Evaluation of observed and modelled aerosol lifetimes using  radioactive tracers of opportunity and an ensemble of 19 global models

Kristiansen, Nina Iren; Stohl, Andreas; Olivié, Dirk; Croft, Betty; Søvde, O. A.; Klein, Heiko; Christoudias, T.; Kunkel, Daniel; Leadbetter, Susan; Lee, Y. H.; Zhang, K.; Tsigaridis, Kostas; Bergman, Tommi; Evangeliou, Nikolaos; Wang, Hailong; Ma, Po‐Lun; Easter, Richard C.; Rasch, Philip J.; Liu, Xiaohong; Pitari, Giovanni; Genova, Glauco Di; Zhao, Shuyun; Balkanski, Yves; Bauer, Susanne E.; Faluvegi, G.; Kokkola, Harri; Martin, Randall V.; Pierce, Jeffrey R.; Schulz, Mıchael; Shindell, D. T.; Tost, H.; Zhang, H.

doi:10.5194/acp-16-3525-2016

Cited by 83 publications

(88 citation statements)

References 78 publications

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“…These wet scavenging developments were also implemented in a GEOS-Chem v9-03-01 simulation of 137 Cs (also using GEOS5 met fields) and evaluated against 137 Cs measurements taken for several weeks following the March 2011 Fukushima Daiichi nuclear power plant accident. Implementation of these scavenging revisions yielded improved agreement with the radionuclide measurements (median ratio of measured to modelled surface-layer concentrations changed from 5.53 to 0.52) and reduced efolding times from 21.8 to 13.2 days, which is close to the measurement value of 14.3 days (Kristiansen et al, 2016). These wet-removal revisions also slightly reduced the mean bias relative to measurements of the number of aerosols larger than 40 nm (N40), 80 nm (N80), and 150 nm (N150) for the same global set of 21 geographically diverse sites as described in D'Andrea et al (2013) (not shown).…”

Section: Simulations and Revisions To Model Parameterizationssupporting

confidence: 58%

Processes controlling the annual cycle of Arctic aerosol number and size distributions

et al. 2016

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Abstract. Measurements at high-Arctic sites (Alert, Nunavut, and Mt. Zeppelin, Svalbard) during the years 2011 to 2013 show a strong and similar annual cycle in aerosol number and size distributions. Each year at both sites, the number of aerosols with diameters larger than 20 nm exhibits a minimum in October and two maxima, one in spring associated with a dominant accumulation mode (particles 100 to 500 nm in diameter) and a second in summer associated with a dominant Aitken mode (particles 20 to 100 nm in diameter). Seasonal-mean aerosol effective diameter from measurements ranges from about 180 in summer to 260 nm in winter. This study interprets these annual cycles with the GEOS-Chem-TOMAS global aerosol microphysics model. Important roles are documented for several processes (new-particle formation, coagulation scavenging in clouds, scavenging by precipitation, and transport) in controlling the annual cycle in Arctic aerosol number and size.Our simulations suggest that coagulation scavenging of interstitial aerosols in clouds by aerosols that have activated to form cloud droplets strongly limits the total number of particles with diameters less than 200 nm throughout the year. We find that the minimum in total particle number in October can be explained by diminishing new-particle formation within the Arctic, limited transport of pollution from lower latitudes, and efficient wet removal. Our simulations indicate that the summertime-dominant Aitken mode is associated with efficient wet removal of accumulation-mode aerosols, which limits the condensation sink for condensable vapours. This in turn promotes new-particle formation and growth. The dominant accumulation mode during spring is associated with build up of transported pollution from outside the Arctic coupled with less-efficient wet-removal processes at colder temperatures. We recommend further attention to the key processes of new-particle formation, interstitial coagulation, and wet removal and their delicate interactions and balance in size-resolved aerosol simulations of the Arctic to reduce uncertainties in estimates of aerosol radiative effects on the Arctic climate.

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Section: Simulations and Revisions To Model Parameterizationssupporting

confidence: 58%

Processes controlling the annual cycle of Arctic aerosol number and size distributions

et al. 2016

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“…Aerosols have a lifetime of the order of days (Kristiansen et al, 2016), and the emissions are converted to forcing without an intermediate concentration step.…”

