Combined Characterisation of GOME and TOMS Total Ozone Using Ground-Based Observations from the NDSC

Several years of total ozone measured from space by the ERS-2 GOME, the Earth Probe Total Ozone Mapping Spectrometer (TOMS), and the ADEOS TOMS, are compared with high-quality ground-based observations associated with the Network for the Detection of Stratospheric Change (NDSC), over an extended latitude range and a variety of geophysical conditions. The comparisons with each spaceborne sensor are combined altogether for investigating their respective solar zenith angle (SZA) dependence, dispersion, and difference of sensitivity. The space- and ground-based data are found to agree within a few percent on average. However, the analysis highlights for both Global Ozone Monitoring Experiment (GOME) and TOMS several sources of discrepancies, including a dependence on the SZA at high latitudes and internal inconsistencies

Almost one year of TROPOMI/S5P total ozone column data: global ground-based validation

Póster presentado en: ATMOS 2018, celebrado en Salzburgo (Austria) del 26 al 29 de noviembre de 2018.In this work we present the validation results of almost one year of TROPOMI Near Real Time (NRTI) and OFFLine (OFFL) data against ground-based quality-assured Brewer and Dobson total ozone column (TOC) measurements deposited in the World Ozone and Ultraviolet Radiation Data Center (WOUDC). Additionally, comparisons to Brewer measurements from the European Brewer Network (EUBREWNET) and the Canadian Network are performed, as well as to twilight zenith-sky measurements obtained with ZSL-DOAS (Zenith Scattered Light Differential Optical Absorption Spectroscopy) instruments, that form part of the SAOZ network (Système d'Analyse par Observation Zénitale) of the Network for the Detection of Atmospheric Composition Change (NDACC). Through the comparison of the TROPOMI measurements to the total ozone ground-based measurements from stations that are distributed globally, as the background truth, the dependence of the new instrument on latitude, cloud properties, solar zenith and viewing angles, among others, is examined. Validation results show that the mean bias and the standard deviation of the percentage difference between TROPOMI and QA ground TOC meet the product requirements

Advancing drug discovery through assay development: a survey of tool compounds within the human solute carrier superfamily

With over 450 genes, solute carriers (SLCs) constitute the largest transporter superfamily responsible for the uptake and efflux of nutrients, metabolites, and xenobiotics in human cells. SLCs are associated with a wide variety of human diseases, including cancer, diabetes, and metabolic and neurological disorders. They represent an important therapeutic target class that remains only partly exploited as therapeutics that target SLCs are scarce. Additionally, many small molecules reported in the literature to target SLCs are poorly characterized. Both features may be due to the difficulty of developing SLC transport assays that fulfill the quality criteria for high-throughput screening. Here, we report one of the main limitations hampering assay development within the RESOLUTE consortium: the lack of a resource providing high-quality information on SLC tool compounds. To address this, we provide a systematic annotation of tool compounds targeting SLCs. We first provide an overview on RESOLUTE assays. Next, we present a list of SLC-targeting compounds collected from the literature and public databases; we found that most data sources lacked specificity data. Finally, we report on experimental tests of 19 selected compounds against a panel of 13 SLCs from seven different families. Except for a few inhibitors, which were active on unrelated SLCs, the tested inhibitors demonstrated high selectivity for their reported targets. To make this knowledge easily accessible to the scientific community, we created an interactive dashboard displaying the collected data in the RESOLUTE web portal (https://re-solute.eu). We anticipate that our open-access resources on assays and compounds will support the development of future drug discovery campaigns for SLCs

Evaluation of the inter-annual variability of stratospheric chemical composition in chemistry-climate models using ground-based multi species time series

