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Abstract African dust is transported to South America (SA) every winter and spring. Hypotheses suggest that either Western or Central North Africa (e.g., Bodélé Depression) is the main source of transported dust, yet these notions remain largely untested with geochemical data. Using 2 years of isotopic measurements (strontium and neodymium) of African dust collected in SA integrated into a statistical model, we identified strong interannual variability in dust source region. Central North Africa supplied 44% of long‐range transported dust in winter 2016 while the Western region accounted for 53% of dust in winter 2014. We propose the variability is due to differences in the strength of the Libyan High and precipitation over the Gulf of Guinea and Atlantic Ocean between the 2 years. Our findings can improve constraints of dust nutrient deposition and predictions of how changes in climate impact the source and magnitude of dust transported to the Amazon.
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Data for Interannual variability in the source location of North African dust transported to the Amazon
Here, we present the data and MixSIAR code that corresponds to the manuscript “Interannual variability in the source location of North African dust transported to the Amazon.” African dust is seasonally transported to the western Tropical Atlantic Ocean (TAO) and South America (SA), including the Amazon Basin. Leading hypotheses suggest that either the Western North African potential source area (PSA) or the Central North African PSA (e.g., Bodélé Depression) is the main source of dust transported to the Amazon. However, these notions remain largely untested with geochemical data. Here, we present a more nuanced hypothesis: both PSAs contribute dust to SA with precipitation and wind patterns determining the dominant source. Our premise is based upon two years of isotopic measurements (strontium and neodymium) of African dust collected in SA integrated into a statistical model in a Bayesian framework. With this approach, we identified strong interannual variability: while the Central PSA supplied 48% in winter 2016, a region within the Western PSA, which we suggest may be located near Niger, Mali, and Algeria accounts for 54% of transport in winter 2014. We propose the variability is due to the strength of the Libyan High and differing amounts of precipitation in the Gulf of Guinea and TAO between the two years. We anticipate that our work will lead to better constraints of dust nutrient deposition and subsequent carbon sequestration in the TAO and Amazon as well as improved model predictions of dust transport. Due to the connection between dust, precipitation, and wind patterns, our work can be used to link changes in climate with past changes in the source and magnitude of dust transported to the Amazon and TAO. This data is associated with the article: Barkley, A.E., Pourmand, A., Longman, J., Sharifi, A., Prospero, J.M., Panechou, K., Bakker, N., Drake, N., Guioiseau, D., Gaston, C.J. Interannual variability in the source location of North African dust transported to the Amazon. Submitted to the Proceedings of the National Academy of Sciences ## Description of the datasets The `data/` folder contains three data sets. `ds01` contains the data collected in this study from 34 samples including the dates of collection and Sr and eNd isotopic ratios. ## Metadata of the trajectory file ds01 is a *csv* file that contain 12 columns. Column 1 presents the date in the format ‘MM:DD:YYYY’ (e.g., 01-30-2014) that sample collection was initiated. Column 2 presents the date ‘MM:DD:YYYY’ (e.g., 01-31-2014) sample collection ended. Column 3 shows the mean 87Sr/86Sr ratio (unitless) measured and Column 4 shows the 95% confidence interval (CI) for each sample run in triplicate. Column 5 shows the 143Nd/144Nd isotopic ratio reported as epsilon neodymium (unitless) and Column 6 presents the 95% CI of the mean epsilon Nd. Columns 7, 9, and 11 show the lead (Pb) isotopic ratios normalized to 204Pb with their corresponding 95% CI in Columns 8, 10, and 12.
