ClimateDISK: Hockey stick

This notebook attempts to follow the WINGS steps necessary to reproduce the hockey stick figure (and allow for upgrades as new data becomes available).

Let’s first import our favorite packages:

import json
import requests
import pandas as pd
import io
import ast
import numpy as np
import pyleoclim as pyleo
import seaborn as sns
import cfr as cfr

Step 1: Data query

For now let’s query the LiPDverse v3 graph directly. For ClimateDisk, we may need to return the datasets URL, send the datasets to WINGS and use PyLiPD in the first component to produce the DataFrame.

url = 'https://linkedearth.graphdb.mint.isi.edu/repositories/LiPDVerse3'

query = """PREFIX le: <http://linked.earth/ontology#>
PREFIX wgs84: <http://www.w3.org/2003/01/geo/wgs84_pos#>


SELECT ?dataSetName ?archiveType ?geo_meanLat ?geo_meanLon ?geo_meanElev 
    ?paleoData_variableName ?paleoData_values ?paleoData_units 
    ?paleoData_proxy ?paleoData_proxyGeneral ?time_variableName ?time_values 
	?time_units ?compilationName ?TSID ?QAlabel where{
    
    ?ds a le:Dataset .
    ?ds le:name ?dataSetName .
    OPTIONAL{?ds le:proxyArchiveType ?archiveType .}
    
    ?ds le:collectedFrom ?loc .
    ?loc wgs84:lat ?geo_meanLat .
    ?loc wgs84:long ?geo_meanLon .
    OPTIONAL {?loc wgs84:alt ?geo_meanElev .}
    
    ?ds le:includesPaleoData ?data .
    ?data le:foundInMeasurementTable ?table .
    ?table le:includesVariable ?var . 
    ?var le:name ?paleoData_variableName .
    ?var le:hasValues ?paleoData_values .
    OPTIONAL{?var le:hasUnits ?paleoData_units .}
    OPTIONAL{?var le:proxy ?paleoData_proxy .}
    OPTIONAL{?var le:proxyGeneral ?paleoData_proxyGeneral .}
    ?var le:partOfCompilation ?compilation . 
    ?compilation le:name ?compilationName .
    VALUES ?compilationName {"iso2k" "Pages2kTemperature"} .
    ?var le:useInGlobalTemperatureAnalysis ?QAlabel .
    FILTER (?QAlabel = True || regex(?QAlabel, "True.*", "i")) .
    
    OPTIONAL{?var le:hasVariableID ?TSID} .
    
    
    ?table le:includesVariable ?timevar .
    ?timevar le:name ?time_variableName .
    FILTER (regex(?time_variableName, "year.*")).
    ?timevar le:hasValues ?time_values .
    OPTIONAL{?timevar le:hasUnits ?time_units .}
    
}"""

response = requests.post(url, data = {'query': query})

data = io.StringIO(response.text)
df = pd.read_csv(data, sep=",")

df['paleoData_values']=df['paleoData_values'].apply(lambda row : json.loads(row) if isinstance(row, str) else row)
df['time_values']=df['time_values'].apply(lambda row : json.loads(row) if isinstance(row, str) else row)

df.head()

