The Japan Aerospace Exploration Agency (JAXA) has developed a novel partial column CO2 (XCO2) dataset from Japan’s Greenhouse Gases Observing Satellite (GOSAT) that partitions XCO2 into contributions from the lower troposphere (surface to ~4 km a.g.l.) and upper troposphere (~4 km to ~12 km a.g.l.). Evaluating this two-layer product is essential for its application in studies of atmospheric CO2 distributions and surface fluxes. Here, we assess the JAXA/GOSAT two-layer XCO2 using aircraft measurements from ACT-America, ATom, NOAA aircraft profiling network, and other available datasets, along with global model ensembles from the OCO-2 Model Intercomparison Project (MIP). GOSAT XCO2 generally agrees well with aircraft measurements in zonal means for both tropospheric layers, demonstrating its ability to capture large-scale vertical CO2 structures. A notable low bias of up to 10 ppm is identified in northern high latitudes (50°–80°N). Over northern midlatitudes, particularly North America where aircraft coverage is most extensive, GOSAT shows better agreement with observations in lower-tropospheric XCO2 than the OCO-2 MIP simulations, suggesting potential biases in model surface fluxes and/or transport. Significant differences between GOSAT and OCO-2 MIP are found in both layers over the Amazon (2–10 ppm), southern China (0–8 ppm), India (5–8 ppm), tropical Africa (2–10 ppm), and the Arctic (>10 ppm). However, limited aircraft data in these regions constrain independent validation. Our findings demonstrate that the GOSAT two-layer XCO2 provides a valuable constraint for identifying possible biases in CO2 fluxes and vertical mixing in current global models and has potential to improve surface CO2 flux inversions.

Abstract 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.

The Representative Concentration Pathways (RCPs) are a set of four harmonized greenhouse gas emission and concentration trajectories, representative of scenarios from the literature, that would result in radiative forcings of 2.6, 4.5, 6.0, and 8.5 Wm − 2 by the year 2100. The RCPs show a particularly large range of methane (CH 4 ) trajectories early in the first quarter of the century, with vastly different implications for near-future climate warming. Using observation-constrained global atmospheric methane estimates from NASA’s Goddard Earth Observing System (GEOS) model, we estimate that atmospheric methane has been consistently accelerating from 2001 through 2024, driven by microbial emissions; only RCP8.5 (associated with a radiative forcing of 8.5 Wm − 2 ) models an acceleration during this time. A continuation of this acceleration would produce average concentrations between 2360 ppb and 2600 ppb (95% confidence) by midcentury, closest to RCP8.5. Thus, scenarios that include methane trajectories between the sharp acceleration of RCP8.5 and the much smaller and more stable growth of RCPs 4.5 and 6 should be resolved. Continued atmospheric monitoring and further investigation of the microbial processes that may be causing this increase, including both agriculture and wetland climate feedbacks, are necessary to produce realistic predictions and mitigation scenarios.

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.

Reanalysis datasets are widely used to understand atmospheric processes; however, different reanalyses may give very different results for the same diagnostics. The Atmospheric Processes And their Role in Climate (APARC; formerly SPARC) Reanalysis Intercomparison Project, or S(soon to be A)-RIP (https://s-rip.github.io/), is a coordinated activity to compare key diagnostics among atmospheric reanalyses, identify differences among reanalyses and their underlying causes, provide guidance on appropriate usage of reanalyses in scientific studies, and contribute to future improvements in the reanalysis products via collaborations with reanalysis centers and data users. S-RIP Phase 1 (completed in early 2022) focused primarily on the upper troposphere and above and processes linking these regions to the troposphere and surface. We are broadening our efforts in Phase 2 (S-RIP2), with new directions including studies of the tropospheric circulation, extreme weather events, and their links to the stratosphere, along with evaluation of chemical reanalyses, both those with a stratosphere / upper troposphere focus and those that focus on air quality applications. This presentation will provide a summary of Phase 1 results and discussion of future directions for S-RIP2, emphasizing applications to composition and chemistry studies and capacity building for Early Career Scientists.

