ALBERT

Beschreibung: Samples for the analysis of dissolved nutrients were collected during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) from the water column, sea ice cores and from special events/locations (e.g., leads, melt ponds, brine, incubation experiments). Samples for dissolved inorganic nutrients (NO3 +NO2 , NO2 , PO4 , Si(OH)4, NH4 ) were analysed onboard during PS122 legs 1 to 3, with duplicate samples collected from CTD casts for later analysis of total dissolved nitrogen (TDN) and total dissolved phosphorus (TDP). From leg 4, all samples collected were stored frozen at -20°C for later analysis. Analyses of stored samples were carried out at the AWI Nutrient Facility between January and March 2021. Nutrient analyses onboard and on land were carried out using a Seal Analytical AA3 continuous flow autoanalyser, controlled by the AACE software version 7.09. Best practice procedures for the measurement of nutrients were adopted following GO-SHIP recommendations (Hydes et al., 2010; Becker et al., 2019). Descriptions of sample collection and handling can be found in the various cruise reports (Haas & Rabe, 2023; Kanzow & Damm, 2023; Rex & Metfies, 2023; Rex & Nicolaus, 2023; Rex & Shupe, 2023). Here we provide data from the water column, obtained from the analysis of discrete samples collected from CTD-Rosette casts from Polarstern (https://sensor.awi.de/?site=search&q=vessel:polarstern:ctd_sbe9plus_321) and Ocean City (https://sensor.awi.de/?site=search&q=vessel:polarstern:ctd_sbe9plus_935). Data from sea ice cores and special events are presented elsewhere. Data from sea ice cores and special events are presented elsewhere. For reference, here we included data from CTD-BTL files associated with nutrient samples. These data are presented by Tippenhauer et al. (2023) Polarstern CTD and Tippenhauer et al. (2023) Ocean City CTD.

Beschreibung: Samples for the analysis of dissolved nutrients were collected during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) from the water column, sea ice cores and from special events/locations (e.g., leads, melt ponds, brine, incubation experiments). Samples for dissolved inorganic nutrients (NO3 +NO2 , NO2 , PO4 , Si(OH)4, NH4 ) were analysed onboard during PS122 legs 1 to 3, with duplicate samples collected from CTD casts for later analysis of total dissolved nitrogen (TDN) and total dissolved phosphorus (TDP). From leg 4, all samples collected were stored frozen at -20°C for later analysis. Analyses of stored samples were carried out at the AWI Nutrient Facility between January and March 2021. Nutrient analyses onboard and on land were carried out using a Seal Analytical AA3 continuous flow autoanalyser, controlled by the AACE software version 7.09. Best practice procedures for the measurement of nutrients were adopted following GO-SHIP recommendations (Hydes et al., 2010; Becker et al., 2019). Descriptions of sample collection and handling can be found in the various cruise reports (Haas & Rabe, 2023; Kanzow & Damm, 2023; Rex & Metfies, 2023; Rex & Nicolaus, 2023; Rex & Shupe, 2023). Here we provide data from the water column, obtained from the analysis of discrete samples collected from CTD-Rosette casts from Polarstern (https://sensor.awi.de/?site=search&q=vessel:polarstern:ctd_sbe9plus_321) and Ocean City (https://sensor.awi.de/?site=search&q=vessel:polarstern:ctd_sbe9plus_935). Data from sea ice cores and special events are presented elsewhere. Data from sea ice cores and special events are presented elsewhere. For reference, here we included data from CTD-BTL files associated with nutrient samples. These data are presented by Tippenhauer et al. (2023) Polarstern CTD and Tippenhauer et al. (2023) Ocean City CTD.

