CoCalc Public Filesatms391geodata / Week5a / Week 5 Exercise 3 - Working and plotting time series (advanced).ipynb
Author: Steve Nesbitt
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Description: Jupyter notebook atms391geodata/Week5a/Week 5 Exercise 3 - Working and plotting time series (advanced).ipynb
Compute Environment: Ubuntu 18.04 (Deprecated)

## 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 xarray as xr

nc_cmi = xr.open_dataset('Champaign_data.nc')
nc_cmi


<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

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.

  |------------|------------|------------|  ...  |------------|-----------|-----
1880         1881         1882         1883    2013         2014        2015       year
0            12           24           36     1596         1608        1620       index of January of each year
0            1            2            3      134          135         136        index of year


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', '1891-11-15T00:00:00.000000000Z', '1891-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1891-01-15 1891-02-15 1891-03-15 ... Attributes: long_name: time bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)> array([[[ 3.12999993]], [[ 1.27999997]], [[-2.45999995]], [[ 0.78999998]], [[-1.23999997]], [[ 0.28999999]], [[-2.62999994]], [[-1.03999998]], [[ 1.68999996]], [[-0.84999998]], [[-1.77999996]], [[ 3.87999991]]]) Coordinates: * 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) (12.0, <xray.DataArray 'time' (time: 12)> array(['1892-01-15T00:00:00.000000000Z', '1892-02-15T00:00:00.000000000Z', '1892-03-15T00:00:00.000000000Z', '1892-04-15T00:00:00.000000000Z', '1892-05-15T00:00:00.000000000Z', '1892-06-15T00:00:00.000000000Z', '1892-07-15T00:00:00.000000000Z', '1892-08-15T00:00:00.000000000Z', '1892-09-15T00:00:00.000000000Z', '1892-10-15T00:00:00.000000000Z', '1892-11-15T00:00:00.000000000Z', '1892-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1892-01-15 1892-02-15 1892-03-15 ... Attributes: long_name: time bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)> array([[[-1.24999997]], [[ 2.36999995]], [[-1.60999996]], [[-1.12999997]], [[-1.71999996]], [[ 0.35999999]], [[-0.42999999]], [[-0.04 ]], [[-0.06 ]], [[ 0.15 ]], [[-1.90999996]], [[-1.84999996]]]) Coordinates: * 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) (13.0, <xray.DataArray 'time' (time: 12)> array(['1893-01-15T00:00:00.000000000Z', '1893-02-15T00:00:00.000000000Z', '1893-03-15T00:00:00.000000000Z', '1893-04-15T00:00:00.000000000Z', '1893-05-15T00:00:00.000000000Z', '1893-06-15T00:00:00.000000000Z', '1893-07-15T00:00:00.000000000Z', '1893-08-15T00:00:00.000000000Z', '1893-09-15T00:00:00.000000000Z', '1893-10-15T00:00:00.000000000Z', '1893-11-15T00:00:00.000000000Z', '1893-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1893-01-15 1893-02-15 1893-03-15 ... Attributes: long_name: time bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)> array([[[-3.68999992]], [[-1.90999996]], [[-1.02999998]], [[-0.85999998]], [[-1.50999997]], [[ 0.66999999]], [[ 0.49999999]], [[-0.80999998]], [[ 0.17 ]], [[-0.18 ]], [[-1.15999997]], [[-0.36999999]]]) Coordinates: * 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) (14.0, <xray.DataArray 'time' (time: 12)> array(['1894-01-15T00:00:00.000000000Z', '1894-02-15T00:00:00.000000000Z', '1894-03-15T00:00:00.000000000Z', '1894-04-15T00:00:00.000000000Z', '1894-05-15T00:00:00.000000000Z', '1894-06-15T00:00:00.000000000Z', '1894-07-15T00:00:00.000000000Z', '1894-08-15T00:00:00.000000000Z', '1894-09-15T00:00:00.000000000Z', '1894-10-15T00:00:00.000000000Z', '1894-11-15T00:00:00.000000000Z', '1894-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') 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]], [[-1.30999997]], [[ 3.27999993]], [[ 0.80999998]], [[ 0. ]], [[ 1.31999997]], [[ 0.32999999]], [[ 0.15 ]], [[ 0.99999998]], [[ 0.02 ]], [[-2.06999995]], [[ 2.18999995]]]) 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) (15.0, <xray.DataArray 'time' (time: 12)> array(['1895-01-15T00:00:00.000000000Z', '1895-02-15T00:00:00.000000000Z', '1895-03-15T00:00:00.000000000Z', '1895-04-15T00:00:00.000000000Z', '1895-05-15T00:00:00.000000000Z', '1895-06-15T00:00:00.000000000Z', '1895-07-15T00:00:00.000000000Z', '1895-08-15T00:00:00.000000000Z', '1895-09-15T00:00:00.000000000Z', '1895-10-15T00:00:00.000000000Z', '1895-11-15T00:00:00.000000000Z', '1895-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') 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]], [[-4.75999989]], [[-0.89999998]], [[ 1.38999997]], [[ 0.39999999]], [[ 0.88999998]], [[-1.13999997]], [[ 0.28999999]], [[ 2.07999995]], [[-3.33999993]], [[-0.97999998]], [[ 0.81999998]]]) Coordinates: * 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) (16.0, <xray.DataArray 'time' (time: 12)> array(['1896-01-15T00:00:00.000000000Z', '1896-02-15T00:00:00.000000000Z', '1896-03-15T00:00:00.000000000Z', '1896-04-15T00:00:00.000000000Z', '1896-05-15T00:00:00.000000000Z', '1896-06-15T00:00:00.000000000Z', '1896-07-15T00:00:00.000000000Z', '1896-08-15T00:00:00.000000000Z', '1896-09-15T00:00:00.000000000Z', '1896-10-15T00:00:00.000000000Z', '1896-11-15T00:00:00.000000000Z', '1896-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') 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)> array([[[ 1.89999996]], [[ 1.05999998]], [[-2.47999994]], [[ 2.97999993]], [[ 3.39999992]], [[ 0.06 ]], [[-0.12 ]], [[ 0.38999999]], [[-1.48999997]], [[-2.08999995]], [[-0.75999998]], [[ 1.77999996]]]) Coordinates: * 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) (17.0, <xray.DataArray 'time' (time: 12)> array(['1897-01-15T00:00:00.000000000Z', '1897-02-15T00:00:00.000000000Z', '1897-03-15T00:00:00.000000000Z', '1897-04-15T00:00:00.000000000Z', '1897-05-15T00:00:00.000000000Z', '1897-06-15T00:00:00.000000000Z', '1897-07-15T00:00:00.000000000Z', '1897-08-15T00:00:00.000000000Z', '1897-09-15T00:00:00.000000000Z', '1897-10-15T00:00:00.000000000Z', '1897-11-15T00:00:00.000000000Z', '1897-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') 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)> array([[[-0.14 ]], [[ 1.16999997]], [[ 0.31999999]], [[-0.54999999]], [[-1.33999997]], [[-0.52999999]], [[ 0.96999998]], [[-1.14999997]], [[ 2.28999995]], [[ 2.19999995]], [[-0.52999999]], [[-1.12999997]]]) Coordinates: * 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) (18.0, <xray.DataArray 'time' (time: 12)> array(['1898-01-15T00:00:00.000000000Z', '1898-02-15T00:00:00.000000000Z', '1898-03-15T00:00:00.000000000Z', '1898-04-15T00:00:00.000000000Z', '1898-05-15T00:00:00.000000000Z', '1898-06-15T00:00:00.000000000Z', '1898-07-15T00:00:00.000000000Z', '1898-08-15T00:00:00.000000000Z', '1898-09-15T00:00:00.000000000Z', '1898-10-15T00:00:00.000000000Z', '1898-11-15T00:00:00.000000000Z', '1898-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1898-01-15 