We analyse historical (1850–2014) atmospheric hydroxyl (OH) and methane lifetime data from Coupled Model Intercomparison Project Phase 6 (CMIP6)/Aerosols and Chemistry Model Intercomparison Project (AerChemMIP) simulations. Tropospheric OH changed little from 1850 up to around 1980, then increased by around 9 % up to 2014, with an associated reduction in methane lifetime. The model-derived OH trends from 1980 to 2005 are broadly consistent with trends estimated by several studies that infer OH from inversions of methyl chloroform and associated measurements; most inversion studies indicate decreases in OH since 2005. However, the model results fall within observational uncertainty ranges. The upward trend in modelled OH since 1980 was mainly driven by changes in anthropogenic near-term climate forcer emissions (increases in anthropogenic nitrogen oxides and decreases in CO). Increases in halocarbon emissions since 1950 have made a small contribution to the increase in OH, whilst increases in aerosol-related emissions have slightly reduced OH. Halocarbon emissions have dramatically reduced the stratospheric methane lifetime by about 15 %–40 %; most previous studies assumed a fixed stratospheric lifetime. Whilst the main driver of atmospheric methane increases since 1850 is emissions of methane itself, increased ozone precursor emissions have significantly modulated (in general reduced) methane trends. Halocarbon and aerosol emissions are found to have relatively small contributions to methane trends. These experiments do not isolate the effects of climate change on OH and methane evolution; however, we calculate residual terms that are due to the combined effects of climate change and non-linear interactions between drivers. These residual terms indicate that non-linear interactions are important and differ between the two methodologies we use for quantifying OH and methane drivers. All these factors need to be considered in order to fully explain OH and methane trends since 1850; these factors will also be important for future trends.

Mineral dust impacts key processes in the Earth system, including the radiation budget, clouds, and nutrient cycles. We evaluate dust aerosols in 16 models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) against multiple reanalyses and observations. We note that both the reanalyses and observations used here have their limitations and particularly that dust emission and deposition in reanalyses are poorly constrained. Most models, and particularly the multi-model ensemble mean (MEM), capture the spatial patterns and seasonal cycles of global dust processes well. However, large uncertainties and inter-model diversity are found. For example, global dust emissions, primarily driven by model-simulated surface winds, vary by a factor of 5 across models, while the MEM estimate is double the amount in reanalyses. The ranges of CMIP6 model-simulated global dust emission, deposition, burden, and optical depth (DOD) are larger than previous generations of models. Models present considerable disagreement in dust seasonal cycles over North China and North America. Here, DOD values are overestimated by most CMIP6 models, with the MEM estimate 1.2–1.7 times larger compared to satellite and reanalysis datasets. Such overestimates can reach up to a factor of 5 in individual models. Models also fail to reproduce some key features of the regional dust distribution, such as dust accumulation along the southern edge of the Himalayas. Overall, there are still large uncertainties in CMIP6 models' simulated dust processes, which feature inconsistent biases throughout the dust life cycle between models, particularly in the relationship connecting dust mass to DOD. Our results imply that modelled dust processes are becoming more uncertain as models become more sophisticated. More detailed output and dust size-resolved variables in particular, relating to the dust cycle in future intercomparison projects, are needed to enable better constraints of global dust cycles and enable the potential identification of observationally constrained links between dust cycles and optical properties.

The role of anthropogenic aerosols in future projections (up to 2100) of summertime precipitation and precipitation extremes over the Asian monsoon region is investigated, by comparing two sets of the Community Earth System Model (CESM1) large ensemble simulations under the Representative Concentration Pathway 8.5 scenario (RCP8.5) and the corresponding scenario with aerosol fixed at 2005 levels (RCP8.5_FixA). The model is verified to be performing well in capturing present-day (1986–2005) climate and precipitation extremes. Our results suggest that the Asian monsoon region would become progressively warmer and wetter in the future under RCP8.5, while precipitation extremes will be significantly aggravated due to anthropogenic aerosol mitigation, particularly over East Asia. Specifically, aerosol reductions are found to shift the distribution of precipitation mean and extremes to larger values. For example, aerosol reductions would result in an increased likelihood of extreme precipitation (e.g. the maximum consecutive 5-day precipitation amount) and related disasters. Sensitivities of changes in precipitation mean and extremes to local warming from aerosol reductions are much larger than that from greenhouse gas increases. This is particularly important over East Asia in accordance with larger magnitudes of aerosol reductions compared to South Asia. Finally, by investigating the response of the climate system to aerosol changes, our findings demonstrate that aerosol induced precipitation changes would be dominated by aerosol–radiation–cloud forcing over northern East Asia and aerosol forcing induced large-scale circulation anomalies over southern East and South Asia.

Anthropogenic aerosol emissions are expected to change rapidly over the coming decades, driving strong, spatially complex trends in temperature, hydroclimate, and extreme events both near and far from emission sources. Under-resourced, highly populated regions often bear the brunt of aerosols’ climate and air quality effects, amplifying risk through heightened exposure and vulnerability. However, many policy-facing evaluations of near-term climate risk, including those in the latest Intergovernmental Panel on Climate Change assessment report, underrepresent aerosols’ complex and regionally diverse climate effects, reducing them to a globally averaged offset to greenhouse gas warming. We argue that this constitutes a major missing element in society’s ability to prepare for future climate change. We outline a pathway towards progress and call for greater interaction between the aerosol research, impact modeling, scenario development, and risk assessment communities.

Using the Community Earth System Model Large Ensemble experiments, we investigate future heatwaves under the Representative Concentration Pathway 8.5 scenario, separating the relative roles of greenhouse gas increases and aerosol reductions. We show that there will be more severe heatwaves (in terms of intensity, duration, and frequency) due to mean warming, with minor contributions from future temperature variability changes. While these changes come primarily from greenhouse gas increases, aerosol reductions contribute significantly over the Northern Hemisphere. Furthermore, per degree of global warming, aerosol reductions induce a significantly stronger response in heatwave metrics relative to greenhouse gas increases. The stronger response to aerosols is associated with aerosol‐cloud interactions, which are still poorly understood and constrained in current climate models. This suggests that there may exist large uncertainties in future heatwave projections, highlighting the critical significance of reducing uncertainties in aerosol‐cloud interactions for reliable projection of climate extremes and effective risk management.

An objectively trained model for tropical cyclone intensity estimation from routine satellite infrared images over the Northwestern Pacific Ocean is presented in this paper. The intensity is correlated to some critical signals extracted from the satellite infrared images, by training the 325 tropical cyclone cases from 1996 to 2007 typhoon seasons. To begin with, deviation angles and radial profiles of infrared images are calculated to extract as much potential predicators for intensity as possible. These predicators are examined strictly and included into (or excluded from) the initial predicator pool for regression manually. Then, the “thinned” potential predicators are regressed to the intensity by performing a stepwise regression procedure, according to their accumulated variance contribution rates to the model. Finally, the regressed model is verified using 52 cases from 2008 to 2009 typhoon seasons. The R² and Root Mean Square Error are 0.77 and 12.01 knot in the independent validation tests, respectively. Analysis results demonstrate that this model performs well for strong typhoons, but produces relatively large errors for weak tropical cyclones.