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502 results for “latitude”
Projected Snow Cover Reductions and Mid-latitude Cyclone Responses in the North American Great Plains, 1986 - 2005
Extratropical cyclones are responsible for major weather events and trends in the mid-latitudes and preferentially develop in regions of enhanced cyclogenesis and proceed along climatological storm tracks. It has been shown that terrestrial snow cover exerts considerable influence on atmospheric baroclinicity which is largely responsible for the aforementioned cyclogeneses and storm tracks. Research about the effect which terrestrial snow cover exerts on cyclones' intensities, trajectories, and precipitation characteristics is limited but indicates a robust relationship with these factors. Many examinations of climate model projections have generally shown a poleward shift in storm tracks by the late 21st century though none have determined the degree to which the coincident poleward shift in snow extent is responsible. A method of imposing 10th, 50th, and 90th percentile values of snow retreat between the late 20th and 21st centuries as projected by 14 models of the Coupled Model Intercomparison Project Phase Five (CMIP5) is used to alter 20 historical cold season cyclones which tracked over or adjacent to the North American Great Plains. Simulations by the Advanced Research version of the Weather Research and Forecast Model (WRF-ARW) are initialized at 0 to 4 days prior to cyclogenesis. Including control and sensitivity testing wherein snow is unaltered or removed entirely, each cyclone case is simulated 25 times for a total of 500 simulations.
Dataset to Schiedung et al. (2024): Millennial-aged pyrogenic carbon in high-latitude mineral soils
<p>Dataset to Schiedung et al. (2024, Communications Earth & Environment): Pyrogenic Carbon is Aged at Millennial Scale in High-Latitude Mineral Soils</p> <p>DOI: <a href="https://doi.org/10.1038/s43247-024-01343-5">10.1038/s43247-024-01343-5</a></p> <p>This repository includes the following files: </p> <p><strong><em>dd_all.csv</em> </strong>- Includes all data for the individual samples that are presented in the manuscript.</p> <p><strong><em>Var_names_dd_all.csv</em> </strong>- Describes all variables in <em>dd_all</em> with corresponding unit </p> <p><strong><em>dd_site_average.csv</em></strong> - Includes all data that has been determined on composite samples for each site or the average of all samples per site </p> <p><strong><em>Var_names_dd_site_average.csv</em></strong> - Describes all variables in <em>dd_site_average.csv</em> with corresponding unit</p> <p>All .csv use "," as separator. </p> <p>This data set is also connected to Schiedung et al. (2022, Catena <a href="https://doi.org/10.1016/j.catena.2022.106194"> https://doi.org/10.1016/j.catena.2022.106194</a> ) and the corresponding repository: <a href="../records/10609291">https://zenodo.org/records/10609291</a></p>
Zero-degree isotherm latitude (ZIL) position over Antarctica: Historical and Projections
<p>This is the dataset associated to the research 'Southward migration of the zero-degree isotherm latitude over the Southern Ocean and the Antarctic Peninsula: extent and implications' published in <i>Science of the Total Environment</i>.</p><p>This repository contains:</p><ul><li><strong>ZIL_ERA5_1957-2020_position.zip:</strong> Historical position of the ZIL for every longitude point in ERA5 from 1957 to 2020 for different <i>seasons</i>. Files named:<ul><li>ZIL_ERA5_1957-2020<i>[season]</i>position.csv<ul><li>Dimensions: [lons, years]</li><li>Units: degrees latitude</li></ul></li></ul></li><li><strong>ZIL_ERA5_1957-2020_timeseries.csv:</strong> Historical spatially averaged position of the ZIL for Antartica (Ant) and the Antarctic Peninsula (AP) in ERA5 from 1957 to 2020 for different <i>seasons</i>. File