1898-02-15 1898-03-15 ... Attributes: long_name: time bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)> array([[[ 3.34999993]], [[ 0.77999998]], [[ 2.45999995]], [[-1.29999997]], [[ 0.26999999]], [[ 0.92999998]], [[ 0.22999999]], [[ 0.18 ]], [[ 1.45999997]], [[-1.16999997]], [[-1.86999996]], [[-1.84999996]]]) Coordinates: * lat (lat) float32 41.0 * lon (lon) float32 -89.0 * 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) (19.0, <xray.DataArray 'time' (time: 12)> array(['1899-01-15T00:00:00.000000000Z', '1899-02-15T00:00:00.000000000Z', '1899-03-15T00:00:00.000000000Z', '1899-04-15T00:00:00.000000000Z', '1899-05-15T00:00:00.000000000Z', '1899-06-15T00:00:00.000000000Z', '1899-07-15T00:00:00.000000000Z', '1899-08-15T00:00:00.000000000Z', '1899-09-15T00:00:00.000000000Z', '1899-10-15T00:00:00.000000000Z', '1899-11-15T00:00:00.000000000Z', '1899-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1899-01-15 1899-02-15 1899-03-15 ... Attributes: long_name: time bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)> array([[[ 0.76999998]], [[-5.08999989]], [[-2.76999994]], [[ 0.46999999]], [[ 0.96999998]], [[ 0.81999998]], [[-0.23999999]], [[ 1.25999997]], [[-0.75999998]], [[ 1.88999996]], [[ 2.36999995]], [[-0.64999999]]]) Coordinates: * lat (lat) float32 41.0 * lon (lon) float32 -89.0 * 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) (20.0, <xray.DataArray 'time' (time: 12)> array(['1900-01-15T00:00:00.000000000Z', '1900-02-15T00:00:00.000000000Z', '1900-03-15T00:00:00.000000000Z', '1900-04-15T00:00:00.000000000Z', '1900-05-15T00:00:00.000000000Z', '1900-06-15T00:00:00.000000000Z', '1900-07-15T00:00:00.000000000Z', '1900-08-15T00:00:00.000000000Z', '1900-09-15T00:00:00.000000000Z', '1900-10-15T00:00:00.000000000Z', '1900-11-15T00:00:00.000000000Z', '1900-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1900-01-15 1900-02-15 1900-03-15 ... Attributes: long_name: time bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)> array([[[ 3.20999993]], [[-2.89999994]], [[-2.19999995]], [[ 0.79999998]], [[ 1.03999998]], [[ 0.03 ]], [[-0.13 ]], [[ 2.66999994]], [[ 1.67999996]], [[ 3.45999992]], [[-0.49999999]], [[ 0.92999998]]]) Coordinates: * lat (lat) float32 41.0 * lon (lon) float32 -89.0 * time (time) datetime64[ns] 1900-01-15 1900-02-15 1900-03-15 ... Attributes: long_name: Surface temperature anomaly units: K cell_methods: time: mean) (21.0, <xray.DataArray 'time' (time: 12)> array(['1901-01-15T00:00:00.000000000Z', '1901-02-15T00:00:00.000000000Z', '1901-03-15T00:00:00.000000000Z', '1901-04-15T00:00:00.000000000Z', '1901-05-15T00:00:00.000000000Z', '1901-06-15T00:00:00.000000000Z', '1901-07-15T00:00:00.000000000Z', '1901-08-15T00:00:00.000000000Z', '1901-09-15T00:00:00.000000000Z', '1901-10-15T00:00:00.000000000Z', '1901-11-15T00:00:00.000000000Z', '1901-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1901-01-15 1901-02-15 1901-03-15 ... 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Attributes: long_name: Surface temperature anomaly units: K cell_methods: time: mean) (22.0, <xray.DataArray 'time' (time: 12)> array(['1902-01-15T00:00:00.000000000Z', '1902-02-15T00:00:00.000000000Z', '1902-03-15T00:00:00.000000000Z', '1902-04-15T00:00:00.000000000Z', '1902-05-15T00:00:00.000000000Z', '1902-06-15T00:00:00.000000000Z', '1902-07-15T00:00:00.000000000Z', '1902-08-15T00:00:00.000000000Z', '1902-09-15T00:00:00.000000000Z', '1902-10-15T00:00:00.000000000Z', '1902-11-15T00:00:00.000000000Z', '1902-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1902-01-15 1902-02-15 1902-03-15 ... 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Attributes: long_name: Surface temperature anomaly units: K cell_methods: time: mean) (23.0, <xray.DataArray 'time' (time: 12)> array(['1903-01-15T00:00:00.000000000Z', '1903-02-15T00:00:00.000000000Z', '1903-03-15T00:00:00.000000000Z', '1903-04-15T00:00:00.000000000Z', '1903-05-15T00:00:00.000000000Z', '1903-06-15T00:00:00.000000000Z', '1903-07-15T00:00:00.000000000Z', '1903-08-15T00:00:00.000000000Z', '1903-09-15T00:00:00.000000000Z', '1903-10-15T00:00:00.000000000Z', '1903-11-15T00:00:00.000000000Z', '1903-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1903-01-15 1903-02-15 1903-03-15 ... 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Attributes: long_name: Surface temperature anomaly units: K cell_methods: time: mean) (24.0, <xray.DataArray 'time' (time: 12)> array(['1904-01-15T00:00:00.000000000Z', '1904-02-15T00:00:00.000000000Z', '1904-03-15T00:00:00.000000000Z', '1904-04-15T00:00:00.000000000Z', '1904-05-15T00:00:00.000000000Z', '1904-06-15T00:00:00.000000000Z', '1904-07-15T00:00:00.000000000Z', '1904-08-15T00:00:00.000000000Z', '1904-09-15T00:00:00.000000000Z', '1904-10-15T00:00:00.000000000Z', '1904-11-15T00:00:00.000000000Z', '1904-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1904-01-15 1904-02-15 1904-03-15 ... 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Attributes: long_name: Surface temperature anomaly units: K cell_methods: time: mean) (25.0, <xray.DataArray 'time' (time: 12)> array(['1905-01-15T00:00:00.000000000Z', '1905-02-15T00:00:00.000000000Z', '1905-03-15T00:00:00.000000000Z', '1905-04-15T00:00:00.000000000Z', '1905-05-15T00:00:00.000000000Z', '1905-06-15T00:00:00.000000000Z', '1905-07-15T00:00:00.000000000Z', '1905-08-15T00:00:00.000000000Z', '1905-09-15T00:00:00.000000000Z', '1905-10-15T00:00:00.000000000Z', '1905-11-15T00:00:00.000000000Z', '1905-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1905-01-15 1905-02-15 1905-03-15 ... Attributes: long_name: time bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)> array([[[-2.67999994]], [[-5.18999988]], [[ 2.87999994]], [[-0.54999999]], [[-0.06 ]], [[ 0.17 ]], [[-1.10999998]], [[ 0.33999999]], [[ 0.94999998]], [[-0.86999998]], [[ 0.15 ]], [[ 0.60999999]]]) Coordinates: * lat (lat) float32 41.0 * lon (lon) float32 -89.0 * time (time) datetime64[ns] 1905-01-15 1905-02-15 1905-03-15 ... Attributes: long_name: Surface temperature anomaly units: K cell_methods: time: mean) (26.0, <xray.DataArray 'time' (time: 12)> array(['1906-01-15T00:00:00.000000000Z', '1906-02-15T00:00:00.000000000Z', '1906-03-15T00:00:00.000000000Z', '1906-04-15T00:00:00.000000000Z', '1906-05-15T00:00:00.000000000Z', '1906-06-15T00:00:00.000000000Z', '1906-07-15T00:00:00.000000000Z', '1906-08-15T00:00:00.000000000Z', '1906-09-15T00:00:00.000000000Z', '1906-10-15T00:00:00.000000000Z', '1906-11-15T00:00:00.000000000Z', '1906-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1906-01-15 1906-02-15 1906-03-15 ... 