Global calculated drift potential of winds in DJF, MAM, JJA, SON
Okin, Gregory (2022), Global calculated drift potential of winds in DJF, MAM, JJA, SON, Dryad, Dataset, https://doi.org/10.5068/D1JT3F
Soil moisture in the active aeolian layer (the top ~2 mm of the soil) impacts dust emission by increasing the threshold for emission, and thus precipitation has the potential to suppress dust emission. The purpose of this study was to use reanalysis and satellite data similar to those used in global and regional dust emission models to calculate the probability that a high wind event happens during the period that antecedent precipitation would have left the active layer wet. The results indicate that the answer to this question is more strongly related to regional climate than soil texture. For more than half of the globe with mean annual precipitation < 500 mm/year, the probability of precipitation influencing dust emission is greater than 30 – 40%. Thus, rain-derived soil moisture in the active layer should not be ignored in models throughout much of the world’s dust producing regions.
Instantaneous surface wind speed data, matching the time period of the available precipitation data, from the Modern Era Retrospective Analysis for Research and Applications v. 2 (MERRA2) was obtained [Molod et al., 2015]. Although this data is available hourly, only 3 hourly data matching the times available in the TRMM precipitation dataset were used. Data were clipped to 60°N to 60°S and regridded to the TRMM 0.25° x 25° grid.
3-month (DJF, MAM, JJA, SON) values for dust uplift potential [DUP, Figure 2, Marsham et al., 2011] were calculated. DUP is an integration of the time that the wind is above a threshold (7 m s-1), weighted by a nonlinear (nearly cubic) function of the excess windspeed (i.e., windspeed minus threshold). DUP was calculated on a seasonal basis from the MERRA2 wind data, and the season with the highest DUP was identified for each 0.25° x 0.25° cell.
Marsham, J. H., Knippertz, P., Dixon, N. S., Parker, D. J., & Lister, G. M. S. (2011). The importance of the representation of deep convection for modeled dust-generating winds over West Africa during summer. Geophysical Research Letters, 38(16), L16803. http://dx.doi.org/10.1029/2011GL048368
Molod, A., Takacs, L., Suarez, M., & Bacmeister, J. (2015). Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2. Geosci. Model Dev., 8(5), 1339-1356. http://www.geosci-model-dev.net/8/1339/2015/
Data are in TIFF/GeoTIFF Format and can be opened in standard image processing (for remote sensing data) or GIS (e.g., ArcGIS, QGIS) software. Because the images are raw data and not image data, an image viewer (suitable for photos, for example) will not display the data correctly and may fail to open the image altogether. The .tfw file is the "world" file that must accompany the .tif image (in the same directory) when opening in order for the image to be properly geographically registered.
Army Research Office, Award: 67787‐EV