ALBERT

Description: The Tropospheric Ozone Assessment Report (TOAR) is an activity of the International Global Atmospheric Chemistry Project. This paper is a component of the report, focusing on the present-day distribution and trends of tropospheric ozone relevant to climate and global atmospheric chemistry model evaluation. Utilizing the TOAR surface ozone database, several figures present the global distribution and trends of daytime average ozone at 2702 non-urban monitoring sites, highlighting the regions and seasons of the world with the greatest ozone levels. Similarly, ozonesonde and commercial aircraft observations reveal ozone’s distribution throughout the depth of the free troposphere. Long-term surface observations are limited in their global spatial coverage, but data from remote locations indicate that ozone in the 21st century is greater than during the 1970s and 1980s. While some remote sites and many sites in the heavily polluted regions of East Asia show ozone increases since 2000, many others show decreases and there is no clear global pattern for surface ozone changes since 2000. Two new satellite products provide detailed views of ozone in the lower troposphere across East Asia and Europe, revealing the full spatial extent of the spring and summer ozone enhancements across eastern China that cannot be assessed from limited surface observations. Sufficient data are now available (ozonesondes, satellite, aircraft) across the tropics from South America eastwards to the western Pacific Ocean, to indicate a likely tropospheric column ozone increase since the 1990s. The 2014–2016 mean tropospheric ozone burden (TOB) between 60˚N–60˚S from five satellite products is 300 Tg ± 4%. While this agreement is excellent, the products differ in their quantification of TOB trends and further work is required to reconcile the differences. Satellites can now estimate ozone’s global long-wave radiative effect, but evaluation is difficult due to limited in situ observations where the radiative effect is greatest.

Description: Non-linear optimal estimates of atmospheric profiles from the Tropospheric Emission Spectrometer (TES) may contain a priori information that varies geographically, which is a confounding factor in the analysis and physical interpretation of an ensemble of profiles. A common strategy is to transform these profile estimates to a common prior using a linear operation thereby facilitating the interpretation of profile variability. However, this operation is dependent on the assumption of not worse than moderate non-linearity near the solution of the non-linear estimate. We examines the robustness of this assumption when exchanging the prior by comparing atmospheric retrievals from the Tropospheric Emission Spectrometer processed with a uniform prior with those processed with a variable prior and converted to a uniform prior following the non-linear retrieval. We find that linearly converting the prior following a non-linear retrieval is shown to have a minor effect on the results as compared to a non-linear retrieval using a uniform prior when compared to the expected total error, with less than 10% of the change in the prior ending up as unbiased fluctuations in the profile estimate results.

Description: We compare Tropospheric Emission Spectrometre (TES) versions 3 and 4, V003 and V004, respectively, nadir-stare ozone profiles with ozonesonde profiles from the Arctic Intensive Ozonesonde Network Study (ARCIONS, http://http://croc.gsfc.nasa.gov/arcions/) during the Arctic Research on the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) field mission. The ozonesonde data are from launches timed to match the Aura's overpass, where 11 coincidences spanned 44° N to 71° N from April to July 2008. Using the TES "stare" observation mode, 32 observations are taken over each coincident ozonesonde launch. By effectively sampling the same air mass 32 times, comparisons are made between the empirically-calculated random errors to the expected random errors from measurement noise, temperature and interfering species, such as water. This study represents the first validation of high latitude (〉60°) TES ozone. We find that the calculated errors are consistent with the actual errors with a similar vertical distribution that varies between 5% and 20% for V003 and V004 TES data. In general, TES ozone profiles are positively biased (by less than 15%) from the surface to the upper troposphere (~1000 to 100 hPa) and negatively biased (by less than 20%) from the upper troposphere to the lower stratosphere (100 to 30 hPa) when compared to the ozonesonde data. Lastly, for V003 and V004 TES data between 44° N and 71° N there is a small variability in the mean biases (from −14 to +15%), mean theoretical errors (from 6 to 13%), and mean random errors (from 9 to 19%).

