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  • Monthly and Annual contour lines of the zero and the positive maximum of the Wind Stress Curl over Western North Atlantic during 1980-2019 and the Gulf Stream path during 1993-2019.
Title: Monthly and Annual contour lines of the zero and the positive maximum of the Wind Stress Curl over Western North Atlantic during 1980-2019 and the Gulf Stream path during 1993-2019.
{"Abstract":["This dataset includes multiple fields: (i) files for monthly and annual fields for the max curl line and the zero curl line at 0.1 degree longitudinal resolutions; (ii) files for monthly and annual GS path obtained from Altimetry and originally processed by Andres (2016) at 0.1 degree longitudinal resolution. The maximum curl line (MCL) and the zero curl line (ZCL) calculations are briefly described here and are based on the original wind data (at 1.25 x 1.25 degree) provided by the Japanese reanalysis (JRA-55; Kobayashi et al., 2015) and available at https://zenodo.org/record/8200832 (Gifford et al. 2023). For details see Gifford, 2023. <\/p>\n\nThe wind stress curl (WSC) fields used for the MCL and ZCL calculations extend from 80W to 45W and 30N to 45N at the 1.25 by 1.25-degree resolution.  The MCL is defined as the maximum WSC values greater than zero within the domain per 1.25 degree longitude. As such, it is a function of longitude and is not a constant WSC value unlike the zero contour. High wind stress curl values that occurred near the coast were not included within this calculation. After MCL at the 1.25 resolution was obtained the line was smoothed with a gaussian smoothing and interpolated on to a 0.1 longitudinal resolution. The smoothed MCL lines at 0.1 degree resolution are provided in separate files for monthly and annual averages (2 files). Similarly, 2 other files (monthly and annual) are provided for the ZCL.    <\/p>\n\nLike the MCL, the ZCL is a line derived from 1.25 degree longitude throughout the domain under the condition that it's the line of zero WSC. The ZCL is constant at 0 and does not vary spatially like the MCL. If there are more than one location of zero curl for a given longitude the first location south of the MCL is selected. Similar to the MCL, the ZCL was smoothed with a gaussian smoothing and interpolated on to a 0.1 longitudinal resolution.   <\/p>\n\nThe above files span the years from 1980 through 2019. So, the monthly files have 480 months starting January 1980, and the annual files have 40 years of data. The files are organized with each row being a new time step and each column being a different longitude. Therefore, the monthly MCL and ZCL files are each 480 x 351 for the 0.1 resolution data. Similarly, the annual files are 40 x 351 for the 0.1 degree resolution data.  <\/p>\n\nNote that the monthly MCLs and ZCLs are obtained from the monthly wind-stress curl fields. The annual MCLs and ZCLs are obtained from the annual wind-stress curl fields.<\/strong><\/p>\n\nSince the monthly curl fields preserves more atmospheric mesoscales than the annual curl fields, the 12-month average of the monthly MCLs and ZCLs will not match with the annual MCLs and ZCLs derived from the annual curl field.  The annual MCLs and ZCLs provided here are obtained from the annual curl fields and representative metrics of the wind forcing on an annual time-scale. <\/p>\n\nFurthermore, the monthly Gulf Stream axis path (25 cm isoheight from Altimeter, reprocessed by Andres (2016) technique) from 1993 through 2019 have been made available here. A total of 324 monthly paths of the Gulf Stream are tabulated. In addition, the annual GS paths for these 27 years (1993-2019) of altimetry era have been put together for ease of use. The monthly Gulf Stream paths have been resampled and reprocessed for uniqueness at every 0.1 degree longitude from 75W to 50W and smoothed with a 100 km (10 point) running average via matlab. The uniqueness has been achieved by using Consolidator algorithm (D\u2019Errico, 2023). <\/p>\n\nEach monthly or annual GS path has 251 points between 75W to 50W at 0.1 degree resolution.  <\/p>"],"Other":["Please contact igifford@earth.miami.edu for any queries.","{"references": ["Andres, M., 2016. On the recent destabilization of the Gulf Stream path downstream of Cape Hatteras. Geophysical Research Letters, 43(18), 9836-9842.", "D'Errico, J., 2023. Consolidator (https://www.mathworks.com/matlabcentral/fileexchange/ 8354-consolidator), MATLAB Central File Exchange. Retrieved June 17, 2023.", "Gifford, Ian. H., 2023. The Synchronicity of the Gulf Stream Free Jet and the Wind Induced Cyclonic Vorticity Pool. MS Thesis, University of Massachusetts Dartmouth. 