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- Award ID(s):
- 1944958
- PAR ID:
- 10397549
- Publisher / Repository:
- University of Miami Libraries
- Date Published:
- Subject(s) / Keyword(s):
- FOS: Earth and related environmental sciences Atmospheric Science dust provenance nutrients North Atlantic
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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{"Abstract":["This data set contains all classifications that the Gravity Spy Machine Learning model for LIGO glitches from the first three observing runs (O1, O2 and O3, where O3 is split into O3a and O3b). Gravity Spy classified all noise events identified by the Omicron trigger pipeline in which Omicron identified that the signal-to-noise ratio was above 7.5 and the peak frequency of the noise event was between 10 Hz and 2048 Hz. To classify noise events, Gravity Spy made Omega scans of every glitch consisting of 4 different durations, which helps capture the morphology of noise events that are both short and long in duration.<\/p>\n\nThere are 22 classes used for O1 and O2 data (including No_Glitch and None_of_the_Above), while there are two additional classes used to classify O3 data.<\/p>\n\nFor O1 and O2, the glitch classes were: 1080Lines, 1400Ripples, Air_Compressor, Blip, Chirp, Extremely_Loud, Helix, Koi_Fish, Light_Modulation, Low_Frequency_Burst, Low_Frequency_Lines, No_Glitch, None_of_the_Above, Paired_Doves, Power_Line, Repeating_Blips, Scattered_Light, Scratchy, Tomte, Violin_Mode, Wandering_Line, Whistle<\/p>\n\nFor O3, the glitch classes were: 1080Lines, 1400Ripples, Air_Compressor, Blip, Blip_Low_Frequency<\/strong>, Chirp, Extremely_Loud, Fast_Scattering<\/strong>, Helix, Koi_Fish, Light_Modulation, Low_Frequency_Burst, Low_Frequency_Lines, No_Glitch, None_of_the_Above, Paired_Doves, Power_Line, Repeating_Blips, Scattered_Light, Scratchy, Tomte, Violin_Mode, Wandering_Line, Whistle<\/p>\n\nIf you would like to download the Omega scans associated with each glitch, then you can use the gravitational-wave data-analysis tool GWpy. If you would like to use this tool, please install anaconda if you have not already and create a virtual environment using the following command<\/p>\n\n```conda create --name gravityspy-py38 -c conda-forge python=3.8 gwpy pandas psycopg2 sqlalchemy```<\/p>\n\nAfter downloading one of the CSV files for a specific era and interferometer, please run the following Python script if you would like to download the data associated with the metadata in the CSV file. We recommend not trying to download too many images at one time. For example, the script below will read data on Hanford glitches from O2 that were classified by Gravity Spy and filter for only glitches that were labelled as Blips with 90% confidence or higher, and then download the first 4 rows of the filtered table.<\/p>\n\n```<\/p>\n\nfrom gwpy.table import GravitySpyTable<\/p>\n\nH1_O2 = GravitySpyTable.read('H1_O2.csv')<\/p>\n\nH1_O2[(H1_O2["ml_label"] == "Blip") & (H1_O2["ml_confidence"] > 0.9)]<\/p>\n\nH1_O2[0:4].download(nproc=1)<\/p>\n\n```<\/p>\n\nEach of the columns in the CSV files are taken from various different inputs: <\/p>\n\n[\u2018event_time\u2019, \u2018ifo\u2019, \u2018peak_time\u2019, \u2018peak_time_ns\u2019, \u2018start_time\u2019, \u2018start_time_ns\u2019, \u2018duration\u2019, \u2018peak_frequency\u2019, \u2018central_freq\u2019, \u2018bandwidth\u2019, \u2018channel\u2019, \u2018amplitude\u2019, \u2018snr\u2019, \u2018q_value\u2019] contain metadata about the signal from the Omicron pipeline. <\/p>\n\n[\u2018gravityspy_id\u2019] is the unique identifier for each glitch in the dataset. <\/p>\n\n[\u20181400Ripples\u2019, \u20181080Lines\u2019, \u2018Air_Compressor\u2019, \u2018Blip\u2019, \u2018Chirp\u2019, \u2018Extremely_Loud\u2019, \u2018Helix\u2019, \u2018Koi_Fish\u2019, \u2018Light_Modulation\u2019, \u2018Low_Frequency_Burst\u2019, \u2018Low_Frequency_Lines\u2019, \u2018No_Glitch\u2019, \u2018None_of_the_Above\u2019, \u2018Paired_Doves\u2019, \u2018Power_Line\u2019, \u2018Repeating_Blips\u2019, \u2018Scattered_Light\u2019, \u2018Scratchy\u2019, \u2018Tomte\u2019, \u2018Violin_Mode\u2019, \u2018Wandering_Line\u2019, \u2018Whistle\u2019] contain the machine learning confidence for a glitch being in a particular Gravity Spy class (the confidence in all these columns should sum to unity). <\/p>\n\n[\u2018ml_label\u2019, \u2018ml_confidence\u2019] provide the machine-learning predicted label for each glitch, and the machine learning confidence in its classification. <\/p>\n\n[\u2018url1\u2019, \u2018url2\u2019, \u2018url3\u2019, \u2018url4\u2019] are the links to the publicly-available Omega scans for each glitch. \u2018url1\u2019 shows the glitch for a duration of 0.5 seconds, \u2018url2\u2019 for 1 seconds, \u2018url3\u2019 for 2 seconds, and \u2018url4\u2019 for 4 seconds.<\/p>\n\n```<\/p>\n\nFor the most recently uploaded training set used in Gravity Spy machine learning algorithms, please see Gravity Spy Training Set on Zenodo.<\/p>\n\nFor detailed information on the training set used for the original Gravity Spy machine learning paper, please see Machine learning for Gravity Spy: Glitch classification and dataset on Zenodo. <\/p>"]}more » « less
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