Impact of cloud droplet number concentration on simulated shortwave fluxes in the northeast Pacific

Tuesday, 16 December 2014
Alexandre Jousse, Lionel Renault and Alexander D Hall, University of California Los Angeles, Los Angeles, CA, United States
Satellite retrievals (e.g. MODIS) show that the cloud droplet number concentration (CDNC) is generally high along the US west coast (~300cm-3), while it drops to smaller values further offshore (~50cm-3). The Weather Research Forecast (WRF) model does not explicitly predict CDNC. For instance, the WRF single moment 6-class (WSM6) microphysics scheme uses a physically based auto-conversion scheme with a tunable CDNC. By default, CDNC is set in WSM6 to a constant value. In this study, we investigate the potential benefits of adding a varying CDNC in WSM6. We run two WRF simulations over the northeast Pacific in 2005. In the first, we use a constant CDNC (default value). In the second one, we implement a spatially and seasonally varying CDNC, which is tuned according to the observed climatology from MODIS. In our results, we show that biases in shortwave fluxes are drastically reduced when the varying CDNC is used. We can attribute the better performance of the simulation with a varying CDNC to more accurate quantification of cloud droplet auto-conversion into raindrops. Thus, our study suggests that in order to represent clouds and their radiative effects in the northeast Pacific realistically, it is necessary to implement an auto-conversion scheme that accounts for the variability in CDNC.