Global Historical Climatology Network Monthly - Version 4
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ASCII Text Files Version 4 Archive Additional Diagnostics Documentation |
The Global Historical Climatology Network–monthly (GHCNm) dataset is a set of monthly climate summaries from thousands of weather stations around the world. The monthly data have periods of record that vary by station with the earliest observations dating to the 18th century. Some station records are purely historic and are no longer updated whereas many others are still in operation and provide short time delay updates that are useful for climate monitoring.
The first release of GHCNm & dates to the the early 1990s (Vose et al. 1992). Subsequent releases include version 2 in 1997(Peterson and Vose, 1997), version 3 in 2011 (Lawrimore et al. 2011) and, most recently, version 4 (Menne et al. 2018). For the moment, GHCNm v4 consists of mean monthly temperature data only. Mean monthly maximum and minimum temperatures as well as monthly total precipitation will be included at a later date.
Relative to previous versions, v4 provides an expanded set of station temperature records as well as more comprehensive uncertainties for the calculation of station and regional temperature trends. The increase in station data comes primarily from the temperature observations available in the Global Historical Climatology Network–daily dataset (GHCNd; Menne et al. 2012), which have been combined with the original monthly sources used in previous versions of GHCNm. Additional station data collected under the auspices of the International Surface Temperature Initiative are also used (ISTI; Rennie et al. 2013) and the data merging process was conducted within the ISTI project. Combining these various sources brings the total number of monthly temperature stations in v4 to approximately 26,000 compared to 7200 in v2 and v3.
Data and inventory files
Directions on Uncompressing and Extracing Files (includes description of inventory file and format of data files [measurement, quality, and source flags)]
- Station-Level Information
Detailed station-level information on the source of the data, monthly mean temperatures and trends for unadjusted and adjusted data are provided in graphical form. An explanation of these figures is provided in the readme.txt file
Quality Assurance
GHCNm v4 uses the same set of quality control (QC) algorithms applied to v3 with some additions. The checks and associated flags are shown in Table 1. Further details regarding the quality control checks are available in the version 4 Algorithm Theoretical Basis Document.
Table 1. Quality Assurance Checks Applied to GHCNm Version 4 Temperatures
Temperature Data Homogenization
Nearly all weather stations undergo changes in the circumstances under which measurements are taken at some point during their history. For example, thermometers require periodic replacement or recalibration and measurement technology has evolved over time. Temperature recording protocols have also changed at many locations from recording temperatures at fixed hours during the day to once-per-day readings of the 24-hour maximum and minimum. “Fixed” land stations are sometime relocated and even minor temperature equipment moves can change the microclimate exposure of the instruments. In other cases, the land use or land cover in the vicinity of an observing site can change over time, which can impact the local environment that instruments are sampling even when measurement practice is stable. All of the these different modifications to the circumstances of recording near surface air temperature can cause systematic shifts in temperature readings from a station that are unrelated to any real variation in local weather and climate. Moreover, the magnitude of these shifts (or “inhomogeneities”) can be large relative to true climate variability. Inhomogeneities can therefore lead to large systematic errors in the computation of climate trends and variability not only for individual station records, but also in spatial averages.
For this reason, detecting and accounting for artifacts associated with changes in observing practice is an important and necessary endeavor in building climate datasets. In GHCNm v4, shifts in monthly temperature series are detected through automated pairwise comparisons of the station series using the algorithm described in Menne and Williams (2009). This procedure, known as the Pairwise Homogenization Algorithm (PHA), systematically evaluates each time series of monthly average surface air temperature to identify cases in which there is an abrupt shift in one station’s temperature series (the “target” series) relative to many other correlated series from other stations in the region (the “reference” series). The algorithm seeks to resolve the timing of shifts for all station series before computing an adjustment factor to compensate for any one particular shift. These adjustment factors are based on the average change in the magnitude of monthly temperature differences between the target station series with the apparent shift and the reference series with no apparent concurrent shifts.
The PHA has undergone extensive evaluation (e.g., Williams et al. 2012) and GHCNm v4 data are provided as both homogenized (adjusted) and unhomogenized (unadjusted). The homogenized data are known by the string "qcf" and the unhomogenized data are designated by the string "qcu". As described in Menne et al. (2018), the PHA is periodically run as an ensemble to quantify the uncertainty of homogenization. Other components of uncertainty are also evaluated. The combined effect of uncertainties for GHCNm v4 are shown in the figure below.
(Right) Total uncertainty for GHCNm v4 mean annual Global Land Surface Air Temperature anomalies. Darker grays show homogenization uncertainties (parametric and missed breaks) and the lighter grays show anomaly and spatial coverage uncertainties. The uncertainties are displayed as cumulative, so the uncertainty bounds depicted in each lighter shade includes the uncertainty of the darker shades (see Menne et al. 2018 for details).
References
GHCNm v4
Menne, M. J., C. N. Williams, B.E. Gleason, J. J Rennie, and J. H. Lawrimore, 2018: The Global Historical Climatology Network Monthly Temperature Dataset, Version 4. J. Climate, in press. https://doi.org/10.1175/JCLI-D-18-0094.1.
GHCNm v3
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, J. Geophys. Res., 116, D19121, doi:10.1029/2011JD016187.
GHCNm v2
Peterson, T. C., and R. S. Vose, 1997: An overview of the Global Historical Climatology Network temperature database. Bull. Amer. Meteor. Soc.,78, 2837–2849.
GHCNm v1
Vose, R. S., R. L. Schmoyer, P. M. Steurer, T. C. Peterson, R. Heim, T. R. Karl, and J. Eischeid, 1992: The Global Historical Climatology Network: Long‐term monthly temperature, precipitation, sea level pressure, and station pressure data, ORNL/CDIAC‐53, 325 pp., Carbon Dioxide Inf. Anal. Cent., Oak Ridge, Tenn.
GHCNd
Menne, M. J., I. Durre, B. G. Gleason, T. Houston, and R. S. Vose, 2012: An overview of the Global Historical Climatology Network Daily dataset. J. Atmos. Oceanic Technol., 29, 897–910, doi:10.1175/JTECH-D-11-00103.1.
Pairwise Homogenization Algorithm (PHA)
Menne, M. J., and C. N. Williams, 2009: Homogenization of temperature series via pairwise comparisons, J. Climate, 22, 1700–1717, doi:10.1175/2008JCLI2263.1.
Williams, C. N., M. J. Menne, and P. W. Thorne, 2012: Benchmarking the performance of pairwise homogenization of surface temperatures in the United States, J. Geophys. Res., 117, D05116, doi:10.1029/2011JD016761.
ISTI Databank
Rennie, J. J., Lawrimore, J. H., Gleason, B. E., Thorne, P. W., Morice, C. P., Menne, M. J., Williams, C. N., de Almeida, W. G., Christy, J. R., Flannery, M., Ishihara, M., Kamiguchi, K., Klein-Tank, A. M. G., Mhanda, A., Lister, D. H., Razuvaev, V., Renom, M., Rusticucci, M., Tandy, J., Worley, S. J., Venema, V., Angel, W., Brunet, M., Dattore, B., Diamond, H., Lazzara, M. A., Le Blancq, F., Luterbacher, J., Mächel, H., Revadekar, J., Vose, R. S., and Yin, X. (2014), The international surface temperature initiative global land surface databank: monthly temperature data release description and methods. Geoscience Data Journal, 1, 75–102. doi: 10.1002/gdj3.8.