Modeling global impacts of heterogeneous loss of HO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; on cloud droplets, ice particles and aerosols

Huijnen, Vincent; Williams, Jonathan; Flemming, Johannes

doi:10.5194/acpd-14-8575-2014

Cited by 34 publications

(43 citation statements)

References 85 publications

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“…At the same time, the calculation of photolysis rates will be updated following Williams et al (2006Williams et al ( , 2012 and coupled to the simulated aerosols. Moreover, the representation of heterogeneous chemistry will be improved following Huijnen et al (2014). Another line of work focuses on improving the representation of stratospheric chemistry through simplified schemes, but this is more a long-term project.…”

Section: Discussionmentioning

confidence: 99%

Simulation of tropospheric chemistry and aerosols with the climate model EC-Earth

et al. 2014

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Abstract. We have integrated the atmospheric chemistry and transport model TM5 into the global climate model EC-Earth version 2.4. We present an overview of the TM5 model and the two-way data exchange between TM5 and the IFS model from the European Centre for Medium-Range Weather Forecasts (ECMWF), the atmospheric general circulation model of EC-Earth. In this paper we evaluate the simulation of tropospheric chemistry and aerosols in a oneway coupled configuration. We have carried out a decadal simulation for present-day conditions and calculated chemical budgets and climatologies of tracer concentrations and aerosol optical depth. For comparison we have also performed offline simulations driven by meteorological fields from ECMWF's ERA-Interim reanalysis and output from the EC-Earth model itself. Compared to the offline simulations, the online-coupled system produces more efficient vertical mixing in the troposphere, which reflects an improvement of the treatment of cumulus convection. The chemistry in the EC-Earth simulations is affected by the fact that the current version of EC-Earth produces a cold bias with too dry air in large parts of the troposphere. Compared to the ERA-Interim driven simulation, the oxidizing capacity in EC-Earth is lower in the tropics and higher in the extratropics. The atmospheric lifetime of methane in EC-Earth is 9.4 years, which is 7 % longer than the lifetime obtained with ERA-Interim but remains well within the range reported in the literature. We further evaluate the model by comparing the simulated climatologies of surface radon-222 and carbon monoxide, tropospheric and surface ozone, and aerosol optical depth against observational data. The work presented in this study is the first step in the development of EC-Earth into an Earth system model with fully interactive atmospheric chemistry and aerosols.

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Section: Discussionmentioning

confidence: 99%

Simulation of tropospheric chemistry and aerosols with the climate model EC-Earth

et al. 2014

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“…A basic initial validation of C-IFS fields can be found in Flemming et al (2015) and Inness et al (2015) and more detailed validation of C-IFS can be found in the validation reports available from http://www.copernicus-atmosphere.eu/. The chemistry scheme implemented in the C-IFS model version used for these experiments is an extended, modified version of the Carbon Bond Mechanism 5 (Yarwood et al, 2005) chemical mechanism as originally implemented in the chemistry transport model TM5 (Tracer Model 5) (Huijnen et al, 2010;Williams et al, 2013;Huijnen et al, 2014). This is a tropospheric chemistry scheme with 54 species and 126 reactions.…”

Section: Atmosmentioning

confidence: 99%

The ENSO signal in atmospheric composition fields: emission-driven versus dynamically induced changes

et al. 2015

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Abstract. The El Niño-Southern Oscillation (ENSO) not only affects meteorological fields but also has a large impact on atmospheric composition. Atmospheric composition fields from the Monitoring Atmospheric Composition and Climate (MACC) reanalysis are used to identify the ENSO signal in tropospheric ozone, carbon monoxide, nitrogen oxide and smoke aerosols, concentrating on the months October to December. During El Niño years, all of these fields have increased concentrations over maritime South East Asia in October. The MACC Composition Integrated Forecasting System (C-IFS) model is used to quantify the relative magnitude of dynamically induced and emission-driven changes in the atmospheric composition fields. While changes in tropospheric ozone are a combination of dynamically induced and emission-driven changes, the changes in carbon monoxide, nitrogen oxides and smoke aerosols are almost entirely emission-driven in the MACC model. The ozone changes continue into December, i.e. after the end of the Indonesian fire season while changes in the other fields are confined to the fire season.

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“…Convective fluxes used for tracer transport are now read from the ERA-Interim input data, instead of calculating them with the Tiedtke scheme (Tiedtke, 1989). Heterogeneous removal of N 2 O 5 on aerosol and cloud particles was updated according to Huijnen et al (2014). The CH 4 surface nudging timescale has also been adjusted.…”

Section: Model Descriptionmentioning

confidence: 99%

Can we explain the observed methane variability after the Mount Pinatubo eruption?

et al. 2016

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Abstract. The CH 4 growth rate in the atmosphere showed large variations after the Pinatubo eruption in June 1991. A decrease of more than 10 ppb yr −1 in the growth rate over the course of 1992 was reported, and a partial recovery in the following year. Although several reasons have been proposed to explain the evolution of CH 4 after the eruption, their contributions to the observed variations are not yet resolved. CH 4 is removed from the atmosphere by the reaction with tropospheric OH, which in turn is produced by O 3 photolysis under UV radiation. The CH 4 removal after the Pinatubo eruption might have been affected by changes in tropospheric UV levels due to the presence of stratospheric SO 2 and sulfate aerosols, and due to enhanced ozone depletion on Pinatubo aerosols. The perturbed climate after the eruption also altered both sources and sinks of atmospheric CH 4 . Furthermore, CH 4 concentrations were influenced by other factors of natural variability in that period, such as El Niño-Southern Oscillation (ENSO) and biomass burning events. Emissions of CO, NO X and non-methane volatile organic compounds (NMVOCs) also affected CH 4 concentrations indirectly by influencing tropospheric OH levels.Potential drivers of CH 4 variability are investigated using the TM5 global chemistry model. The contribution that each driver had to the global CH 4 variability during the period 1990 to 1995 is quantified. We find that a decrease of 8-10 ppb yr −1 CH 4 is explained by a combination of the above processes. However, the timing of the minimum growth rate is found 6-9 months later than observed. The long-term decrease in CH 4 growth rate over the period 1990 to 1995 is well captured and can be attributed to an increase in OH concentrations over this time period. Potential uncertainties in our modelled CH 4 growth rate include emissions of CH 4 from wetlands, biomass burning emissions of CH 4 and other compounds, biogenic NMVOC and the sensitivity of OH to NMVOC emission changes. Two inventories are used for CH 4 emissions from wetlands, ORCHIDEE and LPJ, to investigate the role of uncertainties in these emissions. Although the higher climate sensitivity of ORCHIDEE improves the simulated CH 4 growth rate change after Pinatubo, none of the two inventories properly captures the observed CH 4 variability in this period.

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Modeling global impacts of heterogeneous loss of HO<sub>2</sub> on cloud droplets, ice particles and aerosols

Cited by 34 publications

References 85 publications

Simulation of tropospheric chemistry and aerosols with the climate model EC-Earth

Simulation of tropospheric chemistry and aerosols with the climate model EC-Earth

The ENSO signal in atmospheric composition fields: emission-driven versus dynamically induced changes

Can we explain the observed methane variability after the Mount Pinatubo eruption?

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