Bingqing Zhang

Aerosol radiative forcing represents the largest source of uncertainty for calculating present-day climate sensitivity. This uncertainty has been underestimated in climate models, as they have not adequately accounted for uncertainties in the emissions of aerosols prior to the satellite era. This is especially the case for natural aerosols, such as mineral dust and aerosols from biomass burning. In this paper, we take a step towards reducing this uncertainty by providing a roadmap for improved integration of proxy-based observational constraints on past aerosol variability with model simulations. We first review existing literature on the importance of natural aerosol radiative forcing during historical and paleo-time periods, with a focus on the Last Glacial Maximum and the preindustrial era (ca. 1850) to the present. We then compile existing paleo-data archives and describe their ability to constrain emissions of aerosols, highlighting existing gaps. Model-data combinations are available to estimate radiative effects for dust and volcanoes in several time periods, and for biomass burning for preindustrial to present day, but are not available for other aerosols. Modeled feedbacks of aerosols to climate (W/m²/°C) are 10–100⨉ lower than model-data derived estimates for dust, casting doubt on our ability to successfully model feedbacks in the current generation of Earth system models. Finally, we propose a framework for integrating paleo-archives with models through harmonized datasets, proxy–model translation metrics, and regularized inversion approaches, and identify priority measurements needed to reduce uncertainty in both aerosol loading and radiative-effect efficiency across climate states.

Aerosol radiative forcing represents the largest source of uncertainty for calculating present-day climate sensitivity. This uncertainty has been underestimated in climate models, as they have not adequately accounted for uncertainties in the emissions of aerosols prior to the satellite era. This is especially the case for natural aerosols, such as mineral dust and aerosols from biomass burning. In this paper, we take a step towards reducing this uncertainty by providing a roadmap for improved integration of proxy-based observational constraints on past aerosol variability with model simulations. We first review existing literature on the importance of natural aerosol radiative forcing during historical and paleo-time periods, with a focus on the Last Glacial Maximum and the preindustrial era (ca. 1850) to the present. We then compile existing paleo-data archives and describe their ability to constrain emissions of aerosols, highlighting existing gaps. Model-data combinations are available to estimate radiative effects for dust and volcanoes in several time periods, and for biomass burning for preindustrial to present day, but are not available for other aerosols. Modeled feedbacks of aerosols to climate (W/m²/°C) are 10–100⨉ lower than model-data derived estimates for dust, casting doubt on our ability to successfully model feedbacks in the current generation of Earth system models. Finally, we propose a framework for integrating paleo-archives with models through harmonized datasets, proxy–model translation metrics, and regularized inversion approaches, and identify priority measurements needed to reduce uncertainty in both aerosol loading and radiative-effect efficiency across climate states.

Dansgaard-Oeschger (D-O) events refer to a series of rapid climate fluctuations in the Northern Hemisphere during the last glacial period. These fluctuations are recorded in Greenland ice cores by large, abrupt changes in both water isotopes and aerosol deposition, but the processes driving these changes remain unclear. Here we present new high-resolution, multi-proxy measurements from the Greenland GISP2 ice core spanning eight D-O events. In addition to multiple dust and sea-salt tracers, we provide the first high-resolution biomass burning proxy records from refractory black carbon (rBC) concentration and particle size during these transitions. Bayesian ramp fitting analysis shows that water-isotope and aerosol changes generally occurred within a narrow temporal window with no robust and reproducible sequence. Using a simplified aerosol transport and deposition framework, we find that changes in atmospheric transport and removal processes can explain much of the variability shared among aerosols from distinct source regions. Residual variability shows stronger relationships within than across source classes and, particularly for dust, differs between cold and warm climate states. These results suggest that rapid large-scale atmospheric and hydrological reorganization drove the common aerosol response, while changes in individual source systems contributed to the remaining variability. These new multi-proxy records provide valuable constraints for evaluating climate-model representations of abrupt climate transitions.

Aerosol radiative forcing represents the largest source of uncertainty for calculating present-day climate sensitivity. This uncertainty has been underestimated in climate models, as they have not adequately accounted for uncertainties in the emissions of aerosols prior to the satellite era. This is especially the case for natural aerosols, such as mineral dust and aerosols from biomass burning. In this paper, we take a step towards reducing this uncertainty by providing a roadmap for improved integration of proxy-based observational constraints on past aerosol variability with model simulations. We first review existing literature on the importance of natural aerosol radiative forcing during historical and paleo-time periods, with a focus on the Last Glacial Maximum and the preindustrial era (ca. 1850) to the present. We then compile existing paleo-data archives and describe their ability to constrain emissions of aerosols, highlighting existing gaps. Model-data combinations are available to estimate radiative effects for dust and volcanoes in several time periods, and for biomass burning for preindustrial to present day, but are not available for other aerosols. Modeled feedbacks of aerosols to climate (W/m2/°C) are 10–100⨉ lower than model-data derived estimates for dust, casting doubt on our ability to successfully model feedbacks in the current generation of Earth system models. Finally, we propose a framework for integrating paleo-archives with models through harmonized datasets, proxy–model translation metrics, and regularized inversion approaches, and identify priority measurements needed to reduce uncertainty in both aerosol loading and radiative-effect efficiency across climate states.

