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1. Seaglider observations in Davis Strait; 2005-2014

   https://doi.org/10.18739/A2PC2TB3S  

   Lee, Craig (January 2024, NSF Arctic Data Center)

   The Davis Strait observing system was established in 2004 to advance understanding of the role of Arctic – sub-Arctic interactions in the climate system by collecting sustained measurements of physical, chemical and biological variability at one of the primary gateways that connect the Arctic and subpolar oceans. Efforts began as a collaboration between researchers at the University of Washington’s Applied Physics Laboratory and the Canadian Department of Fisheries and Ocean’s Bedford Institute of Oceanography, but has grown to include researchers from the Greenland Institute of Natural Resources, Greenland Climate Institute, Danish Technological University, University of Alberta and University of Colorado, Boulder. The project is a component of the NSF Arctic Observing and Atlantic Meridional Overturning Networks, and the international Arctic-Subarctic Ocean Flux (ASOF) program, Global Ocean Ship-Based Hydrographic Investigations Program (GO-SHIP), Global Ocean Acidification Observing Network (GOA-ON), Arctic Monitoring Assessment Programme (AMAP) and OceanSITES system. Seaglider observations of temperature and salinity extending from the surface to a maximum of 1000 meter (m) depth were collected in Davis Strait from 2005-2014. Seagliders made repeat transects across Davis Strait between waypoints near 66°45' North (N), 60°30' West (W) and 67°N, 56°30' W (though these waypoints and the pathway of the gliders between the waypoints varied). Acoustic navigation enabled year-round data collection. This dataset contains 15 files, each of which contains Level 3 data from one Seaglider deployment in Davis Strait. Deployments lasted from 11 to 174 days and had a median duration of 83 days. Files contain several temperature and salinity products: data not interpolated or despiked; data despiked and interpolated to a common depth/time grid; and low-pass filtered data. Files also include quality control (QC) flags. Each file also contains depth-averaged current vectors from the Seaglider flight model and surface current vectors estimated from the Seaglider's drift track. Data files are named as follows: sg<seaglider number>_DavisStrait_<deployment data in MmmYY format>_level3.nc Level 2 files containing individual dive data are available by contacting the dataset creator. More details about the project can be found at https://iop.apl.washington.edu/project.php?id=davis. 

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2. Merged Datasets for the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Central Observatory in the Arctic Ocean (2019-2020)

   https://doi.org/10.18739/A2GX44W6J  

   Clemens-Sewall, David; Cox, Christopher; Schulz, Kirstin; Raphael, Ian (January 2024, NSF Arctic Data Center)

   The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) produced a wealth of observational data along the drift of the R/V Polarstern in the Arctic Ocean from October 2019 to September 2020. These data can further process-level understanding and improvements in models. However, the observational records contain temporal gaps and are provided in different formats. One goal of the MOSAiC Single Column Model Working Group (MSCMWG: https://mosaic-expedition.org/science/cross-cutting_groups/) is to provide consistently-formatted, gap-filled, merged datasets representing the conditions at the MOSAiC Central Observatory (the intensively studied region within a few km of R/V Polarstern) that are suitable for driving models on this spatial domain (e.g., single column models, large eddy simulations, etc). The MSCMWG is an open group, please contact the dataset creators if you would like to contribute to future versions of these merged datasets (including new variables). This dataset contains version 1 of these merged datasets, and comprises the variables necessary to force a single column ice model (e.g., Icepack: https://zenodo.org/doi/10.5281/zenodo.1213462). The atmospheric variables are primarily derived from Met City (~66 percent (%) of record, https://doi.org/10.18739/A2PV6B83F), with temporal gaps filled by bias and advection corrected data from Atmospheric Surface Flux Stations ( https://doi.org/10.18739/A2XD0R00S, https://doi.org/10.18739/A25X25F0P, https://doi.org/10.18739/A2FF3M18K). Some residual gaps in shortwave radiation were filled with ARM ship-board radiometer data. Three different options for snowfall precipitation rate (prsn) are provided, based on in-situ observations that precipitation greatly exceeded accumulation on level ice, and accumulation rates varied on different ice types. MOSAiC_kazr_snow_MDF_20191005_20201001.nc uses 'snowfall_rate1' derived from the vertically-pointing, ka-band radar on the vessel (https://doi.org/10.5439/1853942). MOSAiC_Raphael_snow_fyi_MDF_20191005_20201001.nc and MOSAiC_Raphael_snow_syi_MDF_20191005_20201001.nc use snow accumulation measurements from manual mass balance sites (https://doi.org/10.18739/A2NK36626) to derived a pseudo-precipitation. MOSAiC_Raphael_snow_fyi_MDF_20191005_20201001.nc is based on the First Year Ice (fyi) sites. MOSAiC_Raphael_snow_syi_MDF_20191005_20201001.nc is based on the Second Year Ice (syi) sites. The other atmospheric variables for these files are identical. Oceanic variables are in MOSAiC_ocn_MDF_20191006_20200919.nc and are derived from https://doi.org/10.18739/A21J9790B. The data are netCDF files formatted according to the Merged Data File format (https://doi.org/10.5194/egusphere-2023-2413, https://gitlab.com/mdf-makers/mdf-toolkit). The code 'recipes' that were used to produce these data are available at: https://doi.org/10.5281/zenodo.10819497. If you use these datasets, please also cite the appropriate publications: Meteorological variables (excluding precipitation): Cox et al., 2023 (https://doi.org/10.1038/s41597-023-02415-5) Oceanographic variables: Schulz et al., 2023 (https://doi.org/10.31223/X5TT2W) KAZR-derived precipitation: Matrosov et al., 2022 (https://doi.org/10.1525/elementa.2021.00101) Accumulation-derived pseudo-precipitation: Raphael et al., in review. The following are known issues that will be addressed in future dataset releases: 1. Residual gaps occupy approximately 20% of the data record (see addendum) 2. Some transitions to shiprad downwelling shortwave are unreasonable abrupt 3. MDF format does not currently include a field for point-by-point data source Addendum: For atmospheric variables, below indicates the percentage sourced from each dataset (and the amount missing a.k.a NaN) Air Temperature metcity 0.661943 NaN 0.193333 asfs30 0.134910 asfs40 0.008607 asfs50 0.001207 Specific Humidity metcity 0.658890 NaN 0.196298 asfs40 0.008695 Wind Velocity metcity 0.666334 NaN 0.255003 asfs30 0.068828 asfs40 0.008630 asfs50 0.001205 Downwelling Longwave metcity 0.549417 asfs30 0.241502 NaN 0.209081 Downwelling Shortwave metcity 0.674166 NaN 0.158814 asfs30 0.140794 shipradS1 0.026226 Note that the 21 day gap from the end of Central Observatory 2 to the start of Central Observatory 3 occupies 5.8% of the record. 