named:<ul><li>ZIL_ERA5_1957-2020_timeseries.csv<ul><li>Dimensions: [years, season_area]</li><li>Units: degrees latitude</li></ul></li></ul></li><li><strong>ZIL_ERA5_1957-2020_meanposition.csv:</strong> Historical temporally averaged position of the ZIL in ERA5 from 1957 to 2020 for different <i>seasons </i>and <i>months</i>. File named:<ul><li>ZIL_ERA5_1957-2020_meanposition.csv<ul><li>Dimensions: [lons, season/month]</li><li>Units: degrees latitude</li></ul></li></ul></li><li><strong>ZIL_CEMIP6_Historical_position.zip:</strong> Mean position of the ZIL for every longitude point in Historical simulations of CEMIP6 from 1957 to 2014 for different <i>seasons</i>. Files named:<ul><li>ZIL_CEMIP6_Hist_[<i>season</i>]_position.csv<ul><li>Dimensions: [lons, models]</li><li>Units: degrees latitude</li></ul></li></ul></li><li><strong>ZIL_CEMIP6_SSP2-45.zip:</strong> Mean position of the ZIL for every longitude point under the SSP2-4.5 scenario in CEMIP6 for the period 2040-69 and 2070-90 for different <i>seasons</i>. Files named:<ul><li>ZIL_CEMIP6_SSP2-45_2040-69_[<i>season</i>]_position.csv<ul><li>Dimensions: [lons, models]</li><li>Units: degrees latitude</li></ul></li><li>ZIL_CEMIP6_SSP2-45_2070-99_[<i>season</i>]_position.csv<ul><li>Dimensions: [lons, models]</li><li>Units: degrees latitude</li></ul></li></ul></li><li><strong>ZIL_CEMIP6_SSP5-85.zip:</strong> Mean position of the ZIL for every longitude point under the SSP5-8.5 scenario in CEMIP6 for the period 2040-69 and 2070-90 and trends for the period 2015-99 for different <i>seasons</i>. Files named:<ul><li>ZIL_CEMIP6_SSP5-85_2040-69_[<i>season</i>]_position.csv<ul><li>Dimensions: [lons, models]</li><li>Units: degrees latitude</li></ul></li><li>ZIL_CEMIP6_SSP5-85_2070-99_[<i>season</i>]_position.csv<ul><li>Dimensions: [lons, models]</li><li>Units: degrees latitude</li></ul></li></ul></li></ul><p><i><strong>seasons</strong></i> are:</p><ul><li>ANN: Annual mean</li><li>DJF: December-January-February (Summer)</li><li>MAM: March-April-May (Autumn)</li><li>JJA: June-July-August (Winter)</li><li>SON: September-October-November (Spring)</li></ul><p><i><strong>areas</strong></i> are:</p><ul><li>Ant: All Antarctica</li><li>AP: Antarctic Peninsula</li></ul><p><strong>Note:</strong> CEMIPT6 models include a column with CEMIP6 model average</p><p><strong>Version control</strong></p><p>v1.0 - Initial version<br>v1.1 - Change ERA5 dataset calculations from preliminary version of ERA5 to final version of ERA5</p><p> </p><p><strong>How to cite</strong></p><p>If you use this dataset, please cite the accompanying paper as:</p><p> </p><p><strong>Complementary code</strong></p><p>You can find the jupyter notebooks to complement the research in: <a href="https://doi.org/10.5281/zenodo.10063849">https://doi.org/10.5281/zenodo.10063849</a></p><p> </p><p><strong>Contact</strong></p><p>If you have any question, please contact with Sergi at <a href="mailto:sergi.gonzalez@slf.ch">sergi.gonzalez@slf.ch</a></p>
Plant Atlas 2020 — British altitude-by-latitude diagrams
<p>Plant Atlas 2020 is the most comprehensive survey of plants (flowering plants, ferns and charophytes) ever undertaken in Britain and Ireland. It is based on over 30 million records, collected mainly by volunteer recorders of the Botanical Society of Britain and Ireland (BSBI) between 2000 and 2019, as well as previous nationwide surveys undertaken in the 1950s and 1990s. This resource provides the data behind the British altitude-by-latitude diagrams presented in the Plant Atlas book (Stroh et al., 2023) and on the website (www.plantatlas2020.org).</p>
Plant Atlas 2020 — British and Irish species weekly apparency (including by-latitude breakdown for Britain), 2000–2019
