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We analyze tropical ozone (O3) and carbon monoxide (CO) distributions in the upper troposphere (UT) for 2005–2020 using Aura Microwave Limb Sounder (MLS) observations and simulations from the Whole Atmosphere Community Climate Model (WACCM) and two variants of the Community Atmosphere Model with Chemistry (CAM-chem), with each variant using different anthropogenic CO emissions. Trends and variability diagnostics are obtained from multiple linear regression. The MLS zonal mean O3 UT trend for 20° S–20° N is +0.39 ± 0.28 % yr−1; the WACCM and CAM-chem simulations yield similar trends, although the WACCM result is somewhat smaller. Our analyses of gridded MLS data yield positive O3 trends (up to 1.4 % yr−1) over Indonesia and east of that region, as well as over Africa and the Atlantic. These positive mapped O3 trends are generally captured by the simulations but in a more muted way. We find broad similarities (and some differences) between mapped MLS UT O3 trends and corresponding mapped trends of tropospheric column ozone. The MLS zonal mean CO UT trend for 20° S–20° N is −0.25 ± 0.30 % yr−1, while the corresponding CAM-chem trend is 0.0 ± 0.14 % yr−1 when anthropogenic emissions are taken from the Community Emissions Data System (CEDS) version 2. The CAM-chem simulation driven by CAMS-GLOB-ANTv5 emissions yields a tropical mean CO UT trend of 0.22 ± 0.19 % yr−1, in contrast to the slightly negative MLS CO trend. Previously published analyses of total column CO data have shown negative trends. Our tropical composition trend results contribute to continuing international assessments of tropospheric evolution.

We analyze Aura Microwave Limb Sounder (MLS) monthly zonal mean time series of ClO and HOCl between 50∘ S and 50∘ N to estimate upper stratospheric trends in these chlorine species from 2005 through 2020. We compare these observations to those from the Whole Atmosphere Community Climate Model version 6 (WACCM6), run under the specified dynamics configuration. The model sampling follows the MLS coverage in space and local time. We use version 5 MLS ClO zonal mean daytime profiles and similarly binned daytime ClO model profiles from 32 to 1.5 hPa. For MLS HOCl, we use the version 5 offline product derived from daily zonal mean radiances rather than averaged level-2 profiles; MLS HOCl is scientifically useful between 10 and 2 hPa, and the HOCl monthly zonal means are separated into day and night for comparison to WACCM6. We find good agreement (mostly within ∼ 10 %) between the climatological MLS ClO daytime distributions and the model ClO climatology for 2005–2020. The model HOCl climatology, however, underestimates the MLS HOCl climatology by about 30 %. This could well be caused by a combination of fairly large systematic uncertainties in both the model-assumed rate constant for the formation of HOCl and the MLS HOCl retrievals themselves. The model daytime ClO trends versus latitude and pressure agree quite well with those from MLS. MLS-derived near-global upper stratospheric daytime trends between 7 and 2 hPa are −0.73 ± 0.40 % yr−1 for ClO and −0.39 ± 0.35 % yr−1 for HOCl, with 2σ uncertainty estimates used here. The corresponding model decreases are somewhat faster than observed (although the difference is not statistically significant), with trend values of −0.85 ± 0.45 % yr−1 for ClO and −0.64 ± 0.37 % yr−1 for HOCl. Both data and model results point to a faster trend in ClO than in HOCl. The MLS ClO trends are consistent with past estimates of upper stratospheric ClO trends from satellite and ground-based microwave data. As discussed in the past, trends in other species (in particular, positive trends in CH4 and H2O) can lead to a ClO decrease that is faster than the decrease in total inorganic chlorine. Regarding trends in HOCl, positive trends in HO2 can lead to a faster rate of formation for HOCl as a function of time, which partially offsets the decreasing trend in active chlorine. The decreasing trends in upper stratospheric ClO and HOCl provide additional confirmation of the effectiveness of the Montreal Protocol and its amendments, which have led to the early stages of an expected long-term ozone recovery from the effects of ozone-depleting substances.

