Christof Petri

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.

Side by side measurements of CO2 by ground-based Fourier transform spectrometry (FTS)

High resolution solar absorption Fourier transform spectrometry (FTS) is the most precise ground-based remote sensing technique to measure the total column of atmospheric carbon dioxide. For carbon cycle studies as well as for the calibration and validation of spaceborne sensors the instrumental comparability of FTS systems is of critical importance. Retrievals from colocated measurements by two identically constructed FTS systems have been compared for the first time. Under clear sky conditions a precision for the retrieved xCO2 better than ˜0.1% is demonstrated and the instruments agree within ˜0.07%. An important factor in achieving such good comparability of the xCO2 is an accurate sampling of the internal reference laser. A periodic laser mis-sampling leads to ghosts (artificial spectral lines), which are mirrored images from original spectral lines. These ghosts can interfere with the spectral range of interest. The influence of the laser mis-sampling on the retrieved xCO2 and xO2 in the near-IR has been quantified. For a typical misalignment, the ratio of the ghost intensity compared to the intensity of the original spectral line is about 0.18% and in this case the retrieved xCO2 is wrong by 0.26% (1 ppm) and the retrieved xO2 is wrong by 0.2%.

Global land mapping of satellite-observed CO2 total columns using spatio-temporal geostatistics

ABSTRACT This study presents an approach for generating a global land mapping dataset of the satellite measurements of CO2 total column (XCO2) using spatio-temporal geostatistics, which makes full use of the joint spatial and temporal dependencies between observations. The mapping approach considers the latitude-zonal seasonal cycles and spatio-temporal correlation structure of XCO2, and obtains global land maps of XCO2, with a spatial grid resolution of 1° latitude by 1° longitude and temporal resolution of 3 days. We evaluate the accuracy and uncertainty of the mapping dataset in the following three ways: (1) in cross-validation, the mapping approach results in a high correlation coefficient of 0.94 between the predictions and observations, (2) in comparison with ground truth provided by the Total Carbon Column Observing Network (TCCON), the predicted XCO2 time series and those from TCCON sites are in good agreement, with an overall bias of 0.01 ppm and a standard deviation of the difference of 1.22 ppm and (3) in comparison with model simulations, the spatio-temporal variability of XCO2 between the mapping dataset and simulations from the CT2013 and GEOS-Chem are generally consistent. The generated mapping XCO2 data in this study provides a new global geospatial dataset in global understanding of greenhouse gases dynamics and global warming.

Toward a chemical reanalysis in a coupled chemistry-climate model: An evaluation of MOPITT CO assimilation and its impact on tropospheric composition

INSU-CNRS (France); Meteo-France; CNES; Universite Paul Sabatier (Toulouse, France); Research Center Julich (FZJ, Julich, Germany); EU; National Aeronautics and Space Administration (NASA); NSF Office of Polar Programs (OPP); Danish Meteorological Institute; Australian Research Council [DP110101948, LE0668470]; European Commission; Max Planck Society; Fundacao de Amparo a Pesquisa do Estado de Sao Paulo; Conselho Nacional de Desenvolvimento Cientifico (Instituto do Milenio LBA); National Science Foundation; National Science Foundation [Computational and Information Systems Laboratory]; Office of Science (BER) of the U.S. Department of Energy; NASA; NASA [NNX13AK24G, NNX14AN47G]

Simultaneous retrieval of atmospheric CO2 and light path modification from space-based spectroscopic observations of greenhouse gases: methodology and application to GOSAT measurements over TCCON sites

This paper presents an improved photon path length probability density function method that permits simultaneous retrievals of column-average greenhouse gas mole fractions and light path modifications through the atmosphere when processing high-resolution radiance spectra acquired from space. We primarily describe the methodology and retrieval setup and then apply them to the processing of spectra measured by the Greenhouse gases Observing SATellite (GOSAT). We have demonstrated substantial improvements of the data processing with simultaneous carbon dioxide and light path retrievals and reasonable agreement of the satellite-based retrievals against ground-based Fourier transform spectrometer measurements provided by the Total Carbon Column Observing Network (TCCON).

Evaluating GPP and Respiration Estimates Over Northern Midlatitude Ecosystems Using Solar‐Induced Fluorescence and Atmospheric CO2 Measurements

On regional to global scales, few constraints exist on gross primary productivity (GPP) and ecosystem respiration (Re) fluxes. Yet, constraints on these fluxes are critical for evaluating and improving terrestrial biosphere models (TBMs). In this study, we evaluate the seasonal cycle of GPP, Re and net ecosystem exchange (NEE) produced by four TBMs and FLUXCOM, a data-driven model, over northern mid-latitude ecosystems. We evaluate the seasonal cycle of GPP and NEE using solar induced fluorescence (SIF) retrieved from the Global Ozone Monitoring Experiment-2 (GOME-2) and column-averaged dry-air mole fractions of CO2 (XCO2) from the Total Carbon Column Observing Network (TCCON), respectively. We then infer Re by combining constraints on GPP with constraints on NEE from two flux inversions. An ensemble of optimized Re seasonal cycles is generated using five GPP estimates and two NEE estimates. The optimized Re curves generally show high consistency with each other, with the largest differences due to the magnitude of GPP. We find optimized Re exhibits a systematically broader summer maximum than modeled Re, with values lower during June–July and higher during the fall than Re. Further analysis suggests that the differences could be due to seasonal variations in the carbon use efficiency (possibly due to an ecosystem scale Kok effect) and to seasonal variations in the leaf litter and fine root carbon pool. The results suggest that the inclusion of variable carbon use efficiency for autotrophic respiration and ©2018 American Geophysical Union. All Rights Reserved. carbon pool dependence for heterotrophic respiration are important for accurately simulating Re. Keypoints: • Top-down constraints on ecosystem respiration are obtained by combining atmospheric CO2 and solar induced fluorescence observations. • Inferred ecosystem respiration suggests a systematically broader summer maximum than bottom-up estimates over the northern mid-latitudes. • Inferred ecosystem respiration shows high sensitivity to the magnitude of gross primary productivity. ©2018 American Geophysical Union. All Rights Reserved.