Henrik Svensmark’s research while affiliated with Technical University of Denmark and other places

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Publications (77)


Top panel (a) The effective supersaturation Seff as a function of N1% based on airborne observations (black circles from Hudson and Noble (2014)). The solid, dashed black line is a linear regression to the data. The dashed lines are 90% confidence intervals (see Methods). The colored circular symbols are modeled maximum supersaturation in a cloud as a function of N1% for five updraft velocities. The Middle panel (b) Modeled droplet number density Nc activated at a given vertical velocity as a function of N1%, a measure of the total number of cloud condensation nuclei at 1% supersaturation, is shown as the colored circular symbols. Black circular points are observations of Hudson and Noble (2014). Bottom panel (c) is the critical activation diameter dcritical as a function of N1%. The colored circular symbols are the modeled critical diameters as a function of vertical velocity and N1%. Circular black symbols are Köhler theory applied to the measured supersaturation as a function of N1%. The solid, dashed black line is a linear regression to the data. The dashed lines are 90% confidence intervals (see Methods).
Liquid cloud droplet concentration based on Moderate Resolution Imaging Spectroradiometer observations of optical thickness and liquid water path averaged over the period 2003.1.3–2021.12.31. The average droplet density is Naverage = 58.9 cm⁻³, which agrees with previous estimates (Grosvenor et al., 2018).
Global distribution of critical cloud condensation nuclei (CCN) sizes and supersaturation in low marine clouds. (a) Map of the critical CCN diameter based on the regression line in Figure 1 that relates the cloud droplet concentration to cloud supersaturation, and finally Köhler theory, Equation 7, for the critical CCN diameter (mean diameter 24.9 nm). (b) A map of the corresponding effective supersaturation (mean supersaturation 1.25%). (c) As (a) but using the lower bound of the 90% confidence interval to give the critical CCN's maximal sizes (mean diameter 40.2 nm), and (d) The corresponding supersaturation (mean supersaturation 0.6%). Using κ = 0.72 for clean marine air with a cloud fraction threshold is Cf > 0.9.
Panel (a) The number density of cloud condensation nuclei (CCN) measured at a supersaturation of 0.5% based on observations from the Atmospheric Tomography Mission (ATom) as a function of the altitude of measurements where the humidity (measured humidity (RH)) > 0%. The red points are averages based on binned altitude in intervals of 2 km. Panel (b) is the same as (a), but for RH > 75%. Panel (c) displays the longitude and latitude of the ATom flight passes. Panel (d) shows the average CCN number density (1–9 km) as a function of RH at supersaturation 1%, 0.5%, 0.2%, and 0.1%. The green diamond symbols are measurements for which the humidity is larger than an RH threshold (given by the value on the RH‐axis), and the orange triangular symbols are measurements where the RH is a binned interval of 10%.
Supersaturation and Critical Size of Cloud Condensation Nuclei in Marine Stratus Clouds
  • Article
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April 2024

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9 Citations

Henrik Svensmark

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Plain Language Summary Clouds in Earth's atmosphere are of fundamental importance for the climate by regulating the reflection of sunlight into space and interacting with thermal radiation from Earth. Clouds form when moist air ascends and gets supersaturated with water vapor that condenses on aerosol particles of sufficient sizes, which then grow into cloud droplets. The aerosol number‐density and size spectrum influence the resulting cloud properties, and the supersaturation determines which aerosols can be activated into cloud drops. Here, we show that the supersaturation in marine liquid clouds is significantly higher than in the conventional view. As a consequence, much smaller aerosols can serve as cloud condensation nuclei. This can make cloud formation more sensitive to changes in aerosol properties than previously thought. Such a result should be of general interest and lead to a better understanding of aerosol‐cloud interactions, which presently constitute the largest uncertainty in our understanding of climate.

