Trend analysis of greenhouse gases over Europe measured by a network of ground-based remote FTIR instruments

This paper describes the statistical analysis of annual trends in long term datasets of greenhouse gas measurements taken over ten or more years. The analysis technique employs a bootstrap resampling method to determine both the long-term and intra-annual variability of the datasets, together with the uncertainties on the trend values. The method has been applied to data from a European network of ground-based solar FTIR instruments to determine the trends in the tropospheric, stratospheric and total columns of ozone, nitrous oxide, carbon monoxide, methane, ethane and HCFC-22. The suitability of the method has been demonstrated through statistical validation of the technique, and comparison with ground-based in-situ measurements and 3-D atmospheric models.Peer reviewe

Intercomparison of long-term ground-based measurements of tropospheric and stratospheric ozone at Lauder, New Zealand (45S)

Long-term ground-based ozone measurements are crucial to study the recovery of stratospheric ozone as well as the trends of tropospheric ozone. This study is performed in the context of the LOTUS (Long-term Ozone Trends and Uncertainties in the Stratosphere) and TOAR-II (Tropospheric Ozone Assessment Report, phase II) initiatives. We perform an intercomparison study of total column ozone and multiple partial ozone columns between the ground-based measurements available at the Lauder station from 2000 to 2022, which are the Fourier transform infrared (FTIR) spectrometer, Dobson Umkehr, ozonesonde, lidar, and the microwave radiometer. We compare partial columns, defined to provide independent information: one tropospheric and three stratospheric columns. The intercomparison is analyzed using the median of relative differences (the bias) of FTIR with each of the other measurements, the scaled Median Absolute deviation (MADs), and a trend of these differences (measurement drift). The total column shows a bias and strong scatter well within the combined systematic and random uncertainties respectively. There is however a drift of 0.6±0.5 %/decade if we consider the full time series. In the troposphere we find a low bias of -1.9 % with the ozonesondes. No drift is found between the three instruments in the troposphere, which is good for trend studies within TOAR-II. In both the lower and upper stratosphere, we get a negative bias for all instruments with respect to FTIR (between -1.2 % and -6.8 %), but all are within the range of the systematic uncertainties. In the middle stratosphere we seem to find a negative bias of around -5.2 to -6.6 %, pointing towards too high values for FTIR in this partial column. We find no significant drift in the stratosphere between ozonesonde and FTIR for all partial columns. We do observe drift between the FTIR and Umkehr partial columns in the lower and upper stratospheres (2.6±1.1 %/decade and -3.2±0.9 %/decade), with lidar in the midle and upper stratosphere (2.1±0.8 %/decade and -3.7±1.2 %/decade), and with MWR in the midle stratosphere (3.1±1.7 %/decade). These drifts point to the fact that the different observed trends in LOTUS are not due to different sampling, vertical sensitivity or time periods and gaps. However, the difference in trends in LOTUS is reduced by applying a new FTIR retrieval strategy, which changes inputs such as the choice of microwindows, spectroscopy from HITRAN2008 to HITRAN2020, and the regularization method

Validation of MIPAS HNO3 operational data

Nitric acid (HNO3) is one of the key products that are operationally retrieved by the European Space Agency (ESA) from the emission spectra measured by the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) onboard ENVISAT. The product version 4.61/4.62 for the observation period between July 2002 and March 2004 is validated by comparisons with a number of independent observations from ground-based stations, aircraft/balloon campaigns, and satellites. Individual HNO3 profiles of the ESA MIPAS level-2 product show good agreement with those of MIPAS-B and MIPAS-STR (the balloon and aircraft version of MIPAS, respectively), and the balloon-borne infrared spectrometers MkIV and SPIRALE, mostly matching the reference data within the combined instrument error bars. In most cases differences between the correlative measurement pairs are less than 1 ppbv (5-10%) throughout the entire altitude range up to about 38 km (similar to 6 hPa), and below 0.5 ppbv (15-20% or more) above 30 km (similar to 17 hPa). However, differences up to 4 ppbv compared to MkIV have been found at high latitudes in December 2002 in the presence of polar stratospheric clouds. The degree of consistency is further largely affected by the temporal and spatial coincidence, and differences of 2 ppbv may be observed between 22 and 26 km (similar to 50 and 30 hPa) at high latitudes near the vortex boundary, due to large horizontal inhomogeneity of HNO3. Similar features are also observed in the mean differences of the MIPAS ESA HNO3 VMRs with respect to the ground-based FTIR measurements at five stations, aircraft-based SAFIRE-A and ASUR, and the balloon campaign IBEX. The mean relative differences between the MIPAS and FTIR HNO3 partial columns are within +/- 2%, comparable to the MIPAS systematic error of similar to 2%. For the vertical profiles, the biases between the MIPAS and FTIR data are generally below 10% in the altitudes of 10 to 30 km. The MIPAS and SAFIRE HNO3 data generally match within their total error bars for the mid and high latitude flights, despite the larger atmospheric inhomogeneities that characterize the measurement scenario at higher latitudes. The MIPAS and ASUR comparison reveals generally good agreements better than 10-13% at 20-34 km. The MIPAS and IBEX measurements agree reasonably well (mean relative differences within +/- 15%) between 17 and 32 km. Statistical comparisons of the MIPAS profiles correlated with those of Odin/SMR, ILAS-II, and ACE-FTS generally show good consistency. The mean differences averaged over individual latitude bands or all bands are within the combined instrument errors, and generally within 1, 0.5, and 0.3 ppbv between 10 and 40 km (similar to 260 and 4.5 hPa) for Odin/SMR, ILAS-II, and ACE-FTS, respectively. The standard deviations of the differences are between 1 to 2 ppbv. The standard deviations for the satellite comparisons and for almost all other comparisons are generally larger than the estimated measurement uncertainty. This is associated with the temporal and spatial coincidence error and the horizontal smoothing error which are not taken into account in our error budget. Both errors become large when the spatial variability of the target molecule is high.Peer reviewe

An eleven year record of XCO2 estimates derived from GOSAT measurements using the NASA ACOS version 9 retrieval algorithm

The Thermal And Near infrared Sensor for carbon Observation – Fourier Transform Spectrometer (TANSO-FTS) on the Japanese Greenhouse gases Observing SATellite (GOSAT) has been returning data since April 2009. The version 9 (v9) Atmospheric Carbon Observations from Space (ACOS) Level 2 Full Physics (L2FP) retrieval algorithm (Kiel et al., 2019) was used to derive estimates of carbon dioxide (CO2) dry air mole fraction (XCO2) from the TANSO-FTS measurements collected over it's first eleven years of operation. The bias correction and quality filtering of the L2FP XCO2 product were evaluated using estimates derived from the Total Carbon Column Observing Network (TCCON) as well as values simulated from a suite of global atmospheric inverse modeling systems (models). In addition, the v9 ACOS GOSAT XCO2 results were compared with collocated XCO2 estimates derived from NASA's Orbiting Carbon Observatory-2 (OCO-2), using the version 10 (v10) ACOS L2FP algorithm. These tests indicate that the v9 ACOS GOSAT XCO2 product has improved throughput, scatter and bias, when compared to the earlier v7.3 ACOS GOSAT product, which extended through mid 2016. Of the 37 million (M) soundings collected by GOSAT through June 2020, approximately 20 % were selected for processing by the v9 L2FP algorithm after screening for clouds and other artifacts. After post-processing, 5.4 % of the soundings (2M out of 37M) were assigned a “good” XCO2 quality flag, as compared to 3.9 % in v7.3 (< 1M out of 24M). After quality filtering and bias correction, the differences in XCO2 between ACOS GOSAT v9 and both TCCON and models have a scatter (one sigma) of approximately 1 ppm for ocean-glint observations and 1 to 1.5 ppm for land observations. Similarly, global mean biases are less than approximately 0.2 ppm. Seasonal mean biases relative to the v10 OCO-2 XCO2 product are of order 0.1 ppm for observations over land. However, for ocean-glint observations, seasonal mean biases relative to OCO-2 range from 0.2 to 0.6 ppm, with substantial variation in time and latitude. The ACOS GOSAT v9 XCO2 data are available on the NASA Goddard Earth Science Data and Information Services Center (GES-DISC). The v9 ACOS Data User's Guide (DUG) describes best-use practices for the data. This dataset should be especially useful for studies of carbon cycle phenomena that span a full decade or more, and may serve as a useful complement to the shorter OCO-2 v10 dataset, which begins in September 2014

