资源环境科技发展态势分析平台

Deposition of mineral dust into ocean fertilizes ecosystems and influences biogeochemical cycles and climate. In situ observations of dust deposition are scarce, and model simulations depend on the highly parameterized representations of dust processes with few constraints. By taking advantage of satellites' routine sampling on global and decadal scales, we estimate African dust deposition flux and loss frequency (a ratio of deposition flux to mass loading) along the trans-Atlantic transit using the three-dimensional distributions of aerosol retrieved by spaceborne lidar (Cloud-Aerosol Lidar with Orthogonal Polarization [CALIOP]) and radiometers (Moderate Resolution Imaging Spectroradiometer [MODIS], Multiangle Imaging Spectroradiometer [MISR], and Infrared Atmospheric Sounding Interferometer [IASI]). On the basis of a 10-year (2007-2016) and basin-scale average, the amount of dust deposition into the tropical Atlantic Ocean is estimated at 136-222 Tg/year. The 65-83% of satellite-based estimates agree with the in situ climatology within a factor of 2. The magnitudes of dust deposition are highest in boreal summer and lowest in fall, whereas the interannual variability as measured by the normalized standard deviation with mean is largest in spring (28-41%) and smallest (7-15%) in summer. The dust deposition displays high spatial heterogeneity, revealing that the meridional shifts of major dust deposition belts are modulated by the seasonal migration of the intertropical convergence zone. On the basis of the annual and basin mean, the dust loss frequency derived from the satellite observations ranges from 0.078 to 0.100 day(-1), which is lower than model simulations by up to factors of 2 to 5. The most efficient loss of dust occurs in winter, consistent with the higher possibility of low-altitude transported dust in southern trajectories being intercepted by rainfall associated with the intertropical convergence zone. The satellite-based estimates of dust deposition can be used to fill the geographical gaps and extend time span of in situ measurements, study the dust-ocean interactions, and evaluate model simulations of dust processes.