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Attributes: long_name: Surface temperature anomaly units: K cell_methods: time: mean) (27.0, <xray.DataArray 'time' (time: 12)> array(['1907-01-15T00:00:00.000000000Z', '1907-02-15T00:00:00.000000000Z', '1907-03-15T00:00:00.000000000Z', '1907-04-15T00:00:00.000000000Z', '1907-05-15T00:00:00.000000000Z', '1907-06-15T00:00:00.000000000Z', '1907-07-15T00:00:00.000000000Z', '1907-08-15T00:00:00.000000000Z', '1907-09-15T00:00:00.000000000Z', '1907-10-15T00:00:00.000000000Z', '1907-11-15T00:00:00.000000000Z', '1907-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1907-01-15 1907-02-15 1907-03-15 ... 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Attributes: long_name: Surface temperature anomaly units: K cell_methods: time: mean) (28.0, <xray.DataArray 'time' (time: 12)> array(['1908-01-15T00:00:00.000000000Z', '1908-02-15T00:00:00.000000000Z', '1908-03-15T00:00:00.000000000Z', '1908-04-15T00:00:00.000000000Z', '1908-05-15T00:00:00.000000000Z', '1908-06-15T00:00:00.000000000Z', '1908-07-15T00:00:00.000000000Z', '1908-08-15T00:00:00.000000000Z', '1908-09-15T00:00:00.000000000Z', '1908-10-15T00:00:00.000000000Z', '1908-11-15T00:00:00.000000000Z', '1908-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1908-01-15 1908-02-15 1908-03-15 ... Attributes: long_name: time bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)> array([[[ 2.31999995]], [[-0.29999999]], [[ 2.44999995]], [[ 0.46999999]], [[ 0.30999999]], [[-0.49999999]], [[-0.18 ]], [[-0.40999999]], [[ 1.77999996]], [[-0.35999999]], [[ 0.90999998]], [[ 1.15999997]]]) Coordinates: * 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) (29.0, <xray.DataArray 'time' (time: 12)> array(['1909-01-15T00:00:00.000000000Z', '1909-02-15T00:00:00.000000000Z', '1909-03-15T00:00:00.000000000Z', '1909-04-15T00:00:00.000000000Z', '1909-05-15T00:00:00.000000000Z', '1909-06-15T00:00:00.000000000Z', '1909-07-15T00:00:00.000000000Z', '1909-08-15T00:00:00.000000000Z', '1909-09-15T00:00:00.000000000Z', '1909-10-15T00:00:00.000000000Z', '1909-11-15T00:00:00.000000000Z', '1909-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') 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]], [[ 2.15999995]], [[-0.30999999]], [[-1.48999997]], [[-1.10999998]], [[ 0.18 ]], [[-0.82999998]], [[ 1.20999997]], [[-0.50999999]], [[-1.63999996]], [[ 3.52999992]], [[-4.00999991]]]) Coordinates: * 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) (30.0, <xray.DataArray 'time' (time: 12)> array(['1910-01-15T00:00:00.000000000Z', '1910-02-15T00:00:00.000000000Z', '1910-03-15T00:00:00.000000000Z', '1910-04-15T00:00:00.000000000Z', '1910-05-15T00:00:00.000000000Z', '1910-06-15T00:00:00.000000000Z', '1910-07-15T00:00:00.000000000Z', '1910-08-15T00:00:00.000000000Z', '1910-09-15T00:00:00.000000000Z', '1910-10-15T00:00:00.000000000Z', '1910-11-15T00:00:00.000000000Z', '1910-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') 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]], [[-2.15999995]], [[ 6.32999986]], [[ 0.83999998]], [[-2.28999995]], [[-0.79999998]], [[ 0.2 ]], [[-0.43999999]], [[ 0.44999999]], [[ 1.37999997]], [[-2.04999995]], [[-1.99999996]]]) Coordinates: * 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', '1911-03-15T00:00:00.000000000Z', '1911-04-15T00:00:00.000000000Z', '1911-05-15T00:00:00.000000000Z', '1911-06-15T00:00:00.000000000Z', '1911-07-15T00:00:00.000000000Z', '1911-08-15T00:00:00.000000000Z', '1911-09-15T00:00:00.000000000Z', '1911-10-15T00:00:00.000000000Z', '1911-11-15T00:00:00.000000000Z', '1911-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') 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)> array([[[ 2.56999994]], [[ 2.43999995]], [[ 1.39999997]], [[-1.04999998]], [[ 2.50999994]], [[ 2.06999995]], [[ 0.1 ]], [[-0.27999999]], [[ 1.57999996]], [[-0.54999999]], [[-2.97999993]], [[ 2.01999995]]]) Coordinates: * 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', '1912-03-15T00:00:00.000000000Z', '1912-04-15T00:00:00.000000000Z', '1912-05-15T00:00:00.000000000Z', '1912-06-15T00:00:00.000000000Z', '1912-07-15T00:00:00.000000000Z', '1912-08-15T00:00:00.000000000Z', '1912-09-15T00:00:00.000000000Z', '1912-10-15T00:00:00.000000000Z', '1912-11-15T00:00:00.000000000Z', '1912-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') 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)> array([[[-5.41999988]], [[-3.01999993]], [[-3.81999991]], [[ 0.49999999]], [[ 0.87999998]], [[-1.64999996]], [[-0.01 ]], [[-0.99999998]], [[ 0.49999999]], [[ 0.51999999]], [[ 0.46999999]], [[ 1.52999997]]]) Coordinates: * 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) (33.0, <xray.DataArray 'time' (time: 12)> array(['1913-01-15T00:00:00.000000000Z', '1913-02-15T00:00:00.000000000Z', '1913-03-15T00:00:00.000000000Z', '1913-04-15T00:00:00.000000000Z', '1913-05-15T00:00:00.000000000Z', '1913-06-15T00:00:00.000000000Z', '1913-07-15T00:00:00.000000000Z', '1913-08-15T00:00:00.000000000Z', '1913-09-15T00:00:00.000000000Z', '1913-10-15T00:00:00.000000000Z', '1913-11-15T00:00:00.000000000Z', '1913-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') 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)> array([[[ 3.13999993]], [[-1.83999996]], [[-0.55999999]], [[ 0.24999999]], [[-0.14 ]], [[ 0.77999998]], [[ 0.72999998]], [[ 1.60999996]], [[-0.09 ]], [[-0.90999998]], [[ 2.69999994]], [[ 2.77999994]]]) Coordinates: * 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)> array(['1914-01-15T00:00:00.000000000Z', '1914-02-15T00:00:00.000000000Z', '1914-03-15T00:00:00.000000000Z', '1914-04-15T00:00:00.000000000Z', '1914-05-15T00:00:00.000000000Z', '1914-06-15T00:00:00.000000000Z', '1914-07-15T00:00:00.000000000Z', '1914-08-15T00:00:00.000000000Z', '1914-09-15T00:00:00.000000000Z', '1914-10-15T00:00:00.000000000Z', '1914-11-15T00:00:00.000000000Z', '1914-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') 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)> array([[[ 4.18999991]], [[-3.65999992]], [[-0.80999998]], [[-0.44999999]], [[ 0.91999998]], [[ 1.46999997]], [[ 1.02999998]], [[ 0.31999999]], [[ 0.03 ]], [[ 1.54999997]], [[ 0.89999998]], [[-3.36999992]]]) Coordinates: * 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) (35.0, <xray.DataArray 'time' (time: 12)> array(['1915-01-15T00:00:00.000000000Z', '1915-02-15T00:00:00.000000000Z', '1915-03-15T00:00:00.000000000Z', '1915-04-15T00:00:00.000000000Z', '1915-05-15T00:00:00.000000000Z', '1915-06-15T00:00:00.000000000Z', '1915-07-15T00:00:00.000000000Z', '1915-08-15T00:00:00.000000000Z', '1915-09-15T00:00:00.000000000Z', '1915-10-15T00:00:00.000000000Z', '1915-11-15T00:00:00.000000000Z', '1915-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') 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)> array([[[-0.08 ]], [[ 