Simulating age of air and the distribution of SF6_{6} in the stratosphere with the SILAM model

he paper presents a comparative study of age of air (AoA) derived from several approaches: a widely used passive-tracer accumulation method, the SF6 accumulation, and a direct calculation of an ideal-age tracer. The simulations were performed with the Eulerian chemistry transport model SILAM driven with the ERA-Interim reanalysis for 1980–2018. The Eulerian environment allowed for simultaneous application of several approaches within the same simulation and interpretation of the obtained differences. A series of sensitivity simulations revealed the role of the vertical profile of turbulent diffusion in the stratosphere, destruction of SF$_{6}$ in the mesosphere, and the effect of gravitational separation of gases with strongly different molar masses. The simulations reproduced well the main features of the SF$_{6}$ distribution in the atmosphere observed by the MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) satellite instrument. It was shown that the apparent very old air in the upper stratosphere derived from the SF$_{6}$ profile observations is a result of destruction and gravitational separation of this gas in the upper stratosphere and the mesosphere. These processes make the apparent SF$_{6}$ AoA in the stratosphere several years older than the ideal-age AoA, which, according to our calculations, does not exceed 6–6.5 years. The destruction of SF$_{6}$ and the varying rate of emission make SF$_{6}$ unsuitable for reliably deriving AoA or its trends. However, observations of SF$_{6}$ provide a very useful dataset for validation of the stratospheric circulation in a model with the properly implemented SF$_{6}$ loss

Towards the operational estimation of a radiological plume using data assimilation after a radiological accidental atmospheric release

International audienceIn the event of an accidental atmospheric release of radionuclides from a nuclear power plant, accurate real-time forecasting of the activity concentrations of radionuclides is required by the decision makers for the preparation of adequate countermeasures. The accuracy of the forecast plume is highly dependent on the source term estimation. On several academic test cases, including real data, inverse modelling and data assimilation techniques were proven to help in the assessment of the source term. In this paper, a semi-automatic method is proposed for the sequential reconstruction of the plume, by implementing a sequential data assimilation algorithm based on inverse modelling, with a care to develop realistic methods for operational risk agencies. The performance of the assimilation scheme has been assessed through the intercomparison between French and Finnish frameworks. Two dispersion models have been used: Polair3D and Silam developed in two different research centres. Different release locations, as well as different meteorological situations are tested. The existing and newly planned surveillance networks are used and realistically large multiplicative observational errors are assumed. The inverse modelling scheme accounts for strong error bias encountered with such errors. The efficiency of the data assimilation system is tested via statistical indicators. For France and Finland, the average performance of the data assimilation system is strong. However there are outlying situations where the inversion fails because of a too poor observability. In addition, in the case where the power plant responsible for the accidental release is not known, robust statistical tools are developed and tested to discriminate candidate release sites

Towards the operational application of inverse modelling for the source identification and plume forecast of an accidental release of radionuclides

International audienceIn the event of an accidental atmospheric release of radionuclides from a nuclear power plant, accurate real-time forecasting of the activity concentrations of radionuclides is required by the decision makers for the preparation of adequate countermeasures. Yet, the accuracy of the forecast plume is highly dependent on the source term estimation. Inverse modelling and data assimilation techniques should help in that respect. In this presentation, a semi-automatic method is proposed for the sequential reconstruction of the plume, by implementing a sequential data assimilation algorithm based on inverse modelling, with a care to develop realistic methods for operational risk agencies. The performance of the assimilation scheme has been assessed through the intercomparison between French and Finnish frameworks. Three dispersion models have been used: Polair3D, with or without plume-in-grid, both developed at CEREA, and SILAM, developed at FMI. Different release locations, as well as different meteorological situations are tested. The existing and newly planned surveillance networks are used and realistically large observational errors are assumed. Statistical indicators to evaluate the efficiency of the method are presented and the results are discussed. In addition, in the case where the power plant responsible for the accidental release is not known, robust statistical tools aredeveloped and tested to discriminate candidate release sites

Evaluation of the performance of four chemical transport models in predicting the aerosol chemical composition in Europe in 2005

© Author(s) 2016.Four regional chemistry transport models were applied to simulate the concentration and composition of particulate matter (PM) in Europe for 2005 with horizontal resolution 20 km. The modelled concentrations were compared with the measurements of PM chemical composition by the European Monitoring and Evaluation Programme (EMEP) monitoring network. All models systematically underestimated PM10 and PM2:5 by 10–60 %, depending on the model and the season of the year, when the calculated dry PM mass was compared with the measurements. The average water content at laboratory conditions was estimated between 5 and 20% for PM2:5 and between 10 and 25% for PM10. For majority of the PM chemical components, the relative underestimation was smaller than it was for total PM, exceptions being the carbonaceous particles and mineral dust. Some species, such as sea salt and NO3, were overpredicted by the models. There were notable differences between the models’ predictions of the seasonal variations of PM, mainly attributable to different treatments or omission of some source categories and aerosol processes. Benzo(a)pyrene concentrations were overestimated by all the models over the whole year. The study stresses the importance of improving the models’ skill in simulating mineral dust and carbonaceous compounds, necessity for high-quality emissions from wildland fires, as well as the need for an explicit consideration of aerosol water content in model–measurement comparison.Peer reviewedFinal Published versio

Representativeness of point-wise phenological Betula data collected in different parts of Europe

Peer reviewe

The Northern Eurasia Earth Science Partnership: An Example of Science Applied to Societal Needs

