Contents

Precompute model data

Precompute model data

This notebook performs some relatively time-consuming computations, aggregating fluxes and 3D CO2 distributions from the model simulations.

The output is saved locally (within ./models/data-cache), so it should only be necessary to run this notebook once—or not at all if these data already exist (they are saved as part of the repository).

%load_ext autoreload
%autoreload 2

import numpy as np
import models
import emergent_constraint as ec

List the models

The emergent_contraint module has a get_model_tracer_lists method that returns a list of tuples (see API documentation), each with the model name and tracer used.

model_input_lists = []
for c in ['ocean_constraint', 'total_constraint']:
    for key in ['model_tracer_list',]: 
        model_input_lists.extend([t[0] for t in ec.get_model_tracer_lists(c)[key]])

model_list = sorted(list(set(model_input_lists)))
model_list

['CAMSv20r1',
 'CT2017',
 'CT2019B',
 'CTE2018',
 'CTE2020',
 'MIROC',
 'TM5-Flux-m0f',
 'TM5-Flux-mmf',
 'TM5-Flux-mrf',
 'TM5-Flux-mwf',
 's99oc_ADJocI40S_v2020',
 's99oc_SOCCOM_v2020',
 's99oc_v2020']

Spin up a cluster

from ncar_jobqueue import NCARCluster
try:
    cluster
except:
    cluster = NCARCluster(memory='100GB')
    cluster.scale(32)
cluster

from dask.distributed import Client
try: 
    client
except: 
    client = Client(cluster) # Connect this local process to remote workers
client

Client

Client-d2f12df4-118f-11ec-a9bd-ac1f6bc9f55e

Connection method: Cluster object Cluster type: dask_jobqueue.PBSCluster
Dashboard: /proxy/8787/status

Cluster Info

Instantiate model objects

model_objs = {}
for name in model_list:
    model_objs[name] = models.Model(name)
model_objs    

{'CAMSv20r1': <models.core.Model at 0x2b57a129e110>,
 'CT2017': <models.core.Model at 0x2b57a129e350>,
 'CT2019B': <models.core.Model at 0x2b57a129e7d0>,
 'CTE2018': <models.core.Model at 0x2b57a129eb10>,
 'CTE2020': <models.core.Model at 0x2b57a1287550>,
 'MIROC': <models.core.Model at 0x2b57a12874d0>,
 'TM5-Flux-m0f': <models.core.Model at 0x2b57a12879d0>,
 'TM5-Flux-mmf': <models.core.Model at 0x2b57a1287110>,
 'TM5-Flux-mrf': <models.core.Model at 0x2b57a1287290>,
 'TM5-Flux-mwf': <models.core.Model at 0x2b57a1287710>,
 's99oc_ADJocI40S_v2020': <models.core.Model at 0x2b57a1287510>,
 's99oc_SOCCOM_v2020': <models.core.Model at 0x2b57a1287450>,
 's99oc_v2020': <models.core.Model at 0x2b57a1287fd0>}

Compute latitude dependent fluxes

Define a set of lat_ranges and average monthly fluxes in these bounds.

lat_ranges = [
    (-90., -30.), (-90., -35.), (-90., -40), (-90., -45.), (-90., -50.), (-90., -55.), 

]
lat_ranges = [(-90., x) for x in np.arange(-75., -14., 1)] + [(-45., -15.), (-45., -30.)]

%%time
dsets = {}
for name in model_list:
    print(name)    
    for lat_range in lat_ranges:
        dsets[name] = model_objs[name].open_derived_dataset(
                    'flux_ts_monthly', 
                    lat_range=lat_range,
                    persist=True,
                    clobber=False,
                    return_none=True,
                )

CAMSv20r1
CT2017
CT2019B
CTE2018
CTE2020
MIROC
TM5-Flux-m0f
TM5-Flux-mmf
TM5-Flux-mrf
TM5-Flux-mwf
s99oc_ADJocI40S_v2020
s99oc_SOCCOM_v2020
s99oc_v2020
CPU times: user 15.7 ms, sys: 5.31 ms, total: 21 ms
Wall time: 20.6 ms

Zonal mean fluxes

%%time
dsets = {}
for name in model_list:
    print(name)    
    dsets[name] = model_objs[name].open_derived_dataset(
                'fluxes_za_ts_monthly', 
                persist=True,
                clobber=False,
                return_none=True,
            )

CAMSv20r1
CT2017
CT2019B
CTE2018
CTE2020
MIROC
TM5-Flux-m0f
TM5-Flux-mmf
TM5-Flux-mrf
TM5-Flux-mwf
s99oc_ADJocI40S_v2020
s99oc_SOCCOM_v2020
s99oc_v2020
CPU times: user 1.83 ms, sys: 0 ns, total: 1.83 ms
Wall time: 1.45 ms

3D CO2 distribtions

for model in model_list:
    if model in ['CT2019B']:
        model_objs[model].open_derived_dataset(
            'molefractions_surface_daily',
            persist=True,
            clobber=False,            
            return_none=True,                    
        )
        model_objs[model].open_derived_dataset(
            'spo_ts_daily',            
            persist=True,
            clobber=False,           
            return_none=True,                    
        )
        model_objs[model].open_derived_dataset(
            'molefractions_theta_bins',
            kwargs_name='SO-10K-bins-300K_275K',
            lat_bounds=(-80., -45.),
            theta_bins=[(295., 305.), (270., 280.),],
            persist=True,            
            clobber=False,
            return_none=True,        
        )
        model_objs[model].open_derived_dataset(
            'molefractions_theta_bins_sectors',
            kwargs_name='SO-10K-bins-300K_275K',
            lat_bounds=(-80., -45.),
            theta_bins=[(295., 305.), (270., 280.),],
            persist=True,            
            clobber=False,
            return_none=True,  
        )
        model_objs[model].open_derived_dataset(
            'molefractions_z_za', 
            persist=True,
            clobber=False,
            return_none=True,                    
        )

cluster.close()
client.close()

previous

Process surface SF6 observations

next

Observed patterns in atmospheric CO2