Spatial and temporal variability in the hydroxyl (OH) radical: understanding the role of large-scale climate features and their influence on OH through its dynamical and photochemical drivers

Abstract

Abstract. The hydroxyl radical (OH) is the primary atmospheric oxidant responsible for removing many important trace gases, including methane, from the atmosphere. Although robust relationships between OH drivers and modes of climate variability have been shown, the underlying mechanisms between OH and these climate modes, such as the El Niño–Southern Oscillation (ENSO), have not been thoroughly investigated. Here, we use a chemical transport model to perform a 38 year simulation of atmospheric chemistry, in conjunction with satellite observations, to understand the relationship between tropospheric OH and ENSO, Northern Hemispheric modes of variability, the Indian Ocean Dipole, and monsoons. Empirical orthogonal function (EOF) and regression analyses show that ENSO is the dominant mode of global OH variability in the tropospheric column and upper troposphere, responsible for approximately 30 % of the total variance in boreal winter. Reductions in OH due to El Niño are centered over the tropical Pacific and Australia and can be as high as 10 %–15 % in the tropospheric column. The relationship between ENSO and OH is driven by changes in nitrogen oxides in the upper troposphere and changes in water vapor and O1D in the lower troposphere. While the correlations between monsoons or other modes of variability and OH span smaller spatial scales than for ENSO, regional changes in OH can be significantly larger than those caused by ENSO. Similar relationships occur in multiple models that participated in the Chemistry–Climate Model Initiative (CCMI), suggesting that the dependence of OH interannual variability on these well-known modes of climate variability is robust. Finally, the spatial pattern and r2 values of correlation between ENSO and modeled OH drivers – such as carbon monoxide, water vapor, lightning, and, to a lesser extent, NO2 – closely agree with satellite observations. The ability of satellite products to capture the relationship between OH drivers and ENSO provides an avenue to an indirect OH observation strategy and new constraints on OH variability.

A Machine Learning Methodology for the Generation of a Parameterization of the Hydroxyl Radical: a Tool to Improve Computational-Efficiency in Chemistry Climate Models

“…Similarly, the chemical species that are most important for controlling OH distribution on the global scale also tend to have higher Gain values. As discussed above, O3 and NOX chemistry is instrumental in controlling primary and secondary OH production on global scales (e.g., Spivakovsky et al, 2000;Anderson et al, 2021), consistent with their comparatively high Gain values. HCHO, an oxidation product of the reaction of OH with many VOCs, has been found to be a suitable proxy for OH in the remote atmosphere (Wolfe et al, 2019), consistent with its relative importance in both the July and January models.…”

“…The GMI chemical mechanism includes approximately 120 species and 400 reactions, characterizing the photochemistry of the troposphere and stratosphere. Further simulation details, including a description of the emissions, are available elsewhere (Anderson et al, 2021;Strode et al, 2019).…”

“…Finally, we omit NOX and NOy as parameterization inputs because we find that NO2 is sufficient as an input to capture the impact of reactive nitrogen on OH in the parameterization. Because of the importance of NOX in OH production (Spivakovsky et al, 2000;Anderson et al, 2021), we tested performance by substituting different reactive nitrogen species for NO2 as inputs to the parameterization. We trained three additional parameterizations, including ones with NOX, NOy (NOy = NO + NO2 + PAN + 2N2O5 + HNO3 + alkyl nitrates), and the individual NOy species.…”

“…We present a new method for the efficient generation of a parameterization of OH using GBRTs and a full chemistry simulation from a CCM, which serves as the training dataset. Our methodology allows for the parameterization to be easily and rapidly regenerated in response to changes in, for instance, the underlying model chemical mechanism (e.g., updates to the chemical rate constants or absorption crosssections) or model dynamics, which affect many of the variables that influence OH (e.g., Anderson et al, 2021). Likewise, the parameterization can be modified to include new input variables.…”

Investigating the Global OH Radical Distribution Using Steady-State Approximations and Satellite Data

“…The OH increase from 2000-2010 was predominantly due to that in the primary production term (O( 1 D) + H2O) though also to a decrease in the CO sink term (OH + CO). Model studies further show OH interannual variability to be influenced by the El Niño-Southern Oscillation (ENSO), with low OH concentrations being associated with El Niño years and high OH concentrations with La Niña years (Zhao et al, 2020;Anderson et al, 2021).…”