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We describe a new Fortran computer program to solve the system of equations for the NH 4+ –Na + –Ca 2+ –K + –Mg 2+ –SO 42- –NO 3- –Cl - –H 2 O system, based on the algorithms of ISORROPIA II. Specifically, the code solves the system of equations describing the "forward" (gas + aerosol input) metastable state but with algorithm improvements and corrections. These algorithm changes allow the code to deliver more accurate solution results in formal evaluations of accuracy of the roots of the systems of equations, while reducing processing time in practical applications by about 50 %. The improved solution performance results from several implementation improvements relative to the original ISORROPIA algorithms. These improvements include (i) the use of the "interpolate, truncate and project" (ITP) root-finding approach rather than bisection, (ii) the allowance of search interval endpoints as valid roots at the onset of a search, (iii) the use of a more accurate method to solve polynomial subsystems of equations, (iv) the elimination of negative concentrations during iterative solutions, (v) corrections for mass conservation enforcement, and (vi) several code structure improvements. The new code may be run in either a "vectorization" mode wherein a global convergence criterion is used across multiple tests within the same chemical subspace or a "by case-by-case" mode wherein individual test cases are solved with the same convergence criteria. The latter approach was found to be more efficient on the compiler tested here, but users of the code are recommended to test both options on their own systems. The new code has been constructed to explicitly conserve the input mass for all species considered in the solver and is provided as open-source Fortran shareware. [ABSTRACT FROM AUTHOR]

Below-cloud scavenging (BCS) is the process of aerosol removal from the atmosphere between cloud base and the ground by precipitation (e.g., rain or snow), and affects aerosol number or mass concentrations, size distribution, and lifetime. An accurate representation of precipitation phases is important in treating BCS as the efficiency of aerosol scavenging differs significantly between liquid and solid precipitation. The impact of different representations of BCS on existing model biases was examined through implementing a new aerosol BCS scheme in the Environment and Climate Change Canada (ECCC) air quality prediction model GEM-MACH and comparing it with the existing scavenging scheme in the model. Further, the current GEM-MACH employs a single-phase precipitation for BCS: total precipitation is treated as either liquid or solid depending on a fixed environment temperature threshold. Here, we consider co-existing liquid and solid precipitation phases as they are predicted by the GEM microphysics. GEM-MACH simulations, in a local-area domain over the Athabasca oil sands areas, Canada, are compared with observed precipitation samples, with a focus on the particulate base cation NH 4+ ; acidic anions NO 3- , SO 4= , HSO 3- in precipitation; and observed ambient particulate sulfate, ammonium, and nitrate concentrations. Overall, the introduction of the multi-phase approach and the new scavenging scheme enhances GEM-MACH performance compared to previous methods. Including a multi-phase approach leads to altered SO 4= scavenging and impacts the BCS of SO 2 into the aqueous phase over the domain. Sulfate biases improved from +46% to -5% relative to Alberta Precipitation Quality Monitoring Program wet sulfate observations. At Canadian Air and Precipitation Monitoring Network stations the biases became more negative, from -10% to -30% for the tests carried out here. These improvements contrast with prior annual average biases of +200% for SO 4= , indicating enhanced model performance. Improvements in model performance (via scores for correlation coefficient, normalized mean bias, and/or fractional number of model values within a factor of 2 of observations) could also be seen between the base case and the two simulations based on multi-phase partitioning for NO 3- , NH 4+ , and SO 4=. Whether or not these improvements corresponded to increases or decreases in NO 3- and NH 4+ wet deposition varied over the simulation region. The changes were episodic in nature – the most significant changes in wet deposition were likely at specific geographic locations and represent specific cloud precipitation events. The changes in wet scavenging resulted in a higher formation rate and larger concentrations of atmospheric particle sulfate. [ABSTRACT FROM AUTHOR]

Spatiotemporal clustering of data is an important analytical technique with many applications in air quality, including source identification, monitoring network analysis and airshed partitioning. Hierarchical agglomerative clustering is one such algorithm, where sets of input data are grouped by a chosen similarity metric, without requiring any a priori information about the final arrangement of clusters. Modern implementations of the algorithm have O(n² 10 log (n)) computational complexity and O;(n²) memory usage, where n is the number of initial clusters. This dependence can strain the resources of even very large individual computers as the number of initial clusters increases into the tens or hundreds of thousands, for example, to cluster all the points in an air-quality model’s simulation grid as part of airshed analysis (~105 to 106 time series to be clustered). Using two parallelization techniques – the Message Passing Interface (MPI) and Open Multi-Processing (OpenMP) – we have reduced the amount of wallclock time while increasing the memory available to a new hierarchical clustering program, by dividing up the program into blocks which are run on separate CPUs but communicate with each other to produce a single result. The new algorithm opens up new directions for large data analysis which had previously not been possible. Here we present a massively parallelized version of an agglomerative hierarchical clustering algorithm which is able to cluster an entire year of hourly regional air-quality model output (538x540 domain; 290,520 hourly concentration timeseries) in 12 hours 37 minutes of wallclock time, by spreading the computation across 8000 Intel® Xeon® Platinum 8830 CPU cores with a total of 2TB of RAM. We then show how the new algorithm allows a new form of air-quality analysis to be carried out starting from air-quality model output. We present maps of the different airsheds within the model domain, identifying equally unique regions for each chemical species. These regions can be used as an aid in determining the placement of surface air-quality monitors which gives the most representative sampling given a fixed number of monitors, or the number of monitors required for a given level of similarity between airsheds. We then demonstrate the new algorithm’s application towards source apportionment of very large observation data sets, through the analysis of a year of Canada’s hourly National Air Pollution Surveillance Program data, comprising 366,427 original observation vectors, a problem size that would be impossible with other source apportionment programs such as Positive Matrix Factorization. [ABSTRACT FROM AUTHOR]

