Global-scale constraints on light-absorbing anthropogenic iron oxide aerosols Lamb, K.D., et al. (2021), Global-scale constraints on light-absorbing anthropogenic iron oxide aerosols, Nature, doi:10.1038/s41612-021-00171-0. Read more about Global-scale constraints on light-absorbing anthropogenic iron oxide aerosols
Spatial and temporal variability in the hydroxyl (OH) radical: understanding the role of large-scale climate features and their influence on OH through its dynamical and photochemical drivers Anderson, D.C., et al. (2021), Spatial and temporal variability in the hydroxyl (OH) radical: understanding the role of large-scale climate features and their influence on OH through its dynamical and photochemical drivers, Atmos. Chem. Phys., 21, 6481-6508, doi:10.5194/acp-21-6481-2021. Read more about Spatial and temporal variability in the hydroxyl (OH) radical: understanding the role of large-scale climate features and their influence on OH through its dynamical and photochemical drivers
Large hemispheric difference in nucleation mode aerosol concentrations in the lowermost stratosphere at mid and high latitudes Williamson, C.J., et al. (2021), Large hemispheric difference in nucleation mode aerosol concentrations in the lowermost stratosphere at mid and high latitudes, Atmos. Chem. Phys., 21, 9065-9088, doi:10.5194/acp-21-9065-2021. Read more about Large hemispheric difference in nucleation mode aerosol concentrations in the lowermost stratosphere at mid and high latitudes
HCOOH in the Remote Atmosphere: Constraints from Atmospheric Tomography (ATom) Airborne Observations Chen, X., et al. (2021), HCOOH in the Remote Atmosphere: Constraints from Atmospheric Tomography (ATom) Airborne Observations, ACS Earth Space Chem., doi:10.1021/acsearthspacechem.1c00049. Read more about HCOOH in the Remote Atmosphere: Constraints from Atmospheric Tomography (ATom) Airborne Observations
Chemical transport models often underestimate inorganic aerosol acidity in remote regions of the atmosphere Nault, B.A., et al. (2021), Chemical transport models often underestimate inorganic aerosol acidity in remote regions of the atmosphere, Commun Earth Environ, 2, doi:10.1038/s43247-021-00164-0. Read more about Chemical transport models often underestimate inorganic aerosol acidity in remote regions of the atmosphere
Five years of variability in the global carbon cycle: comparing an estimate from the Orbiting Carbon Observatory-2 and process-based models Chen, Z., et al. (2021), Five years of variability in the global carbon cycle: comparing an estimate from the Orbiting Carbon Observatory-2 and process-based models, Environ. Res. Lett., 16, doi:10.1088/1748-9326/abfac1. Read more about Five years of variability in the global carbon cycle: comparing an estimate from the Orbiting Carbon Observatory-2 and process-based models
Linking global terrestrial CO2 fluxes and environmental drivers: inferences from the Orbiting Carbon Observatory 2 satellite and terrestrial biospheric models Chen, Z., et al. (2021), Linking global terrestrial CO2 fluxes and environmental drivers: inferences from the Orbiting Carbon Observatory 2 satellite and terrestrial biospheric models, Atmos. Chem. Phys., 21, 6663-6680, doi:10.5194/acp-21-6663-2021. Read more about Linking global terrestrial CO2 fluxes and environmental drivers: inferences from the Orbiting Carbon Observatory 2 satellite and terrestrial biospheric models
Global methane budget and trend, 2010–2017: complementarity of inverse analyses using in situ (GLOBALVIEWplus CH4 ObsPack) and satellite (GOSAT) observations Lu, X., et al. (2021), Global methane budget and trend, 2010–2017: complementarity of inverse analyses using in situ (GLOBALVIEWplus CH4 ObsPack) and satellite (GOSAT) observations, Atmos. Chem. Phys., 21, 4637-4657, doi:10.5194/acp-21-4637-2021. Read more about Global methane budget and trend, 2010–2017: complementarity of inverse analyses using in situ (GLOBALVIEWplus CH4 ObsPack) and satellite (GOSAT) observations
Machine learning uncovers aerosol size information from chemistry and meteorology to quantify potential cloud-forming particles Nair, A.A., et al. (2021), Machine learning uncovers aerosol size information from chemistry and meteorology to quantify potential cloud-forming particles, Geophys. Res. Lett., doi:10.1029/2021GL094133. Read more about Machine learning uncovers aerosol size information from chemistry and meteorology to quantify potential cloud-forming particles
Aerosol pH Indicator and Organosulfate Detectability from Aerosol Mass Spectrometry Measurements Schueneman, M.K., et al. (2021), Aerosol pH Indicator and Organosulfate Detectability from Aerosol Mass Spectrometry Measurements, Atmos. Meas. Tech., doi:10.5194/amt-2020-339. Read more about Aerosol pH Indicator and Organosulfate Detectability from Aerosol Mass Spectrometry Measurements