Beschreibung: This dataset gives an overview of the abundance of microorganisms (smaller than 20 µm) enumerated using flow cytometry (FCM) during the Multidisciplinary drifting observatory for the study of Arctic Climate (MOSAiC) sampled from ship-based and on-ice CTD rosettes during leg 1, 2, 3, 4 and 5 (November 2019 – September 2020). Additional expedition and sampling details can be found in the ECO-overview paper (Fong et al., to be submitted to Elementa). We thank all persons involved in the expedition of the Research Vessel Polarstern during MOSAiC in 2019-2020 (AWI_PS122_00) as listed in Nixdorf et al. (2021). Flow cytometry (FCM) is a fast, high-throughput method to enumerate the abundance of microorganism (smaller than 20 µm). FCM uses the hydrodynamic focusing of a laminar flow to separate and line up microscopic particles. When particles pass a laser beam, the generated light scattering can be used to estimate their cell size, obtain information about cell granularity and surface characteristics and determine fluorescence from inherent pigments or applied stains, such as DNA binding ones. Photosynthetic microorganisms have auto-fluorescent pigments, such as chlorophylls which in combination with the light scattering properties (cell size) or surface properties, can be used to group them into clusters of similar or identical organism types. Heterotrophic microorganisms, including archaea, bacteria and heterotrophic nanoflagellates, and virus do not have fluorescent pigments and require staining, for example using SYBR Green to stain Nucleic Acids (DNA/RNA) in order to distinguish these cells from other organic and inorganic particles in the sample. Samples for flow cytometric analysis were taken in triplicates or quadruplicates of 1.8 mL of sample water and fixed with 36 μL 25 % glutaraldehyde (0.5 % final concentration) at 4 °C in the dark for approximately 2 hours, then flash frozen in liquid nitrogen and stored at -80 °C until analysis. The abundance of pico- and nano-sized phytoplankton and heterotrophic nanoflagellates (HNF) were determined using an Attune® NxT, Acoustic Focusing Cytometer (Invitrogen by Thermo Fisher Scientific) with a 20 mW 488 nm (blue) laser. Autotrophic pico-and nano-sized plankton were counted directly after thawing and the various groups discriminated based on their red fluorescence (BL3) vs. orange fluorescence (BL2), red fluorescence (BL3) vs. side scatter (SSC) and orange fluorescence (BL2) vs. side scatter (SSC). For HNF analysis, the samples were stained with SYBR Green I for 2 h in the dark and 1-2 mL were subsequently measured at a flow rate of 500 µl min-1 following the protocol of Zubkov et al. 2007. The abundance of virus and bacteria was determined using a FACS Calibur (Becton Dickinson) flow cytometer with a 15 mW 480 nm (blue) laser. Prior analysis of virus and bacteria, samples were first thawed, diluted x10 and x100 with 0.2 μm filtered TE buffer (Tris 10 mM, EDTA 1 mM, pH 8), stained with a green fluorescent nucleic acid dye (SYBR Green I ; Molecular Probes, Eugene, Oregon, USA) and then incubated for 10 min at 80°C in a water bath (Marie et al. 1999). Stained samples were counted at a flow rate of around 60 µL min-1 and different groups discriminated on a biparametric plot of green florescence (BL1) vs. side scatter (SSC). This allowed to distinguish virus particles of different sizes, and different bacterial groups including low nuclear acid (LNA) and high nuclear acid (HNA) bacteria. Names of size groups of photosynthetic and heterotrophic organisms are in accordance to "Standards and Best Practices For Reporting Flow Cytometry Observations: a technical manual (Version 1.1)" (Neeley et al., 2023). A short summary is listed here: RedPico = picophytoplankton (1-2 µm); RedNano = Nanophytoplankton (2-20µm), which includes subgroups RedNano_small (2-5 µm), RedNano_large (5-20 µm); OraPico = Nanophytoplankton with more orange fluorescence; OraNano = Cryptophytes; OraPicoProk = Synechococcus; HetNano = heterotrophic nanoflagellates; HetProk = bacteria (and when present archaea); HetLNA = low nucleic acid (LNA) containing bacteria; HetHNA = high nucleic acid (HNA) containing bacteria with the subgroups HetProk_medium = HNA-bacteria subgroup with less fluorescence signal, HetProk_large = HNA-bacteria subgroup with more fluorescence signal and HetProk_verylarge = HNA-bacteria subgroup with very strong fluorescence signal; Virus = virus-like particles, including size refined subgroups: LFV (low fluorescence virus or small virus); MFV (medium fluorescence virus or medium virus); HFV (high fluorescence virus or large virus) according to Larsen et al., 2008. Exemplary plots showing the gating strategies that were followed can be found in "Interoperable vocabulary for marine microbial flow cytometry" (Thyssen et al., 2022).

