Steven Pawson

Many of today's most significant and urgent scientific questions, including those about the impact of human activity on air quality and radiative forcing, require the synthesis of observations of atmospheric constituents and scientific theory. The past three decades have each seen a step‐change in the coverage, accuracy, and precision of constituent observations, and hence the questions they can address, especially from the growing constellation of Earth‐observing satellites. This paper is the first comprehensive description of the design and implementation of NASA's Constituent Data Assimilation System (CoDAS), a software package for assimilating constituent observations into the Goddard Earth Observing System (GEOS), an integrated collection of Earth system models. CoDAS is a generalized, tracer‐agnostic system that extends previous capabilities to assimilate multiple species from multiple sensors configurable at run time. Data assimilation provides a means of monitoring constituent changes, intercomparing heterogeneous types of observations, and reconciling data and models. Assimilation produces an optimal synthesis of the ingested data and model, taking advantage of the fact that the statistics of model‐data differences are simpler than those of the observations themselves. The use of a model enables the propagation of information from all previous observations across space and time, which can improve estimates even where and when data are unavailable.

Numerical experimentation using an atmospheric configuration of the Goddard Earth Observing System (GEOS) Earth System Model is used to examine the vertical structure of the simulated Quasi‐Biennial Oscillation (QBO). Using an improved representation of the parameterized gravity wave spectrum, it is demonstrated that with sufficient vertical resolution (about 300–500 m grid spacing) in the lower stratosphere, the model can capture a QBO that extends all the way down to the tropical tropopause, in agreement with observations. The statistical characteristics of this simulated QBO closely resemble those of the observed oscillation, including the bimodal wind distribution, amplitude, mean period, and event‐to‐event variations. This substantial improvement over previous model simulations is attributed to both the careful tuning of the parameterized spectrum of traveling gravity waves and the enhanced vertical resolution in this version of the GEOS model.

The launch of the Tropospheric Emissions: Monitoring of Pollution (TEMPO) mission in 2023 marked a new era in air quality monitoring by providing high‐frequency, geostationary observations of column NO ₂ across most of North America. In this study, we present the first implementation of a TEMPO NO ₂ data assimilation system using the Joint Effort for Data assimilation Integration (JEDI) framework. Leveraging a four‐dimensional ensemble variational (4DEnVar) approach and an Ensemble of Data Assimilations (EDA), we demonstrate a novel capability to assimilate hourly NO ₂ retrievals from TEMPO alongside polar‐orbiting TROPOspheric Monitoring Instrument (TROPOMI) data into NASA's GEOS Composition Forecast (GEOS‐CF) model. The system is evaluated over the CONUS region for August 2023, using a suite of independent measurements including Pandora spectrometers, AirNow surface stations, and aircraft‐based observations from Atmospheric Emissions and Reactions Observed from Megacities to Marine Areas (AEROMMA) and Synergistic TEMPO Air Quality Science (STAQS) field campaigns. Results show that the assimilation system successfully integrates geostationary NO ₂ observations, improves model performance in the column, and captures diurnal variability. However, assimilation also leads to systematic reductions in NO ₂ levels, which improves agreement with some data sets (e.g., Pandora, AEROMMA) but degrades comparisons with others (e.g., STAQS). These findings highlight the importance of joint evaluation across platforms and motivate further development of dual‐concentration emission assimilation schemes. While the system imposes high computational costs, primarily from the forecast model, ongoing efforts to integrate AI‐based model emulators offer a promising path toward scalable, real‐time assimilation of geostationary atmospheric composition data.

