Retrievals of Drizzle and Cloud Liquid Water Contents in Stratocumulus and Implications for Subgrid-scale Impacts on Model Autoconversion and Accretion Rates

Download or read book Retrievals of Drizzle and Cloud Liquid Water Contents in Stratocumulus and Implications for Subgrid-scale Impacts on Model Autoconversion and Accretion Rates written by Wei Zhao and published by . This book was released on 2020 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: Marine stratocumulus clouds (Sc) cover large areas of the Earth and have a substantial impact on the Earth's radiative balance by reflecting copious amounts of sunlight away from the Earth and emitting longwave radiation at a temperature close to surface. Cloud and precipitation (drizzle) liquid water content (hereafter CLWC and PLWC) are two of the most important microphysical properties of Sc which directly affect radiative transfer and the hydrological cycle, as well as play a critical role in many microphysical and planetary boundary layer processes. It is thus crucial to determine CLWC and PLWC accurately. Sc in many global climate models (GCMs) are found to precipitate too frequently and too lightly which is likely due in part to the lack of information on the subgrid variability in CLWC and PLWC in the calculation of autoconversion and accretion rates. In most GCMs, the effects of subgrid variability have been either completely ignored or incorporated by multiplying the autoconversion and accretion rates (based on grid-mean values) by an enhancement factor to account for the subgrid variability. This dissertation aims to retrieve CLWC and PLWC jointly for Sc based on a millimeter wavelength radar, and to examine the nature of spatial variability in CLWC and PLWC and its impact on the autoconversion and accretion rates. In particular, we derive enhancement factors for autoconversion and accretion rates based on the radar observations, and examine how the enhancement factors change with different factors such as the length scale (size of a GCM grid) and the frequency of below-cloud precipitation. In the first part of the dissertation (Chapter 2), the CLWC and PLWC are retrieved based on a combination of retrieval techniques including a novel Doppler spectra decomposition method that separates Doppler spectra into a cloud and a precipitation component. The radar Doppler spectra data from a vertically pointing Ka-band cloud radar, along with total liquid water path from a three-channel microwave radiometer (MWR) and radiosonde measurements are used in the retrievals. These observational data in this study were collected at the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Eastern North Atlantic (ENA) site. At the scale of a single radar volume, the uncertainty of our retrieved PLWC is about an order of magnitude. By comparing to in-situ aircraft observations, we find on average they are in a good agreement. On the scale of one day, the uncertainty in the mean CLWC is estimated to be within 30% and the systematic errors in the mean PLWC are estimated to be less than 75%. In the second part of the dissertation (Chapter 3), the variability in CLWC and PLWC and its effects on the grid-mean autoconversion and accretion rates are examined, specifically enhancement factors for autoconversion rate (E[subscript]auto) and accretion rate (E[subscript]accr). In many studies (and model implementations) enhancement factors are formulated under the assumption that variability in cloud and precipitation mixing ratio (water content divided by the air density) can be represented by a bivariate lognormal distribution with three key parameters: (i) the fractional standard deviation of the cloud-water mixing ratio, (ii) the fractional standard deviation of precipitating water mixing ratio, and (iii) the (cross) correlation coefficient (between cloud and precipitation mixing ratio). Therefore, both the enhancement factors and these three parameters are evaluated. Overall, we find that while our retrieved joint distribution is not truly a bivariate lognormal, this framework nonetheless works well given the correct values for the three key parameters. In general, we find that E[subscript]auto and E[subscript]accr increase with grid size and have a maximum when precipitation fraction is about 0.4 - 0.6 (depending somewhat on how precipitation occurrence is defined and grid size). E[subscript]auto stays relatively unchanged due to the assumption made in the retrievals that CLWC increases linearly with height in the cloud. E[subscript]accr generally decreases from cloud base to cloud top although an increase in correlation of q[subscript]c and q[subscript]p and a decrease in the magnitude of the subgrid variability of q[subscript]p have some offsetting effects. In addition, we find that E[subscript]auto and E[subscript]accr have little if any correlations with relative humidity (RH), lower tropospheric stability (LTS), and mean liquid water path (LWP) or mean cloud thickness. However, they are highly correlated with variability in of LWP, cloud thickness and cloud base, suggesting that any knowledge in subgrid variability might be useful in predicting E[subscript]auto and E[subscript]accr.