2.91999993]], [[-2.91999993]], [[ 2.98999993]], [[-1.39999997]], [[-1.80999996]], [[-1.55999997]], [[-2.61999994]], [[ 0.73999998]], [[ 0.89999998]], [[ 1.62999996]], [[ 0.03 ]]]) Coordinates: * 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)> array(['1916-01-15T00:00:00.000000000Z', '1916-02-15T00:00:00.000000000Z', '1916-03-15T00:00:00.000000000Z', '1916-04-15T00:00:00.000000000Z', '1916-05-15T00:00:00.000000000Z', '1916-06-15T00:00:00.000000000Z', '1916-07-15T00:00:00.000000000Z', '1916-08-15T00:00:00.000000000Z', '1916-09-15T00:00:00.000000000Z', '1916-10-15T00:00:00.000000000Z', '1916-11-15T00:00:00.000000000Z', '1916-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1916-01-15 1916-02-15 1916-03-15 ... Attributes: long_name: time bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)> array([[[ 2.22999995]], [[-1.54999997]], [[-0.84999998]], [[-0.96999998]], [[ 0.26999999]], [[-2.18999995]], [[ 2.11999995]], [[ 1.13999997]], [[-0.69999998]], [[-0.38999999]], [[ 0.34999999]], [[-1.92999996]]]) Coordinates: * 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)> array(['1917-01-15T00:00:00.000000000Z', '1917-02-15T00:00:00.000000000Z', '1917-03-15T00:00:00.000000000Z', '1917-04-15T00:00:00.000000000Z', '1917-05-15T00:00:00.000000000Z', '1917-06-15T00:00:00.000000000Z', '1917-07-15T00:00:00.000000000Z', '1917-08-15T00:00:00.000000000Z', '1917-09-15T00:00:00.000000000Z', '1917-10-15T00:00:00.000000000Z', '1917-11-15T00:00:00.000000000Z', '1917-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1917-01-15 1917-02-15 1917-03-15 ... Attributes: long_name: time bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)> array([[[ 0.82999998]], [[-2.81999994]], [[ 0.38999999]], [[-1.29999997]], [[-3.24999993]], [[-1.57999996]], [[-0.09 ]], [[-1.12999997]], [[-0.92999998]], [[-4.3399999 ]], [[ 0.18 ]], [[-5.03999989]]]) Coordinates: * 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)> array(['1918-01-15T00:00:00.000000000Z', '1918-02-15T00:00:00.000000000Z', '1918-03-15T00:00:00.000000000Z', '1918-04-15T00:00:00.000000000Z', '1918-05-15T00:00:00.000000000Z', '1918-06-15T00:00:00.000000000Z', '1918-07-15T00:00:00.000000000Z', '1918-08-15T00:00:00.000000000Z', '1918-09-15T00:00:00.000000000Z', '1918-10-15T00:00:00.000000000Z', '1918-11-15T00:00:00.000000000Z', '1918-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1918-01-15 1918-02-15 1918-03-15 ... Attributes: long_name: time bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)> array([[[-5.77999987]], [[ 0.36999999]], [[ 3.72999992]], [[-1.71999996]], [[ 2.01999995]], [[ 0.23999999]], [[-0.94999998]], [[ 1.93999996]], [[-3.28999993]], [[ 1.39999997]], [[ 0.58999999]], [[ 3.51999992]]]) Coordinates: * lat (lat) float32 41.0 * 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)> array(['1919-01-15T00:00:00.000000000Z', '1919-02-15T00:00:00.000000000Z', '1919-03-15T00:00:00.000000000Z', '1919-04-15T00:00:00.000000000Z', '1919-05-15T00:00:00.000000000Z', '1919-06-15T00:00:00.000000000Z', '1919-07-15T00:00:00.000000000Z', '1919-08-15T00:00:00.000000000Z', '1919-09-15T00:00:00.000000000Z', '1919-10-15T00:00:00.000000000Z', '1919-11-15T00:00:00.000000000Z', '1919-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1919-01-15 1919-02-15 1919-03-15 ... Attributes: long_name: time bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)> array([[[ 3.49999992]], [[ 0.97999998]], [[ 1.20999997]], [[-0.33999999]], [[-1.02999998]], [[ 1.61999996]], [[ 1.08999998]], [[-0.27999999]], [[ 1.23999997]], [[ 1.14999997]], [[-1.06999998]], [[-3.31999993]]]) Coordinates: * lat (lat) float32 41.0 * 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)> array(['1920-01-15T00:00:00.000000000Z', '1920-02-15T00:00:00.000000000Z', '1920-03-15T00:00:00.000000000Z', '1920-04-15T00:00:00.000000000Z', '1920-05-15T00:00:00.000000000Z', '1920-06-15T00:00:00.000000000Z', '1920-07-15T00:00:00.000000000Z', '1920-08-15T00:00:00.000000000Z', '1920-09-15T00:00:00.000000000Z', '1920-10-15T00:00:00.000000000Z', '1920-11-15T00:00:00.000000000Z', '1920-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1920-01-15 1920-02-15 1920-03-15 ... Attributes: long_name: time bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)> array([[[-1.38999997]], [[-0.53999999]], [[ 0.95999998]], [[-2.85999994]], [[-0.76999998]], [[-0.17 ]], [[-1.10999998]], [[-1.02999998]], [[ 1.20999997]], [[ 2.41999995]], [[-1.11999997]], [[ 1.04999998]]]) Coordinates: * lat (lat) float32 41.0 * lon (lon) float32 -89.0 * time (time) datetime64[ns] 1920-01-15 1920-02-15 1920-03-15 ... Attributes: long_name: Surface temperature anomaly units: K cell_methods: time: mean) (41.0, <xray.DataArray 'time' (time: 12)> array(['1921-01-15T00:00:00.000000000Z', '1921-02-15T00:00:00.000000000Z', '1921-03-15T00:00:00.000000000Z', '1921-04-15T00:00:00.000000000Z', '1921-05-15T00:00:00.000000000Z', '1921-06-15T00:00:00.000000000Z', '1921-07-15T00:00:00.000000000Z', '1921-08-15T00:00:00.000000000Z', '1921-09-15T00:00:00.000000000Z', '1921-10-15T00:00:00.000000000Z', '1921-11-15T00:00:00.000000000Z', '1921-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1921-01-15 1921-02-15 1921-03-15 ... Attributes: long_name: time bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)> array([[[ 4.21999991]], [[ 3.03999993]], [[ 4.70999989]], [[ 1.43999997]], [[ 0.80999998]], [[ 2.07999995]], [[ 2.15999995]], [[-0.03 ]], [[ 2.63999994]], [[ 0.14 ]], [[-0.29999999]], [[ 1.37999997]]]) 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', '1922-03-15T00:00:00.000000000Z', '1922-04-15T00:00:00.000000000Z', '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', '1922-11-15T00:00:00.000000000Z', '1922-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1922-01-15 1922-02-15 1922-03-15 ... 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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', '1923-03-15T00:00:00.000000000Z', '1923-04-15T00:00:00.000000000Z', '1923-05-15T00:00:00.000000000Z', '1923-06-15T00:00:00.000000000Z', '1923-07-15T00:00:00.000000000Z', '1923-08-15T00:00:00.000000000Z', '1923-09-15T00:00:00.000000000Z', '1923-10-15T00:00:00.000000000Z', '1923-11-15T00:00:00.000000000Z', '1923-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1923-01-15 1923-02-15 1923-03-15 ... 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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', '1924-03-15T00:00:00.000000000Z', '1924-04-15T00:00:00.000000000Z', '1924-05-15T00:00:00.000000000Z', '1924-06-15T00:00:00.000000000Z', '1924-07-15T00:00:00.000000000Z', '1924-08-15T00:00:00.000000000Z', '1924-09-15T00:00:00.000000000Z', '1924-10-15T00:00:00.000000000Z', '1924-11-15T00:00:00.000000000Z', '1924-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1924-01-15 1924-02-15 1924-03-15 ... 