Inverse model intercomparison projects (MIPs) provide a chance to assess the uncertainties in inversion estimates arising from various sources. However, accurately quantifying ensemble CO2 flux errors remains challenging and often relies on the ensemble spread. This study proposes a method for quantifying the errors in regional net surface–atmosphere CO2 flux estimates from models taken from the Orbiting Carbon Observatory-2 (OCO-2) v10 MIP by using independent airborne CO2 measurements for the period 2015–2017. We first calculate the root mean square error (RMSE) between the ensemble mean of posterior CO2 concentrations and airborne observations and then isolate the CO2 concentration errors caused solely by the ensemble mean of posterior net fluxes by subtracting the observation, representation, and transport errors from seven regions. Our analysis reveals that the flux errors projected onto CO2 space account for 55 %–85 % of the regional average RMSE over the 3 years, ranging from 0.88 to 1.91 ppm. In five regions, the error estimates based on observations exceed those computed from the ensemble spread of posterior fluxes by a factor of 1.33–1.93, implying an underestimation of the actual flux errors, while their magnitudes are comparable in two regions. The adjoint sensitivity analysis identifies that the underestimation of flux errors is prominent where the magnitudes of fossil fuel emissions exceed those of terrestrial-biosphere fluxes by a factor of 3–31 over the 3 years. This suggests the presence of systematic biases in the inversion estimates associated with errors in the prescribed fossil fuel emissions common to all models. Our study emphasizes the value of airborne measurements for quantifying regional errors in ensemble net CO2 flux estimates.

The accurate representation of tropospheric hydroxyl radical (TOH) is crucial for reasonably modeling methane concentrations — a potent greenhouse gas. We use an improved parameterization of TOH using an interpretable and agile machine learning module named ECCOH (pronounced "echo") in NASA's GEOS global model to unravel the intricacies of TOH to its key inputs. However, the accuracy of this model is hampered by the accurate representation of its critical inputs. Fortunately, retrieving trace gases like nitrogen dioxide (NO2) and formaldehyde (HCHO) from space-borne sensors, like the Aura Ozone Monitoring Instrument (OMI), has seen remarkable progress. Consequently, we leverage these observations to assess how they can effectively alleviate some biases in TOH and can help better reproduce its long-term trends. In contrast to the earlier investigations, the refined representation of TOH archives a finer spatial resolution (1x1 degrees), and it is more up to date (2005-2019), allowing for elucidating the impact of recent emission regulations, such as those imposed in China, on TOH. OMI NO2 yields valuable insights over biomass-burning areas in Eastern Europe and central Africa, where our prior emission estimates possess significant biases, mitigating regional TOH biases up to 20%. Oceanic HCHO concentrations, serving as a proxy for TOH due to the predominant chemical pathway of VOC oxidation through OH, are only moderately altered by OMI HCHO, attributed to low signal-to-noise ratios and satisfactory representation of HCHO in the a priori simulations. Ultimately, we disentangle the convoluted map of TOH linear trends by isolating five pivotal inputs to the TOH parameterization, including stratospheric ozone, tropospheric ozone, water vapor, HCHO, and NO2. Our results demonstrate that these five parameters can collectively explain 65% of the variability in TOH trends alone. With the deployment of new satellites with enhanced sensor configurations and better temporal resolutions, our mission at NASA is to exploit those observations to improve the representation of many variables highly linked to TOH.