<p>Plant Atlas 2020 is the most comprehensive survey of plants (flowering plants, ferns and charophytes) ever undertaken in Britain and Ireland. It is based on over 30 million records, collected mainly by volunteer recorders of the Botanical Society of Britain and Ireland (BSBI) between 2000 and 2019, as well as previous nationwide surveys undertaken in the 1950s and 1990s. This resource contains the species weekly “apparency” metrics presented within the Plant Atlas book and website, including a breakdown of species apparency by latitude for Britain. Apparency at the scale presented here (2 x 2 km spatially, smoothed over the period 2000–2019) combines aspects of recorder activity and detectability, with the latter being primarily influenced by phenology at this spatio-temporal scale.</p>
Dataset for: Statistical properties of meso-scale plasma flows in the nightside high-latitude ionosphere
<p>This dataset is a compilation of statistical results from Gabrielse et al. [2018] (<a href="https://doi.org/10.1029/2018JA025440">https://doi.org/10.1029/2018JA025440</a>). If you would like to use the dataset, please contact Christine Gabrielse (cgabrielse@ucla.edu, cgabrielse@gmail.com). Depending on how the results are used, the main authors request co-authorship on publications. </p> <p>The following list describes the columns in each data file labeled, ***_FLOW-DATA-PCvsAO_YYYY.txt <br> Files named ***_FLOW-DATA-PCvsAO_YYYY_poleward.txt are for poleward-directed flows. <br> Each text file is for a different year (YYYY). <br> AO=auroral oval<br> PC=polar cap</p> <p> time [YYYYMMDDhhmmss]<br> flagAO [-1=flow could not be observed. 0=flow could be observed, but was not. 1=flow was observed]<br> flagPC [-1=flow could not be observed. 0=flow could be observed, but was not. 1=flow was observed]<br> FWHMavg_AO [degrees]<br> FWHMkmavg_AO=[km]<br> longtestranges=[ignore]<br> Velmaxavg_AO=[m/s, actual average of max V in each range gate used]<br> VelmaxFITavg_AO=[m/s, determined from the Gaussian fits]<br> FWHMavg_PC=[degrees]<br> FWHMkmavg_PC=[km]<br> Velmaxavg_PC=[m/s, actual average of max V in each range gate used]<br> VelmaxFITavg_PC=[m/s, determined from the Gaussian fits]<br> ;;For the bearings/orientation, see the orientation text files. The following four variables were calculated in a first step but are not<br> ;;those used in the paper. They were not found with the strict selection criteria. Please do not use.<br> mbearingAO=[degrees in magnetic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> mbearingPC=[degrees in magnetic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)] <br> gbearingAO=[degrees in geographic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> gbearingPC=[degrees in geographic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> ;;;;;;;;;;;;;;;<br> minlatAO=[degrees, min geographic latitude of the flow]<br> maxlatAO=[degrees, max geographic latitude of the flow]<br> minlatPC=[degrees, min geographic latitude of the flow]<br> maxlatPC=[degrees, max geographic latitude of the flow]<br> mltAO=[degrees (MLT)]<br> mltPC=[degrees (MLT)]<br> AE=[nT]<br> AL=[nT]<br> SYMH=[nT]<br> IMFBz=[nT]<br> IMFBy=[nT]<br> F107=[sfu]</p> <p>The following list describes the columns in each data file labeled, ***_orientation_YYYY.txt <br> Files named ***_orientation_YYYY_poleward.txt are for poleward-directed flows. <br> Each text file is for a different year (YYYY). <br> The orientation was determined when enough bearings between RGs were available. See Gabrielse et al. [2018] for description. <br> https://doi.org/10.1029/2018JA025440 <br> AO=auroral oval<br> PC=polar cap</p> <p> time [YYYYMMDDhhmmss]<br> mbearingAO [degrees clockwise from magnetic North]<br> gbearingAO [degrees clockwise from geographic North]<br> mbearingPC [degrees clockwise from magnetic North]<br> gbearingPC [degrees clockwise from geographic North]</p> <p>The following list describes the columns in each data file labeled, ***_SPEC_TEST_***_noRG1-2.txt</p> <p> time [YYYYMMDDhhmmss]<br> RG [the range gate number at which the polar cap boundary was determined at RNK, or the auroral oval's equatorial boundary at SAS]</p>
