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Remote Sensing of Carbon Dioxide and Methane in Earth’s Atmosphere

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 72304

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Special Issue Editors

Dr. Prabir K. Patra

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Guest Editor

Department of Atmospheric Science, The University of Alabama in Huntsville, 320 Sparkman Dr., Huntsville, AL 35805, USA
Interests: air pollution; greenhouse gases; modelling studies
Special Issues, Collections and Topics in MDPI journals

Dr. David Crisp

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Guest Editor

Jet Propulsion Laboratory, California Institute of Technology, MS 233-200, 4800 Oak Grove Drive, Pasadena, CA 91109, USA
Interests: Orbiting Carbon Observatory–2 (OCO-2) Science Team Leader

Dr. Thomas Lauvaux

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Guest Editor

Department of Meteorology and Atmospheric Science, Pennsylvania State University, 415 Walker Building, University Park, PA 16802, USA
Interests: carbon cycle science; atmospheric inversion; data assimilation

Special Issue Information

Dear Colleagues,

Carbon dioxide (CO₂) and methane (CH₄) are the two most important greenhouse gases that have led to a significant fraction of the increase in earth’s surface temperature in the past 100 years. Global average concentrations of CO₂ and CH₄ have increased from 296 ppm (parts per million) and 900 ppb (parts per billion) in 1900 to about 405 ppm and 1850 ppb in 2017, respectively. Studies have shown that these increases in concentration are due to the increase in anthropogenic activities on the earth’s surface, leading to higher emissions. However, there has not yet been a clear attribution of the processes involved in this increase of emissions and the role of other environmental factors, mainly due to the lack of observational data coverage. To alleviate the sparseness in observations, satellite remote sensing has become a major focus in the past couple of decades for monitoring greenhouse gases from space. The first dedicated mission for greenhouse gas monitoring was launched by JAXA in 2009, the Greenhouse Gases Observation Satellite (GOSAT). Large amount of observations have also been gathered by SCIAMACHY and AIRS, and a growing number of satellites are now in space in order to make more precise measurements, namely the OCO-2 since 2014, TROPOMI since 2017, TanSAT since 2016, etc. This Special Issue is dedicated to the past progress and new developments in satellite remote sensing of long-lived greenhouse gases, with a focus on CO and CH₄.

Dr. Prabir K. Patra
Dr. David Crisp
Dr. Thomas Lauvaux
Guest Editors

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Keywords

Carbon dioxide
Methane
Remote sensing
Greenhouse gases
Earth’s atmosphere

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24 pages, 3840 KiB

Open AccessArticle

Country-Scale Analysis of Methane Emissions with a High-Resolution Inverse Model Using GOSAT and Surface Observations

by Rajesh Janardanan, Shamil Maksyutov, Aki Tsuruta, Fenjuan Wang, Yogesh K. Tiwari, Vinu Valsala, Akihiko Ito, Yukio Yoshida, Johannes W. Kaiser, Greet Janssens-Maenhout, Mikhail Arshinov, Motoki Sasakawa, Yasunori Tohjima, Douglas E. J. Worthy, Edward J. Dlugokencky, Michel Ramonet, Jgor Arduini, Jost V. Lavric, Salvatore Piacentino, Paul B. Krummel, Ray L. Langenfelds, Ivan Mammarella and Tsuneo Matsunaga Show full author list Hide full author list