	dataSetName	archiveType	geo_meanLat	geo_meanLon	geo_meanElev	paleoData_variableName	paleoData_values	paleoData_units	paleoData_proxy	paleoData_proxyGeneral	time_variableName	time_values	time_units	compilationName	TSID	QAlabel
0	LS16STCL	LakeSediment	50.830	-116.39	1126.0	d18O	[-7.81, -5.91, -9.03, -5.35, -5.61, -5.98, -5....	permil	d18O	NaN	year	[2009.0, 2008.3, 2007.8, 2007.4, 2007.0, 2006....	AD	iso2k	LPD7dc5b9ba	True
1	CO00URMA	Coral	0.933	173.00	6.0	d18O	[-4.8011, -4.725, -4.6994, -4.86, -5.0886, -5....	permil	d18O	NaN	year	[1994.5, 1994.33, 1994.17, 1994.0, 1993.83, 19...	AD	iso2k	Ocean2kHR_177	True
2	CO00URMA	Coral	0.933	173.00	6.0	d18O	[-4.8011, -4.725, -4.6994, -4.86, -5.0886, -5....	permil	d18O	NaN	year	[1994.5, 1994.33, 1994.17, 1994.0, 1993.83, 19...	AD	iso2k	Ocean2kHR_177	True
3	CO00URMA	Coral	0.933	173.00	6.0	d18O	[-4.8011, -4.725, -4.6994, -4.86, -5.0886, -5....	permil	d18O	NaN	year	[1994.5, 1994.33, 1994.17, 1994.0, 1993.83, 19...	AD	iso2k	Ocean2kHR_177	True
4	CO05KUBE	Coral	32.467	-64.70	-12.0	d18O	[-4.15, -3.66, -3.69, -4.07, -3.95, -4.12, -3....	permil	d18O	NaN	year	[1983.21, 1983.13, 1983.04, 1982.96, 1982.88, ...	AD	iso2k	Ocean2kHR_105	True

"The number of records is "+str(len(df.index))

'The number of records is 656'

df['QAlabel'].unique()

array([ True])

df.columns

Index(['dataSetName', 'archiveType', 'geo_meanLat', 'geo_meanLon',
       'geo_meanElev', 'paleoData_variableName', 'paleoData_values',
       'paleoData_units', 'paleoData_proxy', 'paleoData_proxyGeneral',
       'time_variableName', 'time_values', 'time_units', 'compilationName',
       'TSID', 'QAlabel'],
      dtype='object')

Step 2: Remove duplicate rows

To be considered a duplicate:

1. Have the same TSID

2. Have the same paleoData_variableName

3. Have the same paleoData_values

4. Have the same time_values

def is_duplicate(row1,row2):
    if row1['TSID']==row2['TSID']:
        if row1['paleoData_variableName']==row2['paleoData_variableName']:
            if len(row1['paleoData_values']) != len(row2['paleoData_values']):
                equal=False
            else:
                if np.allclose(row1['paleoData_values'], row2['paleoData_values'], atol=0.001)==True:
                    if len(row1['time_values']) != len(row2['time_values']):
                        equal=False
                    else:
                        if np.allclose(row1['time_values'], row2['time_values'], atol=0.001)==True:
                            equal = True
                        else: equal=False
                else:
                    equal=False
        else:
            equal=False    
    else:
        equal = False
    return equal

tsid = df['TSID'].unique()

idx_list = []

for item in tsid:
    df1=df[df['TSID']==item]
    if len(df1.index)>1:
        comp = df1.iloc[0]
        df2 = df1.iloc[1:]

        for index, row in df2.iterrows():
            if is_duplicate(comp,row)==True:
                idx_list.append(index)

df_nodup = df.drop(idx_list)

"There is now "+ str(len(df_nodup.index)) + " timeseries available."

'There is now 612 timeseries available.'

df_nodup.columns

Index(['dataSetName', 'archiveType', 'geo_meanLat', 'geo_meanLon',
       'geo_meanElev', 'paleoData_variableName', 'paleoData_values',
       'paleoData_units', 'paleoData_proxy', 'paleoData_proxyGeneral',
       'time_variableName', 'time_values', 'time_units', 'compilationName',
       'TSID', 'QAlabel'],
      dtype='object')

df_metadata = df_nodup.filter(['dataSetName','archiveType','geo_meanLat', 'geo_meanLon', 'geo_meanElev','paleoData_variableName','paleoData_units','paleoData_proxy','paleoData_proxyGeneral','time_variableName',' time_units','compilationName','TSID'])

df_metadata.columns

Index(['dataSetName', 'archiveType', 'geo_meanLat', 'geo_meanLon',
       'geo_meanElev', 'paleoData_variableName', 'paleoData_units',
       'paleoData_proxy', 'paleoData_proxyGeneral', 'time_variableName',
       'compilationName', 'TSID'],
      dtype='object')

df_metadata.to_csv('metadata.csv')

