ML-TOMCAT: machine-learning-based satellite-corrected global stratospheric ozone profile data set from a chemical transport model

High-quality stratospheric ozone profile data sets are a key requirement for accurate quantification and attribution of long-term ozone changes. Satellite instruments provide stratospheric ozone profile measurements over typical mission durations of 5–15 years. Various methodologies have then been applied to merge and homogenise the different satellite data in order to create long-term observation-based ozone profile data sets with minimal data gaps. However, individual satellite instruments use different measurement methods, sampling patterns and retrieval algorithms which complicate the merging of these different data sets. In contrast, atmospheric chemical models can produce chemically consistent long-term ozone simulations based on specified changes in external forcings, but they are subject to the deficiencies associated with incomplete understanding of complex atmospheric processes and uncertain photochemical parameters. Here, we use chemically self-consistent output from the TOMCAT 3-D chemical transport model (CTM) and a random-forest (RF) ensemble learning method to create a merged 42-year (1979–2020) stratospheric ozone profile data set (ML-TOMCAT V1.0). The underlying CTM simulation was forced by meteorological reanalyses, specified trends in long-lived source gases, solar flux and aerosol variations. The RF is trained using the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) data set over the time periods of the Microwave Limb Sounder (MLS) from the Upper Atmosphere Research Satellite (UARS) (1991–1998) and Aura (2005–2016) missions. We find that ML-TOMCAT shows excellent agreement with available independent satellite-based data sets which use pressure as a vertical coordinate (e.g. GOZCARDS, SWOOSH for non-MLS periods) but weaker agreement with the data sets which are altitude-based (e.g. SAGE-CCI-OMPS, SCIAMACHY-OMPS). We find that at almost all stratospheric levels ML-TOMCAT ozone concentrations are well within uncertainties of the observational data sets. The ML-TOMCAT (V1.0) data set is ideally suited for the evaluation of chemical model ozone profiles from the tropopause to 0.1 hPa and is freely available via https://doi.org/10.5281/zenodo.5651194 (Dhomse et al., 2021).

Evaluation of Version 3 Total and Tropospheric Ozone Columns From Earth Polychromatic Imaging Camera on Deep Space Climate Observatory for Studying Regional Scale Ozone Variations

Discrete wavelength radiance measurements from the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) allows derivation of global synoptic maps of total and tropospheric ozone columns every hour during Northern Hemisphere (NH) Summer or 2 hours during Northern Hemisphere winter. In this study, we present version 3 retrieval of Earth Polychromatic Imaging Camera ozone that covers the period from June 2015 to the present with improved geolocation, calibration, and algorithmic updates. The accuracy of total and tropospheric ozone measurements from EPIC have been evaluated using correlative satellite and ground-based total and tropospheric ozone measurements at time scales from daily averages to monthly means. The comparisons show good agreement with increased differences at high latitudes. The agreement improves if we only accept retrievals derived from the EPIC 317 nm triplet and limit solar zenith and satellite looking angles to 70°. With such filtering in place, the comparisons of EPIC total column ozone retrievals with correlative satellite and ground-based data show mean differences within ±5-7 Dobson Units (or 1.5–2.5%). The biases with other satellite instruments tend to be mostly negative in the Southern Hemisphere while there are no clear latitudinal patterns in ground-based comparisons. Evaluation of the EPIC ozone time series at different ground-based stations with the correlative ground-based and satellite instruments and ozonesondes demonstrated good consistency in capturing ozone variations at daily, weekly and monthly scales with a persistently high correlation (r2 > 0.9) for total and tropospheric columns. We examined EPIC tropospheric ozone columns by comparing with ozonesondes at 12 stations and found that differences in tropospheric column ozone are within ±2.5 DU (or ∼±10%) after removing a constant 3 DU offset at all stations between EPIC and sondes. The analysis of the time series of zonally averaged EPIC tropospheric ozone revealed a statistically significant drop of ∼2–4 DU (∼5–10%) over the entire NH in spring and summer of 2020. This drop in tropospheric ozone is partially related to the unprecedented Arctic stratospheric ozone losses in winter-spring 2019/2020 and reductions in ozone precursor pollutants due to the COVID-19 pandemic.

Using vertical phase differences to better resolve 3D gravity wave structure

Atmospheric gravity waves (GWs) are a critically important dynamical mechanism in the terrestrial atmosphere, with significant effects on weather and climate. They are geographically ubiquitous in the middle and upper atmosphere, and thus, satellite observations are key to characterising their properties and spatial distribution. Nadir-viewing satellite instruments characterise the short horizontal wavelength portion of the GW spectrum, which is important for momentum transport; however, these nadir-sensing instruments have coarse vertical resolutions. This restricts our ability to characterise the 3D structure of these waves accurately, with important implications for our quantitative understanding of how these waves travel and how they drive the atmospheric circulation when they break. Here, we describe, implement and test a new spectral analysis method to address this problem. This method is optimised for the characterisation of waves in any three-dimensional data set where one dimension is of coarse resolution relative to variations in the wave field, a description which applies to GW-sensing nadir-sounding satellite instruments but which is also applicable in other areas of science. We show that our new “2D + 1 ST” method provides significant benefits relative to existing spectrally isotropic methods for characterising such waves. In particular, it is much more able to detect regional and height variations in observed vertical wavelength and able to properly characterise extremely vertically long waves that extend beyond the data volume.