Fermi High-Latitude Extended Sources Catalog (FHES)
<p>The data set contains results of the Fermi High-Latitude Extended Sources Catalog.</p> <p>The FITS file contains the Fermi High-Latitude Extended Sources Catalog (FHES). It provides source extension parameters (or upper limits thereof) and additional information for objects measured in 90 months of observations with the Large Area Telescope (LAT) on board NASA's Fermi satellite.<br> It includes the complete analysis results for 2546 sources. The analysis and data products contained in the catalog are described in detail in the accompanying paper, published in the Astrophysical Journal Supplement, <a href="https://doi.org/10.3847/1538-4365/aacdf7">https://doi.org/10.3847/1538-4365/aacdf7</a>. The preprint of the manuscript can be found here: <a href="https://arxiv.org/abs/1804.08035">https://arxiv.org/abs/1804.08035</a><br> <br> We further provide the 95% lower limits on the intergalactic magnetic field (IGMF) as plain ASCII files for different assumptions on the blazar duty cycles. These correspond to the limits shown in Figure 17 (right panel) of the paper.</p>
Dataset for "Nicolas & Buffett (2023) - Excitation of high-latitude MAC waves in Earth's core, GJI"
<p>Data from the geodynamo model 'Calypso', used as forcings for MAC waves in Earth's core (see Nicolas & Buffett 2023 - Excitation of high-latitude MAC waves in Earth's core, GJI). Code to analyze this data is published at <a href="https://zenodo.org/badge/latestdoi/296985370">zenodo.org/badge/latestdoi/296985370</a>.</p> <p>All files use the netCDF4 format, a format that allows to represent labeled arrays.</p>
Effect of Restoration on Physical and Chemical Peat Properties in Previously Drained Boreal Peatlands, latitude 57-63, Sweden, 2021
The major objective behind peatland restoration is to improve ecosystem services, such as increased biodiversity, increased carbon sequestration, increased groundwater storage, and improved surface water quality. However, a century or more of drained conditions has drastically changed the soil properties in relation to natural wetlands and this is likely to profoundly influence the potential for various biogeochemical peat processes. Thus, peatland restoration may result in undesired impacts and potential environmental threats. Two such undesired effects are increased methane production and increased mercury methylation. In this study, we investigated how nine boreal peatlands across a latitudinal gradient in Sweden have been affected by rewetting after up to a century of drained conditions. Each peatland was sampled for three 50 cm deep peat cores that were analyzed for carbon, nitrogen, δ13C, δ15N, bulk density, and organic matter proportion. Adjacent to each restored peatland, we sampled a corresponding pristine (natural) peatland to facilitate a comparison of how the peat properties have been affected by drainage and subsequent rewetting of the peatlands. Groundwater depth was monitored at all peatland locations to confirm restored conditions at the rewetted peatlands. The results indicate that a long period of drained conditions and subsequent rewetting have changed the peat properties, with differences shown in C/N ratio, dry bulk density, and organic matter content. Rewetting will thus not regenerate a pristine environment. Instead, it creates new conditions to which various biogeochemical processes will respond and these do not necessarily represent conditions prior to disturbance. Our study will provide background information to understand the biogeochemical dynamics in peatlands after restoration, especially since the study covers a large span of nutrient conditions and catchment settings. This understanding will be fundamental for the development of strat