Step 4: Slice for the time frame of interest

We can use Pyleoclim to do this for us

time_slice = []
value_slice = []

for _,row in df_nodup.iterrows():
    ts=pyleo.Series(time = row['time_values'],
                    value = row ['paleoData_values'],
                    time_name = row ['time_variableName'],
                    time_unit = row ['time_units'],
                    value_name = row ['paleoData_variableName'],
                    value_unit = row['paleoData_units'], verbose=False)
    ts_slice = ts.slice([0,2000])
    time_slice.append(ts_slice.time)
    value_slice.append(ts_slice.value)

df_nodup['time_values_slice']=time_slice
df_nodup['paleoData_values_slice'] = value_slice

df_nodup.head()

	dataSetName	archiveType	geo_meanLat	geo_meanLon	geo_meanElev	paleoData_variableName	paleoData_values	paleoData_units	paleoData_proxy	paleoData_proxyGeneral	time_variableName	time_values	time_units	compilationName	TSID	QAlabel	time_values_slice	paleoData_values_slice
0	LS16STCL	LakeSediment	50.8300	-116.3900	1126.0	d18O	[-7.81, -5.91, -9.03, -5.35, -5.61, -5.98, -5....	permil	d18O	NaN	year	[2009.0, 2008.3, 2007.8, 2007.4, 2007.0, 2006....	AD	iso2k	LPD7dc5b9ba	True	[0.299999999999955, 2.90000000000009, 5.700000...	[-5.36, -5.34, -5.12, -5.19, -5.34, -5.32, -6....
1	CO00URMA	Coral	0.9330	173.0000	6.0	d18O	[-4.8011, -4.725, -4.6994, -4.86, -5.0886, -5....	permil	d18O	NaN	year	[1994.5, 1994.33, 1994.17, 1994.0, 1993.83, 19...	AD	iso2k	Ocean2kHR_177	True	[1840.0, 1840.17, 1840.33, 1840.5, 1840.67, 18...	[-4.6914, -4.7834, -4.5927, -4.3615, -4.315, -...
4	CO05KUBE	Coral	32.4670	-64.7000	-12.0	d18O	[-4.15, -3.66, -3.69, -4.07, -3.95, -4.12, -3....	permil	d18O	NaN	year	[1983.21, 1983.13, 1983.04, 1982.96, 1982.88, ...	AD	iso2k	Ocean2kHR_105	True	[1825.21, 1825.29, 1825.38, 1825.46, 1825.54, ...	[-3.1, -3.2, -3.37, -3.79, -3.94, -3.88, -3.74...
5	IC13THQU	GlacierIce	-13.9333	-70.8333	5670.0	d18O	[-18.5905, -16.3244, -16.2324, -17.0112, -18.6...	permil	d18O	NaN	year	[2009, 2008, 2007, 2006, 2005, 2004, 2003, 200...	AD	iso2k	SAm_035	True	[226.0, 227.0, 228.0, 229.0, 230.0, 231.0, 232...	[-18.12, -17.48, -17.33, -17.84, -18.45, -18.8...
6	CO01TUNG	Coral	-5.2170	145.8170	-3.0	d18O	[-4.827, -4.786, -4.693, -4.852, -4.991, -4.90...	permil	d18O	NaN	year	[1993.042, 1992.792, 1992.542, 1992.292, 1992....	AD	iso2k	Ocean2kHR_140	True	[1880.792, 1881.042, 1881.292, 1881.542, 1881....	[-4.801, -4.94, -4.906, -4.792, -4.923, -5.023...

