Climate models have long-standing difficulties simulating the South Pacific Convergence Zone (SPCZ) and its variability. For example, the default Zhang-McFarlane (ZM) convection scheme in the Community Atmosphere Model version 5 (CAM5) produces too much light precipitation and too little heavy precipitation in the SPCZ, with this bias toward light precipitation even more pronounced in the SPCZ than in the tropics as a whole. Here, we show that implementing a recently developed convection scheme in the CAM5 yields significant improvements in the simulated SPCZ during austral summer and discuss the reasons behind these improvements. In addition to intensifying both mean rainfall and its variability in the SPCZ, the new scheme produces a larger heavy rainfall fraction that is more consistent with observations and state-of-the-art reanalyses. This shift toward heavier, more variable rainfall increases both the magnitude and altitude of diabatic heating associated with convective precipitation, intensifying lower tropospheric convergence and increasing the influence of convection on the upper-level circulation. Increased diabatic production of potential vorticity in the upper troposphere intensifies the distortion effect exerted by convection on transient Rossby waves that pass through the SPCZ. Weaker distortion effects in simulations using the ZM scheme allow waves to propagate continuously through the region rather than dissipating locally, further reducing updrafts and weakening convection in the SPCZ. Our results outline a dynamical framework for evaluating model representations of tropical–extratropical interactions within the SPCZ and clarify why convective parameterizations that produce ‘top-heavy’ profiles of deep convective heating better represent the SPCZ and its variability.

Circulation patterns linked to the East Asian winter monsoon (EAWM) affect precipitation, surface temperature, and air quality extremes over East Asia. These circulation patterns can in turn be influenced by aerosol radiative and microphysical effects through diabatic heating and its impacts on atmospheric vorticity. Using global model simulations, we investigate the effects of anthropogenic aerosol emissions and concentration changes on the intensity and variability of the EAWM. Comparison with reanalysis products indicates that the model captures the mean state of the EAWM well. The experiments indicate that anthropogenic aerosol emissions strengthen the Siberian High but weaken the East Asian jet stream, making the land areas of East Asia colder, drier, and snowier. Aerosols reduce mean surface air temperatures by approximately 1.5°C, comparable to about half of the difference between strong and weak EAWM episodes in the control simulation. The mechanisms behind these changes are evaluated by analyzing differences in the potential vorticity budget. Anthropogenic aerosol effects on diabatic heating strengthen anomalous subsidence over southern East Asia, establishing an anticyclonic circulation anomaly that suppresses deep convection and precipitation. Aerosol effects on cloud cover and cloud longwave radiative heating weaken stability over the eastern flank of the Tibetan Plateau, intensifying upslope flow along the western side of the anticyclone. Both circulation anomalies contribute to reducing surface air temperatures through regional impacts on thermal advection and the atmospheric radiative balance.

Machine learning has been widely applied in numerical weather prediction, but the incorporation of new observational sites into models trained on stations with long historical records remains a challenge. Here we propose a post-processing framework consisting of three machine learning methods: station clustering with K-means, temperature prediction based on decision trees, and transfer learning for newly-built stations. We apply this framework to post-processing forecasts of surface air temperature at 301 weather stations in China. The results show significant reductions (as much as 39.4%~20.0%) in the root-mean-square error of operational forecasts at lead times as long as 7 days. Moreover, the use of transfer learning to incorporate new stations improves forecasts at the new site by 36.4% after only one year of data collection. These results demonstrate the potential for clustering and transfer learning to boost existing applications of machine learning techniques in weather forecasting.

The Asian Tropopause Aerosol Layer (ATAL) has emerged in recent decades to play a prominent role in the upper troposphere and lower stratosphere above the Asian monsoon. Although ATAL effects on surface and top-of-atmosphere radiation budgets are well established, the magnitude and variability of ATAL effects on radiative transfer within the tropopause layer remain poorly constrained. Here, we investigate the impacts of various aerosol types and layer structures on clear-sky shortwave radiative heating in the Asian monsoon tropopause layer using reanalysis products and offline radiative transfer simulations. ATAL effects on shortwave radiative heating based on the MERRA-2 aerosol reanalysis are on the order of 10% of mean clear-sky radiative heating within the tropopause layer, although discrepancies among recent reanalysis and forecast products suggest that this ratio could be as small as ~5% or as large as ~25%. Uncertainties in surface and top-of-atmosphere flux effects are also large, with values spanning one order of magnitude at the top-of-atmosphere. ATAL effects on radiative heating peak between 150 hPa and 80 hPa (360 K–400 K potential temperature) along the southern flank of the anticyclone. Clear-sky and all-sky shortwave heating are at local minima in this vertical range, which is situated between the positive influences of monsoon-enhanced water vapor and the negative influence of the ‘ozone valley’ in the monsoon lower stratosphere. ATAL effects also extend further toward the west, where diabatic vertical velocities remain upward despite descent in pressure coordinates.