I thought it might be useful to outline a bit about their history (at least, that which I know) and why there are now plans underway to create an updated global dataset. If I've missed something out, or got something wrong, please let me know in the comments or via email and I'll correct it.
Despite pressure from many in the climate and meteorological community, not all meteorological data are freely shared across the globe. Usually, however, derived quantities can be shared more easily, allowing the study and assessment of individual phenomena and also the climate on a global basis. To this end, climate extremes indices get around the problem of restricted access to in-situ data. By converting daily values of minimum and maximum temperature, and precipitation accumulations (rainfall) to monthly or annual climate extremes, some information can be shared globally.
The other advantage of using climate indices, is that they standardise the way certain extremes are measured. This has a benefit that like can be compared with like; so that heavy rainfall, for example, uses the same threshold around the globe (be that as an actual depth, or as a percentile of a distribution - both have their advantages).
The disadvantage of using climate indices is that they reduce the information content as they are by construction some average or single measurement. This loses some of the detail which might be important in a given situation. Taking heatwaves as an example, is it the maximum temperature reached in the day, or how high a minimum temperature was at night, or how high above a threshold (and which) or the duration, or combinations thereof? And is the humidity relevant? Converting sub-daily or daily observations to a single number for a month or year means some of the "colour" is lost. But, at least the indices allow the event to be studied if there are data sharing constraints.
The World Meteorological Organisation (WMO)/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI) has worked for over a decade to develop a set of indices for use across the globe. They have also developed software and arranged workshops to enable researchers to calculate these indices themselves and contribute to international assessments and fill in data sparse regions. These indices have been used in the creation of a number of datasets.
The HadEX Family Tree
The first set of indices developed by the ETCCDI numbered just 10, and were assessed by Frich et al. (2002).The first global dataset to contain all of the current 27 climate extreme indices recommended by the ETCCDI was HadEX (Alexander et al., 2006). These indices were presented on a 3.75lon x 2.5lat grid from 1951-2003. At its launch, it was the most comprehensive, global, gridded dataset of temperature and precipitation extremes based on daily, in-situ data. HadEX was used in many model evaluation, and detection and attribution studies as well as climate monitoring.
In 2012, HadEX2 was released (Donat et al., 2012a), which expanded the time span of the data from 1901-2010 and increased the number of in-situ stations contributing to the final gridded product (around 7,000 for temperature and 11,000 for precipitation indices). HadEX2 built on the legacy of HadEX, and was able to incorporate data from a number of new initiatives, primarily led by KNMI: the European Climate Assessment and Datasets (ECAD) and its siblings, in Southeast Asia (SACAD) and Latin America (LACAD).
By incorporating data from a number of different sources, which themselves may have slightly different processing levels, quality control procedures etc., HadEX2 could end up with some regional differences as a result. Furthermore, HadEX2 has not been updated (bar some minor fixes in the period immediately after its launch), and so it is no longer useful for climate monitoring. At the expense of spatial coverage, a climate extremes dataset was created from the Global Historical Climate Network Daily dataset (Menne et al., 2012) and is updated annually; GHCNDEX (Donat et al., 2012b). GHCNDEX has been well used for monitoring extremes in e.g. the BAMS State of the Climate report.
A further version was created, based on HadGHCND (Caesar et al., 2006) which slightly adapted methodology used to create the final grids. The HadGHCND product is gridded maximum and minimum temperature fields, and these have been taken to make a "HadGHCNDEX" dataset. Although this only exists for the temperature indices, having three datasets all of which have the same goal and product in mind allows the comparison between different methods, and gives users some idea of the uncertainties in these datasets (something for a later blog).
These four datasets (HadEX, HadEX2, GHCNDEX and HadGHCNDEX) have been used to study the change in moderate extremes and individual extreme events for the last decade or so. The methodology they use has been extended to assess extremes in reanalyses and also climate models (Donat et al., 2014, Sillmann et al., 2013a, 2013b).
HadEX, HadEX2 and HadGHCND are all available from the Met Office Hadley Centre Climate Observations website
References:
Alexander, L. V., et al. (2006), Global observed changes in daily climate extremes of temperature and precipitation, Journal of Geophysical Research-Atmospheres, 111, D05109, doi:10.1029/2005JD006290.
Caesar, J., Alexander, L., and Vose, R, (2006) Large-scale changes in observed daily maximum and minimum temperatures: Creation and analysis of a new gridded data set, J. Geophys. Res., 111, D05101, doi:10.1029/2005JD006280
Donat, M., Alexander, L., Yang, H., Durre, I., Vose, R., Dunn, R., Willett, K., Aguilar, E., Brunet, M., Caesar, J., Hewitson, B., Jack, C., Klein Tank, A. M. G., Kruger, A. C., Marengo, J., Peterson, T. C., Renom, M., Rojas, C. O., Rusticucci, M., Salinger, J., Elrayah, A. S., Sekele, S. S., Srivastava, A. K., Trewin, B., Villarroel, C., Vincent, L. A., Zhai, P., Zhang, X., and Kitching, S (2013a) Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: The HadEX2 dataset, J. Geophys. Res. Atmos., 118, 2098–2118, doi:10.1002/jgrd.50150
Donat, M. G., Alexander, L. V., Yang, H., Durre, I., Vose, R., and Caesar, J. (2013b) Global land-based datasets for monitoring climatic extremes, Bull. Am. Meteorol. Soc., 94, 997–1006, doi:10.1175/BAMS-D-12-00109.1
, (2014) Consistency of Temperature and Precipitation Extremes across Various Global Gridded In Situ and Reanalysis Datasets. J. Climate, 27, 5019–5035, doi:10.1175/JCLI-D-13-00405.1
Frich, P., L. V. Alexander, P. Della-Marta, B. Gleason, M. Haylock, A. Klein Tank, T. Peterson (2002), Observed coherent changes in climatic extremes during the second half of the 20th century, Climate Research, 19, 193–212 doi:10.3354/cr019193
Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E., and Houston, T. G. (2012) An overview of the global historical climatology network daily database, J. Atmos. Oc. Technol., 29, 897–910 doi:10.1175/JTECH-D-11-00103.1
Sillmann, J., Kharin, V. V., Zhang, X., Zwiers, F. W., & Bronaugh, D. (2013). Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation in the present climate. Journal of Geophysical Research: Atmospheres, 118(4), 1716-1733. doi:10.1002/jgrd.50203
Sillmann, J., Kharin, V. V., Zwiers,
F. W., Zhang, X., & Bronaugh, D. (2013). Climate extremes indices in
the CMIP5 multimodel ensemble: Part 2. Future climate projections. Journal of Geophysical Research: Atmospheres, 118(6), 2473-2493. doi:10.1002/jgrd.50188
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