Beschreibung: Melt ponds water sampling for biogeochemical parameters such as dissolved inorganic carbon (DIC), total alkalinity (TA), oxygen isotopes were examined from August to September 2020. To obtain discrete water samples from the melt ponds and leads, we checked the vertical structure and depth of the meltwater layer from the same hole used for the RINKO Profiler by attaching a conductivity sensor (Cond 315i, WTW GmbH, Germany) to a 2-m-long ruler and inserting the ruler into the lead water until the salinity measured with the Cond 315i increased at the meltwater–seawater interface (Nomura et al., 2024) . Water was pumped up with a peristaltic pump through a 2-m-long PTFE tube (L/S Pump Tubing, Masterflex, USA) at depths corresponding to meltwater (surface), the interface between meltwater and seawater (interface), and seawater (bottom). Salinity was measured at each depth by attaching a Cond 315i conductivity sensor to the bottom of the ruler. The tube intake was likewise attached to the bottom of the ruler. Seawater was subsampled into a 250-mL glass vial (Duran Co., Ltd., Germany) for measurement of dissolved inorganic carbon (DIC) and total alkalinity (TA) and a 50-mL glass, screw-cap, narrow-neck vial (VWR international LLC, Germany) for measurement of the oxygen isotopic ratio (δ18O) of the water. Immediately after subsampling for measurement of DIC and TA, a 6.0% (wt.) mercuric chloride (HgCl2) solution (100 µL) was added to stop biological activity. Samples for DIC and TA were stored at +4°C on the R/V Polarstern. Samples for δ18O were stored at room temperature (20°C). During the discrete water sampling, the CO2 concentration in the water column was measured directly on site by passing the water through an equilibrator Liqui-Cel® (G542, S/N: 132462, 3M Company, USA) connected to an infrared gas analyzer (LI-8100A, LI-COR Inc., USA). The analyzer was calibrated with standard gases containing 0.0, 299.3, and 501.3 ppm CO2 before MOSAiC Leg 5. RMS (root means square) noise at 370 ppm with 1 sec signal averaging is 〈1 ppm (https://www.licor.com/env/products/soil-flux/LI-8100a). The equilibrator was connected in the loop for water sampling (vide supra), and a 2-m-long ruler was inserted into the water and kept at that depth until the CO2 was equilibrated with air (about 1 minute) by monitoring the CO2 values. The CO2 concentration was measured at each depth (i.e., surface, interface, and bottom). At the ROV lead sites, vertical CO2 measurements were made every 0.05 m for detailed profiles. The DIC of water was determined by coulometry (Johnson et al., 1985; Johnson, 1992) using a home-made CO2 extraction system (Ono et al., 1998) and a coulometer (CM5012, UIC, Inc., Binghamton, NY, USA). The TA of water was determined by titration (Dickson et al., 2007) using a TA analyzer (ATT-05, Kimoto Electric Co., Ltd., Japan). Both DIC and TA measurements were calibrated with reference seawater materials (Batch AR, AU, and AV; KANSO Technos Co., Ltd., Osaka, Japan) traceable to the Certified Reference Material distributed by Prof. A. G. Dickson (Scripps Institution of Oceanography, La Jolla, CA, USA). Oxygen isotope analyses were carried out at the ISOLAB Facility at AWI Potsdam (hdl:10013/sensor.ddc92f54-4c63-492d-81c7-696260694001) with mass spectrometers (DELTA-S Finnigan MAT, USA): hdl:10013/sensor.af148dea-fe65-4c87-9744-50dc4c81f7c9 and hdl:10013/sensor.62e86761-9fae-4f12-9c10-9b245028ea4c employing the equilibration method (details in Meyer et al., 2000). δ18O values were given in per mil (‰) vs. Vienna standard mean ocean water (V-SMOW) as the standard.