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Three curves illustrate the temporal evolution of the diversity of Phanerozoic marine animal genera. The yellow top curve is from Alroy et al. (2008) (offset +200 on the y‐axis) and depicts the genus‐level marine invertebrate. The middle diversity curve (brown) is major marine animals (offset by +250), and finally, the bottom (green) diversity curve is all marine animals (see section 2: materials and methods). The two bottom curves were calculated using “shareholder‐quorum‐subsampling” (SQS) with q=0.5$$ q=0.5 $$. The error bars are one‐sigma uncertainties. Abbreviations for geological periods are Cm, Cambrian; O, Ordovician; S, Silurian; D, Devonian; C, Carboniferous; P, Permian; Tr, Triassic; J, Jurassic; K, Cretaceous; Pg, Palaeogene; Ng, Neogene.
Change in the fractional shallow marine area as a function of time during the last 400 Ma. The black dashed curve is based on the Exxon on‐lap reconstruction of sea level (Haq et al., 1987; Haq & Schutter, 2008; see text). Paleogeographic maps (Cao et al., 2017) give the global fractional shallow marine area (black solid curve), the fractional shallow marine area of the northern (southern) hemisphere, and brown (yellow) curves, and finally, the fractional shallow marine area of the tropical region (green curve).
Variation in relative supernova frequency using three open cluster catalogues. (1) WEBDA catalogue (273 clusters with distance from solar system ≤850 pc and age ≤ 520 Myr). The DIAS (Dias et al., 2010) catalogue (224 clusters with a distance of 850 pc and age ≤ 520 Myr), and finally, the Kharchenko et al. catalogue (Kharchenko et al., 2005; 258 clusters with a distance ≤850 pc and age ≤ 520 Myr). The black curve is based on the average of the three catalogues. The gray band is one σ$$ \sigma $$ uncertainty, random normal distribution (gray band), or a random Poisson distribution (light gray band; Svensmark, 2012; Provide details on the uncertainties).
Variations in relative supernova history compared with genera of major marine animal groups. The black curve is based on the supernova rates and is given by Equation (4). The brown and light green curves show major marine animal genera normalized with the area of shallow marine margins based on Haq et al. (1987), Haq & Schutter (2008) and Cao et al. (2017), respectively. The dark green curve is based on the marine invertebrate genera‐level diversity curve of Alroy et al. (2008). Finally, the dark green curve is all marine animals normalized with the area of shallow marine margins based on Haq et al. (1987) and Haq & Schutter (2008). The exponent used in Equation (6) is α=0.8$$ \alpha =0.8 $$. The gray area is the 1−σ$$ \sigma $$ variance of the supernovae calculated from a Monte Carlo simulation. The error bars on the genera curves show a minimum 1−σ$$ \sigma $$ uncertainty since an error estimate is unavailable for the areas.
(a) Genera‐level diversity curves for major marine taxonomic groups during the last 400 Ma for different parts of the Earth. The black curve is global, the brown curve is for the northern hemisphere, the yellow curve is for the southern hemisphere, and the green curve is for the tropics. Error bars are 1−σ$$ \sigma $$ uncertainty. (b) Same data as in (a) but normalized with the change in areas shown in Figure 2. Notice that the curves converge towards a common variation resembling the change in supernovae. Error bars are 1−σ$$ \sigma $$ uncertainty. The gray band is an outline of the supernova frequency (see Figure 3).
A persistent influence of supernovae on biodiversity over the Phanerozoic

March 2023

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354 Reads

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3 Citations

It is an open question what has constrained macroevolutionary changes in marine animal diversity on the time scale of the Phanerozoic. Here, we will show that supernovae appear to have significantly influenced the biodiversity of life. After normalizing diversity curves of major animal marine genera by the changes in the area of shallow marine margins, a close correlation between supernovae frequency and biodiversity is obtained. The interpretation is that supernovae influence Earth's climate, which controls the ocean and atmospheric circulation of nutrients. With this, supernovae influence ocean bioproductivity and are speculated to affect genera-level diversity. The implication is a surprisingly influential role of stellar processes on evolution.