Section: Aerosolsmentioning

confidence: 99%

FAIR v1.3: a simple emissions-based impulse response and carbon cycle model

et al. 2018

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Abstract. Simple climate models can be valuable if they are able to replicate aspects of complex fully coupled earth system models. Larger ensembles can be produced, enabling a probabilistic view of future climate change. A simple emissions-based climate model, FAIR, is presented, which calculates atmospheric concentrations of greenhouse gases and effective radiative forcing (ERF) from greenhouse gases, aerosols, ozone and other agents. Model runs are constrained to observed temperature change from 1880 to 2016 and produce a range of future projections under the Representative Concentration Pathway (RCP) scenarios. The constrained estimates of equilibrium climate sensitivity (ECS), transient climate response (TCR) and transient climate response to cumulative CO 2 emissions (TCRE) are 2.86 (2.01 to 4.22) K, 1.53 (1.05 to 2.41) K and 1.40 (0.96 to 2.23) K (1000 GtC) −1 (median and 5-95 % credible intervals). These are in good agreement with the likely Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) range, noting that AR5 estimates were derived from a combination of climate models, observations and expert judgement. The ranges of future projections of temperature and ranges of estimates of ECS, TCR and TCRE are somewhat sensitive to the prior distributions of ECS/TCR parameters but less sensitive to the ERF from a doubling of CO 2 or the observational temperature dataset used to constrain the ensemble. Taking these sensitivities into account, there is no evidence to suggest that the median and credible range of observationally constrained TCR or ECS differ from climate model-derived estimates. The range of temperature projections under RCP8.5 for 2081-2100 in the constrained FAIR model ensemble is lower than the emissions-based estimate reported in AR5 by half a degree, owing to differences in forcing assumptions and ECS/TCR distributions.

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“…For example, both atmospheric transport models may simulate too short a lifetime of particulate iodine. This, as for many other models, was found for Cs-137 attached to particles after the Fukushima Dai-ichi accident (Kristiansen et al, 2016). The inversion would probably try to compensate a too strong loss of mass by increasing the emitted amount.…”

Section: Discussion Of the Source Termsmentioning

confidence: 86%

Bayesian inverse modeling and source location of an unintended <sup>131</sup>I release in Europe in the fall of 2011

et al. 2017

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Abstract. In the fall of 2011, iodine-131 (131I) was detected at several radionuclide monitoring stations in central Europe. After investigation, the International Atomic Energy Agency (IAEA) was informed by Hungarian authorities that 131I was released from the Institute of Isotopes Ltd. in Budapest, Hungary. It was reported that a total activity of 342 GBq of 131I was emitted between 8 September and 16 November 2011. In this study, we use the ambient concentration measurements of 131I to determine the location of the release as well as its magnitude and temporal variation. As the location of the release and an estimate of the source strength became eventually known, this accident represents a realistic test case for inversion models. For our source reconstruction, we use no prior knowledge. Instead, we estimate the source location and emission variation using only the available 131I measurements. Subsequently, we use the partial information about the source term available from the Hungarian authorities for validation of our results. For the source determination, we first perform backward runs of atmospheric transport models and obtain source-receptor sensitivity (SRS) matrices for each grid cell of our study domain. We use two dispersion models, FLEXPART and Hysplit, driven with meteorological analysis data from the global forecast system (GFS) and from European Centre for Medium-range Weather Forecasts (ECMWF) weather forecast models. Second, we use a recently developed inverse method, least-squares with adaptive prior covariance (LS-APC), to determine the 131I emissions and their temporal variation from the measurements and computed SRS matrices. For each grid cell of our simulation domain, we evaluate the probability that the release was generated in that cell using Bayesian model selection. The model selection procedure also provides information about the most suitable dispersion model for the source term reconstruction. Third, we select the most probable location of the release with its associated source term and perform a forward model simulation to study the consequences of the iodine release. Results of these procedures are compared with the known release location and reported information about its time variation. We find that our algorithm could successfully locate the actual release site. The estimated release period is also in agreement with the values reported by IAEA and the reported total released activity of 342 GBq is within the 99 % confidence interval of the posterior distribution of our most likely model.

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Evaluation of observed and modelled aerosol lifetimes using radioactive tracers of opportunity and an ensemble of 19 global models

Cited by 83 publications

References 78 publications

Processes controlling the annual cycle of Arctic aerosol number and size distributions

Processes controlling the annual cycle of Arctic aerosol number and size distributions

FAIR v1.3: a simple emissions-based impulse response and carbon cycle model

Bayesian inverse modeling and source location of an unintended <sup>131</sup>I release in Europe in the fall of 2011

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Evaluation of observed and modelled aerosol lifetimes using radioactive tracers of opportunity and an ensemble of 19 global models

Cited by 83 publications

References 78 publications

Processes controlling the annual cycle of Arctic aerosol number and size distributions

Processes controlling the annual cycle of Arctic aerosol number and size distributions

FAIR v1.3: a simple emissions-based impulse response and carbon cycle model

Bayesian inverse modeling and source location of an unintended &lt;sup&gt;131&lt;/sup&gt;I release in Europe in the fall of 2011

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Bayesian inverse modeling and source location of an unintended <sup>131</sup>I release in Europe in the fall of 2011