Evaluating Cumulus Parameterization Schemes for the Simulation of Arabian Peninsula Winter Rainfall

Abstract

This study investigates the sensitivity of winter seasonal rainfall over the Arabian Peninsula (AP) to different convective physical parameterization schemes using a high-resolution WRF Model. Three different parameterization schemes, Kain–Fritch (KF), Betts–Miller–Janjić (BMJ), and Grell–Freitas (GF), are used in winter simulations from 2001 to 2016. Results from seasonal simulations suggest that simulated AP winter rainfall with KF is in best agreement with observed rainfall in terms of spatial distribution and intensity. Higher spatial correlation coefficients and fewer biases with observations are also obtained with KF. In addition, the regional moisture transport, cloud distribution, and cloud microphysical responses are better simulated by KF. The AP low-level circulation, characterized by the Arabian anticyclone, is well captured by KF and BMJ, but its position is displaced in GF. KF is furthermore successful at simulating the moisture distribution in the lower atmosphere and atmospheric water plumes in the middle troposphere. The higher skill of rainfall simulation with the KF (and to some extent BMJ) is attributed to a better representation of the Arabian anticyclone and subtropical westerly jet, which guides the upper tropospheric synoptic transients and moisture. In addition, the vertical profile of diabatic heating from KF is in better agreement with the observations. Discrepancies in representing the diabatic heating profile by BMJ and GF show discrepancies in instability and in turn precipitation biases. Our results indicate that the selection of subgrid convective parameterization in a high-resolution atmospheric model over the AP is an important factor for accurate regional rainfall simulations.

Analysis of Outdoor Thermal Discomfort Over the Kingdom of Saudi Arabia

“…Herein, we computed the DI using ERA and WRF reanalysis data obtained over the summer months, that is, from June to September (JJAS). We selected these months because of both their extreme heat and the significant changes in summer temperatures reported over the study region during the last few decades (e.g., Attada, Dasari, Chowdary, et al., 2018; Attada, Dasari, Kunchala, et al., 2020; Attada, Kunchala, et al., 2018). We categorized the estimated DI values based on the standard limits (Table 1), and thereafter, we explored its variability and trends over the past four decades.…”

Mid-Century Changes in the Mean and Extreme Climate in the Kingdom of Saudi Arabia and Implications for Water Harvesting and Climate Adaptation

“…Similar to Komurcu et al (2018) [22], we perform convection-permitting simulations, meaning that we use convection parameterization (Kain-Fritsch (K-F) [30]) only in domains 1 and 2 and no convection parametrization is used in domain 3. Use of K-F in WRF has been evaluated and shown to yield reasonable spatial distributions as well as temperature and precipitation patterns similar to observed climatology in the AP [29]. While our highest resolution domain (see Figure 1) includes parts of several countries in the AP, our simulations and analysis are focused on the Kingdom of Saudi Arabia.…”

“…The dynamical downscaling is based on the methodology used in Komurcu et al (2018) [22]: We use bias-corrected Community Earth System Model (CESM) v1.0 projections under RCP 8.5 [24,25] as initial and boundary conditions in the Weather Research and Forecasting Model (WRF) v3.6.1 [26] with three nested domains of 36, 12 and 4 km horizontal resolution ( Figure 1) with 40 vertical atmospheric levels. WRF has been extensively used for weather forecasting and atmospheric research as well as for dynamical downscaling of climate reanalyses and climate projections to study historical climate and future climate change (e.g., [22,[27][28][29]). Similar to Komurcu et al (2018) [22], we perform convection-permitting simulations, meaning that we use convection parameterization (Kain-Fritsch (K-F) [30]) only in domains 1 and 2 and no convection parametrization is used in domain 3.…”

Enhanced Simulation of the Indian Summer Monsoon Rainfall Using Regional Climate Modeling and Continuous Data Assimilation

“…Regional Climate Models (RCMs) dynamically downscaling GCMs fields are the most popular tools to generate high resolution simulations of local atmospheric features over specific regions around the world (e.g., Giorgi and Mearns, 1999;Rinke and Dethloff, 2000;Meehl et al, 2007;Dasari et al, 2010Dasari et al, , 2014Attada et al, 2020). To constraint the RCM simulations to those of the underlying large-scale GCM features of interest, grid (Stauffer and Seaman, 1990) and spectral (Waldron et al, 1996;von Storch et al, 2000) nudging techniques have been applied.…”