Seasonal Prediction Potential for Springtime Dustiness in the United States. Issue 15 (5th August 2019)

• Record Type: Journal Article
• Authors: Pu, Bing; Ginoux, Paul; Kapnick, Sarah B.; Yang, Xiaosong
• Is Part Of: Geophysical research letters. Volume 46:Issue 15(2019)
• Page Start: 9163
• Page End: 9173
• Publication Date: 2019-08-05
• Subjects: dust -- seasonal prediction -- United States -- statistical model, FLOR; Geophysics -- Periodicals; Planets -- Periodicals; Lunar geology -- Periodicals; 550
• DOI: 10.1029/2019GL083703
• Languages: English
• ISSNs: 0094-8276

Abstract

Abstract: Most dust forecast models focus on short, subseasonal lead times, that is, 3 to 6 days, and the skill of seasonal prediction is not clear. In this study we examine the potential of seasonal dust prediction in the United States using an observation‐constrained regression model and key variables predicted by a seasonal prediction model developed at National Oceanic and Atmospheric Administration Geophysical Fluid Dynamics Laboratory, the Forecast‐Oriented Low Ocean Resolution (FLOR) model. Our method shows skillful predictions of spring dustiness 3 to 6 months in advance. It is found that the regression model explains about 71% of the variances of dust event frequency over the Great Plains and 63% over the southwestern United States in March‐May from 2004 to 2016 using predictors from FLOR initialized on 1 December. Variations in springtime dustiness are dominated by springtime climatic factors rather than wintertime factors. Findings here will help development of a seasonal dust prediction system and hazard prevention. Plain Language Summary: Severe dust storms reduce visibility and cause breathing problems and lung diseases, affecting public transportation and safety. Reliable forecasts for dust storms and overall dustiness are therefore important for hazard prevention and resource planning. Most dust forecast models focus on short‐time forecasts extending out only a few days. The capability of seasonal dust prediction in the United States is not clear. Here we use a statistical model and precipitation, surface wind, and ground surface bareness from a seasonal prediction model driven by observational information on 1 December to predict dustiness over major dusty regions in the United States in spring. It is found that our method can largely capture the year‐to‐year variations in dustiness over the Great Plains during March‐April‐May and partially over the southwestern United States. The finding here will help the development of a more reliable seasonal dust prediction system in the future. Key Points: A regression model and ensembles from a seasonal prediction model initialized on 1 December are used to predict springtime dustiness About 71% of the variances of dustiness over the Great Plains and 63% over the southwestern United States from 2004 to 2016 are captured Springtime climatic factors play a more important role in variations in spring dustiness than wintertime factors