Description: We present carbon dioxide (CO2) estimates from the Tropospheric Emission Spectrometer (TES) on the EOS-Aura satellite launched in 2004. For observations between 40° S and 45° N, we find about 1 degree of freedom with peak sensitivity at 511 hPa. The estimated error is ~10 ppm for a single target and about 1.3 ppm for monthly averages on spatial scales of 20°×30°. Monthly spatially-averaged TES results from 2005–2008 processed with a uniform initial guess and prior are compared to CONTRAIL aircraft data over the Pacific ocean, aircraft data at the Southern Great Plains (SGP) ARM site in the southern US, and the Mauna Loa and Samoa surface stations. Comparisons to Mauna Loa observatory show a correlation of 0.92, a standard deviation of 1.3 ppm, a predicted error of 1.2 ppm, and a ~2% low bias, which is subsequently corrected, and comparisons to SGP aircraft data over land show a correlation of 0.67 and a standard deviation of 2.3 ppm. TES data between 40° S and 45° N for 2006–2007 are compared to surface flask data, GLOBALVIEW, the Atmospheric Infrared Sounder (AIRS), and CarbonTracker. Comparison to GLOBALVIEW-CO2 ocean surface sites shows a correlation of 0.60 which drops when TES is offset in latitude, longitude, or time. At these same locations, TES shows a 0.62 and 0.67 correlation to CarbonTracker with TES observation operator at the surface and 5 km, respectively. We also conducted an observing system simulation experiment to assess the potential utility of the TES data for inverse modeling of CO2 fluxes. We find that if biases in the data and model are well characterized, the averaged data have the potential to provide sufficient information to significantly reduce uncertainty on annual estimates of regional CO2 sources and sinks. Averaged pseudo-data at 10°×10° reduced uncertainty in flux estimates by as much as 70% for some tropical regions.

Description: We present carbon dioxide (CO2) estimates from the Tropospheric Emission Spectrometer (TES) on the EOS-Aura satellite launched in 2004. For observations between 40° S and 45° N, we find about 1 degree of freedom with peak sensitivity at 511 hPa. The estimated error is ~10 ppm for a single target and 1.3–2.3 ppm for monthly averages on spatial scales of 20°×30°. Monthly spatially-averaged TES data from 2005–2008 processed with a uniform initial guess and prior are compared to CONTRAIL aircraft data over the Pacific ocean, aircraft data at the Southern Great Plains (SGP) ARM site in the southern US, and the Mauna Loa and Samoa surface stations. Comparisons to Mauna Loa data show a correlation of 0.92, a standard deviation of 1.3 ppm, a predicted error of 1.2 ppm, and a ~2% low bias, which is subsequently corrected. Comparisons to SGP aircraft data over land show a correlation of 0.67 and a standard deviation of 2.3 ppm. TES data between 40° S and 45° N for 2006–2007 are compared to surface flask data, GLOBALVIEW, the Atmospheric Infrared Sounder (AIRS), and CarbonTracker. Comparison to GLOBALVIEW-CO2 ocean surface sites shows a correlation of 0.60 which drops when TES is offset in latitude, longitude, or time. At these same locations, TES shows a 0.62 and 0.67 correlation to CarbonTracker at the surface and 5 km, respectively. We also conducted an observing system simulation experiment to assess the potential utility of the TES data for inverse modeling of CO2 fluxes. We find that if biases in the data and model are well characterized, the averaged data have the potential to provide sufficient information to significantly reduce uncertainty on annual estimates of regional CO2 sources and sinks. Averaged pseudo-data at 10°×10° reduced uncertainty in flux estimates by as much as 70% for some tropical regions.

Description: Improved estimates of NOx from lightning sources are required to understand tropospheric NOx and ozone distributions, the oxidising capacity of the troposphere and corresponding feedbacks between chemistry and climate change. In this paper, we report new satellite ozone observations from the Tropospheric Emission Spectrometer (TES) instrument that can be used to test and constrain the parameterization of the lightning source of NOx in global models. Using the National Lightning Detection (NLDN) and the Long Range Lightning Detection Network (LRLDN) data as well as the HYPSLIT transport and dispersion model, we show that TES provides direct observations of ozone enhanced layers downwind of convective events over the USA in July 2006. We find that the GEOS-Chem global chemistry-transport model with a parameterization based on cloud top height, scaled regionally and monthly to OTD/LIS (Optical Transient Detector/Lightning Imaging Sensor) climatology, captures the ozone enhancements seen by TES. We show that the model's ability to reproduce the location of the enhancements is due to the fact that this model reproduces the pattern of the convective events occurrence on a daily basis during the summer of 2006 over the USA, even though it does not well represent the relative distribution of lightning intensities. However, this model with a value of 6 Tg N/yr for the lightning source (i.e.: with a mean production of 260 moles NO/Flash over the USA in summer) underestimates the intensities of the ozone enhancements seen by TES. By imposing a production of 520 moles NO/Flash for lightning occurring in midlatitudes, which better agrees with the values proposed by the most recent studies, we decrease the bias between TES and GEOS-Chem ozone over the USA in July 2006 by 40%. However, our conclusion on the strength of the lightning source of NOx is limited by the fact that the contribution from the stratosphere is underestimated in the GEOS-Chem simulations.

