The climate impact of ship NO<sub>x</sub> emissions: an improved estimate accounting for plume chemistry

Abstract

Nitrogen oxide (NO x ) emissions from maritime shipping produce ozone (O 3 ) and hydroxyl radicals (OH), which in turn destroy methane (CH 4 ). The balance between this warming (due to O 3 ) and cooling (due to CH 4 ) determines the net effect of ship NO x on climate. Previous estimates of the chemical impact and radiative forcing (RF) of ship NO x have generally assumed that plumes of ship exhaust are instantly diluted into model grid cells spanning hundreds of kilometers, even though this is known to produce biased results. Here we improve the parametric representation of exhaust-gas chemistry developed in the GEOS-Chem chemical transport model (CTM) to provide the first estimate of RF from shipping that accounts for sub-grid-scale ship plume chemistry. The CTM now calculates O 3 production and CH 4 loss both within and outside the exhaust plumes and also accounts for the effect of wind speed. With the improved modeling of plumes, ship NO x perturbations are smaller than suggested by the ensemble of past global modeling studies, but if we assume instant dilution of ship NO x on the grid scale, the CTM reproduces previous model results. Our best estimates of the RF components from increasing ship NO x emissions by 1 Tg(N) yr −1 are smaller than that given in the past literature: +3.4 ± 0.85 mW m −2 (1σ confidence interval) from the short-lived ozone increase, −5.7±1.3 mW m −2 from the CH 4 decrease, and −1.7 ± 0.7 mW m −2 from the long-lived O 3 decrease that accompanies the CH 4 change. The resulting net RF is −4.0 ± 2.0 mW m −2 for emissions of 1 Tg(N) yr −1 . Due to non-linearity in O 3 production as a function of background NO x , RF from large changes in ship NO x emissions, such as the increase since preindustrial times, is about 20 % larger than this RF value for small marginal emission changes. Using sensitivity tests in one CTM, we quantify sources of uncertainty in the RF components and causes of the ±30 % spread in past model results; the main source of uncertainty is the composition of the background atmosphere in the CTM, which is driven by model formulation (±10 to 20 %) and the plausible range of anthropogenic emissions (±10 %).

The Role of Clouds in the Tropospheric NOx Cycle: A New Modeling Approach for Cloud Chemistry and Its Global Implications

“…The differences arise from the locations where NO x is lost. Studies of aviation, ship, and industrial emissions show that marginal changes in NO x abundance have the greatest effect on tropospheric O 3 in low-NO x environments (Lin et al, 1988), particularly at low latitudes and high altitudes (Fry et al, 2012;Fuglestvedt et al, 2008;Holmes et al, 2014;Köhler et al, 2008Köhler et al, , 2013. While aerosols remove NO x mainly from the lower troposphere over industrial regions with high NO x , clouds have a greater impact in remote, high-altitude, low-NO x environments, which gives clouds a disproportionate impact on global O 3 and OH.…”

Global high-resolution simulations of tropospheric nitrogen dioxide using CHASER V4.0

“…Even over remote regions, a highresolution simulation has the potential to improve model performance through considering the effects of nonlinear chemistry in highly concentrated NO x plumes emitted from ships and lightning (Charlton-Perez et al, 2009;Vinken et al, 2011;Gressent et al, 2016). These NO x emission sources in remote regions have significant impacts on climate and air quality (Eyring et al, 2010;Holmes et al, 2014;Banerjee et al, 2014;Finney et al, 2016). It is thus important to clarify the importance of resolving small-scale sources and plumes within a global modeling framework for a better understanding of the global atmospheric environment and chemistryclimate system.…”

Multi-scale modeling of urban air pollution: development and application of a Street-in-Grid model (v1.0) by coupling MUNICH (v1.0) and Polair3D (v1.8.1)

“…Many other "plume-ingrid" (PinG) models have been developed over the following 3 decades (see Karamchandani et al, 2011, for an overview). Later PinG model development efforts have included PinG models for line sources, area sources, and volume sources using various modeling approaches (e.g., Cariolle et al, 2009;Karamchandani et al, 2009;Huszar et al, 2010;Jacobson et al, 2011;Briant and Seigneur, 2013;Holmes et al, 2014;Kim et al, 2014) in order to treat aircraft emissions, ship emissions, traffic emissions from roadways, and fugitive emissions from industrial sites. However, there is currently no integrated model that dynamically combines a Eulerian model with a street-network model.…”