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.
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.
Examples of new science applications enabled by MUSICA.
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 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/community-code/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 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.
Citation: Bulletin of the American Meteorological Society 101, 10; 10.1175/BAMS-D-19-0331.1
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 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.
Limitations in current modeling systems as they relate to the different MUSICA Working Group topics.
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.
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.
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).
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.
Citation: Bulletin of the American Meteorological Society 101, 10; 10.1175/BAMS-D-19-0331.1
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 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. Benchmark mechanisms need to be provided that meet the needs of the user community, with respect to their ability to address scientific problems of current scope and interest as well as possible limitations in computing resources. In addition, seamless connections to other infrastructure components must exist (e.g., connections to solvers and radiative transfer codes). Connecting the chemical mechanisms to emission and deposition modules—i.e., providing protocols that allow connections to be made between emitted/deposited and mechanism species—needs to be given full consideration.
Various unresolved challenges associated with mechanism development remain. For example, as fossil fuel emissions, which currently are the dominant source over much of the developed world, are decreasing as a result of mitigation policies, other sources such as biomass burning or personal care products gain in importance. Including these new emission sources requires identifying appropriate compounds that represent the chemistry, developing reaction pathways for these identified compounds, and applying reduced-chemistry approaches. In many cases, the representative compounds are still being identified; for many of the compounds that have been identified, the available kinetic and mechanistic data are insufficient to develop reliable reaction pathways.
Mechanisms must include gas-phase chemistry, but also must consider condensed-phase partitioning and chemistry in aerosols and clouds. Chemical mechanisms for aerosol and cloud chemistry, which are continually developing, could follow the new paradigm of utilizing SARs to represent organic chemistry in the aqueous phase. The next-generation chemical mechanisms must address the need to represent a broader range of atmospheric processes, in addition to the range of scales and conditions previously described.
Aerosols.
The representation of aerosol populations in current models ranges from the simplest and computationally most efficient bulk approach that assumes a single or fixed size bin (e.g., Colarco et al. 2010) to modal (e.g., Binkowski and Roselle 2003; Liu et al. 2012; Neale et al. 2010; Rasch et al. 2019), and sectional approaches (Wexler et al. 1994; Bessagnet et al. 2004; Zaveri et al. 2008). Fully explicit approaches, which simulate individual particles using a Monte Carlo method (e.g., Riemer et al. 2009) are becoming available. In contrast with the other approaches, the fully explicit approach does not impose any assumptions regarding the size distribution and mixing state of aerosol compositions, yet they are generally too computationally expensive to be included in 3D models. MUSICA will be designed to accommodate any type of aerosol treatment.
In addition to treating the aerosol number and size distribution, the complexity of an aerosol model also results from the treatment of processes that affect their life cycle such as nucleation, coagulation, gas-to-particle partitioning, removal mechanisms, and hygroscopic and optical properties. MUSICA and its underlying modularity will permit greater interoperability of processes within an aerosol model, therefore facilitating advances in aerosol process treatments that can be documented over time. For example, many types of thermodynamic modules (e.g., Jacobson et al. 1996; Nenes et al. 1999; Zaveri et al. 2005) with varying complexity and computational expense have been developed and implemented in various aerosol models. However, in a modular framework, one aerosol model could be chosen for different thermodynamic modules allowing a more direct assessment of errors associated with the treatment of gas-to-particle partitioning. Also, a flexible aerosol framework should lead to a better quantification of impacts of a more accurate representation of the aerosol mixing state (e.g., by allowing a better transition between freshly emitted aerosols that are treated as externally mixed and aged aerosols that are treated as internally mixed in remote regions).
Hundreds of prognostic variables are often needed to represent the size, composition, mixing state, and volatility associated with the multitude of chemical pathways responsible for secondary aerosol formation. In addition, it is often desirable to track anthropogenic, biomass burning, biogenic, and other natural aerosols separately, which further increases the computational burden. MUSICA will enable increased flexibility so that more complex and realistic representation of the aerosol life cycle can be included in air quality simulations in parallel with a computationally more efficient representation for climate simulations.
Physics, transport, subgrid processes.
Physics processes and subgrid-scale transport encompass turbulence, gravity waves, convective transport, vertical mixing, and wet scavenging of trace gases and aerosols in stratiform and convective clouds. This includes chemical transformations within subgrid-scale processes, especially parameterized convection. Wet scavenging and lightning nitrogen oxides (NOx) generation is included here, rather than the “Emissions and deposition” section, because of the reliance of these two processes on the properties of convection as well as resolved clouds.
