Maximilian Reuter

Retrieval of atmospheric CO2 with enhanced accuracy and precision from SCIAMACHY: validation with FTS measurements and comparison with model results

The Bremen Optimal Estimation differential optical absorption spectroscopy (DOAS) (BESD) algorithm for satellite based retrievals of XCO_2 (the column-average dry-air mole fraction of atmospheric CO_2) has been applied to Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) data. It uses measurements in the O_2-A absorption band to correct for scattering of undetected clouds and aerosols. Comparisons with precise and accurate ground-based Fourier transform spectrometer (FTS) measurements at four Total Carbon Column Observing Network (TCCON) sites have been used to quantify the quality of the new SCIAMACHY XCO_2 data set. Additionally, the results have been compared to NOAAs assimilation system CarbonTracker. The comparisons show that the new retrieval meets the expectations from earlier theoretical studies. We find no statistically significant regional XCO_2 biases between SCIAMACHY and the FTS instruments. However, the standard error of the systematic differences is in the range of 0.2 ppm and 0.8 ppm. The XCO_2 single-measurement precision of 2.5 ppm is similar to theoretical estimates driven by instrumental noise. There are no significant differences found for the year-to-year increase as well as for the average seasonal amplitude between SCIAMACHY XCO_2 and the collocated FTS measurements. Comparison of the year-to-year increase and also of the seasonal amplitude of CarbonTracker exhibit significant differences with the corresponding FTS values at Darwin. Here the differences between SCIAMACHY and CarbonTracker are larger than the standard error of the SCIAMACHY values. The difference of the seasonal amplitude exceeds the significance level of 2 standard errors. Therefore, our results suggest that SCIAMACHY may provide valuable additional information about XCO_2, at least in regions with a low density of in situ measurements.

Remote sensing of fugitive methane emissions from oil and gas production in North American tight geologic formations

In the past decade, there has been a massive growth in the horizontal drilling and hydraulic fracturing of shale gas and tight oil reservoirs to exploit formerly inaccessible or unprofitable energy resources in rock formations with low permeability. In North America, these unconventional domestic sources of natural gas and oil provide an opportunity to achieve energy self-sufficiency and to reduce greenhouse gas emissions when displacing coal as a source of energy in power plants. However, fugitive methane emissions in the production process may counter the benefit over coal with respect to climate change and therefore need to be well quantified. Here we demonstrate that positive methane anomalies associated with the oil and gas industries can be detected from space and that corresponding regional emissions can be constrained using satellite observations. On the basis of a mass-balance approach, we estimate that methane emissions for two of the fastest growing production regions in the United States, the Bakken and Eagle Ford formations, have increased by 990 ± 650 ktCH4 yr−1 and 530 ± 330 ktCH4 yr−1 between the periods 2006–2008 and 2009–2011. Relative to the respective increases in oil and gas production, these emission estimates correspond to leakages of 10.1% ± 7.3% and 9.1% ± 6.2% in terms of energy content, calling immediate climate benefit into question and indicating that current inventories likely underestimate the fugitive emissions from Bakken and Eagle Ford.

How Much CO2 Is Taken Up by the European Terrestrial Biosphere

The conventional and established estimates of the amount of carbon taken up by the European terrestrial biosphere from the Atlantic to the Urals rely on two conceptually different types of ground-based measurements. On the one hand, in situ measurements of atmospheric CO2 concentrations are globally obtained at about 100 sites on a regular basis. On the other hand, conventional bottom-up estimates of surface carbon fluxes are obtained from field measurements. Additional in situ measurement sites are needed to better constrain the surface fluxes of the northeastern part of Europe with inverse models, where the strongest uptake is expected. Field campaigns in this region, including flux and biomass measurements, can contribute to bottom-up estimates and serve as an additional anchor point for ABC satellite measurements. Regularly updated inventories and land cover classification are also essential for reliable bottom-up estimates. Likewise, reliable estimates of the flux uncertainties from bottom-up methods that should include all kinds of upscaling uncertainties and propagated measurement errors are essential. In addition to the continuation of existing satellite missions, new satellite missions are needed to provide denser and more accurate and precise measurements of the atmospheric CO2 concentration.

