Estimating the Impact of Sea Surface Temperature Patterns on Mineral Aerosol Emission and Deposition
Open Access
- Author:
- Hoffman, Alexis Lee
- Graduate Program:
- Meteorology
- Degree:
- Master of Science
- Document Type:
- Master Thesis
- Date of Defense:
- July 12, 2013
- Committee Members:
- Chris Eliot Forest, Thesis Advisor/Co-Advisor
- Keywords:
- regional climate
dust
mineral aerosols
sea surface temperature
SST
sensitivity - Abstract:
- This thesis estimates the impacts of sea surface temperature patterns on mineral aerosol emission and deposition. Existing literature has focused on the correlations between sea surface temperature anomalies on precipitation, but not on dust emission or deposition. This study first estimates the sensitivity of variables relating to dust emission and deposition to sea surface temperature patterns, and second, uses the estimated sensitivity to reconstruct the linear response of regional climate to historical sea surface temperature anomalies. We estimate the linear dependence of relevant model variables to anomalous sea surface temperature forcings in the National Center for Atmospheric Research’s Community Atmosphere Model (CAM5.0). We then reconstruct the linear component of the atmospheric response to historical sea surface temperature anomalies to test the accuracy of the estimations and to highlight where sea surface temperature variability largely controls the variability of regional climate. Using this method we found that the dust sources responsible for most of the net global emissions are strongly sensitive to tropical sea surface temperature anomalies, particularly in the Indian and Pacific Oceans. We also found that the reconstructions of the annual dust emission anomalies in subtropical dust sources were able to capture the long-term trends, suggesting that these trends are likely a result of sea surface temperature anomalies. However, the reconstructions of dust concentration anomalies downwind of the North African dust sources were poor when compared with in situ observations, likely because the model tends to underestimate variability in dust concentrations at the deposition sites. These results are important for understanding how changes in dust load currently, and in future scenarios, impact climate, nutrient cycles, and human environments.