We describe a new FORTRAN 90 computer program to solve the system of equations for the NH4+-Na+-Ca2+-K+-Mg2+- SO42--NO3--Cl- system, based on the algorithms of ISORROPIA II, but containing algorithm improvements and corrections. These allow the code to deliver more accurate solution results in formal evaluations of accuracy of the roots of the systems of equations, while reducing processing time in practical applications by about 50%. The improved solution performance results from several implementation improvements relative to the original ISORROPIA algorithms. These improvements include (the use of the 'interpolate, truncate and project' (ITP) root-finding approach rather than bisection, (ii) the allowance of search interval endpoints as valid roots at the onset of a search, (iii) the use of a more accurate method to solve polynomial subsystems of equations, (iv) the elimination of negative concentrations during iterative solutions, (v) corrections for mass conservation enforcement, and (vi) several code structure improvements. The new code may be run in either a "vectorization" mode wherein a global convergence criterion is used across multiple tests within the same chemical subspace, or a "by gridpoint" mode wherein individual test cases are solved with the same convergence criteria. The latter approach was found to be more efficient on the compiler tested here, but users of the code are recommended to test both options on their own systems. We also note that implementation of inorganic chemistry within chemical transport models should take care to retain residual or "free" mass of aerosol species remaining after partitioning, to ensure mass conservation - the new code has been constructed to explicitly conserve the input mass. The new code is provided as open-source FORTRAN 90 shareware. [ABSTRACT FROM AUTHOR]

Below-cloud scavenging is the process of aerosol removal from the atmosphere between cloud-base and the ground by precipitation (e.g. rain or snow), and affects aerosol number/mass concentrations, lifetime and distributions. An accurate representation of precipitation phases is important in treating below-cloud scavenging as the efficiency of aerosol scavenging differs significantly between liquid and solid precipitation. The impact of different representations of below-cloud scavenging on existing model biases (Makar et al, 2018), was examined through implementing a new aerosol below-cloud scavenging scheme (from Wang et al., 2014) and comparing with the GEM-MACH's existing scavenging scheme, based on Slinn (1984). Further, the current GEM-MACH employs a single-phase precipitation for below-cloud scavenging: total precipitation is treated as either liquid or solid depending on a fixed environment temperature threshold. Here, we consider co-existing liquid and solid precipitation phases as they are predicted by the GEM microphysics. GEM-MACH simulations are compared with - observed precipitation samples, with a focus on the particulate base cation NH4 +, acidic anions NO3 -, SO4 =, HSO3 - in precipitation, and ambient particulate sulfate, ammonium and nitrate. Overall, the precipitation-phase partitioning and Wang et al. (2014) scavenging scheme improve GEM-MACH performance relative to earlier approaches. Including multi-phase approach leads to a decrease in SO4 2- scavenging and impacts the below-cloud scavenging of SO2 into the aqueous phase over the domain. Sulphate biases improved from +46% to -5% relative to Alberta Precipitation Quality Monitoring Program wet sulphate observations. At Canadian Air and Precipitation Monitoring Network stations the biases became more negative, from -10% to -30% for the tests carried out here. These may be compared to previously published annual average biases of +200% for SO4 2- from earlier versions of GEM-MACH (Makar et al, 2018), indicating an overall improvement in model performance relative to the prior results. Improvements in model performance (via scores for correlation coefficient, normalized mean bias, and/or fractional number of model values within a factor of two of observations) could also be seen, between the base case and the two simulations based on multiphase partitioning for NO3 -, NH4 +, and SO4 2. Whether or not these improvements corresponded to increases or decreases of NO3 - and NH4 + wet deposition varied over the simulation region. The changes were episodic in nature - the most significant changes in wet deposition were likely at specific geographic locations and represent specific cloud precipitation events. The aerosol scavenging rates of the two schemes differ during liquid precipitation in the size range of 0.1-1 µm, mostly at high precipitation intensity. The two schemes aerosol scavenging diverges for aerosols smaller than 1 µm for solid precipitation at lower intensity (R=0.01 mm/h), while at higher precipitation intensities (R=10 mm/h), the two schemes show bigger differences for aerosols larger than 1 µm. The changes in wet scavenging resulted a higher formation rate and larger concentrations of atmospheric particle sulphate. [ABSTRACT FROM AUTHOR]

