ORCID Profile
0000-0002-4575-0409
Current Organisation
University of Helsinki
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Publisher: Copernicus GmbH
Date: 08-10-2021
DOI: 10.5194/ACP-2021-744
Abstract: Abstract. The formation of ice particles in Earth’s atmosphere strongly influences the dynamics and optical properties of clouds and their impacts on the climate system. Ice formation in clouds is often triggered heterogeneously by ice nucleating particles (INPs) that represent a very low number of particles in the atmosphere. To date, many sources of INPs, such as mineral and soil dust, have been investigated and identified in the lower latitudes. Although less is known about the sources of ice nucleation at higher latitudes, efforts have been made to identify the sources of INPs in the Arctic and boreal environments. In this study, we investigate the INP emission potential from high latitude boreal forests. We introduce the HyICE-2018 measurement c aign conducted in the boreal forest of Hyytiälä, Finland between February and June 2018. The c aign utilized the infrastructure of the SMEAR II research station with additional instrumentation for measuring INPs to quantify the concentrations and sources of INPs in the boreal environment. In this contribution, we describe the measurement infrastructure and operating procedures during HyICE-2018 and we report results from specific time periods where INP instruments were run in parallel for inter-comparison purposes. Our results show that the suite of instruments deployed during HyICE-2018 reports consistent results and therefore lays the foundation for forthcoming results to be considered holistically. In addition, we compare the INP concentration we measured to INP parameterizations, and we show a very good agreement with the Tobo et al. (2013) parameterization developed from measurements conducted in a ponderosa pine forest ecosystem in Colorado, USA.
Publisher: Copernicus GmbH
Date: 17-09-2019
Abstract: Abstract. The accurate representation of ice particles is essential for both remotely sensed estimates of clouds and precipitation and numerical models of the atmosphere. As it is typical in radar retrievals to assume that all snow is composed of aggregate snowflakes, both denser rimed snow and the mixed-phase cloud in which riming occurs may be under-diagnosed in retrievals and therefore difficult to evaluate in weather and climate models. Recent experimental and numerical studies have yielded methods for using triple-frequency radar measurements to interrogate the internal structure of aggregate snowflakes and to distinguish more dense and homogeneous rimed particles from aggregates. In this study we investigate which parameters of the morphology and size distribution of ice particles most affect the triple-frequency radar signature and must therefore be accounted for in order to carry out triple-frequency radar retrievals of snow. A range of ice particle morphologies are represented, using a fractal representation for the internal structure of aggregate snowflakes and homogeneous spheroids to represent graupel-like particles the mass–size and area–size relations are modulated by a density factor. We find that the particle size distribution (PSD) shape parameter and the parameters controlling the internal structure of aggregate snowflakes both have significant influences on triple-frequency radar signature and are at least as important as that of the density factor. We explore how these parameters may be allowed to vary in order to prevent triple-frequency radar retrievals of snow from being over-constrained, using two case studies from the Biogenic Aerosols – Effects of Clouds and Climate (BAECC) 2014 field c aign at Hyytiälä, Finland. In a case including heavily rimed snow followed by large aggregate snowflakes, we show that triple-frequency radar measurements provide a strong constraint on the PSD shape parameter, which can be estimated from an ensemble of retrievals however, resolving variations in the PSD shape parameter has a limited impact on estimates of snowfall rate from radar. Particle density is more effectively constrained by the Doppler velocity than triple-frequency radar measurements, due to the strong dependence of particle fall speed on density. Due to the characteristic signatures of aggregate snowflakes, a third radar frequency is essential for effectively constraining the size of large aggregates. In a case featuring rime splintering, differences in the internal structures of aggregate snowflakes are revealed in the triple-frequency radar measurements. We compare retrievals assuming different aggregate snowflake models against in situ measurements at the surface and show significant uncertainties in radar retrievals of snow rate due to changes in the internal structure of aggregates. The importance of the PSD shape parameter and snowflake internal structure to triple-frequency radar retrievals of snow highlights that the processes by which ice particles interact may need to be better understood and parameterized before triple-frequency radar measurements can be used to constrain retrievals of ice particle morphology.
