Anthropogenic heat generated by human activity contributes to urban and regional climate warming. Due to the resolution and accuracy of existing anthropogenic heat data, it is difficult to analyze and simulate the corresponding effects. This study exploited a new method to estimate high spatial and temporal resolutions of anthropogenic heat based on long-term data of energy consumption and the US Air Force Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) data from 1992 to 2010 across China. Our results showed that, throughout the entire study period, there are apparent increasing trends in anthropogenic heat in three major metropoli, i.e., the Beijing-Tianjin region, the Yangzi River delta and the Pearl River delta. The annual mean anthropogenic heat fluxes for Beijing, Shanghai and Guangzhou in 2010 were 17 Wm⁻², 19 and 7.8 Wm⁻², respectively. Comparisons with previous studies indicate that DMSP-OLS data could provide a better spatial proxy for estimating anthropogenic heat than population density and our analysis shows better performance at large scales for estimation of anthropogenic heat.

Geoengineering has been proposed to stabilize global temperature, but its impacts on crop production and stability are not fully understood. A few case studies suggest that certain crops are likely to benefit from solar dimming geoengineering, yet we show that geoengineering is projected to have detrimental effects for groundnut. Using an ensemble of crop‐climate model simulations, we illustrate that groundnut yields in India undergo a statistically significant decrease of up to 20% as a result of solar dimming geoengineering relative to RCP4.5. It is somewhat reassuring, however, to find that after a sustained period of 50 years of geoengineering crop yields return to the nongeoengineered values within a few years once the intervention is ceased.

The radiance lights data in 2006 from the National Oceanic and Atmospheric Administration Air Force Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) and authoritative energy data distributed by the United State Energy Information Administration were applied to estimate the global distribution of anthropogenic heat flux. A strong linear relationship was found to exist between the anthropogenic heat flux and the DMSP/OLS radiance data. On a global scale, the average value of anthropogenic heat flux is approximately 0.03 W m^–2 and 0.10 W m^–2 for global land area. The results indicate that global anthropogenic heat flux was geographically concentrated and distributed, fundamentally correlating to the economical activities. The anthropogenic heat flux concentrated in the economically developed areas including East Asia, Europe, and eastern North America. The anthropogenic heat flux in the concentrated regions, including the northeastern United States, Central Europe, United Kingdom, Japan, India, and East and South China is much larger than global average level, reaching a large enough value that could affect regional climate. In the center of the concentrated area, the anthropogenic heat flux density may exceed 100 W m^–2, according to the results of the model. In developing areas, including South America, Central and North China, India, East Europe, and Middle East, the anthropogenic heat flux can reach a level of more than 10 W m^–2; however, the anthropogenic heat flux in a vast area, including Africa, Central and North Asia, and South America, is low. With the development of global economy and urban agglomerations, the effect on climate of anthropogenic heat is essential for the research of climate change.

This study analyses the influence of the Asian summer monsoon on volcanic aerosol transport. Realistic, altitude-resolved SO₂emissions of a middle-latitude volcanic eruption (Sarychev 2009) and a tropical volcanic eruption (Nabro 2011) were retrieved and used to initialize the simulations of the long-range transport and dispersion of the sulfate aerosol plumes. The barrier effect of the Asian summer monsoon anticyclone (ASMA) isolated the Sarychev eruption plume outside of the ASMA but constrained the Nabro eruption plume inside of the ASMA, which is most evident in the UTLS region between isotropic surfaces of 360–420 K. Meanwhile, the ASMA could transport a fraction of the plume outside of ASMA quasi-horizontally to the tropical tropopause layer along the southeastern periphery of the anticyclonic circulation, and lift the volcanic plume inside the ASMA anticyclonically across the tropopause with an ascent rate of approximately 0.8 K/day. By enhancing the meridional transport in the UTLS region and lifting volcanic aerosols across the tropopause, the ASMA significantly expanded the potential effects of volcanic eruptions.

Precipitable water vapor (PWV) is one of the most dynamic components of the atmosphere, playing a critical role in precipitation formation, the hydrological cycle, and climate change. This study used SuomiNet Global Positioning System (GPS) data from April 2021 to June 2023 in the United States to comprehensively evaluate 3 and 6 h Global Forecast System (GFS) PWV products (i.e., PWV_3h and PWV_6h). There was high consistency between the GFS PWV and GPS PWV data, with correlation coefficients (Rs) higher than 0.98 and a root mean square error (RMSE) of about 0.23 cm. The PWV_3h product performed slightly better than PWV_6h. PWV tended to be underestimated when PWV > 4 cm, and the degree of underestimation increased with increasing water vapor value. The RMSE showed obvious seasonal and diurnal variations, with the RMSE value in summer (i.e., 0.280 cm) considerably higher than in winter (i.e., 0.158 cm), and nighttime were RMSEs higher than daytime RMSEs. Clear-sky conditions showed smaller RMSEs, while cloudy-sky conditions exhibited a smaller range of monthly RMSEs and higher Rs. PWV demonstrated a clear spatial pattern, with both Rs and RMSEs decreasing with increasing elevation and latitude. Based on these temporal and spatial patterns, Back Propagation neural network and random forest (RF) models were employed, using PWV, Julian day, and geographic information (i.e., latitude, longitude, and elevation) as input data to correct the GFS PWV products. The results indicated that the RF model was more advantageous for water vapor correction, improving overall accuracy by 12.08%. In addition, the accuracy of GFS PWV forecasts during hurricane weather was also evaluated. In this extreme weather, the RMSE of the GFS PWV forecast increased comparably to normal weather, but it remained less than 0.4 cm in most cases.