The tropospheric hydroxyl (TOH) radical is a key player in regulating oxidation of various compounds in Earth's atmosphere. Despite its pivotal role, the spatiotemporal distributions of OH are poorly constrained. Past modeling studies suggest that the main drivers of OH, including NO2, tropospheric ozone (TO3), and H2O(v), have increased TOH globally. However, these findings often offer a global average and may not include more recent changes in diverse compounds emitted on various spatiotemporal scales. Here, we aim to deepen our understanding of global TOH trends for more recent years (2005–2019) at 1×1°. To achieve this, we use satellite observations of HCHO and NO2 to constrain simulated TOH using a technique based on a Bayesian data fusion method, alongside a machine learning module named the Efficient CH4-CO-OH (ECCOH) configuration, which is integrated into NASA's Goddard Earth Observing System (GEOS) global model. This innovative module helps efficiently predict the convoluted response of TOH to its drivers and proxies in a statistical way. Aura Ozone Monitoring Instrument (OMI) NO2 observations suggest that the simulation has high biases for biomass burning activities in Africa and eastern Europe, resulting in a regional overestimation of up to 20 % in TOH. OMI HCHO primarily impacts the oceans, where TOH linearly correlates with this proxy. Five key parameters, i.e., TO3, H2O(v), NO2, HCHO, and stratospheric ozone, can collectively explain 65 % of the variance in TOH trends. The overall trend of TOH influenced by NO2 remains positive, but it varies greatly because of the differences in the signs of anthropogenic emissions. Over the oceans, TOH trends are primarily positive in the Northern Hemisphere, resulting from the upward trends in HCHO, TO3, and H2O(v). Using the present framework, we can tap the power of satellites to quickly gain a deeper understanding of simulated TOH trends and biases.

Abstract The atmospheric CO2 growth rate is a fundamental measure of climate forcing. NOAA's growth rate estimates, derived from in situ observations at the marine boundary layer (MBL), serve as the benchmark in policy and science. However, NOAA's MBL‐based method encounters challenges in accurately estimating the whole‐atmosphere CO2 growth rate at sub‐annual scales. Here we introduce the Growth Rate from Satellite Observations (GRESO) method as a complementary approach to estimate the whole‐atmosphere CO2 growth rate utilizing satellite data. Satellite CO2 observations offer extensive atmospheric coverage that extends the capability of the current NOAA benchmark. We assess the sampling errors of the GRESO and NOAA methods using 10 atmospheric transport model simulations. The simulations generate synthetic OCO‐2 satellite and NOAA MBL data for calculating CO2 growth rates, which are compared against the global sum of carbon fluxes used as model inputs. We find good performance for the NOAA method (R = 0.93, RMSE = 0.12 ppm year−1 or 0.25 PgC year−1). GRESO demonstrates lower sampling errors (R = 1.00; RMSE = 0.04 ppm year−1 or 0.09 PgC year−1). Additionally, GRESO shows better performance at monthly scales than the NOAA method (R = 0.76 vs. 0.47, respectively). Due to CO2's atmospheric longevity, the NOAA method accurately captures growth rates over 5‐year intervals. GRESO's robustness across partial coverage configurations (ocean or land data) shows that satellites can be promising tools for low‐latency CO2 growth rate information, provided the systematic biases are minimized using in situ observations. Along with accurate and calibrated NOAA in situ data, satellite‐derived growth rates can provide information about the global carbon cycle at sub‐annual scales.

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 Sounding (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 over the observed midlatitudes and tropics. We conclude that assimilating SAGE III/ISS SWV into GEOS can continue the SWV climate data record of Aura MLS.

Abstract. Multi-inverse modeling inter-comparison projects (MIPs) provide a chance to assess the uncertainties in inversion estimates arising from various sources such as atmospheric CO2 observations, transport models, and prior fluxes. However, accurately quantifying ensemble CO2 flux errors remains challenging, often relying on the ensemble spread as a surrogate. This study proposes a method to quantify the errors of regional terrestrial biosphere CO2 flux estimates from 10 inverse models within the Orbiting Carbon Observatory-2 (OCO-2) MIP by using independent airborne CO2 measurements for the period 2015–2017. We first calculate the root-mean-square error (RMSE) between the ensemble mean of posterior CO2 concentration estimates and airborne observations and then isolate the CO2 concentration error caused solely by the ensemble mean of posterior terrestrial biosphere CO2 flux estimates by subtracting the errors of observation and transport in seven regions. Our analysis reveals significant regional variations in the average monthly RMSE over three years, ranging from 0.90 to 2.04 ppm. The ensemble flux error projected into CO2 space is a major component that accounts for 58–84 % of the mean RMSE. We further show that in five regions, the observation-based error estimates exceed the atmospheric CO2 errors computed from the ensemble spread of posterior CO2 flux estimates by 1.37–1.89 times, implying an underestimation of the actual ensemble flux error, while their magnitudes are comparable in two regions. By identifying the most sensitive areas to airborne measurements through adjoint sensitivity analysis, we find that the underestimation of flux errors is prominent in eastern parts of Australia and East Asia, western parts of Europe and Southeast Asia, and midlatitude North America, suggesting the presence of systematic biases related to anthropogenic CO2 emissions in inversion estimates. The regions with no underestimation were southeastern Alaska and northeastern South America. Our study emphasizes the value of independent airborne measurements not only for the overall evaluation of inversion performance but also for quantifying regional errors in ensemble terrestrial biosphere flux estimates.