75pp.", "Gifford, Ian, H., Avijit Gangopadhyay, Magdalena Andres, Glen Gawarkiewicz, Hilde Oliver, Adrienne Silver, 2023. Wind Stress, Wind Stress Curl, and Upwelling Velocities in the Northwest Atlantic (80-45W, 30-45N) during 1980-2019, https://zenodo.org/record/8200832.", "Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., Endo, H. and Miyaoka, K., 2015. The JRA-55 reanalysis: General specifications and basic characteristics.\\u202fJournal of the Meteorological Society of Japan. Ser. II,\\u202f93(1), pp.5-48. Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., Endo, H. and Miyaoka, K., 2015. The JRA-55 reanalysis: General specifications and basic characteristics.\\u202fJournal of the Meteorological Society of Japan. Ser. II,\\u202f93(1), pp.5-48."]}"]}  more » « less
Award ID(s):
2123283 1851242
PAR ID:
10448887
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
Zenodo
Date Published:
Edition / Version:
1.0.0
Subject(s) / Keyword(s):
Ocean-Air Interaction Gulf Stream Force and Response Ocean Circulation Wind Stress Curl Altimetry Zero Wind Stress Curl Maximum Wind Stress Curl Gulf Stream Path
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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  1. ; ; ; ; ; (, Zenodo)
    {"Abstract":["This dataset contains three netcdf files that pertain to monthly, seasonal, and annual fields of surface wind stress, wind stress curl, and curl-derived upwelling velocities over the Northwest Atlantic (80-45W, 30-45N) covering a forty year period from 1980 to 2019. Six-hourly surface (10 m) wind speed components from the Japanese 55-year reanalysis (JRA-55; Kobayashi et al., 2015) were processed from 1980 to 2019 over a larger North Atlantic domain of 100W to 10E and 10N to 80N. Wind stress was computed using a modified step-wise formulation, originally based on (Gill, 1982) and a non-linear drag coefficient (Large and Pond, 1981), and later modified for low speeds (Trenberth et al., 1989). See Gifford (2023) for more details.   <\/p>\n\nAfter the six-hourly zonal and meridional wind stresses were calculated, the zonal change in meridional stress (curlx) and the negative meridional change in zonal stress (curly) were found using NumPy\u2019s gradient function in Python (Harris et al., 2020) over the larger North Atlantic domain (100W-10E, 10-80N). The curl (curlx + curly) over the study domain (80-45W, 10-80N) is then extracted, which maintain a constant order of computational accuracy in the interior and along the boundaries for the smaller domain in a centered-difference gradient calculation. <\/p>\n\nThe monthly averages of the 6-hour daily stresses and curls were then computed using the command line suite climate data operators (CDO, Schulzweida, 2022) monmean function. The seasonal (3-month average) and annual averages (12-month average) were calculated in Python using the monthly fields with NumPy (NumPy, Harris et al., 2020). <\/p>\n\nCorresponding upwelling velocities at different time-scales were obtained from the respective curl fields and zonal wind stress by using the Ekman pumping equation of the study by Risien and Chelton (2008; page 2393). Please see Gifford (2023) for more details.   <\/p>\n\nThe files each contain nine variables that include longitude, latitude, time, zonal wind stress, meridional wind stress, zonal change in meridional wind stress (curlx), the negative meridional change in zonal wind stress (curly), total curl, and upwelling. Units of time begin in 1980 and are months, seasons (JFM etc.), and years to 2019. The longitude variable extends from 80W to 45W and latitude is 30N to 45N with uniform 1.25 degree resolution.  <\/p>\n\nUnits of stress are in Pascals, units of curl are in Pascals per meter, and upwelling velocity is described by centimeters per day. The spatial grid is a 29 x 13 longitude x latitude array. <\/p>\n\nFilenames: <\/p>\n\nmonthly_windstress_wsc_upwelling.nc<\/strong>: 480 time steps from 80W to 45W and 30N to 45N.<\/p>\n\nseasonal_windstress_wsc_upwelling.nc<\/strong>: 160 time steps from 80W to 45W and 30N to 45N.<\/p>\n\nannual_windstress_wsc_upwelling.nc<\/strong>: 40 time steps from 80W to 45W and 30N to 45N.<\/p>"],"Other":["Please contact igifford@earth.miami.edu for any queries.","{"references": ["Gifford, I.H., 2023. The Synchronicity of the Gulf Stream Free Jet and the Wind Induced Cyclonic Vorticity Pool. MS Thesis, University of Massachusetts Dartmouth. 