Step 4: Gaussianize

Let’s have Pyleoclim help us with that

value_gaussianize = []

for _,row in df_nodup.iterrows():
    ts=pyleo.Series(time = row['time_values_slice'],
                    value = row ['paleoData_values_slice'],
                    time_name = row ['time_variableName'],
                    time_unit = row ['time_units'],
                    value_name = row ['paleoData_variableName'],
                    value_unit = row['paleoData_units'], verbose=False)
    ts_g = ts.gaussianize()
    value_gaussianize.append(ts_g.value)

df_nodup['paleoData_values_slice_gauss'] = value_gaussianize

df_nodup.head()

	dataSetName	archiveType	geo_meanLat	geo_meanLon	geo_meanElev	paleoData_variableName	paleoData_values	paleoData_units	paleoData_proxy	paleoData_proxyGeneral	time_variableName	time_values	time_units	compilationName	TSID	QAlabel	time_values_slice	paleoData_values_slice	paleoData_values_slice_gauss
0	LS16STCL	LakeSediment	50.8300	-116.3900	1126.0	d18O	[-7.81, -5.91, -9.03, -5.35, -5.61, -5.98, -5....	permil	d18O	NaN	year	[2009.0, 2008.3, 2007.8, 2007.4, 2007.0, 2006....	AD	iso2k	LPD7dc5b9ba	True	[0.299999999999955, 2.90000000000009, 5.700000...	[-5.36, -5.34, -5.12, -5.19, -5.34, -5.32, -6....	[-0.2130875894530718, -0.17422655914690632, 0....
1	CO00URMA	Coral	0.9330	173.0000	6.0	d18O	[-4.8011, -4.725, -4.6994, -4.86, -5.0886, -5....	permil	d18O	NaN	year	[1994.5, 1994.33, 1994.17, 1994.0, 1993.83, 19...	AD	iso2k	Ocean2kHR_177	True	[1840.0, 1840.17, 1840.33, 1840.5, 1840.67, 18...	[-4.6914, -4.7834, -4.5927, -4.3615, -4.315, -...	[-0.9811705980359124, -1.2651460843379647, -0....
4	CO05KUBE	Coral	32.4670	-64.7000	-12.0	d18O	[-4.15, -3.66, -3.69, -4.07, -3.95, -4.12, -3....	permil	d18O	NaN	year	[1983.21, 1983.13, 1983.04, 1982.96, 1982.88, ...	AD	iso2k	Ocean2kHR_105	True	[1825.21, 1825.29, 1825.38, 1825.46, 1825.54, ...	[-3.1, -3.2, -3.37, -3.79, -3.94, -3.88, -3.74...	[1.328615981030611, 0.9458448462285661, 0.4131...
5	IC13THQU	GlacierIce	-13.9333	-70.8333	5670.0	d18O	[-18.5905, -16.3244, -16.2324, -17.0112, -18.6...	permil	d18O	NaN	year	[2009, 2008, 2007, 2006, 2005, 2004, 2003, 200...	AD	iso2k	SAm_035	True	[226.0, 227.0, 228.0, 229.0, 230.0, 231.0, 232...	[-18.12, -17.48, -17.33, -17.84, -18.45, -18.8...	[-0.24533483270695292, 0.3501885714345531, 0.5...
6	CO01TUNG	Coral	-5.2170	145.8170	-3.0	d18O	[-4.827, -4.786, -4.693, -4.852, -4.991, -4.90...	permil	d18O	NaN	year	[1993.042, 1992.792, 1992.542, 1992.292, 1992....	AD	iso2k	Ocean2kHR_140	True	[1880.792, 1881.042, 1881.292, 1881.542, 1881....	[-4.801, -4.94, -4.906, -4.792, -4.923, -5.023...	[0.7315480859971977, 0.03064136195287292, 0.21...

Step 5: Create the composite

Step 5a: Clean up the proxy/variable names

temp = df_nodup[['archiveType','paleoData_variableName']]

temp.groupby(['archiveType','paleoData_variableName'], as_index=False).value_counts()

	archiveType	paleoData_variableName	count
0	Coral	d18O	45
1	GlacierIce	d18O	1
2	LakeSediment	d18O	3
3	LakeSediment	d2H	1
4	Sclerosponge	d18O	3
5	bivalve	d18O	1
6	borehole	temperature	2
7	coral	Sr_Ca	24
8	coral	calcification	2
9	coral	composite	6
10	coral	d18O	23
11	coral	temperature	1
12	documents	temperature	14
13	glacier ice	d18O	6
14	glacier ice	dD	1
15	hybrid	temperature	1
16	lake sediment	RABD660_670	1
17	lake sediment	X_radiograph_dark_layer	1
18	lake sediment	temperature	26
19	lake sediment	thickness	3
20	marine sediment	d18O	1
21	marine sediment	temperature	52
22	sclerosponge	Sr_Ca	4
23	sclerosponge	d18O	3
24	speleothem	d18O	4
25	tree	MXD	52
26	tree	density	4
27	tree	temperature	6
28	tree	trsgi	321