The underlying reconstructions used in the present analysis. The geochemical reconstruction²⁹ is depicted with a solid line, while the lithological reconstruction⁴⁴ is depicted with a dashed line. Note that whereas some of the gross features are similar, a notable difference is the extreme geochemically based reconstructed temperatures derived for the early Phanerozoic.
The Scotese et al.⁴⁴ reconstruction (dashed) is based on lithological data on long‐time scales and oxygen isotope data for medium‐time scales (10–20 Ma). The lithological/geochemical combined reconstruction we use here (i.e., Equation 1) is the solid line. The difference between the nondetrended isotopic reconstruction of Song et al.²⁹ and our combined reconstruction is plotted with the dash‐dotted line. It depicts the systematic secular offset in isotope data discussed above. Without this correction, the data taken at face value would require unrealistically hot temperatures for the ancient oceans.²⁹ Note that the systematic correction of −4°C per 1‰ δ¹⁸O suggested by Veizer and Prokoph²⁸ for the oxygen isotope record of carbonate shells (dotted) is practically identical to the one employed here and based mostly on phosphatic shells.
Phanerozoic average global temperature. Plotted are the geochemical/lithological reconstruction of Scotese et al.⁴⁴ (dotted) and combined geochemical/lithological reconstruction (this study, solid), as well as the modeled temperature (dash‐dotted, green). The additional graphs are the different components in the model: atmospheric ionization (bottom, dashed), CO2 (dash–double‐dotted), and increasing solar luminosity (dash‐dotted, purple). The shaded regions are 1‐σ and 95% confidence error regions.
Distribution of model parameter pairs when the rest are marginalized. Dashed and shaded contours are based on the Scotese model and combined data of this study, respectively. Top left: The amplitude and period of the fast oscillation (presumably the vertical motion of the solar system). Top right: The amplitude and period of the slow oscillation (presumably the spiral arm passages). Bottom left: The amplitude and period of the secondary period modulation of the fast oscillation (presumably due to radial epicyclic motion of the solar system in the galaxy). Bottom right: ∆T×2 is the climate sensitivity to changes in CO2, and λ≡αΔT×2$\lambda \equiv \alpha \Delta {T_{ \times 2}}$, which is the sensitivity of the global temperature to the solar luminosity increase (see text).
Left: The distribution of the radiative forcing of CO2 doubling and climate sensitivity to CO2 doubling, mostly constrained by the model fit to both the solar brightening and CO2 variations. Right: The likelihood of the climate sensitivity (∆T×2) and the CO2 bias parameter (∆Tbias, which quantifies the systematic bias that CO2 has on the δ¹⁸O‐based temperature) when other model parameters are marginalized. The dashed line corresponds to a bias for which the pH and temperature effects on δ¹⁸O cancel out to give no CO2/δ¹⁸O correlation.
The Phanerozoic climate

November 2022

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948 Reads

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10 Citations

We review the long‐term climate variations during the last 540 million years (Phanerozoic Eon). We begin with a short summary of the relevant geological and geochemical datasets available for the reconstruction of long‐term climate variations. We then explore the main drivers of climate that appear to explain a large fraction of these climatic oscillations. The first is the long‐term trend in atmospheric CO2 due to geological processes, while the second is the atmospheric ionization due to the changing galactic environment. Other drivers, such as albedo and geographic effects, are of secondary importance. In this review, we pay particular attention to problems that may affect the measurements of temperature obtained from oxygen isotopes, such as the long‐term changes in the concentration of δ¹⁸O seawater.


Effects of Forbush decreases on clouds determined from PATMOS-x

March 2022

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146 Reads

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6 Citations

Journal of Atmospheric and Solar-Terrestrial Physics

This study examines the relationship between cosmic rays and clouds during Forbush decreases (FDs) to understand the cause-effect relationships between cloud microphysics, cloud condensation nuclei (CCN), and ionisation in the atmosphere. The r e s u l t s of a Monte Carlo analysis of cloud parameters during FDs, which were obtained using newly calibrated satellite data (Pathfinder Atmospheres Extended (PATMOS-x)) from 1978 to 2018, show the connections between some cloud parameters and FDs. For context, FD is the event where the number of cosmic rays arriving in the atmosphere decreases and recovers over several days. Other studies have shown that FDs impacted the cloud fraction, aerosol optical depth, CCN, water content, and cloud effective radius (reff) in the atmosphere. Using the Monte Carlo analysis, nine atmospheric parameters from the dataset were evaluated and exhibited a significant response to FDs. Each added FD event (after the first event) reduces the noise, but only the strongest events add a significant signal (exceptionally when the 2nd and 5th rank FD data are added, the signal/noise ration dropped due to a change in the satellite version). We found that cloud fraction shows statistically significant signals following FDs at an achieved significance level of 0.33%. Cloud emissivity also showed highly significant signals from the analysis; however, these cannot be classified as physical causes of FDs since the response starts a week before the FDs. In contrast, the cloud optical depth, integrated total cloud water over the entire column, and reff did not show any significant signals in the frameworks of the applied methods. The top of the atmosphere brightness temperatures (TABTs) at nominal wavelengths of 3.75, 11.0, and 12.0 μm were analysed again along with surface BTs and showed significant signals. The estimated changes in the BT were determined using a radiative transfer model (Fu-Liou model) and showed consistent results with the observed changes in cloud parameters during FD events. Among the analysed several atmospheric/cloud/aerosol parameters, cloud fraction and TABT at nominal wavelengths of 3.75, 11.0, and 12.0 μm are the only parameters depicting a statistically significant and correct-phase response to FDs.


