Calculating time averages¶
Let's say we want to average the monthly time series data from the previous exercise into annually averaged data.
There are a number of ways to do this. Probably the easiest in this case, since we know the data is monthly, would be to chunk through every 12 data points. Looking at the dates, we see that the data starts in January. So, if we loop every 12 points, and take the average of those, we should be able to get an annual average for each year.
In [22]:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import xray
nc_cmi = xray.open_dataset('Champaign_data.nc')
nc_cmi
Out[22]:
<xray.Dataset>
Dimensions: (lat: 1, lon: 1, nv: 2, time: 1628)
Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1880-01-15 1880-02-15 1880-03-15 ...
* nv (nv) int64 0 1
Data variables:
time_bnds (time, nv) int32 29219 29250 29250 29279 29279 29310 29310 ...
tempanomaly (time, lat, lon) float64 7.94 3.23 0.04 0.07 2.96 0.62 ...
Attributes:
title: GISTEMP Surface Temperature Analysis
institution: NASA Goddard Institute for Space Studies
source: http://data.giss.nasa.gov/gistemp/
Conventions: CF-1.6
history: Created 2015-09-11 09:23:38 by SBBX_to_nc 2.0 - ILAND=1200, IOCEAN=NCDC/ER4, Base: 1951-1980
Let's calculate how many years of data we have.
$$\text{number of years} = \frac{\text{number of months}}{12} $$In [23]:
# How many years do we have?
ntimes=np.shape(nc_cmi['time'])
print(ntimes[0]/12.)
135.666666667
OK, we don't have an even number of months going into years. The data starts in January 1880, so it must go partially into a year. Let's forget about the last partial year, and do the averages starting in January 1880 through the end. We can calculate the number of years to calculate by using numpy's floor command (rounding down to the nearest integer after dividing by 12. We will then create numpy array of averages, nyears long, that will store the data, and the dates.
In [5]:
nyears=np.floor(ntimes[0]/12)
nyears
Out[5]:
135.0
Now, we need to devise an algorithm to average the monthly data by year. This can be accomplished by indexing the data to include every 12 months in a list, and subsetting the array. It can be helpful to think about this as a numberline problem. In the diagram below, the -'s are each month in the dataset (12 per year), and we want to average over each year in the dataset, from 1880-2014.
Thinking about how to go about this, we know that we need to pick the first 12 months of the data to get the 1880 data. This can be accomplished by using indexing of the data and time. Let's try to find the first year, keeping in mind that the first element (January 1880) is index 0, and the last index will not be included (convention of python).
In [7]:
data_1880=nc_cmi['tempanomaly'][0:12]
time_1880=nc_cmi['time'][0:12]
#let's check the time to see if it worked
print time_1880
print(data_1880)
<xray.DataArray 'time' (time: 12)>
array(['1880-01-15T00:00:00.000000000Z', '1880-02-15T00:00:00.000000000Z',
'1880-03-15T00:00:00.000000000Z', '1880-04-15T00:00:00.000000000Z',
'1880-05-15T00:00:00.000000000Z', '1880-06-15T00:00:00.000000000Z',
'1880-07-15T00:00:00.000000000Z', '1880-08-15T00:00:00.000000000Z',
'1880-09-15T00:00:00.000000000Z', '1880-10-15T00:00:00.000000000Z',
'1880-11-15T00:00:00.000000000Z', '1880-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]')
Coordinates:
* time (time) datetime64[ns] 1880-01-15 1880-02-15 1880-03-15 ...
Attributes:
long_name: time
bounds: time_bnds
<xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 7.93999982]],
[[ 3.22999993]],
[[ 0.04 ]],
[[ 0.07 ]],
[[ 2.95999993]],
[[ 0.61999999]],
[[-0.50999999]],
[[ 0. ]],
[[-1.08999998]],
[[-1.74999996]],
[[-5.30999988]],
[[-3.52999992]]])
Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1880-01-15 1880-02-15 1880-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean
OK, we found the first year. Now we need to figure out how to get the subsequent years, automatically. Let's try 1881:
In [8]:
data_1881=nc_cmi['tempanomaly'][12:24]
time_1881=nc_cmi['time'][12:24]
#let's check the time to see if it worked
print time_1881
<xray.DataArray 'time' (time: 12)>
array(['1881-01-15T00:00:00.000000000Z', '1881-02-15T00:00:00.000000000Z',
'1881-03-15T00:00:00.000000000Z', '1881-04-15T00:00:00.000000000Z',
'1881-05-15T00:00:00.000000000Z', '1881-06-15T00:00:00.000000000Z',
'1881-07-15T00:00:00.000000000Z', '1881-08-15T00:00:00.000000000Z',
'1881-09-15T00:00:00.000000000Z', '1881-10-15T00:00:00.000000000Z',
'1881-11-15T00:00:00.000000000Z', '1881-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]')
Coordinates:
* time (time) datetime64[ns] 1881-01-15 1881-02-15 1881-03-15 ...
Attributes:
long_name: time
bounds: time_bnds
Now we need to automate this - we don't want to do this for all the months in the file! Time for a for loop. We can loop over each year by using nyears that we calculated above. Now we need to map from the counter that will go from 0:nyears to the months. From the above, we want to have the first element be the January of each year, and the last December, keeping in mind how python indexing works. We can multiply the counter by 12, then add one to the counter and multiply it by 12 to get the right data.
In [9]:
for i in np.arange(nyears):
print(i,nc_cmi['time'][i*12:((i+1)*12)],nc_cmi['tempanomaly'][i*12:((i+1)*12)])
(0.0, <xray.DataArray 'time' (time: 12)>
array(['1880-01-15T00:00:00.000000000Z', '1880-02-15T00:00:00.000000000Z',
'1880-03-15T00:00:00.000000000Z', '1880-04-15T00:00:00.000000000Z',
'1880-05-15T00:00:00.000000000Z', '1880-06-15T00:00:00.000000000Z',
'1880-07-15T00:00:00.000000000Z', '1880-08-15T00:00:00.000000000Z',
'1880-09-15T00:00:00.000000000Z', '1880-10-15T00:00:00.000000000Z',
'1880-11-15T00:00:00.000000000Z', '1880-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]')
Coordinates:
* time (time) datetime64[ns] 1880-01-15 1880-02-15 1880-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 7.93999982]],
[[ 3.22999993]],
[[ 0.04 ]],
[[ 0.07 ]],
[[ 2.95999993]],
[[ 0.61999999]],
[[-0.50999999]],
[[ 0. ]],
[[-1.08999998]],
[[-1.74999996]],
[[-5.30999988]],
[[-3.52999992]]])
Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1880-01-15 1880-02-15 1880-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(1.0, <xray.DataArray 'time' (time: 12)>
array(['1881-01-15T00:00:00.000000000Z', '1881-02-15T00:00:00.000000000Z',
'1881-03-15T00:00:00.000000000Z', '1881-04-15T00:00:00.000000000Z',
'1881-05-15T00:00:00.000000000Z', '1881-06-15T00:00:00.000000000Z',
'1881-07-15T00:00:00.000000000Z', '1881-08-15T00:00:00.000000000Z',
'1881-09-15T00:00:00.000000000Z', '1881-10-15T00:00:00.000000000Z',
'1881-11-15T00:00:00.000000000Z', '1881-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]')
Coordinates:
* time (time) datetime64[ns] 1881-01-15 1881-02-15 1881-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[-2.79999994]],
[[-1.30999997]],
[[-1.56999996]],
[[-2.64999994]],
[[ 2.76999994]],
[[-1.09999998]],
[[ 0.91999998]],
[[ 1.77999996]],
[[ 2.86999994]],
[[ 1.06999998]],
[[-0.47999999]],
[[ 4.11999991]]])
Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1881-01-15 1881-02-15 1881-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(2.0, <xray.DataArray 'time' (time: 12)>
array(['1882-01-15T00:00:00.000000000Z', '1882-02-15T00:00:00.000000000Z',
'1882-03-15T00:00:00.000000000Z', '1882-04-15T00:00:00.000000000Z',
'1882-05-15T00:00:00.000000000Z', '1882-06-15T00:00:00.000000000Z',
'1882-07-15T00:00:00.000000000Z', '1882-08-15T00:00:00.000000000Z',
'1882-09-15T00:00:00.000000000Z', '1882-10-15T00:00:00.000000000Z',
'1882-11-15T00:00:00.000000000Z', '1882-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]')
Coordinates:
* time (time) datetime64[ns] 1882-01-15 1882-02-15 1882-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 2.31999995]],
[[ 5.55999988]],
[[ 1.62999996]],
[[-0.56999999]],
[[-2.94999993]],
[[-0.94999998]],
[[-1.99999996]],
[[-0.39999999]],
[[-0.21 ]],
[[ 1.72999996]],
[[-0.19 ]],
[[-1.14999997]]])
Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1882-01-15 1882-02-15 1882-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(3.0, <xray.DataArray 'time' (time: 12)>
array(['1883-01-15T00:00:00.000000000Z', '1883-02-15T00:00:00.000000000Z',
'1883-03-15T00:00:00.000000000Z', '1883-04-15T00:00:00.000000000Z',
'1883-05-15T00:00:00.000000000Z', '1883-06-15T00:00:00.000000000Z',
'1883-07-15T00:00:00.000000000Z', '1883-08-15T00:00:00.000000000Z',
'1883-09-15T00:00:00.000000000Z', '1883-10-15T00:00:00.000000000Z',
'1883-11-15T00:00:00.000000000Z', '1883-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]')
Coordinates:
* time (time) datetime64[ns] 1883-01-15 1883-02-15 1883-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[-2.62999994]],
[[-0.90999998]],
[[-2.91999993]],
[[-0.71999998]],
[[-2.29999995]],
[[-0.66999999]],
[[-0.99999998]],
[[-1.75999996]],
[[-2.15999995]],
[[-1.34999997]],
[[ 0.47999999]],
[[ 0.59999999]]])
Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1883-01-15 1883-02-15 1883-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(4.0, <xray.DataArray 'time' (time: 12)>
array(['1884-01-15T00:00:00.000000000Z', '1884-02-15T00:00:00.000000000Z',
'1884-03-15T00:00:00.000000000Z', '1884-04-15T00:00:00.000000000Z',
'1884-05-15T00:00:00.000000000Z', '1884-06-15T00:00:00.000000000Z',
'1884-07-15T00:00:00.000000000Z', '1884-08-15T00:00:00.000000000Z',
'1884-09-15T00:00:00.000000000Z', '1884-10-15T00:00:00.000000000Z',
'1884-11-15T00:00:00.000000000Z', '1884-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]')
Coordinates:
* time (time) datetime64[ns] 1884-01-15 1884-02-15 1884-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[-2.98999993]],
[[ 0.43999999]],
[[-0.73999998]],
[[-1.40999997]],
[[-0.45999999]],
[[ 0.21 ]],
[[-1.53999997]],
[[-1.12999997]],
[[ 1.88999996]],
[[ 1.22999997]],
[[-0.86999998]],
[[-1.49999997]]])
Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1884-01-15 1884-02-15 1884-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(5.0, <xray.DataArray 'time' (time: 12)>
array(['1885-01-15T00:00:00.000000000Z', '1885-02-15T00:00:00.000000000Z',
'1885-03-15T00:00:00.000000000Z', '1885-04-15T00:00:00.000000000Z',
'1885-05-15T00:00:00.000000000Z', '1885-06-15T00:00:00.000000000Z',
'1885-07-15T00:00:00.000000000Z', '1885-08-15T00:00:00.000000000Z',
'1885-09-15T00:00:00.000000000Z', '1885-10-15T00:00:00.000000000Z',
'1885-11-15T00:00:00.000000000Z', '1885-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]')
Coordinates:
* time (time) datetime64[ns] 1885-01-15 1885-02-15 1885-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[-2.69999994]],
[[-5.59999987]],
[[-4.3299999 ]],
[[-1.26999997]],
[[-1.11999997]],
[[-1.00999998]],
[[ 0.30999999]],
[[-1.97999996]],
[[-0.92999998]],
[[-2.10999995]],
[[ 0.16 ]],
[[ 0.49999999]]])
Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1885-01-15 1885-02-15 1885-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(6.0, <xray.DataArray 'time' (time: 12)>
array(['1886-01-15T00:00:00.000000000Z', '1886-02-15T00:00:00.000000000Z',
'1886-03-15T00:00:00.000000000Z', '1886-04-15T00:00:00.000000000Z',
'1886-05-15T00:00:00.000000000Z', '1886-06-15T00:00:00.000000000Z',
'1886-07-15T00:00:00.000000000Z', '1886-08-15T00:00:00.000000000Z',
'1886-09-15T00:00:00.000000000Z', '1886-10-15T00:00:00.000000000Z',
'1886-11-15T00:00:00.000000000Z', '1886-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]')
Coordinates:
* time (time) datetime64[ns] 1886-01-15 1886-02-15 1886-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[-2.61999994]],
[[-0.98999998]],
[[-0.71999998]],
[[ 0.80999998]],
[[ 0.34999999]],
[[-1.02999998]],
[[-0.49999999]],
[[ 0.01 ]],
[[ 0.09 ]],
[[ 0.68999998]],
[[-1.64999996]],
[[-3.89999991]]])
Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1886-01-15 1886-02-15 1886-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(7.0, <xray.DataArray 'time' (time: 12)>
array(['1887-01-15T00:00:00.000000000Z', '1887-02-15T00:00:00.000000000Z',
'1887-03-15T00:00:00.000000000Z', '1887-04-15T00:00:00.000000000Z',
'1887-05-15T00:00:00.000000000Z', '1887-06-15T00:00:00.000000000Z',
'1887-07-15T00:00:00.000000000Z', '1887-08-15T00:00:00.000000000Z',
'1887-09-15T00:00:00.000000000Z', '1887-10-15T00:00:00.000000000Z',
'1887-11-15T00:00:00.000000000Z', '1887-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]')
Coordinates:
* time (time) datetime64[ns] 1887-01-15 1887-02-15 1887-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[-2.08999995]],
[[ 0.33999999]],
[[-1.45999997]],
[[-0.48999999]],
[[ 2.66999994]],
[[ 0.49999999]],
[[ 1.72999996]],
[[-0.74999998]],
[[-0.89999998]],
[[-2.59999994]],
[[-1.06999998]],
[[-0.60999999]]])
Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1887-01-15 1887-02-15 1887-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(8.0, <xray.DataArray 'time' (time: 12)>
array(['1888-01-15T00:00:00.000000000Z', '1888-02-15T00:00:00.000000000Z',
'1888-03-15T00:00:00.000000000Z', '1888-04-15T00:00:00.000000000Z',
'1888-05-15T00:00:00.000000000Z', '1888-06-15T00:00:00.000000000Z',
'1888-07-15T00:00:00.000000000Z', '1888-08-15T00:00:00.000000000Z',
'1888-09-15T00:00:00.000000000Z', '1888-10-15T00:00:00.000000000Z',
'1888-11-15T00:00:00.000000000Z', '1888-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]')
Coordinates:
* time (time) datetime64[ns] 1888-01-15 1888-02-15 1888-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[-3.35999992]],
[[-0.57999999]],
[[-3.20999993]],
[[-0.70999998]],
[[-1.98999996]],
[[-0.08 ]],
[[-0.21 ]],
[[-0.98999998]],
[[-2.07999995]],
[[-2.81999994]],
[[ 0.09 ]],
[[ 1.53999997]]])
Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1888-01-15 1888-02-15 1888-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(9.0, <xray.DataArray 'time' (time: 12)>
array(['1889-01-15T00:00:00.000000000Z', '1889-02-15T00:00:00.000000000Z',
'1889-03-15T00:00:00.000000000Z', '1889-04-15T00:00:00.000000000Z',
'1889-05-15T00:00:00.000000000Z', '1889-06-15T00:00:00.000000000Z',
'1889-07-15T00:00:00.000000000Z', '1889-08-15T00:00:00.000000000Z',
'1889-09-15T00:00:00.000000000Z', '1889-10-15T00:00:00.000000000Z',
'1889-11-15T00:00:00.000000000Z', '1889-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]')
Coordinates:
* time (time) datetime64[ns] 1889-01-15 1889-02-15 1889-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 3.08999993]],
[[-2.69999994]],
[[ 1.75999996]],
[[ 0.28999999]],
[[-0.58999999]],
[[-2.02999995]],
[[-0.67999998]],
[[-1.05999998]],
[[-0.95999998]],
[[-2.76999994]],
[[-0.97999998]],
[[ 6.18999986]]])
Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1889-01-15 1889-02-15 1889-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(10.0, <xray.DataArray 'time' (time: 12)>
array(['1890-01-15T00:00:00.000000000Z', '1890-02-15T00:00:00.000000000Z',
'1890-03-15T00:00:00.000000000Z', '1890-04-15T00:00:00.000000000Z',
'1890-05-15T00:00:00.000000000Z', '1890-06-15T00:00:00.000000000Z',
'1890-07-15T00:00:00.000000000Z', '1890-08-15T00:00:00.000000000Z',
'1890-09-15T00:00:00.000000000Z', '1890-10-15T00:00:00.000000000Z',
'1890-11-15T00:00:00.000000000Z', '1890-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]')
Coordinates:
* time (time) datetime64[ns] 1890-01-15 1890-02-15 1890-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 4.2699999 ]],
[[ 3.30999993]],
[[-2.26999995]],
[[ 0.32999999]],
[[-1.53999997]],
[[ 1.68999996]],
[[ 0.08 ]],
[[-1.69999996]],
[[-1.62999996]],
[[-0.76999998]],
[[ 1.01999998]],
[[ 0.48999999]]])
Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1890-01-15 1890-02-15 1890-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(11.0, <xray.DataArray 'time' (time: 12)>
array(['1891-01-15T00:00:00.000000000Z', '1891-02-15T00:00:00.000000000Z',
'1891-03-15T00:00:00.000000000Z', '1891-04-15T00:00:00.000000000Z',
'1891-05-15T00:00:00.000000000Z', '1891-06-15T00:00:00.000000000Z',
'1891-07-15T00:00:00.000000000Z', '1891-08-15T00:00:00.000000000Z',
'1891-09-15T00:00:00.000000000Z', '1891-10-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1891-01-15 1891-02-15 1891-03-15 ...