CO2 emission of Indonesian fires in 2015 estimated from satellite‐derived atmospheric CO2 concentrations

Indonesia experienced an exceptional number of fires in 2015 as a result of droughts related to the recent El Nino event and human activities. These fires released large amounts of carbon dioxide (CO2) into the atmosphere. Emission databases such as the Global Fire Assimilation System version 1.2 and the Global Fire Emission Database version 4s estimated the CO2 emission to be approximately 1100 MtCO2 in the time period from July to November 2015. This emission was indirectly estimated by using parameters like burned area, fire radiative power, and emission factors. In the study presented in this paper, we estimate the Indonesian fire CO2 emission by using the column-averaged dry air mole fraction of CO2, XCO2, derived from measurements of the Orbiting Carbon Observatory-2 satellite mission. The estimated CO2 emission is 748 ± 209 MtCO2, which is about 30% lower than provided by the emission databases.

A New Method for the Comparison of Trend Data with an Application to Water Vapor

AbstractGlobal total column water vapor trends have been derived from both the Global Ozone Monitoring Experiment (GOME) and the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) satellite data and from globally distributed radiosonde measurements, archived and quality controlled by the Deutscher Wetterdienst (DWD).The control of atmospheric water vapor amount by the hydrological cycle plays an important role in determining surface temperature and its response to the increase in man-made greenhouse effect. As a result of its strong infrared absorption, water vapor is the most important naturally occurring greenhouse gas. Without water vapor, the earth surface temperature would be about 20 K lower, making the evolution of life, as we know it, impossible. The monitoring of water vapor and its evolution in time is therefore of utmost importance for our understanding of global climate change. Comparisons of trends derived from independent water vapor measurements from satellite...

A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering—Part 2: Application to XCO2 Retrievals from OCO-2

Satellite retrievals of the atmospheric dry-air column-average mole fraction of CO 2 (XCO 2 ) based on hyperspectral measurements in appropriate near (NIR) and short wave infrared (SWIR) O 2 and CO 2 absorption bands can help to answer important questions about the carbon cycle but the precision and accuracy requirements for XCO 2 data products are demanding. Multiple scattering of light at aerosols and clouds can be a significant error source for XCO 2 retrievals. Therefore, so called full physics retrieval algorithms were developed aiming to minimize scattering related errors by explicitly fitting scattering related properties such as cloud water/ice content, aerosol optical thickness, cloud height, etc. However, the computational costs for multiple scattering radiative transfer (RT) calculations can be immense. Processing all data of the Orbiting Carbon Observatory-2 (OCO-2) can require up to thousands of CPU cores and the next generation of CO 2 monitoring satellites will produce at least an order of magnitude more data. For this reason, the Fast atmOspheric traCe gAs retrievaL FOCAL has been developed reducing the computational costs by orders of magnitude by approximating multiple scattering effects with an analytic solution of the RT problem of an isotropic scattering layer. Here we confront FOCAL for the first time with measured OCO-2 data and protocol the steps undertaken to transform the input data (most importantly, the OCO-2 radiances) into a validated XCO 2 data product. This includes preprocessing, adaptation of the noise model, zero level offset correction, post-filtering, bias correction, comparison with the CAMS (Copernicus Atmosphere Monitoring Service) greenhouse gas flux inversion model, comparison with NASA’s operational OCO-2 XCO 2 product, and validation with ground based Total Carbon Column Observing Network (TCCON) data. The systematic temporal and regional differences between FOCAL and the CAMS model have a standard deviation of 1.0 ppm. The standard deviation of the single sounding mismatches amounts to 1.1 ppm which agrees reasonably well with FOCAL’s average reported uncertainty of 1.2 ppm. The large scale XCO 2 patterns of FOCAL and NASA’s operational OCO-2 product are similar and the most prominent difference is that FOCAL has about three times less soundings due to the inherently poor throughput (11%) of the MODIS (moderate-resolution imaging spectroradiometer) based cloud screening used by FOCAL’s preprocessor. The standard deviation of the difference between both products is 1.1 ppm. The validation of one year (2015) of FOCAL XCO 2 data with co-located ground based TCCON observations results in a standard deviations of the site biases of 0.67 ppm (0.78 ppm without bias correction) and an average scatter relative to TCCON of 1.34 ppm (1.60 ppm without bias correction).