Fig. 1. The reaction chamber, made of electropolished stainless steel. The front end shows the quartz windows -the collimator and UV lamps are placed in front of those. The back end contains various inlets, in addition to those shown on the side and top of the chamber.
Sulphuric acid aerosols in low oxygen environments

January 2022

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86 Reads

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1 Citation

Journal of Aerosol Science

Experiments on sulphuric acid nucleation in low oxygen atmospheres were done in order to investigate the role of nucleation in the Archean atmosphere. Nu-cleation initiated by photolysis of SO2 and subsequent reaction between atomic O and SO2 was measured with a PSM and a separate CPC. The parameters were <10 ppm O2 with varying levels of SO2 (4 levels from 40 to 105 ppb), RH (3 levels from 0 to 51%), UV light (254 nm, 4 levels from 55 to 100% power), and ionization (2 levels: Background (∼3 cm⁻³ s⁻¹) and increased w. gamma sources (∼42 cm⁻³ s⁻¹)). We find that nucleation is possible under these condi-tions and that the measured formation rates correlate positively with all varied parameters. This suggests that the sulphuric acid nucleation system could have played a role in the Archean atmosphere.


Variation in relative supernova frequency using three open cluster catalogs. WEBDA catalog (273 clusters with distance from solar system ≤850 pc and age ≤500 Myr) (cyan curve). The green curve is the Dias et al. (2010) catalog (224 clusters with distance ≤850 pc and age ≤500 Myr), and finally, the blue curve is the Kharchenko et al. (2005) catalog (258 clusters with distance ≤850 pc and age ≤500 Myr). The black curve is the average of the three catalogs. The gray bands are 1σ uncertainty, random normal distribution (dark gray band), or a Poisson distribution (light gray band).
Top panel: Reconstructed relative cosmic ray intensity over the last 3,500 Ma. Based on star formation data Rocha‐Pinto et al. (2000) and Svensmark (2006a), changes in solar evolution and open cluster data Svensmark (2012) (see text). Bottom panel: Fraction of carbon buried as organic matter in the sediments over the last 3,500 Myr reconstructed from Equation 1. The gray band is one sigma uncertainty. Colored bar at the top of figure shows major glaciations (blue) and geological time subdivision in eons.
Variation in climate over the last 500 Myr estimated by changes in δ¹⁸O, gray circular points, and an 11 Myr average (red curve), together with an estimate based on lithological data (Scotese et al. (2021), orange curve). Note that the temperature curves are inverted. The dark red curve show changes in temperature at the transition between the Permian and Triassic period (The gray bar on the right‐hand scale show the present‐day variations in equatorial temperatures). Note the relative good agreement with changes in supernova frequency in the solar neighborhood, blue curve, which is the mean of the three data curves in Figure 1. The gray bands are 1σ uncertainty in supernovae frequency). The colored band at the top of the figure indicates climatic warm periods (orange), cold periods (blue), glacial periods (white and blue hatched bars), and finally peak glaciations (black and white hatched bars) (Svensmark, 2012). The middle panel gives an estimate of the ice volume during glacial periods. The bottom panel is a histogram showing the evolution of the temperature gradient between polar regions and the equator based on Boucot and Gray (2001). Abbreviations for geological periods are Cm Cambrian, O Ordovician, S Silurian, D Devonian, C Carboniferous, P Permian, Tr Triassic, J Jurassic, K Cretaceous, Pg Palaeogene, Ng Neogene.
Changes in burial fraction f of organic matter over the past 500 Myr based on organic and inorganic carbon‐13 in sediments (δ¹³C in parts per mill) and shown by the scattered points, and compared here with the variations in the local supernova rates (blue curve. The gray band are 1σ uncertainty, see Figure 1). δ¹³C in marine carbonates: Cambrian (not labeled) to Carboniferous C (Saltzman, 2005), Permian P (Grossman et al., 2008; Korte et al., 2005), Permo‐Triassic transition P‐Tr (Kakuwa & Matsumoto, 2006), Triassic T (Korte et al., 2005), Jurassic J to Cretaceous K (Emeis & Weissert, 2009) and Cretaceous K (Jarvis et al., 2006) and Cretaceous to present (Katz et al., 2005). Organic δ¹³C Cambrian to Jurassic (Shields & Veizer, 2002), Jurassic J to Neogene Ng (Falkowski et al., 2005) (For a collection of δ¹³C data see Saltzman & Thomas, 2012). The red line is a smoothing of the burial fraction. The plot starts at −510 Myr.
Average logarithmic variations of trace elements (Mn, Co, Ni, Cu, Zn, As, Se, Mo, Ag, Cd, Sb, Te, Tl, Pb, Bi) in pyrite as proxy for nutrient availability in the oceans (large blue points are average 50% percentile together with average 25% and 75% percentiles). Blue curve is the change in supernovae rate and is the mean of the three data curves in Figure 1. The gray bands are 1σ uncertainty in supernovae frequency. The colored band at the top of the figure indicates climatic warm periods (orange), cold periods (blue), glacial periods (white and blue hatched bars), and finally peak glaciations (black and white hatched bars). Abbreviations for geological periods are Cm Cambrian, O Ordovician, S Silurian, D Devonian, C Carboniferous, P Permian, Tr Triassic, J Jurassic, K Cretaceous, Pg Palaeogene, Ng Neogene.
Supernova Rates and Burial of Organic Matter

