The Tibetan Plateau (TP) extends over the area of 27°-45°N, 70°-105°E, covering a region about a quarter of the size of the Chinese territory (Wu et al. 2007). Surrounded by the Earth’s highest mountains, such as the Himalayas, Pamir, Kunlun Shan and others, it is the highest and most extensive plateau in the world (Kang et al. 2010; Xu et al. 2018; Gu et al. 2020) and has long been known as the roof of the world (Liu and Chen 2000; Qiu et al. 2008; Yao et al. 2019). Mountains in the TP have a strong impact on precipitation distribution, and a knowledge of the characteristics of precipitation is a basic and important requirement for the planning and management of water resources (Xu et al. 2008). Meanwhile, in the summer season, the TP serves as a huge heat source (Zhu et al. 2017), transferring heat from the land surface to the air in the form of sensible heating, latent heat transfer, and effective radiation of the ground (Yeh et al. 1957), with strong surface sensible heating and deep latent heating over the central and eastern regions (Duan et al. 2012), and plays an important role in the onset and maintenance of the Asian summer monsoon (Chen et al. 2017). However, precipitation across the Tibetan Plateau is poorly known compared with many other mountain areas in the world.
The complex climate over the TP is the key component of the regional and global climate system, but the lack of basic observation data makes it difficult to assess the impact that TP has on climate change across scales. Facing the fact that only sparse observation is available from heterogeneously distributed meteorological stations over the TP (Kuang and Jiao 2016; Maussion et al. 2011; Xiao et al. 2016; Li et al. 2018), numerical simulation results have been proven to be a reasonable and reliable complement to enhance the understanding of climate over the TP. Compared to the global climate models (GCMs), regional climate models (RCMs) are able to depict regional heterogeneity and leading to a better understanding of regional to local climate change signals. The main achievements in RCM research are benefited from the increase of simulation length and resolutions (Giorgi et al. 2019). Several studies have shown that added value is obtained by increasing the horizontal resolution of RCMs to capture additional fine-scale weather processes (Jacob et al. 2014; Di Luca et al. 2012; Lucas-Picher et al. 2012). With its complex orography, the TP is very sensitive to the horizontal resolution of RCMs (Gao et al. 2015a, b, 2017b). Gao et al. (2018) found that the WRF model with a resolution of about 30km shows reduced overestimation for extreme precipitation frequency, increased spatial pattern correlations, and more accurate linear trends compared with coarser resolution GCMs and reanalysis over the TP. Xu et al. (2018) showed that the added value of RCM simulation of about 25 km resolution is achieved by affecting the regional air circulation near the ground surface around the edge of the TP, which leads to a redistribution of the transport of atmospheric water vapor.
Convection is considered to be one of the most critical physical processes affecting the occurrence and amount of precipitation (Kukulies et al. 2020; Niu et al. 2020), while cumulus parameterization schemes (CPSs) have been considered to be a primary uncertainty source in precipitation simulations over the TP for coarse resolution (~25km) RCMs (Wang et al. 2021). Attempts have been made to solve the difficulty by further increasing the resolution of RCMs. The gridding space around 10 km is the so-called gray-zone at which resolution the individual convection cells cannot be resolved, but the organized mesoscale convective systems can be explicitly represented (Ou et al. 2020). And when the resolution is reduced to less than 4km, it’s widely referred to as convection-permitting scale. With the gray-zone grid spacing, a CPS may or may not be turned on. In Asia, Chen et al. (2018) found that the WRF at the 9km gray-zone resolution without the use of CPS captures the salient features of the Indian summer monsoon as well as the spatial distributions and temporal evolutions of monsoon rainfall. Taraphdar et al. (2021) evaluated the WRF at the 9km gray-zone resolution over the United Arab Emirates (UAE) and the Middle-East, and found that gray-zone simulations’ performance for the synoptic and meso-scale precipitation are comparable to convection-permitting simulations with optimized model physical packages. Ou et al. (2020), based on WRF simulations at gray-zone resolution with different CPSs and a simulation without CPS over the TP, found that the frequencies and initiation timings for short-duration (1–3 h) and long-duration (> 6 h) precipitation events are well captured by the experiment without CPS concerning the precipitation diurnal cycles.
