
Regional contrasts in dust emission responses to climate
Author(s) -
Zender Charles S.,
Kwon Eun Young
Publication year - 2005
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2004jd005501
Subject(s) - aeolian processes , environmental science , precipitation , climatology , mineral dust , atmospheric sciences , vegetation (pathology) , atmospheric dust , climate change , climate model , aerosol , geology , meteorology , geography , oceanography , geomorphology , medicine , pathology
Time‐series analysis of Earth's major dust source regions reveals common traits in responses of wind erosion to climate anomalies. Lag cross‐correlations of monthly mean aerosol optical depth, precipitation, vegetation, and wind speed are examined from 1979–1993. The response to monthly climate anomalies can differ greatly from the response to seasonal mean climate. The signs, magnitudes, and lags of highly significant ( p < 0.01) correlations show that 14 important mineral dust source areas characterized by Prospero et al. (2002) fall into four response categories. Each category represents distinct mechanisms by which climate anomalies influence subsequent atmospheric dust loading on seasonal to interannual timescales. In most regions, precipitation and vegetation together strongly constrain dust anomalies on multiple timescales. In these regions, dry anomalies increase, and wet anomalies reduce, dust emission. Interestingly, in many other regions the contrary is true: Dust and precipitation anomalies correlate positively, consistent with sediment‐supply factors. The response timescales are consistent with loss of surface crusts (less than 1 month) and with alluvial transport and dessication (interannual lags). Supply‐limited dust emission appears more prevalent than previously thought and is not accounted for in models. Reproducing these wind erodibility responses in models may help remediate underprediction of observed seasonal to interannual dust variability.