January 2022

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541 Reads

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11 Citations

Plain Language Summary The study proposes a surprising link between the burial of organic matter in sediments and stellar processes. The paper has two components; the first component concerns empirical evidence: A close correlation between the fraction of organic matter buried in sediments and changes in supernovae frequency. This correlation is evident during the last 3.5 billion years (see Figure 2) and in close detail over the previous 500 Myr (see Figure 4). All astrophysical data, for example, changes in star formation and the inferred changes in supernovae frequency, are based on peer‐reviewed works, as is the carbon 13 data. The second component of the paper gives a possible justification for the observed correlations. The assumption is that changes in supernovae frequency result in climate change, which changes the mixing in oceans and river runoff and ultimately influences nutrient availability—a high nutrient concentration results in a larger bioproductivity and a larger burial of organic matter in sediments. Again empirical evidence for this connection comes from concentrations of trace elements in pyrite (a proxy of ocean nutrient concentrations) which correlate with supernovae frequency changes over the previous 500 Myr (see Figure 5).


Atmospheric ionization and cloud radiative forcing

October 2021

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761 Reads

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25 Citations

Atmospheric ionization produced by cosmic rays has been suspected to influence aerosols and clouds, but its actual importance has been questioned. If changes in atmospheric ionization have a substantial impact on clouds, one would expect to observe significant responses in Earth’s energy budget. Here it is shown that the average of the five strongest week-long decreases in atmospheric ionization coincides with changes in the average net radiative balance of 1.7 W/m2 (median value: 1.2 W/m2) using CERES satellite observations. Simultaneous satellite observations of clouds show that these variations are mainly caused by changes in the short-wave radiation of low liquid clouds along with small changes in the long-wave radiation, and are almost exclusively located over the pristine areas of the oceans. These observed radiation and cloud changes are consistent with a link in which atmospheric ionization modulates aerosol's formation and growth, which survive to cloud condensation nuclei and ultimately affect cloud formation and thereby temporarily the radiative balance of Earth.


Diffuse sunlight and cosmic rays: Missing pieces of the forest growth change attribution puzzle?

September 2021

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57 Reads

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1 Citation

The Science of The Total Environment

Forest growth changes have been a matter of intense research efforts since the 1980s. Owing to the variety of their environmental causes - mainly atmospheric CO2 increase, atmospheric N deposition, changes in temperature and water availability, and their interactions - their interpretation has remained challenging. Recent isolated researches suggest further effects of neglected environmental factors, namely changes in the diffuse fraction of light, more efficient to photosynthesis, and galactic cosmic rays (GCR), both emphasized in this Discussion paper. With growing awareness of GCR influence on global cloudiness (the cosmoclimatologic theory by H. Svensmark), GCR may thus cause trends in diffuse-light, and distinguishing between their direct/indirect influences on forest growth remains uncertain. This link between cosmic rays and diffuse sunlight also forms an alternative explanation to the geological evidence of a negative correlation between GCR and atmospheric CO2 concentration over the past 500 Myr. After a careful scrutiny of this literature and of key contributions in the field, we draw research options to progress further in this attribution. These include i) observational strategies intending to build on differences in the spatio-temporal dynamics of environmental growth factors, ranging from quasi-experiments to meta-analyses, ii) simulation strategies intending to quantify environmental factor's effects based on process-based ecosystem modelling, in a context where progresses for accounting for diffuse-light fraction are ongoing. Also, the hunt for tree-ring based proxies of GCR may offer the perspective of testing the GCR hypothesis on fully coupled forest growth samples.