Remote Sens. 2020, 12(3), 375; https://doi.org/10.3390/rs12030375 - 24 Jan 2020

Cited by 34 | Viewed by 9697

Abstract

We employed a global high-resolution inverse model to optimize the CH₄ emission using Greenhouse gas Observing Satellite (GOSAT) and surface observation data for a period from 2011–2017 for the two main source categories of anthropogenic and natural emissions. We used the Emission Database for Global Atmospheric Research (EDGAR v4.3.2) for anthropogenic methane emission and scaled them by country to match the national inventories reported to the United Nations Framework Convention on Climate Change (UNFCCC). Wetland and soil sink prior fluxes were simulated using the Vegetation Integrative Simulator of Trace gases (VISIT) model. Biomass burning prior fluxes were provided by the Global Fire Assimilation System (GFAS). We estimated a global total anthropogenic and natural methane emissions of 340.9 Tg CH₄ yr⁻¹ and 232.5 Tg CH₄ yr⁻¹, respectively. Country-scale analysis of the estimated anthropogenic emissions showed that all the top-emitting countries showed differences with their respective inventories to be within the uncertainty range of the inventories, confirming that the posterior anthropogenic emissions did not deviate from nationally reported values. Large countries, such as China, Russia, and the United States, had the mean estimated emission of 45.7 ± 8.6, 31.9 ± 7.8, and 29.8 ± 7.8 Tg CH₄ yr⁻¹, respectively. For natural wetland emissions, we estimated large emissions for Brazil (39.8 ± 12.4 Tg CH₄ yr⁻¹), the United States (25.9 ± 8.3 Tg CH₄ yr⁻¹), Russia (13.2 ± 9.3 Tg CH₄ yr⁻¹), India (12.3 ± 6.4 Tg CH₄ yr⁻¹), and Canada (12.2 ± 5.1 Tg CH₄ yr⁻¹). In both emission categories, the major emitting countries all had the model corrections to emissions within the uncertainty range of inventories. The advantages of the approach used in this study were: (1) use of high-resolution transport, useful for simulations near emission hotspots, (2) prior anthropogenic emissions adjusted to the UNFCCC reports, (3) combining surface and satellite observations, which improves the estimation of both natural and anthropogenic methane emissions over spatial scale of countries. Full article

(This article belongs to the Special Issue Remote Sensing of Carbon Dioxide and Methane in Earth’s Atmosphere)

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15 pages, 4845 KiB

Open AccessArticle

Detection of Methane Emission from a Local Source Using GOSAT Target Observations

by Akihiko Kuze, Nobuhiro Kikuchi, Fumie Kataoka, Hiroshi Suto, Kei Shiomi and Yutaka Kondo

Remote Sens. 2020, 12(2), 267; https://doi.org/10.3390/rs12020267 - 13 Jan 2020

Cited by 21 | Viewed by 6186

Abstract

Emissions of atmospheric methane (CH₄), which greatly contributes to radiative forcing, have larger uncertainties than those for carbon dioxide (CO₂). The Thermal And Near-infrared Sensor for carbon Observation Fourier-Transform Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observing SATellite (GOSAT) launched in 2009 has demonstrated global grid observations of the total column density of CO₂ and CH₄ from space, and thus reduced uncertainty in the global flux estimation. In this paper, we present a case study on local CH₄ emission detection from a single-point source using an available series of GOSAT data. By modifying the grid observation pattern, the pointing mechanism of TANSO-FTS targets a natural gas leak point at Aliso Canyon in Southern California, where the clear-sky frequency is high. To enhance local emission estimates, we retrieved CO₂ and CH₄ partial column-averaged dry-air mole fractions of the lower troposphere (XCO₂ (LT) and XCH₄ (LT)) by simultaneous use of both sunlight reflected from Earth’s surface and thermal emissions from the atmosphere. The time-series data of Aliso Canyon showed a large enhancement that decreased with time after its initial blowout, compared with reference point data and filtered with wind direction simulated by the Weather Research and Forecasting (WRF) model. Full article

(This article belongs to the Special Issue Remote Sensing of Carbon Dioxide and Methane in Earth’s Atmosphere)

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23 pages, 2547 KiB

Open AccessArticle

Sensitivity of Optimal Estimation Satellite Retrievals to Misspecification of the Prior Mean and Covariance, with Application to OCO-2 Retrievals

by Hai Nguyen, Noel Cressie and Jonathan Hobbs

Remote Sens. 2019, 11(23), 2770; https://doi.org/10.3390/rs11232770 - 25 Nov 2019

Cited by 11 | Viewed by 3487

Abstract

Optimal Estimation (OE) is a popular algorithm for remote sensing retrievals, partly due to its explicit parameterization of the sources of error and the ability to propagate them into estimates of retrieval uncertainty. These properties require specification of the prior distribution of the state vector. In many remote sensing applications, the true priors are multivariate and hard to characterize properly. Instead, priors are often constructed based on subject-matter expertise, existing empirical knowledge, and a need for computational expediency, resulting in a “working prior.” This paper explores the retrieval bias and the inaccuracy in retrieval uncertainty caused by explicitly separating the true prior (the probability distribution of the underlying state) from the working prior (the probability distribution used within the OE algorithm), with an application to Orbiting Carbon Observatory-2 (OCO-2) retrievals. We find that, in general, misspecifying the mean in the working prior will lead to biased retrievals, and misspecifying the covariance in the working prior will lead to inaccurate estimates of the retrieval uncertainty, though their effects vary depending on the state-space signal-to-noise ratio of the observing instrument. Our results point towards some attractive properties of a class of uninformative priors that is implicit for least-squares retrievals. Furthermore, our derivations provide a theoretical basis, and an understanding of the trade-offs involved, for the practice of inflating a working-prior covariance in order to reduce the prior’s impact on a retrieval (e.g., for OCO-2 retrievals). Finally, our results also lead to practical recommendations for specifying the prior mean and the prior covariance in OE. Full article

(This article belongs to the Special Issue Remote Sensing of Carbon Dioxide and Methane in Earth’s Atmosphere)

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19 pages, 2379 KiB

Open AccessArticle

Methane Emission Estimates by the Global High-Resolution Inverse Model Using National Inventories

by Fenjuan Wang, Shamil Maksyutov, Aki Tsuruta, Rajesh Janardanan, Akihiko Ito, Motoki Sasakawa, Toshinobu Machida, Isamu Morino, Yukio Yoshida, Johannes W. Kaiser, Greet Janssens-Maenhout, Edward J. Dlugokencky, Ivan Mammarella, Jost Valentin Lavric and Tsuneo Matsunaga

Remote Sens. 2019, 11(21), 2489; https://doi.org/10.3390/rs11212489 - 24 Oct 2019

Cited by 34 | Viewed by 7445

Abstract

We present a global 0.1° × 0.1° high-resolution inverse model, NIES-TM-FLEXPART-VAR (NTFVAR), and a methane emission evaluation using the Greenhouse Gas Observing Satellite (GOSAT) satellite and ground-based observations from 2010–2012. Prior fluxes contained two variants of anthropogenic emissions, Emissions Database for Global Atmospheric Research (EDGAR) v4.3.2 and adjusted EDGAR v4.3.2 which were scaled to match the country totals by national reports to the United Nations Framework Convention on Climate Change (UNFCCC), augmented by biomass burning emissions from Global Fire Assimilation System (GFASv1.2) and wetlands Vegetation Integrative Simulator for Trace Gases (VISIT). The ratio of the UNFCCC-adjusted global anthropogenic emissions to EDGAR is 98%. This varies by region: 200% in Russia, 84% in China, and 62% in India. By changing prior emissions from EDGAR to UNFCCC-adjusted values, the optimized total emissions increased from 36.2 to 46 Tg CH₄ yr⁻¹ for Russia, 12.8 to 14.3 Tg CH₄ yr⁻¹ for temperate South America, and 43.2 to 44.9 Tg CH₄ yr⁻¹ for contiguous USA, and the values decrease from 54 to 51.3 Tg CH₄ yr⁻¹ for China, 26.2 to 25.5 Tg CH₄ yr⁻¹ for Europe, and by 12.4 Tg CH₄ yr⁻¹ for India. The use of the national report to scale EDGAR emissions allows more detailed statistical data and country-specific emission factors to be gathered in place compared to those available for EDGAR inventory. This serves policy needs by evaluating the national or regional emission totals reported to the UNFCCC. Full article

(This article belongs to the Special Issue Remote Sensing of Carbon Dioxide and Methane in Earth’s Atmosphere)

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24 pages, 6103 KiB

Open AccessArticle

Accelerated MCMC for Satellite-Based Measurements of Atmospheric CO₂

by Otto Lamminpää, Jonathan Hobbs, Jenný Brynjarsdóttir, Marko Laine, Amy Braverman, Hannakaisa Lindqvist and Johanna Tamminen

