Global land-based datasets for monitoring climatic extremes

For more than a decade, the World Meteorological Organization (WMO) Commission for Climatology (CCl)/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI) has been facilitating the international coordination of a suite of indices that primarily represent the more extreme aspects of climate. The main aim of this team has been to fill in data gaps using a consistent and traceable approach in order to provide a clear global picture of the long-term variability of extremes, to provide the necessary data to perform appropriate “detection and attribution” studies, and to be able to evaluate climate models and assess their efficacy in simulating and projecting the future of climate extremes. To this end, the ETCCDI held a number of regional workshops over many years, the data from which were used to help create HadEX, the first global land-based, gridded dataset of temperature and precipitation extremes covering the second half of the twentieth century. While HadEX facilitated the analysis of trends in extremes, its relatively short record (1951–2003) and static nature (i.e., it is not updated) presents critical gaps in our ability to adequately assess and monitor changes in extremes. Furthermore, much of the data from the regional workshops is not publically available, making it difficult to independently reproduce the results of HadEX. For these reasons, the authors set out to develop a new dataset to address these issues using the world's largest repository of daily in situ observations of temperature and precipitation—the National Climatic Data Center (NCDC)'s Global Historical Climatology Network (GHCN)-Daily dataset. This article describes the resulting dataset, termed GHCNDEX—an operationally updated, global land gridded dataset of climate extremes. We also demonstrate the application of the dataset for climate change and climate monitoring purposes in addition to assessing some issues regarding uncertainty by comparing the results with existing datasets