Paul C. Loikith*, Ben R. Lintner, J. Kim, H. Lee, J. David Neelin, and Duane E. Waliser Geophys. Res. Lett., 40,3710–3714, DOI: 10.1002/grl.50688, 2013.
Preprint (PDF 2.9 MB).
Group use.
Abstract 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.
Citation Loikith, P. C., B. R. Lintner, J. Kim, H. Lee, J. D. Neelin, and D. E. Waliser, 2013: Classifying reanalysis surface temperature probability density functions (PDFs) over North America with cluster analysis. Geophys. Res. Lett., 40, 3710–3714, doi:10.1002/grl.50688.
An edited version of this paper was published by AGU. Copyright (2013) American Geophysical Union.
To view the published open abstract, go to AGU Paper.