Remote Sens. 2019, 11(17), 2061; https://doi.org/10.3390/rs11172061 - 2 Sep 2019

Cited by 6 | Viewed by 4710

Abstract

Markov Chain Monte Carlo (MCMC) is a powerful and promising tool for assessing the uncertainties in the Orbiting Carbon Observatory 2 (OCO-2) satellite’s carbon dioxide measurements. Previous research in comparing MCMC and Optimal Estimation (OE) for the OCO-2 retrieval has highlighted the issues of slow convergence of MCMC, and furthermore OE and MCMC not necessarily agreeing with the simulated ground truth. In this work, we exploit the inherent low information content of the OCO-2 measurement and use the Likelihood-Informed Subspace (LIS) dimension reduction to significantly speed up the convergence of MCMC. We demonstrate the strength of this analysis method by assessing the non-Gaussian shape of the retrieval’s posterior distribution, and the effect of operational OCO-2 prior covariance’s aerosol parameters on the retrieval. We further show that in our test cases we can use this analysis to improve the retrieval to retrieve the simulated true state significantly more accurately and to characterize the non-Gaussian form of the posterior distribution of the retrieval problem. Full article

(This article belongs to the Special Issue Remote Sensing of Carbon Dioxide and Methane in Earth’s Atmosphere)

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21 pages, 1543 KiB

Open AccessArticle

Pixel Size and Revisit Rate Requirements for Monitoring Power Plant CO₂ Emissions from Space

by Tim Hill and Ray Nassar

Remote Sens. 2019, 11(13), 1608; https://doi.org/10.3390/rs11131608 - 6 Jul 2019

Cited by 28 | Viewed by 5475

Abstract

The observational requirements for space-based quantification of anthropogenic CO

_{2}

emissions are of interest to space agencies and related organizations that may contribute to a possible satellite constellation to support emission monitoring in the future. We assess two key observing characteristics for space-based [...] Read more.

The observational requirements for space-based quantification of anthropogenic CO

_{2}

_{2}

emissions: pixel size and revisit rate, and we introduce a new method utilizing multiple images simultaneously to significantly improve emission estimates. The impact of pixel size ranging from 2–10 km for space-based imaging spectrometers is investigated using plume model simulations, accounting for biases in the observations. Performance of rectangular pixels is compared to square pixels of equal area. The findings confirm the advantage of the smallest pixels in this range and the advantage of square pixels over rectangular pixels. A method of averaging multiple images is introduced and demonstrated to be able to estimate emissions from small sources when the individual images are unable to distinguish the plume. Due to variability in power plant emissions, results from a single overpass cannot be directly extrapolated to annual emissions, the most desired timescale for regulatory purposes. We investigate the number of overpasses required to quantify annual emissions with a given accuracy, based on the mean variability from the 50 highest emitting US power plants. Although the results of this work alone are not sufficient to define the full architecture of a future CO

_{2}

monitoring constellation, when considered along with other studies, they may assist in informing the design of a space-based system to support anthropogenic CO

_{2}

emission monitoring. Full article

(This article belongs to the Special Issue Remote Sensing of Carbon Dioxide and Methane in Earth’s Atmosphere)

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29 pages, 4426 KiB

Open AccessArticle

A Theoretical Analysis for Improving Aerosol-Induced CO₂ Retrieval Uncertainties Over Land Based on TanSat Nadir Observations Under Clear Sky Conditions