While some numerical weather prediction and climate models already have the capability of regional refinement where scale-aware parameterizations have been evaluated (e.g., Fowler et al. 2016, 2020), an important activity is testing and evaluating parameterizations of processes at grid meshes ranging from hundreds of kilometers in size to ∼1 km in size. Ultimately, the parameterizations need to ensure that mass, momentum, and energy are conserved in the transition from explicitly resolved to parameterized scales. However, there are several specific challenges for atmospheric chemistry that we highlight here.
One specific challenge is the vertical resolution (see also the “Whole atmosphere” section). With varying horizontal resolution, the vertical resolution and aspect ratios will also need to change to accurately resolve turbulent vertical mixing of atmospheric constituents, the role of convection and its effect on aerosol processing as well as finescale chemical processes. This may include how to best adapt and modify variable vertical grid structures for boundary layer parameterizations. Additionally, for representing the long-range transport of plumes or troposphere–stratosphere exchange, a vertical resolution of approximately 100 m is needed (Zhuang et al. 2018) and the question remains to what degree this will be feasible in a refined global mesh.
Another specific challenge is seamlessly estimating lightning flash rate. A coarse grid will contain a convective parameterization representing one or many convective storms, while the fine grid will resolve the convective storm. Considering that lightning flash-rate parameterizations are based on bulk properties of a single storm, there will be a need to transition from using storm properties in one grid cell to those in several neighboring grid cells. To confront this challenge, continual evaluation and development of lightning schemes will be needed.
In addition, constituent transport by shallow convection (Grell and Freitas 2014), a process often omitted in models, will need to be incorporated at grid scales larger than hundreds of meters. Shallow convective transport is a means of moving trace gases and aerosols to the free troposphere and simultaneously reducing surface mixing ratios. The MUSICA framework will allow further explorations of subgrid-scale issues such as the segregation of trace gases on small scales through its ability to connect to large-eddy simulation models or via regional to local grid refinement.
Whole atmosphere.
Gravity waves couple the whole atmosphere through momentum and constituent transport and drive the general circulation of the middle atmosphere, while the Asian monsoon and tropopause folds transport constituents between the troposphere and the stratosphere. These phenomena are not adequately resolved in global models, and it is costly to simulate them with a global high-resolution mesh.
Regional refinement in MUSICA will resolve mesoscale gravity waves, reducing the dependence of circulation and transport in the middle and upper atmosphere on parameterizations. Since gravity waves can occur anywhere, adaptive grids would be ideal but are not likely to be realized in the near future. The primary challenges will be to 1) develop a “scale aware” gravity wave scheme that adjusts the parameterized wave spectrum within the grid refinement, 2) redevelop existing parameterizations to be consistent with the resolved gravity wave transports, and 3) address any spurious momentum forcings at the edges of the refinement where the newly resolved waves will decay (due to the coarsening grid) and potentially introduce artifacts in dynamics and transport.
In spite of these challenges, regional refinement in MUSICA presents new opportunities for research and model development. “Nature run” simulations with strategically placed refined meshes, in addition to remote sensing observations, can be used to develop more self-consistent parameterizations of gravity wave dynamics and transport for the global grid (e.g., Gardner et al. 2019). Regional refinement will better resolve key ocean–atmosphere coupling in subseasonal-to-seasonal forecasting regions, like the east Pacific, as well as finer horizontal transport in the Asian monsoon region (Table 1). However, vertical resolution remains the primary limit to modeling whole atmosphere coupling, and there is a need to explore the possibility of vertical refinement (Holt et al. 2016; Daniels et al. 2016; Garcia and Richter 2018).
Model evaluation and data assimilation.
The integration with observations will be a critical aspect of MUSICA. MUSICA needs to be capable of supporting field campaign design and analyses, satellite calibration and validation, retrieval algorithm development, and correlative analysis between different atmospheric quantities as well as data assimilation and inverse modeling. By appropriately accounting for the differences in the representation of processes at multiple scales, MUSICA will allow reducing uncertainties in the representativeness of the modeled and observed states of chemical constituents (e.g., Janjíc et al. 2017), and as a consequence, will improve the utility of model evaluation and interpretation. As such, MUSICA will be essential for fully exploiting available observational constraints and gains from, for example, the upcoming geostationary satellite constellation (Judd et al. 2018; Chance et al. 2019) (Table 1).