ML-TOMCAT: Machine-Learning-Based Satellite-Corrected Global Stratospheric Ozone Profile Dataset from a Chemical Transport Model

High quality stratospheric ozone profile datasets are a key requirement for accurate quantification and attribution of long-term ozone changes. Satellite instruments obtain stratospheric ozone profile measurements over typical mission durations of 5–15 years. Various methodologies have then been applied to merge and homogenise the different satellite data in order to create longer term observation-based ozone profile datasets with minimal data gaps. However, individual satellite instruments use different measurement methods, sampling patterns and retrieval algorithms which complicate the merging of these different datasets. In contrast, atmospheric chemical models can produce chemically consistent long-term ozone simulations based on specified changes in external forcings, but they are subject to the deficiencies associated with incomplete understanding of complex atmospheric processes and uncertain photochemical parameters. Here, we use chemically self-consistent output from the TOMCAT 3-D chemical transport model (CTM) and a Random-Forest (RF) ensemble learning method to create a merged 42-year (1979–2020) stratospheric ozone profile dataset (ML-TOMCAT V1.0). The underlying CTM simulation was forced by meteorological reanalyses, specified trends in long-lived source gas, solar flux and aerosol variations. The RF is trained using the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) dataset over the time periods of the Microwave Limb Sounder (MLS) from the Upper Atmosphere Research Satellite (UARS) (1991–1998) and Aura (2005–2016) missions. We find that ML-TOMCAT shows excellent agreement with available independent satellite-based datasets which use pressure as the vertical coordinate (e.g. GOZCARDS, SWOOSH for non-MLS periods) but weaker agreement with the datasets which are height-based (e.g. SAGE–CCI–OMPS, SCIAMACHY-OMPS). We find that at almost all stratospheric levels ML-TOMCAT ozone concentrations are well within uncertainties in the observational datasets. The ML-TOMCAT dataset is thus ideally suited for the evaluation of model ozone profiles from the tropopause to 0.1 hPa. ML-TOMCAT data is freely available via https://zenodo.org/record/4997959#.YNzleUlKiUk (Dhomse et al., 2021).

Directionally dependent Lambertian-equivalent reflectivity (DLER) of the Earth's surface measured by the GOME-2 satellite instruments

In this paper we introduce the new concept of directionally dependent Lambertian-equivalent reflectivity (DLER) of the Earth's surface retrieved from satellite observations. This surface DLER describes Lambertian (isotropic) surface reflection which is extended with a dependence on the satellite viewing geometry. We apply this concept to data of the GOME-2 satellite instruments to create a global database of the reflectivity of the Earth's surface, providing surface DLER for 26 wavelength bands between 328 and 772 nm as a function of the satellite viewing angle via a second-degree polynomial parameterisation. The resolution of the database grid is 0.25∘ by 0.25∘, but the real, intrinsic spatial resolution varies over the grid from 1.0∘ by 1.0∘ to 0.5∘ by 0.5∘ down to 0.25∘ by 0.25∘ by applying dynamic gridding techniques. The database is based on more than 10 years (2007–2018) of GOME-2 data from the MetOp-A and MetOp-B satellites. The relation between DLER and bi-directional reflectance distribution function (BRDF) surface reflectance is studied using radiative transfer simulations. For the shorter wavelengths (λ<500 nm), there are significant differences between the two. For instance, at 463 nm the difference can go up to 6 % at 30∘ solar zenith angle. The study also shows that, although DLER and BRDF surface reflectances have different properties, they are comparable for the longer wavelengths (λ>500 nm). Based on this outcome, the GOME-2 surface DLER is compared with MODIS surface BRDF data from MODIS band 1 (centred around 645 nm) using both case studies and global comparisons. The conclusion of this validation is that the GOME-2 DLER compares well to MODIS BRDF data and that it does so much better than the non-directional LER database. The DLER approach for describing surface reflectivity is therefore an important improvement over the standard isotropic (non-directional) LER approaches used in the past. The GOME-2 surface DLER database can be used for the retrieval of atmospheric properties from GOME-2 and from previous satellite instruments like GOME and SCIAMACHY. It will also be used to support retrievals from the future Sentinel-5 UVNS (ultraviolet, visible, near-infrared, and short-wave infrared) satellite instrument.

Using Vertical Phase Differences to Better Resolve 3D Gravity Wave Structure

Atmospheric gravity waves (GWs) are a critically-important dynamical process in the terrestrial atmosphere, with significant effects on weather and climate. They are geographically ubiquitous in the middle and upper atmosphere, and thus satellite observations are key to characterising their properties and spatial distribution. Nadir-viewing satellite instruments characterise the short-horizontal-wavelength portion of the GW spectrum, which is important for momentum transport, well; however, these nadir-sensing instruments have coarse vertical resolutions. This restricts our ability to characterise the 3D structure of these waves accurately, with important implications for our quantitative understanding of how these waves travel and how they drive the atmospheric circulation when they break. Here, we describe, implement and test a new spectral analysis method to address this problem. This method is optimised for the characterisation of waves in any three-dimensional dataset where one dimension is of coarse resolution relative to variations in the wave field, a description which applies to GW-sensing nadir-sounding satellite instruments but which is also applicable in other areas of science. We show that this "2D+1 ST" method provides significant benefits relative to existing spectrally-isotropic methods for characterising such waves. In particular, it is much more able to detect regional and height variations in observed vertical wavelength, and able to properly characterise extremely vertically long waves that extend beyond the data volume.

Gas and heat fluxes during multiple effusive eruptions of Piton de la Fournaise (Réunion) and their implications for magmatic processes

&lt;p&gt;Piton de la Fournaise (La R&amp;#233;union, France) is one of the most active volcanoes in the world, producing frequent effusive basaltic eruptions of varying duration. These eruptions are accompanied by strong thermal infrared (TIR) signals and significant sulfur dioxide (SO&lt;sub&gt;2&lt;/sub&gt;) emissions detected by satellite instruments. The high frequency of eruptions provides an extensive dataset, which allows us to explore the relationships between eruptive heat, mass and gas fluxes. Five eruptions with different temporal trends of erupted mass flux have been selected for this study: April 2007, May 2015, August-October 2015, February 2019 and April 2020. For each of them, we estimated SO&lt;sub&gt;2&lt;/sub&gt; emission from three ultraviolet satellite instruments (the Ozone Monitoring Instrument OMI, the Ozone Mapping and Profiler Suite OMPS and the Tropospheric Monitoring Instrument TROPOMI). The total SO&lt;sub&gt;2&lt;/sub&gt; emission for each eruption has been estimated for an extensive range of sulfur (S) content within melt inclusions and the matrix using a petrological approach and the erupted magma masses obtained from MODIS TIR satellite data. Preliminary results show that, assuming the estimated SO&lt;sub&gt;2&lt;/sub&gt; emission falls within the 30% error of the SO&lt;sub&gt;2&lt;/sub&gt; mass detected by each satellite instrument, the implied magmatic sulfur contents are in good agreement with expected values for basaltic eruptions. Given pre-eruptive S contents between 200 and 750 ppm, estimated SO&lt;sub&gt;2&lt;/sub&gt; emissions for the May 2015 eruption are consistent with an eruption largely fed by degassed magma. However, for the February 2019 eruption, there is a discrepancy between the three satellite sensors. Whereas the TROPOMI and the OMI instruments provide almost the same range of magmatic sulfur content (300-1100 ppm), the OMPS gives a higher range (700 to 1900 ppm) suggesting that fresh, undegassed magma was also involved in this eruption. Petrologic analysis of the pre-eruptive sulfur content will allow us to validate the satellite data and, in turn, to validate the ground-based SO&lt;sub&gt;2&lt;/sub&gt; data from the NOVAC network operated by the&amp;#160;Observatoire Volcanologique du Piton de la Fournaise&amp;#160;(OVPF). Our approach yields insights into the characteristics of the magma reservoir supplying effusive events (e.g., eruptive degassing processes and the ratio of intrusive to extrusive magma) from space-based sensors.&lt;/p&gt;

Model estimations of geophysical variability between satellite measurements of ozone profiles

In order to validate satellite measurements of atmospheric composition, it is necessary to understand the range of random and systematic uncertainties inherent in the measurements. On occasions where measurements from two different satellite instruments do not agree within those estimated uncertainties, a common explanation is that the difference can be assigned to geophysical variability, i.e., differences due to sampling the atmosphere at different times and locations. However, the expected geophysical variability is often left ambiguous and rarely quantified. This paper describes a case study where the geophysical variability of O3 between two satellite instruments – ACE-FTS (Atmospheric Chemistry Experiment – Fourier Transform Spectrometer) and OSIRIS (Optical Spectrograph and InfraRed Imaging System) – is estimated using simulations from climate models. This is done by sampling the models CMAM (Canadian Middle Atmosphere Model), EMAC (ECHAM/MESSy Atmospheric Chemistry), and WACCM (Whole Atmosphere Community Climate Model) throughout the upper troposphere and stratosphere at times and geolocations of coincident ACE-FTS and OSIRIS measurements. Ensemble mean values show that in the lower stratosphere, O3 geophysical variability tends to be independent of the chosen time coincidence criterion, up to within 12 h; and conversely, in the upper stratosphere geophysical variation tends to be independent of the chosen distance criterion, up to within 2000 km. It was also found that in the lower stratosphere, at altitudes where there is the greatest difference between air composition inside and outside the polar vortex, the geophysical variability in the southern polar region can be double of that in the northern polar region. This study shows that the ensemble mean estimates of geophysical variation can be used when comparing data from two satellite instruments to optimize the coincidence criteria, allowing for the use of more coincident profiles while providing an estimate of the geophysical variation within the comparison results.