What should be included in a work plan, and what resources are needed to address the research objectives identified above?

How can atmospheric dispersion modeling techniques provide reliable emission estimates in atmospheric inversion modeling that starts with GHG concentration data?

What are the advantages/disadvantages of the mixed method (Eulerian and Lagrangian) vs. pure Eulerian? How can we make their solutions converge for non-reactive species?

• Develop infrastructure to provide reliable emission estimates using GHG concentration observations as key input

Goal

• One or more GHG weather prediction systems with integrated source estimation

Solution

• Atmospheric prediction models that include GHG concentrations
• Data Assimilation (DA) systems that can ingest both GHG concentration �and traditional meteorological observations and produce improved �estimates of concentrations and sources/sinks
• GHG and other observations required to estimate sources/sinks

System Components

• Clearly defined formal requirements and evaluation metrics
• Guidance from system design experts
• Observing system simulation experiments (OSSEs)

Systems Design Process

• Movement of GHG through the PBL
• Atmospheric convection, microphysics, clouds, radiation and other physics
• Land/sea interaction with the PBL
• Vegetation modeling, especially for GHG sources/sinks

Model Improvements

• Ability to estimate sources/sinks of GHG from concentration observations
• Evaluation of relative capabilities of variational and ensemble systems
• Capability to provide useful uncertainty estimates
• Coupled land/biosphere/atmosphere DA

DA System Needs

• OSSEs to quantify information available from current and possible future observations
• Accurate estimates of observation errors, especially for GHG concentrations
• Use of observations from diverse sources: in-situ, remote sensing, drone, aircraft, satellite
• Observations of short-lived tracers

Observing System Requirements

• Synergy with infrastructure development efforts for other prediction applications
• Leveraging ongoing efforts in chemical weather prediction
• Use of observations that are not currently being assimilated
• Use of DA systems to guide improvement in models and observing systems

Opportunities

• Appropriate personnel for system design tasks
• Significant computational resources that will be essential for OSSEs and related system design explorations
• DA systems compatible with the models and observations explored as part of the design process

Resources Needed

What should be included in a work plan, and what resources are needed to address the research objectives identified above?

Are there measurement opportunities that can provide an improved understanding of turbulent processes and dispersion in the planetary boundary layer (PBL) that will contribute directly to the reduction in uncertainty in source attribution of GHG concentrations?

Generate & Synthesize Data

Expedite Innovations

Overarching Challenge: How to prioritize and target observations?

• Multiscale problem
• Co-locating multiscale observation footprints & continuous observations
• Point in situ observations—surface fluxes, mean concentrations
• Vertical and horizontal variability in GHG concentrations, atmospheric thermodynamics and dynamics
• ABL depth, entrainment processes, ground surface conditions

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• Targeting necessary locations over a wide range of disparate sources and sinks
• Observations must go beyond the flat and homogeneous terrain assumptions to characterize the impacts of complex terrain and multi-scale surface and vegetation heterogeneities.
• Observations must focus also on understudied regions and conditions—especially the stable boundary layer

Specific Measurement Challenges

Communication and Collaboration Challenges

Instrumentation Limitations

• Assay existing infrastructure for new measurements
• Use to evaluate models and identify deficiencies and gaps in datasets
• To inform new field campaigns
• Bolster networking and team-building

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• Resource pooling
• Instrumentation
• Data processing protocols
• Data sharing
• Modeling tools

Synthesis of existing datasets: Across climate and land use types support of MMRV

• Create & support large-scale networks (e.g., Ribbit network, similar to Purple Air)
• Develop inexpensive, small, portable, and robust sensors sensors that could be employed in ensembles
• Use with open-source drone technology
• New opportunities for citizen involvement and support (e.g., Purple Air for air quality)

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• Create & promote affordable instrumentation leasing and training programs, e.g., Center for Transformative Environmental Monitoring Programs (CTEMPs), such as
• Ground-based remote sensing equipment
• GHG flux sensors (particularly for methane)
• Distributed temperature sensing

Develop new technologies to facilitate additional measurement opportunities

ribbitne twork.org/

purpleair.com

• Obtaining a full assay of existing data across climatic and land-use types
• Direct collaborations between specialists in modeling and in observations will be crucial to the dataset-synthesis process
• Development of a diverse workforce
• Targeted field campaigns

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• Understanding the resources needed to develop new observational technologies will require further study

Resources Needed

What should be included in a work plan, and what resources are needed to address the research objectives identified above?