Mechanisms mediating plant distributions across estuarine landscapes in a low-latitude tidal estuary
Understanding of how plant communities are organized and will respond to global changes requires an understanding of how plant species respond to multiple environmental gradients. We examined the mechanisms mediating the distribution patterns of tidal marsh plants along an estuarine gradient in Georgia using a combination of field transplant experiments and monitoring. Our results could not be fully explained by the “competition-to-stress hypothesis” (the current paradigm explaining plant distributions across estuarine landscapes). This hypothesis states that the upstream limits of plant distributions are determined by competition, and the downstream limits by abiotic stress. We found that competition was generally strong in freshwater and brackish marshes, and that conditions in brackish and salt marshes were stressful to freshwater marsh plants, results consistent with the competition-to-stress hypothesis. Four other aspects of our results, however, were not explained by the competition-to-stress hypothesis. First, several halophytes found the freshwater habitat stressful, and performed best (in the absence of competition) in brackish or salt marshes. Second, the upstream distribution of one species was determined by the combination of both abiotic and biotic (competition) factors. Third, marsh productivity (estimated by standing biomass) was a better predictor of relative biotic interaction intensity (RII) than was salinity or flooding, suggesting that productivity is a better indicator of plant stress than salinity or flooding gradients. Fourth, facilitation played a role in mediating the distribution patterns of some plants. Our results illustrate that even apparently simple abiotic gradients can encompass surprisingly complex processes mediating plant distributions.
Contrasting plant adaptation strategies to latitude in the native and invasive range of Spartina alterniflora: geographic survey (2014) and Common garden (2015-2017)
We examined trait differences and evolution across geographic clines among continents of the intertidal grass Spartina alterniflora within its invasive and native ranges. Between September and November 2014, we sampled vegetative and reproductive traits in the field at 20 sites over 20° latitude in China (invasive range) and 28 sites over 17° latitude in the US (native range). We grew both Chinese and US plants in a greenhouse common garden for three years (2015 - 2017) to determine if differences in performance of S. alterniflora between the introduced and native ranges were due to genetic differences or differences in abiotic conditions.
A set of allsky camera images from latitude 55 degrees North
<p>A set of R G and B images from an allsky camera situated in Copenhagen, Denmark. The images have been darksubtracted and split into these 16-bit FITS format images. Each image is the sum of something like 9 PNG images which each, originally, were 14 bit images from a ZWO camera cmos detector.</p>
Water column changes under ice during diferent winters in a mid-latitude Mediterranean high mountain lake - Dataset
<p>Dataset of the research article <em>Water column changes under ice during diferent winters in a mid-latitude Mediterranean high mountain lake.</em></p> <p>Granados, I., Toro, M., Giralt, S., Camacho, A., Montes, C., 2020. Water column changes under ice during different winters in a mid-latitude Mediterranean high mountain lake. Aquatic Sciences 82, 30. <a href="https://doi.org/10/ggmkhv">https://doi.org/10/ggmkhv</a></p> <p> </p>
Dataset from Holding et al. (2019)––Seasonal and spatial patterns of primary production in a high latitude fjord
