A scPDSI-based global data set of dry and wet spells for 1901-2009: Variations in the self-calibrating PDSI

G. Van Der Schrier, J. Barichivich, K. R. Briffa, Philip Jones

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279 Citations (Scopus)

Abstract

Global maps of monthly self-calibrating Palmer Drought Severity Index (scPDSI) have been calculated for the period 1901–2009 based on the CRU TS 3.10.01 data sets. This work addresses some concerns with regard to monitoring of global drought conditions using the traditional Palmer Drought Severity Index. First, the scPDSI has a similar range of variability in diverse climates making it a more suitable metric for comparing the relative availability of moisture in different regions. Second, the more physically based Penman-Monteith parameterization for potential evapotranspiration is used, calculated using the actual vegetation cover rather than a reference crop. Third, seasonal snowpack dynamics are considered in the water balance model. The leading mode of variability in the new data set represents a trend towards drying conditions in some parts of the globe between 1950 and 1985 but accounts for less than 9% of the total variability. Increasing temperature and potential evapotranspiration explain part of the drying trend. However, local trends in most of the drying regions are not significant. Previously published evidence of unusually strong or widespread drying is not supported by the evidence in this work. A fundamental aspect of the calculation of scPDSI is the selection of a calibration period. When this period does not include the most recent part of the record, trends towards more extreme conditions are amplified. It is shown that this is the principal reason for different published interpretations of the scale of recent global drying and not, as recently claimed, the use of simplified forcing data.
Original languageEnglish
Pages (from-to)4025-4048
Number of pages24
JournalJournal of Geophysical Research: Atmospheres
Volume118
Issue number10
DOIs
Publication statusPublished - 27 May 2013

Keywords

  • drought
  • self-calibrating PDSI
  • soil moisture
  • snow model
  • global coverage

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