ALBERT

Description: Observations of aerosol scattering and absorption offer valuable information about aerosol composition. We apply a simulation of the Ultraviolet Aerosol Index (UVAI), a method of detecting aerosol absorption from satellite observations, to interpret UVAI values observed by the Ozone Monitoring Instrument (OMI) from 2005 to 2015 to understand global trends in aerosol composition. We conduct our simulation using the vector radiative transfer model VLIDORT with aerosol fields from the global chemical transport model GEOS-Chem. We examine the 2005–2015 trends in individual aerosol species from GEOS-Chem and apply these trends to the UVAI simulation to calculate the change in simulated UVAI due to the trends in individual aerosol species. We find that global trends in the UVAI are largely explained by trends in absorption by mineral dust, absorption by brown carbon, and scattering by secondary inorganic aerosol. Trends in absorption by mineral dust dominate the simulated UVAI trends over North Africa, the Middle East, East Asia, and Australia. The UVAI simulation resolves observed negative UVAI trends well over Australia, but underestimates positive UVAI trends over North Africa and Central Asia near the Aral Sea and underestimates negative UVAI trends over East Asia. We find evidence of an increasing dust source from the desiccating Aral Sea that may not be well represented by the current generation of models. Trends in absorption by brown carbon dominate the simulated UVAI trends over biomass burning regions. The UVAI simulation reproduces observed negative trends over central South America and West Africa, but underestimates observed UVAI trends over boreal forests. Trends in scattering by secondary inorganic aerosol dominate the simulated UVAI trends over the eastern United States and eastern India. The UVAI simulation slightly overestimates the observed positive UVAI trends over the eastern United States and underestimates the observed negative UVAI trends over India. Quantitative simulation of the OMI UVAI offers new insight into global trends in aerosol composition.

Description: Observations of aerosol scattering and absorption offer valuable information about aerosol composition. We apply a simulation of the Ultraviolet Aerosol Index (UVAI), a method of detecting aerosol absorption from satellite observations, to interpret UVAI values observed by the Ozone Monitoring Instrument (OMI) over 2005–2015 to understand global trends in aerosol composition. We conduct our simulation using the vector radiative transfer model VLIDORT with aerosol fields from the global chemical transport model GEOS-Chem. We examine the 2005–2015 trends in individual aerosol species from GEOS-Chem, and apply these trends to the UVAI simulation to calculate the change in simulated UVAI due to the trends in individual aerosol species. We find that global trends in the UVAI are largely explained by trends in absorption by mineral dust, absorption by brown carbon, and scattering by secondary inorganic aerosol. Trends in absorption by mineral dust dominate the simulated UVAI trends over North Africa, the Middle-East, East Asia, and Australia. The UVAI simulation well resolves observed negative UVAI trends over Australia, but underestimates positive UVAI trends over North Africa and Central Asia near the Aral Sea, and underestimates negative UVAI trends over East Asia. We find evidence of an increasing dust source from the desiccating Aral Sea, that may not be well represented by the current generation of models. Trends in absorption by brown carbon dominate the simulated UVAI trends over biomass burning regions. The UVAI simulation reproduces observed negative trends over central South America and West Africa, but underestimates observed UVAI trends over boreal forests. Trends in scattering by secondary inorganic aerosol dominate the simulated UVAI trends over the eastern United States and eastern India. The UVAI simulation slightly overestimates the observed positive UVAI trends over the eastern United States, and underestimates the observed negative UVAI trends over India. Quantitative simulation of the OMI UVAI offers new insight into global trends in aerosol composition.