The Ion and Charged Aerosol Growth Enhancement (ION‐CAGE) code: A numerical model for the growth of charged and neutral aerosols

September 2020

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272 Reads

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8 Citations

Abstract The presence of small ions influences the growth dynamics of a size distribution of aerosols. Specifically, the often neglected mass of small ions influences the aerosol growth rate, which may be important for terrestrial cloud formation. To this end, we develop the Ion and Charged Aerosol Growth Enhancement (ION‐CAGE) code, a numerical model to calculate the growth of a species of aerosols in the presence of charge, which explicitly includes terms for ion condensation. It is shown that a positive contribution to aerosol growth rate is obtained by increasing the ion‐pair concentration through this ion condensation effect, consistent with recent experimental findings. The ion condensation effect is then compared to aerosol growth from charged aerosol coagulation, which is seen to be independent of ion‐pair concentration. Growth rate enhancements by ion condensation are largest for aerosol sizes less than ∼25 nm and increases proportional to the ion concentration. The effect of ion condensation is expected to be most important over pristine marine areas. The model source code is made available through a public repository.


Stochastic effects in H 2 SO 4 -H 2 O cluster growth

April 2020

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32 Reads

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2 Citations

The nucleation of sulfuric acid-water clusters plays a significant role in the formation of aerosols. Based on a recently developed particle Monte Carlo (MC) Code, we analyze how the growth of sulfuric acid-water clusters is influenced by stochastic fluctuations. We here consider samples of H2SO4-H2O clusters at T = 200 K with a relative humidity of 50%, with particle concentrations between 10⁵ and 10⁷ cm–3 in volumes between 10–6 and 10–2 cm³. We present the temporal evolution of the formation rate and of the size distribution as well as growth rates and the onset time of the nucleation above a given cluster size with and without constant production of new monomers. Clear evidence is revealed by the MC code that fluctuations result in a faster growth rate of the smallest clusters compared to deterministic continuum models that do not contain the stochastic effects. The faster growth of small clusters in turn influences the growth of larger clusters. Depending on the volume size, the onset time for clusters larger than 0.85 nm varies between 1000 s and 20,000 s for n=105 cm–3 and between 10 s and 100 s for n=107 cm–3. Copyright © 2020 American Association for Aerosol Research


Citations (57)


... In marine environments, supersaturations of SS = 0.5% can be reached (Gong et al., 2023;Svensmark et al., 2024). Based 470 on the results in Fig. 8, this means that due to surface tension lowering, a large part of the Aitken mode particles can be activated (D dry,crit (SS = 0.5 %) ≈ 50 nm) despite having a high organic content. ...

Reference:

The surface tension and CCN activation of sea spray aerosol particles
Supersaturation and Critical Size of Cloud Condensation Nuclei in Marine Stratus Clouds

... Svensmark et al. 17 argued that if a near-Earth supernova were to occur, the increase in ionising radiation would strongly increase cloud condensation nuclei (CCN) concentrations. Svensmark 18 proposed that SN strongly influences climate and, in turn, ocean circulation and marine biodiversity during the Phanerozoic. ...

A persistent influence of supernovae on biodiversity over the Phanerozoic

... In such a long period, the tectonics play important role, as the Earth's continents and oceans are moving (see Figure 1 for a couple of examples) and clearly the distribution of land and sea on the globe affects climate. In addition, the solar radiation was not constant but rose consistently, with a total 5% increase over the Phanerozoic [15]. It is also asserted that the cosmic ray flux has a large effect on the climate and this flux had variations, including a cycle with a period of about 145 million years, corresponding to the passage of the solar system through one of the two sets of spiral arms of the Milky Way [15]. ...

