CO_2 Annual and Semiannual Cycles From Multiple Satellite Retrievals and Models

Satellite CO_2 retrievals from the Greenhouse gases Observing SATellite (GOSAT), Atmospheric Infrared Sounder (AIRS), and Tropospheric Emission Spectrometer (TES) and in situ measurements from the National Oceanic and Atmospheric Administration - Earth System Research Laboratory (NOAA-ESRL) Surface CO_2 and Total Carbon Column Observing Network (TCCON) are utilized to explore the CO_2 variability at different altitudes. A multiple regression method is used to calculate the CO_2 annual cycle and semiannual cycle amplitudes from different data sets. The CO_2 annual cycle and semiannual cycle amplitudes for GOSAT X_(CO2) and TCCON X_(CO2) are consistent but smaller than those seen in the NOAA-ESRL surface data. The CO_2 annual and semiannual cycles are smallest in the AIRS midtropospheric CO_2 compared with other data sets in the Northern Hemisphere. The amplitudes for the CO_2 annual cycle and semiannual cycle from GOSAT, TES, and AIRS CO_2 are small and comparable to each other in the Southern Hemisphere. Similar regression analysis is applied to the Model for OZone And Related chemical Tracers-2 and CarbonTracker model CO_2. The convolved model CO_2 annual cycle and semiannual cycle amplitudes are similar to those from the satellite CO_2 retrievals, although the models tend to underestimate the CO_2 seasonal cycle amplitudes in the Northern Hemisphere midlatitudes and underestimate the CO_2 semiannual cycle amplitudes in the high latitudes. These results can be used to better understand the vertical structures for the CO_2 annual cycle and semiannual cycle and help identify deficiencies in the models, which are very important for the carbon budget study

A31N-03: Lower-Tropospheric CO2 from Near-Infrared ACOS-GOSAT Observations

We present two new products from near-infrared GOSAT observations: lower tropospheric (LMT, from 0-2.5 km) and upper tropospheric/stratospheric (U, above 2.5 km) carbon dioxide partial columns. We compare these new products to aircraft profiles and remote surface flask measurements and find that the seasonal and year-to-year variations in the new partial columns significantly improve over the ACOS-GOSAT initial guess/a priori, with distinct patterns in the LMT and U seasonal cycles which match validation data. For land monthly averages, we find errors of 1.9, 0.7, and 0.8 ppm for retrieved GOSAT LMT, U, and XCO2; for ocean monthly averages, we find errors of 0.7, 0.5, and 0.5 ppm for retrieved GOSAT LMT, U, and XCO2. In the southern hemisphere biomass burning season, the new partial columns show similar patterns to MODIS fire maps and MOPITT multispectral CO for both vertical levels, despite a flat ACOS-GOSAT prior, and CO/CO2 emission factor consistent with published values. The difference of LMT and U, useful for evaluation of model transport error, has also been validated with monthly average error of 0.8 (1.4) ppm for ocean (land). The new LMT partial column is more locally influenced than the U partial column, meaning that local fluxes can now be separated from CO2 transported from far away

El Niño, the 2006 Indonesian peat fires, and the distribution of atmospheric methane

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110818/1/grl50937.pd

Satellite measurements of peroxyacetyl nitrate from the Cross-Track Infrared Sounder: comparison with ATom aircraft measurements

We present an overview of an optimal estimation algorithm to retrieve peroxyacetyl nitrate (PAN) from single-field-of-view Level 1B radiances measured by the Cross-Track Infrared Sounder (CrIS). CrIS PAN retrievals show peak sensitivity in the mid-troposphere, with degrees of freedom for signal less than or equal to 1.0. We show comparisons with two sets of aircraft measurements from the Atmospheric Tomography Mission (ATom), the PAN and Trace Hydrohalocarbon ExpeRiment (PANTHER) and the Georgia Tech chemical ionization mass spectrometer (GT-CIMS). We find a systematic difference between the two aircraft datasets, with vertically averaged mid-tropospheric values from the GT-CIMS around 14 % lower than equivalent values from PANTHER. However, the two sets of aircraft measurements are strongly correlated (R2 value of 0.92) and do provide a consistent view of the large-scale variation of PAN. We demonstrate that the retrievals of PAN from CrIS show skill in measurement of these large-scale PAN distributions in the remote mid-troposphere compared to the retrieval prior. The standard deviation of individual CrIS–aircraft differences is 0.08 ppbv, which we take as an estimate of the uncertainty of the CrIS mid-tropospheric PAN for a single satellite field of view. The standard deviation of the CrIS–aircraft comparisons for averaged CrIS retrievals (median of 20 satellite coincidences with each aircraft profile) is lower at 0.05 ppbv. This would suggest that the retrieval error is reduced with averaging, although not with the square root of the number of observations. We find a negative bias of the order of 0.1 ppbv in the CrIS PAN results with respect to the aircraft measurements. This bias shows a dependence on column water vapor. We provide a water-vapor-dependent bias correction for use with the CrIS PAN data.</p