<p>Unprecedented melting of the Greenland Ice Sheet (GrIS) is impacting the coastal ocean, and its effects on fjord ecology remain understudied. It has been suggested that as glaciers retreat, primary production regimes may be altered, rendering fjords less productive. Here we present data from the paper Holding et al. (2019). Seasonal and spatial patterns of primary production in a high-latitude fjord affected by Greenland Ice Sheet run-off. <em>Biogeosciences</em>, <em>16</em>(19), 3777-3792, /doi.org/10.5194/bg-16-3777-2019. This paper investigates patterns of primary productivity in a northeast Greenland fjord (Young Sound, 74°N), which receives run-off from the GrIS via land-terminating glaciers. This dataset includes measures of size fractioned primary production and chlorophyll <em>a </em>biomass, as well as CTD data and biochemical parameters. Furthermore, primary production was measured using photosynthesis v. irradiance (PI) curves, thus PI curve parameters are also available. The data were taken during the ice-free season along a spatial gradient of meltwater influence. </p> <p>We thank Egon Frandsen, Kunuk Lennert, and Ivali Lennert for excellent assistance during fieldwork. This research has beensupported by the Danish Environmental Protection Agency’s programme for Arctic research (DANCEA) (grant no. MST-112-0023), The Carlsberg Foundation (grant no. 2013_01_0532), the Norwegian Research Council (Mi- croPolar) (grant no. RCN 225956), and the European Commission, H2020 Research Infrastructures (GrIS-Melt (grant no. 752325) and INTAROS (grant no. 727890)). </p>
Model output and analysis scripts for "High-latitude precipitation as a driver of multicentennial variability of the AMOC in a climate model of intermediate complexity"
<p>Here, we provide annually averaged model output from a 3000-year control simulation of PlaSim–LSG, a climate model of intermediate complexity. Processed variables and a Jupyter notebook to reproduce all figures of the manuscript (Mehling et al.: "High-latitude precipitation as a driver of multicentennial variability of the AMOC in a climate model of intermediate complexity") can also be found in this repository.</p> <p>In addition, a Python implementation of the three-box model proposed in the manuscript can be found in the notebook <em>boxmodel.ipynb</em>.</p>
Database of Nightside, High-latitude Ionosphere Meso-scale Flow Characteristics
<p>This database is a compilation of nightside, high-latitude ionosphere meso-scale flow characteristics built on those used in Gabrielse et al. [2018] (<a href="https://doi.org/10.1029/2018JA025440">https://doi.org/10.1029/2018JA025440</a>). It is the most complete version. If you would like to use the database, please contact Christine Gabrielse (cgabrielse@ucla.edu, cgabrielse@gmail.com, and/or christine.gabrielse@aero.org). Depending on how the results are used, the main authors request co-authorship on publications that utilize this database. </p> <p>The methodology and selection criteria can be found in Gabrielse et al. [2018] (<a href="https://doi.org/10.1029/2018JA025440">https://doi.org/10.1029/2018JA025440</a>). </p> <p>The following list describes the columns in each data file labeled, ***_FLOW-DATA-PCvsAO_YYYY.txt <br> The first three letters (RNK or SAS) designate the station used (Rankin Inlet or Saskatoon).<br> Files named ***_FLOW-DATA-PCvsAO_poleward_YYYY.txt are for poleward-directed flows. <br> Each text file is for a different year (YYYY). <br> <br> AO=Auroral Oval for Rankin Inlet; equatorward of the auroral oval for Saskatoon (not used)<br> PC=Polar Cap for Rankin Inlet; Auroral Oval for Saskatoon</p> <p>(Note: the data files for RNK and SAS have the same format, so the PC designator means flows above the pertinent boundary (polar cap boundary for RNK, auroral oval equatorward boundary at SAS) and the AO designator means flows below the pertinent boundary.)</p> <p> time [YYYYMMDDhhmmss]<br> flagAO [-1=flow could not be observed. 0=flow could be observed, but was not. 1=flow was observed]<br> flagPC [-1=flow could not be observed. 