The Phanerozoic climate

... Particle acceleration and coronal plasma heating (10-30 million Kelvin) are two phenomena linked to fluctuations in solar radiation that they saw. Earth's atmosphere is greatly impacted by solar flares, which release radiation throughout the electromagnetic spectrum, with a concentration in UV and X-ray emissions [9,10] . Extreme UV radiation can increase by 4%-6% at the solar cycle's zenith, changing the composition of the stratosphere and altering air circulation patterns worldwide. ...

Effects of Forbush decreases on clouds determined from PATMOS-x

Journal of Atmospheric and Solar-Terrestrial Physics

... Undoubtedly, the Precambrian included significant volcanic activity, as there is much evidence in the rock record (e.g., (Gambeta et al., 2021;Maurice et al., 2021;Nawrocki et al., 2021). Sulfur emissions in the Neoarchean could exceed at least one order of magnitude higher than today (Marty et al., 2019;Enghoff et al., 2022). The volcanic activity provides the input of sulfur into the atmosphere, where it is mixed with atmospheric moisture and water to form acid clouds in the form of rain or aerosols. ...

Sulphuric acid aerosols in low oxygen environments

Journal of Aerosol Science

... Iron meteorites are regarded as medium of the signal for the cosmic-ray-flux [13]. The black curve represents the variation in relative supernova frequency presented by [31], which shows a weak graphical correlation with the other two curves. However, a generally increasing variation of supernova frequency (black arrows > derivative) may obviously correlate with the passageway of the solar system through the spiral arms of the Milky Way galaxy [10] [14]. ...

Supernova Rates and Burial of Organic Matter

... A period-doubling fluid potential (9) between points 1 and 1' correlates large distances of bifurcating fractal lateral points 1, 1′ → 2 → … → 1k → … → 2k (dotted line).Tidal tensile forces in Equation (9) explain CR and CMB as well the correlated stability of objects similar toFigure 2. Global temperature (9) yields a climateweather relation to CR and CMB and to a one-dimensional model. Previous hypotheses already suggest a relation between CR, atmospheric clouds and global temperature[28]-[32]. A self-interaction between CR and atmospheric clouds as part of FZU supports a continuous creation of matter near nontrivial zeros znt of ζ(z). ...

Atmospheric ionization and cloud radiative forcing

... These factors aside, forest growth changes can also be attributed to complex cosmoclimatologic processes such as incidence of solar radiation and galactic cosmic rays (Bontemps and Svensmark, 2022). Direct solar radiation has been found to be less efficient for terrestrial photosynthesis and productivity, compared to diffuse light which has better light penetration in forest canopies (Urban et al., 2007). ...

Diffuse sunlight and cosmic rays: Missing pieces of the forest growth change attribution puzzle?
  • Citing Article
  • September 2021

The Science of The Total Environment

... Considering only the unhydrated process of Reaction (R3), the rate constant is 4-5 orders of magnitude lower than the rate constant obtained for the SO 2 + O − 3 → SO − 3 + O 2 reaction (Fehsenfeld and Ferguson, 1974;Bork et al., 2012). Despite this difference, the oxidation process follows a similar mechanism to the one presented by Bork et al. for the SO 2 + O − 3 → SO − 3 + O 2 reaction, consisting of the oxygen transfer from O 3 to SO 2 . ...

Structures and reaction rates of the gaseous oxidation of SO2 by an O3(H2O)0-5 cluster – a density functional theory investigation

... The efficiency of nucleation is facilitated by the intense electric field of ions, which shifts the thermodynamic phase equilibrium towards the condensed phase (Aragones et al., 2011;Maerzke & Siepmann, 2010;Svishchev & Hayward, 1999), as well as by the pronounced inhomogeneity of this field, which forces polar molecules of water vapour to be drawn into the region of a stronger field, i.e., towards the ion. This process has garnered significant interest from researchers for both fundamental exploration and practical applications such as the development of systems for extracting moisture from the atmospheric air and artificial influencing weather patterns (Harrison et al., 2020;Kasparian et al., 2012;Keasler et al., 2010;Svensmark et al., 2020;Yel & Cremaschi, 2024). This process has also attracted attention in the fields of microfluidics and surface science (Kurra et al., 2010). ...

The Ion and Charged Aerosol Growth Enhancement (ION‐CAGE) code: A numerical model for the growth of charged and neutral aerosols