Nasa GISS Surface Temperature (GISTEMP) Analysis

core

Files Size Format Created Updated License Source
3 169kB csv zip 7 months ago 3 months ago ODC-PDDL-1.0 Global Annual Temperature Anomalies (Land), 1880-2014 Global Annual Temperature Anomalies (Land+Ocean), 1880-2014 Hemispheric Temperature Anomalies (Land+Ocean), 1880-2014 Global Annual Temperature Anomalies (Land+Ocean) for three latitude bands, 1900-2014
Nasa GISS Surface Temperature (GISTEMP) Analysis. Four different series are provided: Global Annual Temperature Anomalies (Land) 1880-2014, Global Annual Temperature Anomalies (Land and Ocean) 1880-2014, Hemispheric Temperature Anomalies (Land+ Ocean) 1880-2014 and Annual Temperature anomalies read more
Download

Data Files

File Description Size Last changed Download
global-temp-annual 6kB csv (6kB) , json (19kB)
global-temp-5yr 6kB csv (6kB) , json (19kB)
global-temp-anomalies_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 16kB zip (16kB)

global-temp-annual  

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

Field information

Field Name Order Type (Format) Description
Year 1 date (%Y-%m-%d) YYYY
Land 2 number Global annual anomalies computed from land data, in degrees C
Land and Ocean 3 number Global annual anomalies computed from land and ocean data, in degrees C
N Hem 4 number Northern hemisphere annual anomalies computed from land and ocean data, in degrees C
S Hem 5 number Southern hemisphere annual anomalies computed from land and ocean data, in degrees C
Band 1 6 number Latitude band (90N-23.6N, 30% of global area) annual anomalies computed from land and ocean data, in degrees C
Band 2 7 number Latitude band (23.6N-23.6S, 40% of global area) annual anomalies computed from land and ocean data, in degrees C
Band 3 8 number Latitude band (23.6S-90S, 30% of global area) annual anomalies computed from land and ocean data, in degrees C

global-temp-5yr  

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

Field information

Field Name Order Type (Format) Description
Year 1 date (%Y-%m-%d) YYYY
Land 2 number Global 5-year anomalies mean computed from land data, in degrees C
Land and Ocean 3 number Global 5-year anomalies mean computed from land and ocean data, in degrees C
N Hem 4 number Northern hemisphere 5-year anomalies mean computed from land and ocean data, in degrees C
S Hem 5 number Southern hemisphere 5-year anomalies mean computed from land and ocean data, in degrees C
Band 1 6 number Latitude band (90N-23.6N, 30% of global area) 5-year anomalies mean computed from land and ocean data, in degrees C
Band 2 7 number Latitude band (23.6N-23.6S, 40% of global area) 5-year anomalies mean computed from land and ocean data, in degrees C
Band 3 8 number Latitude band (23.6S-90S, 30% of global area) 5-year anomalies mean computed from land and ocean data, in degrees C

Import into your tool

Data-cli or just data is the program to get and post your data with the datahub.
Download CLI tool and use it with the datahub almost like you use git with the github:

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

# Get resources

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

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

curl -L https://datahub.io/core/global-temp-anomalies/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-anomalies/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-anomalies/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-anomalies/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-anomalies/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

Nasa GISS Surface Temperature (GISTEMP) Analysis. Four different series are provided: Global Annual Temperature Anomalies (Land) 1880-2014, Global Annual Temperature Anomalies (Land and Ocean) 1880-2014, Hemispheric Temperature Anomalies (Land+ Ocean) 1880-2014 and Annual Temperature anomalies (Land + Ocean) for three latitude bands that cover 30%, 40% and 30% of the global area, respectively, 1900-2014.

Data

Period of Record

1880-2014 (Anomalies are relative to the 1951-80 base period means.)

Description

The NASA GISS Surface Temperature (GISTEMP) analysis provides a measure of the changing global surface temperature with monthly resolution for the period since 1880, when a reasonably global distribution of meteorological stations was established. The input data Ruedy et al. use for the analysis, collected by many national meteorological services around the world, are the adjusted data of the Global Historical Climatology Network (GHCN) Vs. 3 (this represents a change from prior use of unadjusted Vs. 2 data) (Peterson and Vose, 1997 and 1998), United States Historical Climatology Network (USHCN) data, and SCAR (Scientific Committee on Antarctic Research) data from Antarctic stations. Documentation of the basic analysis method is provided by Hansen et al. (1999), with several modifications described by Hansen et al. (2001). The GISS analysis is updated monthly, however CDIAC’s presentation of the data here is updated annually.

Key finding

The global mean temperature for 2014 was the warmest on record (see Trends section for further details)

Sources

Preparation

Requirements

Python 2 together with modules urllib and csv are required in order to process the data.

Processing

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

make

Resources

The raw data are stored in ./archive/. The processed data can be found in ./data.

License

Data

Data are sourced from US Federal government funded agency and no copyright restrictions are applied. More specifically:

If you wish to use a diagram, image, graph, table, or other materials from the CDIAC website and are concerned with obtaining permission and possible copyright restrictions, there should be no concerns. All of the reports, graphics, data, and other information on the CDIAC website are freely and publicly available without copyright restrictions.*

Additional work

All the additional work made to build this Data Package is 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/

Citations

Ruedy, R., M. Sato, and K. Lo. 2015. NASA GISS Surface Temperature (GISTEMP) Analysis. In Trends: A Compendium of Data on Global Change. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tenn., U.S.A. doi: 10.3334/CDIAC/cli.001 .

Datapackage.json