Published July 31, 2023 | Version 1.0.0
Dataset Open

Wind Stress, Wind Stress Curl, and Upwelling Velocities in the Northwest Atlantic (80-45W, 30-45N) during 1980-2019

Creators

  • 1. University of Massachusetts Dartmouth
  • 2. Woods Hole Oceanographic Institution

Contributors

Data curator:

  • 1. University of Massachusetts Dartmouth
  • 2. University of Massachusetts
  • 3. Woods Hole Oceanographic Institution
  • 4. Professor

Description

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.   

After 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’s 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. 

The 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). 

Corresponding 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.   

The 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.  

Units 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. 

Filenames: 

monthly_windstress_wsc_upwelling.nc: 480 time steps from 80W to 45W and 30N to 45N.

seasonal_windstress_wsc_upwelling.nc: 160 time steps from 80W to 45W and 30N to 45N.

annual_windstress_wsc_upwelling.nc: 40 time steps from 80W to 45W and 30N to 45N.

Notes

Please contact igifford@earth.miami.edu for any queries.

Files

Files (12.7 MB)

Name Size Download all
md5:f3ea89a8cc068690c258db52c66926c0
765.9 kB Download
md5:e4d8eabe23399771a152ecbf29149d8e
8.9 MB Download
md5:105a65ab95e44d81f9659056bff3d40f
3.0 MB Download

Additional details

Funding

U.S. National Science Foundation
Collaborative Research: Investigating the Asymmetries and Temporally Varying Nature of Gulf Stream Ring Formation 2123283
U.S. National Science Foundation
Scholarships to Accelerate Engineering Leadership and Identity in Graduate Students 2130252
U.S. National Science Foundation
Implementation of a Contextualized Computing Pedagogy in STEM Core Courses and Its Impact on Undergraduate Student Academic Success, Retention, and Graduation 2030552

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, 357–362 (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. Journal of the Meteorological Society of Japan. Ser. II, 93(1), pp.5-48.
  • Large, W.G. and Pond, S., 1981. Open ocean momentum flux measurements in moderate to strong winds. Journal of physical oceanography, 11(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. Journal of Physical Oceanography, 38(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. Journal of Climate, 2(12), pp.1507-1516.