Many of today’s most significant and urgent scientific questions, including those about the impact of human activity on air quality and radiative forcing, require the synthesis of observations of atmospheric constituents and scientific theory. The past three decades have each seen a step-change in the coverage, accuracy, and precision of constituent observations, and hence the questions they can address, especially from the growing constellation of Earth-observing satellites. This paper describes the design and implementation of NASA’s Constituent Data Assimilation System (CoDAS), an open-source software package for assimilating constituent observations into the Goddard Earth Observing System (GEOS), a collection of integrated models of the Earth’s atmosphere, ocean, and land surface. Data assimilation provides a means of monitoring constituent changes, intercomparing heterogeneous types of observations, and reconciling data and models. Assimilation produces an optimal synthesis of the ingested data and model, taking advantage of the fact that the statistics of model-data differences are simpler than those of the observations themselves. The use of a model enables the propagation of information from all previous observations across space and time, which can improve estimates even where and when data are unavailable.

The launch of the Tropospheric Emissions: Monitoring of Pollution (TEMPO) mission in 2023 marked a new era in air quality monitoring by providing high-frequency, geostationary observations of column NO2 across most of North America. In this study, we present the first implementation of a TEMPO NO2 data assimilation system using the Joint Effort for Data assimilation Integration (JEDI) framework. Leveraging a four-dimensional ensemble variational (4DEnVar) approach and an Ensemble of Data Assimilations (EDA), we demonstrate a novel capability to assimilate hourly NO2 retrievals from TEMPO alongside polar-orbiting TROPOspheric Monitoring Instrument (TROPOMI) data into NASA’s GEOS Composition Forecast (GEOS-CF) model. The system is evaluated over the CONUS region for August 2023, using a suite of independent measurements including Pandora spectrometers, AirNow surface stations, and aircraft-based observations from Atmospheric Emissions and Reactions Observed from Megacities to Marine Areas (AEROMMA) and Synergistic TEMPO Air Quality Science (STAQS) field campaigns. Results show that the assimilation system successfully integrates geostationary NO2 observations, improves model performance in the column, and captures diurnal variability. However, assimilation also leads to systematic reductions in NO2 levels, which improves agreement with some datasets (e.g., Pandora, AEROMMA) but degrades comparisons with others (e.g., STAQS). These findings highlight the importance of joint evaluation across platforms and motivate further development of dual-concentration emission assimilation schemes. While the system imposes high computational costs, primarily from the forecast model, ongoing efforts to integrate AI-based model emulators offer a promising path toward scalable, real-time assimilation of geostationary atmospheric composition data.

Accurately modeling the large‐scale transport of trace gases and aerosols is critical for interpreting past (and projecting future) changes in atmospheric composition. Simulations of the stratospheric mean age‐of‐air continue to show persistent biases in chemistry climate models, although the drivers of these biases are not well understood. Here we identify one driver of simulated stratospheric transport differences among various NASA Global Earth Observing System (GEOS) candidate model versions under consideration for the upcoming GEOS Retrospective analysis for the Century (GEOS‐R21C). In particular, we show that the simulated age‐of‐air values are sensitive to the so‐called “remapping” algorithm used within the finite‐volume dynamical core, which controls how individual material surfaces are vertically interpolated back to standard pressure levels after each horizontal advection time step. Differences in the age‐of‐air resulting from changes within the remapping algorithm approach 1 year over the high latitude middle stratosphere—or about 30% climatological mean values—and imprint on several trace gases, including methane () and nitrous oxide (O). These transport sensitivities reflect, to first order, changes in the strength of tropical upwelling in the lower stratosphere (70–100 hPa) which are driven by changes in resolved wave convergence over northern midlatitudes as (critical lines of) wave propagation shift in latitude. Our results strongly support continued examination of the role of numerics in contributing to transport biases in composition modeling.

Stratospheric water vapor (SWV) is a greenhouse gas that has an important, yet uncertain, impact on the Earth's climate through its radiative effect and feedback. As the climate changes, it is thus critical to monitor and understand changes in SWV. NASA's Microwave Limb Sounder (MLS) aboard the Aura satellite has observed SWV since 2004 but is reaching end of life. The Stratospheric Aerosol and Gas Experiment (SAGE) missions observe SWV as well, with the SAGE III instrument operating on the International Space Station (ISS) since 2017. We use the constituent data assimilation capabilities of NASA's Goddard Earth Observing System (GEOS) to demonstrate that the up to 30 SAGE III/ISS profiles each day provide a useful constraint over the observed midlatitudes and tropics. We conclude that by assimilating SAGE III/ISS SWV into GEOS we can largely continue to monitor SWV after Aura MLS.

