期刊
GEOPHYSICAL RESEARCH LETTERS
卷 40, 期 14, 页码 3710-3714出版社
AMER GEOPHYSICAL UNION
DOI: 10.1002/grl.50688
关键词
surface temperature PDFs; cluster analysis; reanalysis; North America
资金
- National Aeronautics and Space Administration
- NSF [ExArch 1125798]
- NOAA [NA11OAR4310099]
- New Jersey Agricultural Experiment Station Hatch grant [NJ07102]
- NASA National Climate Assessment project [11-NCA11-0028]
- NASA AIST project [AIST-QRS-12-0002]
- Direct For Computer & Info Scie & Enginr [1125798] Funding Source: National Science Foundation
- Directorate For Geosciences [1102838] Funding Source: National Science Foundation
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 33years 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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