Attributes:
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bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1891-01-15 1891-02-15 1891-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
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array(['1892-01-15T00:00:00.000000000Z', '1892-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1892-01-15 1892-02-15 1892-03-15 ...
Attributes:
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bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1892-01-15 1892-02-15 1892-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
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array(['1893-01-15T00:00:00.000000000Z', '1893-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1893-01-15 1893-02-15 1893-03-15 ...
Attributes:
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bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1893-01-15 1893-02-15 1893-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
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array(['1894-01-15T00:00:00.000000000Z', '1894-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1894-01-15 1894-02-15 1894-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 2.32999995]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1894-01-15 1894-02-15 1894-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
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array(['1895-01-15T00:00:00.000000000Z', '1895-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1895-01-15 1895-02-15 1895-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[-1.82999996]],
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* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1895-01-15 1895-02-15 1895-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
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Coordinates:
* time (time) datetime64[ns] 1896-01-15 1896-02-15 1896-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1896-01-15 1896-02-15 1896-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
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Coordinates:
* time (time) datetime64[ns] 1897-01-15 1897-02-15 1897-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1897-01-15 1897-02-15 1897-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
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Coordinates:
* time (time) datetime64[ns] 1898-01-15 1898-02-15 1898-03-15 ...
Attributes:
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bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* time (time) datetime64[ns] 1898-01-15 1898-02-15 1898-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
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Coordinates:
* time (time) datetime64[ns] 1899-01-15 1899-02-15 1899-03-15 ...
Attributes:
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bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* lat (lat) float32 41.0
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* time (time) datetime64[ns] 1899-01-15 1899-02-15 1899-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
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Attributes:
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bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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Attributes:
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units: K
cell_methods: time: mean)
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Attributes:
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Attributes:
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units: K
cell_methods: time: mean)
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Attributes:
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Attributes:
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cell_methods: time: mean)
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Attributes:
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Attributes:
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cell_methods: time: mean)
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Attributes:
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Attributes:
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cell_methods: time: mean)
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Attributes:
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Attributes:
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* time (time) datetime64[ns] 1906-01-15 1906-02-15 1906-03-15 ...
Attributes:
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bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1906-01-15 1906-02-15 1906-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
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array(['1907-01-15T00:00:00.000000000Z', '1907-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1907-01-15 1907-02-15 1907-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 2.49999994]],
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* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1907-01-15 1907-02-15 1907-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
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array(['1908-01-15T00:00:00.000000000Z', '1908-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1908-01-15 1908-02-15 1908-03-15 ...
Attributes:
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bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1908-01-15 1908-02-15 1908-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
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Coordinates:
* time (time) datetime64[ns] 1909-01-15 1909-02-15 1909-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 2.34999995]],
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* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1909-01-15 1909-02-15 1909-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
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array(['1910-01-15T00:00:00.000000000Z', '1910-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1910-01-15 1910-02-15 1910-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 0.96999998]],
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* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1910-01-15 1910-02-15 1910-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(31.0, <xray.DataArray 'time' (time: 12)>
array(['1911-01-15T00:00:00.000000000Z', '1911-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1911-01-15 1911-02-15 1911-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1911-01-15 1911-02-15 1911-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(32.0, <xray.DataArray 'time' (time: 12)>
array(['1912-01-15T00:00:00.000000000Z', '1912-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1912-01-15 1912-02-15 1912-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1912-01-15 1912-02-15 1912-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
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Coordinates:
* time (time) datetime64[ns] 1913-01-15 1913-02-15 1913-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1913-01-15 1913-02-15 1913-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(34.0, <xray.DataArray 'time' (time: 12)>
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Coordinates:
* time (time) datetime64[ns] 1914-01-15 1914-02-15 1914-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1914-01-15 1914-02-15 1914-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
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array(['1915-01-15T00:00:00.000000000Z', '1915-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1915-01-15 1915-02-15 1915-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1915-01-15 1915-02-15 1915-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(36.0, <xray.DataArray 'time' (time: 12)>
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Coordinates:
* time (time) datetime64[ns] 1916-01-15 1916-02-15 1916-03-15 ...
Attributes:
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bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1916-01-15 1916-02-15 1916-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(37.0, <xray.DataArray 'time' (time: 12)>
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Coordinates:
* time (time) datetime64[ns] 1917-01-15 1917-02-15 1917-03-15 ...
Attributes:
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bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1917-01-15 1917-02-15 1917-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(38.0, <xray.DataArray 'time' (time: 12)>
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Coordinates:
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Attributes:
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bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1918-01-15 1918-02-15 1918-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(39.0, <xray.DataArray 'time' (time: 12)>
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Coordinates:
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Attributes:
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bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1919-01-15 1919-02-15 1919-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(40.0, <xray.DataArray 'time' (time: 12)>
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Coordinates:
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Attributes:
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* time (time) datetime64[ns] 1920-01-15 1920-02-15 1920-03-15 ...
