The Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA)

AMERICAN METEOROLOGICAL SOCIETY OCTOBER 2020 E1743 The Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA) Gabriele G. Pfister, Sebastian D. Eastham, Avelino F. Arellano, Bernard Aumont, Kelley C. Barsanti, Mary C. Barth, Andrew Conley, Nicholas A. Davis, Louisa K. Emmons, Jerome D. Fast, Arlene M. Fiore,, Benjamin Gaubert, Steve Goldhaber, Claire Granier,, Georg A. Grell, Marc Guevara, Daven K. Henze, Alma Hodzic, Xiaohong Liu, Daniel R. Marsh,, John J. Orlando, John M. C. Plane, Lorenzo M. Polvani, Karen H. Rosenlof, Allison L. Steiner, Daniel J. Jacob, and Guy P. Brasseur ABSTRACT: To explore the various couplings across space and time and between ecosystems in a consistent manner, atmospheric modeling is moving away from the fractured limited-scale modeling strategy of the past toward a unification of the range of scales inherent in the Earth system. This paper describes the forward-looking Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA), which is intended to become the next-generation community infrastructure for research involving atmospheric chemistry and aerosols. MUSICA will be developed collaboratively by the National Center for Atmospheric Research (NCAR) and university and government researchers, with the goal of serving the international research and applications communities. The capability of unifying various spatiotemporal scales, coupling to other Earth system components, and process-level modularization will allow advances in both fundamental and applied research in atmospheric composition, air quality, and climate and is also envisioned to become a platform that addresses the needs of policy makers and stakeholders. https://doi.org/10.1175/BAMS-D-19-0331.1 Corresponding author: Gabriele Pfister, [email protected] In final form 5 May 2020 ©2020 American Meteorological Society For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy. Article Unauthenticated | Downloaded 05/19/21 12:46 PM UTC AMERICAN METEOROLOGICAL SOCIETY OCTOBER 2020 E1744 AFFILIATIONS: Pfister, Barth, Conley, Davis, Emmons, Gaubert, Hodzic, Orlando, and Brasseur—Atmospheric Chemistry Observations and Modeling Laboratory, National Center for Atmospheric Research, Boulder, Colorado; Eastham—Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, Massachusetts; Arellano—Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, Arizona; Aumont—LISA, UMR CNRS 7583, Université Paris-Est-Créteil, Université de Paris, Institut Pierre Simon Laplace, Créteil, France; Barsanti—Department of Chemical and Environmental Engineering, and Center for Environmental Research and Technology, Bourns College of Engineering, University of California, Riverside, Riverside, California; Fast—Pacific Northwest National Laboratory, Richland, Washington; Fiore—Lamont–Doherty Earth Observatory, Columbia University, Palisades, and Department of Earth and Environmental Sciences, Columbia University, New York, New York; Goldhaber—Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, Colorado; Granier—Observatoire Midi-Pyrénées, Université de Toulouse, Toulouse, France, and NOAA/Earth System Research Laboratory, Boulder, Colorado; Grell—NOAA/Earth System Research Laboratory, Boulder, Colorado; Guevara—Earth Sciences Department, Barcelona Supercomputing Center, Barcelona, Spain; Henze—University of Colorado Boulder, Boulder, Colorado; Liu—Department of Atmospheric Sciences, Texas A&M University, College Station, Texas; Marsh—Atmospheric Chemistry Observations and Modeling Laboratory, National Center for Atmospheric Research, Boulder, Colorado, and University of Leeds, Leeds, United Kingdom; Plane—University of Leeds, Leeds, United Kingdom; Polvani—Department of Earth and Environmental Sciences, Columbia University, New York, New York; Rosenlof—NOAA/Chemical Sciences Laboratory, Boulder, Colorado; Steiner—Climate and Space Sciences and Engineering, University of Michigan, Ann Arbor, Michigan; Jacob—Department of Earth and Planetary Sciences, Harvard University, Cambridge, Massachusetts Empirical and modeling studies have provided strong evidence of dynamical and chemical coupling across the range of spatial and temporal scales inherent in the Earth system (e.g., Prinn 2012). Current chemical transport models, however, inadequately account for the two-way coupling of atmospheric chemistry with other Earth system components over the range of urban/local to regional to global scales and from the surface up to the top of the atmosphere. As a result, the predictability of local air quality from regional models is currently limited by the insufficient representation of large-scale feedbacks and prescribed land and ocean state, while predictability of future atmospheric trace constituents is hampered by either the coarse resolution of global chemical climate models or the initial and boundary impacts affecting limited area