Climatic clustering and longitudinal analysis with impacts on food, bioenergy, and pandemics

Journal Article · Thu Sep 15 00:00:00 EDT 2022 · Phytobiomes Journal

DOI:https://doi.org/10.1094/pbiomes-02-22-0007-r· OSTI ID:1891420

Lagergren, John  ^[1]; Cashman McDevitt, Mikaela  ^[1]; Melesse Vergara, Veronica G.  ^[2]; Eller, Paul R. ^[1]; Machado Gazolla, Joao Gabriel Felipe  ^[1]; Chhetri, Hari B. ^[1]; Streich, Jared ^[1]; Climer, Sharlee ^[3]; Thornton, Peter E.  ^[1]; Joubert, Wayne  ^[1]; Jacobson, Daniel  ^[1]

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1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States)
3. Univ. of Missouri, St. Louis, MO (United States)

Predicted growth in world population will put unparalleled stress on the need for sustainable energy and global food production, as well as increase the likelihood of future pandemics. In this work, we identify high-resolution environmental zones in the context of a changing climate and predict longitudinal processes relevant to these challenges. We do this using exhaustive vector comparison methods that measure the climatic similarity between all locations on earth at high geospatial resolution relative to global-scale analyses. The results are captured as networks, in which edges between geolocations are defined if their historical climate similarities exceed a threshold. We apply Markov clustering and our novel Correlation of Correlations method to the resulting climatic networks, which provides unprecedented agglomerative and longitudinal views of climatic relationships across the globe. The methods performed here resulted in the fastest (9.37x10¹⁸ operations/sec) and one of the largest (168.7x10²¹ operations) scientific computations ever performed, with more than 100 quadrillion edges considered for a single climatic network. Our climatic analysis reveals areas of the world experiencing rapid environmental changes, which can have important implications for global carbon fluxes and zoonotic spillover events. Correlation and network analyses of this kind are widely applicable across computational and predictive biology domains, including systems biology, ecology, carbon cycles, biogeochemistry, and zoonosis research.

• Research Organization: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Bioenergy Innovation (CBI); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER); National Institute on Aging (NIA); National Institutes of Health (NIH)
• Grant/Contract Number: AC05-00OR22725; AC02-05CH11231
• OSTI ID: 1891420
• Journal Information: Phytobiomes Journal, Vol. 6, Issue 3; ISSN 2471-2906
• Publisher: American Phytopathological Society (APS)
• Country of Publication: United States
• Language: English

Related Subjects

59 BASIC BIOLOGICAL SCIENCES