The temperature sensitivity of fine particulate matter (PM2.5) critically influences air quality and human health under a warming climate, yet models struggle to accurately reproduce observed sensitivities. This study improves the representation of PM2.5-temperature relationships in the chemical transport model GEOS-Chem through targeted improvements and analyses of the underlying drivers based on simulations across the contiguous US (2000–2022). Our simulations reveal that chemical production processes, particularly isoprene secondary organic aerosol (SOA) and sulfate formation, determine the magnitude of PM2.5 sensitivity in the eastern US. In the western US, primary emissions drive the increasing PM2.5-temperature sensitivity. Transport processes contribute to interannual variability in PM2.5 sensitivity across all regions. We quantified the contributions from individual temperature-sensitive processes for the first time. Sulfate concentration plays a pivotal role in modulating the sensitivity of isoprene SOA due to its direct influence on isoprene SOA formation. Furthermore, the increased SO2 emissions on warm days dictates both the magnitude and variability of sulfate sensitivity in the eastern and central US. In the western US, however, sulfate sensitivity is primarily controlled by the temperature response of hydroxyl radicals (⚫OH). These findings highlight the impact of anthropogenic emission reductions on declining PM2.5–temperature sensitivity in the eastern US, improve our understanding of climate-driven air quality changes, and underscore the importance of accurately representing temperature-dependent processes in future air quality projections.

The current study focuses on the interconnection between wildfires and the atmosphere and more precisely on the radiative effect of wildfire emissions on a global scale. Specifically, the effective radiative forcing (ERF) of present-day wildfire emissions relative to pre-industrial conditions is determined. Atmosphere-only simulations were performed using EC-Earth3, and three wildfire-emission datasets were introduced: BB4CMIP6 and two reconstructed alternatives, one derived from the BB4CMIP6 dataset and one derived from the fire model LPJ-LMfire. Our simulations indicate that the main drivers of ERF are the changes in cloud cover and surface albedo caused by the present-day wildfire emissions.

Climate change poses direct and indirect threats to public health, including exacerbating air pollution. However, the influence of rising temperature on air quality remains highly uncertain in the United States, particularly under rapid reduction in anthropogenic emissions. Here, we examined the sensitivity of surface-level fine particulate matter (PM_2.5) and ozone (O₃) to summer temperature anomalies in the contiguous US as well as their decadal changes using high-resolution datasets generated by machine learning. Our findings demonstrate that in the eastern US, stringent emission control strategies have significantly reduced the positive responses of PM_2.5 and O₃ to summer temperature, thereby lowering the population exposure associated with warming-induced air quality deterioration. In contrast, PM_2.5 in the western US became more sensitive to temperature, highlighting the urgent need to manage and mitigate the impact of worsening wildfires. Our results have important implications for air quality management and risk assessments of future climate change.

Simulation of secondary organic aerosol (SOA) in models has been an uncertain component in determining the impacts of atmospheric aerosols on air quality and climate. Recent studies have shown that reactive chlorine can rapidly oxidize volatile organic compounds (VOCs), trigger SOA formation, and alter other oxidants, thus having a potentially significant effect on SOA, which has not been thoroughly investigated at the global scale. Here, we developed a chlorine-SOA simulation within a global chemical transport model along with updated anthropogenic continental chlorine emissions. Our simulations demonstrate that chlorine chemistry increases the annual mean boundary layer SOA by 5-12% over most continents while decreasing SOA by 5-11% over northern Atlantic and Pacific oceans, which are in the right direction to narrow existing discrepancies between models and observations. Notably, sensitivity simulations in China with observed high chlorine levels capture the temporal variations of both observed fine Cl^- and organic aerosol, showing an increase in SOA by more than 100%. Our study also reveals that polluted regions, which have ample emissions of both chlorine species and VOCs, exhibit potential chlorinated SOA, which are commonly toxic, contributing up to 15% to total SOA.

Biomass burning plays an important role in climate-forcing and atmospheric chemistry. The drivers of fire activity over the past two centuries, however, are hotly debated and fueled by poor constraints on the magnitude and trends of preindustrial fire regimes. As a powerful tracer of biomass burning, reconstructions of paleoatmospheric carbon monoxide (CO) can provide valuable information on the evolution of fire activity across the preindustrial to industrial transition. Here too, however, significant disagreements between existing CO records currently allow for opposing fire histories. In this study, we reconstruct a continuous record of Antarctic ice core CO between 1821 and 1995 CE to overlap with direct atmospheric observations. Our record indicates that the Southern Hemisphere CO burden ([CO]) increased by 50% from a preindustrial mixing ratio of ca. 35 ppb to ca. 53 ppb by 1995 CE with more variability than allowed for by state-of-the-art chemistry-climate models, suggesting that historic CO dynamics have been not fully accounted for. Using a 6-troposphere box model, a 40 to 50% decrease in Southern Hemisphere biomass-burning emissions, coincident with unprecedented rates of early 20th century anthropogenic land-use change, is identified as a strong candidate for this mismatch.

Estimating fire emissions prior to the satellite era is challenging because observations are limited, leading to large uncertainties in the calculated aerosol climate forcing following the preindustrial era. This challenge further limits the ability of climate models to accurately project future climate change. Here, we reconstruct a gridded dataset of global biomass burning emissions from 1750 to 2010 using inverse analysis that leveraged a global array of 31 ice core records of black carbon deposition fluxes, two different historical emission inventories as a priori estimates, and emission-deposition sensitivities simulated by the atmospheric chemical transport model GEOS-Chem. The reconstructed emissions exhibit greater temporal variabilities which are more consistent with paleoclimate proxies. Our ice core constrained emissions reduced the uncertainties in simulated cloud condensation nuclei and aerosol radiative forcing associated with the discrepancy in preindustrial biomass burning emissions. The derived emissions can also be used in studies of ocean and terrestrial biogeochemistry.