Attributes:
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units: K
cell_methods: time: mean)
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Coordinates:
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Attributes:
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1921-01-15 1921-02-15 1921-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(42.0, <xray.DataArray 'time' (time: 12)>
array(['1922-01-15T00:00:00.000000000Z', '1922-02-15T00:00:00.000000000Z',
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'1922-05-15T00:00:00.000000000Z', '1922-06-15T00:00:00.000000000Z',
'1922-07-15T00:00:00.000000000Z', '1922-08-15T00:00:00.000000000Z',
'1922-09-15T00:00:00.000000000Z', '1922-10-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1922-01-15 1922-02-15 1922-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 0.2 ]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1922-01-15 1922-02-15 1922-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(43.0, <xray.DataArray 'time' (time: 12)>
array(['1923-01-15T00:00:00.000000000Z', '1923-02-15T00:00:00.000000000Z',
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'1923-05-15T00:00:00.000000000Z', '1923-06-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1923-01-15 1923-02-15 1923-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 3.61999992]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1923-01-15 1923-02-15 1923-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(44.0, <xray.DataArray 'time' (time: 12)>
array(['1924-01-15T00:00:00.000000000Z', '1924-02-15T00:00:00.000000000Z',
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'1924-05-15T00:00:00.000000000Z', '1924-06-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1924-01-15 1924-02-15 1924-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[-1.73999996]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1924-01-15 1924-02-15 1924-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(45.0, <xray.DataArray 'time' (time: 12)>
array(['1925-01-15T00:00:00.000000000Z', '1925-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1925-01-15 1925-02-15 1925-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 0.25999999]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1925-01-15 1925-02-15 1925-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(46.0, <xray.DataArray 'time' (time: 12)>
array(['1926-01-15T00:00:00.000000000Z', '1926-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1926-01-15 1926-02-15 1926-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 1.49999997]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1926-01-15 1926-02-15 1926-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(47.0, <xray.DataArray 'time' (time: 12)>
array(['1927-01-15T00:00:00.000000000Z', '1927-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1927-01-15 1927-02-15 1927-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 1.01999998]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1927-01-15 1927-02-15 1927-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(48.0, <xray.DataArray 'time' (time: 12)>
array(['1928-01-15T00:00:00.000000000Z', '1928-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1928-01-15 1928-02-15 1928-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 2.00999996]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1928-01-15 1928-02-15 1928-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(49.0, <xray.DataArray 'time' (time: 12)>
array(['1929-01-15T00:00:00.000000000Z', '1929-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1929-01-15 1929-02-15 1929-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[-1.75999996]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1929-01-15 1929-02-15 1929-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(50.0, <xray.DataArray 'time' (time: 12)>
array(['1930-01-15T00:00:00.000000000Z', '1930-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1930-01-15 1930-02-15 1930-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[-1.82999996]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1930-01-15 1930-02-15 1930-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(51.0, <xray.DataArray 'time' (time: 12)>
array(['1931-01-15T00:00:00.000000000Z', '1931-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1931-01-15 1931-02-15 1931-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 3.75999992]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1931-01-15 1931-02-15 1931-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(52.0, <xray.DataArray 'time' (time: 12)>
array(['1932-01-15T00:00:00.000000000Z', '1932-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1932-01-15 1932-02-15 1932-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 5.03999989]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1932-01-15 1932-02-15 1932-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(53.0, <xray.DataArray 'time' (time: 12)>
array(['1933-01-15T00:00:00.000000000Z', '1933-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1933-01-15 1933-02-15 1933-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 6.28999986]],
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* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1933-01-15 1933-02-15 1933-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(54.0, <xray.DataArray 'time' (time: 12)>
array(['1934-01-15T00:00:00.000000000Z', '1934-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1934-01-15 1934-02-15 1934-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1934-01-15 1934-02-15 1934-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(55.0, <xray.DataArray 'time' (time: 12)>
array(['1935-01-15T00:00:00.000000000Z', '1935-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1935-01-15 1935-02-15 1935-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1935-01-15 1935-02-15 1935-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(56.0, <xray.DataArray 'time' (time: 12)>
array(['1936-01-15T00:00:00.000000000Z', '1936-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1936-01-15 1936-02-15 1936-03-15 ...
Attributes:
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bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1936-01-15 1936-02-15 1936-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(57.0, <xray.DataArray 'time' (time: 12)>
array(['1937-01-15T00:00:00.000000000Z', '1937-02-15T00:00:00.000000000Z',
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'1937-05-15T00:00:00.000000000Z', '1937-06-15T00:00:00.000000000Z',
'1937-07-15T00:00:00.000000000Z', '1937-08-15T00:00:00.000000000Z',
'1937-09-15T00:00:00.000000000Z', '1937-10-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1937-01-15 1937-02-15 1937-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 1.48999997]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1937-01-15 1937-02-15 1937-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(58.0, <xray.DataArray 'time' (time: 12)>
array(['1938-01-15T00:00:00.000000000Z', '1938-02-15T00:00:00.000000000Z',
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'1938-05-15T00:00:00.000000000Z', '1938-06-15T00:00:00.000000000Z',
'1938-07-15T00:00:00.000000000Z', '1938-08-15T00:00:00.000000000Z',
'1938-09-15T00:00:00.000000000Z', '1938-10-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1938-01-15 1938-02-15 1938-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 1.40999997]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1938-01-15 1938-02-15 1938-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(59.0, <xray.DataArray 'time' (time: 12)>
array(['1939-01-15T00:00:00.000000000Z', '1939-02-15T00:00:00.000000000Z',
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'1939-05-15T00:00:00.000000000Z', '1939-06-15T00:00:00.000000000Z',
'1939-07-15T00:00:00.000000000Z', '1939-08-15T00:00:00.000000000Z',
'1939-09-15T00:00:00.000000000Z', '1939-10-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1939-01-15 1939-02-15 1939-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 4.10999991]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1939-01-15 1939-02-15 1939-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(60.0, <xray.DataArray 'time' (time: 12)>
array(['1940-01-15T00:00:00.000000000Z', '1940-02-15T00:00:00.000000000Z',
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'1940-05-15T00:00:00.000000000Z', '1940-06-15T00:00:00.000000000Z',
'1940-07-15T00:00:00.000000000Z', '1940-08-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1940-01-15 1940-02-15 1940-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[-5.06999989]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1940-01-15 1940-02-15 1940-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(61.0, <xray.DataArray 'time' (time: 12)>
array(['1941-01-15T00:00:00.000000000Z', '1941-02-15T00:00:00.000000000Z',
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'1941-05-15T00:00:00.000000000Z', '1941-06-15T00:00:00.000000000Z',
'1941-07-15T00:00:00.000000000Z', '1941-08-15T00:00:00.000000000Z',
'1941-09-15T00:00:00.000000000Z', '1941-10-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1941-01-15 1941-02-15 1941-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 2.19999995]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1941-01-15 1941-02-15 1941-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(62.0, <xray.DataArray 'time' (time: 12)>
array(['1942-01-15T00:00:00.000000000Z', '1942-02-15T00:00:00.000000000Z',
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'1942-05-15T00:00:00.000000000Z', '1942-06-15T00:00:00.000000000Z',
'1942-07-15T00:00:00.000000000Z', '1942-08-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1942-01-15 1942-02-15 1942-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 1.38999997]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1942-01-15 1942-02-15 1942-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(63.0, <xray.DataArray 'time' (time: 12)>
array(['1943-01-15T00:00:00.000000000Z', '1943-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1943-01-15 1943-02-15 1943-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 0.43999999]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1943-01-15 1943-02-15 1943-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(64.0, <xray.DataArray 'time' (time: 12)>
array(['1944-01-15T00:00:00.000000000Z', '1944-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1944-01-15 1944-02-15 1944-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 4.19999991]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1944-01-15 1944-02-15 1944-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(65.0, <xray.DataArray 'time' (time: 12)>
array(['1945-01-15T00:00:00.000000000Z', '1945-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1945-01-15 1945-02-15 1945-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[-0.48999999]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1945-01-15 1945-02-15 1945-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(66.0, <xray.DataArray 'time' (time: 12)>
array(['1946-01-15T00:00:00.000000000Z', '1946-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1946-01-15 1946-02-15 1946-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 2.32999995]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1946-01-15 1946-02-15 1946-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(67.0, <xray.DataArray 'time' (time: 12)>
array(['1947-01-15T00:00:00.000000000Z', '1947-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1947-01-15 1947-02-15 1947-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 3.62999992]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1947-01-15 1947-02-15 1947-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(68.0, <xray.DataArray 'time' (time: 12)>
array(['1948-01-15T00:00:00.000000000Z', '1948-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1948-01-15 1948-02-15 1948-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[-1.93999996]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1948-01-15 1948-02-15 1948-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(69.0, <xray.DataArray 'time' (time: 12)>
array(['1949-01-15T00:00:00.000000000Z', '1949-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1949-01-15 1949-02-15 1949-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 2.76999994]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1949-01-15 1949-02-15 1949-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(70.0, <xray.DataArray 'time' (time: 12)>
array(['1950-01-15T00:00:00.000000000Z', '1950-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1950-01-15 1950-02-15 1950-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 3.66999992]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1950-01-15 1950-02-15 1950-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(71.0, <xray.DataArray 'time' (time: 12)>