The variability of stratospheric chemical composition occurs on a broad spectrum of timescales, ranging from day to decades. A large part of the variability appears to be driven by external forcings such as volcanic aerosols, solar activity, halogen loading, levels of greenhouse gases (GHG), and modes of climate variability (quasi-biennial oscillation (QBO), El Niño-Southern Oscillation (ENSO)). We estimate the contributions of different external forcings to the interannual variability of stratospheric chemical composition and evaluate how well 3-D chemistry-climate models (CCMs) can reproduce the observed response-forcing relationships. We carry out multivariate regression analyses on long time series of observed and simulated time series of several traces gases in order to estimate the contributions of individual forcings and unforced variability to their internannual variability. The observations are typically decadal time series of ground-based data from the international Network for the Detection of Atmospheric Composition Change (NDACC) and the CCM simulations are taken from the CCMVal-2 REF-B1 simulations database. The chemical species considered are column O3, HCl, NO2, and N2O. We check the consistency between observations and model simulations in terms of the forced and internal components of the total interannual variability (externally forced variability and internal variability) and identify the driving factors in the interannual variations of stratospheric chemical composition over NDACC measurement sites. Overall, there is a reasonably good agreement between regression results from models and observations regarding the externally forced interannual variability. A much larger fraction of the observed and modelled interannual variability is explained by external forcings in the tropics than in the extratropics, notably in polar regions. CCMs are able to reproduce the amplitudes of responses in chemical composition to specific external forcings. However, CCMs tend to underestimate very substantially the internal variability and hence the total interannual variability for almost all species considered. This lack of internal variability in CCMs might partly originate from the surface forcing of these CCMs by analysed SSTs. The results illustrate the potential of NDACC ground-based observations for evaluating CCMs

Recent Arctic ozone depletion: Is there an impact of climate change?

After the well-reported record loss of Arctic stratospheric ozone of up to 38% in the winter 2010–2011, further large depletion of 27% occurred in the winter 2015–2016. Record low winter polar vortex temperatures, below the threshold for ice polar stratospheric cloud (PSC) formation, persisted for one month in January 2016. This is the first observation of such an event and resulted in unprecedented dehydration/denitrification of the polar vortex. Although chemistry–climate models (CCMs) generally predict further cooling of the lower stratosphere with the increasing atmospheric concentrations of greenhouse gases (GHGs), significant differences are found between model results indicating relatively large uncertainties in the predictions. The link between stratospheric temperature and ozone loss is well understood and the observed relationship is well captured by chemical transport models (CTMs). However, the strong dynamical variability in the Arctic means that large ozone depletion events like those of 2010–2011 and 2015–2016 may still occur until the concentrations of ozone-depleting substances return to their 1960 values. It is thus likely that the stratospheric ozone recovery, currently anticipated for the mid-2030s, might be significantly delayed. Most important in order to predict the future evolution of Arctic ozone and to reduce the uncertainty of the timing for its recovery is to ensure continuation of high-quality ground-based and satellite ozone observations with special focus on monitoring the annual ozone loss during the Arctic winter

Validation of ozone measurements from the Atmospheric Chemistry Experiment (ACE)

This paper presents extensive bias determination analyses of ozone observations from the Atmospheric Chemistry Experiment (ACE) satellite instruments: the ACE Fourier Transform Spectrometer (ACE-FTS) and the Measurement of Aerosol Extinction in the Stratosphere and Troposphere Retrieved by Occultation (ACE-MAESTRO) instrument. Here we compare the latest ozone data products from ACE-FTS and ACE-MAESTRO with coincident observations from nearly 20 satellite-borne, airborne, balloon-borne and ground-based instruments, by analysing volume mixing ratio profiles and partial column densities. The ACE-FTS version 2.2 Ozone Update product reports more ozone than most correlative measurements from the upper troposphere to the lower mesosphere. At altitude levels from 16 to 44 km, the average values of the mean relative differences are nearly all within +1 to +8%. At higher altitudes (45 60 km), the ACE-FTS ozone amounts are significantly larger than those of the comparison instruments, with mean relative differences of up to +40% (about + 20% on average). For the ACE-MAESTRO version 1.2 ozone data product, mean relative differences are within +/- 10% (average values within +/- 6%) between 18 and 40 km for both the sunrise and sunset measurements. At higher altitudes (similar to 35-55 km), systematic biases of opposite sign are found between the ACE-MAESTRO sunrise and sunset observations. While ozone amounts derived from the ACE-MAESTRO sunrise occultation data are often smaller than the coincident observations (with mean relative differences down to -10%), the sunset occultation profiles for ACE-MAESTRO show results that are qualitatively similar to ACE-FTS, indicating a large positive bias (mean relative differences within +10 to +30%) in the 45-55 km altitude range. In contrast, there is no significant systematic difference in bias found for the ACE-FTS sunrise and sunset measurements