Nan Peng's research while affiliated with Lanzhou University and other places

Cloud overlap, referring to distinct cloud layers occurring over the same location, is essential for accurately calculating the atmospheric radiation transfer in numerical models, which, in turn, enhances our ability to predict future climate change. In this study, we analyze multi-year cloud overlap properties observed from the Ka-band Zenith Radar (KAZR) at the Semi-Arid Climate and Environment Observatory of Lanzhou University’s (SACOL) site. We conduct a series of statistical analyses and determine the suitable temporal-spatial resolution of 1 h with a 360 m scale for data analysis. Our findings show that the cloud overlap parameter and total cloud fraction are maximized during winter-spring and minimized in summer-autumn, and the extreme value of decorrelation length usually lags one or two seasons. Additionally, we find the cloud overlap assumption has distinct effects on the cloud fraction bias for different cloud types. The random overlap leads to the minimum bias of the cloud fraction for Low-Middle-High (LMH), Low-Middle (LM), and Middle-High (MH) clouds, while the maximum overlap is for Low (L), Middle (M), and High (H) clouds. We also incorporate observations from satellite-based active sensors, including CloudSat, Cloud-Aerosol Lidar, and Infrared Pathfinder Satellite Observations (CALIPSO), to refine our study area and specific cases by considering the total cloud fraction and sample size from different datasets. Our analysis reveals that the representativeness of random overlap strengthens and then weakens with increasing layer separations. The decorrelation length varies with the KAZR, CloudSat-CALIPSO, CloudSat, and CALIPSO datasets, measuring 1.43 km, 2.18 km, 2.58 km, and 1.11 km, respectively. For H, MH, and LMH clouds, the average cloud overlap parameter from CloudSat-CALIPSO aligns closely with KAZR. For L, M, and LM clouds, when the level separation of cloud layer pairs are less than 1 km, the representative assumption obtained from different datasets are maximum overlap.

Different aerosol types exhibit distinct radiative effects in different regions, attributed to their unique optical characteristics and regional distributions. This study focuses on North China, which is impacted by both natural and anthropogenic aerosols with high concentrations and a variety of aerosol types. While many studies on aerosol direct radiative effects have been conducted in this region, the majority have focused on a specific type of aerosol or overall aerosol, leaving limited research on the direct radiative effects and contributions of different aerosol types. In this study, we use CALIPSO satellite data from 2011 to 2020 to investigate concentrations and distributions of different aerosol types. The results reveal that dust, polluted dust, polluted continental/smoke, and elevated smoke are the dominant aerosol types in North China. Based on the radiative closure experiment, we systematically calculate the radiative effects of different aerosol types and their corresponding contributions to the energy budget by combining satellite data with the Fu–Liou radiative transfer model. The annual average net aerosol direct radiative effect (ADRE) of North China is −6.1 and −13.43 W m−2 at the TOA and surface, respectively, causing a net warming effect of 7.33 W m−2 in the atmosphere. For each main aerosol type, dust contributes 93% to the shortwave ADRE in the western dust source region, while polluted dust mainly contributes 31% and 45% of the total ADRE, in Northwest China and North China Plain, respectively. Anthropogenic pollutant aerosols account for 58% of the total ADRE in Northeast China. This study holds great significance in elucidating the dominant aerosol types and their concentrations in North China, comprehending the impacts of different aerosol types on the local energy balance.