Available evaluation tools should provide basic statistical metrics for evaluating the ability of the model to fit large observational datasets, for analyzing complex time series and for establishing statistical relations between the concentrations of different species. A database of evaluation datasets will be compiled, with the ability for a user to select which datasets are appropriate for the model evaluation (e.g., satellite curtains and maps, aircraft tracks, surface stations, sonde profiles) Given the selected datasets, output will be sampled at the appropriate co-temporal and collocated points and written to files for easy comparison.
Data assimilation tools will build upon current capabilities, e.g., the Data Assimilation Research Testbed (DART; Anderson et al. 2009; Gaubert et al. 2016), the Community Gridpoint Statistical Interpolation (GSI) tool (Shao et al. 2016), or the Joint Effort for Data Assimilation Integration (JEDI, www.jcsda.org/jcsda-project-jedi). Similarly, evaluation and diagnostic tools will complement existing tools, e.g., the CESM diagnostics package (www.cesm.ucar.edu/working_groups/Atmosphere/amwg-diagnostics-package/), the ObsPack Diagnostics (Masarie et al. 2014) or the Aerosol Modeling Testbed (Fast et al. 2011). MUSICA requires more advanced evaluation and assimilation tools that span a wide range of spatial and temporal scales and needs to provide interfaces that make it easy for users to digest their own data. Evaluation tools also need to enable direct model to model intercomparison.
Chemical data assimilation faces specific challenges. These include high dimensionality and nonlinearity, irregular frequency distribution, representativeness, mass conservation issues, model errors, parameter uncertainties, and more importantly the need to account for emissions (e.g., surface boundary conditions). Methods will also have to account for the tight coupling within the chemical system (between multiple constituents of varying lifetimes) and across Earth system components (e.g., Carmichael et al. 2008; Sandu and Chai 2011; Bocquet et al. 2015).
The development of tangent linear and adjoint capabilities, along with efforts to improve and refine the ensemble forecast capability, will be an integral part of MUSICA’s development and not ex post facto as has been the case in the past. This offers a more effective and efficient development strategy, which can minimize many of the pitfalls (e.g., use of ad hoc approaches, high computational costs, redundant use of resources). Although still considered a frontier question, multiscale data assimilation (e.g., Nadeem et al. 2018) will be a target for MUSICA.
Technical challenges remain when assimilating chemical data on unstructured grids with variable resolution, particularly with regard to mass-conserving interpolation and grid transformation, similar to the challenges faced with emissions data (“Emissions and deposition” section). Development of scale-aware and consistent observation operators (i.e., transformation of states and parameters from model to observation spaces) is important. More advanced algorithms on localization and inflation (for ensemble Kalman filter and hybrid methods) and more flexible assimilation windows to specify different length scales (for 4D-Var) need to be developed to fully exploit the multiscale nature of the infrastructure.
Example implementations and current status
MUSICA is designed to be implemented under different configurations with, for example, different dynamical cores and different formulations of physical and chemical processes. This could include offline systems such as the GEOS-Chem High Performance model (Eastham et al. 2018), or online systems such as the ECMWF Integrated Forecast System (Huijnen et al. 2019). Once the models are adapted for compliance with CCPP and the principles underlying MUSICA, code and knowledge exchange could be significantly accelerated. However, a first implementation (MUSICA V0) will be achieved by configuring MUSICA within the CESM framework, which enables full interactions with ocean, ice, and land models.
As part of the System for Integrated Modeling of the Atmosphere (SIMA) project, CESM is being extended to support cross-scale simulations and includes the hydrostatic Spectral Element (SE) dynamical core with variable resolution in the atmosphere model (Lauritzen et al. 2018). This system allows global simulations with regional refinement down to a few kilometers. The addition of the Model for Prediction Across Scales–Atmosphere (MPAS-A; Skamarock et al. 2012, 2018) dynamical core and its physics parameterizations are underway and will extend the infrastructure to nonhydrostatic scales. A gas-phase-only version of the Chemistry and Aerosol Suite—the Model Independent Chemistry Module (MICM)—is being included in the early version of the model infrastructure and the inclusion of aerosols is underway. In a first release, the suite of Model for Ozone and Related Chemical Tracers (MOZART) mechanisms (Emmons et al. 2020) is integrated through MICM and a gas-phase chemistry box model version based on MICM (MusicBox) will be released to the community together with MUSICA V0 in summer 2020. Other chemical schemes are planned to be added to the framework by and with their developers, such as the mechanism used in the GEOS-Chem model (Bey et al. 2001).