The Stratosphere-troposphere Processes and their Role in Climate (SPARC) Data Initiative (SPARC, 2017) performed the first comprehensive assessment of currently available stratospheric composition measurements obtained from an international suite of space-based limb sounders. The initiative's main objectives were (1) to assess the state of data availability, (2) to compile time series of vertically resolved, zonal monthly mean trace gas and aerosol fields, and (3) to perform a detailed intercomparison of these time series, summarizing useful information and highlighting differences among datasets. The datasets extend over the region from the upper troposphere to the lower mesosphere (300–0.1 hPa) and are provided on a common latitude–pressure grid. They cover 26 different atmospheric constituents including the stratospheric trace gases of primary interest, ozone (O3) and water vapor (H2O), major long-lived trace gases (SF6, N2O, HF, CCl3F, CCl2F2, NOy), trace gases with intermediate lifetimes (HCl, CH4, CO, HNO3), and shorter-lived trace gases important to stratospheric chemistry including nitrogen-containing species (NO, NO2, NOx, N2O5, HNO4), halogens (BrO, ClO, ClONO2, HOCl), and other minor species (OH, HO2, CH2O, CH3CN), and aerosol. This overview of the SPARC Data Initiative introduces the updated versions of the SPARC Data Initiative time series for the extended time period 1979–2018 and provides information on the satellite instruments included in the assessment: LIMS, SAGE I/II/III, HALOE, UARS-MLS, POAM II/III, OSIRIS, SMR, MIPAS, GOMOS, SCIAMACHY, ACE-FTS, ACE-MAESTRO, Aura-MLS, HIRDLS, SMILES, and OMPS-LP. It describes the Data Initiative's top-down climatological validation approach to compare stratospheric composition measurements based on zonal monthly mean fields, which provides upper bounds to relative inter-instrument biases and an assessment of how well the instruments are able to capture geophysical features of the stratosphere. An update to previously published evaluations of O3 and H2O monthly mean time series is provided. In addition, example trace gas evaluations of methane (CH4), carbon monoxide (CO), a set of nitrogen species (NO, NO2, and HNO3), the reactive nitrogen family (NOy), and hydroperoxyl (HO2) are presented. The results highlight the quality, strengths and weaknesses, and representativeness of the different datasets. As a summary, the current state of our knowledge of stratospheric composition and variability is provided based on the overall consistency between the datasets. As such, the SPARC Data Initiative datasets and evaluations can serve as an atlas or reference of stratospheric composition and variability during the “golden age” of atmospheric limb sounding. The updated SPARC Data Initiative zonal monthly mean time series for each instrument are publicly available and accessible via the Zenodo data archive (Hegglin et al., 2020).

We have analyzed near-global stratospheric data (and mesospheric data as well for H2O) in terms of absolute abundances, variability, and trends for O3, H2O, HCl, N2O, and HNO3, based on Aura Microwave Limb Sounder (MLS) data, as well as longer-term series from the Global OZone Chemistry And Related trace gas Data records for the Stratosphere (GOZCARDS). While we emphasize the evaluation of stratospheric models via data comparisons through 2014 to free-running (FR-WACCM) and specified dynamics (SD-WACCM) versions of the Community Earth System Model version 1 (CESM1) Whole Atmosphere Community Climate Model (WACCM), we also highlight observed stratospheric changes, using the most recent data from MLS. Regarding highlights from the satellite data, we have used multiple linear regression to derive trends based on zonal mean time series from Aura MLS data alone, between 60∘ S and 60∘ N. In the upper stratosphere, MLS O3 shows increases over 2005–2018 at ∼0.1–0.3 % yr−1 (depending on altitude and latitude) with 2σ errors of ∼0.2 % yr−1. For the lower stratosphere (LS), GOZCARDS O3 data for 1998–2014 point to small decreases between 60∘ S and 60∘ N, but the trends are more positive if the starting year is 2005. Southern midlatitudes (30–60∘ S) exhibit near-zero or slightly positive LS trends for 1998–2018. The LS O3 trends based on 2005–2018 MLS data are most positive (0.1–0.2 % yr−1) at these southern midlatitudes, although marginally statistically significant, in contrast to slightly negative or near-zero trends for 2005–2014. Given the high variability in LS O3, and the high sensitivity of trends to the choice of years used, especially for short periods, further studies are required for a robust longer-term LS trend result. For H2O, upper-stratospheric and mesospheric trends from GOZCARDS 1992–2010 data are near zero (within ∼0.2 % yr−1) and significantly smaller than trends (within ∼0.4–0.7 % yr−1) from MLS for 2005–2014 or 2005–2018. The latter short-term positive H2O trends are larger than expected from changes resulting from long-term increases in methane. We note that the very shallow solar flux maximum of solar cycle 24 has contributed to fairly large short-term mesospheric and upper-stratospheric H2O trends since 2005. However, given known drifts in the MLS H2O time series, MLS H2O trend results, especially after 2010, should be viewed as upper limits. The MLS data also show regions and periods