Northern Eurasia, the largest landmass in the northern extratropics, accounts for ~20% of the global land area. However, little is known about how the biogeochemical cycles, energy and water cycles, and human activities specific to this carbon-rich, cold region interact with global climate. A major concern is that changes in the distribution of land-based life, as well as its interactions with the environment, may lead to a self-reinforcing cycle of accelerated regional and global warming. With this as its motivation, the Northern Eurasian Earth Science Partnership Initiative (NEESPI) was formed in 2004 to better understand and quantify feedbacks between northern Eurasian and global climates. The first group of NEESPI projects has mostly focused on assembling regional databases, organizing improved environmental monitoring of the region, and studying individual environmental processes. That was a starting point to addressing emerging challenges in the region related to rapidly and simultaneously changing climate, environmental, and societal systems. More recently, the NEESPI research focus has been moving toward integrative studies, including the development of modeling capabilities to project the future state of climate, environment, and societies in the NEESPI domain. This effort will require a high level of integration of observation programs, process studies, and modeling across disciplines

Ilmansaasteiden haittakustannusmalli Suomelle (IHKU)

Ilmansaasteiden haittakustannusmalli Suomelle (IHKU) –hankkeen tavoitteena oli arvioida kotimaisten ilmansaastepäästöjen vaikutuksia pienhiukkaspitoisuuksiin ja niistä aiheutuvien terveyshaittojen kustannuksiin. Laskentamalliin sisällytettiin ilmansaasteiden ympäristöhaitoista vain pienhiukkaspitoisuuksien vaikutus ihmisten terveyteen, sillä pienhiukkaset on merkittävin ihmisten terveyteen vaikuttava ilman epäpuhtaus, ja sen arviointiin on olemassa vakiintuneet laskentamenetelmät. Muita haittoja ja aiempia vastaavia tutkimuksia on tarkasteltu kirjallisuuskatsauksessa. IHKU-malli on ensimmäinen koko Suomelle tehty, tärkeimmät ilmansaasteet kattava kehikko haittakustannusten arvioitiin. Hanke toteutettiin yhteistyössä Suomen ympäristökeskuksen (SYKE), Ilmatieteen laitoksen (IL) ja Terveyden ja hyvinvoinninlaitoksen (THL) kanssa. Kokonaisuudesta vastasi SYKE. Tässä raportissa esitetään mallinnusmenetelmät, joiden avulla on laskettu hankkeen keskeinen tulos: asiantuntijakäyttöön tarkoitettu haittakustannusmalli. Malli antaa keskiarvioistetut yksikkökustannukset valittujen ilmansaasteiden aiheuttamille terveyshaitoille päästökorkeudesta ja –sijainnista riippuen. Helppokäyttöistä mallia voi hyödyntää esimerkiksi ilmansuojelustrategioita suunniteltaessa ja erilaisten toimenpiteiden kustannustehokkuutta vertailtaessa. Raportissa on myös annettu esimerkkejä mahdollisista sovelluskohteista ja tulosten tulkinnasta. Yksikkökustannukset kotimaisten päästöjen terveyshaitoille ovat suuruusluokaltaan verrattavissa muissa maissa saatuihin tuloksiin, ja ne osoittavat että päästövähennyksistä on mahdollista saada merkittävää rahallista hyötyä myös Suomessa, jossa hengitysilman pienhiukkaspitoisuudet ovat verrattain alhaisi

Ilmansaasteiden haittakustannusmalli Suomelle (IHKU)

Ilmansaasteiden haittakustannusmalli Suomelle (IHKU) –hankkeen tavoitteena oli arvioida kotimaisten ilmansaastepäästöjen vaikutuksia pienhiukkaspitoisuuksiin ja niistä aiheutuvien terveyshaittojen kustannuksiin. Laskentamalliin sisällytettiin ilmansaasteiden ympäristöhaitoista vain pienhiukkaspitoisuuksien vaikutus ihmisten terveyteen, sillä pienhiukkaset on merkittävin ihmisten terveyteen vaikuttava ilman epäpuhtaus, ja sen arviointiin on olemassa vakiintuneet laskentamenetelmät. Muita haittoja ja aiempia vastaavia tutkimuksia on tarkasteltu kirjallisuuskatsauksessa. IHKU-malli on ensimmäinen koko Suomelle tehty, tärkeimmät ilmansaasteet kattava kehikko haittakustannusten arvioitiin. Hanke toteutettiin yhteistyössä Suomen ympäristökeskuksen (SYKE), Ilmatieteen laitoksen (IL) ja Terveyden ja hyvinvoinninlaitoksen (THL) kanssa. Kokonaisuudesta vastasi SYKE. Tässä raportissa esitetään mallinnusmenetelmät, joiden avulla on laskettu hankkeen keskeinen tulos: asiantuntijakäyttöön tarkoitettu haittakustannusmalli. Malli antaa keskiarvioistetut yksikkökustannukset valittujen ilmansaasteiden aiheuttamille terveyshaitoille päästökorkeudesta ja –sijainnista riippuen. Helppokäyttöistä mallia voi hyödyntää esimerkiksi ilmansuojelustrategioita suunniteltaessa ja erilaisten toimenpiteiden kustannustehokkuutta vertailtaessa. Raportissa on myös annettu esimerkkejä mahdollisista sovelluskohteista ja tulosten tulkinnasta. Yksikkökustannukset kotimaisten päästöjen terveyshaitoille ovat suuruusluokaltaan verrattavissa muissa maissa saatuihin tuloksiin, ja ne osoittavat että päästövähennyksistä on mahdollista saada merkittävää rahallista hyötyä myös Suomessa, jossa hengitysilman pienhiukkaspitoisuudet ovat verrattain alhaisi