Publisher: Copernicus GmbH
Date: 19-04-2022
Abstract: Abstract. The formation of ice particles in Earth's atmosphere strongly influences the dynamics and optical properties of clouds and their impacts on the climate system. Ice formation in clouds is often triggered heterogeneously by ice-nucleating particles (INPs) that represent a very low number of particles in the atmosphere. To date, many sources of INPs, such as mineral and soil dust, have been investigated and identified in the low and mid latitudes. Although less is known about the sources of ice nucleation at high latitudes, efforts have been made to identify the sources of INPs in the Arctic and boreal environments. In this study, we investigate the INP emission potential from high-latitude boreal forests in the mixed-phase cloud regime. We introduce the HyICE-2018 measurement c aign conducted in the boreal forest of Hyytiälä, Finland, between February and June 2018. The c aign utilized the infrastructure of the Station for Measuring Ecosystem-Atmosphere Relations (SMEAR) II, with additional INP instruments, including the Portable Ice Nucleation Chamber I and II (PINC and PINCii), the SPectrometer for Ice Nuclei (SPIN), the Portable Ice Nucleation Experiment (PINE), the Ice Nucleation SpEctrometer of the Karlsruhe Institute of Technology (INSEKT) and the Microlitre Nucleation by Immersed Particle Instrument (µL-NIPI), used to quantify the INP concentrations and sources in the boreal environment. In this contribution, we describe the measurement infrastructure and operating procedures during HyICE-2018, and we report results from specific time periods where INP instruments were run in parallel for inter-comparison purposes. Our results show that the suite of instruments deployed during HyICE-2018 reports consistent results and therefore lays the foundation for forthcoming results to be considered holistically. In addition, we compare measured INP concentrations to INP parameterizations, and we observe good agreement with the Tobo et al. (2013) parameterization developed from measurements conducted in a ponderosa pine forest ecosystem in Colorado, USA.
Publisher: Copernicus GmbH
Date: 18-03-2019
DOI: 10.5194/AMT-2019-100
Abstract: Abstract. The accurate representation of ice particles is essential for both remotely-sensed estimates of cloud and precipitation and numerical models of the atmosphere. As it is typical in radar retrievals to assume that all snow is composed of unrimed aggregate snowflakes, both denser rimed snow and the mixed-phase cloud in which riming occurs may be under-diagnosed in retrievals, and therefore difficult to evaluate in weather and climate models. Recent experimental and numerical studies have yielded methods for using triple-frequency radar measurements to distinguish fractal aggregate snowflakes from more dense and homogeneous rimed particles. In this study we investigate which parameters of the particle size distribution (PSD) and morphology of ice particles are most important to the triple-frequency radar signature of snow, in order to carry out an optimal estimation retrieval using triple-frequency Doppler radar observations. We represent a range of ice particle morphologies using a fractal model for aggregate snowflakes and homogeneous spheroids to represent rimed graupel-like particles, and modulate the prefactor and exponent of the particles' mass-size relations with a density factor. We find that for both fractal particles and homogeneous spheroids the PSD shape has a greater influence on the triple-frequency radar signature than the density factor, and show that the PSD shape must be allowed to vary to adequately constrain a triple-frequency radar retrieval of snow. We then demonstrate a novel triple-frequency Doppler radar retrieval of three parameters of the PSD as well as particle density, and show that the estimated snow rate, PSD and bulk density compare well against in situ observations at the surface. In a case study of compact rimed snow, we find that triple-frequency radar measurements provide a strong constraint on the estimation of PSD shape, but a relatively weak constraint on particle density, which we find can be more directly estimated from the Doppler velocity due to the relation between particle density and fallspeed. Including variations in PSD shape as well as particle morphology allows for a better representation of the triple-frequency radar signatures of rimed and unrimed snow, and suggests the potential for making new insights into the interaction between particles during aggregation and riming mechanisms. However, we find that improved representation of the PSD shape has a limited impact on improved estimates of snow rate from radar. The importance of the PSD shape to triple-frequency radar retrievals of snow suggests that further work is needed to account for variations in PSD shape before triple-frequency radar measurements can be used to better constrain particle morphology.