75pp.", "Gill, A. E. (1982). Atmosphere-ocean dynamics (Vol. 30). Academic Press.", "Harris, C.R., Millman, K.J., van der Walt, S.J. et al. Array programming with NumPy. Nature 585, 357\\u2013362 (2020). DOI: 10.1038/s41586-020-2649-2.", "Japan Meteorological Agency/Japan (2013), JRA-55: Japanese 55-year Reanalysis, Daily 3-Hourly and 6-Hourly Data, https://doi.org/10.5065/D6HH6H41, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, Boulder, Colo. (Updated monthly.)", "Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., Endo, H. and Miyaoka, K., 2015. The JRA-55 reanalysis: General specifications and basic characteristics.\\u202fJournal of the Meteorological Society of Japan. Ser. II,\\u202f93(1), pp.5-48.", "Large, W.G. and Pond, S., 1981. Open ocean momentum flux measurements in moderate to strong winds.\\u202fJournal of physical oceanography,\\u202f11(3), pp.324-336.", "Risien, C.M. and Chelton, D.B., 2008. A global climatology of surface wind and wind stress fields from eight years of QuikSCAT scatterometer data.\\u202fJournal of Physical Oceanography,\\u202f38(11), pp.2379-2413.", "Schulzweida, Uwe. (2022). CDO User Guide (2.1.0). Zenodo. https://doi.org/10.5281/zenodo.7112925.", "Trenberth, K.E., Large, W.G. and Olson, J.G., 1989. The effective drag coefficient for evaluating wind stress over the oceans.\\u202fJournal of Climate,\\u202f2(12), pp.1507-1516."]}"]} 
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  2. ; ; (, Zenodo)
    {"Abstract":["Gulf Stream paths (daily, monthly, and annual) from 1993-01-01 to 2023-12-31 are identified via the longest 25-cm sea surface height contour in the Northwest Atlantic (75°W–55°W; 33°N–43°N) from the daily 1/8° resolution maps of absolute dynamic topography from the E.U. Copernicus Marine Service product Global Ocean Gridded Level 4 Sea Surface Heights and Derived Variables Reprocessed 1993 Ongoing, following the methodology of Andres (2016). The daily sea surface height fields are averaged to monthly and annual fields to identify the corresponding monthly and annual Gulf Stream paths. Additionally, an updated Gulf Stream destabilization point time series (1993–2023), which builds upon the work of Andres (2016), was generated using the E.U. Copernicus Marine Service product Global Ocean Gridded Level 4 Sea Surface Heights and Derived Variables Reprocessed 1993 Ongoing (1/8°). Similar to Andres (2016), the monthly Gulf Stream path is identified as the 25-cm SSH contour from absolute dynamic topography maps. The 12 monthly mean paths are divided yearly into 0.5° longitude bins (from 75°W to 55°W). In some months, the Gulf Stream can take a meandering path and contort over itself in an “S” curve. In these cases, the northernmost latitude is used in the variance calculation to resolve the issue of multiple latitudes for a single longitude. The variance of the Gulf Stream position (latitude) is then calculated for each year using the 12 monthly mean paths. The destabilization point is defined as the first downstream distance (longitude) at which the variance of the Gulf Stream position exceeds 0.4(°)2, which differs from the original threshold value of 0.5(°)2 in Andres (2016). The threshold value of 0.4(°)2 is the 70th percentile of variance for all years, which marks the transition from a relatively stable jet to an unstable, meandering current in the new higher-resolution (1/8°) maps of absolute dynamic topography.\n\nThanks to improvements in processing and combining satellite altimeter data (Taburet et al., 2019), in recent years the maps of absolute dynamic topography are different than the maps used by Andres (2016), which had 1/4° resolution. To account for the differences in the resolution of the data and corrections to the processing standards of altimeter data, a new threshold value was chosen that is consistent with the methods of Andres (2016), i.e., the threshold still signifies the transition between a stable and unstable Gulf Stream. However, a lower threshold value is necessary in the new absolute dynamic topography maps since finer-resolution data can separate distinct local maxima in variance, which could be smoothed together in coarser data, and may cause the destabilization point to be identified further downstream if the threshold were not adjusted. The 70th percentile of variance (0.4(°)2) for all years (1993–2023) was chosen as the threshold because the distribution of variance is right-skewed with a long tail and the 70th percentile separate lower variance associated with meridional shifts in the Gulf Stream path from the extreme, vigorous meadnering that occurs downstream of the "destabilization point".\n\nThe daily, monthly, annual Gulf Stream paths, and the updated destabilization point time series were generated using the E.U. Copernicus Marine Service product Global Ocean Gridded Level 4 Sea Surface Heights and Derived Variables Reprocessed 1993 Ongoing (https://doi.org/10.48670/moi-00148). \n\n \n\n "]} 
