PT - JOURNAL ARTICLE AU - John Lagergren AU - Mikaela Cashman AU - Verónica G. Melesse Vergara AU - Paul R. Eller AU - Joao Gabriel Felipe Machado Gazolla AU - Hari B. Chhetri AU - Jared Streich AU - Sharlee Climer AU - Peter Thornton AU - Wayne Joubert AU - Daniel Jacobson TI - Climatic clustering and longitudinal analysis with impacts on food, bioenergy, and pandemics AID - 10.1101/2021.09.30.462568 DP - 2021 Jan 01 TA - bioRxiv PG - 2021.09.30.462568 4099 - http://biorxiv.org/content/early/2021/10/01/2021.09.30.462568.short 4100 - http://biorxiv.org/content/early/2021/10/01/2021.09.30.462568.full AB - 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. The results are captured as networks, in which edges between geolocations are defined if their historical climates exceed a similarity threshold. We then 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 (9.37 × 1018 operations/sec) and one of the largest (168.7 × 1021 operations) scientific computations ever performed, with more than 100 quadrillion edges considered for a single climatic network. Correlation and network analysis methods of this kind are widely applicable across computational and predictive biology domains, including systems biology, ecology, carbon cycles, biogeochemistry, and zoonosis research.Competing Interest StatementThe authors have declared no competing interest.