Estimate of carbonyl sulfide tropical oceanic surface fluxes using Aura Tropospheric Emission Spectrometer observations

Quantifying the carbonyl sulfide (OCS) land/ocean fluxes contributes to the understanding of both the sulfur and carbon cycles. The primary sources and sinks of OCS are very likely in a steady state because there is no significant observed trend or interannual variability in atmospheric OCS measurements. However, the magnitude and spatial distribution of the dominant ocean source are highly uncertain due to the lack of observations. In particular, estimates of the oceanic fluxes range from approximately 280 Gg S yr^(−1) to greater than 800 Gg S yr^(−1), with the larger flux needed to balance a similarly sized terrestrial sink that is inferred from NOAA continental sites. Here we estimate summer tropical oceanic fluxes of OCS in 2006 using a linear flux inversion algorithm and new OCS data acquired by the Aura Tropospheric Emissions Spectrometer (TES). Modeled OCS concentrations based on these updated fluxes are consistent with HIAPER Pole‐to‐Pole Observations during 4th airborne campaign and improve significantly over the a priori model concentrations. The TES tropical ocean estimate of 70 ± 16 Gg S in June, when extrapolated over the whole year (about 840 ± 192 Gg S yr^(−1), supports the hypothesis proposed by Berry et al. (2013) that the ocean flux is in the higher range of approximately 800 Gg S yr^(−1)

Ozone-CO Correlations Determined by the TES Satellite Instrument in Continental Outflow Regions

Collocated measurements of tropospheric ozone (O3) and carbon monoxide (CO) from the Tropospheric Emission Spectrometer (TES) aboard the EOS Aura satellite provide information on O3-CO correlations to test our understanding of global anthropogenic influence on O3. We examine the global distribution of TES O3-CO correlations in the middle troposphere (618 hPa) for July 2005 and compare to correlations generated with the GEOS-Chem chemical transport model and with ICARTT aircraft observations over the eastern United States (July 2004). The TES data show significant O3-CO correlations downwind of polluted continents, with dO3/dCO enhancement ratios in the range 0.4–1.0 mol mol−1 and consistent with ICARTT data. The GEOS-Chem model reproduces the O3-CO enhancement ratios observed in continental outflow, but model correlations are stronger and more extensive. We show that the discrepancy can be explained by spectral measurement errors in the TES data. These errors will decrease in future data releases, which should enable TES to provide better information on O3-CO correlations.Earth and Planetary SciencesEngineering and Applied Science

El Niño, the 2006 Indonesian peat fires, and the distribution of atmospheric methane

Dry conditions from a moderate El Niño during the fall of 2006 resulted in enhanced burning in Indonesia with fire emissions of CO approximately 4–6 times larger than the prior year. Here we use new tropospheric methane and CO data from the Aura Tropospheric Emission Spectrometer and new CO profile measurements from the Terra Measurements of Pollution in the Troposphere (MOPITT) satellite instruments with the Goddard Earth Observing System (GEOS)‐Chem model to estimate methane emissions of 4.25 ± 0.75 Tg for October–November 2006 from these fires. Errors in convective parameterization in GEOS‐Chem, evaluated by comparing MOPITT and GEOS‐Chem CO profiles, are the primary uncertainty of the emissions estimate. The El Niño related Indonesian fires increased the tropical distribution of atmospheric methane relative to 2005, indicating that tropical biomass burning can compensate for expected decreases in tropical wetland methane emissions from reduced rainfall during El Niño as found in previous studies

Ozone-CO Correlations Determined by the TES Satellite Instrument in Continental Outflow Regions

Collocated measurements of tropospheric ozone (O3) and carbon monoxide (CO) from the Tropospheric Emission Spectrometer (TES) aboard the EOS Aura satellite provide information on O3-CO correlations to test our understanding of global anthropogenic influence on O3. We examine the global distribution of TES O3-CO correlations in the middle troposphere (618 hPa) for July 2005 and compare to correlations generated with the GEOS-Chem chemical transport model and with ICARTT aircraft observations over the eastern United States (July 2004). The TES data show significant O3-CO correlations downwind of polluted continents, with dO3/dCO enhancement ratios in the range 0.4–1.0 mol mol−1 and consistent with ICARTT data. The GEOS-Chem model reproduces the O3-CO enhancement ratios observed in continental outflow, but model correlations are stronger and more extensive. We show that the discrepancy can be explained by spectral measurement errors in the TES data. These errors will decrease in future data releases, which should enable TES to provide better information on O3-CO correlations.Earth and Planetary SciencesEngineering and Applied Science