Editing IPCC:AR6/WGI/Chapter-6 (section)

=== 6.3.6 Implications of SLCF Abundances for Atmospheric Oxidizing Capacity ===

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The atmospheric oxidising capacity is determined primarily by tropospheric hydroxyl (OH) radical and to a smaller extent by NO <sub>3</sub> radical, ozone, hydrogen peroxide (H <sub>2</sub> O <sub>2</sub> ) and halogen radicals. OH is the main sink for many SLCFs, including methane, halogenated compounds (HCFCs and HFCs), CO and NMVOCs, controlling their lifetimes and consequently their abundance and climate influence. OH-initiated oxidation of methane, CO and NMVOCs in the presence of NO <sub>x</sub> leads to the production of tropospheric ozone. OH also contributes to the formation of aerosols from oxidation of SO <sub>2</sub> to sulphate and NMVOCs to secondary organic aerosols. The evolution of the atmospheric oxidising capacity of the Earth driven by human activities and natural processes is, therefore, of significance for climate and air-quality concerns.

The main source of tropospheric OH is the photoexcitation of tropospheric ozone that creates an electronically excited oxygen atom which reacts with water vapour producing OH. A secondary source of importance for global OH is the recycling of peroxy radicals formed by the reaction of OH with reduced and partly oxidized species, including methane, CO and NMVOCs. In polluted air, NO <sub>x</sub> emissions control the secondary OH production, while in pristine air it occurs via other mechanisms involving, in particular, isoprene ( [[#Lelieveld--2016|Lelieveld et al., 2016]] ; [[#Wennberg--2018|Wennberg et al., 2018]] ). Knowledge of the effect of isoprene oxidation on OH recycling has evolved tremendously over the past decade, facilitating mechanistic explanation of elevated OH concentrations observed in locations characterised by low NO <sub>x</sub> levels (Hofzumahaus et al. , 2009; Paulot et al. , 2009; Peeters et al. , 2009, 2014; Fuchs et al. , 2013) . Since AR5, the inclusion of improved chemical mechanisms in some CTMs suggest advances in understanding of the global OH budget, however, these improvements have yet to be incorporated in CMIP6-generation ESMs.

As a result of the complex photochemistry, tropospheric OH abundance is sensitive to changes in SLCF emissions as well as climate. Increases in methane, CO and NMVOCs reduce OH while increases in water vapour and temperature, incoming solar radiation, NO <sub>x</sub> and tropospheric ozone enhance OH. The OH level thus responds to climate change and climate variability via its sensitivity to temperature and water vapour, as well as the influence of climate on natural emissions (e.g., wetland methane emissions, lightning NO <sub>x</sub> , BVOCs, fire emissions) with consequent feedbacks on climate (Section 6.4.5). Climate modes of variability, like El Niño–Southern Oscillation, also contribute to OH variability via changes in lightning NO <sub>x</sub> emissions and deep convection (Turner et al. , 2018) , and fire emissions ( [[#Rowlinson--2019|Rowlinson et al., 2019]] ).

Global-scale OH observations are non-existent because of its extremely short lifetime (around 1 second) and therefore global OH abundance and its time variations are either inferred from atmospheric measurements of methyl chloroform (MCF; [[#Prinn--2018|Prinn et al., 2018]] and references therein) or derived from global atmospheric chemistry models ( [[#Lelieveld--2016|Lelieveld et al., 2016]] ). The AR5 reported small interannual OH variations in the 2000s based on atmospheric inversions of MCF observations (within ±5%) and global CCMs and CTMs (within ±3%) ( [[#Ciais--2013|Ciais et al., 2013]] ).