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3. Sea Ice Forecasting using Attention-based Ensemble LSTM

   Ali, Sahara; Huang, Yiyi; Huang, Xin; Wang, Jianwu (January 2021, Proceedings of Tackling Climate Change with Machine Learning Workshop at International Conference on Machine Learning (ICML 2021))

   Accurately forecasting Arctic sea ice from sub- seasonal to seasonal scales has been a major scientific effort with fundamental challenges at play. In addition to physics-based earth system models, researchers have been applying multiple statistical and machine learning models for sea ice forecast- ing. Looking at the potential of data-driven sea ice forecasting, we propose an attention-based Long Short Term Memory (LSTM) ensemble method to predict monthly sea ice extent up to 1 month ahead. Using daily and monthly satellite retrieved sea ice data from NSIDC and atmospheric and oceanic variables from ERA5 reanalysis product for 39 years, we show that our multi-temporal ensemble method outperforms several baseline and recently proposed deep learning models. This will substantially improve our ability in predicting future Arctic sea ice changes, which is fundamental for forecasting transporting routes, resource development, coastal erosion, threats to Arctic coastal communities and wildlife. 

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4. Longyearbyen (LYR) Super Dual Auroral Radar Network (SuperDARN) High Frequency (HF) Radar Ground Scatter Data (2016-2018)

   https://doi.org/10.18739/A22J6859G  

   Thomas, Evan; Shepherd, Simon; Lorentzen, Dag (January 2023, NSF Arctic Data Center)

   The Super Dual Auroral Radar Network (SuperDARN) is an international network of ground-based, space weather radars which have operated continuously in the Arctic and Antarctic regions for more than 30 years. These high-frequency (HF) radars use over-the-horizon (OTH) radio wave propagation to detect ionospheric plasma structures across ranges of several thousand kilometers (km). As a byproduct of this technique, the transmitted radar signals frequently reflect from the Earth's surface and can be observed as ground backscatter echoes. The monthly files in this dataset contain maps of daily ground backscatter observations from the Longyearbyen (LYR) SuperDARN HF radar binned onto an equal-area 24 km grid. The LYR radar is located in Svalbard, Norway (78.15°N, 16.07°E) and is operated by the University Centre in Svalbard (Principal Investigator: Dag A. Lorentzen, dagl@unis.no) with funding support from the University Centre in Svalbard (UNIS) and the ConocoPhillips and Lundin Arctic Approach Research Project. 

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5. A spatially and vertically resolved global grid of dissolved barium concentrations in seawater determined using Gaussian Process Regression machine learning

   https://doi.org/10.26008/1912/bco-dmo.885506.2  

   Horner, Tristan J.; Mete, Oyku Z. (January 2023, Biological and Chemical Oceanography Data Management Office (BCO-DMO))

   We present a spatially and vertically resolved global grid of dissolved barium concentrations ([Ba]) in seawater determined using Gaussian Process Regression machine learning. This model was trained using 4,345 quality-controlled GEOTRACES data from the Arctic, Atlantic, Pacific, and Southern Oceans. Model output was validated by assessing the accuracy of [Ba] simulations in the Indian Ocean, noting that none of the Indian Ocean data were seen by the model during training. We identify a model that can accurate predict [Ba] in the Indian Ocean using seven features: depth, temperature, salinity, as well as dissolved dioxygen, phosphate, nitrate, and silicate concentrations. This model achieves a mean absolute percentage error of 6.0 %, which we assume represents the generalization error. This model was used to simulate [Ba] on a global basis using predictor data from the World Ocean Atlas 2018. The global model of [Ba] is on a 1°x 1° grid with 102 depth levels from 0 to 5,500 m. The dissolved [Ba] output was then used to simulate dissolved Ba* (barium-star), which is the difference between 'observed' and [Ba] predicted from co-located [Si]. Lastly, [Ba] data were combined with temperature, salinity, and pressure data from the World Ocean Atlas to calculate the saturation state of seawater with respect to barite. The model reveals that the volume-weighted mean oceanic [Ba] and and saturation state are 89 nmol/kg and 0.82, respectively. These results imply that the total marine Ba inventory is 122(±7) ×10¹² mol and that the ocean below 1,000 m is at barite equilibrium. 

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