What processes need to be better represented in the models to minimize location and quantification uncertainties?

• Improve representation of PBL structure, pollutant transport within the PBL and interaction between the PBL and free troposphere, greenhouse gas surface fluxes, and tropospheric chemistry

Goal

• Appropriate staffing support to conduct the needed research.
• Computational resources supporting the modeling and data science efforts.

Resources Needed

• Initial hurdle of air quality emission modelers and GHG emission modelers working together to develop a consistent emissions modeling framework

Collaboration

• PBL height, growth, and collapse; PBL parameterizations
• Atmospheric stability
• Turbulence parameterizations
• Convective transport
• Entrainment/detrainment processes
• Surface properties, such as surface heat, moisture, and momentum fluxes
• Aerosol cloud radiation impacts on surface heating
• Local scale circulation patterns (i.e., mountain-valley flow and sea breeze circulations)

Address uncertainties in model representation of PBL structure, evolution, and processes impacting GHG transport through model evaluation and further model development, including in areas with complex topography and spatial heterogeneity �(i.e., coastlines)

• Create eddy-resolved benchmark simulations with high spatial resolutions to help guide the further development of parameterization schemes for coarser model simulations

Create

• Coordinate efforts with air quality community to address common GHG and air quality related surface fluxes (i.e., anthropogenic emissions and biogenic fluxes)

Coordinate

• Advance prediction capability by including tropospheric chemistry, two way feedback between aerosols and meteorology, and biogeochemical and biogeophysical feedbacks

Advance

What should be included in a work plan, and what resources are needed to address the research objectives identified above?

What protocols do we need to set in place for a robust intercomparison and assessment of the impact of improvements in modeling of the PBL and GHG exchanges between Earth’s surface and its atmosphere?

• Develop protocols for model intercomparison and assessment that span the diverse approaches used for GHG-relevant modeling of the atmosphere, land, and emissions
• Provide quantitative targets for model assessment that are appropriate across modeling methods that account for differences in represented scales
• Support development of novel diagnostic methods that connect modeling approaches and elucidate strengths and weaknesses of different methods

Goals

• Engage disparate sub-disciplines that have historically worked nearly independently from each other.
• Identify commonalities and differences in modeling approaches, terminology, and validation standards

Collaboration

• Adopt a standard definition of PBL height that can be applied across modeling approaches and be determined from standard observations
• Develop a standardized set of observational data that is accessible to the community

Standards

• Develop and deploy software for diagnostics and metrics in support of MMRV

Software

• Building on successes of past model intercomparison activities - from CMIP to RCEMIP to TransCom2 - may provide foundations for cyberinfrastructure and experimental design principles

Build on Past Success

• Unify the disparate approaches to PBL modeling framed by the common challenge of understanding GHG transport
• Opportunity for synergy that will advance PBL modeling across disciplines

Unified Approach

• Results of model intercomparison will feedback on the design of observational networks by identifying key quantities needed for MMRV of GHG emissions and transport

Feedback Loop

• Project support for organizing task teams and coordination among them
• Virtual and in-person collaborative workshops

Logistical Support

• Computational resources for running models and performing analysis
• Archival storage for the data
• A community portal for data access

Cyberinfrastructure

• Contributing model results to a common repository
• Developing standards for PBL height and other relevant diagnostics used for validation and evaluation
• Co-designing diagnostic software

Community engagement

Workshop Organization

• Hanne Mauriello�(UCAR/CPAESS - PI and workshop planning)
• Cindy Bruyère�(UCAR/CPAESS - co-PI and workshop planning)
• Wendy Gram�(UCAR/COMET - facilitation)
• Bob Henson�(Independent - writing/editing)
• Glen Romine�(NCAR Directorate - workshop planning)
• Maggie Costley�(UCAR/CPAESS - event planning)