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Global Temperature Time Series

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core

Files Size Format Created Updated License Source
3 510kB csv zip 1 year ago 4 months ago Open Data Commons Public Domain Dedication and License v1.0 GISTEMP Global Land-Ocean Temperature Index Global component of Climate at a Glance (GCAG)
Global Temperature Time Series. Data are included from the GISS Surface Temperature (GISTEMP) analysis and the global component of Climate at a Glance (GCAG). Two datasets are provided: 1) global monthly mean and 2) annual mean temperature anomalies in degrees Celsius from 1880 to the read more
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Data Files

Download files in this dataset

File Description Size Last changed Download
annual 5kB csv (5kB) , json (13kB)
monthly 80kB csv (80kB) , json (186kB)
global-temp_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 51kB zip (51kB)

annual  

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This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
Source 1 string
Year 2 year YYYY
Mean 3 number Average global mean temperature anomalies in degrees Celsius relative to a base period. GISTEMP base period: 1951-1980. GCAG base period: 20th century average.

monthly  

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This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
Source 1 string
Date 2 date (%Y-%m-%d) YYYY-MM
Mean 3 number Monthly mean temperature anomalies in degrees Celsius relative to a base period. GISTEMP base period: 1951-1980. GCAG base period: 20th century average.

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Integrate this dataset into your favourite tool

Use our data-cli tool designed for data wranglers:

data get https://datahub.io/core/global-temp
data info core/global-temp
tree core/global-temp
# Get a list of dataset's resources
curl -L -s https://datahub.io/core/global-temp/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/core/global-temp/r/0.csv

curl -L https://datahub.io/core/global-temp/r/1.csv

curl -L https://datahub.io/core/global-temp/r/2.zip

If you are using R here's how to get the data you want quickly loaded:

install.packages("jsonlite", repos="https://cran.rstudio.com/")
library("jsonlite")

json_file <- 'https://datahub.io/core/global-temp/datapackage.json'
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# get list of all resources:
print(json_data$resources$name)

# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
  if(json_data$resources$datahub$type[i]=='derived/csv'){
    path_to_file = json_data$resources$path[i]
    data <- read.csv(url(path_to_file))
    print(data)
  }
}

Note: You might need to run the script with root permissions if you are running on Linux machine

Install the Frictionless Data data package library and the pandas itself:

pip install datapackage
pip install pandas

Now you can use the datapackage in the Pandas:

import datapackage
import pandas as pd

data_url = 'https://datahub.io/core/global-temp/datapackage.json'

# to load Data Package into storage
package = datapackage.Package(data_url)

# to load only tabular data
resources = package.resources
for resource in resources:
    if resource.tabular:
        data = pd.read_csv(resource.descriptor['path'])
        print (data)

For Python, first install the `datapackage` library (all the datasets on DataHub are Data Packages):

pip install datapackage

To get Data Package into your Python environment, run following code:

from datapackage import Package

package = Package('https://datahub.io/core/global-temp/datapackage.json')

# print list of all resources:
print(package.resource_names)

# print processed tabular data (if exists any)
for resource in package.resources:
    if resource.descriptor['datahub']['type'] == 'derived/csv':
        print(resource.read())

If you are using JavaScript, please, follow instructions below:

Install data.js module using npm:

  $ npm install data.js

Once the package is installed, use the following code snippet:

const {Dataset} = require('data.js')

const path = 'https://datahub.io/core/global-temp/datapackage.json'

// We're using self-invoking function here as we want to use async-await syntax:
;(async () => {
  const dataset = await Dataset.load(path)
  // get list of all resources:
  for (const id in dataset.resources) {
    console.log(dataset.resources[id]._descriptor.name)
  }
  // get all tabular data(if exists any)
  for (const id in dataset.resources) {
    if (dataset.resources[id]._descriptor.format === "csv") {
      const file = dataset.resources[id]
      // Get a raw stream
      const stream = await file.stream()
      // entire file as a buffer (be careful with large files!)
      const buffer = await file.buffer
      // print data
      stream.pipe(process.stdout)
    }
  }
})()

Read me

Global Temperature Time Series. Data are included from the GISS Surface Temperature (GISTEMP) analysis and the global component of Climate at a Glance (GCAG). Two datasets are provided: 1) global monthly mean and 2) annual mean temperature anomalies in degrees Celsius from 1880 to the present.

Data

Description

  1. GISTEMP Global Land-Ocean Temperature Index:

Combined Land-Surface Air and Sea-Surface Water Temperature Anomalies [i.e. deviations from the corresponding 1951-1980 means]. Global-mean monthly […] and annual means, 1880-present, updated through most recent month.

  1. Global component of Climate at a Glance (GCAG):

Global temperature anomaly data come from the Global Historical Climatology Network-Monthly (GHCN-M) data set and International Comprehensive Ocean-Atmosphere Data Set (ICOADS), which have data from 1880 to the present. These two datasets are blended into a single product to produce the combined global land and ocean temperature anomalies. The available timeseries of global-scale temperature anomalies are calculated with respect to the 20th century average […].

Citations

  1. GISTEMP: NASA Goddard Institute for Space Studies (GISS) Surface Temperature Analysis, Global Land-Ocean Temperature Index.
  2. NOAA National Climatic Data Center (NCDC), global component of Climate at a Glance (GCAG).

Sources

Additional Data

  • Upstream datasets:
  • Other:
    • HadCRUT4 time series data are not included in the published Data Package at this time because of the dataset’s restrictive terms and conditions. However, the data preparation script supports processing the dataset.

Preparation

Requirements

Data preparation requires Python 2.

Processing

Run the following script from this directory to download and process the data:

make data

Hundredths of degrees Celsius in the GISTEMP Global Land-Ocean Temperature Index data are converted to degrees Celsius.

A HadCRUT4 processing script is available but not run by default.

Resources

The raw data are output to ./tmp. The processed data are output to ./data.

License

ODC-PDDL-1.0

This Data Package and these datasets are made available under the Public Domain Dedication and License v1.0 whose full text can be found at: http://www.opendatacommons.org/licenses/pddl/1.0/

Notes

The upstream datasets do not impose any specific restrictions on using these data in a public or commercial product:


Keywords and keyphrases: Global Temperature, gistemp, Global Temperature Time Series, global monthly mean data temperature anomalies, global annual mean temperature anomalies.
Datapackage.json

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