Get the standard archive and variable names:

archive_std = pd.read_csv('archiveType.csv')
df_archive = archive_std[['lipdName','synonym']].reset_index()

syn = []
for _,row in df_archive.iterrows():
    syn.append(row['synonym'].lower().replace(' ',""))
df_archive['synonym_reg']=syn

var_std=pd.read_csv('paleoData_variableName.csv')
df_var = var_std[['lipdName','synonym']].reset_index()

syn_var=[]
for _,row in df_var.iterrows():
    try:
        syn_var.append(row['synonym'].lower().replace(' ',"").replace('/',"").replace('_',""))
    except:
        syn_var.append(row['synonym'])
df_var['synonym_reg']=syn_var

#let's do the archive first

archiveType_lipd = []

for _, row in df_nodup.iterrows():
    match = row['archiveType'].lower().replace(' ',"").replace('/',"").replace('_',"")
    f = df_archive[df_archive['synonym_reg']==match]
    if type(f) == pd.core.frame.DataFrame and not f.empty:
        archiveType_lipd.append(f['lipdName'].iloc[0])
    elif type(f) == pd.core.series.Series:
        archiveType_lipd.append(f['lipdName'])
    else:
        archiveType_lipd.append(row['archiveType'])

#let's work on varname

variableName = []

for _, row in df_nodup.iterrows():
    match = row['paleoData_variableName'].lower().replace(' ',"").replace('/',"").replace('_',"")
    f = df_var[df_var['synonym_reg']==match]
    if type(f) == pd.core.frame.DataFrame and not f.empty:
        variableName.append(f['lipdName'].iloc[0])
    elif type(f) == pd.core.series.Series:
        variableName.append(f['lipdName'])
    else:
        variableName.append(row['archiveType'])

ptype = []
for idx,item in enumerate(variableName):
    ptype.append(archiveType_lipd[idx]+'.'+item)    

Put it in our DataFrame:

df_nodup['ptype']=ptype

Step 5b: Set the style for the figures

colors_dict = {}

for idx, item in enumerate(set(ptype)):
    if 'marinesediment' in item.lower():
        colors_dict[item] = sns.xkcd_rgb['brown']
    elif 'lakesediment' in item.lower():
        colors_dict[item]= sns.xkcd_rgb['maroon']
    elif 'coral' in item.lower():
        colors_dict[item]= sns.xkcd_rgb['orange']
    elif 'ice' in item.lower():
        colors_dict[item]= sns.xkcd_rgb['sky blue']
    elif 'borehole' in item.lower():
        colors_dict[item]= sns.xkcd_rgb['dark blue']
    elif 'sclerosponge' in item.lower():
        colors_dict[item]= sns.xkcd_rgb['amber']
    elif 'speleothem' in item.lower():
        colors_dict[item]= sns.xkcd_rgb['mauve']
    elif 'wood' in item.lower():
        colors_dict[item]= sns.xkcd_rgb['forest green']
    elif 'bivalve' in item.lower():
        colors_dict[item]= sns.xkcd_rgb['peach']
    else:
        colors_dict[item]= 'k'  

markers_dict = {}

for idx, item in enumerate(set(ptype)):
    if 'temperature' in item.lower():
        markers_dict[item] = 'P'
    elif 'srca' in item.lower().replace('/',''):
        markers_dict[item]= 'X'
    elif 'd18o' in item.lower():
        markers_dict[item] = 'o'
    elif 'calcification' in item.lower():
        markers_dict[item] = 'H'
    elif 'composite' in item.lower():
        markers_dict[item] = '<'
    elif 'd2h' in item.lower():
        markers_dict[item] = 'd'
    elif 'reflectance' in item.lower():
        markers_dict[item] = '>'
    elif 'thickness' in item.lower():
        markers_dict[item] = 'h'
    elif 'mxd' in item.lower():
        markers_dict[item] = '8'
    elif 'density' in item.lower():
        markers_dict[item] = '^'
    elif 'width' in item.lower():
        markers_dict[item] = 'v'
    else:
        markers_dict[item] = 's'