of small HCl increases in the lower stratosphere, within the context of the longer-term stratospheric decrease in HCl, as well as interhemispheric–latitudinal differences in short-term HCl tendencies. We observe similarities in such short-term tendencies, and interhemispheric asymmetries therein, for lower-stratospheric HCl and HNO3, while N2O trend profiles exhibit anti-correlated patterns. In terms of the model evaluation, climatological averages for 2005–2014 from both FR-WACCM and SD-WACCM for O3, H2O, HCl, N2O, and HNO3 compare favorably with Aura MLS data averages over this period. However, the models at mid- to high latitudes overestimate mean MLS LS O3 values and seasonal amplitudes by as much as 50 %–60 %; such differences appear to implicate, in part, a transport-related model issue. At lower-stratospheric high southern latitudes, variations in polar winter and spring composition observed by MLS are well matched by SD-WACCM, with the main exception being for the early winter rate of decrease in HCl, which is too slow in the model. In general, we find that the latitude–pressure distributions of annual and semiannual oscillation amplitudes derived from MLS data are properly captured by the model amplitudes. In terms of closeness of fit diagnostics for model–data anomaly series, not surprisingly, SD-WACCM (driven by realistic dynamics) generally matches the observations better than FR-WACCM does. We also use root mean square variability as a more valuable metric to evaluate model–data differences. We find, most notably, that FR-WACCM underestimates observed interannual variability for H2O; this has implications for the time period needed to detect small trends, based on model predictions. The WACCM O3 trends generally agree (within 2σ uncertainties) with the MLS data trends, although LS trends are typically not statistically different from zero. The MLS O3 trend dependence on latitude and pressure is matched quite well by the SD-WACCM results. For H2O, MLS and SD-WACCM positive trends agree fairly well, but FR-WACCM shows significantly smaller increases; this discrepancy for FR-WACCM is even more pronounced for longer-term GOZCARDS H2O records. The larger discrepancies for FR-WACCM likely arise from its poorer correlations with cold point temperatures and with quasi-biennial oscillation (QBO) variability. For HCl, while some expected decreases in the global LS are seen in the observations, there are interhemispheric differences in the trends, and increasing tendencies are suggested in tropical MLS data at 68 hPa, where there is only a slight positive trend in SD-WACCM. Although the vertical gradients in MLS HCl trends are well duplicated by SD-WACCM, the model trends are always somewhat more negative; this deserves further investigation. The original MLS N2O product time series yield small positive LS tropical trends (2005–2012), consistent with models and with rates of increase in tropospheric N2O. However, longer-term series from the more current MLS N2O standard product are affected by instrument-related drifts that have also impacted MLS H2O. The LS short-term trend profiles from MLS N2O and HNO3 at midlatitudes in the two hemispheres have different signs; these patterns are well matched by SD-WACCM trends for these species. These model–data comparisons provide a reminder that the QBO and other dynamical factors affect decadal variability in a major way, notably in the lower stratosphere, and can thus significantly hinder the goals of robustly extracting (and explaining) small underlying long-term trends. The data sets and tools discussed here for model evaluation could be expanded to comparisons of species or regions not included here, as well as to comparisons between a variety of chemistry–climate models.

The Ramsar Convention on Wetlands is an international framework through which countries identify and protect important wetlands. Yet Ramsar wetlands are under substantial anthropogenic pressure worldwide, and tracking ecological change relies on multitemporal data sets. Here, we evaluated the spatial extent, temporal change, and anthropogenic threat to Ramsar wetlands at a national scale across China to determine whether their management is currently sustainable. We analyzed Landsat data to examine wetland dynamics and anthropogenic threats at the 57 Ramsar wetlands in China between 1980 and 2018. Results reveal that Ramsar sites play important roles in preventing wetland loss compared to the dramatic decline of wetlands in the surrounding areas. However, there are declines in wetland area at 18 Ramsar sites. Among those, six lost a wetland area greater than 100 km2, primarily caused by agricultural activities. Consistent expansion of anthropogenic land covers occurred within 43 (75%) Ramsar sites, and anthropogenic threats from land cover change were particularly notable in eastern China. Aquaculture pond expansion and Spartina alterniflora invasion were prominent threats to coastal Ramsar wetlands. The observations within China’s Ramsar sites, which in management regulations have higher levels of protection than other wetlands, can help track progress towards achieving United Nations Sustainable Development Goals (SDGs). The study findings suggest that further and timely actions are required to control the loss and degradation of wetland ecosystems.

