Abstract
Advancing the understanding of how variations in the climate over the ocean influences the weather over the United States can be aided by developing marine climatic indices. Herein, wind component indices are developed using nearly 125 years of wind observations from ships. A new technique using probability density functions for the values of meridional and zonal wind components is developed to create indices for a user-selected region and accumulation interval (e.g., annual or seasonal) over a climatological period. The index is a measure of the shift in the likelihood of values above or below a threshold for a given season or year as compared to the long-term (e.g., 125 year) probability distribution. The new index method is demonstrated using ship-based wind observations for select regions of the Atlantic Ocean. Ship observations are extracted from release 3.0.0 of the International Comprehensive Ocean-Atmosphere Data Set. Prior to index creation, an assessment of wind data quality is completed, and suspect observations are removed. The method to create a probabilistic wind component index is described along with a metric of the uncertainty in the calculated index. Two wind component indices, for regions in the north Atlantic and eastern Gulf of Mexico, are presented to demonstrate the technique. Using the Gulf of Mexico index as a case study, we compare the wind component indices to precipitation measured over the Gulf coastal states and identify several relationships between multi-year changes in winds in the eastern Gulf of Mexico and precipitation on a seasonal basis. Exploring the spatiotemporal patterns of the onshore/offshore component wind indices derived from seasonal wind forecasts could provide a metric for future prediction of seasonal or annual precipitation to support the agricultural sector. The index method demonstrated can be applied to other spatiotemporal regions for different parameters and using other source datasets.
Highlights
Climate variability on time scales from seasons, years, and decades has long been recognized as one of the factors that influences synoptic-scale weather
Where Pindex is the predicted standardized precipitation index, M1 and M2 are the linear coefficients relating to the zonal standardized wind index (Nindex) and the meridional standardized wind index (Eindex), respectively, and C is an intercept coefficient
The seasonally generated multiple linear regression models for eastern Texas/southwestern Louisiana, including the regression curves, root-mean squared errors (RMSE) and coefficients of determination (r2) for the three periods superimposed on the seasonal precipitation series (Figure 13), quantify the wind index and precipitation relationships
Summary
Climate variability on time scales from seasons, years, and decades has long been recognized as one of the factors that influences synoptic-scale weather. The warm phase of the El Niño Southern Oscillation (ENSO) influences an anomalously wet and cool boreal winter season for the southeast United States, while the cold phase of ENSO prompts a relatively warm and dry boreal. By identifying and quantifying variations in both atmospheric and ocean circulations, pinpointing the consequential weather patterns and impact severity for a particular region can be achieved. Circulation patterns are typically assessed by a climatic index (e.g., American Meteorological Society, 2000): a diagnostic tool used to interpret the past climate, as well as monitor the current climate. Climatic indices provide a metric, typically a numeric value derived from one or more climate elements (e.g., wind, temperature, and precipitation), of the climate system that is easy to calculate and understand
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