Description: We use simultaneous observations of tropospheric ozone and outgoing longwave radiation (OLR) sensitivity to tropospheric ozone from the Tropospheric Emission Spectrometer (TES) to evaluate model tropospheric ozone and its effect on OLR simulated by a suite of chemistry-climate models that participated in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). The ensemble mean of ACCMIP models show a persistent but modest tropospheric ozone low bias (5–20 ppb) in the Southern Hemisphere (SH) and modest high bias (5–10 ppb) in the Northern Hemisphere (NH) relative to TES ozone for 2005–2010. These ozone biases have a significant impact on the OLR. Using TES instantaneous radiative kernels (IRK), we show that the ACCMIP ensemble mean tropospheric ozone low bias leads up to 120 mW m−2 OLR high bias locally but zonally compensating errors reduce the global OLR high bias to 39 ± 41 m Wm−2 relative to TES data. We show that there is a correlation (R2 = 0.59) between the magnitude of the ACCMIP OLR bias and the deviation of the ACCMIP preindustrial to present day (1750–2010) ozone radiative forcing (RF) from the ensemble ozone RF mean. However, this correlation is driven primarily by models whose absolute OLR bias from tropospheric ozone exceeds 100 m Wm−2. Removing these models leads to a mean ozone radiative forcing of 394 ± 42 m Wm−2. The mean is about the same and the standard deviation is about 30% lower than an ensemble ozone RF of 384 ± 60 m Wm−2 derived from 14 of the 16 ACCMIP models reported in a companion ACCMIP study. These results point towards a profitable direction of combining satellite observations and chemistry-climate model simulations to reduce uncertainty in ozone radiative forcing.

Description: We present satellite based ozone profile estimates derived by combining radiances measured at thermal infrared (TIR) wavelengths from the Aura Tropospheric Emission Spectrometer (TES) and ultraviolet (UV) wavelengths measured by the Aura Ozone Monitoring Instrument (OMI). The advantage of using these combined wavelengths and instruments for sounding ozone over either instrument alone is improved sensitivity near the surface as well as the capability to consistently resolve the lower troposphere, upper troposphere, and lower stratosphere for scenes with varying geophysical states. For example, the vertical resolution of ozone estimates from either TES or OMI varies strongly by surface albedo and temperature. Typically, TES provides 1.6 degrees of freedom for signal (DOFS) and OMI provides less than 1 DOFS in the troposphere. The combination provides 2 DOFS in the troposphere with approximately 0.4 DOFS for near surface ozone (surface to 700 hPa). We evaluated these new ozone profile estimates with ozonesonde measurements and found that calculated errors for the joint TES and OMI ozone profile estimates are in reasonable agreement with actual errors as derived by the root-mean-square (RMS) difference between the ozonesondes and the joint TES/OMI ozone estimates. We also used a common a priori profile in the retrievals in order to evaluate the capability of different retrieval approaches on capturing near-surface ozone variability. We found that the vertical resolution of the joint TES/OMI ozone profile estimates shows significant improvements on quantifying variations in near-surface ozone with RMS differences of 49.9% and correlation coefficient of R = 0.58 for the TES/OMI near-surface estimates as compared to 67.2% RMS difference and R = 0.33 for TES and 115.8% RMS difference and R = 0.09 for OMI. This comparison removes the impacts of using the climatological a priori in the retrievals. However, it results in artificially large sonde/retrieval differences. The TES/OMI ozone profiles from the production code of joint retrievals will use climatological a priori and therefore will have more realistic ozone estimates than those from using a common a priori volume mixing ratio profile.

Description: Thermal infrared radiances from the Tropospheric Emission Spectrometer (TES) between 10 and 15 μm contain significant carbon dioxide (CO2) information, however the CO2 signal must be separated from radiative interference from temperature, surface and cloud parameters, water, and other trace gases. Validation requires data sources spanning the range of TES CO2 sensitivity, which is approximately 2.5 to 12 km with peak sensitivity at about 5 km and the range of TES observations in latitude (40° S to 40° N) and time (2005–2011). We therefore characterize Tropospheric Emission Spectrometer (TES) CO2 version 5 biases and errors through comparisons to ocean and land-based aircraft profiles and to the CarbonTracker assimilation system. We compare to ocean profiles from the first three Hiaper Pole-to-Pole Observations (HIPPO) campaigns between 40° S and 40° N with measurements between the surface and 14 km and find that TES CO2 estimates capture the seasonal and latitudinal gradients observed by HIPPO CO2 measurements. Actual errors range from 0.8–1.8 ppm, depending on the campaign and pressure level, and are approximately 1.6–2 times larger than the predicted errors. The bias of TES versus HIPPO is within 1 ppm for all pressures and datasets; however, several of the sub-tropical TES CO2 estimates are lower than expected based on the calculated errors. Comparisons to land aircraft profiles from the United States Southern Great Plains (SGP) Atmospheric Radiation Measurement (ARM) between 2005 and 2011 measured from the surface to 5 km to TES CO2 show good agreement with an overall bias of −0.3 ppm to 0.1 ppm and standard deviations of 0.8 to 1.0 ppm at different pressure levels. Extending the SGP aircraft profiles above 5 km using AIRS or CONTRAIL measurements improves comparisons with TES. Comparisons to CarbonTracker (version CT2011) show a persistent spatially dependent bias pattern and comparisons to SGP show a time-dependent bias of −0.2 ppm yr−1. We also find that the predicted sensitivity of the TES CO2 estimates is too high, which results from using a multi-step retrieval for CO2 and temperature. We find that the averaging kernel in the TES product corrected by a pressure-dependent factor accurately reflects the sensitivity of the TES CO2 product.

Description: Evaluating surface fluxes of CH4 using total column data requires models to accurately account for the transport and chemistry of methane in the free-troposphere and stratosphere, thus reducing sensitivity to the underlying fluxes. Vertical profiles of methane have increased sensitivity to surface fluxes because lower tropospheric methane is more sensitive to surface fluxes than a total column. Resolving the free troposphere from the lower-troposphere also helps to evaluate the impact of transport and chemistry uncertainties on estimated surface fluxes. Here we demonstrate the potential for estimating lower tropospheric CH4 concentrations through the combination of free-tropospheric methane measurements from the Aura Tropospheric Emission Spectrometer (TES) and XCH4 (dry-mole air fraction of methane) from the Greenhouse Gases Observing Satellite Thermal And Near Infrared for Carbon Observations (GOSAT TANSO, herein GOSAT for brevity). The mean precision of these estimates are calculated to be ~ 23 ppb for a monthly average on a 4 × 5 latitude/longitude degree grid making these data suitable for evaluating lower-tropospheric methane concentrations. Smoothing error is approximately 10 ppb or less. The accuracy is primarily determined by knowledge error of XCO2, used to estimate XCH4 from the GOSAT CH4/CO2 "proxy" retrieval. For example, we use different XCO2 fields to quantify XCH4 from the GOSAT CH4/CO2 retrieval, one from Carbontracker and another from the NASA Carbon Monitoring System, and find that differences of up to approximately 60 ppb are possible with a mean value of approximately 35 ppb or less for any given latitude for these lower-tropospheric methane estimates using these two different XCO2 distributions. We show that these lower-tropospheric concentrations are more directly sensitive to the underlying fluxes than a total column using the GEOS-Chem model. In particular, we compare these lower-tropospheric methane estimates with those from the GEOS-Chem model for July 2009 to determine if these data can capture methane enhancements associated with the high-latitude methane fluxes because both TES and GOSAT separately do not show much sensitivity to methane from these sources. We find that the spatial patterns and magnitude of lower tropospheric methane concentrations from GEOS-Chem over Northern European and Siberian wetland fluxes are consistent with these data but modeled concentrations are much larger than measured over Canadian wetland fluxes. Transport of methane significantly affects lower-tropospheric methane concentrations over S.E. Asia as both data and model show methane enhancements that are shifted away from their sources. A possible new finding is that there is no representation of a strong source between the Black and Caspian seas.