The representation of upper tropospheric–lower stratospheric (UTLS) jet and tropopause characteristics is compared in five modern high-resolution reanalyses for 1980 through 2014. Climatologies of upper tropospheric jet, subvortex jet (the lowermost part of the stratospheric vortex), and multiple tropopause frequency distributions in MERRA (Modern-Era Retrospective analysis for Research and Applications), ERA-I (ERA-Interim; the European Centre for Medium-Range Weather Forecasts, ECMWF, interim reanalysis), JRA-55 (the Japanese 55-year Reanalysis), and CFSR (the Climate Forecast System Reanalysis) are compared with those in MERRA-2. Differences between alternate products from individual reanalysis systems are assessed; in particular, a comparison of CFSR data on model and pressure levels highlights the importance of vertical grid spacing. Most of the differences in distributions of UTLS jets and multiple tropopauses are consistent with the differences in assimilation model grids and resolution – for example, ERA-I (with coarsest native horizontal resolution) typically shows a significant low bias in upper tropospheric jets with respect to MERRA-2, and JRA-55 (the Japanese 55-year Reanalysis) a more modest one, while CFSR (with finest native horizontal resolution) shows a high bias with respect to MERRA-2 in both upper tropospheric jets and multiple tropopauses. Vertical temperature structure and grid spacing are especially important for multiple tropopause characterizations. Substantial differences between MERRA and MERRA-2 are seen in mid- to high-latitude Southern Hemisphere (SH) winter upper tropospheric jets and multiple tropopauses as well as in the upper tropospheric jets associated with tropical circulations during the solstice seasons; some of the largest differences from the other reanalyses are seen in the same times and places. Very good qualitative agreement among the reanalyses is seen between the large-scale climatological features in UTLS jet and multiple tropopause distributions. Quantitative differences may, however, have important consequences for transport and variability studies. Our results highlight the importance of considering reanalyses differences in UTLS studies, especially in relation to resolution and model grids; this is particularly critical when using high-resolution reanalyses as an observational reference for evaluating global chemistry–climate models.

This study investigates the representativeness of two types of orbital sampling applied to stratospheric temperature and trace gas fields. Model fields are sampled using real sampling patterns from the Aura Microwave Limb Sounder (MLS), the HALogen Occultation Experiment (HALOE) and the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS). The MLS sampling acts as a proxy for a dense uniform sampling pattern typical of limb emission sounders, while HALOE and ACE-FTS represent coarse nonuniform sampling patterns characteristic of solar occultation instruments. First, this study revisits the impact of sampling patterns in terms of the sampling bias, as previous studies have done. Then, it quantifies the impact of different sampling patterns on the estimation of trends and their associated detectability. In general, we find that coarse nonuniform sampling patterns may introduce non-negligible errors in the inferred magnitude of temperature and trace gas trends and necessitate considerably longer records for their definitive detection. Lastly, we explore the impact of these sampling patterns on tropical vertical velocities derived from stratospheric water vapor measurements. We find that coarse nonuniform sampling may lead to a biased depiction of the tropical vertical velocities and, hence, to a biased estimation of the impact of the mechanisms that modulate these velocities. These case studies suggest that dense uniform sampling such as that available from limb emission sounders provides much greater fidelity in detecting signals of stratospheric change (for example, fingerprints of greenhouse gas warming and stratospheric ozone recovery) than coarse nonuniform sampling such as that of solar occultation instruments.

