Institution
Finnish Meteorological Institute
Government•Helsinki, Finland•
About: Finnish Meteorological Institute is a government organization based out in Helsinki, Finland. It is known for research contribution in the topics: Aerosol & Snow. The organization has 1367 authors who have published 6120 publications receiving 228339 citations. The organization is also known as: Ilmatieteen laitos & Meteorologiska institutet.
Topics: Aerosol, Snow, Solar wind, Environmental science, Magnetosphere
Papers published on a yearly basis
Papers
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University of Colorado Boulder1, Carnegie Mellon University2, Paul Scherrer Institute3, University at Albany, SUNY4, University of California, Berkeley5, Swiss Federal Laboratories for Materials Science and Technology6, University of California, Davis7, State University of New York System8, University of Eastern Finland9, Finnish Meteorological Institute10, University of Helsinki11, Stockholm University12, Texas A&M University13, Max Planck Society14, University of Tokyo15, University of New Hampshire16, National Oceanic and Atmospheric Administration17
TL;DR: A unifying model framework describing the atmospheric evolution of OA that is constrained by high–time-resolution measurements of its composition, volatility, and oxidation state is presented, which can serve as a basis for improving parameterizations in regional and global models.
Abstract: Organic aerosol (OA) particles affect climate forcing and human health, but their sources and evolution remain poorly characterized. We present a unifying model framework describing the atmospheric evolution of OA that is constrained by high-time-resolution measurements of its composition, volatility, and oxidation state. OA and OA precursor gases evolve by becoming increasingly oxidized, less volatile, and more hygroscopic, leading to the formation of oxygenated organic aerosol (OOA), with concentrations comparable to those of sulfate aerosol throughout the Northern Hemisphere. Our model framework captures the dynamic aging behavior observed in both the atmosphere and laboratory: It can serve as a basis for improving parameterizations in regional and global models.
3,104 citations
Potsdam Institute for Climate Impact Research1, University of Bayreuth2, University of California, Berkeley3, Institut national de la recherche agronomique4, Dresden University of Technology5, Max Planck Society6, ETH Zurich7, South Dakota State University8, Academy of Sciences of the Czech Republic9, Finnish Forest Research Institute10, Finnish Meteorological Institute11, Oak Ridge National Laboratory12, Centre national de la recherche scientifique13, University of Helsinki14, Weizmann Institute of Science15
TL;DR: In this paper, the authors analyse the effect of extrapolation of night-time values of ecosystem respiration into the daytime; this is usually done with a temperature response function that is derived from long-term data sets.
Abstract: This paper discusses the advantages and disadvantages of the different methods that separate net ecosystem exchange (NEE) into its major components, gross ecosystem carbon uptake (GEP) and ecosystem respiration (Reco). In particular, we analyse the effect of the extrapolation of night-time values of ecosystem respiration into the daytime; this is usually done with a temperature response function that is derived from long-term data sets. For this analysis, we used 16 one-year-long data sets of carbon dioxide exchange measurements from European and US-American eddy covariance networks. These sites span from the boreal to Mediterranean climates, and include deciduous and evergreen forest, scrubland and crop ecosystems. We show that the temperature sensitivity of Reco, derived from long-term (annual) data sets, does not reflect the short-term temperature sensitivity that is effective when extrapolating from night- to daytime. Specifically, in summer active ecosystems the long
2,881 citations
TL;DR: An adaptive Metropolis (AM) algorithm, where the Gaussian proposal distribution is updated along the process using the full information cumulated so far, which establishes here that it has the correct ergodic properties.
Abstract: A proper choice of a proposal distribution for Markov chain Monte Carlo methods, for example for the Metropolis-Hastings algorithm, is well known to be a crucial factor for the convergence of the algorithm. In this paper we introduce an adaptive Metropolis (AM) algorithm, where the Gaussian proposal distribution is updated along the process using the full information cumulated so far. Due to the adaptive nature of the process, the AM algorithm is non-Markovian, but we establish here that it has the correct ergodic properties. We also include the results of our numerical tests, which indicate that the AM algorithm competes well with traditional Metropolis-Hastings algorithms, and demonstrate that the AM algorithm is easy to use in practical computation.
2,511 citations
Technische Universität München1, Central Institution for Meteorology and Geodynamics2, University of Tartu3, Swedish Museum of Natural History4, University of Latvia5, Humboldt University of Berlin6, University of Ljubljana7, MeteoSwiss8, Trinity College, Dublin9, Autonomous University of Barcelona10, Norwegian University of Life Sciences11, Norwegian Meteorological Institute12, Finnish Meteorological Institute13, Czech Hydrometeorological Institute14, Environment Agency15, Wageningen University and Research Centre16, University of Oslo17
TL;DR: In this article, the authors used an enormous systematic phenological network data set of more than 125 000 observational series of 542 plant and 19 animal species in 21 European countries (1971-2000) and concluded that previously published results of phenological changes were not biased by reporting or publication predisposition.
Abstract: Global climate change impacts can already be tracked in many physical and biological systems; in particular, terrestrial ecosystems provide a consistent picture of observed changes. One of the preferred indicators is phenology, the science of natural recurring events, as their recorded dates provide a high-temporal resolution of ongoing changes. Thus, numerous analyses have demonstrated an earlier onset of spring events for mid and higher latitudes and a lengthening of the growing season. However, published single-site or single-species studies are particularly open to suspicion of being biased towards predominantly reporting climate change-induced impacts. No comprehensive study or meta-analysis has so far examined the possible lack of evidence for changes or shifts at sites where no temperature change is observed. We used an enormous systematic phenological network data set of more than 125 000 observational series of 542 plant and 19 animal species in 21 European countries (1971–2000). Our results showed that 78% of all leafing, flowering and fruiting records advanced (30% significantly) and only 3% were significantly delayed, whereas the signal of leaf colouring/fall is ambiguous. We conclude that previously published results of phenological changes were not biased by reporting or publication predisposition: the average advance of spring/summer was 2.5 days decade � 1 in Europe. Our analysis of 254 mean national time series undoubtedly demonstrates that species’ phenology is responsive to temperature of the preceding
2,457 citations
TL;DR: In this paper, the formation rate of 3-nm particles is often in the range 0.01-10 cm −3 s −1 in the boundary layer in urban areas and in coastal areas and industrial plumes.
2,028 citations
Authors
Showing all 1489 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| Markku Kulmala | 142 | 1487 | 85179 |
| Michael Schmitt | 134 | 2007 | 114667 |
| Douglas R. Worsnop | 99 | 408 | 41676 |
| Timo Vesala | 86 | 491 | 43130 |
| Jean-Loup Bertaux | 82 | 410 | 24131 |
| Tuukka Petäjä | 82 | 526 | 30572 |
| D. R. Worsnop | 78 | 203 | 20938 |
| Ari Laaksonen | 77 | 345 | 24124 |
| Jouni J. K. Jaakkola | 74 | 318 | 16534 |
| Risto Hillamo | 69 | 284 | 13591 |
| Ray Leuning | 64 | 131 | 19297 |
| Veli-Matti Kerminen | 64 | 269 | 15754 |
| Harald U. Frey | 59 | 340 | 11958 |
| V. A. Sergeev | 56 | 213 | 9997 |
| Kari E. J. Lehtinen | 56 | 281 | 13774 |