Vanessa Sherlock

The Total Carbon Column Observing Network

A global network of ground-based Fourier transform spectrometers has been founded to remotely measure column abundances of CO2, CO, CH4, N2O and other molecules that absorb in the near-infrared. These measurements are directly comparable with the near-infrared total column measurements from space-based instruments. With stringent requirements on the instrumentation, acquisition procedures, data processing and calibration, the Total Carbon Column Observing Network (TCCON) achieves an accuracy and precision in total column measurements that is unprecedented for remote-sensing observations (better than 0.25% for CO2). This has enabled carbon-cycle science investigations using the TCCON dataset, and allows the TCCON to provide a link between satellite measurements and the extensive ground-based in situ network.

Daily and 3‐hourly variability in global fire emissions and consequences for atmospheric model predictions of carbon monoxide

Attribution of the causes of atmospheric trace gas and aerosol variability often requires the use of high resolution time series of anthropogenic and natural emissions inventories. Here we developed an approach for representing synoptic- and diurnal-scale temporal variability in fire emissions for the Global Fire Emissions Database version 3 (GFED3). We disaggregated monthly GFED3 emissions during 2003–2009 to a daily time step using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived measurements of active fires from Terra and Aqua satellites. In parallel, mean diurnal cycles were constructed from Geostationary Operational Environmental Satellite (GOES) Wildfire Automated Biomass Burning Algorithm (WF_ABBA) active fire observations. Daily variability in fires varied considerably across different biomes, with short but intense periods of daily emissions in boreal ecosystems and lower intensity (but more continuous) periods of burning in savannas. These patterns were consistent with earlier field and modeling work characterizing fire behavior dynamics in different ecosystems. On diurnal timescales, our analysis of the GOES WF_ABBA active fires indicated that fires in savannas, grasslands, and croplands occurred earlier in the day as compared to fires in nearby forests. Comparison with Total Carbon Column Observing Network (TCCON) and Measurements of Pollution in the Troposphere (MOPITT) column CO observations provided evidence that including daily variability in emissions moderately improved atmospheric model simulations, particularly during the fire season and near regions with high levels of biomass burning. The high temporal resolution estimates of fire emissions developed here may ultimately reduce uncertainties related to fire contributions to atmospheric trace gases and aerosols. Important future directions include reconciling top-down and bottom up estimates of fire radiative power and integrating burned area and active fire time series from multiple satellite sensors to improve daily emissions estimates.

Process-evaluation of tropospheric humidity simulated by general circulation models using water vapor isotopologues: 1. Comparison between models and observations

The goal of this study is to determine how H2O and HDO measurements in water vapor can be used to detect and diagnose biases in the representation of processes controlling tropospheric humidity in atmospheric general circulation models (GCMs). We analyze a large number of isotopic data sets (four satellite, sixteen ground-based remote-sensing, five surface in situ and three aircraft data sets) that are sensitive to different altitudes throughout the free troposphere. Despite significant differences between data sets, we identify some observed HDO/H2O characteristics that are robust across data sets and that can be used to evaluate models. We evaluate the isotopic GCM LMDZ, accounting for the effects of spatiotemporal sampling and instrument sensitivity. We find that LMDZ reproduces the spatial patterns in the lower and mid troposphere remarkably well. However, it underestimates the amplitude of seasonal variations in isotopic composition at all levels in the subtropics and in midlatitudes, and this bias is consistent across all data sets. LMDZ also underestimates the observed meridional isotopic gradient and the contrast between dry and convective tropical regions compared to satellite data sets. Comparison with six other isotope-enabled GCMs from the SWING2 project shows that biases exhibited by LMDZ are common to all models. The SWING2 GCMs show a very large spread in isotopic behavior that is not obviously related to that of humidity, suggesting water vapor isotopic measurements could be used to expose model shortcomings. In a companion paper, the isotopic differences between models are interpreted in terms of biases in the representation of processes controlling humidity. Copyright

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.

Methane retrievals from greenhouse gases observing satellite (GOSAT) shortwave infrared measurements: performance comparison of proxy and physics retrieval algorithms

We compare two conceptually different methods for determining methane column-averaged mixing ratios image from Greenhouse Gases Observing Satellite (GOSAT) shortwave infrared (SWIR) measurements. These methods account differently for light scattering by aerosol and cirrus. The proxy method retrieves a CO_2 column which, in conjunction with prior knowledge on CO_2 acts as a proxy for scattering effects. The physics-based method accounts for scattering by retrieving three effective parameters of a scattering layer. Both retrievals are validated on a 19-month data set using ground-based X_CH_4 at 12 stations of the Total Carbon Column Observing Network (TCCON), showing comparable performance: for the proxy retrieval we find station-dependent retrieval biases from −0.312% to 0.421% of X_CH_4 a standard deviation of 0.22% and a typical precision of 17 ppb. The physics method shows biases between −0.836% and −0.081% with a standard deviation of 0.24% and a precision similar to the proxy method. Complementing this validation we compared both retrievals with simulated methane fields from a global chemistry-transport model. This identified shortcomings of both retrievals causing biases of up to 1ings and provide a satisfying validation of any methane retrieval from space-borne SWIR measurements, in our opinion it is essential to further expand the network of TCCON stations.

