.391e+06
     DYT                     (nlat, nlon) float64 5.94e+06 5.94e+06 ... 5.046e+06
     vonkar                  float64 0.4
     dz                      (z_t) float32 1e+03 1e+03 1e+03 ... 2.5e+04 2.5e+04
     transport_regions       (transport_reg) |S384 b'Global Ocean - Marginal S...
     ...                      ...
     salt_to_ppt             float64 1e+03
     photoC_TOT_zint_100m    (time, member_id) float64 ...
     photoC_diat_zint_100m   (time, member_id) float64 ...
     POC_FLUX_100m           (time, member_id) float64 ...
     NPP_diat_frac_100m      (time, member_id) float64 0.3839 0.3697 ... 0.2938
     ep_ratio_100m           (time, member_id) float64 0.1475 0.1459 ... 0.1339,
 'ocn.SSP3-7.0.pop.h': <xarray.Dataset>
 Dimensions:                 (lat_aux_grid: 395, member_id: 3, moc_comp: 3, moc_z: 61, nlat: 384, nlon: 320, time: 86, transport_comp: 5, transport_reg: 2, z_t: 60, z_t_150m: 15, z_w: 60, z_w_bot: 60, z_w_top: 60)
 Coordinates: (12/13)
   * member_id               (member_id) int64 4 10 11
   * lat_aux_grid            (lat_aux_grid) float32 -79.49 -78.95 ... 89.47 90.0
     TLONG                   (nlat, nlon) float64 320.6 321.7 ... 319.4 319.8
   * z_w_top                 (z_w_top) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05
     TLAT                    (nlat, nlon) float64 -79.22 -79.22 ... 72.19 72.19
   * moc_z                   (moc_z) float32 0.0 1e+03 2e+03 ... 5.25e+05 5.5e+05
     ...                      ...
   * z_t                     (z_t) float32 500.0 1.5e+03 ... 5.125e+05 5.375e+05
     ULONG                   (nlat, nlon) float64 321.1 322.3 ... 319.6 320.0
   * z_w_bot                 (z_w_bot) float32 1e+03 2e+03 ... 5.25e+05 5.5e+05
   * z_w                     (z_w) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05
     ULAT                    (nlat, nlon) float64 -78.95 -78.95 ... 72.41 72.41
   * time                    (time) int64 2015 2016 2017 2018 ... 2098 2099 2100
 Dimensions without coordinates: moc_comp, nlat, nlon, transport_comp, transport_reg
 Data variables: (12/58)
     nsurface_u              float64 8.297e+04
     DXU                     (nlat, nlon) float64 2.397e+06 ... 1.391e+06
     DYT                     (nlat, nlon) float64 5.94e+06 5.94e+06 ... 5.046e+06
     vonkar                  float64 0.4
     dz                      (z_t) float32 1e+03 1e+03 1e+03 ... 2.5e+04 2.5e+04
     transport_regions       (transport_reg) |S384 b'Global Ocean - Marginal S...
     ...                      ...
     salt_to_ppt             float64 1e+03
     photoC_TOT_zint_100m    (time, member_id) float64 ...
     photoC_diat_zint_100m   (time, member_id) float64 ...
     POC_FLUX_100m           (time, member_id) float64 ...
     NPP_diat_frac_100m      (time, member_id) float64 0.3778 0.3623 ... 0.3396
     ep_ratio_100m           (time, member_id) float64 0.1462 0.1449 ... 0.1399,
 'ocn.SSP2-4.5.pop.h': <xarray.Dataset>
 Dimensions:                 (lat_aux_grid: 395, member_id: 3, moc_comp: 3, moc_z: 61, nlat: 384, nlon: 320, time: 86, transport_comp: 5, transport_reg: 2, z_t: 60, z_t_150m: 15, z_w: 60, z_w_bot: 60, z_w_top: 60)
 Coordinates: (12/13)
   * member_id               (member_id) int64 4 10 11
   * lat_aux_grid            (lat_aux_grid) float32 -79.49 -78.95 ... 89.47 90.0
     TLONG                   (nlat, nlon) float64 320.6 321.7 ... 319.4 319.8
   * z_w_top                 (z_w_top) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05
     TLAT                    (nlat, nlon) float64 -79.22 -79.22 ... 72.19 72.19
   * moc_z                   (moc_z) float32 0.0 1e+03 2e+03 ... 5.25e+05 5.5e+05
     ...                      ...
   * z_t                     (z_t) float32 500.0 1.5e+03 ... 5.125e+05 5.375e+05
     ULONG                   (nlat, nlon) float64 321.1 322.3 ... 319.6 320.0
   * z_w_bot                 (z_w_bot) float32 1e+03 2e+03 ... 5.25e+05 5.5e+05
   * z_w                     (z_w) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05
     ULAT                    (nlat, nlon) float64 -78.95 -78.95 ... 72.41 72.41
   * time                    (time) int64 2015 2016 2017 2018 ... 2098 2099 2100
 Dimensions without coordinates: moc_comp, nlat, nlon, transport_comp, transport_reg
 Data variables: (12/58)
     nsurface_u              float64 8.297e+04
     DXU                     (nlat, nlon) float64 2.397e+06 ... 1.391e+06
     DYT                     (nlat, nlon) float64 5.94e+06 5.94e+06 ... 5.046e+06
     vonkar                  float64 0.4
     dz                      (z_t) float32 1e+03 1e+03 1e+03 ... 2.5e+04 2.5e+04
     transport_regions       (transport_reg) |S384 b'Global Ocean - Marginal S...
     ...                      ...
     salt_to_ppt             float64 1e+03