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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', '1925-03-15T00:00:00.000000000Z', '1925-04-15T00:00:00.000000000Z', '1925-05-15T00:00:00.000000000Z', '1925-06-15T00:00:00.000000000Z', '1925-07-15T00:00:00.000000000Z', '1925-08-15T00:00:00.000000000Z', '1925-09-15T00:00:00.000000000Z', '1925-10-15T00:00:00.000000000Z', '1925-11-15T00:00:00.000000000Z', '1925-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1925-01-15 1925-02-15 1925-03-15 ... 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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', '1926-03-15T00:00:00.000000000Z', '1926-04-15T00:00:00.000000000Z', '1926-05-15T00:00:00.000000000Z', '1926-06-15T00:00:00.000000000Z', '1926-07-15T00:00:00.000000000Z', '1926-08-15T00:00:00.000000000Z', '1926-09-15T00:00:00.000000000Z', '1926-10-15T00:00:00.000000000Z', '1926-11-15T00:00:00.000000000Z', '1926-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1926-01-15 1926-02-15 1926-03-15 ... 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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', '1927-03-15T00:00:00.000000000Z', '1927-04-15T00:00:00.000000000Z', '1927-05-15T00:00:00.000000000Z', '1927-06-15T00:00:00.000000000Z', '1927-07-15T00:00:00.000000000Z', '1927-08-15T00:00:00.000000000Z', '1927-09-15T00:00:00.000000000Z', '1927-10-15T00:00:00.000000000Z', '1927-11-15T00:00:00.000000000Z', '1927-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1927-01-15 1927-02-15 1927-03-15 ... 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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', '1928-03-15T00:00:00.000000000Z', '1928-04-15T00:00:00.000000000Z', '1928-05-15T00:00:00.000000000Z', '1928-06-15T00:00:00.000000000Z', '1928-07-15T00:00:00.000000000Z', '1928-08-15T00:00:00.000000000Z', '1928-09-15T00:00:00.000000000Z', '1928-10-15T00:00:00.000000000Z', '1928-11-15T00:00:00.000000000Z', '1928-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1928-01-15 1928-02-15 1928-03-15 ... 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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', '1929-03-15T00:00:00.000000000Z', '1929-04-15T00:00:00.000000000Z', '1929-05-15T00:00:00.000000000Z', '1929-06-15T00:00:00.000000000Z', '1929-07-15T00:00:00.000000000Z', '1929-08-15T00:00:00.000000000Z', '1929-09-15T00:00:00.000000000Z', '1929-10-15T00:00:00.000000000Z', '1929-11-15T00:00:00.000000000Z', '1929-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1929-01-15 1929-02-15 1929-03-15 ... 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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', '1930-03-15T00:00:00.000000000Z', '1930-04-15T00:00:00.000000000Z', '1930-05-15T00:00:00.000000000Z', '1930-06-15T00:00:00.000000000Z', '1930-07-15T00:00:00.000000000Z', '1930-08-15T00:00:00.000000000Z', '1930-09-15T00:00:00.000000000Z', '1930-10-15T00:00:00.000000000Z', '1930-11-15T00:00:00.000000000Z', '1930-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1930-01-15 1930-02-15 1930-03-15 ... 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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', '1931-03-15T00:00:00.000000000Z', '1931-04-15T00:00:00.000000000Z', '1931-05-15T00:00:00.000000000Z', '1931-06-15T00:00:00.000000000Z', '1931-07-15T00:00:00.000000000Z', '1931-08-15T00:00:00.000000000Z', '1931-09-15T00:00:00.000000000Z', '1931-10-15T00:00:00.000000000Z', '1931-11-15T00:00:00.000000000Z', '1931-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1931-01-15 1931-02-15 1931-03-15 ... 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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', '1932-03-15T00:00:00.000000000Z', '1932-04-15T00:00:00.000000000Z', '1932-05-15T00:00:00.000000000Z', '1932-06-15T00:00:00.000000000Z', '1932-07-15T00:00:00.000000000Z', '1932-08-15T00:00:00.000000000Z', '1932-09-15T00:00:00.000000000Z', '1932-10-15T00:00:00.000000000Z', '1932-11-15T00:00:00.000000000Z', '1932-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1932-01-15 1932-02-15 1932-03-15 ... 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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', '1933-03-15T00:00:00.000000000Z', '1933-04-15T00:00:00.000000000Z', '1933-05-15T00:00:00.000000000Z', '1933-06-15T00:00:00.000000000Z', '1933-07-15T00:00:00.000000000Z', '1933-08-15T00:00:00.000000000Z', '1933-09-15T00:00:00.000000000Z', '1933-10-15T00:00:00.000000000Z', '1933-11-15T00:00:00.000000000Z', '1933-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1933-01-15 1933-02-15 1933-03-15 ... 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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', '1934-03-15T00:00:00.000000000Z', '1934-04-15T00:00:00.000000000Z', '1934-05-15T00:00:00.000000000Z', '1934-06-15T00:00:00.000000000Z', '1934-07-15T00:00:00.000000000Z', '1934-08-15T00:00:00.000000000Z', '1934-09-15T00:00:00.000000000Z', '1934-10-15T00:00:00.000000000Z', '1934-11-15T00:00:00.000000000Z', '1934-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1934-01-15 1934-02-15 1934-03-15 ... 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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', '1935-03-15T00:00:00.000000000Z', '1935-04-15T00:00:00.000000000Z', '1935-05-15T00:00:00.000000000Z', '1935-06-15T00:00:00.000000000Z', '1935-07-15T00:00:00.000000000Z', '1935-08-15T00:00:00.000000000Z', '1935-09-15T00:00:00.000000000Z', '1935-10-15T00:00:00.000000000Z', '1935-11-15T00:00:00.000000000Z', '1935-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1935-01-15 1935-02-15 1935-03-15 ... 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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', '1936-03-15T00:00:00.000000000Z', '1936-04-15T00:00:00.000000000Z', '1936-05-15T00:00:00.000000000Z', '1936-06-15T00:00:00.000000000Z', '1936-07-15T00:00:00.000000000Z', '1936-08-15T00:00:00.000000000Z', '1936-09-15T00:00:00.000000000Z', '1936-10-15T00:00:00.000000000Z', '1936-11-15T00:00:00.000000000Z', '1936-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1936-01-15 1936-02-15 1936-03-15 ... 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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', '1937-03-15T00:00:00.000000000Z', '1937-04-15T00:00:00.000000000Z', '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', '1937-11-15T00:00:00.000000000Z', '1937-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1937-01-15 1937-02-15 1937-03-15 ... 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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', '1938-03-15T00:00:00.000000000Z', '1938-04-15T00:00:00.000000000Z', '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', '1938-11-15T00:00:00.000000000Z', '1938-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1938-01-15 1938-02-15 1938-03-15 ... 