.ipynb .pdf

Binder

Transient CO₂

%load_ext autoreload
%autoreload 2

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

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

import utils
import data_collections
import plot

from ncar_jobqueue import NCARCluster

cluster = NCARCluster(memory='10 GB')
cluster.scale(10) #adapt(minimum_jobs=0, maximum_jobs=24)
client

/glade/u/home/mgrover/miniconda3/envs/cesm2-marbl/lib/python3.7/site-packages/distributed/node.py:164: UserWarning: Port 8787 is already in use.
Perhaps you already have a cluster running?
Hosting the HTTP server on port 40653 instead
  expected, actual

Client Scheduler: tcp://10.12.206.57:44211 Dashboard: https://jupyterhub.hpc.ucar.edu/stable/user/mgrover/proxy/40007/status

Cluster Workers: 24 Cores: 24 Memory: 558.72 GiB

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

query = dict(
    experiment=experiments,
    stream='pop.h',
)
glb_int = data_collections.global_integral_timeseries_ann(query)
glb_avg = data_collections.global_mean_timeseries_ann(query)
glb_int

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

dsets_int = glb_int.to_dataset_dict('FG_CO2', clobber=False)
dsets_avg = glb_avg.to_dataset_dict(['ATM_CO2', 'SST',], prefer_derived=True, clobber=False)

assert dsets_int.keys() == dsets_avg.keys()

dsets = {}
for key in dsets_int.keys():
    dsets[key] = xr.merge((dsets_int[key], dsets_avg[key]))

