WRF v.3.9 sensitivity to land surface model and horizontal resolution changes over North America

Abstract

Abstract. Understanding the differences between regional simulations of land–atmosphere interactions and near-surface conditions is crucial for a more reliable representation of past and future climate. Here, we explore the effect of changes in the model's horizontal resolution on the simulated energy balance at the surface and near-surface conditions using the Weather Research and Forecasting (WRF) model. To this aim, an ensemble of 12 simulations using three different horizontal resolutions (25, 50 and 100 km) and four different land surface model (LSM) configurations over North America from 1980 to 2013 is developed. Our results show that finer resolutions lead to higher surface net shortwave radiation and maximum temperatures at mid and high latitudes. At low latitudes over coastal areas, an increase in resolution leads to lower values of sensible heat flux and higher values of latent heat flux, as well as lower values of surface temperatures and higher values of precipitation, and soil moisture in summer. The use of finer resolutions leads then to an increase in summer values of latent heat flux and convective and non-convective precipitation and soil moisture at low latitudes. The effect of the LSM choice is larger than the effect of horizontal resolution on the near-surface temperature conditions. By contrast, the effect of the LSM choice on the simulation of precipitation is weaker than the effect of horizontal resolution, showing larger differences among LSM simulations in summer and over regions with high latent heat flux. Comparison between observations and the simulation of daily maximum and minimum temperatures and accumulated precipitation indicates that the CLM4 LSM yields the lowest biases in maximum and minimum mean temperatures but the highest biases in extreme temperatures. Increasing horizontal resolution leads to larger biases in accumulated precipitation over all regions particularly in summer. The reasons behind this are related to the partition between convective and non-convective precipitation, specially noticeable over western USA.

Performance of Seven Land Surface Schemes in the WRFv4.3 Model for Simulating Precipitation in the Record‐Breaking Meiyu Season Over the Yangtze–Huaihe River Valley in China

“…Second, the Meiyu season is one of the main causes of flood disasters in East Asia. Many studies have evaluated the effectiveness of different LSMs in various simulations of precipitation (García‐García et al., 2022; Yan et al., 2021; Zhong et al., 2021), but few have evaluated LSMs for simulating precipitation in the record‐breaking Meiyu season. In the present study, we assessed seven LSMs in the latest WRF model (version 4.3) to determine the LSM that obtained the most reasonable simulations of precipitation during the Meiyu event in 2020 over the YHRV region of East China, which was the worst Meiyu event since 1961.…”

Examining the Role of the Land Surface on Convection Using High‐Resolution Model Forecasts Over the Southeastern United States

“…Differences in the treatment of land and vegetation processes in land surface models (LSMs) impact surface sensible and latent heat fluxes, convective mixing, boundary layer growth, and the transport of moisture into the upper troposphere (e.g., Anthes, 1984; Duda et al., 2017; Pielke, 2001; Santanello et al., 2018). Modeling‐based studies have examined the interactions between the land surface and the atmosphere to discuss land impacts on downstream cloud growth (e.g., García‐García et al., 2021; Kirkpatrick et al., 2011; Knist et al., 2020; PaiMazumder et al., 2012; Pei et al., 2014) and LSM perturbation studies can be used to gauge the impacts of systematic changes in land surface properties (Deng et al., 2016; Duda et al., 2017; Orth et al., 2016; Zhu et al., 2018). Ground‐based observational studies have examined the land‐atmosphere coupling conditions leading to deep convection (e.g., Lareau, 2020; Qiu & Williams, 2020; Van Weverberg et al., 2018; Zhang & Klein, 2010) and high temporal resolution data from geostationary satellites have also been used to better understand the processes leading to CI (Bikos et al., 2012; Griffin et al., 2021; Henderson et al., 2021; Mecikalski et al., 2015; Senf & Deneke, 2017).…”