regional models (e.g., National Research Council 2012; Bellucci et al. 2015; Huang et al. 2017; Neal et al. 2017; Im et al. 2018). Also, predictions of weather and climate often neglect the two-way interactions with atmospheric trace constituents. However, in order to establish warning systems and develop adaptation and mitigation strategies, decision-makers need accurate predictions of atmospheric composition, weather, and climate on time scales from hours to weeks to seasonal to decadal, along with reliable quantification of their uncertainties. To meet future challenges, future modeling systems need to have the ability to 1) change spatial scales in a consistent manner, 2) resolve multiple spatial scales in a single simulation, 3) couple model components that represent different Earth system processes, and 4) easily mix and match model components as needed for a specific application. This requires moving away from the fractured modeling activities of the past and bridging the gap between regional chemical weather models and global chemical climate models. It requires coupling from the local emission scale all the way up to the global forcing scale within a single framework. Unauthenticated | Downloaded 05/19/21 12:46 PM UTC AMERICAN METEOROLOGICAL SOCIETY OCTOBER 2020 E1745 This vision motivates the development of Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA). MUSICA is not a single model, but a set of infrastructure concepts and requirements with rigorously defined standards that will enable studying atmospheric composition across all relevant scales. It describes a unified and modular framework with consistent scale-aware modeling approaches, i.e., approaches that are not dependent on model resolution and that can be applied to any Earth system model. It will account for feedbacks between all components of the Earth system, thereby permitting the exploration of interactions between atmospheric chemistry and weather and climate. Evaluation and data assimilation will be essential components of MUSICA. To date, the lack of coordination between the different atmospheric chemistry models has made it difficult to accurately assess the benefits of different model components (Fast et al. 2011). MUSICA’s flexible modular design will break down the problem of simulating atmospheric composition into the representation of individual processes that are described by separate modules. MUSICA can be configured in different ways appropriate for the scientific question on hand. This design will facilitate direct intercomparison of individual modules in a single framework, opening the way to quantify uncertainties of individual processes and enable co-development of individual components. MUSICA will enable exploration of new science topics (Table 1) addressing important, frontier issues by overcoming constraints posed by limited scale awareness of current models and frameworks. MUSICA is envisioned to become a central tool for research but needs to be applicable to a wide range of users and should also be suitable for operational applications. This requires the system to be open source, transferable, efficient, and user friendly. These new approaches to a modular unified framework are too large a challenge to be achieved by a single organization. MUSICA and its components are developed by the atmospheric research community under the coordination of the National Center for Atmospheric Research (NCAR) Atmospheric Chemistry Observations and Modeling (ACOM) Laboratory and in support of the National Science Foundation (NSF) Atmospheric and Geospace Sciences (AGS) Atmospheric Chemistry program. The current status and the future design plans for MUSICA are shared here with the entire community, with an invitation to actively engage in the design and development so that the framework is built to address the needs of the wider community. Several strategic partnerships with prominent research groups and organizations in the United States and elsewhere in the world are being established to foster this community development. MUSICA is guided by a steering group with committed representatives of the broad research community and by working groups each led by three co-chairs, who represent the wider research community including national and international universities, and research organizations (see www2.acom.ucar.edu/sections/musica-governance for a list of members). The working groups are open and invite members from the community to join them. The NCAR ACOM Laboratory serves as the lead organizer and coordinator of MUSICA. It is intended that MUSICA development will connect with other unified atmospheric chemistry modeling initiatives and leverage ongoing activities. Examples include the Modular Earth Submodel System (MESSy; Jöckel et al. 2010; Garny et al. 2019), the Multiscale Online Nonhydrostatic Atmosphere Chemistry (MONARCH) model (Badia et al. 