Global CO2 fluxes inferred from surface air-sample measurements and from TCCON retrievals of the CO2 total column

We present the first estimate of the global distribution of CO_2 surface fluxes from 14 stations of the Total Carbon Column Observing Network (TCCON). The evaluation of this inversion is based on 1) comparison with the fluxes from a classical inversion of surface air-sample-measurements, and 2) comparison of CO_2 mixing ratios calculated from the inverted fluxes with independent aircraft measurements made during the two years analyzed here, 2009 and 2010. The former test shows similar seasonal cycles in the northern hemisphere and consistent regional carbon budgets between inversions from the two datasets, even though the TCCON inversion appears to be less precise than the classical inversion. The latter test confirms that the TCCON inversion has improved the quality (i.e., reduced the uncertainty) of the surface fluxes compared to the assumed or prior fluxes. The consistency between the surface-air-sample-based and the TCCON-based inversions despite remaining flaws in transport models opens the possibility of increased accuracy and robustness of flux inversions based on the combination of both data sources and confirms the usefulness of space-borne monitoring of the CO_2 column.

Total Column Carbon Observing Network (TCCON)

A network of ground-based, sun-viewing, near-IR, Fourier transform spectrometers has been established to accurately measure atmospheric greenhouse gases such as CO2, CO, N2O, and CH4.

Increase in the vertical column abundance of HCFC‐22 (CHClF2) above Lauder, New Zealand, between 1985 and 1994

Total column abundances of CHClF2 (HCFC-22) have been retrieved from high-resolution infrared solar absorption spectra recorded at the Network for the Detection of Stratospheric Change (NDSC) station in Lauder, New Zealand (370 m altitude, 45.04°S latitude, 169.68°E longitude). The analysis, based on nonlinear least squares fittings to the unresolved 2v6 band Q branch of CH35ClF2 at 829.05 cm−1, has been applied to a time series of 670 spectra recorded on 394 days between May 1985 and November 1994. The measurements indicate exponential and linear (referenced to the beginning of 1994) increase rates of (7.5±0.3)% yr−1 and (5.9±0.2)% yr−1, 1 σ, corresponding to a doubling of the total column abundance over the 9.5-year measurement period. Of the two models the exponential increase model yields a slightly better fit to the data than the linear model. A HCFC-22 south/north hemispheric ratio of 0.83 ±0.04, 1σ, is derived by comparing the Lauder column measurements with column measurements from the International Scientific Station of the Jungfraujoch (46.5°N, 8.0°E), after correction for the altitude difference between the two sites. Using a second, independent method in which the N2O column serves as a surrogate air mass, we have used the Lauder measurements and similar measurements from Table Mountain (34.4°N) to calculate a south/north ratio of 0.91±0.10.

Corrigendum to "A multi-year methane inversion using SCIAMACHY, accounting for systematic errors using TCCON measurements" published in Atmos. Chem. Phys., 14, 3991–4012, 2014

S. Houweling1,2, M. Krol1,2,3, P. Bergamaschi4, C. Frankenberg5, E. J. Dlugokencky6, I. Morino7, J. Notholt8, V. Sherlock9, D. Wunch10, V. Beck11, C. Gerbig11, H. Chen12,13, E. A. Kort14, T. Rockmann2, and I. Aben1 1SRON Netherlands Institute for Space Research, Utrecht, the Netherlands 2Institute for Marine and Atmospheric Research (IMAU), Utrecht University, Utrecht, the Netherlands 3Department of Meteorology and Air Quality (MAQ), Wageningen University and Research Centre, Wageningen, the Netherlands 4European Commission Joint Research Centre, Institute for Environment and Sustainability, Ispra (Va), Italy 5Jet Propulsion Laboratory, Pasadena, CA, USA 6NOAA Earth System Research Laboratory, Global Monitoring Division, Boulder, CO, USA 7Center for Global Environmental Research, National Institute for Environmental Studies (NIES) Onogawa 16-2, Tsukuba, Ibaraki 305-8506, Japan 8Institute of Environmental Physics, University of Bremen, Bremen, Germany 9National Institute of Water and Atmospheric Research (NIWA), P.O. Box 14-901, Wellington, New Zealand 10Caltech, Pasadena, CA, USA 11Max Planck Institute for Biogeochemistry, Jena, Germany 12Center for Isotope Research (CIO), University of Groningen, the Netherlands 13CIRES, University of Colorado, Boulder, CO, USA 14Department of Atmospheric, Oceanic and Space Sciences, University of Michigan, Ann Arbor, MI, USA

A multi-year methane inversion using SCIAMACHY, accounting for systematic errors using TCCON measurements (vol 14, pg 3991, 2014)

S. Houweling1,2, M. Krol1,2,3, P. Bergamaschi4, C. Frankenberg5, E. J. Dlugokencky6, I. Morino7, J. Notholt8, V. Sherlock9, D. Wunch10, V. Beck11, C. Gerbig11, H. Chen12,13, E. A. Kort14, T. Rockmann2, and I. Aben1 1SRON Netherlands Institute for Space Research, Utrecht, the Netherlands 2Institute for Marine and Atmospheric Research (IMAU), Utrecht University, Utrecht, the Netherlands 3Department of Meteorology and Air Quality (MAQ), Wageningen University and Research Centre, Wageningen, the Netherlands 4European Commission Joint Research Centre, Institute for Environment and Sustainability, Ispra (Va), Italy 5Jet Propulsion Laboratory, Pasadena, CA, USA 6NOAA Earth System Research Laboratory, Global Monitoring Division, Boulder, CO, USA 7Center for Global Environmental Research, National Institute for Environmental Studies (NIES) Onogawa 16-2, Tsukuba, Ibaraki 305-8506, Japan 8Institute of Environmental Physics, University of Bremen, Bremen, Germany 9National Institute of Water and Atmospheric Research (NIWA), P.O. Box 14-901, Wellington, New Zealand 10Caltech, Pasadena, CA, USA 11Max Planck Institute for Biogeochemistry, Jena, Germany 12Center for Isotope Research (CIO), University of Groningen, the Netherlands 13CIRES, University of Colorado, Boulder, CO, USA 14Department of Atmospheric, Oceanic and Space Sciences, University of Michigan, Ann Arbor, MI, USA