The version 10 (v10) Atmospheric Carbon Observations from Space (ACOS) Level 2 full-physics (L2FP) retrieval algorithm has been applied to multiyear records of observations from NASA's Orbiting Carbon Observatory 2 and 3 sensors (OCO-2 and OCO-3, respectively) to provide estimates of the carbon dioxide (CO2) column-averaged dry-air mole fraction (XCO2). In this study, a number of improvements to the ACOS v10 L2FP algorithm are described. The post-processing quality filtering and bias correction of the XCO2 estimates against multiple truth proxies are also discussed. The OCO v10 data volumes and XCO2 estimates from the two sensors for the time period of August 2019 through February 2022 are compared, highlighting differences in spatiotemporal sampling but demonstrating broad agreement between the two sensors where they overlap in time and space. A number of evaluation sources applied to both sensors suggest they are broadly similar in data and error characteristics. Mean OCO-3 differences relative to collocated OCO-2 data are approximately 0.2 and −0.3 ppm for land and ocean observations, respectively. Comparison of XCO2 estimates to collocated Total Carbon Column Observing Network (TCCON) measurements shows root mean squared errors (RMSEs) of approximately 0.8 and 0.9 ppm for OCO-2 and OCO-3, respectively. An evaluation against XCO2 fields derived from atmospheric inversion systems that assimilated only near-surface CO2 observations, i.e., did not assimilate satellite CO2 measurements, yielded RMSEs of 1.0 and 1.1 ppm for OCO-2 and OCO-3, respectively. Evaluation of uncertainties in XCO2 over small areas, as well as XCO2 biases across land–ocean crossings, also indicates similar behavior in the error characteristics of both sensors. Taken together, these results demonstrate a broad consistency of OCO-2 and OCO-3 XCO2 measurements, suggesting they may be used together for scientific analyses.

The MERRA-2 Stratospheric Composition Reanalysis of Aura Microwave Limb Sounder (M2-SCREAM) is a new reanalysis of stratospheric ozone, water vapor, hydrogen chloride (HCl), nitric acid (HNO3) and nitrous oxide (N2O) between 2004 and the present (with a latency of several months). The assimilated fields are provided at a 50-km horizontal resolution and at a three-hourly frequency. M2-SCREAM assimilates version 4.2 Microwave Limb Sounder (MLS) profiles of the five constituents alongside total ozone column from the Ozone Monitoring Instrument. Dynamics and tropospheric water vapor are constrained by the MERRA-2 reanalysis. The assimilated species are in excellent agreement with the MLS observations, except for HNO3 in polar night, where data are not assimilated. Comparisons against independent observations show that the reanalysis realistically captures the spatial and temporal variability of all the assimilated constituents. In particular, the standard deviations of the differences between M2-SCREAM and constituent mixing ratio data from The Atmospheric Chemistry Experiment Fourier Transform Spectrometer are much smaller than the standard deviations of the measured constituents. Evaluation of the reanalysis against aircraft data and balloon-borne frost point hygrometers indicates faithful representations of small-scale structures in the assimilated water vapor, HNO3 and ozone fields near the tropopause. Comparisons with independent observations and a process-based analysis of the consistency of the assimilated constituent fields with the MERRA-2 dynamics and with large-scale stratospheric processes demonstrate the utility of M2-SCREAM for scientific studies of chemical and transport variability on time scales ranging from hours to decades. Analysis uncertainties and guidelines for data usage are provided.

