Seasonal prediction potential for springtime dustiness in the U.S.

Most dust forecast models focus on a short, sub-seasonal lead times, i.e., three to six days, and the skill of seasonal prediction is not clear. In this study we examine the potential of seasonal dust prediction in the U.S. using an observation-constrained regression model and key variables predicted by a seasonal prediction model developed at NOAA Geophysical Fluid Dynamics Laboratory, the Forecast-Oriented Low Ocean Resolution (FLOR) Model.

Our method shows skillful predictions of spring dustiness three to six 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 U.S. in March-May from 2004 to 2016 using predictors from FLOR initialized on December 1^st. 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.