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  3. ; ; ; (, Dryad)
    {"Abstract":["This dataset contains monthly average output files from the iCAM6\n simulations used in the manuscript "Enhancing understanding of the\n hydrological cycle via pairing of process-oriented and isotope ratio\n tracers," in review at the Journal of Advances in Modeling Earth\n Systems. A file corresponding to each of the tagged and isotopic variables\n used in this manuscript is included. Files are at 0.9° latitude x 1.25°\n longitude, and are in NetCDF format. Data from two simulations are\n included: 1) a simulation where the atmospheric model was\n "nudged" to ERA5 wind and surface pressure fields, by adding an\n additional tendency (see section 3.1 of associated manuscript), and 2) a\n simulation where the atmospheric state was allowed to freely evolve, using\n only boundary conditions imposed at the surface and top of atmosphere.\n Specific information about each of the variables provided is located in\n the "usage notes" section below. Associated article abstract:\n The hydrologic cycle couples the Earth's energy and carbon budgets\n through evaporation, moisture transport, and precipitation. Despite a\n wealth of observations and models, fundamental limitations remain in our\n capacity to deduce even the most basic properties of the hydrological\n cycle, including the spatial pattern of the residence time (RT) of water\n in the atmosphere and the mean distance traveled from evaporation sources\n to precipitation sinks. Meanwhile, geochemical tracers such as stable\n water isotope ratios provide a tool to probe hydrological processes, yet\n their interpretation remains equivocal despite several decades of use. As\n a result, there is a need for new mechanistic tools that link variations\n in water isotope ratios to underlying hydrological processes. Here we\n present a new suite of \u201cprocess-oriented tags,\u201d which we use to explicitly\n trace hydrological processes within the isotopically enabled Community\n Atmosphere Model, version 6 (iCAM6). Using these tags, we test the\n hypotheses that precipitation isotope ratios respond to parcel rainout,\n variations in atmospheric RT, and preserve information regarding\n meteorological conditions during evaporation. We present results for a\n historical simulation from 1980 to 2004, forced with winds from the ERA5\n reanalysis. We find strong evidence that precipitation isotope ratios\n record information about atmospheric rainout and meteorological conditions\n during evaporation, but little evidence that precipitation isotope ratios\n vary with water vapor RT. These new tracer methods will enable more robust\n linkages between observations of isotope ratios in the modern hydrologic\n cycle or proxies of past terrestrial environments and the environmental\n processes underlying these observations.  "],"Methods":["Details about the simulation setup can be found in section 3 of the\n associated open-source manuscript, "Enhancing understanding of the\n hydrological cycle via pairing of process\u2010oriented and isotope ratio\n tracers." In brief, we conducted two simulations of the atmosphere\n from 1980-2004 using the isotope-enabled version of the Community\n Atmosphere Model 6 (iCAM6) at 0.9x1.25° horizontal resolution, and with 30\n vertical hybrid layers spanning from the surface to ~3 hPa. In the first\n simulation, wind and surface pressure fields were "nudged"\n toward the ERA5 reanalysis dataset by adding a nudging tendency,\n preventing the model from diverging from observed/reanalysis wind fields.