Climate extremes such as droughts, floods, heatwaves, frosts, and windstorms add considerable variability to the global year‐to‐year increase in atmospheric CO 2 through their influence on terrestrial ecosystems. While the impact of droughts on terrestrial ecosystems has received considerable attention, the response to flooding is not well understood. To improve upon this knowledge, the impact of the 2019 anomalously wet conditions over the Midwest and Southern US on CO 2 vegetation fluxes is examined in the context of 2017–2018 when such precipitation anomalies were not observed. CO 2 is simulated with NASA's Global Earth Observing System (GEOS) combined with the Low‐order Flux Inversion, where fluxes of CO 2 are estimated using a suite of remote sensing measurements including greenness, night lights, and fire radiative power as well as with a bias correction based on insitu observations. Net ecosystem exchange CO 2 tracers are separated into the three regions covering the Midwest, South, and Eastern Texas and adjusted to match CO 2 observations from towers located in Iowa, Mississippi, and Texas. Results indicate that for the Midwestern region consisting primarily of corn and soybeans crops, flooding contributes to a 15%–25% reduction of annual net carbon uptake in 2019 in comparison to 2017 and 2018. These results are supported by independent reports of changes in agricultural activity. For the Southern region, comprised mainly of non‐crop vegetation, annual net carbon uptake is enhanced in 2019 by about 10%–20% in comparison to 2017 and 2018. These outcomes show the heterogeneity in effects that excess wetness can bring to diverse ecosystems.

Tropical lands play an important role in the global carbon cycle yet their contribution remains uncertain owing to sparse observations. Satellite observations of atmospheric carbon dioxide (CO) have greatly increased spatial coverage over tropical regions, providing the potential for improved estimates of terrestrial fluxes. Despite this advancement, the spread among satellite-based and in-situ atmospheric CO flux inversions over northern tropical Africa (NTA), spanning 0-24◦N, remains large. Satellite-based estimates of an annual source of 0.8-1.45 PgC yr challenge our understanding of tropical and global carbon cycling. Here, we compare posterior mole fractions from the suite of inversions participating in the Orbiting Carbon Observatory 2 (OCO-2) Version 10 Model Intercomparison Project (v10 MIP) with independent in-situ airborne observations made over the tropical Atlantic Ocean by the NASA Atmospheric Tomography (ATom) mission during four seasons. We develop emergent constraints on tropical African CO fluxes using flux-concentration relationships defined by the model suite. We find an annual flux of 0.14 ± 0.39 PgC yr (mean and standard deviation) for NTA, 2016-2018. The satellite-based flux bias suggests a potential positive concentration bias in OCO-2 B10 and earlier version retrievals over land in NTA during the dry season. Nevertheless, the OCO-2 observations provide improved flux estimates relative to the in situ observing network at other times of year, indicating stronger uptake in NTA during the wet season than the in-situ inversion estimates.

The past decade has witnessed a growing interest in the quickly developing field of chemical composition reanalyses, that is, multiyear records of assimilated observations of atmospheric constituent gases. Composition reanalyses typically assimilate observations of atmospheric constituents using full chemistry and transport models driven by assimilated meteorology. Most, although not all, of these reanalyses to date focus on tropospheric composition. This presentation introduces a new chemical reanalysis of stratospheric constituents developed and produced at NASA’s Global Modeling and Assimilation Office (GMAO). Named Global Earth Observing System (GEOS) Stratospheric Composition Reanalysis with Aura MLS (GEOS-SCREAM), this product consists of assimilated global three-dimensional fields of stratospheric ozone, water vapor, hydrogen chloride (HCl), nitric acid (HNO3), and nitrous oxide (N2O) mixing ratios and covers the period since the beginning of MLS observations in September 2004 to 2021. The assimilated instantaneous fields are produced at a three-hourly frequency. GEOS-SCREAM assimilates version 4.2 MLS profiles of the five constituents alongside total ozone column from the Aura Ozone Monitoring Instrument with the recently developed Constituent Data Assimilation System. It is also constrained by tropospheric water vapor from several satellite sensors and in situ measurements with the existing MERRA-2 meteorological data assimilation system. GEOS-SCREAM provides an accurate and dynamically consistent high-resolution data record of the five constituents, all of which are of primary importance to stratospheric chemistry and transport studies. We will present a description of GEOS-SCREAM and selected results of a process-based evaluation of this product using independent data. We will also discuss potential scientific applications of GEOS-SCREAM and outline plans for an upcoming comprehensive composition reanalysis that is being developed at NASA GMAO.