Beschreibung: Leads play an important role in the exchange of heat, gases, vapour, and particles between seawater and the atmosphere in ice-covered polar oceans. In summer, these processes can be modified significantly by the formation of a meltwater layer at the surface, yet we know little about the dynamics of meltwater layer formation and persistence. During the drift campaign of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC), we examined how variation in lead width, re-freezing, and mixing events affected the vertical structure of lead waters during late summer in the central Arctic. At the beginning of the 4-week survey period, a meltwater layer occupied the surface 0.8 m of the lead, and temperature and salinity showed strong vertical gradients. Stable oxygen isotopes indicate that the meltwater consisted mainly of sea ice meltwater rather than snow meltwater. During the first half of the survey period (before freezing), the meltwater layer thickness decreased rapidly as lead width increased and stretched the layer horizontally. During the latter half of the survey period (after freezing of the lead surface), stratification weakened and the meltwater layer became thinner before disappearing completely due to surface ice formation and mixing processes. Removal of meltwater during surface ice formation explained about 43% of the reduction in thickness of the meltwater layer. The remaining approximate 57% could be explained by mixing within the water column initiated by disturbance of the lower boundary of the meltwater layer through wind-induced ice floe drift. These results indicate that rapid, dynamic changes to lead water structure can have potentially significant effects on the exchange of physical and biogeochemical components throughout the atmosphere–lead–underlying seawater system.

Beschreibung: The Arctic is warming faster than anywhere else on Earth, prompting glacial melt, permafrost thaw, and sea ice decline. These severe consequences induce feedbacks that contribute to amplified warming, affecting weather and climate globally. Aerosols and clouds play a critical role in regulating radiation reaching the Arctic surface. However, the magnitude of their effects is not adequately quantified, especially in the central Arctic where they impact the energy balance over the sea ice. Specifically, aerosols called ice nucleating particles (INPs) remain understudied yet are necessary for cloud ice production and subsequent changes in cloud lifetime, radiative effects, and precipitation. Here, we report observations of INPs in the central Arctic over a full year, spanning the entire sea ice growth and decline cycle. Further, these observations are size-resolved, affording valuable information on INP sources. Our results reveal a strong seasonality of INPs, with lower concentrations in the winter and spring controlled by transport from lower latitudes, to enhanced concentrations of INPs during the summer melt, likely from marine biological production in local open waters. This comprehensive characterization of INPs will ultimately help inform cloud parameterizations in models of all scales.

Beschreibung: To investigate the balance between net photo- and heterotrophy throughout the Arctic autumn-winter-spring transition, we assessed the abundances of O2 and Ar in surface waters by means of membrane-inlet mass spectrometry . We derived biologically mediated O2 super-/undersaturation (ΔO2/Ar), reflecting the difference between gross primary production and the community’s combined autotrophic and heterotrophic respiration (i.e., ‘net community production’, NCP). We present first results on the magnitude of NCP over the autumn-winter-spring transition and extrapolate biological carbon drawdown and release. Further correlation with biological and chemical parameters assessed during MOSAiC is used to identify the controls on net community production and to better understand the ecological mechanisms that drive biogeochemical fluxes in the rapidly changing Arctic.

Beschreibung: Multiomics approaches need to be applied in the central Arctic Ocean to benchmark biodiversity change and to identify novel species and their genes. As part of MOSAiC, EcoOmics will therefore be essential for conservation and sustainable bioprospecting in one of the least explored ecosystems on Earth.

Beschreibung: The original version of this Article contained an error in Fig. 4, in which the y-axis should read “INPs (L−1 of air)” instead of “INPs x 10−3 (L−1 of air)”. This has been corrected in both the PDF and HTML versions of the Article.

Beschreibung: The rapid melt of snow and sea ice during the Arctic summer provides a significant source of low-salinity meltwater to the surface ocean on the local scale. The accumulation of this meltwater on, under, and around sea ice floes can result in relatively thin meltwater layers in the upper ocean. Due to the small-scale nature of these upper-ocean features, typically on the order of 1 m thick or less, they are rarely detected by standard methods, but are nevertheless pervasive and critically important in Arctic summer. Observations during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in summer 2020 focused on the evolution of such layers and made significant advancements in understanding their role in the coupled Arctic system. Here we provide a review of thin meltwater layers in the Arctic, with emphasis on the new findings from MOSAiC. Both prior and recent observational datasets indicate an intermittent yet longlasting (weeks to months) meltwater layer in the upper ocean on the order of 0.1 m to 1.0 m in thickness, with a large spatial range. The presence of meltwater layers impacts the physical system by reducing bottom ice melt and allowing new ice formation via false bottom growth. Collectively, the meltwater layer and false bottoms reduce atmosphere-ocean exchanges of momentum, energy, and material.The impacts on the coupled Arctic system are far-reaching, including acting as a barrier for nutrient and gas exchange and impacting ecosystem diversity and productivity.