The Network for the Detection of Atmospheric Composition Change (NDACC) is an international global network of more than 90 stations making high-quality measurements of atmospheric composition that began official operations in 1991 after 5 years of planning. Apart from sonde measurements, all measurements in the network are performed by ground-based remote-sensing techniques. Originally named the Network for the Detection of Stratospheric Change (NDSC), the name of the network was changed to NDACC in 2005 to better reflect the expanded scope of its measurements. The primary goal of NDACC is to establish long-term databases for detecting changes and trends in the chemical and physical state of the atmosphere (mesosphere, stratosphere, and troposphere) and to assess the coupling of such changes with climate and air quality. NDACC's origins, station locations, organizational structure, and data archiving are described. NDACC is structured around categories of ground-based observational techniques (sonde, lidar, microwave radiometers, Fourier-transform infrared, UV-visible DOAS (differential optical absorption spectroscopy)-type, and Dobson–Brewer spectrometers, as well as spectral UV radiometers), timely cross-cutting themes (ozone, water vapour, measurement strategies, cross-network data integration), satellite measurement systems, and theory and analyses. Participation in NDACC requires compliance with strict measurement and data protocols to ensure that the network data are of high and consistent quality. To widen its scope, NDACC has established formal collaborative agreements with eight other cooperating networks and Global Atmosphere Watch (GAW). A brief history is provided, major accomplishments of NDACC during its first 25 years of operation are reviewed, and a forward-looking perspective is presented.

Isoprene is the dominant nonmethane organic compound emitted to the atmosphere, where it drives ozone and aerosol production, modulates atmospheric oxidation, and interacts with the global nitrogen cycle. Isoprene emissions are highly variable and uncertain, as is the nonlinear chemistry coupling isoprene and its primary sink, the hydroxyl radical. Space‐based isoprene measurements can help close the gap on these uncertainties, and when combined with concurrent formaldehyde data provide a new constraint on atmospheric oxidation regimes. Here, we present a next‐generation machine‐learning isoprene retrieval for the Cross‐track Infrared Sounder (CrIS) that provides improved sensitivity, lower noise, and thus higher space‐time resolution than earlier approaches. The Retrieval of Organics from CrIS Radiances (ROCR) isoprene measurements compare well with previous space‐based retrievals as well as with the first‐ever ground‐based isoprene column measurements, with 20%–50% discrepancies that reflect differing sources of systematic uncertainty. An ensemble of sensitivity tests points to the spectral background and isoprene profile specification as the most relevant uncertainty sources in the ROCR framework. We apply the ROCR isoprene algorithm to the full CrIS record from 2012 to 2020, showing that it can resolve fine‐scale spatial gradients at daily resolution over the world's isoprene hotspots. Results over North America and Amazonia highlight emergent connections between isoprene abundance and daily‐to‐interannual variations in temperature and nitrogen oxides.

Understanding the atmospheric distribution of water (H ${}_2$ O) is crucial for global warming studies and climate change mitigation. In this context, reliable satellite data are extremely valuable for their global and continuous coverage, once their quality has been assessed. Short-wavelength infrared spectra are acquired by the Thermal And Near-infrared Sensor for carbon Observation-Fourier Transform Spectrometer (TANSO-FTS) aboard the Greenhouse gases Observing Satellite (GOSAT). From these, column-averaged dry-air mole fractions of carbon dioxide, methane and water vapor (XH ${}_2$ O) have been retrieved at the National Institute for Environmental Studies (NIES, Japan) and are available as a Level 2 research product. We compare the NIES XH ${}_2$ O data, Version 02.21, with retrievals from the ground-based Total Carbon Column Observing Network (TCCON, Version GGG2014). The datasets are in good overall agreement, with GOSAT data showing a slight global low bias of −3.1% $\pm$ 24.0%, good consistency over different locations (station bias of −1.53% $\pm$ 10.35%) and reasonable correlation with TCCON (R = 0.89). We identified two potential sources of discrepancy between the NIES and TCCON retrievals over land. While the TCCON XH ${}_2$ O amounts can reach 6000–7000 ppm when the atmospheric water content is high, the correlated NIES values do not exceed 5500 ppm. This could be due to a dry bias of TANSO-FTS in situations of high humidity and aerosol content. We also determined that the GOSAT-TCCON differences directly depend on the altitude difference between the TANSO-FTS footprint and the TCCON site. Further analysis will account for these biases, but the NIES V02.21 XH ${}_2$ O product, after public release, can already be useful for water cycle studies.