2017), and NOAA’s Unified Forecast System (UFS) community effort, which is aimed at producing a multiscale operational global numerical forecast system. Infrastructure design The specific goal of MUSICA is to produce a new modular and flexible infrastructure that will enable chemistry and aerosols to be simulated over all relevant atmospheric scales in a single, coherent fashion. Coupling of a stand-alone chemistry component to other atmospheric processes (e.g., aerosol–cloud interactions or convective scavenging) and Earth system Unauthenticated | Downloaded 05/19/21 12:46 PM UTC AMERICAN METEOROLOGICAL SOCIETY OCTOBER 2020 E1746 Table 1. Examples of new science applications enabled by MUSICA. Past and Current Approach Future Approach including MUSICA Impacts of the Asian monsoon on weather and climate Hemispheric to global impacts without resolving convection or surface air quality over the monsoon region. More realistic predictions by resolving local air quality and convection in monsoon region consistently with global impacts. Exploiting the future constellation of geostationary satellites for atmospheric composition Global analysis at resolutions coarser than that of observations or regional analysis without considering the benefits of the entire constellation and those of polar orbiting satellites. Matching measurement resolution over the key regions together with global feedbacks results in more seamless prediction of long-range transport and more accurate source attributions between regions of the globe. Subseasonal to seasonal predictions of air quality and weather Hampered by global simulations not fully resolving regional climate-relevant phenomena and with limited accuracy and information content at human impact scales. Global coupled predictions with strategically placed high resolution over key areas (e.g., El Niño region) improves simulated regional climate variability, air–sea coupling, and exposure-relevant scales. Stratospheric intrusions Global models with coarse resolution near the upper troposphere–lower stratosphere or regional models dependent on boundary conditions. Higher horizontal and vertical resolutions over tropopause fold regions will allow better representation of frontal passages and the filaments associated with intrusions. Long-range pollution transport and urban air quality Global models providing boundary conditions to regional models only consider one-way transport and are inconsistent in nature. Seamless two-way feedback from local to global and global to local scales. Intercontinental-/global-scale transport of chemical layers Coarse vertical and spatial resolution and associated numerical diffusion prevents simulation of the layered structure of the troposphere. Adaptive model resolution preserves chemical layers and enables assessment of their global impacts. Aerosols seeding extreme events (e.g., hurricanes) High resolution over impact regions but coarse resolution over aerosol source regions and/or from lateral boundary conditions leads to poor aerosol prediction and affects feedback on extreme event predictions. High resolution enabled over impacts and aerosol source regions in a consistent framework with fully enabled feedback of meteorology, chemistry, and dynamics and between ocean and atmosphere. Feedback loop of climate change on trace gas and aerosol gas concentrations Global simulations with coarse resolution over highemissions regions impact the accuracy of simulated pollutant life cycles and land–sea–atmosphere exchange. Global feedbacks with increased spatial resolution over high-emission regions better represent the life cycles of short-lived pollutants and land–sea–atmosphere exchange. Gravity wave processes impacting stratosphere and mesosphere temperature and mixing Global simulations with general circulation, chemistry, and climate dependent on parameterized wave sources, characteristics, and transport; or costly high-resolution “nature runs.” Better resolution of the gravity wave spectrum within the refined region and a more internally consistent gravity wave parameterization on the global grid. Effect of megacities on global atmospheric composition and climate Disconnected spatial and temporal scales, separate models for local/regional and global impacts. A fully coupled system accounts for detailed chemistry/ emissions over megacities, and enables quantifying their impacts on remote regions (e.g., Arctic) and the global atmosphere. Chemical data assimilation (CDA) and evaluation CDA in models is generally done separately from meteorological DA and limited to updating atmospheric concentrations. Evaluation tools have been developed separately from DA. CDA will be co-developed as integral part of MUSICA with the objective of updating concentrations and inputs (e.g., emissions) efficiently. Commonalities between DA and evaluation will be addressed in parallel. Air quality (AQ) under a changing climate Use downscaling methods to provide meteorological and chemical initial and boundary conditions to a regional AQ model. Conduct an ensemble of simulations under various future scenarios with a single self-consistent model with sufficient resolution to simulate key AQ metrics. Unauthenticated | Downloaded 05/19/21 12:46 PM UTC AMERICAN METEOROLOGICAL SOCIETY OCTOBER 2020 E1747 components (e.g., land–sea atmospheric exchange) will leverage community-developed software tools. The Earth System Modeling Framework (ESMF, https://sourceforge.net/projects/ esmf/) and National Unified Operational Prediction Capability (NUOPC, www.earthsystemcog. org/projects/nuopc/refmans) provide flexible and tested tools for coupling models and grid interpolation. The Common Community Physics Package (CCPP, https://dtcenter.org/communitycode/common-community-physics-package-ccpp) (Theurich et al. 2016), which will be adopted by MUSICA, includes a metadata standard that allows a model build system to check that all required fields are present. This infrastructure can be used to make sure all required fields are either provided by the host model or are read in from a data file. MUSICA design will enable its components to be connected to any three-dimensional (3D) global or regional atmosphere model, or to any 1D single column or 0D box model through the CCPP. It will also have the capability to be driven by atmospheric reanalyses. The infrastructure will provide and extend the functionality of existing models [e.g., the Community Atmosphere Model with Chemistry (CAM-chem) and Whole Atmosphere Community Climate Model (WACCM), both embedded in the Community Earth System Model (CESM; Hurrell et al. 2013), GEOS-Chem (Bey et al. 2001), or the Weather Research and Forecasting Model with Chemistry (WRF-Chem; Grell et al. 2005; Fast et al. 2006)], but will also incorporate and advance models of chemistry and aerosols down to turbulence-resolved scales (e.g., Kim et al. 2012). In a first configuration, MUSICA will be configured within the CESM framework (“Example implementations and current status” section). At the heart of MUSICA is the Chemistry and Aerosol Suite (Fig. 1), which provides updates to chemical states of gases and aerosols within the broader atmospheric simulation framework. The suite is being designed to support a range of gas phase schemes (“Chemical schemes” section) and aerosol representations (“Aerosols” section) and includes all associated processes (e.g., photolysis-rate calculations, nucleation, coagulation, thermodynamics) in separate modules. It is to be incorporated through the CCPP to enable its connection to any physical component that is compliant with the interface. Any atmospheric dynamics model that uses CCPP will be able to use those physical components. This modular structure with a standardized interface simplifies the intercomparison of individual modules and opens the way to quantifying uncertainties of individual processes, e.g., directly assessing the impact of different aerosol schemes on radiative forcing. MUSICA will be capable of coupling to biogeochemistry treated in land and ocean models and provide a platform for both short-term air quality predictions and fully coupled Earth system simulations. Closely tied to the development of MUSICA is the development of community Co-benefits of greenhouse gas–emission reduction policies Conduct separate inconsistent simulations using regional or global AQ models and global climate models to investigate impacts on AQ and climate change from the move toward a net-zero-emissions future. Conduct simulations under net-zero future scenarios with a single self-consistent model with sufficient resolution to simulate key AQ metrics and accurately simulate climate change. Top-down emission estimates Either coarse resolution or inconsistency in modeling and emissions when constraining sources and sinks of long-lived species. Improved accuracy and consistency by simulating transport and chemistry of long-lived species consistently across all scales. Land surface coupling Coarser-resolution climate models are limited in their representation of land–atmosphere couplings, such as biogenic emissions and dry deposition of atmospheric constituents. Many regional models lack full coupling between land and atmosphere processes. Land–atmosphere coupling and regionally finer resolution improves representation of meteorology, biogenic emissions and wet and dry deposition (e.g., simulating effect of acid rain on vegetation). Table 1. (Continued). Unauthenticated | Downloaded 05/19/21 12:46 PM UTC AMERICAN METEOROLOGICAL SOCIETY OCTOBER 2020 E1748 tools for processing input data such as emissions onto flexible model grids (“Emissions and deposition” section), a common model evaluation and diagnostics framework, and data assimilation capabilities (“Model evaluation and data