Atom CO and Aerosols in the Context of Interannual Variability: A Satellite View

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Attribution of chemistry-climate model initiative (CCMI) ozone radiative flux bias from satellites

The top-of-atmosphere (TOA) outgoing longwave flux over the 9.6 µm ozone band is a fundamental quantity for understanding chemistry–climate coupling. However, observed TOA fluxes are hard to estimate as they exhibit considerable variability in space and time that depend on the distributions of clouds, ozone (O3), water vapor (H2O), air temperature (Ta), and surface temperature (Ts). Benchmarking present-day fluxes and quantifying the relative influence of their drivers is the first step for estimating climate feedbacks from ozone radiative forcing and predicting radiative forcing evolution. To that end, we constructed observational instantaneous radiative kernels (IRKs) under clear-sky conditions, representing the sensitivities of the TOA flux in the 9.6 µm ozone band to the vertical distribution of geophysical variables, including O3, H2O, Ta, and Ts based upon the Aura Tropospheric Emission Spectrometer (TES) measurements. Applying these kernels to present-day simulations from the Chemistry-Climate Model Initiative (CCMI) project as compared to a 2006 reanalysis assimilating satellite observations, we show that the models have large differences in TOA flux, attributable to different geophysical variables. In particular, model simulations continue to diverge from observations in the tropics, as reported in previous studies of the Atmospheric Chemistry Climate Model Intercomparison Project (ACCMIP) simulations. The principal culprits are tropical middle and upper tropospheric ozone followed by tropical lower tropospheric H2O. Five models out of the eight studied here have TOA flux biases exceeding 100 mW m−2 attributable to tropospheric ozone bias. Another set of five models have flux biases over 50 mW m−2 due to H2O. On the other hand, Ta radiative bias is negligible in all models (no more than 30 mW m−2). We found that the atmospheric component (AM3) of the Geophysical Fluid Dynamics Laboratory (GFDL) general circulation model and Canadian Middle Atmosphere Model (CMAM) have the lowest TOA flux biases globally but are a result of cancellation of opposite biases due to different processes. Overall, the multi-model ensemble mean bias is −133±98  mW m−2, indicating that they are too atmospherically opaque due to trapping too much radiation in the atmosphere by overestimated tropical tropospheric O3 and H2O. Having too much O3 and H2O in the troposphere would have different impacts on the sensitivity of TOA flux to O3 and these competing effects add more uncertainties on the ozone radiative forcing. We find that the inter-model TOA outgoing longwave radiation (OLR) difference is well anti-correlated with their ozone band flux bias. This suggests that there is significant radiative compensation in the calculation of model outgoing longwave radiation

Analysis of Ozone in Cloudy Versus Clear Sky Conditions

Convection impacts ozone concentrations by transporting ozone vertically and by lofting ozone precursors from the surface, while the clouds and lighting associated with convection affect ozone chemistry. Observations of the above-cloud ozone column (Ziemke et al., 2009) derived from the OMI instrument show geographic variability, and comparison of the above-cloud ozone with all-sky tropospheric ozone columns from OMI indicates important regional differences. We use two global models of atmospheric chemistry, the GMI chemical transport model (CTM) and the GEOS-5 chemistry climate model, to diagnose the contributions of transport and chemistry to observed differences in ozone between areas with and without deep convection, as well as differences in clean versus polluted convective regions. We also investigate how the above-cloud tropospheric ozone from OMI can provide constraints on the relationship between ozone and convection in a free-running climate simulation as well as a CTM

Global 3-D Land-Ocean-Atmosphere Model for Mercury: Present-Day Versus Preindustrial Cycles and Anthropogenic Enrichment Factors for Deposition

We develop a mechanistic representation of land-atmosphere cycling in a global 3-D ocean-atmosphere model of mercury (GEOS-Chem). The resulting land-ocean-atmosphere model is used to construct preindustrial and present biogeochemical cycles of mercury, to examine the legacy of past anthropogenic emissions, to map anthropogenic enrichment factors for deposition, and to attribute mercury deposition in the United States. Land emission in the model includes prompt recycling of recently deposited mercury (600 Mg a−1 for present day), soil volatilization (550 Mg a−1), and evapotranspiration (550 Mg a−1). The spatial distribution of soil concentrations is derived from local steady state between land emission and deposition in the preindustrial simulation, augmented for the present day by a 15% increase in the soil reservoir distributed following the pattern of anthropogenic deposition. Mercury deposition and hence emission are predicted to be highest in the subtropics. Our atmospheric lifetime of mercury against deposition (0.50 year) is shorter than past estimates because of our accounting of Hg(0) dry deposition, but recycling from surface reservoirs results in an effective lifetime of 1.6 years against transfer to long-lived reservoirs in the soil and deep ocean. Present-day anthropogenic enrichment of mercury deposition exceeds a factor of 5 in continental source regions. We estimate that 68% of the deposition over the United States is anthropogenic, including 20% from North American emissions (20% primary and <1% recycled through surface reservoirs), 31% from emissions outside North America (22% primary and 9% recycled), and 16% from the legacy of anthropogenic mercury accumulated in soils and the deep ocean.Earth and Planetary SciencesEngineering and Applied Science

