In [1]:
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      <header>Kanon Kino</header>
Kanon Kino

About me

Doctoral program of Department of Earth and Planetary Science, the University of Tokyo.

Atmosphere and Ocean Research Institute (AORI) & Institute of Industrial Science (IIS)

CV (2021.8.15 updated)

Personal HomePage (jump to Jimdo)

Miscellaneous

Publications

GCMs

In [2]:
import xarray as xr
import matplotlib.pyplot as plt
import matplotlib.colors as mc
from matplotlib.ticker import AutoMinorLocator
import cartopy.crs as ccrs
from cartopy.util import add_cyclic_point
import cartopy.feature as feature
from cmocean import cm as cmo
from netCDF4 import Dataset as NetCDFFile
import statsmodels.api as sm
import statsmodels.tools.eval_measures as smte
import os
import datetime
import paramiko
import pandas as pd
import datetime
import numpy as np
import matplotlib.dates as mdates
from cdo import *
cdo =Cdo()
%matplotlib inline

Job monitors

Current date and time

In [3]:
now = datetime.datetime.now()
print (now.strftime("%Y-%m-%d %H:%M"))
2021-10-28 13:45

The following jobs are currently running on isotope3

In [4]:
client = paramiko.SSHClient()
client.load_system_host_keys()
client.connect('isotope3.iis.u-tokyo.ac.jp', username='kanon')

stdin, stdout, stderr = client.exec_command('qstat')
queue_status = stdout.readlines()
queue_status = [l.split() for l in queue_status]
def check_everything():
    if len(queue_status) != 0:
        queue_df = pd.DataFrame(queue_status[2:])
        queue_df.columns = ['JOB ID', 'JOB NAME', 'USERNAME', 'CPU TIME USE', 'STATUS', 'QUEUE']
        if any(queue_df['USERNAME'].str.startswith('kanon')):
                return queue_df[(queue_df['USERNAME'] == 'kanon')]
        else:
            print("no jobs running on isotope3")
        return None
check_everything()
no jobs running on isotope3

Experimental Setups

Period $\mathsf{CO_2}$ [ppm] $\mathsf{N_2O}$ [ppb] $\mathsf{CH_4}$ [ppb] CFC Eccenctirity Obliquity [°] Precession [vpid] Solar Constant [$\mathsf{W/m^2}$] $\mathsf{\delta^{18}O_{sw}}$ [‰]
PI 284.3 273 808 0 0.016764 23.459 100.33 1366.12 0
LGM 190.0 200 375 0 0.018994 22.949 114.42 1366.12 +1
  • $\mathsf{\delta^{18}O_{sw}}$ in LGM: following Werner et al., Nature com., 2018
  • GLOMAP (Paul et al.. 2020)
  • MIROC-SST (Sherriff-Tadano et al. PMIP2020 and Vadsaria et al. PMIP2020.)
  • GLAC-1D (Tarasov and Peltier GJI 2002, Tarasov et al. EPSL 2012, Briggs et al. QSR 2014 and Abe-Ouchi et al. Nature 2013.)
  • The other surface conditions (e.g. vegetation, LAI, soil types, rivers...): same as PI, but the ice sheets regions are masked.

Please ask me for the passwords!