array(['1951-01-15T00:00:00.000000000Z', '1951-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1951-01-15 1951-02-15 1951-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 1.30999997]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1951-01-15 1951-02-15 1951-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(72.0, <xray.DataArray 'time' (time: 12)>
array(['1952-01-15T00:00:00.000000000Z', '1952-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1952-01-15 1952-02-15 1952-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 3.08999993]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1952-01-15 1952-02-15 1952-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(73.0, <xray.DataArray 'time' (time: 12)>
array(['1953-01-15T00:00:00.000000000Z', '1953-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1953-01-15 1953-02-15 1953-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 3.58999992]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1953-01-15 1953-02-15 1953-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(74.0, <xray.DataArray 'time' (time: 12)>
array(['1954-01-15T00:00:00.000000000Z', '1954-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1954-01-15 1954-02-15 1954-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 1.08999998]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1954-01-15 1954-02-15 1954-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(75.0, <xray.DataArray 'time' (time: 12)>
array(['1955-01-15T00:00:00.000000000Z', '1955-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1955-01-15 1955-02-15 1955-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 1.18999997]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1955-01-15 1955-02-15 1955-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(76.0, <xray.DataArray 'time' (time: 12)>
array(['1956-01-15T00:00:00.000000000Z', '1956-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1956-01-15 1956-02-15 1956-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 0.59999999]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1956-01-15 1956-02-15 1956-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(77.0, <xray.DataArray 'time' (time: 12)>
array(['1957-01-15T00:00:00.000000000Z', '1957-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1957-01-15 1957-02-15 1957-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[-1.18999997]],
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1957-01-15 1957-02-15 1957-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(78.0, <xray.DataArray 'time' (time: 12)>
array(['1958-01-15T00:00:00.000000000Z', '1958-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1958-01-15 1958-02-15 1958-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1958-01-15 1958-02-15 1958-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(79.0, <xray.DataArray 'time' (time: 12)>
array(['1959-01-15T00:00:00.000000000Z', '1959-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1959-01-15 1959-02-15 1959-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1959-01-15 1959-02-15 1959-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(80.0, <xray.DataArray 'time' (time: 12)>
array(['1960-01-15T00:00:00.000000000Z', '1960-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1960-01-15 1960-02-15 1960-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1960-01-15 1960-02-15 1960-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(81.0, <xray.DataArray 'time' (time: 12)>
array(['1961-01-15T00:00:00.000000000Z', '1961-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1961-01-15 1961-02-15 1961-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1961-01-15 1961-02-15 1961-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(82.0, <xray.DataArray 'time' (time: 12)>
array(['1962-01-15T00:00:00.000000000Z', '1962-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1962-01-15 1962-02-15 1962-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1962-01-15 1962-02-15 1962-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(83.0, <xray.DataArray 'time' (time: 12)>
array(['1963-01-15T00:00:00.000000000Z', '1963-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1963-01-15 1963-02-15 1963-03-15 ...
Attributes:
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bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1963-01-15 1963-02-15 1963-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(84.0, <xray.DataArray 'time' (time: 12)>
array(['1964-01-15T00:00:00.000000000Z', '1964-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1964-01-15 1964-02-15 1964-03-15 ...
Attributes:
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bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1964-01-15 1964-02-15 1964-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(85.0, <xray.DataArray 'time' (time: 12)>
array(['1965-01-15T00:00:00.000000000Z', '1965-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1965-01-15 1965-02-15 1965-03-15 ...
Attributes:
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bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1965-01-15 1965-02-15 1965-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(86.0, <xray.DataArray 'time' (time: 12)>
array(['1966-01-15T00:00:00.000000000Z', '1966-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1966-01-15 1966-02-15 1966-03-15 ...
Attributes:
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bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1966-01-15 1966-02-15 1966-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(87.0, <xray.DataArray 'time' (time: 12)>
array(['1967-01-15T00:00:00.000000000Z', '1967-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1967-01-15 1967-02-15 1967-03-15 ...
Attributes:
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bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1967-01-15 1967-02-15 1967-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(88.0, <xray.DataArray 'time' (time: 12)>
array(['1968-01-15T00:00:00.000000000Z', '1968-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1968-01-15 1968-02-15 1968-03-15 ...
Attributes:
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bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1968-01-15 1968-02-15 1968-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(89.0, <xray.DataArray 'time' (time: 12)>
array(['1969-01-15T00:00:00.000000000Z', '1969-02-15T00:00:00.000000000Z',
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Coordinates:
* time (time) datetime64[ns] 1969-01-15 1969-02-15 1969-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1969-01-15 1969-02-15 1969-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
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array(['1970-01-15T00:00:00.000000000+0000',
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Coordinates:
* time (time) datetime64[ns] 1970-01-15 1970-02-15 1970-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1970-01-15 1970-02-15 1970-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
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array(['1971-01-15T00:00:00.000000000+0000',
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Coordinates:
* time (time) datetime64[ns] 1971-01-15 1971-02-15 1971-03-15 ...
Attributes:
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bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[-1.26999997]],
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* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1971-01-15 1971-02-15 1971-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(92.0, <xray.DataArray 'time' (time: 12)>
array(['1972-01-15T00:00:00.000000000+0000',
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Coordinates:
* time (time) datetime64[ns] 1972-01-15 1972-02-15 1972-03-15 ...
Attributes:
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bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1972-01-15 1972-02-15 1972-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
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array(['1973-01-15T00:00:00.000000000+0000',
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Coordinates:
* time (time) datetime64[ns] 1973-01-15 1973-02-15 1973-03-15 ...
Attributes:
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bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1973-01-15 1973-02-15 1973-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
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Coordinates:
* time (time) datetime64[ns] 1974-01-15 1974-02-15 1974-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1974-01-15 1974-02-15 1974-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
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Coordinates:
* time (time) datetime64[ns] 1975-01-15 1975-02-15 1975-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1975-01-15 1975-02-15 1975-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(96.0, <xray.DataArray 'time' (time: 12)>
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Coordinates:
* time (time) datetime64[ns] 1976-01-15 1976-02-15 1976-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1976-01-15 1976-02-15 1976-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
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Coordinates:
* time (time) datetime64[ns] 1977-01-15 1977-02-15 1977-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1977-01-15 1977-02-15 1977-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
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Coordinates:
* time (time) datetime64[ns] 1978-01-15 1978-02-15 1978-03-15 ...
Attributes:
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bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1978-01-15 1978-02-15 1978-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
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Coordinates:
* time (time) datetime64[ns] 1979-01-15 1979-02-15 1979-03-15 ...
Attributes:
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bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 1979-01-15 1979-02-15 1979-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
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Coordinates:
* time (time) datetime64[ns] 1980-01-15 1980-02-15 1980-03-15 ...
Attributes:
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bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* time (time) datetime64[ns] 1980-01-15 1980-02-15 1980-03-15 ...
Attributes:
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units: K
cell_methods: time: mean)
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Coordinates:
* time (time) datetime64[ns] 1981-01-15 1981-02-15 1981-03-15 ...
Attributes:
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bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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Attributes:
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units: K
cell_methods: time: mean)
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Coordinates:
* time (time) datetime64[ns] 1982-01-15 1982-02-15 1982-03-15 ...
Attributes:
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Attributes:
long_name: Surface temperature anomaly
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cell_methods: time: mean)
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* time (time) datetime64[ns] 1983-01-15 1983-02-15 1983-03-15 ...
Attributes:
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bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
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* time (time) datetime64[ns] 1984-01-15 1984-02-15 1984-03-15 ...
Attributes:
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Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
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* time (time) datetime64[ns] 1985-01-15 1985-02-15 1985-03-15 ...
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bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
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* time (time) datetime64[ns] 1986-01-15 1986-02-15 1986-03-15 ...
Attributes:
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Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
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* time (time) datetime64[ns] 1987-01-15 1987-02-15 1987-03-15 ...
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Attributes:
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units: K
cell_methods: time: mean)
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* time (time) datetime64[ns] 1988-01-15 1988-02-15 1988-03-15 ...
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Attributes:
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units: K
cell_methods: time: mean)
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* time (time) datetime64[ns] 1989-01-15 1989-02-15 1989-03-15 ...
Attributes:
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Attributes:
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units: K
cell_methods: time: mean)
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* time (time) datetime64[ns] 1990-01-15 1990-02-15 1990-03-15 ...
