Station Data. Zonal Means: Series or Seasonal Cycle.
These updated files incorporate reports for the previous month and also late reports and corrections for earlier months. January 15, NASA has posted a news release about the annual global temperature anomaly.
July 15, The difference between raw data and the unadjusted data has been clarified on our FAQ page. If the FAQ does not answer your question, please address your inquiry to Dr.
Makiko Sato , and Dr. Ken Lo. This research was initiated by Dr. James E.
Hansen, now retired. It is currently led by Dr. Gavin Schmidt. When referencing the GISTEMP v4 data provided here, please cite both this webpage and also our most recent scholarly publication about the data.
Global temperature anomalies from 1880 to 2018
In citing the webpage, be sure to include the date of access. The basic GISS temperature analysis scheme was defined in the late s by James Hansen when a method of estimating global temperature change was needed for comparison with one-dimensional global climate models.
The scheme was based on the finding that the correlation of temperature change was reasonably strong for stations separated by up to km, especially at middle and high latitudes. This fact proved sufficient to obtain useful estimates for global mean temperature changes.
Our first published results Hansen et al. The early analysis scheme went through a series of enhancements that are listed and illustrated on the History Page. The analysis method was fully documented in Hansen and Lebedeff , including quantitative estimates of the error in annual and 5-year mean temperature change.
This was done by sampling at station locations a spatially complete data set of a long run of a global climate model, which was shown to have realistic spatial and temporal variability.
This however only addresses the error due to incomplete spatial coverage of measurements.
A more complete uncertainty analysis was published recently and forms the core of the Uncertainty website. As there are other potential sources of error, such as urban warming near meteorological stations, many other methods have been used to verify the approximate magnitude of inferred global warming. These methods include inference of surface temperature change from vertical temperature profiles in the ground bore holes at many sites around the world, rate of glacier retreat at many locations, and studies by several groups of the effect of urban and other local human influences on the global temperature record.
All of these yield consistent estimates of the approximate magnitude of global warming, which reached about 0.
GLOBAL SURFACE TEMPERATURE CHANGE
Further affirmation of the reality of the warming is its spatial distribution, which has largest values at locations remote from any local human influence, with a global pattern consistent with that expected for response to global climate forcings larger in the Northern Hemisphere than the Southern Hemisphere, larger at high latitudes than low latitudes, larger over land than over ocean.
An updated documentation Hansen et al.
A multi-year smoothing is applied to fully remove the annual cycle and improve information content in temperature graphs. One of the improvements — introduced in — was the implementation of a method to address the problem of urban warming: The urban and peri-urban i. Urban stations without nearby rural stations are dropped.
This preserves local short-term variability without affecting long term trends. Originally, the classification of stations was based on population size near that station; the current analysis uses satellite-observed night lights to determine which stations are located in urban and peri-urban areas. Graphs and tables are updated around the middle of every month using the current adjusted GHCN data.
The new file incorporates reports for the previous month as well as late reports and corrections for earlier months. We maintain a running record of any modifications made to the analysis on our Updates to Analysis page. The programs assume a Unix-like operating system and require familiarity with Python for installation and use.
Data for Downloading
The following are plain-text files in tabular format of temperature anomalies, i. Also available are various FORTRAN programs and instructions to create time series of regular gridded anomaly maps from the basic files.
Copies of many of our papers are available in the GISS publications database. Citation When referencing the GISTEMP v4 data provided here, please cite both this webpage and also our most recent scholarly publication about the data. Lenssen, N. Schmidt, J. Hansen, M.
GISTEMP v4 Figures
Menne, A. Persin, R. Ruedy, and D. Background of the GISS Analysis The basic GISS temperature analysis scheme was defined in the late s by James Hansen when a method of estimating global temperature change was needed for comparison with one-dimensional global climate models. Documentation and Assessment of Results The analysis method was fully documented in Hansen and Lebedeff , including quantitative estimates of the error in annual and 5-year mean temperature change.
GISS Homogenization Urban Adjustment One of the improvements — introduced in — was the implementation of a method to address the problem of urban warming: The urban and peri-urban i.
Schmidt Website Curator: Robert B. Schmunk Page updated: