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An improved cloud detection algorithm for the Aura Microwave Limb Sounder (MLS) is presented. This new algorithm is based on a feedforward artificial neural network and uses as input, for each MLS limb scan, a vector consisting of 1710 brightness temperatures provided by MLS observations from 15 different tangent altitudes and up to 13 spectral channels in each of 10 different MLS bands. The model has been trained on global cloud properties reported by Aqua's Moderate Resolution Imaging Spectroradiometer (MODIS). In total, the colocated MLS–MODIS data set consists of 162 117 combined scenes sampled on 208 d over 2005–2020. A comparison to the current MLS cloudiness flag used in “Level 2” processing reveals a huge improvement in classification performance. For previously unseen data, the algorithm successfully detects > 93 % of profiles affected by clouds, up from ∼ 16 % for the Level 2 flagging. At the same time, false positives reported for actually clear profiles are comparable to the Level 2 results. The classification performance is not dependent on geolocation but slightly decreases over low-cloud-cover regions. The new cloudiness flag is applied to determine average global cloud cover maps over 2015–2019, successfully reproducing the spatial patterns of mid-level to high clouds seen in MODIS data. It is also applied to four example cloud fields to illustrate its reliable performance for different cloud structures with varying degrees of complexity. Training a similar model on MODIS-retrieved cloud top pressure (pCT) yields reliable predictions with correlation coefficients > 0.82. It is shown that the model can correctly identify > 85 % of profiles with pCT < 400 hPa. Similar to the cloud classification model, global maps and example cloud fields are provided, which reveal good agreement with MODIS results. The combination of the cloudiness flag and predicted cloud top pressure provides the means to identify MLS profiles in the presence of high-reaching convection.

The NASA Goddard Earth Observing System (GEOS) Composition Forecast (GEOS-CF) provides recent estimates and 5-day forecasts of atmospheric composition to the public in near-real time. To do this, the GEOS Earth system model is coupled with the GEOS-Chem tropospheric-stratospheric unified chemistry extension (UCX) to represent composition from the surface to the top of the GEOS atmosphere (0.01 hPa). The GEOS-CF system is described, including updates made to the GEOS-Chem UCX mechanism within GEOS-CF for improved representation of stratospheric chemistry. Comparisons are made against balloon, lidar, and satellite observations for stratospheric composition, including measurements of ozone (O

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.

Sulphur dioxide (SO2) is an important atmospheric constituent, particularly in the aftermath of volcanic eruptions. These events can inject large amounts of SO2 into the lower stratosphere, where it is oxidised to form sulphate aerosols; these in turn have a significant effect on the climate. The MLS instrument on the Aura satellite has observed the SO2 mixing ratio in the upper troposphere and lower stratosphere from August 2004 to the present, during which time a number of volcanic eruptions have significantly affected those regions of the atmosphere. We describe the MLS SO2 data and how various volcanic events appear in the data. As the MLS SO2 data are currently not validated we take some initial steps towards their validation. First we establish the level of internal consistency between the three spectral regions in which MLS is sensitive to SO2. We compare SO2 column values calculated from MLS data to total column values reported by the OMI instrument. The agreement is good in cases where the SO2 is clearly at altitudes above 147 hPa.

The interrelated geographical and temporal variability seen in more than seven years of tropical and subtropical upper tropospheric (215 hPa) ozone, carbon monoxide and cloud ice water content (IWC) observations by the Aura Microwave Limb Sounder (MLS) are presented. Observed ozone abundances and their variability (geographical and temporal) agree to within 10–15 ppbv with records from sonde observations. MLS complements these (and other) observations with global coverage and simultaneous measurements of related parameters. Previously-reported phenomena such as the ozone "wave one" feature are clearly seen in the MLS observations, as is a double peak in ozone abundance over tropical East Africa, with enhanced abundances in both May to June and September to November. While repeatable seasonal cycles are seen in many regions, they are often accompanied by significant interannual variability. Ozone seasonal cycles in the southern tropics and subtropics tend to be more distinct (i.e., annually repeatable) than in the northern. By contrast, carbon monoxide shows distinct seasonal cycles in many northern subtropical regions, notably from India to the Eastern Pacific. Deep convection (as indicated by large values of IWC) is typically associated with reductions in upper tropospheric ozone. Convection over polluted regions is seen to significantly enhance upper tropospheric carbon monoxide. While some regions show statistically significant correlations among ozone, carbon monoxide and IWC, simple correlations fall well short of accounting for the observed variability. The observed interrelated variations and metrics of annual and interannual variability described here represent a new resource for validation of atmospheric chemistry models.

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; poor CTM performance in the upper stratosphere and lower mesosphere results primarily from biases in the diabatic descent in assimilated analyses.

During several periods since 2005 the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on Envisat has performed observations dedicated to the region of the upper troposphere/lower stratosphere (UTLS). For the duration of November/December 2005 global distributions of temperature and several trace gases from MIPAS UTLS-1 mode measurements have been retrieved using the IMK/IAA (Institut für Meteorologie und Klimaforschung/Instituto de Astrofísica de Andalucía) scientific processor. In the UTLS region a vertical resolution of 2.5 to 3 km has been achieved. The retrieved temperature, H2O, O3, HNO3, N2O, and relative humidity over ice are intercompared with the Microwave Limb Sounder (MLS/Aura) v2.2 data. In general, MIPAS and MLS temperatures agree within ±4 K over the whole pressure range of 316–0.68 hPa. Systematic, latitude-independent differences of −2 to −4 K (MIPAS-MLS) at 121 hPa are explained by previously observed biases in the MLS v2.2 temperature retrievals. Temperature differences of −4 K up to 12 K above 10.0 hPa are present similarly in MIPAS and MLS with respect to ECMWF (European Centre for Medium-Range Weather Forecasts) and are likely due to deficiencies of the ECMWF analysis data. MIPAS and MLS stratospheric volume mixing ratios (vmr) of H2O agree within ±1 ppmv, with indication of oscillations between 146 and 26 hPa in the MLS dataset. Tropical upper tropospheric values of relative humidity over ice measured by the two instruments differ by ±20% in the pressure range ~146 to 68 hPa. These differences are mainly caused by the MLS temperature biases. Ozone mixing ratios agree within 0.5 ppmv (10 to 20%) between 68 and 14 hPa. At pressures smaller than 10 hPa, MIPAS O3 vmr are higher than MLS by an average of 0.5 ppmv (10%). General agreement between MIPAS and MLS HNO3 is within the range of −1.0 (−10%) to 1.0 ppbv (20%). MIPAS HNO3 is 1.0 ppbv (10%) higher compared to MLS in the height range of 46 to 10 hPa over the Northern Hemisphere. Over the tropics at 31.6 hPa MLS shows a low bias of more than 1 ppbv (>50%). In general, MIPAS and MLS N2O vmr agree within 20 to 40 ppbv (20 to 40%). Differences in the height range between 100 to 21 hPa are attributed to a known 20% positive bias in MIPAS N2O data.

We have studied the effects of the 2006 El Niño on tropospheric O3 and CO at tropical and sub-tropical latitudes measured from the OMI and MLS instruments on the Aura satellite. The 2006 El Niño-induced drought caused forest fires (largely set to clear land) to burn out of control during October and November in the Indonesian region. The effects of these fires are clearly seen in the enhancement of CO concentration measured from the MLS instrument. We have used a global model of atmospheric chemistry and transport (GMI CTM) to quantify the relative importance of biomass burning and large scale transport in producing observed changes in tropospheric O3 and CO. The model results show that during October and November biomass burning and meteorological changes contributed almost equally to the observed increase in tropospheric O3 in the Indonesian region. The biomass component was 4–6 DU but it was limited to the Indonesian region where the fires were most intense. The dynamical component was 4–8 DU but it covered a much larger area in the Indian Ocean extending from South East Asia in the north to western Australia in the south. By December 2006, the effect of biomass burning was reduced to zero and the observed changes in tropospheric O3 were mostly due to dynamical effects. The model results show an increase of 2–3% in the global burden of tropospheric ozone. In comparison, the global burden of CO increased by 8–12%.

We assess the quality of the version 2.2 (v2.2) ClO measurements from the Microwave Limb Sounder (MLS) on the Earth Observing System Aura satellite. The MLS v2.2 ClO data are scientifically useful over the range 100 to 1 hPa, with a single- profile precision of similar to 0.1 ppbv throughout most of the vertical domain. Vertical resolution is similar to 3-4 km. Comparisons with climatology and correlative measurements from a variety of different platforms indicate that both the amplitude and the altitude of the peak in the ClO profile in the upper stratosphere are well determined by MLS. The latitudinal and seasonal variations in the ClO distribution in the lower stratosphere are also well determined, but a substantial negative bias is present in both daytime and nighttime mixing ratios at retrieval levels below (i. e., pressures larger than) 22 hPa. Outside of the winter polar vortices, this negative bias can be eliminated by subtracting gridded or zonal mean nighttime values from the individual daytime measurements. In studies for which knowledge of lower stratospheric ClO mixing ratios inside the winter polar vortices to better than a few tenths of a ppbv is needed, however, day - night differences are not recommended and the negative bias must be corrected for by subtracting the estimated value of the bias from the individual measurements at each affected retrieval level.