0=flow could be observed, but was not. 1=flow was observed]<br> FWHMavg_AO [degrees]<br> FWHMkmavg_AO=[km]<br> longtestranges=[ignore]<br> Velmaxavg_AO=[m/s, actual average of max V in each range gate used]<br> VelmaxFITavg_AO=[m/s, determined from the Gaussian fits]<br> FWHMavg_PC=[degrees]<br> FWHMkmavg_PC=[km]<br> Velmaxavg_PC=[m/s, actual average of max V in each range gate used]<br> VelmaxFITavg_PC=[m/s, determined from the Gaussian fits]</p> <p>;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;<br> For the bearings/orientation, see the orientation text files. The following four variables were calculated in a first step but are not<br> those used in the paper. They were not found with the strict selection criteria. **Please do not use.**<br> mbearingAO=[degrees in magnetic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> mbearingPC=[degrees in magnetic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)] <br> gbearingAO=[degrees in geographic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> gbearingPC=[degrees in geographic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> ;;;;;;;;;;;;;;;<br> minlatAO=[degrees, min geographic latitude of the flow]<br> maxlatAO=[degrees, max geographic latitude of the flow]<br> minlatPC=[degrees, min geographic latitude of the flow]<br> maxlatPC=[degrees, max geographic latitude of the flow]<br> mltAO=[degrees (MLT)]<br> mltPC=[degrees (MLT)]<br> AE=[nT]<br> AL=[nT]<br> SYMH=[nT]<br> IMFBy=[nT]<br> IMFBz=[nT] <br> F107=[sfu]</p> <p>The following list describes the columns in each data file labeled, ***_orientation_YYYY.txt <br> Files named ***_orientation_poleward_YYYY.txt are for poleward-directed flows. <br> Each text file is for a different year (YYYY). <br> The orientation was determined when enough bearings between RGs were available. See Gabrielse et al. [2018] for description. <br> https://doi.org/10.1029/2018JA025440 <br> AO=auroral oval<br> PC=polar cap</p> <p> time [YYYYMMDDhhmmss]<br> mbearingAO [degrees clockwise from magnetic North]<br> gbearingAO [degrees clockwise from geographic North]<br> mbearingPC [degrees clockwise from magnetic North]<br> gbearingPC [degrees clockwise from geographic North]</p> <p>The following list describes the columns in each data file labeled, ***_SPEC_TEST_***_noRG1-2.txt</p> <p> time [YYYYMMDDhhmmss]<br> RG [the range gate number at which the polar cap boundary was determined at RNK, or the auroral oval's equatorial boundary at SAS]</p>
DMS emission dataset (latitudes >45N)
<p># README file for (sub)Arctic DMS emission dataset (latitudes >45N), v1.0.0<br> ===========================================================================</p> <p>Martí Galí Tàpias, 2019-06-12<br> http://orcid.org/0000-0002-5587-1271</p> <p>Address requests for additional data and questions to:<br> marti.gali.tapias@gmail.com</p> <p>This dataset contains satellite-derived time series of:<br> * dmspt (sea-surface dimethylsulfoniopropionate -DMSPt- concentration, in µmol/m3 or equivalent nmol/L)<br> * dms (sea-surface dimethylsulfide -DMS- concentration, in µmol/m3 or equivalent nmol/L)<br> * fdms (sea-air DMS flux, in µmol/m2/d)<br> * ice concentration (0 to 1)</p> <p>The DMSPt dataset was produced as described by:<br> Galí, M., Devred, E., Levasseur, M., Royer, S. J., & Babin, M. (2015). A remote sensing algorithm for planktonic dimethylsulfoniopropionate (DMSP) and an analysis of global patterns. Remote Sensing of Environment, 171, 171-184. https://doi.org/10.1016/j.rse.2015.10.012</p> <p>The DMS dataset was produced as described by:</p> <p>Galí, M., Devred, E., Babin, M., & Levasseur, M. (2019). Decadal increase in Arctic dimethylsulfide emission. Accepted in PNAS. doi:10.1073/pnas.1904378116.