We describe the publicly available data from the Global OZone Chemistry And Related trace gas Data records for the Stratosphere (GOZCARDS) project and provide some results, with a focus on hydrogen chloride (HCl), water vapor (H2O), and ozone (O3). This data set is a global long-term stratospheric Earth system data record, consisting of monthly zonal mean time series starting as early as 1979. The data records are based on high-quality measurements from several NASA satellite instruments and the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) on SCISAT. We examine consistency aspects between the various data sets. To merge ozone records, the time series are debiased relative to SAGE II (Stratospheric Aerosol and Gas Experiments) values by calculating average offsets versus SAGE II during measurement overlap periods, whereas for other species the merging derives from an averaging procedure during overlap periods. The GOZCARDS files contain mixing ratios on a common pressure–latitude grid, as well as standard errors and other diagnostics; we also present estimates of systematic uncertainties in the merged products. Monthly mean temperatures for GOZCARDS were also produced, based directly on data from the Modern-Era Retrospective analysis for Research and Applications. The GOZCARDS HCl merged product comes from the Halogen Occultation Experiment (HALOE), ACE-FTS and lower-stratospheric Aura Microwave Limb Sounder (MLS) data. After a rapid rise in upper-stratospheric HCl in the early 1990s, the rate of decrease in this region for 1997–2010 was between 0.4 and 0.7 % yr−1. On 6–8-year timescales, the rate of decrease peaked in 2004–2005 at about 1 % yr−1, and it has since levelled off, at ~ 0.5 % yr−1. With a delay of 6–7 years, these changes roughly follow total surface chlorine, whose behavior versus time arises from inhomogeneous changes in the source gases. Since the late 1990s, HCl decreases in the lower stratosphere have occurred with pronounced latitudinal variability at rates sometimes exceeding 1–2 % yr−1. Recent short-term tendencies of lower-stratospheric and column HCl vary substantially, with increases from 2005 to 2010 for northern midlatitudes and deep tropics, but decreases (increases) after 2011 at northern (southern) midlatitudes. For H2O, the GOZCARDS product covers both stratosphere and mesosphere, and the same instruments as for HCl are used, along with Upper Atmosphere Research Satellite (UARS) MLS stratospheric H2O data (1991–1993). We display seasonal to decadal-type variability in H2O from 22 years of data. In the upper mesosphere, the anticorrelation between H2O and solar flux is now clearly visible over two full solar cycles. Lower-stratospheric tropical H2O has exhibited two periods of increasing values, followed by fairly sharp drops (the well-documented 2000–2001 decrease and a recent drop in 2011–2013). Tropical decadal variability peaks just above the tropopause. Between 1991 and 2013, both in the tropics and on a near-global basis, H2O has decreased by ~ 5–10 % in the lower stratosphere, but about a 10 % increase is observed in the upper stratosphere and lower mesosphere. However, such tendencies may not represent longer-term trends. For ozone, we used SAGE I, SAGE II, HALOE, UARS and Aura MLS, and ACE-FTS data to produce a merged record from late 1979 onward, using SAGE II as the primary reference. Unlike the 2 to 3 % increase in near-global column ozone after the late 1990s reported by some, GOZCARDS stratospheric column O3 values do not show a recent upturn of more than 0.5 to 1 %; long-term interannual column ozone variations from GOZCARDS are generally in very good agreement with interannual changes in merged total column ozone (Version 8.6) data from SBUV instruments. A brief mention is also made of other currently available, commonly formatted GOZCARDS satellite data records for stratospheric composition, namely those for N2O and HNO3.

An unusually strong and prolonged stratospheric sudden warming (SSW) in January 2006 was the first major SSW for which globally distributed long-lived trace gas data are available covering the upper troposphere through the lower mesosphere. We use Aura Microwave Limb Sounder (MLS), Atmospheric Chemistry Experiment-Fourier Transform Spectrometer (ACE-FTS) data, the SLIMCAT Chemistry Transport Model (CTM), and assimilated meteorological analyses to provide a comprehensive picture of transport during this event. The upper tropospheric ridge that triggered the SSW was associated with an elevated tropopause and layering in trace gas profiles in conjunction with stratospheric and tropospheric intrusions. Anomalous poleward transport (with corresponding quasi-isentropic troposphere-to-stratosphere exchange at the lowest levels studied) in the region over the ridge extended well into the lower stratosphere. In the middle and upper stratosphere, the breakdown of the polar vortex transport barrier was seen in a signature of rapid, widespread mixing in trace gases, including CO, H2O, CH4 and N2O. The vortex broke down slightly later and more slowly in the lower than in the middle stratosphere. In the middle and lower stratosphere, small remnants with trace gas values characteristic of the pre-SSW vortex lingered through the weak and slow recovery of the vortex. The upper stratospheric vortex quickly reformed, and, as enhanced diabatic descent set in, CO descended into this strong vortex, echoing the fall vortex development. Trace gas evolution in the SLIMCAT CTM agrees well with that in the satellite trace gas data from the upper troposphere through the middle stratosphere. In the upper stratosphere and lower mesosphere, the SLIMCAT simulation does not capture the strong descent of mesospheric CO and H2O values into the reformed vortex; this poor CTM performance in the upper stratosphere and lower mesosphere results primarily from biases in the diabatic descent in assimilated analyses.