No abstract

Homogeneity of climate data is the basis for quantitative assessment of climate change. By using the MASH method, this work examined and corrected the homogeneity of the daily data including average, minimum, and maximum temperature and precipitation during 1978-2015 from 404/397 national meteorological stations in North China. Based on the meteorological station metadata, the results are analyzed and the differences before and after homogenization are compared. e results show that breakpoints are present pervasively in these temperature data. Most of them appeared after 2000. e stations with a host of breakpoints are mainly located in Beijing, Tianjin, and Hebei Province, where meteorological stations are densely distributed. e numbers of breakpoints in the daily precipitation series in North China during 1978-2015 also culminated in 2000. e reason for these breakpoints, called inhomogeneity, may be the large-scale replacement of meteorological instruments after 2000. After correction by the MASH method, the annual average temperature and minimum temperature decrease by 0.04°C and 0.06°C, respectively, while the maximum temperature increases by 0.01°C. e annual precipitation declines by 0.96 mm. e overall trends of temperature change before and after the correction are largely consistent, while the homogeneity of individual stations is significantly improved. Besides, due to the correction, the majority series of the precipitation are reduced and the correction amplitude is relatively large. During 1978-2015, the temperature in North China shows a rise trend, while the precipitation tends to decrease.

The outdoor events of the 2022 Winter Olympics and Paralympics will be held in the mountain areas of Beijing–Zhangjiakou, North China, where there is a complete reliance on artificial snow production owing to the dry and cold weather conditions. To assess how favorable the meteorological conditions are to snowmaking at the mountain venues, we reconstructed the daily wet-bulb temperature by adopting the thin-plate smoothing spline function method, and then assessed the potential number of snowmaking days at eight weather stations (928–2098 m a.s.l.) from October to the next April (i.e., the ski season) during the period 1978–2017. Results showed that artificial snow production would have been possible on 121(±14) to 171(±12) days on average at the stations with the increases of altitude, and the number of days decreased at rates of 4.3–5.1 days per decade across four decades of the study period. The cause of the decrease was the warming trend, which affected the number of days in low-altitude sites simultaneously, but the reduction was delayed with increased elevation. At monthly scale, the number of snowmaking days was robust in wintertime but reduced in other months of the ski season, particularly in March in more recent sub-periods at high-altitude stations, which was determined by the increase in high values of daily mean wet-bulb temperature. Further improvements in assessing snowmaking conditions require detailed microclimatic studies to reduce the uncertainties caused by meteorological conditions, as well as combination with model-based methods to determine potential future changes.

Large-scale agricultural production in North China makes the study of precipitation in this area vital. The performance of the Integrated Merged Multisatellite Retrievals for the Global Precipitation Measurement (IMERG) and the Climate Prediction Center morphing technique (CMORPH) precipitation products for 2015 was evaluated against daily precipitation data from 404 rain gauges in North China. Relative errors, correlation coefficients, Pearson’s chi-squared test values, and root mean square errors, as well as the probability of detection (POD), false alarm ratio, and critical success index, were used to analyze the accuracy of both IMERG and CMORPH precipitation products on daily, monthly, and seasonal timescales. The probability density function (PDF) was also considered. Overall, both products overestimated ground precipitation, especially in summer. Positive correlation coefficients between satellite-derived and rain-gauge monthly precipitation data were higher over plains and coastal areas, compared with plateau regions. The PODs of both IMERG and CMORPH data were highest in summer. The PODs of IMERG data were much higher than for CMORPH data in autumn. The PODs over coastal regions, plains, and plateaus at lower latitudes also were considerably better than over inland and plateau areas at higher latitudes. The precipitation products performed best over coastal areas, plains, and areas with high rainfall. Both CMORPH and IMERG products were prone to identifying non-rainy days as rainy days. They also overestimated light (0.1-9.9 mm d-1) and moderate (10-24.9 mm d-1) precipitation events, although the IMERG product was more sensitive to precipitation. Accordingly, we find that both of these satellite-derived precipitation products require further modification to enable them to substitute for gauge precipitation data in North China.

An urban heat island (UHI) is a phenomenon whereby the temperature in an urban area is significantly warmer than it a rural area. To further advance the characterization and understanding of UHIs within urban areas, nighttime light measured by the Day/Night Band (DNB) onboard the Visible Infrared Imaging Radiometer Suite (VIIRS) and the land surface temperature (LST) data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) combined with principal component analysis (PCA) are used here. Beijing (highly developed) and Pyongyang (less developed) are selected as the two case studies. Linear correlation analysis is first used, with higher correlations being found between DNB and LST data at nighttime than between population and LST data for both cities, although none of the correlation coefficients are particularly high because of noise. Principal component analysis (PCA), a method that can remove random noise, is used to extract more useful information. Two types of PCA are conducted, focusing on spatial (S) and temporal (T) patterns. The results of the S-mode PCA reveal that the typical temporal variation is a seasonal cycle for both LST and DNB data in Beijing and Pyongyang. Furthermore, there are monthly cycles for DNB data related to the moon phase in two cities. The T-mode PCA results show important spatial information, while the spatial pattern of the first mode explains over 50% of the variation. This study is among the first to demonstrate the advantages of using urban light to study the spatial variation of urban heat, especially for nighttime urban temperatures measured from space, at the street and neighborhood scales.