Accurate estimation of the spatiotemporal variations of solar radiation is crucial for assessing and utilizing solar energy, one of the fastest‐growing and most important clean and renewable resources. Based on observations from 2,379 meteorological stations along with scare solar radiation observations, the random forest (RF) model is employed to construct a high‐density network of daily global solar radiation (DGSR) and its spatiotemporal variations in China. The RF‐estimated DGSR is in good agreement with site observations across China, with an overall correlation coefficient (R) of 0.95, root‐mean‐square error of 2.34 MJ/m², and mean bias of −0.04 MJ/m². The geographical distributions of R values, root‐mean‐square error, and mean bias values indicate that the RF model has high predictive performance in estimating DGSR under different climatic and geographic conditions across China. The RF model further reveals that daily sunshine duration, daily maximum land surface temperature, and day of year play dominant roles in determining DGSR across China. In addition, compared with other models, the RF model exhibits a more accurate estimation performance for DGSR. Using the RF model framework at the national scale allows the establishment of a high‐resolution DGSR network, which can not only be used to effectively evaluate the long‐term change in solar radiation but also serve as a potential resource to rationally and continually utilize solar energy.

Aerosol pollution elicits considerable public concern due to the adverse influence on air quality, climate change, and human health. Outside of emissions, haze formation is closely related to meteorological conditions, especially relative humidity (RH). Partly due to insufficient investigations on the aerosol hygroscopicity, the accuracy of pollution prediction in Central China is limited. In this study, taking Wuhan as a sample city, we investigated the response of aerosol pollution to RH during wintertime based on in-situ measurements. The results show that, aerosol pollution in Wuhan is dominated by PM_2.5 (aerodynamic particle size not larger than 2.5 μm) on wet days (RH ≥ 60%), with the averaged mass fraction of 0.62 for PM₁₀. Based on the RH dependence of aerosol light scattering (f (RH)), aerosol hygroscopicity was evaluated and shows the high dependence on the particle size distribution and chemical compositions. f (RH = 80%) in Wuhan was 2.18 (±0.73), which is comparable to that measured in the Pearl River Delta and Yangtze River Delta regions for urban aerosols, and generally greater than values in Beijing. Ammonium (NH₄⁺), sulfate (SO₄²⁻), and nitrate (NO₃⁻) were enhanced by approximately 2.5-, 2-, and 1.5-fold respectively under wet conditions, and the ammonia-rich conditions in wintertime efficiently promoted the formation of SO₄²⁻ and NO₃⁻, especially at high RH. These secondary ions play an important role in aggravating the pollution level and aerosol light scattering. This study has important implications for understanding the roles of RH in aerosol pollution aggravation over Central China, and the fitted equation between f (RH) and RH may be helpful for pollution forecasting in this region.

The accuracy of the Atmospheric Infrared Sounder (AIRS) water vapor product in China is as yet unknown due to the lack of collocated in situ sounding observations. Based on high‐resolution soundings at 1400 Beijing time from 113 radiosonde sites across China, along with the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Global Positioning System (GPS) data sets, a preliminary assessment has been conducted of AIRS water vapor mixing ratio (q) and precipitable water vapor (PWV) products in June 2013 and June 2014. Comparison between AIRS and radiosonde data suggests that the correlation coefficient (R) and mean bias of these two q products in China exhibit a distinct geographical dependence (with the highest R values in northwest China). This suggests that the AIRS q product tends to be underestimated in southeast China where cloud cover prevails, but overestimated in northwest China where cloud cover is sparse. With regard to the height‐resolved distribution, the q products from both AIRS and radiosondes tend to decrease with increasing altitude, irrespective of the particular region. The spatial distribution of AIRS PWV is consistent with that from radiosonde‐derived PWV, except in south China where the AIRS PWV data set is considerably underestimated. The accuracy of the AIRS water vapor product tends to be impaired under highly cloudy conditions, corroborating the notion of clouds affecting the retrieval of AIRS PWV. Our findings highlight the importance of afternoon sounding measurements in validating AIRS data and call for the improved understanding of the role of water vapor in the context of global climate change.

A long-term time series of ice sheet surface elevation change (SEC) is an essential parameter to assess the impact of climate change. In this study, we used an updated plane-fitting least-squares regression strategy to generate a 30-year surface elevation time series for the Greenland Ice Sheet (GrIS) at monthly temporal resolution and 5×5 km grid spatial resolution using ERS-1 (European Remote Sensing), ERS-2, Envisat, and CryoSat-2 satellite radar altimeter observations obtained between August 1991 and December 2020. The ingenious corrections for intermission bias were applied using an updated plane-fitting least-squares regression strategy. Empirical orthogonal function (EOF) reconstruction was used to supplement the sparse monthly gridded data attributable to poor observations in the early years. Validation using both airborne laser altimeter observations and the European Space Agency GrIS Climate Change Initiative (CCI) product indicated that our merged surface elevation time series is reliable. The accuracy and dispersion of errors of SECs of our results were 19.3 % and 8.9 % higher, respectively, than those of CCI SECs and even 30.9 % and 19.0 % higher, respectively, in periods from 2006–2010 to 2010–2014. Further analysis showed that our merged time series could provide detailed insight into GrIS SEC on multiple temporal (up to 30 years) and spatial scales, thereby providing an opportunity to explore potential associations between ice sheet change and climatic forcing. The merged surface elevation time series data are available at https://doi.org/10.11888/Glacio.tpdc.271658 (Zhang et al., 2021).