Reset the plotting style:

cfr.visual.STYLE.colors_dict = colors_dict
cfr.visual.STYLE.markers_dict = markers_dict

Step 5c: Create the Proxy DataBase

Let’s try to get one ProxyRecord into cfr:

row = df_nodup.iloc[0,:]

f = cfr.ProxyRecord(pid = row['TSID'],
                   time = row['time_values_slice'],
                   value = row['paleoData_values_slice_gauss'],
                   lat = row['geo_meanLat'],
                   lon = row['geo_meanLon'],
                   elev = row['geo_meanElev'],
                   ptype = row['ptype'],
                   tags = None,
                   value_name = row['paleoData_variableName'],
                   value_unit = row['paleoData_units'],
                   time_name = row['time_units'],
                   seasonality = None)

f.plot()

(<Figure size 1200x400 with 2 Axes>,
 {'ts': <AxesSubplot: title={'center': 'LPD7dc5b9ba (LakeSediment.d18O) @ (lat:50.83, lon:243.61)'}, xlabel='AD [yr]', ylabel='d18O [permil]'>,
  'map': <GeoAxesSubplot: >})

Now let’s try to get the proxy database sorted out:

proxy_dict = {}

for _,row in df_nodup.iterrows():
    proxy_dict[row['TSID']] = cfr.ProxyRecord(pid = row['TSID'],
                   time = row['time_values_slice'],
                   value = row['paleoData_values_slice_gauss'],
                   lat = row['geo_meanLat'],
                   lon = row['geo_meanLon'],
                   elev = row['geo_meanElev'],
                   ptype = row['ptype'],
                   tags = None,
                   value_name = row['paleoData_variableName'],
                   value_unit = row['paleoData_units'],
                   time_name = row['time_units'],
                   seasonality = None)

pdb = cfr.ProxyDatabase(proxy_dict)

fig, ax = pdb.plot()

Step 5d: Calibrate against instrumental data

import xarray as xr

ds = xr.open_dataset('./data/HadCRUT.5.0.1.0.analysis.anomalies.ensemble_mean.nc')
ds

<xarray.Dataset>
Dimensions:           (time: 2071, latitude: 36, longitude: 72, bnds: 2)
Coordinates:
  * time              (time) datetime64[ns] 1850-01-16T12:00:00 ... 2022-07-1...
  * latitude          (latitude) float64 -87.5 -82.5 -77.5 ... 77.5 82.5 87.5
  * longitude         (longitude) float64 -177.5 -172.5 -167.5 ... 172.5 177.5
    realization       int64 ...
Dimensions without coordinates: bnds
Data variables:
    tas_mean          (time, latitude, longitude) float64 ...
    time_bnds         (time, bnds) datetime64[ns] ...
    latitude_bnds     (latitude, bnds) float64 ...
    longitude_bnds    (longitude, bnds) float64 ...
    realization_bnds  (bnds) int64 ...
Attributes:
    comment:      2m air temperature over land blended with sea water tempera...
    history:      Data set built at: 2022-09-06T13:35:49+00:00
    institution:  Met Office Hadley Centre / Climatic Research Unit, Universi...
    licence:      HadCRUT5 is licensed under the Open Government Licence v3.0...
    reference:    C. P. Morice, J. J. Kennedy, N. A. Rayner, J. P. Winn, E. H...
    source:       CRUTEM.5.0.1.0 HadSST.4.0.0.0
    title:        HadCRUT.5.0.1.0 blended land air temperature and sea-surfac...
    version:      HadCRUT.5.0.1.0
    Conventions:  CF-1.7

xarray.Dataset

• Dimensions:
  • time: 2071
  • latitude: 36
  • longitude: 72
  • bnds: 2
• Coordinates: (4)
  • time
    (time)
    datetime64[ns]
    1850-01-16T12:00:00 ... 2022-07-...