Publisher: Copernicus GmbH
Date: 31-07-2020
DOI: 10.5194/ACP-2020-683
Abstract: Abstract. Ice-nucleating particles (INPs) trigger the formation of cloud ice crystals in the atmosphere. Therefore, they strongly influence cloud microphysical and optical properties, as well as precipitation and the life cycle of clouds. Improving weather forecasting and climate projection requires an appropriate formulation of atmospheric INP concentrations. This remains challenging, as the global INP distribution and variability depend on a variety of aerosol types and sources, and neither their short-term variability nor their long-term seasonal cycles are well covered by continuous measurements. Here, we provide the first year-long set of observations with a pronounced INP seasonal cycle in a boreal forest environment. Besides the observed seasonal cycle in INP concentrations with a minimum in wintertime and maxima in early and late summer, we also provide indications for a seasonal variation in the prevalent INP type. We show that the seasonal dependency of INP concentrations and prevalent INP types is most likely driven by the abundance of biogenic aerosol. As current parameterizations do not reproduce this variability, we suggest a new parameterization approach, which considers the seasonal variation of INP concentrations. For this, we use the ambient air temperature as a proxy for the season which affects the source strength of biogenic emissions and by that the INP abundance over the boreal forest areas. Furthermore, we provide new INP parameterizations based on the Ice Nucleation Active Surface Site (INAS) approach, which specifically describes the ice nucleation activity of boreal aerosols particles prevalent in different seasons. Our results characterize the boreal forest as an important but variable INP source and provide new perspectives to describe these new findings in atmospheric models.
Publisher: American Geophysical Union (AGU)
Date: 18-12-2018
DOI: 10.1029/2018JD028603
Publisher: Copernicus GmbH
Date: 16-03-2021
Abstract: Abstract. Ice-nucleating particles (INPs) trigger the formation of cloud ice crystals in the atmosphere. Therefore, they strongly influence cloud microphysical and optical properties and precipitation and the life cycle of clouds. Improving weather forecasting and climate projection requires an appropriate formulation of atmospheric INP concentrations. This remains challenging as the global INP distribution and variability depend on a variety of aerosol types and sources, and neither their short-term variability nor their long-term seasonal cycles are well covered by continuous measurements. Here, we provide the first year-long set of observations with a pronounced INP seasonal cycle in a boreal forest environment. Besides the observed seasonal cycle in INP concentrations with a minimum in wintertime and maxima in early and late summer, we also provide indications for a seasonal variation in the prevalent INP type. We show that the seasonal dependency of INP concentrations and prevalent INP types is most likely driven by the abundance of biogenic aerosol. As current parameterizations do not reproduce this variability, we suggest a new mechanistic description for boreal forest environments which considers the seasonal variation in INP concentrations. For this, we use the ambient air temperature measured close to the ground at 4.2 m height as a proxy for the season, which appears to affect the source strength of biogenic emissions and, thus, the INP abundance over the boreal forest. Furthermore, we provide new INP parameterizations based on the Ice Nucleation Active Surface Site (INAS) approach, which specifically describes the ice nucleation activity of boreal aerosols particles prevalent in different seasons. Our results characterize the boreal forest as an important but variable INP source and provide new perspectives to describe these new findings in atmospheric models.
Publisher: Elsevier BV
Date: 04-2017
Location: Russian Federation
No related grants have been discovered for Dmitri Moisseev.