Computation and analysis of atmospheric carbon dioxide annual mean growthrates from satellite observations during 2003–2016

frared satellite sensors over almost a decade and a half. The authors show that their estimated growth rates are in line with NOAA growth rates computed from marine boundary layer sites, and variations in the growth rate are correlated with expected mechanisms such as the ENSO cycle and anthropogenic emissions. This is all reasonable. However, I do not think that Atmospheric Chemistry and Physics is the correct journal for publishing this manuscript, because the manuscript does not present anything new about either the atmosphere or surface processes that influence the atmosphere (my comments on variation partitioning follow later). What I learned from this manuscript is that the merged XCO2 data product Obs4MIPs gives global CO2 growth rates that are reasonable, in line with other estimates, and can be correlated with known factors influencing the carbon budget. This is a perfectly fine message, but it’s primarily a message about the Obs4MIPs data product, and therefore a better venue for it would be an alternative measurementor data-focused journal such as Atmospheric Measurement Techniques or Earth System Science Data. If the authors insist on publishing this in ACP and the editor agrees, I would strongly suggest making this a technical note instead of a research article.

A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering—Part 1: Radiative Transfer and a Potential OCO-2 XCO2 Retrieval Setup

Satellite retrievals of the atmospheric dry-air column-average mole fraction of CO 2 (XCO 2 ) based on hyperspectral measurements in appropriate near (NIR) and short wave infrared (SWIR) O 2 and CO 2 absorption bands can help to answer important questions about the carbon cycle but the precision and accuracy requirements for XCO 2 data products are demanding. Multiple scattering of light at aerosols and clouds can be a significant error source for XCO 2 retrievals. Therefore, so called full physics retrieval algorithms were developed aiming to minimize scattering related errors by explicitly fitting scattering related properties such as cloud water/ice content, aerosol optical thickness, cloud height, etc. However, the computational costs for multiple scattering radiative transfer (RT) calculations can be immense. Processing all data of the Orbiting Carbon Observatory-2 (OCO-2) can require up to thousands of CPU cores and the next generation of CO 2 monitoring satellites will produce at least an order of magnitude more data. Here we introduce the Fast atmOspheric traCe gAs retrievaL FOCAL including a scalar RT model which approximates multiple scattering effects with an analytic solution of the RT problem of an isotropic scattering layer and a Lambertian surface. The computational performance is similar to an absorption only model and currently determined by the convolution of the simulated spectra with the instrumental line shape function (ILS). We assess FOCAL’s quality by confronting it with accurate multiple scattering vector RT simulations using SCIATRAN. The simulated scenarios do not cover all possible geophysical conditions but represent, among others, some typical cloud and aerosol scattering scenarios with optical thicknesses of up to 0.7 which have the potential to survive the pre-processing of a XCO 2 algorithm for real OCO-2 measurements. Systematic errors of XCO 2 range from −2.5 ppm (−6.3‰) to 3.0 ppm (7.6‰) and are usually smaller than ±0.3 ppm (0.8‰). The stochastic uncertainty of XCO 2 is typically about 1.0 ppm (2.5‰). FOCAL simultaneously retrieves the dry-air column-average mole fraction of H 2 O (XH 2 O) and the solar induced chlorophyll fluorescence at 760 nm (SIF). Systematic and stochastic errors of XH 2 O are most times smaller than ±6 ppm and 9 ppm, respectively. The systematic SIF errors are always below 0.02 mW/m 2 /sr/nm, i.e., it can be expected that instrumental or forward model effects causing an in-filling of the used Fraunhofer lines will dominate the systematic errors when analyzing actually measured data. The stochastic uncertainty of SIF is usually below 0.3 mW/m 2 /sr/nm. Without understating the importance of analyzing synthetic measurements as presented here, the actual retrieval performance can only be assessed by analyzing measured data which is subject to part 2 of this publication.