Estimating the Sensitivity of Regional Dust Sources to Sea Surface Temperature Anomaly Patterns

Abstract:

Mineral aerosols are an increasingly important component of the climate system that affect the radiative budget, nutrient cycles, and human environments. Dust emissions are largely controlled by regional climate factors such as atmospheric stability, precipitation, soil moisture, and vegetation. Regional climates, particularly within the tropics, are affected by teleconnections excited by sea surface temperatures. We therefore explore the impact of sea surface temperature (SST) anomaly patterns on local climates in major dust source regions (including southern Africa, the Arabian Desert, the Lake Eyre basin, and three others in North Africa) to help understand variability in the global dust cycle. We investigate the sensitivity of regional climate variables impacting mineral aerosol emissions to global SST anomaly patterns by estimating the global teleconnection operator (GTO), which relates regional climate responses to SST anomaly patterns. We estimate the GTO using the NCAR Community Atmosphere Model version 5.0 (CAM5.0) forced by an ensemble of randomly perturbed climatological SST fields.

Variability in dust emissions are connected to SST anomaly patterns in the tropical oceans, particularly in the Indian and western Pacific Oceans. Teleconnections excited by remote SST anomalies typically modify dust emissions via near-surface circulation changes that impact friction velocity. However, the impact of SST-driven changes on threshold friction velocity can be on the same order of magnitude as those of friction velocity, suggesting the impact of SST anomalies on surface conditions are also significant. We reconstruct historical climates using the GTO and compare the results to a non-linear model and observations to assess the GTO capabilities and to identify ocean basins with the strongest influence on major dust source regions. Recognizing SST anomaly patterns as a component of internal variability in regional dust emissions helps characterize the impact of human influences on the dust cycle as well as improve predictions of dust and their climate impacts.