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bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
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* time (time) datetime64[ns] 1990-01-15 1990-02-15 1990-03-15 ...
Attributes:
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units: K
cell_methods: time: mean)
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* time (time) datetime64[ns] 1991-01-15 1991-02-15 1991-03-15 ...
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Attributes:
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units: K
cell_methods: time: mean)
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* time (time) datetime64[ns] 1992-01-15 1992-02-15 1992-03-15 ...
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Attributes:
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units: K
cell_methods: time: mean)
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* time (time) datetime64[ns] 1993-01-15 1993-02-15 1993-03-15 ...
Attributes:
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Attributes:
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cell_methods: time: mean)
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* time (time) datetime64[ns] 1994-01-15 1994-02-15 1994-03-15 ...
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Attributes:
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units: K
cell_methods: time: mean)
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* time (time) datetime64[ns] 1995-01-15 1995-02-15 1995-03-15 ...
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Attributes:
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Attributes:
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[[-0.99999998]],
[[ 0.15 ]],
[[ 3.09999993]]])
Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 2012-01-15 2012-02-15 2012-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(133.0, <xray.DataArray 'time' (time: 12)>
array(['2013-01-15T00:00:00.000000000+0000',
'2013-02-15T00:00:00.000000000+0000',
'2013-03-15T00:00:00.000000000+0000',
'2013-04-15T00:00:00.000000000+0000',
'2013-05-15T00:00:00.000000000+0000',
'2013-06-15T00:00:00.000000000+0000',
'2013-07-15T00:00:00.000000000+0000',
'2013-08-15T00:00:00.000000000+0000',
'2013-09-15T00:00:00.000000000+0000',
'2013-10-15T00:00:00.000000000+0000',
'2013-11-15T00:00:00.000000000+0000',
'2013-12-15T00:00:00.000000000+0000'], dtype='datetime64[ns]')
Coordinates:
* time (time) datetime64[ns] 2013-01-15 2013-02-15 2013-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[ 2.78999994]],
[[ 0.46999999]],
[[-2.12999995]],
[[-1.38999997]],
[[ 0.22 ]],
[[ 0.15 ]],
[[-0.58999999]],
[[-0.2 ]],
[[ 1.16999997]],
[[ 0. ]],
[[-1.11999997]],
[[-1.42999997]]])
Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 2013-01-15 2013-02-15 2013-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
(134.0, <xray.DataArray 'time' (time: 12)>
array(['2014-01-15T00:00:00.000000000+0000',
'2014-02-15T00:00:00.000000000+0000',
'2014-03-15T00:00:00.000000000+0000',
'2014-04-15T00:00:00.000000000+0000',
'2014-05-15T00:00:00.000000000+0000',
'2014-06-15T00:00:00.000000000+0000',
'2014-07-15T00:00:00.000000000+0000',
'2014-08-15T00:00:00.000000000+0000',
'2014-09-15T00:00:00.000000000+0000',
'2014-10-15T00:00:00.000000000+0000',
'2014-11-15T00:00:00.000000000+0000',
'2014-12-15T00:00:00.000000000+0000'], dtype='datetime64[ns]')
Coordinates:
* time (time) datetime64[ns] 2014-01-15 2014-02-15 2014-03-15 ...
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)>
array([[[-2.24999995]],
[[-3.41999992]],
[[-2.35999995]],
[[-0.15 ]],
[[ 0.49999999]],
[[ 0.73999998]],
[[-2.03999995]],
[[ 0.03 ]],
[[-0.09 ]],
[[ 0.23999999]],
[[-3.06999993]],
[[ 2.26999995]]])
Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* time (time) datetime64[ns] 2014-01-15 2014-02-15 2014-03-15 ...
Attributes:
long_name: Surface temperature anomaly
units: K
cell_methods: time: mean)
Perfect. Now we just need to calculate the average temperature anomaly in each of those ranges:
In [10]:
for i in np.arange(nyears):
print(i,nc_cmi['time'][i*12],np.mean(nc_cmi['tempanomaly'][i*12:((i+1)*12)]))
(0.0, <xray.DataArray 'time' ()>
numpy.datetime64('1880-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1880-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.22249999502673745))
(1.0, <xray.DataArray 'time' ()>
numpy.datetime64('1881-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1881-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.30166665992389124))
(2.0, <xray.DataArray 'time' ()>
numpy.datetime64('1882-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1882-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.23499999474734068))
(3.0, <xray.DataArray 'time' ()>
numpy.datetime64('1883-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1883-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-1.2783333047603567))
(4.0, <xray.DataArray 'time' ()>
numpy.datetime64('1884-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1884-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.5724999872036278))
(5.0, <xray.DataArray 'time' ()>
numpy.datetime64('1885-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1885-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-1.6733332959314187))
(6.0, <xray.DataArray 'time' ()>
numpy.datetime64('1886-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1886-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.7883333157127103))
(7.0, <xray.DataArray 'time' ()>
numpy.datetime64('1887-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1887-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.39416665785635513))
(8.0, <xray.DataArray 'time' ()>
numpy.datetime64('1888-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1888-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-1.1999999731779099))
(9.0, <xray.DataArray 'time' ()>
numpy.datetime64('1889-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1889-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.0366666658471028))
(10.0, <xray.DataArray 'time' ()>
numpy.datetime64('1890-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1890-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.2733333272238572))
(11.0, <xray.DataArray 'time' ()>
numpy.datetime64('1891-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1891-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.08833333135892947))
(12.0, <xray.DataArray 'time' ()>
numpy.datetime64('1892-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1892-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.5933333200712999))
(13.0, <xray.DataArray 'time' ()>
numpy.datetime64('1893-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1893-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.8483333143716058))
(14.0, <xray.DataArray 'time' ()>
numpy.datetime64('1894-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1894-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.6708333183390399))
(15.0, <xray.DataArray 'time' ()>
numpy.datetime64('1895-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1895-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.5899999868124723))
(16.0, <xray.DataArray 'time' ()>
numpy.datetime64('1896-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1896-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.3858333247092863))
(17.0, <xray.DataArray 'time' ()>
numpy.datetime64('1897-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1897-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.13166666372368732))
(18.0, <xray.DataArray 'time' ()>
numpy.datetime64('1898-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1898-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.289166660203288))
(19.0, <xray.DataArray 'time' ()>
numpy.datetime64('1899-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1899-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.07999999821186066))
(20.0, <xray.DataArray 'time' ()>
numpy.datetime64('1900-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1900-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.6741666515978674))
(21.0, <xray.DataArray 'time' ()>
numpy.datetime64('1901-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1901-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.061666665288309254))
(22.0, <xray.DataArray 'time' ()>
numpy.datetime64('1902-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1902-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.03499999921768904))
(23.0, <xray.DataArray 'time' ()>
numpy.datetime64('1903-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1903-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.4449999900534749))
(24.0, <xray.DataArray 'time' ()>
numpy.datetime64('1904-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1904-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-1.037499976810068))
(25.0, <xray.DataArray 'time' ()>
numpy.datetime64('1905-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1905-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.4466666566828887))
(26.0, <xray.DataArray 'time' ()>
numpy.datetime64('1906-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1906-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.2024999954737723))
(27.0, <xray.DataArray 'time' ()>
numpy.datetime64('1907-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1907-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.3983333244298895))
(28.0, <xray.DataArray 'time' ()>
numpy.datetime64('1908-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1908-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.6374999857507646))
(29.0, <xray.DataArray 'time' ()>
numpy.datetime64('1909-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1909-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.039166665791223444))
(30.0, <xray.DataArray 'time' ()>
numpy.datetime64('1910-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1910-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.03583333253239592))
(31.0, <xray.DataArray 'time' ()>
numpy.datetime64('1911-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1911-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.8191666483568648))
(32.0, <xray.DataArray 'time' ()>
numpy.datetime64('1912-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1912-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.8766666470716397))
(33.0, <xray.DataArray 'time' ()>
numpy.datetime64('1913-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1913-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.7041666509273151))
(34.0, <xray.DataArray 'time' ()>
numpy.datetime64('1914-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1914-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.17666666271785894))
(35.0, <xray.DataArray 'time' ()>
numpy.datetime64('1915-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1915-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.09833333113541205))
(36.0, <xray.DataArray 'time' ()>
