ALBERT

Description: Foraminifera are a species-rich phylum of rhizarian protists that are highly abundant in most marine environments. Molecular methods such as metabarcoding have revealed a high, yet undescribed diversity of Foraminifera. However, so far only one molecular marker, the 18S ribosomal RNA, was available for metabarcoding studies on Foraminifera. Primers that allow amplification of foraminiferal mitochondrial cytochrome oxidase I (COI) and identification of Foraminifera species were recently published. Here we test the performance of these primers for the amplification of whole foraminiferal communities, and compare their performance to that of the highly degenerate LerayXT primers, which amplify the same COI region in a wide range of eukaryotes. We applied metabarcoding to 48 samples taken along three transects spanning a North Sea beach in the Netherlands from dunes to the low tide level, and analysed both sediment samples and meiofauna samples, which contained taxa between 42 mm and 1 mm in body size obtained by decantation from sand samples. We used single-cell metabarcoding (Girard et al., 2022) to generate a COI reference library containing 32 species of Foraminifera, and used this to taxonomically annotate our community metabarcoding data. Our analyses show that the highly degenerate LerayXT primers do not amplify Foraminifera, while the Foraminifera primers are highly Foraminifera- specific, with about 90% of reads assigned to Foraminifera and amplifying taxa from all major groups, i.e., monothalamids, Globothalamea, and Tubothalamea. We identified 176 Foraminifera ASVs and found a change in Foraminifera community composition along the beach transects from high tide to low tide level, and a dominance of single-chambered monothalamid Foraminifera. Our results highlight that COI metabarcoding can be a powerful tool for assessing Foraminiferal communities.

Description: Laboratory O2 clumped-isotope data (as D36 values measured at Rice University) for air occluded in ice core GISP2-D spanning gas ages of 18000-21000 ky BP for the Last Glacial Maximum (LGM). Modeled atmospheric history for the LGM via outputs of the GISS-E2.1 driven and Data Assimilation (DAv2.0) driven chemical transport model incorporated into a 2-box model of the atmosphere.

Description: Within the European Space Agency funded Oceanographic datasets for acidification (OceanSODA) project, the University of Exeter (UNEXE) produced the OceanSODA-UNEXE dataset (v1.0) which is an optimal dataset of the surface ocean carbonate system in the Amazon and Congo River outflows. All four main carbonate system variables, total alkalinity (TA), dissolved inorganic carbon (DIC), the partial pressure of carbon dioxide (pCO2) and pH are provided on monthly 1° × 1° grids along with additional carbonate system parameters. The uncertainties within these data have been assessed using independent in situ database (Land et.al 2022). A paper detailing the methodology used to optimally construct and then evaluate this dataset is currently being written. Each netCDF4 dataset file contains 10 or more years of data; the full carbonate system is provided for 2010-2020 in the Amazon outflow (defined as 2°S and 24°N and between 70°W and 31°W) datasets and the full carbonate system is provided for the period 2002-2016 in the Congo outflow (defined as 10°S and 4°N and between 2°W and 16°E). Variables are stored on a 180° by 360° latitude grid with a time dimension (defined as the months from January 1957 to December 2021). Following the methodology of Land et al. (2019), TA and DIC were derived using empirical algorithms from the published literature that use combinations of inputs that include sea surface temperature (SST), sea surface salinity (SSS) datasets and nutrients (silicate (SiO4-), nitrate (NO3-), phosphate (PO4-) or dissolved oxygen (DO). TA and the inputs used to derive it (e.g. SST and SSS) are within the netCDF files prefixed with _TA, whereas DIC and the inputs used to derive it (SST and SSS) are within the netCDF files prefixed with _DIC. The full carbonate system equations (calculating for surface waters) were run twice with PyCO2SYS V1.7 (Humphreys et al., 2022), using the same TA, DIC, SiO4- and PO4- along with the SST and SST datasets from the respective DIC or TA netCDF files. The variables computed with PyCO2SYS are the carbonate ion (CO3-2), the bicarbonate ion (HCO3-), hydrogen ions (H+) ,pH on the total scale, pH on the free scale, pH on the seawater scale, the partial pressure of carbon dioxide (pCO2), the fugacity of carbon dioxide (fCO2),the saturation state of calcite and the saturation state of aragonite. A full list of variables and references for all input data can be found in Table 1. All variable fields have an associated uncertainty field; this uncertainty has the same abbreviated variable name along with the suffix uncertainty (e.g. TA_uncertainty). SST, SSS and nutrient input data uncertainties come from their respective dataset accuracy assessments and dataset references (Table 1). TA and DIC uncertainty is the combined standard uncertainty from the algorithm and input data evaluation determined using the methods of Land et al. (2019) which are consistent with the uncertainty methods of (JCGM, 2008). Uncertainties for the remaining variables were determined by propagating the TA, DIC, SST and SSS uncertainties through PyCO2SYS using a forward finite difference approach (Humphreys et al., 2022).

