New Technique for Creating Long-term Wind Indices Shows Broad Applications
Researchers from The Florida State University and Pennsylvania State University, funded by CPO’s Climate Observations and Modeling (COM) program, have developed and demonstrated a new approach to creating climatic indices using observational wind data. Climatic indices provide a metric or value used to interpret past climate and monitor the current climate. For example, marine climatic indices help scientists understand how variations in climate over the ocean can influence weather patterns over the United States. Usually these indices are developed from atmospheric pressure observations, which are useful but have limited availability over longer periods of time. In a new study, published in Frontiers in Earth Science, the COM-supported researchers showcase a new technique for developing climatic indices from surface wind direction observations, datasets which have a long record and play a fundamental role in atmospheric and oceanic circulation variability.
This study uses nearly 125 years of wind observations from ships (the International Comprehensive Ocean-Atmosphere Data Set) to develop new climatic indices in the Atlantic basin derived from wind speed and direction observations. In two parts, the study details how wind direction-based indices can be scientifically viable and similar to well-accepted indices already in use, as well as useful for explaining variability in non-wind observations. First, the authors validate their new index technique by creating a wind index for the North Atlantic Oscillation (NAO) that statistically performs similar to current, pressure-based NAO indices on the multiannual scale. Second, they then demonstrate the application potential of this new technique using a case study to show a statistically significant relationship between their wind index in the eastern Gulf of Mexico and precipitation patterns in the southern United States. The case study has important implications not just for understanding climate variability but also for potential agricultural and other industrial applications. While the study uses a specific wind dataset, the index method the researchers showcase is broadly applicable to a variety of climate indices, not just those based on directional wind data, and can also be applied to other regions and time periods.
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