Interannual Variability and Trends of CH4, CO and OH Using the Computationally-Efficient CH4-CO-OH (ECCOH) Module

Methane (CH4) is the second most important anthropogenic greenhouse gas (GHG). Its 100-year global warming potential (GWP) is 34 times larger than that for carbon dioxide. The 100-year integrated GWPof CH4 is sensitive to changes in hydroxyl radical (OH) levels.Oxidation of CH4 and carbon monoxide (CO) by OH is the main loss process, thus affecting the oxidizing capacity of the atmosphere and contributing to the global ozone background. Limitations of using archived, monthly OH fields for studies of methane's and COs evolution are that feedbacks of the CH4-CO-OH system on methane, CO and OH are not captured. In this study, we employ the computationally Efficient CH4-CO-OH (ECCOH) module (Elshorbany et al., 2015) to investigate the nonlinear feedbacks of the CH4-CO-OH system on the interannual variability and trends of the CH4, CO, OH system

Implications of CO Bias for Ozone and Methane Lifetime in a CCM

A low bias in carbon monoxide compared to observations at high latitudes is a common feature of chemistry climate models. CO bias can both indicate and contribute to a bias in modeled OH and methane lifetime. This study examines possible causes of CO bias in the ACCMIP simulation of the GEOSCCM, and considers how attributing the CO bias to uncertainty in CO emissions versus biases in other constituents impacts the relationship between CO bias and methane lifetime. We use a simplified model of CO tagged by source with specified OH to quantify the sensitivity of the CO bias to changes in CO emissions or OH concentration, comparing the modeled CO to surface and MOPITT observations. The simplified model shows that decreasing OH in the northern hemisphere removes most of the global mean and inter-hemispheric bias in surface CO. We then use results from this analysis to explore how adjusting CO sources in the CCM impacts the concentrations of ozone, OH and methane. The CCM simulation also exhibits biases in ozone and water vapor compared to observations. We use a parameterized CO-OH-CH4 model that takes ozone and water vapor as inputs to the parameterization to examine whether correcting water and ozone biases can alter OH enough to remove the CO bias. Through this analysis, we aim to better quantify the relationship between CO bias and model biases in ozone concentrations and methane lifetime

Air-Sea Exchange in the Global Mercury Cycle

We present results from a new global atmospheric mercury model coupled with a mixed layer slab ocean. The ocean model describes the interactions of the mixed layer with the atmosphere and deep ocean, as well as conversion between elemental, divalent, and nonreactive mercury species. Our global mean aqueous concentrations of 0.07 pM elemental, 0.80 pM reactive, and 1.51 pM total mercury agree with observations. The ocean provides a 14.1 Mmol yr−1 source of mercury to the atmosphere, at the upper end of previous estimates. Re-emission of previously deposited mercury constitutes 89% of this flux. Ocean emissions are largest in the tropics and downwind of industrial regions. Midlatitude ocean emissions display a large seasonal cycle induced by biological productivity. Oceans contribute 54% (36%) of surface atmospheric mercury in the Southern (Northern) Hemisphere. We find a large net loss of mercury to the deep ocean (8.7 Mmol yr−1), implying a ∼0.7%/year increase in deep ocean concentrations.Earth and Planetary SciencesEngineering and Applied Science

Atmospheric Constituents in GEOS-5: Components for an Earth System Model

The GEOS-S model is being developed for weather and climate processes, including the implementation of "Earth System" components. While the stratospheric chemistry capabilities are mature, we are presently extending this to include predictions of the tropospheric composition and chemistry - this includes CO2, CH4, CO, nitrogen species, etc. (Aerosols are also implemented, but are beyond the scope of this paper.) This work will give an overview of our chemistry modules, the approaches taken to represent surface emissions and uptake of chemical species, and some studies of the sensitivity of the atmospheric circulation to changes in atmospheric composition. Results are obtained through focused experiments and multi-decadal simulations