IOD’s impact after removing the impact of ENSO

IOD: Indian Ocean Dipole

Load Modules

import numpy as np
import sacpy as scp
import matplotlib.pyplot as plt

Load SST Data

# load sst
sst = scp.load_sst()['sst']
sst

<xarray.DataArray 'sst' (time: 372, lat: 13, lon: 57)>
[275652 values with dtype=float64]
Coordinates:
  * time     (time) datetime64[ns] 1991-01-16T12:00:00 ... 2021-12-16T12:00:00
  * lon      (lon) int64 20 25 30 35 40 45 50 55 ... 270 275 280 285 290 295 300
  * lat      (lat) int64 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30

xarray.DataArray

'sst'

• time: 372
• lat: 13
• lon: 57

• ...

  [275652 values with dtype=float64]

• Coordinates: (3)
  • time
    (time)
    datetime64[ns]
    1991-01-16T12:00:00 ... 2021-12-...

    array(['1991-01-16T12:00:00.000000000', '1991-02-15T00:00:00.000000000',
           '1991-03-16T12:00:00.000000000', ..., '2021-10-16T12:00:00.000000000',
           '2021-11-16T12:00:00.000000000', '2021-12-16T12:00:00.000000000'],
          dtype='datetime64[ns]')

  • lon
    (lon)
    int64
    20 25 30 35 40 ... 285 290 295 300

    array([ 20,  25,  30,  35,  40,  45,  50,  55,  60,  65,  70,  75,  80,  85,
            90,  95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155,
           160, 165, 170, 175, 180, 185, 190, 195, 200, 205, 210, 215, 220, 225,
           230, 235, 240, 245, 250, 255, 260, 265, 270, 275, 280, 285, 290, 295,
           300])

  • lat
    (lat)
    int64
    -30 -25 -20 -15 -10 ... 15 20 25 30

    array([-30, -25, -20, -15, -10,  -5,   0,   5,  10,  15,  20,  25,  30])

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1991-01-16 12:00:00', '1991-02-15 00:00:00',
                   '1991-03-16 12:00:00', '1991-04-16 00:00:00',
                   '1991-05-16 12:00:00', '1991-06-16 00:00:00',
                   '1991-07-16 12:00:00', '1991-08-16 12:00:00',
                   '1991-09-16 00:00:00', '1991-10-16 12:00:00',
                   ...
                   '2021-03-16 12:00:00', '2021-04-16 12:00:00',
                   '2021-05-16 12:00:00', '2021-06-16 12:00:00',
                   '2021-07-16 12:00:00', '2021-08-16 12:00:00',
                   '2021-09-16 12:00:00', '2021-10-16 12:00:00',
                   '2021-11-16 12:00:00', '2021-12-16 12:00:00'],
                  dtype='datetime64[ns]', name='time', length=372, freq=None))

  • lon
    PandasIndex

    PandasIndex(Int64Index([ 20,  25,  30,  35,  40,  45,  50,  55,  60,  65,  70,  75,  80,
                 85,  90,  95, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145,
                150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 205, 210,
                215, 220, 225, 230, 235, 240, 245, 250, 255, 260, 265, 270, 275,
                280, 285, 290, 295, 300],
               dtype='int64', name='lon'))

  • lat
    PandasIndex

    PandasIndex(Int64Index([-30, -25, -20, -15, -10, -5, 0, 5, 10, 15, 20, 25, 30], dtype='int64', name='lat'))

• Attributes: (0)

# get ssta
ssta = scp.get_anom(sst,method=1)
# ssta[0].plot()

Get Nino3.4 and IOD index

# calculate Nino3.4
Nino34 = ssta.loc[:,-5:5,190:240].mean(axis=(1,2))

IODW = ssta.loc[:,-10:10,50:70].mean(axis=(1,2))
IODE = ssta.loc[:,-10:0,90:110].mean(axis=(1,2))
IODI = +IODW - IODE

build Data for MLR

X = np.vstack([np.array(Nino34),np.array(IODI)]).T
# multiple linear regression
MLR = scp.MultLinReg(X,np.array(ssta))

MLR

MultLinReg, x.shape = (372, 2), y.shape = (372, 13, 57)

Plot

import sacpy.Map
import cartopy.crs as ccrs
fig = plt.figure(figsize=[7, 3])
ax = plt.axes(projection=ccrs.PlateCarree(central_longitude=180))
lon ,lat = np.array(ssta.lon) , np.array(ssta.lat)
m = ax.scontourf(lon,lat,MLR.slope[1])
n = ax.sig_plot(lon,lat,MLR.pv_i[1],color="k",marker="..")
ax.init_map(stepx=50, smally=2.5)
plt.colorbar(m)
plt.savefig("../pic/MLR.png",dpi=200)

<Figure size 640x480 with 0 Axes>