by Xi Chen, Yi Liu, Dongxu Yang, Zhaonan Cai, Hongbin Chen and Maohua Wang

Remote Sens. 2019, 11(9), 1061; https://doi.org/10.3390/rs11091061 - 5 May 2019

Cited by 6 | Viewed by 3974

Abstract

Aerosols significantly affect carbon dioxide (CO₂) retrieval accuracy and precision by modifying the light path. Hyperspectral measurements in the near infrared and shortwave infrared (NIR/SWIR) bands from the generation of new greenhouse gas satellites (e.g., the Chinese Global Carbon Dioxide Monitoring Scientific Experimental Satellite, TanSat) contain aerosol information for correction of scattering effects in the retrieval. Herein, a new approach is proposed for optimizing the aerosol model used in the TanSat CO₂ retrieval algorithm to reduce CO₂ uncertainties associated with aerosols. The weighting functions of hyperspectral observations with respect to elements in the state vector are simulated by a forward radiative transfer model. Using the optimal estimation method (OEM), the information content and each component of the CO₂ column-averaged dry-air mole fraction (XCO₂) retrieval errors from the TanSat simulations are calculated for typical aerosols which are described by Aerosol Robotic Network (AERONET) inversion products at selected sites based on the a priori and measurement assumptions. The results indicate that the size distribution parameters (r_eff, v_eff), real refractive index coefficient of fine mode (a_r^f) and fine mode fraction (fmf) dominate the interference errors, with each causing 0.2–0.8 ppm of XCO₂ errors. Given that only 4–7 degrees of freedom for signal (DFS) of aerosols can be obtained simultaneously and CO₂ information decreases as more aerosol parameters are retrieved, four to seven aerosol parameters are suggested as the most appropriate for inclusion in CO₂ retrieval. Focusing on only aerosol-induced XCO₂ errors, forward model parameter errors, rather than interference errors, are dominant. A comparison of these errors across different aerosol parameter combination groups reveals that fewer aerosol-induced XCO₂ errors are found when retrieving seven aerosol parameters. Therefore, the model selected as the optimal aerosol model includes aerosol optical depth (AOD), peak height of aerosol profile (H_p), width of aerosol profile (H_w), effective variance of fine mode aerosol (v_eff^f), effective radius of coarse mode aerosol (r_eff^c), coefficient a of the real part of the refractive index for the fine mode and coarse mode (a_r^f and a_r^c), with the lowest error of less than 1.7 ppm for all aerosol and surface types. For marine aerosols, only five parameters (AOD, H_p, H_w, r_eff^c and a_r^c) are recommended for the low aerosol information. This optimal aerosol model therefore offers a theoretical foundation for improving CO₂ retrieval precision from real TanSat observations in the future. Full article

(This article belongs to the Special Issue Remote Sensing of Carbon Dioxide and Methane in Earth’s Atmosphere)

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16 pages, 5696 KiB

Open AccessArticle

Increase of Atmospheric Methane Observed from Space-Borne and Ground-Based Measurements

by Mingmin Zou, Xiaozhen Xiong, Zhaohua Wu, Shenshen Li, Ying Zhang and Liangfu Chen

Remote Sens. 2019, 11(8), 964; https://doi.org/10.3390/rs11080964 - 23 Apr 2019

Cited by 16 | Viewed by 6565

Abstract

It has been found that the concentration of atmospheric methane (CH₄) has rapidly increased since 2007 after a decade of nearly constant concentration in the atmosphere. As an important greenhouse gas, such an increase could enhance the threat of global warming. To better quantify this increasing trend, a novel statistic method, i.e. the Ensemble Empirical Mode Decomposition (EEMD) method, was used to analyze the CH₄ trends from three different measurements: the mid–upper tropospheric CH₄ (MUT) from the space-borne measurements by the Atmospheric Infrared Sounder (AIRS), the CH₄ in the marine boundary layer (MBL) from NOAA ground-based in-situ measurements, and the column-averaged CH₄ in the atmosphere (X_CH4) from the ground-based up-looking Fourier Transform Spectrometers at Total Carbon Column Observing Network (TCCON) and the Network for the Detection of Atmospheric Composition Change (NDACC). Comparison of the CH₄ trends in the mid–upper troposphere, lower troposphere, and the column average from these three data sets shows that, overall, these trends agree well in capturing the abrupt CH₄ increase in 2007 (the first peak) and an even faster increase after 2013 (the second peak) over the globe. The increased rates of CH₄ in the MUT, as observed by AIRS, are overall smaller than CH₄ in MBL and the column-average CH₄. During 2009–2011, there was a dip in the increase rate for CH₄ in MBL, and the MUT-CH₄ increase rate was almost negligible in the mid-high latitude regions. The increase of the column-average CH₄ also reached the minimum during 2009–2011 accordingly, suggesting that the trends of CH₄ are not only impacted by the surface emission, however that they also may be impacted by other processes like transport and chemical reaction loss associated with [OH]. One advantage of the EEMD analysis is to derive the monthly rate and the results show that the frequency of the variability of CH₄ increase rates in the mid–high northern latitude regions is larger than those in the tropics and southern hemisphere. Full article

(This article belongs to the Special Issue Remote Sensing of Carbon Dioxide and Methane in Earth’s Atmosphere)

