4.7 Article

Identifying a Leading Predictor of Arctic Stratospheric Ozone for April Precipitation in Eastern North America

Journal

REMOTE SENSING
Volume 14, Issue 19, Pages -

Publisher

MDPI
DOI: 10.3390/rs14195040

Keywords

Arctic stratospheric ozone; precipitation; prediction; WACCM; statistical linear model

Funding

  1. National Natural Science Foundation of China [42122037, 41975047, 41875047, 12135003]
  2. Postdoctoral Innovative Talent Support Program of China [BX20220039]

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There is a strong correlation between Arctic stratospheric ozone and precipitation in eastern North America. Anomalies in Arctic stratospheric ozone can affect tropospheric circulation over the North Pacific and North America, leading to changes in precipitation. High Arctic stratospheric ozone suppresses precipitation, while low Arctic stratospheric ozone enhances precipitation. Additionally, a statistical linear model based on Arctic stratospheric ozone can accurately predict April precipitation.
An analysis of the relationship between changes in Arctic stratospheric ozone (ASO) and precipitation in eastern North America (38 degrees-54 degrees N, 65 degrees-87 degrees W; PENA) was performed using observational and reanalysis data coupled with the Whole Atmosphere Community Climate Model version 4 (WACCM4). We found that March ASO exhibits a strong correlation with PENA in April, indicating that the one-month leading ASO exerts a potentially strong impact on April PENA. Changes in tropospheric circulation over the North Pacific and North America can be influenced by ASO anomalies via stratosphere-troposphere interactions. Increased ASO typically results in the transport of drier, colder air from northwest to eastern North America and suppresses local convective activity by enhancing regional downwelling. These conditions lead to a decrease in regional atmospheric water vapor content (1000-600 hPa). Abnormally high ASO may therefore suppress precipitation, whereas abnormally low ASO serves to enhance precipitation, and the finding is supported by WACCM4 simulations incorporating these ASO anomaly signals. We also present an ASO-based statistical linear model for predicting April PENA. Results confirm that the linear model reproduces April PENA for both training and testing periods, based on March ASO, demonstrating the reliability and stability of this linear model. This study verifies that ASO is a viable predictor for projecting April PENA and thus improving forecasts of regional seasonal precipitation.

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