\n In the second simulation, no additional nudging tendency was included, and\n the model was allowed to evolve 'freely' with only boundary\n conditions provided at the top (e.g., incoming solar radiation) and bottom\n (e.g., observed sea surface temperatures) of the model. In addition to the\n isotopic variables, our simulation included a suite of\n 'process-oriented tracers,' which we describe in section 2 of\n the manuscript. These variables are meant to track a property of water\n associated with evaporation, condensation, or atmospheric transport."],"Other":["Metadata are provided about each of the files below; moreover, since the\n attached files are NetCDF data - this information is also provided with\n the data files. NetCDF metadata can be accessed using standard tools\n (e.g., ncdump). Each file has 4 variables: the tagged quantity, and the\n associated coordinate variables (time, latitude, longitude). The latter\n three are identical across all files, only the tagged quantity changes.\n Twelve files are provided for the nudged simulation, and an additional\n three are provided for the free simulations: Nudged simulation files\n iCAM6_nudged_1980-2004_mon_RHevap: Mass-weighted mean evaporation source\n property: RH (%) with respect to surface temperature.\n iCAM6_nudged_1980-2004_mon_Tevap: Mass-weighted mean evaporation source\n property: surface temperature in Kelvin\n iCAM6_nudged_1980-2004_mon_Tcond: Mass-weighted mean condensation\n property: temperature (K) iCAM6_nudged_1980-2004_mon_columnQ: Total\n (vertically integrated) precipitable water (kg/m2).  Not a tagged\n quantity, but necessary to calculate depletion times in section 4.3 (e.g.,\n Fig. 11 and 12). iCAM6_nudged_1980-2004_mon_d18O: Precipitation d18O (\u2030\n VSMOW) iCAM6_nudged_1980-2004_mon_d18Oevap_0: Mass-weighted mean\n evaporation source property - d18O of the evaporative flux (e.g., the\n 'initial' isotope ratio prior to condensation), (\u2030 VSMOW)\n iCAM6_nudged_1980-2004_mon_dxs: Precipitation deuterium excess (\u2030 VSMOW) -\n note that precipitation d2H can be calculated from this file and the\n precipitation d18O as d2H = d-excess - 8*d18O.\n iCAM6_nudged_1980-2004_mon_dexevap_0: Mass-weighted mean evaporation\n source property - deuterium excess of the evaporative flux\n iCAM6_nudged_1980-2004_mon_lnf: Integrated property - ln(f) calculated\n from the constant-fractionation d18O tracer (see section 3.2).\n iCAM6_nudged_1980-2004_mon_precip: Total precipitation rate in m/s. Note\n there is an error in the metadata in this file - it is total\n precipitation, not just convective precipitation.\n iCAM6_nudged_1980-2004_mon_residencetime: Mean atmospheric water residence\n time (in days). iCAM6_nudged_1980-2004_mon_transportdistance: Mean\n atmospheric water transport distance (in km). Free simulation files\n iCAM6_free_1980-2004_mon_d18O: Precipitation d18O (\u2030 VSMOW)\n iCAM6_free_1980-2004_mon_dxs: Precipitation deuterium excess (\u2030 VSMOW) -\n note that precipitation d2H can be calculated from this file and the\n precipitation d18O as d2H = d-excess - 8*d18O.\n iCAM6_free_1980-2004_mon_precip: Total precipitation rate in m/s. Note\n there is an error in the metadata in this file - it is total\n precipitation, not just convective precipitation."]} 
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  4. ; ; (, Zenodo)
    {"Abstract":["This dataset consists of weekly trajectory information of Gulf Stream Warm Core Rings from 2000-2010. This work builds upon Silver et al. (2022a) ( https://doi.org/10.5281/zenodo.6436380) which contained Warm Core Ring trajectory information from 2011 to 2020. Combining the two datasets a total of 21 years of weekly Warm Core Ring trajectories can be obtained. An example of how to use such a dataset can be found in Silver et al. (2022b).<\/p>\n\nThe format of the dataset is similar to that of  Silver et al. (2022a), and the following description is adapted from their dataset. This dataset is comprised of individual files containing each ring\u2019s weekly center location and its area for 374 WCRs present between January 1, 2000 and December 31, 2010. Each Warm Core Ring is identified by a unique alphanumeric code 'WEyyyymmddA', where 'WE' represents a Warm Eddy (as identified in the analysis charts); 'yyyymmdd' is the year, month and day of formation; and the last character 'A' represents the sequential sighting of the eddies in a particular year. Continuity of a ring which passes from one year to the next is maintained by the same character in the first sighting.  