The impact of ENSO on the climate in the second year

The aim of this page to the calculate how ENSO influences the second year June July August SSTA

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

Load Data

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

<xarray.DataArray 'sst' (time: 372, lat: 13, lon: 57)>
array([[[        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        ...,
        [        nan,         nan,         nan, ...,         nan,
         -0.43838512, -0.39504153],
        [        nan,         nan,         nan, ...,  0.05971687,
         -0.00956711, -0.17235787],
        [        nan,         nan,         nan, ...,  0.25652925,
         -0.00416474, -0.07999759]],

       [[        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
...
        [        nan,         nan,         nan, ...,         nan,
          0.15849629,  0.20575899],
        [        nan,         nan,         nan, ...,  0.4969    ,
          0.56747427,  0.38412402],
        [        nan,         nan,         nan, ...,  0.69595955,
          0.53821822,  0.63045126]],

       [[        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        [        nan,         nan,         nan, ...,         nan,
                 nan,         nan],
        ...,
        [        nan,         nan,         nan, ...,         nan,
          0.40377714,  0.33947234],
        [        nan,         nan,         nan, ...,  0.76328241,
          0.59248761,  0.58278422],
        [        nan,         nan,         nan, ...,  0.92944068,
          0.69763707,  0.58009631]]])
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
    month    (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12

xarray.DataArray

'sst'

• time: 372
• lat: 13
• lon: 57

• nan nan nan -0.3645 -0.09718 ... nan 0.8515 0.9294 0.6976 0.5801

  array([[[        nan,         nan,         nan, ...,         nan,
                   nan,         nan],
          [        nan,         nan,         nan, ...,         nan,
                   nan,         nan],
          [        nan,         nan,         nan, ...,         nan,
                   nan,         nan],
          ...,
          [        nan,         nan,         nan, ...,         nan,
           -0.43838512, -0.39504153],
          [        nan,         nan,         nan, ...,  0.05971687,
           -0.00956711, -0.17235787],
          [        nan,         nan,         nan, ...,  0.25652925,
           -0.00416474, -0.07999759]],
  
         [[        nan,         nan,         nan, ...,         nan,
                   nan,         nan],
          [        nan,         nan,         nan, ...,         nan,
                   nan,         nan],
          [        nan,         nan,         nan, ...,         nan,
                   nan,         nan],
  ...
          [        nan,         nan,         nan, ...,         nan,
            0.15849629,  0.20575899],
          [        nan,         nan,         nan, ...,  0.4969    ,
            0.56747427,  0.38412402],
          [        nan,         nan,         nan, ...,  0.69595955,
            0.53821822,  0.63045126]],
  
         [[        nan,         nan,         nan, ...,         nan,
                   nan,         nan],
          [        nan,         nan,         nan, ...,         nan,
                   nan,         nan],
          [        nan,         nan,         nan, ...,         nan,
                   nan,         nan],
          ...,
          [        nan,         nan,         nan, ...,         nan,
            0.40377714,  0.33947234],
          [        nan,         nan,         nan, ...,  0.76328241,
            0.59248761,  0.58278422],
          [        nan,         nan,         nan, ...,  0.92944068,
            0.69763707,  0.58009631]]])

• Coordinates: (4)
  • 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])

  • month
    (time)
    int64
    1 2 3 4 5 6 7 ... 6 7 8 9 10 11 12

    array([ 1,  2,  3, ..., 10, 11, 12])

• 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 Nino3.4 Index

Nino34 = ssta.loc[:,-5:5,190:240].mean(axis=(1,2))
DJF_nino34 = scp.XrTools.spec_moth_yrmean(Nino34,"DJF")
DJF_nino34.plot()

[<matplotlib.lines.Line2D at 0x16440bcd0>]

Get JJA SSTA data

JJA_ssta = scp.XrTools.spec_moth_yrmean(ssta, "JJA")
JJA_ssta.time,DJF_nino34.time

(<xarray.DataArray 'time' (time: 31)>
 array([1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,
        2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,
        2015, 2016, 2017, 2018, 2019, 2020, 2021])
 Coordinates:
   * time     (time) int64 1991 1992 1993 1994 1995 ... 2017 2018 2019 2020 2021
     month    (time) int64 6 6 6 6 6 6 6 6 6 6 6 6 6 ... 6 6 6 6 6 6 6 6 6 6 6 6,
 <xarray.DataArray 'time' (time: 30)>
 array([1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,
        2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,
        2015, 2016, 2017, 2018, 2019, 2020])
 Coordinates:
   * time     (time) int64 1991 1992 1993 1994 1995 ... 2016 2017 2018 2019 2020
     month    (time) int64 12 12 12 12 12 12 12 12 12 ... 12 12 12 12 12 12 12 12)

Linear Regression between DJF Nino3.4 Index and JJA SSTA

reg = scp.LinReg(DJF_nino34[:], JJA_ssta[1:])

Plot

import cartopy.crs as ccrs
import sacpy.Map
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,reg.slope)
n = ax.sig_plot(lon,lat,reg.p_value,color="k",marker="///")
ax.init_map(stepx=50, smally=2.5)
plt.colorbar(m)
# plt.savefig("../pic/ENSO_Next_year_JJA.png",dpi=300)

<matplotlib.colorbar.Colorbar at 0x16596be80>

<Figure size 640x480 with 0 Axes>