     photoC_TOT_zint_100m    (time, member_id) float64 ...
     photoC_diat_zint_100m   (time, member_id) float64 ...
     POC_FLUX_100m           (time, member_id) float64 ...
     NPP_diat_frac_100m      (time, member_id) float64 0.3764 0.3713 ... 0.3498
     ep_ratio_100m           (time, member_id) float64 0.1466 0.1453 ... 0.1427,
 'ocn.historical.pop.h': <xarray.Dataset>
 Dimensions:                 (lat_aux_grid: 395, member_id: 11, moc_comp: 3, moc_z: 61, nlat: 384, nlon: 320, time: 165, transport_comp: 5, transport_reg: 2, z_t: 60, z_t_150m: 15, z_w: 60, z_w_bot: 60, z_w_top: 60)
 Coordinates: (12/13)
   * member_id               (member_id) int64 1 2 3 4 5 6 7 8 9 10 11
   * lat_aux_grid            (lat_aux_grid) float32 -79.49 -78.95 ... 89.47 90.0
     TLONG                   (nlat, nlon) float64 320.6 321.7 ... 319.4 319.8
   * z_w_top                 (z_w_top) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05
     TLAT                    (nlat, nlon) float64 -79.22 -79.22 ... 72.19 72.19
   * moc_z                   (moc_z) float32 0.0 1e+03 2e+03 ... 5.25e+05 5.5e+05
     ...                      ...
   * z_t                     (z_t) float32 500.0 1.5e+03 ... 5.125e+05 5.375e+05
     ULONG                   (nlat, nlon) float64 321.1 322.3 ... 319.6 320.0
   * z_w_bot                 (z_w_bot) float32 1e+03 2e+03 ... 5.25e+05 5.5e+05
   * z_w                     (z_w) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05
     ULAT                    (nlat, nlon) float64 -78.95 -78.95 ... 72.41 72.41
   * time                    (time) int64 1850 1851 1852 1853 ... 2012 2013 2014
 Dimensions without coordinates: moc_comp, nlat, nlon, transport_comp, transport_reg
 Data variables: (12/58)
     nsurface_u              float64 8.297e+04
     DXU                     (nlat, nlon) float64 2.397e+06 ... 1.391e+06
     DYT                     (nlat, nlon) float64 5.94e+06 5.94e+06 ... 5.046e+06
     vonkar                  float64 0.4
     dz                      (z_t) float32 1e+03 1e+03 1e+03 ... 2.5e+04 2.5e+04
     transport_regions       (transport_reg) |S384 b'Global Ocean - Marginal S...
     ...                      ...
     salt_to_ppt             float64 1e+03
     photoC_TOT_zint_100m    (time, member_id) float64 ...
     photoC_diat_zint_100m   (time, member_id) float64 ...
     POC_FLUX_100m           (time, member_id) float64 ...
     NPP_diat_frac_100m      (time, member_id) float64 0.3471 0.3643 ... 0.3742
     ep_ratio_100m           (time, member_id) float64 0.1445 0.1477 ... 0.1462,
 'ocn.SSP1-2.6.pop.h': <xarray.Dataset>
 Dimensions:                 (lat_aux_grid: 395, member_id: 3, moc_comp: 3, moc_z: 61, nlat: 384, nlon: 320, time: 86, transport_comp: 5, transport_reg: 2, z_t: 60, z_t_150m: 15, z_w: 60, z_w_bot: 60, z_w_top: 60)
 Coordinates: (12/13)
   * member_id               (member_id) int64 4 10 11
   * lat_aux_grid            (lat_aux_grid) float32 -79.49 -78.95 ... 89.47 90.0
     TLONG                   (nlat, nlon) float64 320.6 321.7 ... 319.4 319.8
   * z_w_top                 (z_w_top) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05
     TLAT                    (nlat, nlon) float64 -79.22 -79.22 ... 72.19 72.19
   * moc_z                   (moc_z) float32 0.0 1e+03 2e+03 ... 5.25e+05 5.5e+05
     ...                      ...
   * z_t                     (z_t) float32 500.0 1.5e+03 ... 5.125e+05 5.375e+05
     ULONG                   (nlat, nlon) float64 321.1 322.3 ... 319.6 320.0
   * z_w_bot                 (z_w_bot) float32 1e+03 2e+03 ... 5.25e+05 5.5e+05
   * z_w                     (z_w) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05
     ULAT                    (nlat, nlon) float64 -78.95 -78.95 ... 72.41 72.41
   * time                    (time) int64 2015 2016 2017 2018 ... 2098 2099 2100
 Dimensions without coordinates: moc_comp, nlat, nlon, transport_comp, transport_reg
 Data variables: (12/58)
     nsurface_u              float64 8.297e+04
     DXU                     (nlat, nlon) float64 2.397e+06 ... 1.391e+06
     DYT                     (nlat, nlon) float64 5.94e+06 5.94e+06 ... 5.046e+06
     vonkar                  float64 0.4
     dz                      (z_t) float32 1e+03 1e+03 1e+03 ... 2.5e+04 2.5e+04
     transport_regions       (transport_reg) |S384 b'Global Ocean - Marginal S...
     ...                      ...
     salt_to_ppt             float64 1e+03
     photoC_TOT_zint_100m    (time, member_id) float64 ...
     photoC_diat_zint_100m   (time, member_id) float64 ...
     POC_FLUX_100m           (time, member_id) float64 ...
     NPP_diat_frac_100m      (time, member_id) float64 0.3775 0.3596 ... 0.3228
     ep_ratio_100m           (time, member_id) float64 0.1462 0.1446 ... 0.1406}

