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A simple framework to quantitatively describe monthly precipitation and temperature climatology

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)3161–3174
Number of pages14
JournalInternational Journal of Climatology
Issue number3
Early online date27 Oct 2015
DateAccepted/In press - 25 Sep 2015
DateE-pub ahead of print - 27 Oct 2015
DatePublished (current) - Jul 2016


Climate descriptors and classifications are vital for ordering past, current and future climatic conditions. Yet, these parsimonious descriptors of climatic conditions only capture specific aspects of this climate signal, and lose all other information available in the observations. As a result, climate descriptions are often not physically insightful when they are applied in other studies. In this study, we show that a sinusoidal function with an annual period can adequately describe the vast majority of monthly precipitation and temperature climates around the world. This finding allows us to synthesize intra-annual monthly precipitation and temperature climatology using 5 indices that are easy to interpret. The indices describe (i) the mean precipitation rate (P ̅), (ii) the mean temperature (T ̅), (iii) the seasonal precipitation amplitude (δ_P), (iv) the seasonal temperature amplitude (Δ_T), and (v) the phase difference between the precipitation and temperature regimes (s_d). The combination of the 5 indices describes the relative time series of precipitation and temperature climatology, in contrast to earlier proposed similarity indices that only capture specific aspects of these time series. We demonstrate how the framework can reproduce many earlier proposed indices and classifications, and provide an example how the framework can be used to classify regions. We argue that the framework provides comprehensive insight into global climatology and can function as a quantitative conceptual basis for climate descriptions among different sciences.

    Research areas

  • climate, precipitation, temperature, similarity index, analytical framework

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    Licence: CC BY-NC


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