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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', '1939-03-15T00:00:00.000000000Z', '1939-04-15T00:00:00.000000000Z', '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', '1939-11-15T00:00:00.000000000Z', '1939-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1939-01-15 1939-02-15 1939-03-15 ... 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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', '1940-03-15T00:00:00.000000000Z', '1940-04-15T00:00:00.000000000Z', '1940-05-15T00:00:00.000000000Z', '1940-06-15T00:00:00.000000000Z', '1940-07-15T00:00:00.000000000Z', '1940-08-15T00:00:00.000000000Z', '1940-09-15T00:00:00.000000000Z', '1940-10-15T00:00:00.000000000Z', '1940-11-15T00:00:00.000000000Z', '1940-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1940-01-15 1940-02-15 1940-03-15 ... 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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', '1941-03-15T00:00:00.000000000Z', '1941-04-15T00:00:00.000000000Z', '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', '1941-11-15T00:00:00.000000000Z', '1941-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1941-01-15 1941-02-15 1941-03-15 ... 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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', '1942-03-15T00:00:00.000000000Z', '1942-04-15T00:00:00.000000000Z', '1942-05-15T00:00:00.000000000Z', '1942-06-15T00:00:00.000000000Z', '1942-07-15T00:00:00.000000000Z', '1942-08-15T00:00:00.000000000Z', '1942-09-15T00:00:00.000000000Z', '1942-10-15T00:00:00.000000000Z', '1942-11-15T00:00:00.000000000Z', '1942-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1942-01-15 1942-02-15 1942-03-15 ... 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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', '1943-03-15T00:00:00.000000000Z', '1943-04-15T00:00:00.000000000Z', '1943-05-15T00:00:00.000000000Z', '1943-06-15T00:00:00.000000000Z', '1943-07-15T00:00:00.000000000Z', '1943-08-15T00:00:00.000000000Z', '1943-09-15T00:00:00.000000000Z', '1943-10-15T00:00:00.000000000Z', '1943-11-15T00:00:00.000000000Z', '1943-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1943-01-15 1943-02-15 1943-03-15 ... 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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', '1944-03-15T00:00:00.000000000Z', '1944-04-15T00:00:00.000000000Z', '1944-05-15T00:00:00.000000000Z', '1944-06-15T00:00:00.000000000Z', '1944-07-15T00:00:00.000000000Z', '1944-08-15T00:00:00.000000000Z', '1944-09-15T00:00:00.000000000Z', '1944-10-15T00:00:00.000000000Z', '1944-11-15T00:00:00.000000000Z', '1944-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1944-01-15 1944-02-15 1944-03-15 ... 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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', '1955-03-15T00:00:00.000000000Z', '1955-04-15T00:00:00.000000000Z', '1955-05-15T00:00:00.000000000Z', '1955-06-15T00:00:00.000000000Z', '1955-07-15T00:00:00.000000000Z', '1955-08-15T00:00:00.000000000Z', '1955-09-15T00:00:00.000000000Z', '1955-10-15T00:00:00.000000000Z', '1955-11-15T00:00:00.000000000Z', '1955-12-15T00:00:00.000000000Z'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1955-01-15 1955-02-15 1955-03-15 ... 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Attributes: long_name: Surface temperature anomaly units: K cell_methods: time: mean) (110.0, <xray.DataArray 'time' (time: 12)> array(['1990-01-15T00:00:00.000000000+0000', '1990-02-15T00:00:00.000000000+0000', '1990-03-15T00:00:00.000000000+0000', '1990-04-15T00:00:00.000000000+0000', '1990-05-15T00:00:00.000000000+0000', '1990-06-15T00:00:00.000000000+0000', '1990-07-15T00:00:00.000000000+0000', '1990-08-15T00:00:00.000000000+0000', '1990-09-15T00:00:00.000000000+0000', '1990-10-15T00:00:00.000000000+0000', '1990-11-15T00:00:00.000000000+0000', '1990-12-15T00:00:00.000000000+0000'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1990-01-15 1990-02-15 1990-03-15 ... 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Attributes: long_name: Surface temperature anomaly units: K cell_methods: time: mean) (111.0, <xray.DataArray 'time' (time: 12)> array(['1991-01-15T00:00:00.000000000+0000', '1991-02-15T00:00:00.000000000+0000', '1991-03-15T00:00:00.000000000+0000', '1991-04-15T00:00:00.000000000+0000', '1991-05-15T00:00:00.000000000+0000', '1991-06-15T00:00:00.000000000+0000', '1991-07-15T00:00:00.000000000+0000', '1991-08-15T00:00:00.000000000+0000', '1991-09-15T00:00:00.000000000+0000', '1991-10-15T00:00:00.000000000+0000', '1991-11-15T00:00:00.000000000+0000', '1991-12-15T00:00:00.000000000+0000'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1991-01-15 1991-02-15 1991-03-15 ... 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Attributes: long_name: Surface temperature anomaly units: K cell_methods: time: mean) (112.0, <xray.DataArray 'time' (time: 12)> array(['1992-01-15T00:00:00.000000000+0000', '1992-02-15T00:00:00.000000000+0000', '1992-03-15T00:00:00.000000000+0000', '1992-04-15T00:00:00.000000000+0000', '1992-05-15T00:00:00.000000000+0000', '1992-06-15T00:00:00.000000000+0000', '1992-07-15T00:00:00.000000000+0000', '1992-08-15T00:00:00.000000000+0000', '1992-09-15T00:00:00.000000000+0000', '1992-10-15T00:00:00.000000000+0000', '1992-11-15T00:00:00.000000000+0000', '1992-12-15T00:00:00.000000000+0000'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1992-01-15 1992-02-15 1992-03-15 ... 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Attributes: long_name: Surface temperature anomaly units: K cell_methods: time: mean) (113.0, <xray.DataArray 'time' (time: 12)> array(['1993-01-15T00:00:00.000000000+0000', '1993-02-15T00:00:00.000000000+0000', '1993-03-15T00:00:00.000000000+0000', '1993-04-15T00:00:00.000000000+0000', '1993-05-15T00:00:00.000000000+0000', '1993-06-15T00:00:00.000000000+0000', '1993-07-15T00:00:00.000000000+0000', '1993-08-15T00:00:00.000000000+0000', '1993-09-15T00:00:00.000000000+0000', '1993-10-15T00:00:00.000000000+0000', '1993-11-15T00:00:00.000000000+0000', '1993-12-15T00:00:00.000000000+0000'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1993-01-15 1993-02-15 1993-03-15 ... 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Attributes: long_name: Surface temperature anomaly units: K cell_methods: time: mean) (114.0, <xray.DataArray 'time' (time: 12)> array(['1994-01-15T00:00:00.000000000+0000', '1994-02-15T00:00:00.000000000+0000', '1994-03-15T00:00:00.000000000+0000', '1994-04-15T00:00:00.000000000+0000', '1994-05-15T00:00:00.000000000+0000', '1994-06-15T00:00:00.000000000+0000', '1994-07-15T00:00:00.000000000+0000', '1994-08-15T00:00:00.000000000+0000', '1994-09-15T00:00:00.000000000+0000', '1994-10-15T00:00:00.000000000+0000', '1994-11-15T00:00:00.000000000+0000', '1994-12-15T00:00:00.000000000+0000'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1994-01-15 1994-02-15 1994-03-15 ... 