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Global Historical Climatology Network Monthly - Version 3

GHCN-Monthly Version 3 ASCII Text Files

Effective October 26, 2018, the Global Historical Climatology Network-Monthly (GHCN-M) version 4 dataset of monthly mean temperature has replaced GHCN-M version 3. Scientists first developed the Global Historical Climatology Network, Monthly (GHCN-M) temperature dataset in the early 1990s (Vose et al. 1992). Release of the second version was in 1997 following extensive efforts to increase the number of stations and length of the data record (Peterson and Vose, 1997). Methods for removing inhomogeneities from the data record associated with non-climatic influences such as changes in instrumentation, station environment, and observing practices that occur over time were also included in the version 2 release (Peterson and Easterling, 1994; Easterling and Peterson 1995). Since that time efforts have focused on continued improvements in dataset development methods including new quality control processes and advanced techniques for removing data inhomogeneities (Menne and Williams, 2009). Effective May 2, 2011, the Global Historical Climatology Network-Monthly (GHCN-M) version 3 dataset of monthly mean temperature has replaced GHCN-M version 2 as the dataset for operational climate monitoring activities. The formal designation is ghcnm.x.y.z[optionally -betan].yyyymmdd where:

  • x = major upgrades of unspecified nature to either qc, adjustments, or station configurations and accompanied by a peer reviewed manuscript
  • y = substantial modifications to the dataset, including a new set of stations or additional quality control algorithms, accompanied by a technical note
  • z = minor revisions to both data and processing software that are tracked in "status and errata"
  • yyyy = year in which the update to the dataset occurred
  • mm = month in which the update to the dataset occurred
  • dd = day in which the update to the dataset occurred

Each evening as part of the GHCN-M update process, labeling of the output files with the current version number and date and time stamped to indicate when the update occurred. For example, the filename for the dataset produced on April 28, 2011 will include the string "v3.0.0.20110428" to indicate the developed data uses version 3.0.0 station data and quality control and homogeneity adjustment methods. This notation also will indicate the most recent update occurred in the year 2011, in the 4th month (April) and the 28thday of the month. Staff will place the data on line at the conclusion of each update process and will be available via the link noted above. This versioning format facilitates documentation and communication of updates and modifications that occur as a normal part of the life of this climate dataset.

Data Access

Quality Assurance

With the development of GHCN-M v3, new quality control (QC) procedures were instituted using methods established as part of other dataset development efforts during the past five years. Scientists created the QC algorithms based on methods used for the GHCN-Daily temperature dataset and subsequently applied to USHCN-Monthly Version 2 data (Menne et al. 2009). The QC process used in GHCN-M v3 consists of the seven checks listed in Table 1. We perform the QC algorithms in the order listed in the table. Scientists selected the thresholds shown in column two based on their performance evaluated using the method outlined in Durre et al. (2008).

Quality Assurance Checks Applied to GHCN-Monthly Version 3 Temperature Table

Data Problem Description of Check
Consecutive Month duplication Used to identify duplicate retransmission and mislabeling of previous month's temperature for current month. Occurs in GTS transmitted CLIMAT bulletins
Series Duplication Identifies duplication of data between years
Streak Identifies runs of the same value in four or more consecutive months
Isolated Value Identifies months that are isolated in time. One to three consecutive months that are separated from other non-missing months by 36 or more consecutive months of missing values
Climatological Outlier Identifies temperatures that exceed their respective climatological means for the corresponding station and calendar month by at least five standard deviations
Spatial Inconsistency Identifies temperatures whose anomalies differ by more than 4°C from concurrent anomalies at the five nearest neighboring stations whose temperature anomalies are well correlated with the target (correlation >0.7 for the corresponding calendar month)

Homogeneity Adjustment

Many surface weather stations undergo minor relocations through their history of observation. Stations may also be subject to changes in instrumentation as measurement technology evolves. Further, the land use/land cover in the vicinity of an observing site may also change with time. Such modifications to an observing site have the potential to alter a thermometer's microclimate exposure characteristics and/or change the bias of measurements, the impact of which can be a systematic shift in the mean level of temperature readings that is unrelated to true climate variations. Homogenization is the process of removing such “non-climatic” artifacts in a climate time series.

The GHCN–M version 3 temperature data make use of processing improvements that included a new method for the homogenization of temperature data. Automated pairwise comparisons of mean monthly temperature series (Menne and Williams 2009) form the basis for adjustments to the apparent impacts of documented and undocumented inhomogeneities.

In this approach, numerous combinations of temperature series in a region are compared to identify cases of abrupt shifts in one station series relative to many others. The algorithm starts by forming a large number of pairwise difference series between serial monthly temperature values from a region.

In an automated and reproducible way, each difference series undergoes statistical evaluation for abrupt shifts. After the algorithm identifies all of the shifts attributed to the appropriate station within the network, adjustment apply to each target shift. Adjustments are determined by estimating the magnitude of change in pairwise difference series between the target series and highly correlated neighboring series that have no apparent shifts at the same time.


Durre, I., M.J. Menne, and R.S. Vose, 2008: Strategies for evaluating quality assurance procedures. Journal of Applied Meteorology and Climatology, 47, 1785–1791, doi:10.1175/2007JAMC1706.1.

Easterling, D.R., and T.C. Peterson, 1995: A new method for detecting undocumented discontinuities in climatological time series. International Journal of Climatology, 15, 369–377. doi:10.1002/joc.3370150403.

Lawrimore, J.H., M.J. Menne, B.E. Gleason, C.N. Williams, D.B. Wuertz, R.S. Vose, and J. Rennie, 2011: An overview of the Global Historical Climatology Network monthly mean temperature data set, version 3. Journal of Geophysical Research, 116, D19121, doi:10.1029/2011JD016187.

Menne, M.J., and C.N. Williams Jr., 2009: Homogenization of temperature series via pairwise comparisons. Journal of Climate, 22, 1700–1717, doi:10.1175/2008JCLI2263.1.

Peterson, T.C., and D.R. Easterling, 1994: Creation of homogeneous composite climatological reference series. International Journal of Climatology, 14, 671–679, doi:10.1002/joc.3370140606.

Peterson, T.C., and R.S. Vose, 1997: An overview of the Global Historical Climatology Network temperature database. Bulletin of the American Meteorological Society, 78, 2837–2849, doi:10.1175/1520-0477(1997)078%3C2837:AOOTGH%3E2.0.CO;2.


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Citing and Metadata

Information is available on the how uses can cite the dataset and view the Metadata.