import seaborn as sns

current_palette = sns.color_palette('colorblind', 8)
sns.palplot(current_palette)

exp_colors = {
    'PI': current_palette.as_hex()[-1],
    'historical': 'k',
    'SSP1-2.6': current_palette.as_hex()[0],
    'SSP2-4.5': current_palette.as_hex()[1],
    'SSP3-7.0': current_palette.as_hex()[2],
    'SSP5-8.5': current_palette.as_hex()[3],
}

../_images/transient-biological-pump_9_0.png

variable_labels = dict(
    time='Year',
    FG_CO2='Air-sea CO$_2$ flux [Pg C yr$^{-1}$]',
    photoC_TOT_zint_100m='NPP (z > -100m) [Pg C yr$^{-1}$]',
    photoC_TOT_zint='NPP [Pg C yr$^{-1}$]',
    POC_FLUX_100m='POC flux (100 m) [Pg C yr$^{-1}$]',
    ATM_CO2='Atmospheric CO$_2$ [ppm]',
    SST='Temperature [°C]',
    ep_ratio_100m='ep ratio',
    NPP_diat_frac_100m='Diatom fraction of NPP',
)
varlist_all = varlist + ['NPP_diat_frac_100m', 'ep_ratio_100m']

nrow = 2
ncol = 2
#fig, axs = plt.subplots(nrow, ncol, figsize=(4*ncol, 3*nrow), constrained_layout=False)

fig = plt.figure(figsize=(12, 9))
gs = gridspec.GridSpec(
    nrows=2, ncols=2, 
    hspace=0.25,
    wspace=0.22,
)
axs = np.empty((2, 2)).astype(object)
for i, j in product(range(2), range(2)):
    axs[i, j] = plt.subplot(gs[i, j])


X = [['time', 'time'], ['time', 'time'],]
Y = [['photoC_TOT_zint_100m', 'POC_FLUX_100m'], ['ep_ratio_100m', 'NPP_diat_frac_100m'],]

for i, j in product(range(2), range(2)):
    ax = axs[i, j]
    x = X[i][j]
    y = Y[i][j]
    for exp in experiments:
        if exp == 'piControl':
            continue
        ds = dsets_ts[f'ocn.{exp}.pop.h']
        #for member_id in ds.member_id.values:
        #    ax.plot(ds.time, ds[v].sel(member_id=member_id), '-', alpha=0.25, linewidth=1, color=exp_colors[exp])
        ax.plot(ds[x], ds[y].mean('member_id'), '-', linewidth=2, color=exp_colors[exp], label=exp)

    ax.set_ylabel(variable_labels[y])    
    ax.legend()
    ax.set_xlabel(variable_labels[x])
utils.label_plots(fig, [ax for ax in axs.ravel()], xoff=-0.03, yoff=0.015)   

utils.savefig(f'global-timeseries-bio-pump.pdf')

../_images/transient-biological-pump_12_0.png

fig = plt.figure(figsize=(12, 6))
prj = ccrs.Robinson(central_longitude=305.0)

titles = dict(
    photoC_TOT_zint_100m='NPP', 
    POC_FLUX_100m='POC export (100m)', 
    ep_ratio_100m='Export ratio',
    NPP_diat_frac_100m='Fraction of NPP by diatoms',
)


nrow, ncol = 2, 2 
gs = gridspec.GridSpec(
    nrows=nrow, ncols=ncol*3, 
    width_ratios=(1, 0.02, 0.02)*ncol,
    wspace=0.15, 
    hspace=0.1,
)

axs = np.empty((nrow, ncol)).astype(object)
caxs= np.empty((nrow, ncol)).astype(object)
for i, j in product(range(nrow), range(ncol)):    
    axs[i, j] = plt.subplot(gs[i, j*3], projection=prj)
    caxs[i, j] = plt.subplot(gs[i, j*3+1])

    
levels = dict(
    photoC_TOT_zint_100m=np.arange(-10, 11, 1),
    POC_FLUX_100m=np.arange(-4, 4.2, 0.2),
    ep_ratio_100m=np.arange(-10, 11, 1)/100.,
    NPP_diat_frac_100m=np.arange(-0.75, 0.76, 0.1)
)

cmap_field = cmocean.cm.balance

for n, field in enumerate(['photoC_TOT_zint_100m', 'POC_FLUX_100m', 'ep_ratio_100m', 'NPP_diat_frac_100m']):
    
    i, j = np.unravel_index(n, axs.shape)
    
    ax = axs[i, j]
   
    cf = ax.contourf(
        dsp.TLONG, dsp.TLAT, dsp[field],
        levels=levels[field],
        extend='both',
        cmap=cmap_field,
        norm=colors.BoundaryNorm(levels[field], ncolors=cmap_field.N),
        transform=ccrs.PlateCarree(),
    )

    land = ax.add_feature(
        cartopy.feature.NaturalEarthFeature(
            'physical','land','110m',
            edgecolor='face',
            facecolor='gray'
        )
    )  
                             
    ax.set_title(titles[field]) #dsp[field].attrs['title_str'])

    
    cb = plt.colorbar(cf, cax=caxs[i, j])
    if 'units' in dsp[field].attrs:
        cb.ax.set_title(dsp[field].attrs['units'])
    
utils.label_plots(fig, [ax for ax in axs.ravel()], xoff=0.02, yoff=0)       
utils.savefig('bio-pump-change-2100-SSP5-8.5.pdf')

../_images/transient-biological-pump_15_0.png

del client
del cluster

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Transient CO₂

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Conclusions and Future Development