Predicting April Precipitation in the Northwestern United States Based on Arctic Stratospheric Ozone and Local Circulation

• Published in: Frontiers in Earth Science
• Main Authors: Xuan Ma, Fei Xie
• Format: Article in Journal/Newspaper
• Language: English
• Published: Frontiers Media S.A. 2020
• Subjects: Arctic stratospheric ozone; local circulation; regression model; prediction; precipitation; Science; Q; Arctic; Pacific
• Online Access: https://doi.org/10.3389/feart.2020.00056; https://doaj.org/article/a5f40e77a42b4b47a4093e8fc7eb34b1

Using observations and reanalysis, we develop a linear regression model to predict April precipitation in the northwestern United States (PNWUS) with a 1-month lead. Not only does this model reproduce April PNWUS for the training period, but also its predictions are robust and reliable for the independent test period. Two independent factors, Arctic stratospheric ozone (ASO) and geopotential height in western North America at 700 hPa (H700WNA), are used to construct the linear regression model. Based on observations, the possible mechanism by which ASO and H700WNA affect PNWUS is as follows. April circulation anomalies over the North Pacific related to March ASO changes can extend eastward to the western United States, causing April PNWUS anomalies. When the April H700WNA is abnormally high, there will be an anti-cyclonic circulation anomaly over the northwestern United States, which not only inhibits water-vapor transport from the North Pacific to the northwestern United States but also suppresses convective activity. This process also influences the April PNWUS. A transient experiment using a climate model with a longer period also agrees with the above result from observations. Using the predicted April H700WNA from the climate forecast system (CFS) of the National Centers for Environmental Prediction (NCEP) based on March data and the observed March ASO, the April PNWUS predicted by this statistical model is closer to observations than the April PNWUS predicted directly by the CFS using March data.