Figure 3 shows surface ozone from a 1° uniform global-resolution simulation, which is compared to preliminary results from a MUSICA V0 simulation with a regionally refined grid of ∼14-km resolution over the contiguous United States embedded in a 1° global resolution (as shown in Fig. 2a). Despite the fact that both simulations are nudged to meteorological reanalyses, the regional refinement leads to changes not only over the higher-resolution region, but also affects the outflow and leads to differences over the downwind regions. Evaluation of these first MUSICA simulations is still underway but the infrastructure is now at a stage where assessment of individual processes and of scale awareness is possible and the increased value of a multiscale framework can be tested. It also provides a framework conducive for active engagement with the community.
Surface ozone for 1800 UTC 9 Aug 2013 (top) simulated at uniform global 1° horizontal resolution and (bottom) with a preliminary MUSICA V0 global 1° simulation that refines to 14-km resolution over the contiguous United States.
Citation: Bulletin of the American Meteorological Society 101, 10; 10.1175/BAMS-D-19-0331.1
An offline mass-conserving emission processing tool for mapping a variety of inventories to unstructured grid meshes with regional refinement will become available together with MUSICA V0. Development of a single online and offline emission tool for MUSICA has started and involves restructuring of the HEMCO emission module (Keller et al. 2014). The current CESM evaluation and diagnostics tool is being adapted to work with the framework. Sharing these beta versions with the wider community provides a framework for co-development and testing.
As for data assimilation, the adoption of MUSICA Version 0 with existing capabilities (e.g., DART) is envisaged to be straightforward and will require less than two years to develop and implement. We also foresee that a development of an observation package can be leveraged across different activities (model development, evaluation and testing, data assimilation).
Community involvement and outlook
The development of a multiscale infrastructure that meets the scientific needs of the entire user community represents an exciting challenge that necessitates strong community involvement and partnerships among different organizations and different disciplines ranging from laboratory studies and field experiments to statistics and computational sciences and from molecular chemistry to space physics. The community is invited at an early stage to engage in the development and design of the MUSICA framework. The new capabilities represented by MUSICA will deepen existing, and establish new, working relations of the research community with a variety of users ranging from the research community to stakeholders. MUSICA will contribute to both advancing the science and to providing relevant and actionable information for the development of mitigation policies or warning systems.
Within the next few years, MUSICA will gradually replace the current suite of community chemistry models supported by NCAR and is envisioned to also integrate the capabilities of other modeling capabilities in the community. It will provide efficiencies through consolidation of model development and training efforts and provide a single point of entry for the majority of end-users. The transition phase will be dictated by the progress of MUSICA in providing at a minimum the capabilities of current models. The transition will be accompanied by educational activities including user guides and in-person and online tutorials. MUSICA User Tutorials will occur annually, but we envision the community taking part in offering training opportunities (e.g., through regional MUSICA “hubs”).
After the challenges posed by the multiscale nature of the new infrastructure are met, the atmospheric community will be able to leverage MUSICA to advance our understanding of the multiscale couplings across the range of spatial scales, throughout the whole atmosphere, and across different components of the Earth system. The MUSICA framework, with its inherent modularity and flexibility and ability to tailor configurations to specific issues, as well as its emphasis on global community participation, is an excellent vehicle to address modern, multiscale, and complex problems. MUSICA is envisioned to become an engine of convergent research by not only advancing chemistry/aerosol/air quality modeling capabilities but by providing a framework for advancing research in the entire Earth system sciences.
Acknowledgments
The National Center for Atmospheric Research is sponsored by the National Science Foundation. The authors thank Rebecca Schwantes, Forrest Lacey, and Olivia Clifton (NCAR) for valuable contributions to the manuscript. We further acknowledge the valuable suggestions by three anonymous reviewers. Daniel Jacob, Sebastian Eastham, and Kelley Barsanti acknowledge support from the NSF Atmospheric Chemistry Program. Jerome Fast is supported by the U.S. Department of Energy’s Atmospheric System Research (ASR) program. Xiaohong Liu acknowledges support from the U.S. Department of Energy’s Earth System Modeling Development Program.
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