Since AR5, there is much closer agreement in the estimates of interannual variations in global mean OH derived from atmospheric inversions, empirical reconstruction, and global CCMs and ESMs, with an estimate of 2–3% over the 1980–2015 period (Table 6.7). While the different methodologies agree on the occurrence of small interannual variations, there is much debate over the longer-term global OH trend. Two studies using multi-box model inversions of MCF and methane observations suggest large positive and negative trends since the 1990s in global mean OH ( [[#Rigby--2017|Rigby et al., 2017]] ; [[#Turner--2017|Turner et al., 2017]] ), however, both find that observational constraints are weak, such that a wide range of multi-annual OH variations are possible. Indeed, [[#Naus--2019|Naus et al. (2019)]] find an overall positive global OH trend over the past two decades (Table 6.7) after accounting for uncertainties and biases in atmospheric MCF and methane inversions, confirming the weakness in observational constraints for deriving OH trends. Global ESMs, CCMs and CTMs exhibit increasing global OH after 1980 contrary to the lack of trend derived from some atmospheric inversions and empirical reconstructions (Table 6.7). In particular, a three-member ensemble of ESMs participating in the AerChemMIP/CMIP6 agrees that global OH has increased since 1980 by around 9% (Figure 6.9) with an associated reduction in methane lifetime ( [[#Stevenson--2020|Stevenson et al., 2020]] ). This positive OH trend is in agreement with the OH increase of about 7% derived by assimilating global-scale satellite observations of CO over the 2002–2013 period (with CO declining trends) into a CCM (Section 6.3.4; [[#Gaubert--2017|Gaubert et al., 2017]] ). Multi-model sensitivity analysis suggests that increasing OH since 1980 is predominantly driven by changes in anthropogenic SLCF emissions with the complementary influence of increasing NO <sub>x</sub> and decreasing CO emissions ( [[#Stevenson--2020|Stevenson et al., 2020]] ).

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'''Table 6.7 |''' '''Summary of global OH trends and interannual variability from studies post 2010.'''

{| class="wikitable"
|-
| '''Reference'''

| '''Time Period'''

| '''OH Trends and IAV'''

| '''Approach'''

|-
| colspan="4"| Inversion and Empirical Methods Based on Observations

|-
| [[#Montzka--2011|Montzka et al. (2011)]]

| 1998–2007

| 2.3 ± 1.3% (IAV)

| 3D inversion

|-
| [[#Ciais--2013|Ciais et al. (2013)]]

| 2000s

| within ±5% (IAV)

| AR5 based on inversions

|-
| [[#McNorton--2016|McNorton et al. (2016)]]

| 1993–2011

| ±2.3% (IAV)

| Box-model inversion

|-
| [[#Rigby--2017|Rigby et al. (2017)]]

| 1980–2014

| 10% increase from the late 1990s–2004; 10% decrease from 2004–2014

| Box-model inversion

|-
| [[#Turner--2017|Turner et al. (2017)]]

| 1983–2015

| about 7% increase in 1991–2001; 7% decrease in 2003–2016

| Box-model inversion

|-
| [[#Nicely--2018|Nicely et al. (2018)]]

| 1980–2015

| 1.6% (IAV)

| Empirical reconstruction

|-
| [[#McNorton--2018|McNorton et al. (2018)]]

| 2003–2015

| 1.8 ± 0.4% decrease

| 3D inversion

|-
| [[#Naus--2019|Naus et al. (2019)]]

| 1994–2015

| 3.8 ± 3.2% increase

| Box-model inversion

|-
| [[#Patra--2021|Patra et al. (2021)]]

| 1996–2015

| 2–3% IAV, no trend

| 3D inversion

|-
| colspan="4"| Global CTMs, CCMs and ESMs

|-
| [[#John--2012|John et al. (2012)]]

| 1860–2005

1980–2000

| 6% decrease

About 3% increase

| CCM

|-
| [[#Holmes--2013|Holmes et al. (2013)]]

| 1997–2009

| 0.7–1.1% (IAV)

| Multi-model CTMs

|-
| [[#Ciais--2013|Ciais et al. (2013)]]

| 2000s

| within ±3% (IAV)

| AR5 based on CCMs

|-
| [[#Murray--2013|Murray et al. (2013)]]

| 1998–2006

| increasing trend

| 3D CTM

|-
| [[#Naik--2013|Naik et al. (2013)]]

| 1980–2000

1850–2000

| 3.5 ± 2.2 % increase

−0.6 ± 8.8%

| Multi-model CCMs/CTMs

|-
| [[#Murray--2014|Murray et al. (2014)]]

| 1770s–1990s

| 5.3% increase

| CCM

|-
| [[#Dalsøren--2016|Dalsøren et al. (2016)]]

| 1970–2012

| 8% increase

| 3D CTM

|-
| [[#Gaubert--2017|Gaubert et al. (2017)]]

| 2002–2013

| 7% increase

| CCM with assimilated satellite CO observations

|-
| [[#Zhao--2019|]] [[#Zhao--2019|]] [[#Zhao--2019|Zhao et al. (2019)]]

| 1960–2010

1980–2000

| 1.9 ± 1.2 % (IAV)