Validation of HNO3, ClONO2, and N2O5 from the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS)

The Atmospheric Chemistry Experiment (ACE) satellite was launched on 12 August 2003. Its two instruments measure vertical profiles of over 30 atmospheric trace gases by analyzing solar occultation spectra in the ultraviolet/visible and infrared wavelength regions. The reservoir gases HNO3, ClONO2, and N2O5 are three of the key species provided by the primary instrument, the ACE Fourier Transform Spectrometer (ACE-FTS). This paper describes the ACE-FTS version 2.2 data products, including the N2O5 update, for the three species and presents validation comparisons with available observations. We have compared volume mixing ratio (VMR) profiles of HNO3, ClONO2, and N2O5 with measurements by other satellite instruments (SMR, MLS, MIPAS), aircraft measurements (ASUR), and single balloon-flights (SPIRALE, FIRS-2). Partial columns of HNO3 and ClONO2 were also compared with measurements by ground-based Fourier Transform Infrared (FTIR) spectrometers. Overall the quality of the ACE-FTS v2.2 HNO3 VMR profiles is good from 18 to 35 km. For the statistical satellite comparisons, the mean absolute differences are generally within ±1 ppbv ±20%) from 18 to 35 km. For MIPAS and MLS comparisons only, mean relative differences lie within±10% between 10 and 36 km. ACE-FTS HNO3 partial columns (~15–30 km) show a slight negative bias of −1.3% relative to the ground-based FTIRs at latitudes ranging from 77.8° S–76.5° N. Good agreement between ACE-FTS ClONO2 and MIPAS, using the Institut für Meteorologie und Klimaforschung and Instituto de Astrofísica de Andalucía (IMK-IAA) data processor is seen. Mean absolute differences are typically within ±0.01 ppbv between 16 and 27 km and less than +0.09 ppbv between 27 and 34 km. The ClONO2 partial column comparisons show varying degrees of agreement, depending on the location and the quality of the FTIR measurements. Good agreement was found for the comparisons with the midlatitude Jungfraujoch partial columns for which the mean relative difference is 4.7%. ACE-FTS N2O5 has a low bias relative to MIPAS IMK-IAA, reaching −0.25 ppbv at the altitude of the N2O5 maximum (around 30 km). Mean absolute differences at lower altitudes (16–27 km) are typically −0.05 ppbv for MIPAS nighttime and ±0.02 ppbv for MIPAS daytime measurements

Ensemble-based satellite-derived carbon dioxide and methane column-averaged dry-air mole fraction data sets (2003-2018) for carbon and climate applications

Satellite retrievals of column-averaged dry-air mole fractions of carbon dioxide (CO₂) and methane (CH₄), denoted XCO₂ and XCH₄, respectively, have been used in recent years to obtain information on natural and anthropogenic sources and sinks and for other applications such as comparisons with climate models. Here we present new data sets based on merging several individual satellite data products in order to generate consistent long-term climate data records (CDRs) of these two Essential Climate Variables (ECVs). These ECV CDRs, which cover the time period 2003–2018, have been generated using an ensemble of data products from the satellite sensors SCIAMACHY/ENVISAT and TANSO-FTS/GOSAT and (for XCO₂) for the first time also including data from the Orbiting Carbon Observatory 2 (OCO-2) satellite. Two types of products have been generated: (i) Level 2 (L2) products generated with the latest version of the ensemble median algorithm (EMMA) and (ii) Level 3 (L3) products obtained by gridding the corresponding L2 EMMA products to obtain a monthly 5∘×5∘ data product in Obs4MIPs (Observations for Model Intercomparisons Project) format. The L2 products consist of daily NetCDF (Network Common Data Form) files, which contain in addition to the main parameters, i.e., XCO₂ or XCH₄, corresponding uncertainty estimates for random and potential systematic uncertainties and the averaging kernel for each single (quality-filtered) satellite observation. We describe the algorithms used to generate these data products and present quality assessment results based on comparisons with Total Carbon Column Observing Network (TCCON) ground-based retrievals. We found that the XCO₂ Level 2 data set at the TCCON validation sites can be characterized by the following figures of merit (the corresponding values for the Level 3 product are listed in brackets) – single-observation random error (1σ): 1.29 ppm (monthly: 1.18 ppm); global bias: 0.20 ppm (0.18 ppm); and spatiotemporal bias or relative accuracy (1σ): 0.66 ppm (0.70 ppm). The corresponding values for the XCH₄ products are single-observation random error (1σ): 17.4 ppb (monthly: 8.7 ppb); global bias: −2.0 ppb (−2.9 ppb); and spatiotemporal bias (1σ): 5.0 ppb (4.9 ppb). It has also been found that the data products exhibit very good long-term stability as no significant long-term bias trend has been identified. The new data sets have also been used to derive annual XCO₂ and XCH₄ growth rates, which are in reasonable to good agreement with growth rates from the National Oceanic and Atmospheric Administration (NOAA) based on marine surface observations. The presented ECV data sets are available (from early 2020 onwards) via the Climate Data Store (CDS, https://cds.climate.copernicus.eu/, last access: 10 January 2020) of the Copernicus Climate Change Service (C3S, https://climate.copernicus.eu/, last access: 10 January 2020)

Advanced exploitation of ground-based Fourier transform infrared observations for tropospheric studies over Europe: achievements of the UFTIR project

Solar absorption measurements using Fourier transform infrared (FTIR) spectrometry carry information about the atmospheric abundances of many constituents, including information about their vertical distributions in the troposphere and the stratosphere. Such observations have regularly been made since many years as a contribution to the NDSC (Network for the Detection of Stratospheric Change). They are the only ground-based remote sensing observations available nowadays that carry information about key atmospheric trace species in the free troposphere, among which the most important greenhouse gases. The European UFTIR project (Time series of Upper Free Troposphere observations from a European ground-based FTIR network, http://www.nilu.no/uftir) has focused on maximizing the information content of FTIR long-term monitoring data of some direct and indirect greenhouse gases (CH4, N2O, O3,HCFC-22, and CO and C2H6, respectively). The UFTIR network includes six NDSC stations in Western Europe, covering the polar to subtropical regions. At several stations of the network, the observations span more than a decade. Existing spectral time series have been reanalyzed according to a common optimized retrieval strategy, in order to derive distinct tropospheric and stratospheric abundances of the abovementioned target gases. A bootstrap resampling method has been implemented to evaluate trends of the tropospheric and total burdens of the target gases, including their uncertainties. In parallel, simulations of the target time series have been made with the Oslo CTM2 model: comparisons between the model results and the observations provide valuable information to improve the model, and in particular, to optimize emission estimates that are used as inputs to the model simulations, and to explain the observed trends. The final results of the project will be presented, and ways to proceed will be discussed

Complement C1q-dependent excitatory and inhibitory synapse elimination by astrocytes and microglia in Alzheimer's disease mouse models

Microglia and complement can mediate neurodegeneration in Alzheimer's disease (AD). By integrative multi-omics analysis, here we show that astrocytic and microglial proteins are increased in Tau P301S synapse fractions with age and in a C1q-dependent manner. In addition to microglia, we identified that astrocytes contribute substantially to synapse elimination in Tau P301S hippocampi. Notably, we found relatively more excitatory synapse marker proteins in astrocytic lysosomes, whereas microglial lysosomes contained more inhibitory synapse material. C1q deletion reduced astrocyte-synapse association and decreased astrocytic and microglial synapses engulfment in Tau P301S mice and rescued synapse density. Finally, in an AD mouse model that combines β-amyloid and Tau pathologies, deletion of the AD risk gene Trem2 impaired microglial phagocytosis of synapses, whereas astrocytes engulfed more inhibitory synapses around plaques. Together, our data reveal that astrocytes contact and eliminate synapses in a C1q-dependent manner and thereby contribute to pathological synapse loss and that astrocytic phagocytosis can compensate for microglial dysfunction