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Geophysical Research Letters

Classifying reanalysis surface temperature probability density functions (PDFs) over North America with cluster analysis

Authors

  • P. C. Loikith,

    Corresponding author
    1. NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
    • Corresponding author: P. C. Loikith, NASA Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr., Pasadena, CA 91109, USA. (paul.c.loikith@jpl.nasa.gov)

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  • B. R. Lintner,

    1. Department of Environmental Sciences, Rutgers, The State University of New Jersey, New Brunswick, New Jersey, USA
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  • J. Kim,

    1. Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, California, USA
    2. Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, California, USA
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  • H. Lee,

    1. NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
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  • J. D. Neelin,

    1. Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, California, USA
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  • D. E. Waliser

    1. NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
    2. Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, California, USA
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Abstract

[1] An important step in projecting future climate change impacts on extremes involves quantifying the underlying probability distribution functions (PDFs) of climate variables. However, doing so can prove challenging when multiple models and large domains are considered. Here an approach to PDF quantification using k-means clustering is considered. A standard clustering algorithm (with k = 5 clusters) is applied to 33 years of daily January surface temperature from two state-of-the-art reanalysis products, the North American Regional Reanalysis and the Modern Era Retrospective Analysis for Research and Applications. The resulting cluster assignments yield spatially coherent patterns that can be broadly related to distinct climate regimes over North America, e.g., low variability over the tropical oceans or temperature advection across stronger or weaker gradients. This technique has the potential to be a useful and intuitive tool for evaluation of model-simulated PDF structure and could provide insight into projections of future changes in temperature.

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