Different cloud types have distinct radiative effects on the energy budget of the earth–atmosphere system. To better understand the cloud radiative impacts, it is necessary to distinguish the effects of different cloud types, which can be achieved through the cloud radar data that can provide cloud profiles for both day-to-day and diurnal variations. In this study, we use 6-year high-temporal resolution data from the Ka-Band Zenith Radar (KAZR) at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) site to analyze the physical properties and radiative effects of main cloud types. The three types of clouds that occur most frequently at the SACOL site are single-layer ice clouds, single-layer water clouds, and double-layer clouds with the annual occurrence frequencies being 29.1%, 3.4%, and 8.3%, respectively. By using the Fu–Liou radiative transfer model simulation, it is found that the distinct diurnal variations of both the occurrence frequency and their macro- and micro-physical properties significantly affect the cloud-radiation. On annual mean, the single-layer ice clouds have a positive radiative forcing of 7.4 W/m² to heat the system, which is a result of reflecting 12.9 W/m² shortwave (SW) radiation and retaining 20.3 W/m² longwave (LW) radiation; while the single-layer water clouds and double-layer clouds have much stronger SW cooling effect than LW warming effect, causing a net negative forcing of 8.5 W/m². Although all these clouds have an overall small cooling effect of 1.1 W/m² on the annual radiative energy budget, the significant differences of the diurnal and seasonal distributions for different type clouds can lead to distinct radiative forcing. Especially the LW warming effect induced by the exclusive ice clouds in the cold season may have an important contribution to the rapid winter warming over the semi-arid regions.

Diurnal cycle of cirrus cloud (DCCci) can affect cloud interactions with both the solar radiation and terrestrial radiation. However, evaluation on how DCCci influences the radiative effects is relatively few, especially in the semi-arid region, which is one of the most sensitive areas in response to global climate change. In this study, we investigate the physical properties and associated radiative effects of DCCci using two-year, high-resolution Ka-band Zenith Radar (KZAR) observations at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) site in Northwest China. We find that cirrus clouds occur more frequently during nighttime than during daytime, with a maximum occurrence frequency of 48% at midnight and a minimum occurrence frequency of 35% at midday, which drastically influences the diurnal variation of cirrus radiative effects. The diurnal variation of cirrus cloud radiative forcing (CRF) is calculated using the Fu-Liou model, involving 96 cirrus profiles per day to represent DCCci accurately. In each season, the diurnal cycle amplitude of CRF is more than 40 W/m² for shortwave (SW), and less than 16 W/m² for longwave (LW). During daytime, the net CRF at the top of the atmosphere (TOA) ranges from −16 to 30 W/m²; during nighttime, it varies from 30 to 33 W/m². Based on the accurate simulation of CRF with DCCci, we then calculate the daily-mean CRF and compare it with the simulated results derived from different averaged cirrus profiles that do not fully represent the diurnal cycle, to evaluate the radiative biases induced by not having accurate DCCci. We find that the absolute bias of net CRF at the TOA can reach 11 W/m² at the SACOL site when only one daily averaged cirrus property profile is used in the simulation, demonstrating that neglecting DCCci in the model will result in significant bias of cirrus net CRF. This evaluation suggests that DCCci need to be well considered in climate models to reduce the uncertainty of cirrus radiative effects.

Dust events not only cause local ecosystem degradation and desertification, but also have profound impacts on regional and global climate system, as well as air quality and human health. Dust events in Xinjiang Basin, as the important dust source of Eastern Asia, have undergone a significant change under the global warming background and may be in a new active period after 2000, which is worthy of study. This study provides the temporal and spatial variations of dust events in the Xinjiang Basin based on surface meteorological station observation data during 1960–2015. The results show that Southern Xinjiang is the main dust occurrence region where dust events are significantly more than that in the Northern Xinjiang, and each year more than 73% of dust events occurred in spring and summer. The dust index (DI), which is defined to represent the large-scale variation of dust event, shows a significant downward trend during the past 56 years with a linear decreasing rate −8.2 years⁻¹ in Southern Xinjiang. The DI is positively correlated to surface wind speed with a mean correlation coefficient of 0.79. The declining trend of surface wind speed could explain dust events variation during 1960–2000. But in the new active period after 2000, the increase of DI is not consistent with the rising wind speed with the correlation coefficient decreasing to 0.34. It is found that, compared with 1960–1999, the average annual precipitation and frequency increased by 17.4 and 13% during 2000–2015, respectively, and the NDVI also increased at the same time, which indicates that the surface condition changes induced by the increase of precipitation might suppress the occurrence of dust. Moreover, the analysis of high-altitude wind field shows that the variation of the East Asian general circulation’s intensity, dominating the upper-level wind fields in the Xinjiang basin, will change the surface wind speed and precipitation, and further affect the occurrence of dust events.