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 highperformance 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° x 0:625°) or finer become routinely feasible. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Black carbon (BC) contributes to Arctic warming, yet sources of Arctic BC and their geographic contributions remain uncertain. We interpret a series of recent airborne (NETCARE 2015; PAMARCMiP 2009 and 2011 campaigns) and ground-based measurements (at Alert, Barrow and Ny-Âlesund) from multiple methods (thermal, laser incandescence and light absorption) with the GEOS-Chem global chemical transport model and its adjoint to attribute the sources of Arctic BC. This is the first comparison with a chemical transport model of refractory BC (rBC) measurements at Alert. The springtime airborne measurements performed by the NETCARE campaign in 2015 and the PAMARCMiP campaigns in 2009 and 2011 offer BC vertical profiles extending to above 6 km across the Arctic and include profiles above Arctic ground monitoring stations. Our simulations with the addition of seasonally varying domestic heating and of gas flaring emissions are consistent with ground-based measurements of BC concentrations at Alert and Barrow in winter and spring (rRMSE< 13 %) and with airborne measurements of the BC vertical profile across the Arctic (rRMSED 17 %) except for an underestimation in the middle troposphere (500-700 hPa). Sensitivity simulations suggest that anthropogenic emissions in eastern and southern Asia have the largest effect on the Arctic BC column burden both in spring (56 %) and annually (37 %), with the largest contribution in the middle troposphere (400-700 hPa). Anthropogenic emissions from northern Asia contribute considerable BC (27% in spring and 43% annually) to the lower troposphere (below 900 hPa). Biomass burning contributes 20% to the Arctic BC column annually. At the Arctic surface, anthropogenic emissions from northern Asia (40-45 %) and eastern and southern Asia (20-40 %) are the largest BC contributors in winter and spring, followed by Europe (16-36 %). Biomass burning from North America is the most important contributor to all stations in summer, especially at Barrow. Our adjoint simulations indicate pronounced spatial heterogeneity in the contribution of emissions to the Arctic BC column concentrations, with noteworthy contributions from emissions in eastern China (15 %) and western Siberia (6.5 %). Although uncertain, gas flaring emissions from oilfields in western Siberia could have a striking impact (13 %) on Arctic BC loadings in January, comparable to the total influence of continental Europe and North America (6.5% each in January). Emissions from as far as the Indo-Gangetic Plain could have a substantial influence (6.3% annually) on Arctic BC as well. [ABSTRACT FROM AUTHOR]

Black carbon (BC) contributes to both degraded air quality and Arctic warming, however sources of Arctic BC and their geographic contributions remain uncertain. We interpret a series of recent airborne and ground-based measurements with the GEOS-Chem global chemical transport model and its adjoint to attribute the sources of Arctic BC. The springtime airborne measurements performed by the NETCARE campaign in 2015 and the PAMARCMiP campaigns in 2009 and 2011 offer BC vertical profiles extending to > 6 km across the Arctic and include profiles above Arctic ground monitoring stations. Long-term ground-based measurements are examined from multiple methods (thermal, laser incandescence and light absorption) at Alert (2011-2013), Barrow (2009-2015) and Ny-Ålesund (2009-2014) stations. Our simulations with the addition of gas flaring emissions are consistent with ground-based measurements of BC concentrations at Alert and Barrow in winter and spring (rRMSE < 13 %), and with airborne measurements of BC vertical profile across the Arctic (rRMSE = 17 %). Sensitivity simulations suggest that anthropogenic emissions in eastern and southern Asia are the largest source of the Arctic BC column both in spring (56 %) and annually (37 %), with larger contributions aloft than near the surface (e.g. a contribution of 66 % between 400-700 hPa and of 46 % below 900 hPa in spring). Anthropogenic emissions from northern Asia contribute considerable BC to the lower troposphere (a contribution of 27 % in spring and of 43 % annually below 900 hPa). Biomass burning has a substantial contribution to Arctic BC below 400 hPa of 25 % annually, despite minor influence in spring (< 10 %). Surface BC is largely influenced by anthropogenic emissions in winter and spring, and by biomass burning in summer. At Alert and Barrow, anthropogenic emissions from northern Asia are the largest source of BC (> 50 %) in winter and those from eastern and southern Asia are the largest in spring (~ 40 %). At Ny-Ålesund, anthropogenic emissions from Europe (~ 30 %) and northern Asia (~ 30 %) are major sources in winter and early spring. Biomass burning from North America is the most important contributor to surface BC at all stations in summer, especially at Barrow where North American biomass burning contributes more than 90 % of BC in July and August. Our adjoint simulations indicate pronounced spatial and seasonal heterogeneity in the contribution of emissions to the Arctic BC column concentrations with noteworthy contributions from emissions in eastern China (15 %) and western Siberia (6.5 %). Although uncertain, gas flaring emissions from oilfields in western Siberia could have a striking impact (13 %) on Arctic BC loadings in January, comparable to the total influence of continental Europe and North America (6.5 % each in January). [ABSTRACT FROM AUTHOR]