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Attributes: long_name: Surface temperature anomaly units: K cell_methods: time: mean) (115.0, <xray.DataArray 'time' (time: 12)> array(['1995-01-15T00:00:00.000000000+0000', '1995-02-15T00:00:00.000000000+0000', '1995-03-15T00:00:00.000000000+0000', '1995-04-15T00:00:00.000000000+0000', '1995-05-15T00:00:00.000000000+0000', '1995-06-15T00:00:00.000000000+0000', '1995-07-15T00:00:00.000000000+0000', '1995-08-15T00:00:00.000000000+0000', '1995-09-15T00:00:00.000000000+0000', '1995-10-15T00:00:00.000000000+0000', '1995-11-15T00:00:00.000000000+0000', '1995-12-15T00:00:00.000000000+0000'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1995-01-15 1995-02-15 1995-03-15 ... 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Attributes: long_name: Surface temperature anomaly units: K cell_methods: time: mean) (116.0, <xray.DataArray 'time' (time: 12)> array(['1996-01-15T00:00:00.000000000+0000', '1996-02-15T00:00:00.000000000+0000', '1996-03-15T00:00:00.000000000+0000', '1996-04-15T00:00:00.000000000+0000', '1996-05-15T00:00:00.000000000+0000', '1996-06-15T00:00:00.000000000+0000', '1996-07-15T00:00:00.000000000+0000', '1996-08-15T00:00:00.000000000+0000', '1996-09-15T00:00:00.000000000+0000', '1996-10-15T00:00:00.000000000+0000', '1996-11-15T00:00:00.000000000+0000', '1996-12-15T00:00:00.000000000+0000'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1996-01-15 1996-02-15 1996-03-15 ... 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Attributes: long_name: Surface temperature anomaly units: K cell_methods: time: mean) (117.0, <xray.DataArray 'time' (time: 12)> array(['1997-01-15T00:00:00.000000000+0000', '1997-02-15T00:00:00.000000000+0000', '1997-03-15T00:00:00.000000000+0000', '1997-04-15T00:00:00.000000000+0000', '1997-05-15T00:00:00.000000000+0000', '1997-06-15T00:00:00.000000000+0000', '1997-07-15T00:00:00.000000000+0000', '1997-08-15T00:00:00.000000000+0000', '1997-09-15T00:00:00.000000000+0000', '1997-10-15T00:00:00.000000000+0000', '1997-11-15T00:00:00.000000000+0000', '1997-12-15T00:00:00.000000000+0000'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1997-01-15 1997-02-15 1997-03-15 ... 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Attributes: long_name: Surface temperature anomaly units: K cell_methods: time: mean) (118.0, <xray.DataArray 'time' (time: 12)> array(['1998-01-15T00:00:00.000000000+0000', '1998-02-15T00:00:00.000000000+0000', '1998-03-15T00:00:00.000000000+0000', '1998-04-15T00:00:00.000000000+0000', '1998-05-15T00:00:00.000000000+0000', '1998-06-15T00:00:00.000000000+0000', '1998-07-15T00:00:00.000000000+0000', '1998-08-15T00:00:00.000000000+0000', '1998-09-15T00:00:00.000000000+0000', '1998-10-15T00:00:00.000000000+0000', '1998-11-15T00:00:00.000000000+0000', '1998-12-15T00:00:00.000000000+0000'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 1998-01-15 1998-02-15 1998-03-15 ... 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Attributes: long_name: Surface temperature anomaly units: K cell_methods: time: mean) (121.0, <xray.DataArray 'time' (time: 12)> array(['2001-01-15T00:00:00.000000000+0000', '2001-02-15T00:00:00.000000000+0000', '2001-03-15T00:00:00.000000000+0000', '2001-04-15T00:00:00.000000000+0000', '2001-05-15T00:00:00.000000000+0000', '2001-06-15T00:00:00.000000000+0000', '2001-07-15T00:00:00.000000000+0000', '2001-08-15T00:00:00.000000000+0000', '2001-09-15T00:00:00.000000000+0000', '2001-10-15T00:00:00.000000000+0000', '2001-11-15T00:00:00.000000000+0000', '2001-12-15T00:00:00.000000000+0000'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 2001-01-15 2001-02-15 2001-03-15 ... 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Attributes: long_name: Surface temperature anomaly units: K cell_methods: time: mean) (123.0, <xray.DataArray 'time' (time: 12)> array(['2003-01-15T00:00:00.000000000+0000', '2003-02-15T00:00:00.000000000+0000', '2003-03-15T00:00:00.000000000+0000', '2003-04-15T00:00:00.000000000+0000', '2003-05-15T00:00:00.000000000+0000', '2003-06-15T00:00:00.000000000+0000', '2003-07-15T00:00:00.000000000+0000', '2003-08-15T00:00:00.000000000+0000', '2003-09-15T00:00:00.000000000+0000', '2003-10-15T00:00:00.000000000+0000', '2003-11-15T00:00:00.000000000+0000', '2003-12-15T00:00:00.000000000+0000'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 2003-01-15 2003-02-15 2003-03-15 ... 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Attributes: long_name: Surface temperature anomaly units: K cell_methods: time: mean) (125.0, <xray.DataArray 'time' (time: 12)> array(['2005-01-15T00:00:00.000000000+0000', '2005-02-15T00:00:00.000000000+0000', '2005-03-15T00:00:00.000000000+0000', '2005-04-15T00:00:00.000000000+0000', '2005-05-15T00:00:00.000000000+0000', '2005-06-15T00:00:00.000000000+0000', '2005-07-15T00:00:00.000000000+0000', '2005-08-15T00:00:00.000000000+0000', '2005-09-15T00:00:00.000000000+0000', '2005-10-15T00:00:00.000000000+0000', '2005-11-15T00:00:00.000000000+0000', '2005-12-15T00:00:00.000000000+0000'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 2005-01-15 2005-02-15 2005-03-15 ... Attributes: long_name: time bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)> array([[[ 1.91999996]], [[ 3.28999993]], [[-0.1 ]], [[ 1.32999997]], [[-1.13999997]], [[ 1.56999996]], [[ 0.69999998]], [[ 1.08999998]], [[ 2.27999995]], [[ 0.63999999]], [[ 1.77999996]], [[-1.46999997]]]) Coordinates: * lat (lat) float32 41.0 * lon (lon) float32 -89.0 * time (time) datetime64[ns] 2005-01-15 2005-02-15 2005-03-15 ... Attributes: long_name: Surface temperature anomaly units: K cell_methods: time: mean) (126.0, <xray.DataArray 'time' (time: 12)> array(['2006-01-15T00:00:00.000000000+0000', '2006-02-15T00:00:00.000000000+0000', '2006-03-15T00:00:00.000000000+0000', '2006-04-15T00:00:00.000000000+0000', '2006-05-15T00:00:00.000000000+0000', '2006-06-15T00:00:00.000000000+0000', '2006-07-15T00:00:00.000000000+0000', '2006-08-15T00:00:00.000000000+0000', '2006-09-15T00:00:00.000000000+0000', '2006-10-15T00:00:00.000000000+0000', '2006-11-15T00:00:00.000000000+0000', '2006-12-15T00:00:00.000000000+0000'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 2006-01-15 2006-02-15 2006-03-15 ... Attributes: long_name: time bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)> array([[[ 7.16999984]], [[ 0.86999998]], [[ 1.40999997]], [[ 2.72999994]], [[ 0.13 ]], [[ 0.16 ]], [[ 1.15999997]], [[ 1.05999998]], [[-1.33999997]], [[-1.62999996]], [[ 1.48999997]], [[ 3.40999992]]]) Coordinates: * lat (lat) float32 