In [1]:
<body for="html-export">
      <div class="mume markdown-preview  ">
      <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)




In [2]:
import xarray as xr
import matplotlib.pyplot as plt
import matplotlib.colors as mc
from matplotlib.ticker import AutoMinorLocator
import 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 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 =
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.connect('', 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')]
            print("no jobs running on isotope3")
        return None
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!