The middle and upper atmosphere plays a critical role in linking the lower atmosphere forcing with ionospheric variability, especially during strong atmospheric activities. This study examines the dynamical response in the altitude range from ∼20 to 80 km to a major Sudden Stratospheric Warming (SSW) event peaking on 11 February 2018. We compare the reanalysis product of the Modern‐Era Retrospective Analysis for Research and Applications Version 2 (MERRA‐2) from the Goddard Earth Observing System (GEOS) to the satellite observations by Thermosphere Ionosphere and Mesosphere Electric Dynamics (TIMED)/Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) TIMED/SABER that are not assimilated in MERRA‐2. Our study shows that the zonal mean wind and temperature and planetary wave 1 and 2 variations are generally consistent between the reanalysis and observations. We also identify a strong ∼6 day wave propagating both westward and eastward with zonal wavenumber‐1 with the westward propagating component likely generated by baroclinic/barotropic instability. However, important disagreements arise specifically above ∼60 km, where the wind and temperature are not well represented in MERRA‐2, causing differences in the day‐to‐day development of 6 day wave. This study highlights the need for additional assimilation of mesospheric data and development of high‐altitude vertically extended GEOS model.

Stratospheric water vapor (SWV) is a greenhouse gas that has a significant, yet uncertain, impact on the Earth’s climate through its radiative effect and feedback. As the climate changes, it is thus critical to monitor and understand changes in SWV. NASA’s Microwave Limb Sounder (MLS) aboard the Aura satellite has observed SWV since 2004 but will soon reach end of life. The Stratospheric Aerosol and Gas Experiment (SAGE) missions observe SWV as well, with the SAGE III instrument operating on the International Space Station (ISS) since 2017. We use the constituent data assimilation capabilities of NASA’s Goddard Earth Observing System (GEOS) to demonstrate that the up to 30 SAGE III/ISS profiles each day provide a useful constraint on SWV from 500 to 1500 K over the observed midlatitudes and tropics. We conclude that by assimilating SAGE III/ISS SWV into GEOS we can continue to monitor SWV after Aura MLS.

In the face of a changing climate, the understanding, predictions, and projections of natural and human systems are increasingly crucial to prepare and cope with extremes and cascading hazards, determine unexpected feedbacks and potential tipping points, inform long‐term adaptation strategies, and guide mitigation approaches. Increasingly complex socio‐economic systems require enhanced predictive information to support advanced practices. Such new predictive challenges drive the need to fully capitalize on ambitious scientific and technological opportunities. These include the unrealized potential for very high‐resolution modeling of global‐to‐local Earth system processes across timescales, reduction of model biases, enhanced integration of human systems and the Earth Systems, better quantification of predictability and uncertainties; expedited science‐to‐service pathways, and co‐production of actionable information with stakeholders. Enabling technological opportunities include exascale computing, advanced data storage, novel observations and powerful data analytics, including artificial intelligence and machine learning. Looking to generate community discussions on how to accelerate progress on U.S. climate predictions and projections, representatives of Federally‐funded U.S. modeling groups outline here perspectives on a six‐pillar national approach grounded in climate science that builds on the strengths of the U.S. modeling community and agency goals. This calls for an unprecedented level of coordination to capitalize on transformative opportunities, augmenting and complementing current modeling center capabilities and plans to support agency missions. Tangible outcomes include projections with horizontal spatial resolutions finer than 10 km, representing extremes and associated risks in greater detail, reduced model errors, better predictability estimates, and more customized projections to support next generation climate services.