</p> <p>Galí, M., Levasseur, M., Devred, E., Simó, R., & Babin, M. (2018). Sea-surface dimethylsulfide (DMS) concentration from satellite data at global and regional scales. Biogeosciences, 15(11), 3497-3519. https://doi.org/10.5194/bg-15-3497-2018</p> <p> </p> <p>Code for the DMSPt_SAT and DMS_SAT algorithms: https://github.com/mgali/DMS-SAT_ALGORITHM</p> <p><br> ## File format</p> <p>MetCDF files are self-describing: they contain one “variable of interest” and all the corresponding temporal and geographic dimensions.</p> <p>## File naming convention</p> <p>variable_sensorYYYY_nD_28km.nc</p> <p>Both the DMSPt and DMS satellite datasets are based on ocean color data obtained from NASA Ocean Color website. Chlorophyll was derived from the GSM algorithm, hence _gsm was appended to the variable names.</p> <p>DMSPt and DMS were produced for 2 satellite sensors.<br> * S = SeaWiFS, years 1998 to 2007<br> * A = MODIS-Aqua, years 2003 to 2016 (the last year is empty from October on)</p> <p>FDMS was calculated using ERA-Interim wind speed and SST, and ice cover from NSIDC. Two different gas exchange parameterizations were used, whose names are appended to the variable names:<br> * W97 (Woolf 1997)<br> * N00GBC (Nightingale et al. 2000 GBC –do not confound with Nightingale et al. 2000 GL)</p> <p>FDMS was also calculated using climatological DMS fields (W97 parameterization) while retaining the interannual variability of all the other variables involved in FDMS calculation:<br> * S19982007 DMS climatology<br> * A20032016 DMS climatology<br> * L11 DMS climatology (interpolation based climatology of Lana et al. 2011 GBC)</p> <p>Ice cover (ice) is also provided for the full period 1998-2016.</p> <p>## Directory naming convention</p> <p>Each directory is named using the names of variable it contains.</p>
The impact of 11 May 2024 super geomagnetic storm on the plasma distribution over the Indian equatorial/low latitude ionospheric region
<p>The file contains the data set and the software for the generation the plots used in the manuscript " The impact of 11 May 2024 super geomagnetic storm on the plasma distribution over the Indian equatorial/low latitude ionospheric region".</p>
Supplementary Information to: "Living on the edge: Response of rudist bivalves (Hippuritida) to hot and highly seasonal climate in the low-latitude Saiwan site, Oman"
<p>This dataset contains supplementary information required to understand and reproduce the study detailed in our manuscript titled "<em>Living on the edge: Response of rudist bivalves (Hippuritida) to hot and highly seasonal climate in the low-latitude Saiwan site, Oman</em>" which was submitted for publication to Palaeogeography, Palaeoclimatology, Palaeoecology.</p>
Series expansions for converting between auxiliary latitudes
<p>This data accompanies the paper</p> <blockquote> <p>C. F. F. Karney,<br><a href="https://doi.org/10.1080/00396265.2023.2217604">On auxiliary latitudes</a>,<br>Survey Review <strong>56</strong>(395), 165-180 (2024).</p> <p>Journal article: <a href="https://doi.org/10.1080/00396265.2023.2217604">doi:10.1080/00396265.2023.2217604</a></p> <p>Preprint: <a href="https://arxiv.org/abs/2212.05818">https://arxiv.org/abs/2212.05818</a></p> </blockquote> <p>Files:</p> <ul> <li><code>auxlat.mac</code>: the Maxima code used to obtain the series expansions. Instructions for using this code are included in the file.</li> <li><code>auxvals40.mac</code>: the 40th-order series in Maxima notation.</li> <li><code>auxvals6.m</code>, <code>auxvals16.m</code>, and <code>auxvals40.m</code>: the 6th-, 16th-, and 40th-order series as Octave/MATLAB code. The 40th-order series are given as floating-point numbers; the others are as exact fractions.</li> <li><code>AuxLatitude.hpp</code>, <code>AuxLatitude.cpp</code>, and <code>example-AuxLatitude.cpp</code>: C++ code implementing the conversions between auxiliary latitudes both using the series expansions and with direct evaluation of the defining equations.</li> </ul>
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