Dataset Information

Phytoplankton Impact on Marine Cloud Microphysical Properties Over the Northeast Atlantic Ocean.

ABSTRACT: The current understanding of the impact of natural cloud condensation nuclei (CCN) variability on cloud properties in marine air is low, thus contributing to climate prediction uncertainty. By analyzing cloud remote sensing observations (2009-2015) at Mace Head (west coast of Ireland), we show the oceanic biota impact on the microphysical properties of stratiform clouds over the Northeast Atlantic Ocean. During spring to summer (seasons of enhanced oceanic biological activity), clouds typically host a higher number of smaller droplets resulting from increased aerosol number concentration in the CCN relevant-size range. The induced increase in cloud droplet number concentration (+100%) and decrease in their radius (-14%) are comparable in magnitude to that generated by the advection of anthropogenically influenced air masses over the background marine boundary layer. Cloud water content and albedo respond to marine CCN perturbations with positive adjustments, making clouds brighter as the number of droplets increases. Cloud susceptibility to marine aerosols overlaps with a large variability of cloud macrophysical and optical properties primarily affected by the meteorological conditions. The above findings suggest the existence of a potential feedback mechanism between marine biota and the marine cloud-climate system.

SUBMITTER: Mansour K

PROVIDER: S-EPMC9285769 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Phytoplankton Impact on Marine Cloud Microphysical Properties Over the Northeast Atlantic Ocean.

Mansour Karam K Rinaldi Matteo M Preißler Jana J Decesari Stefano S Ovadnevaite Jurgita J Ceburnis Darius D Paglione Marco M Facchini Maria C MC O'Dowd Colin C

Journal of geophysical research. Atmospheres : JGR 20220524 10

The current understanding of the impact of natural cloud condensation nuclei (CCN) variability on cloud properties in marine air is low, thus contributing to climate prediction uncertainty. By analyzing cloud remote sensing observations (2009-2015) at Mace Head (west coast of Ireland), we show the oceanic biota impact on the microphysical properties of stratiform clouds over the Northeast Atlantic Ocean. During spring to summer (seasons of enhanced oceanic biological activity), clouds typically ...[more]

PMID: 35860437

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Project description:Cloud drop number concentrations (N d) over the western North Atlantic Ocean (WNAO) are generally highest during the winter (DJF) and lowest in summer (JJA), in contrast to aerosol proxy variables (aerosol optical depth, aerosol index, surface aerosol mass concentrations, surface cloud condensation nuclei (CCN) concentrations) that generally peak in spring (MAM) and JJA with minima in DJF. Using aircraft, satellite remote sensing, ground-based in situ measurement data, and reanalysis data, we characterize factors explaining the divergent seasonal cycles and furthermore probe into factors influencing N d on seasonal timescales. The results can be summarized well by features most pronounced in DJF, including features associated with cold-air outbreak (CAO) conditions such as enhanced values of CAO index, planetary boundary layer height (PBLH), low-level liquid cloud fraction, and cloud-top height, in addition to winds aligned with continental outflow. Data sorted into high- and low-N d days in each season, especially in DJF, revealed that all of these conditions were enhanced on the high-N d days, including reduced sea level pressure and stronger wind speeds. Although aerosols may be more abundant in MAM and JJA, the conditions needed to activate those particles into cloud droplets are weaker than in colder months, which is demonstrated by calculations of the strongest (weakest) aerosol indirect effects in DJF (JJA) based on comparing N d to perturbations in four different aerosol proxy variables (total and sulfate aerosol optical depth, aerosol index, surface mass concentration of sulfate). We used three machine learning models and up to 14 input variables to infer about most influential factors related to N d for DJF and JJA, with the best performance obtained with gradient-boosted regression tree (GBRT) analysis. The model results indicated that cloud fraction was the most important input variable, followed by some combination (depending on season) of CAO index and surface mass concentrations of sulfate and organic carbon. Future work is recommended to further understand aspects uncovered here such as impacts of free tropospheric aerosol entrainment on clouds, degree of boundary layer coupling, wet scavenging, and giant CCN effects on aerosol-N d relationships, updraft velocity, and vertical structure of cloud properties such as adiabaticity that impact the satellite estimation of N d.

Project description:The Atlantic herring Clupea harengus L has a vast geographical distribution and a complex population structure with a few very large migratory units and many small local populations. Each population has its own spawning ground and/or time, thereby maintaining their genetic integrity. Several herring populations migrate between common feeding grounds and over-wintering areas resulting in frequent mixing of populations. Thus, many herring fisheries are based on mixed populations of different demographic status. In order to avoid over-exploitation of weak populations and to conserve biodiversity, understanding the population structure and population mixing is important for maintaining biologically sustainable herring fisheries. The aim of this study was to investigate the genetic population structure of herring in the Faroese and surrounding waters, and to develop genetic markers for distinguishing between four herring management units (often called stocks), namely the Norwegian spring-spawning herring (NSSH), Icelandic summer-spawning herring (ISSH), North Sea autumn-spawning herring (NSAH), and Faroese autumn-spawning herring (FASH). Herring from the four stocks were sequenced at low coverage, and single nucleotide polymorphisms (SNPs) were called and used for population structure analysis and individual assignment. An ancestry-informative SNP panel with 118 SNPs was developed and tested on 240 individuals. The results showed that all four stocks appeared to be genetically differentiated populations, but at lower levels of differentiation between FASH and ISSH than the other two populations. Overall assignment rate with the SNP panel was 80.7%, and agreement between the genetic and traditional visual assignment was 75.5%. The NSAH and NSSH samples had the highest assignment rate (100% and 98.3%, respectively) and highest agreement between traditional and genetic assignment methods (96.6% and 94.9%, respectively). The FASH and ISSH samples had substantially lower assignment rates (72.9% and 51.7%, respectively) and agreement between traditional and genetic methods (39.5% and 48.4%, respectively).

Dataset Information

Phytoplankton Impact on Marine Cloud Microphysical Properties Over the Northeast Atlantic Ocean.

Publications

Phytoplankton Impact on Marine Cloud Microphysical Properties Over the Northeast Atlantic Ocean.

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