numpy.datetime64('1916-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1916-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.20583332873259982))
(37.0, <xray.DataArray 'time' ()>
numpy.datetime64('1917-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1917-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-1.5899999644607306))
(38.0, <xray.DataArray 'time' ()>
numpy.datetime64('1918-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1918-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.17249999614432454))
(39.0, <xray.DataArray 'time' ()>
numpy.datetime64('1919-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1919-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.39583332448576886))
(40.0, <xray.DataArray 'time' ()>
numpy.datetime64('1920-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1920-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.27916666042680544))
(41.0, <xray.DataArray 'time' ()>
numpy.datetime64('1921-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1921-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(1.8574999584816396))
(42.0, <xray.DataArray 'time' ()>
numpy.datetime64('1922-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1922-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.831666648077468))
(43.0, <xray.DataArray 'time' ()>
numpy.datetime64('1923-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1923-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.18416666255022088))
(44.0, <xray.DataArray 'time' ()>
numpy.datetime64('1924-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1924-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-1.0308333102924128))
(45.0, <xray.DataArray 'time' ()>
numpy.datetime64('1925-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1925-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.28249999368563294))
(46.0, <xray.DataArray 'time' ()>
numpy.datetime64('1926-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1926-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.4499999899417162))
(47.0, <xray.DataArray 'time' ()>
numpy.datetime64('1927-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1927-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.3499999921768904))
(48.0, <xray.DataArray 'time' ()>
numpy.datetime64('1928-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1928-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.06333333191772302))
(49.0, <xray.DataArray 'time' ()>
numpy.datetime64('1929-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1929-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.6266666526595751))
(50.0, <xray.DataArray 'time' ()>
numpy.datetime64('1930-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1930-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.6158333195683857))
(51.0, <xray.DataArray 'time' ()>
numpy.datetime64('1931-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1931-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(1.8666666249434154))
(52.0, <xray.DataArray 'time' ()>
numpy.datetime64('1932-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1932-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.4191666572975616))
(53.0, <xray.DataArray 'time' ()>
numpy.datetime64('1933-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1933-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(1.1041666419866185))
(54.0, <xray.DataArray 'time' ()>
numpy.datetime64('1934-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1934-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.8758333137569329))
(55.0, <xray.DataArray 'time' ()>
numpy.datetime64('1935-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1935-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.047499998938292265))
(56.0, <xray.DataArray 'time' ()>
numpy.datetime64('1936-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1936-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.11833333068837722))
(57.0, <xray.DataArray 'time' ()>
numpy.datetime64('1937-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1937-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.17666666271785894))
(58.0, <xray.DataArray 'time' ()>
numpy.datetime64('1938-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1938-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(1.2999999709427357))
(59.0, <xray.DataArray 'time' ()>
numpy.datetime64('1939-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1939-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(1.0524999764747918))
(60.0, <xray.DataArray 'time' ()>
numpy.datetime64('1940-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1940-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.5091666552859048))
(61.0, <xray.DataArray 'time' ()>
numpy.datetime64('1941-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1941-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(1.0258333104041715))
(62.0, <xray.DataArray 'time' ()>
numpy.datetime64('1942-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1942-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.28083332705621916))
(63.0, <xray.DataArray 'time' ()>
numpy.datetime64('1943-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1943-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.12083333063249786))
(64.0, <xray.DataArray 'time' ()>
numpy.datetime64('1944-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1944-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.5374999879859388))
(65.0, <xray.DataArray 'time' ()>
numpy.datetime64('1945-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1945-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.12249999726191163))
(66.0, <xray.DataArray 'time' ()>
numpy.datetime64('1946-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1946-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(1.129166641427825))
(67.0, <xray.DataArray 'time' ()>
numpy.datetime64('1947-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1947-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.04333333236475786))
(68.0, <xray.DataArray 'time' ()>
numpy.datetime64('1948-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1948-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.23749999469146132))
(69.0, <xray.DataArray 'time' ()>
numpy.datetime64('1949-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1949-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.824999981559813))
(70.0, <xray.DataArray 'time' ()>
numpy.datetime64('1950-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1950-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.6649999851360917))
(71.0, <xray.DataArray 'time' ()>
numpy.datetime64('1951-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1951-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.5208333216918012))
(72.0, <xray.DataArray 'time' ()>
numpy.datetime64('1952-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1952-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.6774999848566949))
(73.0, <xray.DataArray 'time' ()>
numpy.datetime64('1953-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1953-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(1.219999972730875))
(74.0, <xray.DataArray 'time' ()>
numpy.datetime64('1954-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1954-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(1.001666644277672))
(75.0, <xray.DataArray 'time' ()>
numpy.datetime64('1955-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1955-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.5416666545594732))
(76.0, <xray.DataArray 'time' ()>
numpy.datetime64('1956-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1956-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.43916665685052675))
(77.0, <xray.DataArray 'time' ()>
numpy.datetime64('1957-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1957-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.2733333272238572))
(78.0, <xray.DataArray 'time' ()>
numpy.datetime64('1958-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1958-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.5324999880976975))
(79.0, <xray.DataArray 'time' ()>
numpy.datetime64('1959-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1959-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.2291666615443925))
(80.0, <xray.DataArray 'time' ()>
numpy.datetime64('1960-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1960-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.4541666565152506))
(81.0, <xray.DataArray 'time' ()>
numpy.datetime64('1961-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1961-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.07666666495303313))
(82.0, <xray.DataArray 'time' ()>
numpy.datetime64('1962-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1962-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.2199999950826168))
(83.0, <xray.DataArray 'time' ()>
numpy.datetime64('1963-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1963-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.21916666176790991))
(84.0, <xray.DataArray 'time' ()>
numpy.datetime64('1964-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1964-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.4324999903328717))
(85.0, <xray.DataArray 'time' ()>
numpy.datetime64('1965-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1965-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.0041666665735344095))
(86.0, <xray.DataArray 'time' ()>
numpy.datetime64('1966-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1966-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.45583332314466435))
(87.0, <xray.DataArray 'time' ()>
numpy.datetime64('1967-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1967-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.40999999083578587))
(88.0, <xray.DataArray 'time' ()>
numpy.datetime64('1968-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1968-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.1708333295149108))
(89.0, <xray.DataArray 'time' ()>
numpy.datetime64('1969-01-15T00:00:00.000000000Z')
Coordinates:
time datetime64[ns] 1969-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.43833332353581983))
(90.0, <xray.DataArray 'time' ()>
numpy.datetime64('1970-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 1970-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.06499999854713678))
(91.0, <xray.DataArray 'time' ()>
numpy.datetime64('1971-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 1971-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.17666666271785894))
(92.0, <xray.DataArray 'time' ()>
numpy.datetime64('1972-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 1972-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.5958333200154206))
(93.0, <xray.DataArray 'time' ()>
numpy.datetime64('1973-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 1973-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.8166666484127442))
(94.0, <xray.DataArray 'time' ()>
numpy.datetime64('1974-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 1974-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.10833333091189463))
