Analysis of Four Years of Global XCO2 Anomalies as Seen by Orbiting Carbon Observatory-2

Abstract

NASA's carbon dioxide mission, Orbiting Carbon Observatory-2, began operating in September 2014. In this paper, we analyze four years (2015)(2016)(2017)(2018) of global (60 • S-60 • N) XCO 2 anomalies and their annual variations and seasonal patterns. We show that the anomaly patterns in the column-averaged CO 2 dry air mole fraction, XCO 2 , are robust and consistent from year-to-year. We evaluate the method by comparing the anomalies to fluxes from anthropogenic, biospheric, and biomass burning and to model-simulated local concentration enhancements. We find that, despite the simplicity of the method, the anomalies describe the spatio-temporal variability of XCO 2 (including anthropogenic emissions and seasonal variability related to vegetation and biomass burning) consistently with more complex model-based approaches. We see, for example, that positive anomalies correspond to fossil fuel combustion over the major industrial areas (e.g., China, eastern USA, central Europe, India, and the Highveld region in South Africa), shown as large positive XCO 2 enhancements in the model simulations. We also find corresponding positive anomalies and fluxes over biomass burning areas during different fire seasons. On the other hand, the largest negative anomalies correspond to the growing season in the northern middle latitudes, characterized by negative XCO 2 enhancements from simulations and high solar-induced chlorophyll fluorescence (SIF) values (indicating the occurrence of photosynthesis). The largest discrepancies between the anomaly patterns and the model-based results are observed in the tropical regions, where OCO-2 shows persistent positive anomalies over every season of every year included in this study. Finally, we demonstrate how XCO 2 anomalies enable the detection of anthropogenic signatures for several local scale case studies, both in the Northern and Southern Hemisphere. In particular, we analyze the XCO 2 anomalies collocated with the recent TROPOspheric Monitoring Instrument NO 2 observations (used as indicator of anthropogenic fossil fuel combustion) over the Highveld region in South Africa. The results highlight the capability of satellite-based observations to monitor natural and man-made CO 2 signatures on global scale. all over the world and show levels that are about 100 ppm higher than they were in 1958, when they were about 315 ppm. However, the measurement networks are still denser in the Western world.One of the most important applications of CO 2 measurements is to infer the surface CO 2 fluxes, i.e., sources and sinks (or emissions into and removal from the atmosphere). A variety of methods are commonly used to derive CO 2 fluxes from spatially resolved estimates of column-averaged CO 2 dry air mole fraction, XCO 2 . These methods can be broadly classified as analytic mass balance, plume dispersion, or atmospheric inversion methods. All three approaches predict or optimize surface fluxes needed to reproduce the observed XCO 2 distribution in the presence of the time-varying wind field. Groun...

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

“…China (e.g., Zhang et al, 2020;Bauwens et al, 2020), but for CO 2 such an assessment is more challenging because of small XCO 2 changes on top of a large background. For example, over extended anthropogenic source areas such as East China, the XCO 2 enhancement due to anthropogenic emissions is typically only approximately 1 -2 ppm (0.25% -0.5% of 400 ppm) or even less (see, e.g., Schneising et al, 2008Schneising et al, , 2013Hakkarainen et al, 2016Hakkarainen et al, , 2019; and this study). A 10% emission reduction would therefore only change the regional XCO 2 enhancement by 0.1 to 0.2 ppm.…”

“…They applied their method to the OCO-2 Level 2 XCO 2 data product to filter out trends and seasonal variations in order to isolate CO 2 source/sink signals. Hakkarainen et al, 2019, explain their method as follows: "In order to obtain the background, we calculate the daily medians for each 10-degree latitude band and linearly interpolate the resulting values to each OCO-2 data point. The median was chosen because it better represents the typical value in each latitude band, and it is not skewed towards extreme values".…”

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

“…Retrievals of XCO 2 from the satellite sensors SCIA-MACHY/ ENVISAT (Burrows et al, 1995;Bovensmann et al, 1999;Reuter et al, 2010Reuter et al, , 2011 and TANSO-FTS/GOSAT (Kuze et al, 2016) and from the Orbiting Carbon Observatory-2 (OCO-2) satellite (Crisp et al, 2004;O'Dell et al, , 2018 have been used in recent years to obtain information on natural CO 2 sources and sinks (e.g. Basu et al, 2013;Chevallier et al, 2014;Chevallier, 2015;Reuter et al, 2014aReuter et al, , 2017cSchneising et al, 2014;Houweling et al, 2015;Kaminski et al, 2017;Liu et al, 2017;Yin et al, 2018;Palmer et al, 2019;Miller and Michalak, 2020), 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.…”

A methodology to constrain carbon dioxide emissions from coal-fired power plants using satellite observations of co-emitted nitrogen dioxide

“…Anthropogenic CO 2 emissions have been estimated from space-based CO 2 observations, but the existing satellite CO 2 sensors are designed to provide constraints on natural CO 2 sources and sinks (Basu et al, 2013;Houweling et al, 2015) and thus their capability for monitoring anthropogenic point sources is limited (Nassar et al, 2017). Observations from sensors, including the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIA-MACHY; Burrows et al, 1995), Greenhouse gases Observing SATellite (GOSAT; Yokota et al, 2009), and Orbiting Carbon Observatory-2 (OCO-2; Crisp, 2015), show statistically significant enhancements over metropolitan regions (Kort et al, 2012;Schneising et al, 2013;Janardanan et al, 2016;Buchwitz et al, 2018;Reuter et al, 2019;.…”