4.6 ± 2.4 % increase

| Multi-model CCMs/CTMs

|-
| [[#Stevenson--2020|Stevenson et al. (2020)]]

| 1980–2014

1850–1980

| 9% increase

no trend

| Multi-model ESMs

|}

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[[File:297c58cb2efbea12401a3db5425cecee IPCC_AR6_WGI_Figure_6_9.png]]

'''Figure 6.9 |''' '''Time evolution of global annual mean tropospheric hydroxyl (OH) over the historical period''' , '''expressed as a percentage anomaly relative to the mean over 1998–2007.''' '''(a)''' Results from three CMIP6 models, including UKESM1-0LL (green), GFDL-ESM4 (blue), and CESM2-WACCM (red), are shown; the shaded light green and light red bands show mean over multiple ensemble members for UKESM1-0LL (3) and CESM2-WACCM (3) models, respectively with the multi-model mean anomalies shown in thick black line. '''(b)''' Multi-model mean OH anomalies for the 1980–2014 period compared with those derived from observational-based inversions from Montzka et al., (2011); Rigby et al., (2017); Turner et al., (2017); Nicely et al., (2018); Naus et al., (2019); Patra et al., (2021) in the zoomed box. Further details on data sources and processing are available in the chapter data table (Table 6.SM.3).

Over paleo time scales, proxy-based observational constraints from methane and formaldehyde suggest tropospheric OH to be a factor of two to four lower in the Last Glacial Maximum (LGM) relative to pre-industrial levels, though these estimates are highly uncertain ( [[#Alexander--2015|Alexander and Mickley, 2015]] ) . Global models, in contrast, exhibit no change in tropospheric OH (and consequently in methane lifetime) at the LGM relative to the pre-industrial period (Murray et al. , 2014; Quiquet et al. , 2015) , however, the sign and magnitude of OH changes are sensitive to model predictions of changes in natural emissions, including lightning NO <sub>x</sub> and BVOCs, and model representation of isoprene oxidation chemistry ( [[#Achakulwisut--2015|Achakulwisut et al., 2015]] ; [[#Hopcroft--2017|Hopcroft et al., 2017]] ).

Regarding change since the pre-industrial era, at the time of the AR5, the ensemble mean of 17 global models participating in ACCMIP indicated little change in tropospheric OH from 1850–2000. This was due to the competing and finally offsetting changes in factors enhancing or reducing OH with a consequent small decline in methane lifetime ( [[#Naik--2013|Naik et al., 2013]] ; [[#Voulgarakis--2013|Voulgarakis et al., 2013]] ). However, there was large diversity in both the sign and magnitude of past OH changes across the individual models attributed to the disparate implementation of chemical and physical processes ( [[#Nicely--2017|Nicely et al., 2017]] ; [[#Wild--2020|Wild et al., 2020]] ). Analysis of historical simulations from three CMIP6 ESMs indicates little change in global mean OH from 1850 to about 1980 ( [[#Stevenson--2020|Stevenson et al., 2020]] ). However, there is no observational evidence of changes in global OH since 1850 up to the early 1980s to evaluate the ESMs.

In summary, global mean tropospheric OH does not show a significant trend from 1850 up to around 1980 ( ''low confidence'' ). There is conflicting information from global models constrained by emissions versus observationally constrained inversion methods over the 1980–2014 period. A positive trend since 1980 (about 9% increase over 1980–2014) is a robust feature among ESMs and CCMs and there is ''medium confidence'' that this trend is mainly driven by increases in global anthropogenic NO <sub>x</sub> emissions and decreases in CO emissions. There is ''limited evidence'' and ''medium agreement'' for positive trends or absence of trends inferred from observation-constrained methods. Overall, there is ''medium confidence'' that global mean OH has remained stable or exhibited a positive trend since the 1980s.

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