Land surface memory effects on dust emission in a Mongolian temperate grassland

Aeolian processes in temperate grasslands are unique in that the plant growth-decay cycle, soil moisture/snowpack dynamics, and induced grazing interactively affect seasonal and interannual variations of dust emission. This study uses process-based ecosystem model DAYCENT and unique saltation flux measurements to (1) identify primary land surface factors that control dust emission with soil moisture and vegetation components (live grasses, standing dead grasses, and litter) in a Mongolian grassland and (2) test the dead-leaf hypothesis proposed by previous observational studies that correlates plant biomass in summer and dust events the following spring. In general, the DAYCENT model realistically simulates seasonal and interannual variations of the vegetation components and soil moisture that were captured by field observations during 2003-2010. Then, the land surface components are correlated with measured daily saltation flux in the springs of 2008-2009 and the frequency of monthly dusty days during March-June 2002-2010. Results show that dust emission had a similar amplitude of significant correlation with wind speed and a combination of all land surface components, which demonstrates a memory of the preceding year. The memory analysis reveals that vegetation and soil moisture anomalies during spring dust emission are significantly autocorrelated with the preceding year's (autumn) corresponding anomalies, which were controlled by rainfall during a given summer. Most importantly, of the vegetation components, the standing dead grasses had the strongest memory and simultaneous correlation with spring dust emission, suggesting the validity of the dead-leaf hypothesis.