Nitrogen dioxide has been monitored at the International Scientific Station at the Jungfraujoch (46°N, 8°E;) since June 1990 and at Sodankyla (67°N, 27°E) since January 1990. NO₂ vertical column abundances are measured during the morning and evening twilights by application of the differential absorption method using the sunlight scattered at zenith in the visible range. The available time series shows a significant reduction of NO₂ starting in winter 1992, after the eruption of the Mount Pinatubo volcano. A maximum decrease of about 35% is observed in January 1992 at both stations. The continued series of observations shows the recovery of the NO₂ column until August 1995. These results are compared with two‐dimensional chemical model calculations, including the effect of heterogeneous reactions on observed Pinatubo aerosols. In general, the modeled NO₂ columns agree qualitatively with the observations although the amplitude of the seasonal variation is underestimated, possibly due to internal limitations of the model which, for example, does not include diurnal changes. The observed and calculated NO₂ percent changes are in good agreement, which confirms quantitatively the impact of the heterogeneous chemistry on stratospheric NO₂.

The nationwide lockdown due to the COVID-19 pandemic in 2020 reduced industrial and human activities in China. In this study, we investigate atmospheric carbon monoxide (CO) concentration changes during the lockdown from observations at the surface and from two satellites (TROPOspheric Monitoring Instrument (TROPOMI) and Infrared Atmospheric Sounding Interferometer (IASI)). It is found that the average CO surface concentration in 2020 was close to that in 2019 before the lockdown, and became 18.7% lower as compared to 2019 during the lockdown. The spatial variation of the change in the CO surface concentration is high, with an 8–27% reduction observed for Beijing, Shanghai, Chengdu, Zhengzhou, and Guangzhou, and almost no change in Wuhan. The TROPOMI and IASI satellite observations show that the CO columns decreased by 2–13% during the lockdown in most regions in China. However in South China, there was an 8.8% increase in the CO columns observed by TROPOMI and a 36.7% increase observed by IASI, which is contrary to the 23% decrease in the surface CO concentration. The enhancement of the CO column in South China is strongly affected by the fire emissions transported from Southeast Asia. This study provides an insight into the impact of COVID-19 on CO concentrations both at the surface and in the columns in China, and it can be extended to evaluate other areas using the same approach.

Volcanic ash is emitted by most eruptions, sometimes reaching the stratosphere. In addition to its climate effect, ash may have a significant impact on civilian flights. Currently, the horizontal distribution of ash aerosols is quite extensively studied, but not its vertical profile, while of high importance for both applications mentioned. Here, we study the sensitivity of the thermal infrared spectral range to the altitude distribution of volcanic ash, based on similar work that was undertaken on mineral dust. We use measurements by the Infrared Atmospheric Sounding Interferometer (IASI) instruments onboard the MetOp satellite series. The retrieval method that we develop for the ash vertical profile is based on the optimal estimation formalism. This method is applied to study the eruption of the Chilean volcano Puyehue, which started on the 4th of June 2011. The retrieved profiles agree reasonably well with Cloud-Aerosol LiDAR with Orthogonal Polarization (CALIOP) measurements, and our results generally agree with literature studies of the same eruption. The retrieval strategy presented here therefore is very promising for improving our knowledge of the vertical distribution of volcanic ash and obtaining a global 3D ash distribution twice a day. Future improvements of our retrieval strategy are also discussed.