dsets

{'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)
   * z_t_150m                (z_t_150m) float32 500.0 1.5e+03 ... 1.45e+04
     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
   * z_t                     (z_t) float32 500.0 1.5e+03 ... 5.125e+05 5.375e+05
   * moc_z                   (moc_z) float32 0.0 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
   * z_w_bot                 (z_w_bot) float32 1e+03 2e+03 ... 5.25e+05 5.5e+05
   * z_w_top                 (z_w_top) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05
   * lat_aux_grid            (lat_aux_grid) float32 -79.49 -78.95 ... 89.47 90.0
     ULONG                   (nlat, nlon) float64 321.1 322.3 ... 319.6 320.0
   * time                    (time) int64 1850 1851 1852 1853 ... 2012 2013 2014
 Dimensions without coordinates: moc_comp, nlat, nlon, transport_comp, transport_reg
 Data variables: (12/56)
     TAREA                   (nlat, nlon) float64 1.125e+13 ... 7.432e+12
     sound                   float64 1.5e+05
     ocn_ref_salinity        float64 34.7
     days_in_norm_year       timedelta64[ns] 365 days
     sflux_factor            float64 0.1
     HUS                     (nlat, nlon) float64 2.397e+06 ... 1.473e+06
     ...                      ...
     salt_to_Svppt           float64 1e-09
     rho_sw                  float64 1.026
     nsurface_u              float64 8.297e+04
     FG_CO2                  (time, member_id) float64 ...
     ATM_CO2                 (time, member_id) float64 ...
     SST                     (time, member_id) float64 ...,
 '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)
   * z_t_150m                (z_t_150m) float32 500.0 1.5e+03 ... 1.45e+04
     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
   * z_t                     (z_t) float32 500.0 1.5e+03 ... 5.125e+05 5.375e+05
   * moc_z                   (moc_z) float32 0.0 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
   * z_w_bot                 (z_w_bot) float32 1e+03 2e+03 ... 5.25e+05 5.5e+05
   * z_w_top                 (z_w_top) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05
   * lat_aux_grid            (lat_aux_grid) float32 -79.49 -78.95 ... 89.47 90.0
     ULONG                   (nlat, nlon) float64 321.1 322.3 ... 319.6 320.0
   * time                    (time) int64 2015 2016 2017 2018 ... 2098 2099 2100
 Dimensions without coordinates: moc_comp, nlat, nlon, transport_comp, transport_reg
 Data variables: (12/56)
     TAREA                   (nlat, nlon) float64 1.125e+13 ... 7.432e+12
     sound                   float64 1.5e+05
     ocn_ref_salinity        float64 34.7
     days_in_norm_year       timedelta64[ns] 365 days
     sflux_factor            float64 0.1
     HUS                     (nlat, nlon) float64 2.397e+06 ... 1.473e+06
     ...                      ...
     salt_to_Svppt           float64 1e-09
     rho_sw                  float64 1.026
     nsurface_u              float64 8.297e+04
     FG_CO2                  (time, member_id) float64 ...
     ATM_CO2                 (time, member_id) float64 ...
     SST                     (time, member_id) float64 ...,
 '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)
   * z_t_150m                (z_t_150m) float32 500.0 1.5e+03 ... 1.45e+04
     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
   * z_t                     (z_t) float32 500.0 1.5e+03 ... 5.125e+05 5.375e+05
   * moc_z                   (moc_z) float32 0.0 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
   * z_w_bot                 (z_w_bot) float32 1e+03 2e+03 ... 5.25e+05 5.5e+05
   * z_w_top                 (z_w_top) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05
   * lat_aux_grid            (lat_aux_grid) float32 -79.49 -78.95 ... 89.47 90.0
     ULONG                   (nlat, nlon) float64 321.1 322.3 ... 319.6 320.0
   * time                    (time) int64 2015 2016 2017 2018 ... 2098 2099 2100
 Dimensions without coordinates: moc_comp, nlat, nlon, transport_comp, transport_reg
 Data variables: (12/56)
     TAREA                   (nlat, nlon) float64 1.125e+13 ... 7.432e+12
     sound                   float64 1.5e+05
     ocn_ref_salinity        float64 34.7
     days_in_norm_year       timedelta64[ns] 365 days
     sflux_factor            float64 0.1
     HUS                     (nlat, nlon) float64 2.397e+06 ... 1.473e+06
     ...                      ...
     salt_to_Svppt           float64 1e-09
     rho_sw                  float64 1.026
     nsurface_u              float64 8.297e+04
     FG_CO2                  (time, member_id) float64 ...
     ATM_CO2                 (time, member_id) float64 ...
     SST                     (time, member_id) float64 ...,
 '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)
   * z_t_150m                (z_t_150m) float32 500.0 1.5e+03 ... 1.45e+04
     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
   * z_t                     (z_t) float32 500.0 1.5e+03 ... 5.125e+05 5.375e+05
   * moc_z                   (moc_z) float32 0.0 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
   * z_w_bot                 (z_w_bot) float32 1e+03 2e+03 ... 5.25e+05 5.5e+05
   * z_w_top                 (z_w_top) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05
   * lat_aux_grid            (lat_aux_grid) float32 -79.49 -78.95 ... 89.47 90.0
     ULONG                   (nlat, nlon) float64 321.1 322.3 ... 319.6 320.0
   * time                    (time) int64 2015 2016 2017 2018 ... 2098 2099 2100
 Dimensions without coordinates: moc_comp, nlat, nlon, transport_comp, transport_reg
 Data variables: (12/56)
     TAREA                   (nlat, nlon) float64 1.125e+13 ... 7.432e+12
     sound                   float64 1.5e+05
     ocn_ref_salinity        float64 34.7
     days_in_norm_year       timedelta64[ns] 365 days
     sflux_factor            float64 0.1
     HUS                     (nlat, nlon) float64 2.397e+06 ... 1.473e+06
     ...                      ...
     salt_to_Svppt           float64 1e-09
     rho_sw                  float64 1.026
     nsurface_u              float64 8.297e+04
     FG_CO2                  (time, member_id) float64 ...
     ATM_CO2                 (time, member_id) float64 ...
     SST                     (time, member_id) float64 ...,
 '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)
   * z_t_150m                (z_t_150m) float32 500.0 1.5e+03 ... 1.45e+04
     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
   * z_t                     (z_t) float32 500.0 1.5e+03 ... 5.125e+05 5.375e+05
   * moc_z                   (moc_z) float32 0.0 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
   * z_w_bot                 (z_w_bot) float32 1e+03 2e+03 ... 5.25e+05 5.5e+05
   * z_w_top                 (z_w_top) float32 0.0 1e+03 2e+03 ... 5e+05 5.25e+05
   * lat_aux_grid            (lat_aux_grid) float32 -79.49 -78.95 ... 89.47 90.0
     ULONG                   (nlat, nlon) float64 321.1 322.3 ... 319.6 320.0
   * time                    (time) int64 2015 2016 2017 2018 ... 2098 2099 2100
 Dimensions without coordinates: moc_comp, nlat, nlon, transport_comp, transport_reg
 Data variables: (12/56)
     TAREA                   (nlat, nlon) float64 1.125e+13 ... 7.432e+12
     sound                   float64 1.5e+05
     ocn_ref_salinity        float64 34.7
     days_in_norm_year       timedelta64[ns] 365 days
     sflux_factor            float64 0.1
     HUS                     (nlat, nlon) float64 2.397e+06 ... 1.473e+06
     ...                      ...
     salt_to_Svppt           float64 1e-09
     rho_sw                  float64 1.026
     nsurface_u              float64 8.297e+04
     FG_CO2                  (time, member_id) float64 ...
     ATM_CO2                 (time, member_id) float64 ...
     SST                     (time, member_id) float64 ...}

del client
del cluster

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],
}

variable_labels = dict(
    year='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]',
    time='',
)

y = 'FG_CO2'
x = 'SST'

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', 'ATM_CO2'],]
Y = [['ATM_CO2', 'SST'], ['FG_CO2', 'FG_CO2'],]

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:
        ds = dsets[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.mean('member_id')[x], ds.mean('member_id')[y], '-', 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-FG_CO2.pdf')

baseline = dsets['ocn.historical.pop.h'].SST.isel(time=slice(0, 10)).mean()
for exp, ds in dsets.items():
    if 'SSP' in exp:
        warming = ds.SST.isel(time=-1).mean() - baseline
        print(f'{exp}: {warming.values:0.2f}')

ocn.SSP5-8.5.pop.h: 4.60
ocn.SSP3-7.0.pop.h: 3.33
ocn.SSP1-2.6.pop.h: 1.72
ocn.SSP2-4.5.pop.h: 2.54

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