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31 pages, 7525 KiB

Open AccessArticle

Evaluation and Analysis of the Seasonal Cycle and Variability of the Trend from GOSAT Methane Retrievals

by Ella Kivimäki, Hannakaisa Lindqvist, Janne Hakkarainen, Marko Laine, Ralf Sussmann, Aki Tsuruta, Rob Detmers, Nicholas M. Deutscher, Edward J. Dlugokencky, Frank Hase, Otto Hasekamp, Rigel Kivi, Isamu Morino, Justus Notholt, David F. Pollard, Coleen Roehl, Matthias Schneider, Mahesh Kumar Sha, Voltaire A. Velazco, Thorsten Warneke, Debra Wunch, Yukio Yoshida and Johanna Tamminen Show full author list Hide full author list

Remote Sens. 2019, 11(7), 882; https://doi.org/10.3390/rs11070882 - 11 Apr 2019

Cited by 19 | Viewed by 8064

Abstract

Methane (

{CH}_{4}

) is a potent greenhouse gas with a large temporal variability. To increase the spatial coverage, methane observations are increasingly made from satellites that retrieve the column-averaged dry air mole fraction of methane (

{XCH}_{4}

). To understand [...] Read more.

Methane (

{CH}_{4}

{XCH}_{4}

). To understand and quantify the spatial differences of the seasonal cycle and trend of

{XCH}_{4}

in more detail, and to ultimately help reduce uncertainties in methane emissions and sinks, we evaluated and analyzed the average

{XCH}_{4}

seasonal cycle and trend from three Greenhouse Gases Observing Satellite (GOSAT) retrieval algorithms: National Institute for Environmental Studies algorithm version 02.75, RemoTeC

{CH}_{4}

Proxy algorithm version 2.3.8 and RemoTeC

{CH}_{4}

Full Physics algorithm version 2.3.8. Evaluations were made against the Total Carbon Column Observing Network (TCCON) retrievals at 15 TCCON sites for 2009–2015, and the analysis was performed, in addition to the TCCON sites, at 31 latitude bands between latitudes 44.43°S and 53.13°N. At latitude bands, we also compared the trend of GOSAT

{XCH}_{4}

retrievals to the NOAA’s Marine Boundary Layer reference data. The average seasonal cycle and the non-linear trend were, for the first time for methane, modeled with a dynamic regression method called Dynamic Linear Model that quantifies the trend and the seasonal cycle, and provides reliable uncertainties for the parameters. Our results show that, if the number of co-located soundings is sufficiently large throughout the year, the seasonal cycle and trend of the three GOSAT retrievals agree well, mostly within the uncertainty ranges, with the TCCON retrievals. Especially estimates of the maximum day of

{XCH}_{4}

agree well, both between the GOSAT and TCCON retrievals, and between the three GOSAT retrievals at the latitude bands. In our analysis, we showed that there are large spatial differences in the trend and seasonal cycle of

{XCH}_{4}

. These differences are linked to the regional

{CH}_{4}

sources and sinks, and call for further research. Full article

(This article belongs to the Special Issue Remote Sensing of Carbon Dioxide and Methane in Earth’s Atmosphere)

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20 pages, 11015 KiB

Open AccessArticle

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

by Janne Hakkarainen, Iolanda Ialongo, Shamil Maksyutov and David Crisp

Remote Sens. 2019, 11(7), 850; https://doi.org/10.3390/rs11070850 - 9 Apr 2019

Cited by 67 | Viewed by 11412

Abstract

NASA’s carbon dioxide mission, Orbiting Carbon Observatory-2, began operating in September 2014. In this paper, we analyze four years (2015–2018) of global (60°S–60°N) XCO₂ anomalies and their annual variations and seasonal patterns. We show that the anomaly patterns in the column-averaged CO₂ dry air mole fraction, XCO₂, 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 XCO₂ (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 XCO₂ 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 XCO₂ 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 XCO₂ 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 XCO₂ anomalies collocated with the recent TROPOspheric Monitoring Instrument NO₂ 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 CO₂ signatures on global scale. Full article

(This article belongs to the Special Issue Remote Sensing of Carbon Dioxide and Methane in Earth’s Atmosphere)

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