Frontal boundaries have been shown to cause large changes in CO 2 mole‐fractions, but clouds and the complex vertical structure of fronts make these gradients difficult to observe. It remains unclear how the column average CO 2 dry air mole‐fraction (XCO 2 ) changes spatially across fronts, and how well airborne lidar observations, data assimilation systems, and numerical models without assimilation capture XCO 2 frontal contrasts (ΔXCO 2, i.e., warm minus cold sector average of XCO 2 ). We demonstrated the potential of airborne Multifunctional Fiber Laser Lidar (MFLL) measurements in heterogeneous weather conditions (i.e., frontal environment) to investigate the ΔXCO 2 during four seasonal field campaigns of the Atmospheric Carbon and Transport‐America (ACT‐America) mission. Most frontal cases in summer (winter) reveal higher (lower) XCO 2 in the warm (cold) sector than in the cold (warm) sector. During the transitional seasons (spring and fall), no clear signal in ΔXCO 2 was observed. Intercomparison among the MFLL, assimilated fields from NASA's Global Modeling and Assimilation Office (GMAO), and simulations from the Weather Research and Forecasting‐—Chemistry (WRF‐Chem) showed that (a) all products had a similar sign of ΔXCO 2 though with different levels of agreement in ΔXCO 2 magnitudes among seasons; (b) ΔXCO 2 in summer decreases with altitude; and (c) significant challenges remain in observing and simulating XCO 2 frontal contrasts. A linear regression analyses between ΔXCO 2 for MFLL versus GMAO, and MFLL versus WRF‐Chem for summer‐2016 cases yielded a correlation coefficient of 0.95 and 0.88, respectively. The reported ΔXCO 2 variability among four seasons provide guidance to the spatial structures of XCO 2 transport errors in models and satellite measurements of XCO 2 in synoptically‐active weather systems.

Accurate estimates of carbon–climate feedbacks require an independent means for evaluating surface flux models at regional scales. The altitude-integrated enhancement (AIE) derived from the Arctic Carbon Atmospheric Profiles (Arctic-CAP) project demonstrates the utility of this bulk quantity for surface flux model evaluation. This bulk quantity leverages background mole fraction values from the middle free troposphere, is agnostic to uncertainties in boundary layer height, and can be derived from model estimates of mole fractions and vertical gradients. To demonstrate the utility of the bulk quantity, six airborne profiling surveys of atmospheric carbon dioxide (CO2), methane (CH4), and carbon monoxide (CO) throughout Alaska and northwestern Canada between April and November 2017 were completed as part of NASA's Arctic–Boreal Vulnerability Experiment (ABoVE). The Arctic-CAP sampling strategy involved acquiring vertical profiles of CO2, CH4, and CO from the surface to 5 km altitude at 25 sites around the ABoVE domain on a 4- to 6-week time interval. All Arctic-CAP measurements were compared to a global simulation using the Goddard Earth Observing System (GEOS) modeling system. Comparisons of the AIE bulk quantity from aircraft observations and GEOS simulations of atmospheric CO2, CH4, and CO highlight the fidelity of the modeled surface fluxes. The model–data comparison over the ABoVE domain reveals that while current state-of-the-art models and flux estimates are able to capture broad-scale spatial and temporal patterns in near-surface CO2 and CH4 concentrations, more work is needed to resolve fine-scale flux features that are captured in CO observations.