Abstract. The growth rate of atmospheric carbon dioxide (CO2) reflects the net effect of emissions and uptake resulting from anthropogenic and natural carbon sources and sinks. Annual mean CO2 growth rates have been determined from satellite retrievals of column-averaged dry-air mole fractions of CO2, i.e. XCO2, for the years 2003 to 2016. The XCO2 growth rates agree with National Oceanic and Atmospheric Administration (NOAA) growth rates from CO2 surface observations within the uncertainty of the satellite-derived growth rates (mean difference ± standard deviation: 0.0±0.3 ppm year−1; R: 0.82). This new and independent data set confirms record-large growth rates of around 3 ppm year−1 in 2015 and 2016, which are attributed to the 2015–2016 El Niño. Based on a comparison of the satellite-derived growth rates with human CO2 emissions from fossil fuel combustion and with El Niño Southern Oscillation (ENSO) indices, we estimate by how much the impact of ENSO dominates the impact of fossil-fuel-burning-related emissions in explaining the variance of the atmospheric CO2 growth rate. Our analysis shows that the ENSO impact on CO2 growth rate variations dominates that of human emissions throughout the period 2003–2016 but in particular during the period 2010–2016 due to strong La Niña and El Niño events. Using the derived growth rates and their uncertainties, we estimate the probability that the impact of ENSO on the variability is larger than the impact of human emissions to be 63 % for the time period 2003–2016. If the time period is restricted to 2010–2016, this probability increases to 94 %.
Abstract. This work presents the latest release (v9.0) of the University of Leicester GOSAT Proxy XCH4 dataset. Since the launch of the GOSAT satellite in 2009, these data have been produced by the UK National Centre for Earth Observation (NCEO) as part of the ESA Greenhouse Gas Climate Change Initiative (GHG-CCI) and Copernicus Climate Change Services (C3S) projects. With now over a decade of observations, we outline the many scientific studies achieved using past versions of these data in order to highlight how this latest version may be used in the future. We describe in detail how the data are generated, providing information and statistics for the entire processing chain from the L1B spectral data through to the final quality-filtered column-averaged dry-air mole fraction (XCH4) data. We show that out of the 19.5 million observations made between April 2009 and December 2019, we determine that 7.3 million of these are sufficiently cloud-free (37.6 %) to process further and ultimately obtain 4.6 million (23.5 %) high-quality XCH4 observations. We separate these totals by observation mode (land and ocean sun glint) and by month, to provide data users with the expected data coverage, including highlighting periods with reduced observations due to instrumental issues. We perform extensive validation of the data against the Total Carbon Column Observing Network (TCCON), comparing to ground-based observations at 22 locations worldwide. We find excellent agreement with TCCON, with an overall correlation coefficient of 0.92 for the 88 345 co-located measurements. The single-measurement precision is found to be 13.72 ppb, and an overall global bias of 9.06 ppb is determined and removed from the Proxy XCH4 data. Additionally, we validate the separate components of the Proxy (namely the modelled XCO2 and the XCH4∕XCO2 ratio) and find these to be in excellent agreement with TCCON. In order to show the utility of the data for future studies, we compare against simulated XCH4 from the TM5 model. We find a high degree of consistency between the model and observations throughout both space and time. When focusing on specific regions, we find average differences ranging from just 3.9 to 15.4 ppb. We find the phase and magnitude of the seasonal cycle to be in excellent agreement, with an average correlation coefficient of 0.93 and a mean seasonal cycle amplitude difference across all regions of −0.84 ppb. These data are available at https://doi.org/10.5285/18ef8247f52a4cb6a14013f8235cc1eb (Parker and Boesch, 2020).
Abstract. 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).
Abstract. Nitrogen dioxide is both a primary pollutant with direct health effects and a key precursor of the secondary pollutant ozone. This paper reports on the development, characterisation and test flight of the Atmospheric Nitrogen Dioxide Imager (ANDI) remote sensing system. The ANDI system includes an imaging UV/Vis grating spectrometer able to capture scattered sunlight spectra for the determination of tropospheric nitrogen dioxide (NO 2 ) concentrations by way of DOAS slant column density and vertical column density measurements.Results are shown for an ANDI test flight over Leicester City in the UK on a cloud-free winter day in February 2013. Retrieved NO 2 columns gridded to a surface resolution of 80 m × 20 m revealed hotspots in a series of locations around Leicester City, including road junctions, the train station, major car parks, areas of heavy industry, a nearby airport (East Midlands) and a power station (Ratcliffe-on-Soar). In the city centre the dominant source of NO 2 emissions was identified as road traffic, contributing to a background concentration as well as producing localised hotspots. Quantitative analysis revealed a significant urban increment over the city centre which increased throughout the flight.
Abstract.A novel tropospheric NO 2 DOAS retrieval algorithm optimised for a nadir-viewing satellite instrument imaging polluted areas is proposed in this work. Current satellite DOAS retrievals have relied on using a solar reference spectrum to derive a total slant column, then using either model assimilation or spatial filtering to derive the tropospheric component. In the ERrs-DOAS (Earth radiance reference sector DOAS) algorithm, tropospheric NO 2 slant columns are derived using spectra averaged from measurements over unpolluted regions, thus removing the need for post-hoc separation techniques, though some residual stratospheric biases may still remain. To validate the ERrs-DOAS algorithm, DOAS retrievals were performed on modelled spectra created by the radiative transfer model SCIA-TRAN, as well as L1B Earth radiance data measured by the NASA/KNMI Ozone Monitoring Instrument (OMI). It was found that retrievals using an Earth radiance reference produce spatial distributions of tropospheric NO 2 over eastern China during June 2005 that highly correlate with those derived using existing retrieval algorithms. Comparisons with slant columns retrieved by the operational NO 2 retrieval algorithm for OMI (OMNO2A) show that the ERrs-DOAS algorithm greatly reduces the presence of artificial across-track biases (stripes) caused by calibration errors, as well as the removal of path length enhancement in off-nadir pixels. Analysis of Pacific SCDs suggests that the algorithm also produces a 27 % reduction in retrieval uncertainty, though this may be partially due to biases introduced by differences in the retrieval algorithm settings. The ERrs-DOAS technique also reveals absorption features over the Sahara and similar regions characteristic of sand and liquid H 2 O absorption, as first discovered in the analysis of GOME-2 NO 2 retrievals.
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