    axis :

    T

    bounds :

    time_bnds

    standard_name :

    time

    long_name :

    time

    array(['1850-01-16T12:00:00.000000000', '1850-02-15T00:00:00.000000000',
           '1850-03-16T12:00:00.000000000', ..., '2022-05-16T12:00:00.000000000',
           '2022-06-16T00:00:00.000000000', '2022-07-16T12:00:00.000000000'],
          dtype='datetime64[ns]')

  • latitude
    (latitude)
    float64
    -87.5 -82.5 -77.5 ... 82.5 87.5

    axis :

    Y

    bounds :

    latitude_bnds

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    array([-87.5, -82.5, -77.5, -72.5, -67.5, -62.5, -57.5, -52.5, -47.5, -42.5,
           -37.5, -32.5, -27.5, -22.5, -17.5, -12.5,  -7.5,  -2.5,   2.5,   7.5,
            12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,  47.5,  52.5,  57.5,
            62.5,  67.5,  72.5,  77.5,  82.5,  87.5])

  • longitude
    (longitude)
    float64
    -177.5 -172.5 ... 172.5 177.5

    axis :

    X

    bounds :

    longitude_bnds

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    array([-177.5, -172.5, -167.5, -162.5, -157.5, -152.5, -147.5, -142.5, -137.5,
           -132.5, -127.5, -122.5, -117.5, -112.5, -107.5, -102.5,  -97.5,  -92.5,
            -87.5,  -82.5,  -77.5,  -72.5,  -67.5,  -62.5,  -57.5,  -52.5,  -47.5,
            -42.5,  -37.5,  -32.5,  -27.5,  -22.5,  -17.5,  -12.5,   -7.5,   -2.5,
              2.5,    7.5,   12.5,   17.5,   22.5,   27.5,   32.5,   37.5,   42.5,
             47.5,   52.5,   57.5,   62.5,   67.5,   72.5,   77.5,   82.5,   87.5,
             92.5,   97.5,  102.5,  107.5,  112.5,  117.5,  122.5,  127.5,  132.5,
            137.5,  142.5,  147.5,  152.5,  157.5,  162.5,  167.5,  172.5,  177.5])

  • realization
    ()
    int64
    ...

    bounds :

    realization_bnds

    units :

    1

    standard_name :

    realization

    [1 values with dtype=int64]

• Data variables: (5)
  • tas_mean
    (time, latitude, longitude)
    float64
    ...

    long_name :

    blended air_temperature_anomaly over land with sea_water_temperature_anomaly

    units :

    K

    cell_methods :

    area: mean (interval: 5.0 degrees_north 5.0 degrees_east) time: mean (interval: 1 month) realization: mean

    [5368032 values with dtype=float64]

  • time_bnds
    (time, bnds)
    datetime64[ns]
    ...

    [4142 values with dtype=datetime64[ns]]

  • latitude_bnds
    (latitude, bnds)
    float64
    ...

    [72 values with dtype=float64]

  • longitude_bnds
    (longitude, bnds)
    float64
    ...

    [144 values with dtype=float64]

  • realization_bnds
    (bnds)
    int64
    ...

    [2 values with dtype=int64]

• Indexes: (3)
  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1850-01-16 12:00:00', '1850-02-15 00:00:00',
                   '1850-03-16 12:00:00', '1850-04-16 00:00:00',
                   '1850-05-16 12:00:00', '1850-06-16 00:00:00',
                   '1850-07-16 12:00:00', '1850-08-16 12:00:00',
                   '1850-09-16 00:00:00', '1850-10-16 12:00:00',
                   ...
                   '2021-10-16 12:00:00', '2021-11-16 00:00:00',
                   '2021-12-16 12:00:00', '2022-01-16 12:00:00',
                   '2022-02-15 00:00:00', '2022-03-16 12:00:00',
                   '2022-04-16 00:00:00', '2022-05-16 12:00:00',
                   '2022-06-16 00:00:00', '2022-07-16 12:00:00'],
                  dtype='datetime64[ns]', name='time', length=2071, freq=None))