Description: There are few multi-decadal observations of atmospheric aerosols worldwide. This study applies global hourly visibility (Vis) observations at more than 3000 stations to investigate historical trends in atmospheric haze over 1945–1996 for the US, and over 1973–2013 for Europe and eastern Asia. A comprehensive data screening and processing framework is developed and applied to minimize uncertainties and construct monthly statistics of inverse visibility (1/Vis). This data processing includes removal of relatively clean cases with high uncertainty, and change point detection to identify and separate methodological discontinuities such as the introduction of instrumentation. Although the relation between 1/Vis and atmospheric extinction coefficient (bext) varies across different stations, spatially coherent trends of the screened 1/Vis data exhibit consistency with the temporal evolution of collocated aerosol measurements, including the bext trend of −2.4 % yr−1 (95 % CI: −3.7, −1.1 % yr−1) vs. 1/Vis trend of −1.6 % yr−1 (95 % CI: −2.4, −0.8 % yr−1) over the US for 1989–1996, and the fine aerosol mass (PM2.5) trend of −5.8 % yr−1 (95 % CI: −7.8, −4.2 % yr−1) vs. 1/Vis trend of −3.4 % yr−1 (95 % CI: −4.4, −2.4 % yr−1) over Europe for 2006–2013. Regional 1/Vis and Emissions Database for Global Atmospheric Research (EDGAR) sulfur dioxide (SO2) emissions are significantly correlated over the eastern US for 1970–1995 (r = 0.73), over Europe for 1973–2008 (r ∼ 0.9) and over China for 1973–2008 (r ∼ 0.9). Consistent "reversal points" from increasing to decreasing in SO2 emission data are also captured by the regional 1/Vis time series (e.g., late 1970s for the eastern US, early 1980s for western Europe, late 1980s for eastern Europe, and mid 2000s for China). The consistency of 1/Vis trends with other in situ measurements and emission data demonstrates promise in applying these quality assured 1/Vis data for historical air quality studies.

Description: Global modeling of atmospheric composition is a grand computational challenge because of the need to simulate large coupled systems of chemical species interacting with transport on all scales. Off-line chemical transport models (CTMs), where the chemical continuity equations are solved using meteorological data as input, have the advantages of simplicity and reproducibility, and are important vehicles for developing knowledge that can then be transferred to Earth system models. However, they have generally not been designed to take advantage of massively parallel computing architectures. Here we develop such a high-performance capability (GCHP) for GEOS-Chem, a CTM driven by GEOS meteorological data from the NASA Goddard Earth Observation System (GEOS) and used by hundreds of research groups worldwide. GCHP is a grid-independent implementation of GEOS-Chem using the Earth System Modeling Framework (ESMF) that permits the same standard model to be run in a distributed-memory framework, scalable from six cores on a single node up to hundreds of cores distributed across a network. GCHP also allows GEOS-Chem to take advantage of the native GEOS cubed-sphere grid for greater accuracy and computational efficiency in simulating transport. GCHP enables GEOS-Chem simulations to be conducted with high computational scalability up to at least 500 cores, so that global simulations of stratosphere-troposphere oxidant-aerosol chemistry at C180 spatial resolution (~0.5°×0.625°) or finer become routinely feasible.

Description: Global modeling of atmospheric chemistry is a grand computational challenge because of the need to simulate large coupled systems of ∼100–1000 chemical species interacting with transport on all scales. Offline chemical transport models (CTMs), where the chemical continuity equations are solved using meteorological data as input, have usability advantages and are important vehicles for developing atmospheric chemistry knowledge that can then be transferred to Earth system models. However, they have generally not been designed to take advantage of massively parallel computing architectures. Here, we develop such a high-performance capability for GEOS-Chem (GCHP), a CTM driven by meteorological data from the NASA Goddard Earth Observation System (GEOS) and used by hundreds of research groups worldwide. GCHP is a grid-independent implementation of GEOS-Chem using the Earth System Modeling Framework (ESMF) that permits the same standard model to operate in a distributed-memory framework for massive parallelization. GCHP also allows GEOS-Chem to take advantage of the native GEOS cubed-sphere grid for greater accuracy and computational efficiency in simulating transport. GCHP enables GEOS-Chem simulations to be conducted with high computational scalability up to at least 500 cores, so that global simulations of stratosphere–troposphere oxidant–aerosol chemistry at C180 spatial resolution (∼0.5∘×0.625∘) or finer become routinely feasible.