(95.0, <xray.DataArray 'time' ()>
numpy.datetime64('1975-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 1975-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.2941666600915293))
(96.0, <xray.DataArray 'time' ()>
numpy.datetime64('1976-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 1976-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.458333323088785))
(97.0, <xray.DataArray 'time' ()>
numpy.datetime64('1977-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 1977-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.22333332834144434))
(98.0, <xray.DataArray 'time' ()>
numpy.datetime64('1978-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 1978-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.8649999806657434))
(99.0, <xray.DataArray 'time' ()>
numpy.datetime64('1979-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 1979-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.9149999795481563))
(100.0, <xray.DataArray 'time' ()>
numpy.datetime64('1980-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 1980-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.03166666595886151))
(101.0, <xray.DataArray 'time' ()>
numpy.datetime64('1981-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 1981-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.22666666160027185))
(102.0, <xray.DataArray 'time' ()>
numpy.datetime64('1982-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 1982-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.26416666076208156))
(103.0, <xray.DataArray 'time' ()>
numpy.datetime64('1983-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 1983-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.1333333303531011))
(104.0, <xray.DataArray 'time' ()>
numpy.datetime64('1984-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 1984-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.18916666243846217))
(105.0, <xray.DataArray 'time' ()>
numpy.datetime64('1985-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 1985-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.30333332655330497))
(106.0, <xray.DataArray 'time' ()>
numpy.datetime64('1986-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 1986-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.6791666514861087))
(107.0, <xray.DataArray 'time' ()>
numpy.datetime64('1987-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 1987-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(1.2808333047044773))
(108.0, <xray.DataArray 'time' ()>
numpy.datetime64('1988-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 1988-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.25499999430030584))
(109.0, <xray.DataArray 'time' ()>
numpy.datetime64('1989-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 1989-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.5024999887682498))
(110.0, <xray.DataArray 'time' ()>
numpy.datetime64('1990-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 1990-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(1.1841666401984792))
(111.0, <xray.DataArray 'time' ()>
numpy.datetime64('1991-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 1991-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(1.093333308895429))
(112.0, <xray.DataArray 'time' ()>
numpy.datetime64('1992-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 1992-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.024999999441206455))
(113.0, <xray.DataArray 'time' ()>
numpy.datetime64('1993-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 1993-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.4074999908916652))
(114.0, <xray.DataArray 'time' ()>
numpy.datetime64('1994-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 1994-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.1791666626619796))
(115.0, <xray.DataArray 'time' ()>
numpy.datetime64('1995-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 1995-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.09249999793246388))
(116.0, <xray.DataArray 'time' ()>
numpy.datetime64('1996-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 1996-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.7183333172773322))
(117.0, <xray.DataArray 'time' ()>
numpy.datetime64('1997-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 1997-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.23666666137675443))
(118.0, <xray.DataArray 'time' ()>
numpy.datetime64('1998-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 1998-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(1.89749995758757))
(119.0, <xray.DataArray 'time' ()>
numpy.datetime64('1999-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 1999-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(1.1791666403102379))
(120.0, <xray.DataArray 'time' ()>
numpy.datetime64('2000-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 2000-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.4241666571858029))
(121.0, <xray.DataArray 'time' ()>
numpy.datetime64('2001-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 2001-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.9816666447247068))
(122.0, <xray.DataArray 'time' ()>
numpy.datetime64('2002-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 2002-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.8916666467363635))
(123.0, <xray.DataArray 'time' ()>
numpy.datetime64('2003-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 2003-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.11749999737367034))
(124.0, <xray.DataArray 'time' ()>
numpy.datetime64('2004-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 2004-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.5166666551182667))
(125.0, <xray.DataArray 'time' ()>
numpy.datetime64('2005-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 2005-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.9908333111864825))
(126.0, <xray.DataArray 'time' ()>
numpy.datetime64('2006-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 2006-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(1.3849999690428376))
(127.0, <xray.DataArray 'time' ()>
numpy.datetime64('2007-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 2007-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(1.0108333107394476))
(128.0, <xray.DataArray 'time' ()>
numpy.datetime64('2008-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 2008-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.21749999513849616))
(129.0, <xray.DataArray 'time' ()>
numpy.datetime64('2009-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 2009-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.11833333068837722))
(130.0, <xray.DataArray 'time' ()>
numpy.datetime64('2010-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 2010-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.7233333171655735))
(131.0, <xray.DataArray 'time' ()>
numpy.datetime64('2011-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 2011-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(0.8349999813362956))
(132.0, <xray.DataArray 'time' ()>
numpy.datetime64('2012-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 2012-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(2.185833284476151))
(133.0, <xray.DataArray 'time' ()>
numpy.datetime64('2013-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 2013-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.17166666282961765))
(134.0, <xray.DataArray 'time' ()>
numpy.datetime64('2014-01-15T00:00:00.000000000+0000')
Coordinates:
time datetime64[ns] 2014-01-15
Attributes:
long_name: time
bounds: time_bnds, <xray.DataArray 'tempanomaly' ()>
array(-0.7999999821186066))
Let's create a numpy array to store the data in, as well as the times, being careful of the data type:
In [12]:
tempanom_annual=np.zeros(int(nyears))
times_annual=np.zeros(int(nyears),dtype="datetime64")
Let's roll: (note: we'll just pick the first month of the year as the time)
In [15]:
for i in np.arange(int(nyears)):
tempanom_annual[i]=nc_cmi['tempanomaly'][i*12:((i+1)*12)].mean(dim='time')
# times_annual[i]=nc_cmi['time'][i*12]
print(tempanom_annual)
plt.plot(tempanom_annual)
[ 0.2225 0.30166666 0.23499999 -1.2783333 -0.57249999 -1.6733333 -0.78833332 -0.39416666 -1.19999997 -0.03666667 0.27333333 0.08833333 -0.59333332 -0.84833331 0.67083332 -0.58999999 0.38583332 0.13166666 0.28916666 -0.08 0.67416665 -0.06166667 -0.035 -0.44499999 -1.03749998 -0.44666666 0.2025 -0.39833332 0.63749999 -0.03916667 0.03583333 0.81916665 -0.87666665 0.70416665 0.17666666 -0.09833333 -0.20583333 -1.58999996 0.1725 0.39583332 -0.27916666 1.85749996 0.83166665 0.18416666 -1.03083331 0.28249999 -0.44999999 0.34999999 0.06333333 -0.62666665 0.61583332 1.86666662 0.41916666 1.10416664 0.87583331 0.0475 0.11833333 -0.17666666 1.29999997 1.05249998 -0.50916666 1.02583331 0.28083333 -0.12083333 0.53749999 -0.1225 1.12916664 0.04333333 0.23749999 0.82499998 -0.66499999 -0.52083332 0.67749998 1.21999997 1.00166664 0.54166665 0.43916666 0.27333333 -0.53249999 0.22916666 -0.45416666 -0.07666666 -0.22 -0.21916666 0.43249999 -0.00416667 -0.45583332 -0.40999999 -0.17083333 -0.43833332 -0.065 0.17666666 -0.59583332 0.81666665 0.10833333 0.29416666 -0.45833332 0.22333333 -0.86499998 -0.91499998 -0.03166667 0.22666666 -0.26416666 0.13333333 0.18916666 -0.30333333 0.67916665 1.2808333 0.25499999 -0.50249999 1.18416664 1.09333331 0.025 -0.40749999 0.17916666 0.0925 -0.71833332 -0.23666666 1.89749996 1.17916664 0.42416666 0.98166664 0.89166665 0.1175 0.51666666 0.99083331 1.38499997 1.01083331 -0.2175 -0.11833333 0.72333332 0.83499998 2.18583328 -0.17166666 -0.79999998]
Out[15]:
[<matplotlib.lines.Line2D at 0x7fd8122b19d0>]
xray has built in functionality for this, to make it really easy! Some examples:
http://xray.readthedocs.org/en/stable/time-series.html
http://xray.readthedocs.org/en/stable/examples/weather-data.html#toy-weather-data
In [18]:
nc_cmi_ann=nc_cmi.resample('12MS', dim='time', how='mean')
In [19]:
print(nc_cmi_ann)
<xray.Dataset>
Dimensions: (lat: 1, lon: 1, nv: 2, time: 136)
Coordinates:
* lat (lat) float32 41.0
* lon (lon) float32 -89.0
* nv (nv) int64 0 1
* time (time) datetime64[ns] 1880-01-01 1881-01-01 1882-01-01 ...
Data variables:
time_bnds (time, nv) float64 2.939e+04 2.942e+04 2.975e+04 2.978e+04 ...
tempanomaly (time, lat, lon) float64 0.2225 0.3017 0.235 -1.278 -0.5725 ...
Write the data file out, easy!!
In [20]:
nc_cmi_ann.to_netcdf('nc_cmi_ann.nc')
Plot the original and annually-averaged time series.
In [21]:
plt.figure(figsize=(11,8.5)) #create a new figure
plt.plot(nc_cmi['time'],np.squeeze(nc_cmi['tempanomaly']),'b',alpha=0.5)
plt.plot(nc_cmi_ann['time'],np.squeeze(nc_cmi_ann['tempanomaly']),'r',linewidth=2.0)
plt.legend(['Monthly averages','Annual Averages'])
plt.xlabel('Year')
plt.ylabel('Temperature Anomaly (degrees C)')
plt.title('GISTEMP Temperature Anomalies near Champaign, IL')
plt.show()
In [ ]: