Vertical Profile of Ozone Derived from Combined MLS and TES Satellite Observations

Ozone is one of the most important gases in the atmosphere as it plays different roles based on the levels it presents. The ozone layer in the stratosphere protects life on Earth by absorbing ultraviolet (UV) radiance while harming life at ground-level. In order to better understand the source of ozone pollution, transport of ozone, stratosphere-troposphere exchange of ozone, it is necessary to estimate the vertical profile of ozone. In this study, we derive the vertical ozone profile throughout the troposphere to the stratosphere by combing ozone retrievals from MLS (Microwave Limb Sounder) and TES (Tropospheric Emission Spectrometer). The combination algorithm is based on the MLS and TES retrieved vertical profiles of ozone, and averaging kernels of MLS, which represent the vertical sensitivity of the instrument. The combination algorithm was applied to the pairs of MLS and TES over the globe in 2007 as examples. The combined vertical profiles were compared with ozonesonde observations for validation, which indicate that the combined products extract information from MLS and TES have less biases than that of MLS or TES alone in general in both stratosphere and troposphere under certain quantitative criteria.

Sensitivity of Optimal Estimation Satellite Retrievals to Misspecification of the Prior Mean and Covariance, with Application to OCO-2 Retrievals

Optimal Estimation (OE) is a popular algorithm for remote sensing retrievals, partly due to its explicit parameterization of the sources of error and the ability to propagate them into estimates of retrieval uncertainty. These properties require specification of the prior distribution of the state vector. In many remote sensing applications, the true priors are multivariate and hard to characterize properly. Instead, priors are often constructed based on subject-matter expertise, existing empirical knowledge, and a need for computational expediency, resulting in a “working prior.” This paper explores the retrieval bias and the inaccuracy in retrieval uncertainty caused by explicitly separating the true prior (the probability distribution of the underlying state) from the working prior (the probability distribution used within the OE algorithm), with an application to Orbiting Carbon Observatory-2 (OCO-2) retrievals. We find that, in general, misspecifying the mean in the working prior will lead to biased retrievals, and misspecifying the covariance in the working prior will lead to inaccurate estimates of the retrieval uncertainty, though their effects vary depending on the state-space signal-to-noise ratio of the observing instrument. Our results point towards some attractive properties of a class of uninformative priors that is implicit for least-squares retrievals. Furthermore, our derivations provide a theoretical basis, and an understanding of the trade-offs involved, for the practice of inflating a working-prior covariance in order to reduce the prior’s impact on a retrieval (e.g., for OCO-2 retrievals). Finally, our results also lead to practical recommendations for specifying the prior mean and the prior covariance in OE.

Global O3-CO Correlations in a Chemistry and Transport Model During July-August: Evaluation with TES Satellite Observations and Sensitivity to Input Meteorological Data and Emissions

We examine the capability of the Global Modeling Initiative (GMI) chemistry and transport model to reproduce global mid-tropospheric (618hPa) O₃-CO correlations determined by the measurements from Tropospheric Emission Spectrometer (TES) aboard NASA's Aura satellite during boreal summer (July-August). The model is driven by three meteorological data sets (fvGCM with sea surface temperature for 1995, GEOS4-DAS for 2005, and MERRA for 2005), allowing us to examine the sensitivity of model O₃-CO correlations to input meteorological data. Model simulations of radionuclide tracers (²²²Rn, ²¹⁰Pb, and ⁷Be) are used to illustrate the differences in transport-related processes among the meteorological data sets. Simulated O₃ values are evaluated with climatological ozone profiles from ozonesonde measurements and satellite tropospheric O₃ columns. Despite the fact that three simulations show significantly different global and regional distributions of O₃ and CO concentrations, all simulations show similar patterns of O₃-CO correlations on a global scale. These patterns are consistent with those derived from TES observations, except in the tropical easterly biomass burning outflow regions. Discrepancies in regional O₃-CO correlation patterns in the three simulations may be attributed to differences in convective transport, stratospheric influence, and subsidence, among other processes. To understand how various emissions drive global O₃-CO correlation patterns, we examine the sensitivity of GMI/MERRA model-calculated O₃ and CO concentrations and their correlations to emission types (fossil fuel, biomass burning, biogenic, and lightning NO_x emissions). Fossil fuel and biomass burning emissions are mainly responsible for the strong positive O₃-CO correlations over continental outflow regions in both hemispheres. Biogenic emissions have a relatively smaller impact on O₃-CO correlations than other emissions, but are largely responsible for the negative correlations over the tropical eastern Pacific, reflecting the fact that O₃ is consumed and CO generated during the atmospheric oxidation process of isoprene under low NO_x conditions. We find that lightning NO_x emissions degrade both positive correlations at mid-/high- latitudes and negative correlations in the tropics because ozone production downwind of lightning NO_x emissions is not directly related to the emission and transport of CO. Our study concludes that O₃-CO correlations may be used effectively to constrain the sources of regional tropospheric O₃ in global 3-D models, especially for those regions where convective transport of pollution plays an important role.

Simulating the Black Saturday 2009 smoke plume with an interactive composition-climate model: sensitivity to emissions amount, timing and injection height

We simulated the high-altitude smoke plume from the early February 2009 Black Saturday bushfires in southeastern Australia using the NASA GISS ModelE2. To the best of our knowledge, this is the first single-plume analysis of biomass burning emissions injected directly into the upper-troposphere/lower stratosphere (UTLS) using a full-complexity composition-climate model. We compared simulated carbon monoxide (CO) to a new Aura TES/MLS joint CO retrieval, focusing on the plume's initial transport eastward, anticyclonic circulation to the north of New Zealand, westward transport in the lower stratospheric easterlies, and arrival over Africa at the end of February. Our goal was to determine the sensitivity of the simulated plume to prescribed injection height, emissions amount and emissions timing from different sources for a full complexity model when compared to Aura. The most realistic plumes were obtained using injection heights in the UTLS, including one drawn from ground-based radar data. A six-hour emissions pulse or emissions tied to independent estimates of hourly fire behavior produced a more realistic plume in the lower stratosphere compared to the same emissions amount being released evenly over 12 or 24-hours. Simulated CO in the plume was highly sensitive to the differences between emissions amounts estimated from the Global Fire Emissions Database and from detailed, ground-based estimates of fire growth. The emissions amount determined not only the CO concentration of the plume, but the proportion of the plume that entered the stratosphere. We speculate that this is due to either or both non-linear CO loss with a weakened OH sink, or plume self-lofting driven by shortwave absorption of the co-emitted aerosols.