Description: Stress maps show the orientation of the current maximum horizontal stress (SHmax) in the earth's crust. Assuming that the vertical stress (SV) is a principal stress, SHmax defines the orientation of the 3D stress tensor; the minimum horizontal stress Shmin is than perpendicular to SHmax. In stress maps SHmax orientations are represented as lines of different lengths. The length of the line is a measure of the quality of data and the symbol shows the stress indicator and the color the stress regime. The stress data are freely available and part of the World Stress Map (WSM) project. For more information about the data and criteria of data analysis and quality mapping are plotted along the WSM website at http://www.world-stress-map.org. The stress map of Taiwan 2022 is based on the WSM database release 2016. However, all data records have been checked and we added a large number of new data from earthquake focal mechanisms from the national earthquake catalog and from publications. The total number of data records has increased from n=401 in the WSM 2016 to n=6,498 (4,234 with A-C quality) in the stress map of Taiwan 2022 The update with earthquake focal mechanims is even larger since another 1313 earthquake focal mechanism data records beyond the scale of this map have been added to the WSM database. The digital version of the stress map is a layered pdf file generated with GMT (Wessel et al., 2019). It also provide estimates of the mean SHmax orientation on a regular 0.1° grid using the tool stress2grid (Ziegler and Heidbach, 2019). Two mean SHmax orientations are estimated with search radii of r=25 and 50 km, respectively, and with weights according to distance and data quality. The stress map and data are available on the landing page at https://doi.org/10.5880/WSM.Taiwan2022 where further information is provided. The earthquake focal mechanism that are used for this stress map are provided by the Taiwan Earthquake Research Center (TEC) available at the TEC Data Center (https://tec.earth.sinica.edu.tw).

Description: We construct and examine the prototype of a deep learning-based ground-motion model (GMM) that is both fully data driven and nonergodic. We formulate ground-motion modeling as an image processing task, in which a specific type of neural network, the U-Net, relates continuous, horizontal maps of earthquake predictive parameters to sparse observations of a ground-motion intensity measure (IM). The processing of map-shaped data allows the natural incorporation of absolute earthquake source and observation site coordinates, and is, therefore, well suited to include site-, source-, and path-specific amplification effects in a nonergodic GMM. Data-driven interpolation of the IM between observation points is an inherent feature of the U-Net and requires no a priori assumptions. We evaluate our model using both a synthetic dataset and a subset of observations from the KiK-net strong motion network in the Kanto basin in Japan. We find that the U-Net model is capable of learning the magnitude–distance scaling, as well as site-, source-, and path-specific amplification effects from a strong motion dataset. The interpolation scheme is evaluated using a fivefold cross validation and is found to provide on average unbiased predictions. The magnitude–distance scaling as well as the site amplification of response spectral acceleration at a period of 1 s obtained for the Kanto basin are comparable to previous regional studies.

Description: Raw data acquired by position sensors on board RV SONNE during expedition SO292 were processed to receive a validated master track which can be used as reference of further expedition data. During SO292 the motion reference unit Kongsberg SeaTex AS MRU-5 combined with Kongsberg SeaTex AS Seapath 320 and two GPS receivers SAAB MGL-4 were used as navigation sensors. Data were downloaded from DAVIS SHIP data base (https://dship.bsh.de) with a resolution of 1 sec. Processing and evaluation of the data is outlined in the data processing report. Processed data are provided as a master track with 1 sec resolution derived from the position sensors' data selected by priority and a generalized track with a reduced set of the most significant positions of the master track.

Description: Data from autonomous, drifting buoy with a floating chamber to measure insitu air-sea carbon dioxide (CO2) fluxes during RV Falkor cruise FK191120 in the southern Pacific during November-December 2019. The technique is described in detail in Ribas-Ribas et al. (2018) (https://doi.org/10.1525/elementa.275). The buoy is equipped with a sensor to measure aqueous and atmospheric partial pressure of CO2 (pCO2), and to monitor the increase or loss of CO2 inside the chamber. One complete cycle including two chamber measurements last 70 minutes. The buoy can be deployed for more than 15 hours, and at wind speeds of up to 10 m/s. Floating chambers are known to overestimate fluxes due to the creation of additional turbulence at the water surface. We check that by measuring turbulence with two Acoustic Doppler Velocimeter (ADV), one directly underneath the center of the floating chamber (equipped with an inertial motion unit) and the other one positioned sideways to measure turbulence outside the perimeter of the buoy.

Description: Data from autonomous, drifting buoy with a floating chamber to measure insitu air-sea carbon dioxide (CO2) fluxes during RV Falkor cruise FK191120 in the southern Pacific during November-December 2019. The technique is described in detail in Ribas-Ribas et al. (2018) (https://doi.org/10.1525/elementa.275). The buoy is equipped with a sensor to measure aqueous and atmospheric partial pressure of CO2 (pCO2), and to monitor the increase or loss of CO2 inside the chamber. One complete cycle including two chamber measurements last 70 minutes. The buoy can be deployed for more than 15 hours, and at wind speeds of up to 10 m/s. Floating chambers are known to overestimate fluxes due to the creation of additional turbulence at the water surface. We check that by measuring turbulence with two Acoustic Doppler Velocimeter (ADV), one directly underneath the center of the floating chamber (equipped with an inertial motion unit) and the other one positioned sideways to measure turbulence outside the perimeter of the buoy.

Description: Data from autonomous, drifting buoy with a floating chamber to measure insitu air-sea carbon dioxide (CO2) fluxes during RV Falkor cruise FK191120 in the southern Pacific during November-December 2019. The technique is described in detail in Ribas-Ribas et al. (2018) (https://doi.org/10.1525/elementa.275). The buoy is equipped with a sensor to measure aqueous and atmospheric partial pressure of CO2 (pCO2), and to monitor the increase or loss of CO2 inside the chamber. One complete cycle including two chamber measurements last 70 minutes. The buoy can be deployed for more than 15 hours, and at wind speeds of up to 10 m/s. Floating chambers are known to overestimate fluxes due to the creation of additional turbulence at the water surface. We check that by measuring turbulence with two Acoustic Doppler Velocimeter (ADV), one directly underneath the center of the floating chamber (equipped with an inertial motion unit) and the other one positioned sideways to measure turbulence outside the perimeter of the buoy.