A Blueprint for a Joint Meteorology and Atmospheric Composition Program

For greenhouse gas (GHG) observations to more effectively inform climate management strategies, we must be able to better identify the timing, location, and magnitude of surface emissions and removals. Making atmospheric composition data actionable requires improved traceability to surface fluxes. For this, we need better observation of the vertical distribution of trace gases and better modeling of vertical atmospheric mixing. Uncertainty in vertical transport and mixing has been especially problematic because of two factors: (1) long tracer lifetimes can lead to accumulation of vertical mixing errors over time and space, and (2) covariance of vertical mixing with surface fluxes confounds attribution of trace gas data to surface fluxes. These problems are exacerbated by the presence of clouds and wind shear, which can obscure the origin of trace gases. A new generation of models and space-based GHG and wind remote sensing techniques is emerging. These tools show promise for observing and simulating the small scales at which vertical mixing occurs, with near-global coverage. Spaceborne GHG missions will continue to close spatial and temporal sampling gaps, increasingly target collocated species (CO2, CH4, CO, NOx), and vertical gradients (via multi-spectral lidar and spectrometers) for improved sectoral attribution of carbon emissions and removals. Wind missions leveraging passive and active techniques to track the motion of cloud and trace gas spatial features, cloud liquid and ice hydrometeors (radar and lidar), and air/particulates (lidar) are improving our ability to track vertical and horizontal motion within and around clouds. High-resolution numerical weather prediction and climate models and machine learning-driven forecasting that resolve deep convection and permit shallow convection are improving the statistics of vertical mixing at regional scales. The combination of wind and GHG observations with high-resolution models will strengthen our knowledge of GHG mixing, connecting surface exchange to atmospheric abundances. To provide scientific guidance on how to bring these modeling and observing tools together for more accurate GHG and air quality climate data, the Earth science community needs to move beyond single instrument teams to tackle integrated science challenges. We recommend the development and coordination of a joint meteorology and atmospheric composition program, whose goal is to vastly improve GHG source and sink quantification while simultaneously advancing our understanding of vertical atmospheric mixing. We envision a three-tiered model-observation integration approach to reduce uncertainty in vertical mixing based on existing and future observations: · Diagnosis–Comparing models to observations to identify process uncertainty, · Optimization–Assimilation of observations into models to optimize parameters and state, and · Prediction–Forward and inverse simulation using calibrated model ensembles. A key component of this approach is the development of testbeds to inform vertical mixing, building on coordinated programs such as the European Union-led Carbon Atmospheric Tracer Research to Improve Numerics and Evaluation (CATRINE) project. These recommendations are supported by the National Academies Earth Science Decadal Survey Midterm findings to expand collaboration opportunities, and to more actively engage the modeling communities.