For example, the first ring in 2002 having a trailing alphabet of 'F' indicates that five rings were carried over from 2001 which were still observed on January 1, 2002. Each ring has its own netCDF (.nc) filename following its alphanumeric code. Each file contains 4 variables, \u201cLon\u201d- the ring center\u2019s weekly longitude, \u201cLat\u201d- the ring center\u2019s weekly latitude, \u201cArea\u201d - the rings weekly size in km2<\/sup>, and \u201cDate\u201d in days - representing the days since Jan 01, 0000. <\/p>\n\nThe process of creating the WCR tracking dataset follows the same methodology of the previously generated WCR census (Gangopadhyay et al., 2019, 2020). The Jenifer Clark\u2019s Gulf Stream Charts used to create this dataset are 2-3 times a week from 2000-2010. Thus, we used approximately 1560 Charts for the 10 years of analysis. All of these charts were reanalyzed between 75° and 55°W using QGIS 2.18.16 (2016) and geo-referenced on a WGS84 coordinate system (Decker, 1986). <\/p>\n\n <\/p>\n\nSilver, A., Gangopadhyay, A, & Gawarkiewicz, G. (2022a). Warm Core Ring Trajectories in the Northwest Atlantic Slope Sea (2011-2020) (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6436380<\/p>\n\nSilver, A., Gangopadhyay, A., Gawarkiewicz, G., Andres, M., Flierl, G., & Clark, J. (2022b). Spatial Variability of Movement, Structure, and Formation of Warm Core Rings in the Northwest Atlantic Slope Sea. Journal of Geophysical Research: Oceans<\/em>, 127<\/em>(8), e2022JC018737. https://doi.org/10.1029/2022JC018737 <\/p>\n\nGangopadhyay, A., G. Gawarkiewicz, N. Etige, M. Monim and J. Clark, 2019. An Observed Regime Shift in the Formation of Warm Core Rings from the Gulf Stream, Nature - Scientific Reports, https://doi.org/10.1038/s41598-019-48661-9. www.nature.com/articles/s41598-019-48661-9.<\/p>\n\nGangopadhyay, A., N. Etige, G. Gawarkiewicz, A. M. Silver, M. Monim and J. Clark, 2020.  A Census of the Warm Core Rings of the Gulf Stream (1980-2017). Journal of Geophysical Research, Oceans, 125, e2019JC016033. https://doi.org/10.1029/2019JC016033.<\/p>\n\nQGIS Development Team. QGIS Geographic Information System (2016).<\/p>\n\nDecker, B. L. World Geodetic System 1984. World geodetic system 1984 (1986).<\/p>\n\n <\/p>"],"Other":["Funded by two NSF US grants OCE-1851242, OCE-212328","{"references": ["Silver, A., Gangopadhyay, A, & Gawarkiewicz, G. (2022). Warm Core Ring Trajectories in the Northwest Atlantic Slope Sea (2011-2020) (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6436380", "Silver, A., Gangopadhyay, A., Gawarkiewicz, G., Andres, M., Flierl, G., & Clark, J. (2022b). Spatial Variability of Movement, Structure, and Formation of Warm Core Rings in the Northwest Atlantic Slope Sea.\\u00a0Journal of Geophysical Research: Oceans,\\u00a0127(8), e2022JC018737.\\u00a0https://doi.org/10.1029/2022JC018737", "Gangopadhyay, A., G. Gawarkiewicz, N. Etige, M. Monim and J. Clark, 2019. An Observed Regime Shift in the Formation of Warm Core Rings from the Gulf Stream, Nature - Scientific Reports, https://doi.org/10.1038/s41598-019-48661-9. www.nature.com/articles/s41598-019-48661-9.", "Gangopadhyay, A., N. Etige, G. Gawarkiewicz, A. M. Silver, M. Monim and J. Clark, 2020. A Census of the Warm Core Rings of the Gulf Stream (1980-2017). Journal of Geophysical Research, Oceans, 125, e2019JC016033. https://doi.org/10.1029/2019JC016033.", "QGIS Development Team. QGIS Geographic Information System (2016).", "Decker, B. L. World Geodetic System 1984. World geodetic system 1984 (1986)."]}"]} 
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  5. ; ; ; (, Journal of Geophysical Research: Oceans)
    Abstract The Gulf Stream system is dominated by strong mesoscale variability that can obscure any seasonal signals in Gulf Stream strength. Nevertheless, seasonal variability of the Gulf Stream is important for local weather and climate and can influence amplification of hurricane intensity and storm tracks. We investigate seasonal variability of the speed of the Gulf Stream after it detaches from Cape Hatteras, using high‐resolution along‐track altimeter data. The altimeter data show a significant seasonal cycle in the Gulf Stream axis speed, peaking in summer. The seasonal variability in the Gulf Stream axis velocity is related to changes in the local wind stress curl and changes in the density difference across the Gulf Stream. Wind forcing affects the Gulf Stream year‐round, while changes in the density difference have the largest impact in summer. Overall, changes in the wind stress curl and upper ocean density difference across the Gulf Stream can explain roughly 40% of the seasonal Gulf Stream speed variability in summer. 
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