41.0 * lon (lon) float32 -89.0 * time (time) datetime64[ns] 2006-01-15 2006-02-15 2006-03-15 ... Attributes: long_name: Surface temperature anomaly units: K cell_methods: time: mean) (127.0, <xray.DataArray 'time' (time: 12)> array(['2007-01-15T00:00:00.000000000+0000', '2007-02-15T00:00:00.000000000+0000', '2007-03-15T00:00:00.000000000+0000', '2007-04-15T00:00:00.000000000+0000', '2007-05-15T00:00:00.000000000+0000', '2007-06-15T00:00:00.000000000+0000', '2007-07-15T00:00:00.000000000+0000', '2007-08-15T00:00:00.000000000+0000', '2007-09-15T00:00:00.000000000+0000', '2007-10-15T00:00:00.000000000+0000', '2007-11-15T00:00:00.000000000+0000', '2007-12-15T00:00:00.000000000+0000'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 2007-01-15 2007-02-15 2007-03-15 ... Attributes: long_name: time bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)> array([[[ 2.90999993]], [[-3.19999993]], [[ 4.23999991]], [[-1.05999998]], [[ 1.98999996]], [[ 0.77999998]], [[-0.53999999]], [[ 2.11999995]], [[ 1.52999997]], [[ 2.64999994]], [[ 0.45999999]], [[ 0.24999999]]]) Coordinates: * lat (lat) float32 41.0 * lon (lon) float32 -89.0 * time (time) datetime64[ns] 2007-01-15 2007-02-15 2007-03-15 ... Attributes: long_name: Surface temperature anomaly units: K cell_methods: time: mean) (128.0, <xray.DataArray 'time' (time: 12)> array(['2008-01-15T00:00:00.000000000+0000', '2008-02-15T00:00:00.000000000+0000', '2008-03-15T00:00:00.000000000+0000', '2008-04-15T00:00:00.000000000+0000', '2008-05-15T00:00:00.000000000+0000', '2008-06-15T00:00:00.000000000+0000', '2008-07-15T00:00:00.000000000+0000', '2008-08-15T00:00:00.000000000+0000', '2008-09-15T00:00:00.000000000+0000', '2008-10-15T00:00:00.000000000+0000', '2008-11-15T00:00:00.000000000+0000', '2008-12-15T00:00:00.000000000+0000'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 2008-01-15 2008-02-15 2008-03-15 ... Attributes: long_name: time bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)> array([[[ 1.10999998]], [[-0.66999999]], [[-0.07 ]], [[-0.2 ]], [[-1.31999997]], [[ 0.65999999]], [[-0.17 ]], [[-0.50999999]], [[ 0.50999999]], [[-0.56999999]], [[-0.18 ]], [[-1.19999997]]]) Coordinates: * lat (lat) float32 41.0 * lon (lon) float32 -89.0 * time (time) datetime64[ns] 2008-01-15 2008-02-15 2008-03-15 ... Attributes: long_name: Surface temperature anomaly units: K cell_methods: time: mean) (129.0, <xray.DataArray 'time' (time: 12)> array(['2009-01-15T00:00:00.000000000+0000', '2009-02-15T00:00:00.000000000+0000', '2009-03-15T00:00:00.000000000+0000', '2009-04-15T00:00:00.000000000+0000', '2009-05-15T00:00:00.000000000+0000', '2009-06-15T00:00:00.000000000+0000', '2009-07-15T00:00:00.000000000+0000', '2009-08-15T00:00:00.000000000+0000', '2009-09-15T00:00:00.000000000+0000', '2009-10-15T00:00:00.000000000+0000', '2009-11-15T00:00:00.000000000+0000', '2009-12-15T00:00:00.000000000+0000'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 2009-01-15 2009-02-15 2009-03-15 ... Attributes: long_name: time bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)> array([[[-1.40999997]], [[ 1.90999996]], [[ 1.74999996]], [[-0.27999999]], [[ 0.09 ]], [[ 0.25999999]], [[-2.36999995]], [[-0.90999998]], [[ 0.16 ]], [[-2.50999994]], [[ 2.97999993]], [[-1.08999998]]]) Coordinates: * lat (lat) float32 41.0 * lon (lon) float32 -89.0 * time (time) datetime64[ns] 2009-01-15 2009-02-15 2009-03-15 ... Attributes: long_name: Surface temperature anomaly units: K cell_methods: time: mean) (130.0, <xray.DataArray 'time' (time: 12)> array(['2010-01-15T00:00:00.000000000+0000', '2010-02-15T00:00:00.000000000+0000', '2010-03-15T00:00:00.000000000+0000', '2010-04-15T00:00:00.000000000+0000', '2010-05-15T00:00:00.000000000+0000', '2010-06-15T00:00:00.000000000+0000', '2010-07-15T00:00:00.000000000+0000', '2010-08-15T00:00:00.000000000+0000', '2010-09-15T00:00:00.000000000+0000', '2010-10-15T00:00:00.000000000+0000', '2010-11-15T00:00:00.000000000+0000', '2010-12-15T00:00:00.000000000+0000'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 2010-01-15 2010-02-15 2010-03-15 ... Attributes: long_name: time bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)> array([[[-0.66999999]], [[-1.69999996]], [[ 2.34999995]], [[ 2.93999993]], [[ 1.04999998]], [[ 1.66999996]], [[ 1.15999997]], [[ 1.91999996]], [[ 0.55999999]], [[ 0.94999998]], [[ 0.95999998]], [[-2.50999994]]]) Coordinates: * lat (lat) float32 41.0 * lon (lon) float32 -89.0 * time (time) datetime64[ns] 2010-01-15 2010-02-15 2010-03-15 ... Attributes: long_name: Surface temperature anomaly units: K cell_methods: time: mean) (131.0, <xray.DataArray 'time' (time: 12)> array(['2011-01-15T00:00:00.000000000+0000', '2011-02-15T00:00:00.000000000+0000', '2011-03-15T00:00:00.000000000+0000', '2011-04-15T00:00:00.000000000+0000', '2011-05-15T00:00:00.000000000+0000', '2011-06-15T00:00:00.000000000+0000', '2011-07-15T00:00:00.000000000+0000', '2011-08-15T00:00:00.000000000+0000', '2011-09-15T00:00:00.000000000+0000', '2011-10-15T00:00:00.000000000+0000', '2011-11-15T00:00:00.000000000+0000', '2011-12-15T00:00:00.000000000+0000'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 2011-01-15 2011-02-15 2011-03-15 ... Attributes: long_name: time bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)> array([[[-0.89999998]], [[ 0.65999999]], [[ 0.89999998]], [[ 0.61999999]], [[-0.03 ]], [[ 0.90999998]], [[ 2.38999995]], [[ 0.93999998]], [[-0.75999998]], [[ 0.22999999]], [[ 2.18999995]], [[ 2.86999994]]]) Coordinates: * lat (lat) float32 41.0 * lon (lon) float32 -89.0 * time (time) datetime64[ns] 2011-01-15 2011-02-15 2011-03-15 ... Attributes: long_name: Surface temperature anomaly units: K cell_methods: time: mean) (132.0, <xray.DataArray 'time' (time: 12)> array(['2012-01-15T00:00:00.000000000+0000', '2012-02-15T00:00:00.000000000+0000', '2012-03-15T00:00:00.000000000+0000', '2012-04-15T00:00:00.000000000+0000', '2012-05-15T00:00:00.000000000+0000', '2012-06-15T00:00:00.000000000+0000', '2012-07-15T00:00:00.000000000+0000', '2012-08-15T00:00:00.000000000+0000', '2012-09-15T00:00:00.000000000+0000', '2012-10-15T00:00:00.000000000+0000', '2012-11-15T00:00:00.000000000+0000', '2012-12-15T00:00:00.000000000+0000'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 2012-01-15 2012-02-15 2012-03-15 ... Attributes: long_name: time bounds: time_bnds, <xray.DataArray 'tempanomaly' (time: 12, lat: 1, lon: 1)> array([[[ 4.23999991]], [[ 3.62999992]], [[ 8.31999981]], [[ 1.29999997]], [[ 2.80999994]], [[ 0.87999998]], [[ 2.94999993]], [[ 0.12 ]], [[-0.26999999]], [[-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]
[<matplotlib.lines.Line2D at 0x7fd8122b19d0>]

xray has built in functionality for this, to make it really easy! Some examples:

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 [ ]: