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

Abstract

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).

Can a regional-scale reduction of atmospheric CO<sub>2</sub> during the COVID-19 pandemic be detected from space? A case study for East China using satellite XCO<sub>2</sub> retrievals

“…The extent to which COVID-19 related emission reductions can be resolved on smaller scales -such as power plants or cities (e.g., Nassar et al, 2017;Reuter et al, 2019;Zheng et al, 2020a;Wu et al, 2020) has not yet been investigated in detail (to the best of our knowledge). For this purpose, XCO 2 retrievals from NASA's OCO-3 mission are also very promising, especially because of its Snapshot Area Map (SAM) mode, which permits the mapping of XCO 2 over ~80 km by 80 km areas around https://doi.org/10.5194/amt-2020-386 Preprint.…”

“…, on anthropogenic CO 2 emissions (e.g., Schneising et al, 2008Schneising et al, , 2013Reuter et al, 2014bReuter et al, , 2019Nassar et al, 2017;Schwandner et al, 2017;Matsunaga and Maksyutov, 2018;Miller et al, 2019;Labzovskii et al, 2019;Wu et al, 2020;Zheng et al, 2020a;Ye et al, 2020) and for other applications such as climate model assessments (e.g., Lauer et al, 2017;Gier et al, 2020) or data assimilation (e.g., Massart et al, 2016).…”

Reply to comments from reviewer #1

“…We have added a paragraph devoted to the discussion on the limitations and future directions: "There are several ways to improve on this analysis in the future. With more available future scenario simulations, the analysis can be extended for a longer time series, making use of longer observational timeseries, such as the one introduced in Reuter et al (2020). Higher temporal resolution of the models would enable studies on the effect of the diurnal cycle of CO 2 on the monthly mean and also allow for the construction of a co-located time series with the Level 2 satellite data.…”

Spatially resolved evaluation of Earth system models with satellite column-averaged CO<sub>2</sub>

“…All figures in this paper were produced with the Earth System Model Evaluation Tool (ESMValTool) version 2.0 (v2.0) (Righi et al, 2020;Eyring et al, 2020;Lauer et al, 2020).…”