ALBERT

Description: Emissions from aircraft engines contribute to atmospheric NOx, driving changes in both the climate and in surface air quality. Existing atmospheric models typically assume instant dilution of emissions into large-scale grid cells, neglecting non-linear, small-scale processes occurring in aircraft wakes. They also do not explicitly simulate the formation of ice crystals, which could drive local chemical processing. This assumption may lead to errors in estimates of aircraft-attributable ozone production, and in turn to biased estimates of aviation's current impacts on the atmosphere and the effect of future changes in emissions. This includes black carbon emissions, on which contrail ice forms. These emissions are expected to reduce as biofuel usage increases, but their chemical effects are not well captured by existing models. To address this problem, we develop a Lagrangian model that explicitly models the chemical and microphysical evolution of an aircraft plume. It includes a unified tropospheric–stratospheric chemical mechanism that incorporates heterogeneous chemistry on background and aircraft-induced aerosols. Microphysical processes are also simulated, including the formation, persistence, and chemical influence of contrails. The plume model is used to quantify how the long-term (24 h) atmospheric chemical response to an aircraft plume varies in response to different environmental conditions, engine characteristics, and fuel properties. We find that an instant-dilution model consistently overestimates ozone production compared to the plume model, up to a maximum error of ∼200 % at cruise altitudes. Instant dilution of emissions also underestimates the fraction of remaining NOx, although the magnitude and sign of the error vary with season, altitude, and latitude. We also quantify how changes in black carbon emissions affect plume behavior. Our results suggest that a 50 % reduction in black carbon emissions, as may be possible through blending with certain biofuels, may lead to thinner, shorter-lived contrails. For the cases that we modeled, these contrails sublimate ∼5 % to 15 % sooner and are 10 % to 22 % optically thinner. The conversion of emitted NOx to HNO3 and N2O5 falls by 16 % and 33 %, respectively, resulting in chemical feedbacks that are not resolved by instant-dilution approaches. The persistent discrepancies between results from the instant-dilution approach and from the aircraft plume model demonstrate that a parameterization of effective emission indices should be incorporated into 3-D atmospheric chemistry transport models.

Description: Emissions from aircraft engines contribute to atmospheric NOx, driving changes in both the climate and in surface air quality. Existing atmospheric models typically assume instant dilution of emissions into large-scale grid cells, neglecting non-linear, small-scale processes occurring in aircraft wakes. They also do not explicitly simulate the formation of ice crystals, which could drive local chemical processing. This assumption may lead to errors in estimates of aircraft-attributable ozone production, and in turn to biased estimates of aviation’s current impacts on the atmosphere and the effect of future changes in emissions. This includes soot emissions, on which contrail ice forms. These emissions are expected to reduce as biofuel usage increases, but their chemical effects are not well captured by existing models. To address this problem, we develop a Lagrangian model which explicitly models the chemical and microphysical evolution of an aircraft plume. It includes a unified tropospheric-stratospheric chemical mechanism that incorporates heterogeneous chemistry on background and aircraft-induced aerosols. Microphysical processes are also simulated, including the formation, persistence, and chemical influence of contrails. The plume model is used to quantify how the long-term (24-hour) atmospheric chemical response to an aircraft plume varies in response to different environmental conditions, and engine characteristics, and fuel properties. We find that an instant dilution model consistently overestimates ozone production compared to the plume model, up to a maximum error of ~ 200 % at cruise altitudes. Instant dilution of emissions also underestimates the fraction of remaining NOx, although the magnitude and sign of the error vary with season, altitude, and latitude. We also quantify how changes in soot emissions affect plume behavior. Our results show that a 50 % reduction in black carbon emissions, as may be possible through blending with certain biofuels, leads to contrails which evaporate ~ 9 % faster and are 14 % optically thinner. The conversion of emitted NOx to HNO3 and N2O5 falls by 65 % and 69 % respectively, resulting in chemical feedbacks which are not resolved by instant-dilution approaches. The persistent discrepancies between results from the instant dilution approach and from the aircraft plume model demonstrate that a parametrization of effective emission indices should be incorporated into 3-D atmospheric chemistry transport models.

Description: Emissions are a central component of atmospheric chemistry models. The Harmonized Emissions Component (HEMCO) is a software component for computing emissions from a user-selected ensemble of emission inventories and algorithms. It allows users to re-grid, combine, overwrite, subset, and scale emissions from different inventories through a configuration file and with no change to the model source code. The configuration file also maps emissions to model species with appropriate units. HEMCO can operate in offline stand-alone mode, but more importantly it provides an online facility for models to compute emissions at runtime. HEMCO complies with the Earth System Modeling Framework (ESMF) for portability across models. We present a new version here, HEMCO 3.0, that features an improved three-layer architecture to facilitate implementation into any atmospheric model and improved capability for calculating emissions at any model resolution including multiscale and unstructured grids. The three-layer architecture of HEMCO 3.0 includes (1) the Data Input Layer that reads the configuration file and accesses the HEMCO library of emission inventories and other environmental data, (2) the HEMCO Core that computes emissions on the user-selected HEMCO grid, and (3) the Model Interface Layer that re-grids (if needed) and serves the data to the atmospheric model and also serves model data to the HEMCO Core for computing emissions dependent on model state (such as from dust or vegetation). The HEMCO Core is common to the implementation in all models, while the Data Input Layer and the Model Interface Layer are adaptable to the model environment. Default versions of the Data Input Layer and Model Interface Layer enable straightforward implementation of HEMCO in any simple model architecture, and options are available to disable features such as re-gridding that may be done by independent couplers in more complex architectures. The HEMCO library of emission inventories and algorithms is continuously enriched through user contributions so that new inventories can be immediately shared across models. HEMCO can also serve as a general data broker for models to process input data not only for emissions but for any gridded environmental datasets. We describe existing implementations of HEMCO 3.0 in (1) the GEOS-Chem “Classic” chemical transport model with shared-memory infrastructure, (2) the high-performance GEOS-Chem (GCHP) model with distributed-memory architecture, (3) the NASA GEOS Earth System Model (GEOS ESM), (4) the Weather Research and Forecasting model with GEOS-Chem (WRF-GC), (5) the Community Earth System Model Version 2 (CESM2), and (6) the NOAA Global Ensemble Forecast System – Aerosols (GEFS-Aerosols), as well as the planned implementation in the NOAA Unified Forecast System (UFS). Implementation of HEMCO in CESM2 contributes to the Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA) by providing a common emissions infrastructure to support different simulations of atmospheric chemistry across scales.