assimilation” section). MUSICA components and working groups The seven existing MUSICA working groups are established and invite members from the community to become active members. The following sections provide a brief overview of the working group topics and discuss expected developments, challenges and scientific gains expected from the new infrastructure, and how limitations in current modeling systems (Table 2) can be overcome. Model architecture. MUSICA needs to be fundamentally flexible to allow individual components to be used in multiple atmospheric host models and data needs to be communicated between individual modules through well-defined standardized interfaces. Data requirements from each module are communicated during the time of model configuration and configuration tests are being implemented to ensure that a valid and compatible set of model components is selected. Users of MUSICA will be able to select parameterizations in a user-friendly way. A dictionary of chemical properties will ensure that physical and chemical quantities are defined consistently between all components of the framework. For MUSICA, monolithic codes will be broken into separate interoperable modules (Fig. 1), thus a user can choose a set of modules during configuration and also change the order of module calls (e.g., test calling transport routines before or after chemistry routines). Emissions and observational data or any other input data will be accessed and remapped to the model grid during runtime. A remapping tool is responsible for the full 3D conservative remapping required by the user specified model grid and the source data grids. A clearly defined layer will provide a connection for code related to the evaluation of the model, modification of model data through assimilation (“Model evaluation and data assimilation” section), and delivery of emissions data (“Emissions and deposition” section). These processes abstract the data sources from the model data, thus minimizing problems such as storing data on alternative grids, communication costs, and load balancing. It will eliminate the need for users to implement regridding solutions themselves. MUSICA will simplify the user experience, clarify data dependencies between parameterizations, make the configurations of scientific model runs and corresponding datasets more traceable, support unit testing and integration testing, broaden and simplify model evaluation, and simplify updating and adding new schemes and parameterizations. Important goals include ensuring that MUSICA can be run on many different computer systems, designing clear processes for code improvements, and satisfying many different types of users. Last, MUSICA and all its components must guarantee open access and provide comprehensive benchmark scenarios and user guides. Fig. 1. MUSICA defines a framework to integrate chemistry into any atmospheric host model. It includes coupling to emissions, evaluation, and data assimilation tools and enables linking to other Earth system components, all through well-defined standardized interfaces. All physics and chemistry processes operate on individual columns; cross-column processes have to be handled by the host model. Unauthenticated | Downloaded 05/19/21 12:46 PM UTC AMERICAN METEOROLOGICAL SOCIETY OCTOBER 2020 E1749 Table 2. Limitations in current modeling systems as they relate to the different MUSICA Working Group topics. Model architecture • Physical and chemical processes are typically packaged together into a single monolithic code base with complex data interdependencies, but this slows down scientific advancement and also prevents fair comparisons and rigorous assessments of different process treatments. This inflexible approach further complicates modifying or replacing different process modules in models. Emissions and deposition • Current models require extensive preprocessing of emissions inventory files to match the model horizontal resolution, the model chemical scheme, etc. This also consumes disk space, challenges incorporating feedbacks of dynamics and meteorology on emissions, and often requires users to develop their own regridding tools. • Using offline inventories introduces inconsistencies between land use and vegetation information used in the model and that used to generate the different types of anthropogenic and natural emissions • Current dry deposition schemes crudely parameterize the dependence on meteorology and biophysics, leading to poor understanding of the impact of dry deposition on atmospheric chemistry and vegetation. Chemical schemes • Current chemical mechanisms are not flexible and targeted toward a limited scale and application. • Mechanism development, which is based on a set of handwritten equations, starting simple and adding complexity as needed (Sarwar et al. 2008; Carter 2010; Stockwell et al. 1997; Emmons et al. 2010; Bates and Jacob 2019; Wang