  • latitude
    PandasIndex

    PandasIndex(Index([-87.5, -82.5, -77.5, -72.5, -67.5, -62.5, -57.5, -52.5, -47.5, -42.5,
           -37.5, -32.5, -27.5, -22.5, -17.5, -12.5,  -7.5,  -2.5,   2.5,   7.5,
            12.5,  17.5,  22.5,  27.5,  32.5,  37.5,  42.5,  47.5,  52.5,  57.5,
            62.5,  67.5,  72.5,  77.5,  82.5,  87.5],
          dtype='float64', name='latitude'))

  • longitude
    PandasIndex

    PandasIndex(Index([-177.5, -172.5, -167.5, -162.5, -157.5, -152.5, -147.5, -142.5, -137.5,
           -132.5, -127.5, -122.5, -117.5, -112.5, -107.5, -102.5,  -97.5,  -92.5,
            -87.5,  -82.5,  -77.5,  -72.5,  -67.5,  -62.5,  -57.5,  -52.5,  -47.5,
            -42.5,  -37.5,  -32.5,  -27.5,  -22.5,  -17.5,  -12.5,   -7.5,   -2.5,
              2.5,    7.5,   12.5,   17.5,   22.5,   27.5,   32.5,   37.5,   42.5,
             47.5,   52.5,   57.5,   62.5,   67.5,   72.5,   77.5,   82.5,   87.5,
             92.5,   97.5,  102.5,  107.5,  112.5,  117.5,  122.5,  127.5,  132.5,
            137.5,  142.5,  147.5,  152.5,  157.5,  162.5,  167.5,  172.5,  177.5],
          dtype='float64', name='longitude'))

• Attributes: (9)

  comment :

  2m air temperature over land blended with sea water temperature at a depth of 20cm expressed as monthly anomalies relative to 1961-1990 climatology.

  history :

  Data set built at: 2022-09-06T13:35:49+00:00

  institution :

  Met Office Hadley Centre / Climatic Research Unit, University of East Anglia

  licence :

  HadCRUT5 is licensed under the Open Government Licence v3.0 except where otherwise stated. To view this licence, visit https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3

  reference :

  C. P. Morice, J. J. Kennedy, N. A. Rayner, J. P. Winn, E. Hogan, R. E. Killick, R. J. H. Dunn, T. J. Osborn, P. D. Jones and I. R. Simpson (2021), An updated assessment of near-surface temperature change from 1850: the HadCRUT5 data set, Journal of Geophysical Research: Atmospheres, 126, e2019JD032361. https://doi.org/10.1029/2019JD032361

  source :

  CRUTEM.5.0.1.0 HadSST.4.0.0.0

  title :

  HadCRUT.5.0.1.0 blended land air temperature and sea-surface temperature anomaly data set

  version :

  HadCRUT.5.0.1.0

  Conventions :

  CF-1.7

obs = cfr.ClimateField().load_nc(
    './data/HadCRUT.5.0.1.0.analysis.anomalies.ensemble_mean.nc',
    vn='tas_mean', lat_name='latitude', lon_name='longitude',
)
obs = obs.rename('tas')

Step 5e: make the composite

pdb.make_composite(obs,bin_width=10)

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fig, ax = pdb.plot_composite()
current_title = ax['var'].title.get_text().split(',',1)[-1]
ax['var'].set_title('Multi-Proxy, '+current_title)

Text(0.5, 1.0, 'Multi-Proxy,  605 records, bin_width=10')

pdb.composite.keys()

dict_keys(['df_proxy', 'df_obs', 'proxy_comp', 'proxy_comp_time', 'proxy_comp_value', 'proxy_sq_low_time', 'proxy_sq_high_time', 'proxy_sq_low_value', 'proxy_sq_high_value', 'proxy_num', 'proxy_num_time', 'proxy_num_value', 'obs_comp', 'obs_comp_time', 'obs_comp_value', 'bin_width', 'intercept', 'slope', 'r2'])