Future directions in RCM research are discussed by Giorgi et al. (2019), with a highlight on the transition to convection-permitting modeling systems. Benefited from the rapid development of high-performance computing resources, convection-permitting models (CPMs) is becoming affordable for climate study, which could explicitly resolve the deep convection (Liang et al. 2004; Dai 2006; Prein et al. 2015; Zhang and Chen 2016), eliminate the biases resulted from the application of CPSs, and narrow the uncertainty from model physics (Weisman et al. 1997; Miura 2007; Schlemmer et al. 2011; Satoh et al. 2014; Ban et al. 2015), especially over regions with prevailing convective activities. The added values, such as improved simulations of the buildup and melting of snowpack as well as improvements of temperature at a height of 2m related to improved representation of orography, have been found in CPM climate simulations. Many studies have also demonstrated the other benefits of using CPMs, including the ability to capture observed precipitation diurnal cycles over subtropics (Fosser et al. 2015; Guo et al. 2019, 2020; Li et al. 2020; Yun et al. 2020), well replicating the spatial distribution of precipitation over complex terrain (Grell et al. 2000; Prein et al. 2013; Rasmussen et al. 2014; Gao et al. 2020), and even capable of representing the spatial-temporal scales and the organization of tropical convection at the nearly global scale (Schiwitalla et al. 2020). However, it is also important to mention that CPM climate simulations are not the cure for all model biases. The largest added value can be found on small spatial and temporal scales (<100 km and subdaily) or in regions with steep orography (Prein et al. 2015) such as TP. Li et al. (2021) demonstrated that CPM is a promising tool for dynamic downscaling over the TP with its higher ability to depict the precipitation frequency and intensity. Zhou et al. (2021) found that CPM outperforms the High Asia Refined regional reanalysis (HAR v2, Wang et al. 2020) for 10-m wind speed and precipitation with obviously reduced wet bias over the TP. Furthermore, process-based analysis methods can reveal deeper insights into the more physically and dynamically consistent atmospheric phenomena in CPM climate simulations. Lin et al. (2018) showed that simulation with finer resolutions (especially 2 km) can diminish the positive precipitation bias over the TP through decreased water vapor transport which is reflected mostly in the weakened wind speed. However, modeling clouds remains a challenge even with CPMs that still requires several parameterizations (shallow convection, microphysics and clouds) that need to be adapted for finer resolutions (Kendon et al. 2021). Thus, to date, one of the main challenges associated with the use of CPMs lies in their heavy computational requirements and demanding output storage sizes (Schär et al. 2020). Another challenge lies in the lack of reliable high temporal and spatial resolution gridded observations, affecting the evaluation of the CPM simulations, and especially the assessment of their added value, often linked with sub-daily time scales and extremes. The above challenges limit the characterizations of the different sources of CPM uncertainties and hamper their uptake in climate change assessments and impact studies (Lucas-Picher et al. 2021; Prein et al. 2017, 2020).
Both gray-zone and CPM simulations are at their earlier stages in regional climate application, and there are still few studies in intercomparing simulations at gray-zone scale and convection-permitting scale especially over the TP during the past years. A gap exists in understanding the added value from gray-zone to convection-permitting scale, in which a significant increase in computational resources is needed. Furthermore, previous studies with CPM over the TP were mostly limited to short-term simulation. In this study, two types of high-resolution experiments using the WRF model, the gray-zone resolution of 9km with no CPS and the convection-permitting (CP) resolution of 3km, are performed over the TP for the summer of 2009 to 2018. By comparing the two sets of simulation results, we can: (1) evaluate the model’s performance with various resolutions in reproducing the spatiotemporal characteristics of surface summer climate over the TP; (2) identify the added value of convection-permitting simulation over complex terrain; and (3) isolate the contribution of the convection-permitting experiments in improving the simulation of regional climate processes.
The article is organized as follows. Section 2 describes the model and experimental design, data and methodology. Section 3 presents the main results as well as the comparison with the observations, including the added value of CPM simulation. Section 4 discusses the possible reasons for explaining the excessive precipitation and higher 2-m air temperature in CPM simulation. Finally, major conclusions are presented in Section 5.