et al. 2019) is becoming more challenging as complexity increases. Aerosols • Modules that calculate aerosol processes are typically strongly tied to the aerosol representation of the host model, which complicates porting aerosol process treatments between host models with different aerosol representations. The required level of complexity increases when considering aerosol–cloud interactions or interstitial and cloud-borne aerosol species. Physics, transport, subgrid processes • Subgrid physics processes can be sensitive to the grid mesh sizes and therefore are often tuned within a limited range of spatial scales for optimizing model results. • Most chemistry transport models assume that each grid cell is well mixed, although it has been shown with largeeddy simulations that reactants can be segregated causing effective reaction rates to be different from those in the well-mixed grid cell (e.g., Ouwersloot et al. 2011; Kim et al. 2016). • Currently, boundary layer parameterizations are employed by the chemistry transport model and constituents are transported as passive tracers. However, fast chemistry alters trace gas vertical gradients, which affects their transport efficiency. Whole atmosphere • Gravity waves are a dominant driver of the vertical constituent transport and circulation of the middle and upper atmosphere, connecting the whole atmosphere to solar processes and Earth’s surface (Fritts and Alexander 2003; Grygalashvyly et al. 2012; Smith et al. 2013; Gardner and Liu 2016; Hendrickx et al. 2018). These waves must be parameterized as their 10–100-km scales are too small to be resolved on a global mesh, but there is evidence their transports are severely underrepresented in models (Garcia et al. 2014; Smith et al. 2015; Huang et al. 2015; Millán et al. 2015; Yue et al. 2015; Gardner et al. 2016). Model evaluation and DA • Current evaluation packages are limited in scope and inflexible at incorporating new observational datasets. • The inability to conduct a more advanced ensemble analysis and prediction via stochastic parameterization across scales and chemical species, leads to insufficient characterization of model skill. • Not considering multiscale coupled chemical DA leads to suboptimal exploitation of information content in observations and in accounting for chemical feedback processes. • Mismatches in the representation of observational and model data result in systematic errors in model evaluation and source attribution. Emissions and deposition. A critical component of chemistry models is the specification of anthropogenic, biomass burning, and natural emissions. Historic and up-to-date emission inventories will be made available in MUSICA, in cooperation with international groups developing these inventories, and efforts will be made to achieve consistency among global, regional, and local emission inventories. Unauthenticated | Downloaded 05/19/21 12:46 PM UTC AMERICAN METEOROLOGICAL SOCIETY OCTOBER 2020 E1750 The spatial resolution of emission inventories is constantly improving: the resolution of some global inventories is currently on the order of 0.1° (e.g., Crippa et al. 2016; Granier et al. 2018), close to that of regional inventories at 7 km (Kuenen et al. 2014). Nevertheless, such resolutions might still be too coarse for modeling some scenarios, e.g., urban air quality at the city scale. Moreover, the proxies used to spatially allocate the emissions may often not be representative of the real-world spatial emission patterns (e.g., use of population density to distribute residential wood combustion emissions). An online capability for regridding, applying temporal variation and vertical distribution of emissions, and combining different inventories (e.g., with different sectors and/or different resolutions) is needed. This will eliminate the need for creating model-configured emission files as a preprocessing step. More importantly, it will allow accounting for impacts of dynamics and meteorology on emissions such as changes in fugitive emissions with temperature or plume dispersion for point emissions. To accomplish this, we use the examples of the Harvard–NASA Emissions Component (HEMCO; Keller et al. 2014) and the High-Elective Resolution Modeling Emission System (HERMES; Guevara et al. 2019) emission models, including collaborating with these teams. Conservative regridding tools that can handle unstructured grid meshes with regional refinement capabilities (Fig. 2) are needed. Applying diurnal, day-of-week, holiday, and seasonal variations, as well as vertical distribution (e.g., power plant stack height), to standard inventories is critical for high-resolution air quality modeling, but may not be necessary for coarse-resolution global modeling. Emission temporal profiles should account for spatial variations across countries and regions due to variable sociodemographic habits (e.g., influence of local crop calendars on the application of fertilizers) and climate conditions (e.g., relationship between outdoor temperature and residential combustion emissions). An online emission capability for MUSICA should enable efficient updating of inventories such as the ability to predict preliminary emissions for the most recent years, not typically available in standard inventories (e.g., by applying sector and pollutant-dependent Fig. 2. Examples of unstructured grids with regional refinement over the contiguous United States: (a) the Spectral Element (SE) dynamical core (Zarzycki et al. 2014) and (b) the Model for Prediction Across Scales (MPAS; Michaelis et al. 2019) dynamical core. Unauthenticated | Downloaded 05/19/21 12:46 PM UTC AMERICAN METEOROLOGICAL SOCIETY OCTOBER 2020 E1751 extrapolations) or adding emission sectors that are not captured in inventories (e.g., traffic dust resuspension emissions). Detailed maps of vegetation and land use will be necessary for the spatial distribution of anthropogenic emissions linked to, e.g., agriculture practices, and for natural emissions (biogenic, dust, etc.), and should be consistent with those used in the development of fire emissions and the land type used in any other part of the simulation. The integration of emission models into the emission tools, in addition to the inventories themselves, should be considered. Such models need to combine activity data and emission factors collected at a fine spatial scale (e.g., road level) with detailed emission estimation algorithms that represent the different factors influencing the emission processes. Moreover, these models should include functionalities for automatically manipulating and performing spatial operations on geometric objects (raster, shapefiles) so that they can be implemented in any region at high resolution. Some emission sources are already calculated online in many models, such as biogenic emissions (Guenther et al. 2012; Hudman et al. 2012), lightning nitric oxide (NO) emissions (“Physics, transport, subgrid processes” section), or dust and sea salt emissions (Mahowald et al. 2006a,b). These emissions depend on environmental variables in nonlinear ways, requiring special care to make the underlying parameterization scale aware and linking to land and ocean models. The degree to which depositional processes should be resolved for accurate simulation at different scales is uncertain at this stage. While recent work points to the value of coupling atmospheric and land models through terrestrial dry deposition (e.g., Paulot et al. 2018; Clifton et al. 2020), a major question is whether more complex models such as multilayer canopy models, including those with in-canopy ambient chemistry, more accurately capture dry deposition at regional scales. A common framework for testing different approaches for scale-aware dry deposition modeling would expedite advancing our understanding of dry deposition’s role in the Earth system. Chemical schemes. It is both a challenge and opportunity to provide accurate yet computationally efficient chemical mechanisms for diverse regions (local to global, troposphere to upper atmosphere) and time scales (hours to centuries). The development, testing, validation, and tracing of new chemical mechanisms will be made simpler in MUSICA’s infrastructure design. New chemical mechanisms should be flexible and could be tailored to address different types of problems—e.g., a long-term global climate study might require a different mechanism than a short-term air quality study. Albeit a large challenge, the potential to apply different mechanisms at different locations/altitudes within a single model run (Santillana et al. 2010; Shen et al. 2020) should be explored. Perhaps most exciting are the tools with which next-generation chemical mechanisms will likely be built in concert with the traditional mechanisms and methods alluded to in Table 2. Models like the Generator of Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A; Aumont et al. 2005) and the Statewide Air Pollution Research Center (SAPRC) Mechanism Generation (MechGen) system (Carter 2019) use structure–activity relationships (SARs) to estimate kinetic parameters for unstudied reactions, and may contain millions of chemical species and reactions. These fully explicit mechanisms could replace traditional methods for mechanism development and could be reduced through some combination of machine learning and more traditional approaches (Szopa et al. 2005) to lumped/parameterized mechanisms of magnitude and content appropriate to answer a specific science problem. As with other aspects of MUSICA, modularity is critical. There is a clear need for an adaptive approach (Shen et al. 2020) or other methods that allow for facile switching between different mechanisms including user-provided mechanisms. 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