The Global Mesoscale Eddy Available Potential Energy Field in Models and Observations
Global maps of the mesoscale eddy available potential energy (EAPE) field at a depth of 500 m are created using potential density anomalies in a high‐resolution 1/12.5° global ocean model. Maps made from both a free‐running simulation and a data‐assimilative reanalysis of the HYbrid Coordinate Ocean...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article in Journal/Newspaper |
| Language: | unknown |
| Published: |
U.S. Government Printing Office
2017
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/2027.42/141214 https://doi.org/10.1002/2017JC013136 |
| _version_ | 1835010290866978816 |
|---|---|
| author | Luecke, C.A. Arbic, B.K. Bassette, S.L. Richman, J.G. Shriver, J.F. Alford, M.H. Smedstad, O.M. Timko, P.G. Trossman, D.S. Wallcraft, A.J. |
| author_facet | Luecke, C.A. Arbic, B.K. Bassette, S.L. Richman, J.G. Shriver, J.F. Alford, M.H. Smedstad, O.M. Timko, P.G. Trossman, D.S. Wallcraft, A.J. |
| author_sort | Luecke, C.A. |
| collection | Unknown |
| description | Global maps of the mesoscale eddy available potential energy (EAPE) field at a depth of 500 m are created using potential density anomalies in a high‐resolution 1/12.5° global ocean model. Maps made from both a free‐running simulation and a data‐assimilative reanalysis of the HYbrid Coordinate Ocean Model (HYCOM) are compared with maps made by other researchers from density anomalies in Argo profiles. The HYCOM and Argo maps display similar features, especially in the dominance of western boundary currents. The reanalysis maps match the Argo maps more closely, demonstrating the added value of data assimilation. Global averages of the simulation, reanalysis, and Argo EAPE all agree to within about 10%. The model and Argo EAPE fields are compared to EAPE computed from temperature anomalies in a data set of “moored historical observations” (MHO) in conjunction with buoyancy frequencies computed from a global climatology. The MHO data set allows for an estimate of the EAPE in high‐frequency motions that is aliased into the Argo EAPE values. At MHO locations, 15–32% of the EAPE in the Argo estimates is due to aliased motions having periods of 10 days or less. Spatial averages of EAPE in HYCOM, Argo, and MHO data agree to within 50% at MHO locations, with both model estimates lying within error bars observations. Analysis of the EAPE field in an idealized model, in conjunction with published theory, suggests that much of the scatter seen in comparisons of different EAPE estimates is to be expected given the chaotic, unpredictable nature of mesoscale eddies.Key PointsGlobal maps of the mesoscale eddy available potential energy are made from a HYCOM simulation and reanalysisModeled eddy available potential energy compares well to Argo observations globally, and to moored instruments locallyModel‐data comparisons of eddy available potential energy exhibit intrinsic scatter Peer Reviewed https://deepblue.lib.umich.edu/bitstream/2027.42/141214/1/jgrc22559_am.pdf ... |
| format | Article in Journal/Newspaper |
| genre | Arctic |
| genre_facet | Arctic |
| id | ftumdeepblue:oai:deepblue.lib.umich.edu:2027.42/141214 |
| institution | Open Polar |
| language | unknown |
| op_collection_id | ftumdeepblue |
| op_relation | http://hdl.handle.net/2027.42/141214 doi:10.1002/2017JC013136 Journal of Geophysical Research: Oceans Roemmich, D., & Owens, B. W. ( 2000 ). The argo project: Global ocean observations for understanding and prediction of climate variability. Oceanography, 13, 45 – 50. Flierl, G. R., & McWilliams, J. C. ( 1977 ). Sampling requirements for measuring moments of eddy variability. Journal of Marine Research, 35, 797 – 820. Gnanadesikan, A. ( 1999 ). A simple predictive model for the structure of the oceanic pycnocline. Science, 283, 2077 – 2079. Hecht, W. M., & Hasumi, H. ( 2008 ). Ocean modeling in an eddying regime, Geophysical monograph. Washington, DC: American Geophysical Union. Huang, R. X. ( 1998 ). Mixing and available potential energy in a Boussinesq ocean. Journal of Physical Oceanography, 28, 669 – 678. Jacobs, G. A., Barron, C. N., & Rhodes, R. C. ( 2001 ). Mesoscale characteristics. Journal of Geophysical Research, 106 ( C9 ), 19581 – 19595. https://doi.org/10.1029/2000JC000669 Kang, D., & Fringer, O. ( 2010 ). On the calculation of available potential energy in internal wave fields. Journal of Physical Oceanography, 40, 2539 – 2545. Kuragano, T., & Kamachi, M. ( 2000 ). Global statistical space‐time scales of oceanic variability estimated from the topex/poseidon altimeter data. Journal of Geophysical Research, 105 ( C1 ), 955 – 974. https://doi.org/10.1029/1999JC900247 Locarnini, R. A., Mishonov, A. V., Antonov, J. I., Boyer, T. P., Garcia, H. E., Baranova, O. K., … Johnson, D. R. ( 2010 ). World Ocean Atlas 2009, volume 1: Temperature. In NOAA atlas NESDIS 68. Washington, DC: U.S. Government Printing Office. Lumpkin, R., & Pazos, M. ( 2007 ). Lagrangian analysis and prediction of coastal and ocean dynamics. Cambridge, UK: Cambridge University Press. https://doi.org/10.1017/CBO9780511535901 Maltrud, M. E., & McClean, J. L. ( 2005 ). An eddy resolving global 1/10 ocean simulation. Ocean Modelling, 8 ( 12 ), 31 – 54. https://doi.org/10.1016/j.ocemod.2003.12.001 McClean, J. L., Bader, D. C., Bryan, F. O., Maltrud, M. E., Dennis, J. M., Mirin, A. A., … Worley, P. H. ( 2011 ). A prototype two‐decade fully‐coupled fine‐resolution ccsm simulation. Ocean Modelling, 39 ( 1 ), 10 – 30. https://doi.org/10.1016/j.ocemod.2011.02.011 McDougall, T. J., & Barker, P. M. ( 2011 ). Getting started with TEOS‐10 and the Gibbs seawater (GSW) oceanographic toolbox (Rep. SCOR/IAPSO WG127). Metzger, E. J., Smedstad, O. M., Thoppil, P. G., Hurlburt, H. E., Cummings, J. A., Wallcraft, A. J., … DeHaan, C. J. ( 2014 ). US Navy operational global ocean and Arctic ice prediction systems. Oceanography, 27 ( 3 ), 32 – 43. https://doi.org/10.5670/oceanog.2014.66 Munk, W., & Wunsch, C. ( 1998 ). Abyssal recipes II: Energetics of tidal and wind mixing. Deep Sea Research I: Oceanographic Research Papers, 45, 1977 – 2010. Penduff, T., Barnier, B., Molines, J.‐M., & Madec, G. ( 2006 ). On the use of current meter data to assess the realism of ocean model simulations. Ocean Modelling, 11, 399 – 416. Richman, J. G., Wunsch, C., & Hogg, N. G. ( 1977 ). Space and time scales of mesoscale motion in the western north atlantic. Reviews of Geophysics, 15 ( 4 ), 385 – 420. https://doi.org/10.1029/RG015i004p00385 Roullet, G., Capet, X., & Maze, G. ( 2014 ). Global interior eddy available potential energy diagnosed from argo floats. Geophysical Research Letters, 41, 1651 – 1656. https://doi.org/10.1002/2013GL059004 Rudnick, D. L., & Ferrari, R. ( 1999 ). Compensation of horizontal temperature and salinity gradients in the ocean mixed layer. Science, 283 ( 5401 ), 526 – 529. https://doi.org/10.1126/science.283.5401.526 Saenz, J. A., Hogg, A. M., Hughes, G. O., & Griffiths, R. W. ( 2012 ). Mechanical power input from buoyancy and wind to the circulation in an ocean model. Geophysical Research Letters, 39, L13605. https://doi.org/10.1029/2012GL052035 Saha, S., Moorthi, S., Pan, H.‐L., Wu, X., Wang, J., Nadiga, S., … Goldberg, M. ( 2010 ). The NCEP climate forecast system reanalysis. Bulletin of the American Meteorological Society, 91 ( 8 ), 1015 – 1057. https://doi.org/10.1175/2010BAMS3001.1 Scott, R. B., Arbic, B. K., Chassignet, E. P., Coward, A. C., Maltrud, M., Merryfield, W. J., … Varghese, A. ( 2010 ). Total kinetic energy in four global eddying ocean circulation models and over 5000 current meter records. Ocean Modelling, 32, 157 – 169. Thoppil, P. G., Richman, J. G., & Hogan, P. J. ( 2011 ). Energetics of a global ocean circulation model compared to observations. Geophysical Research Letters, 38, L15607. https://doi.org/10.1029/2011GL048347 Timko, P. G., Arbic, B. K., Richman, J. G., Scott, R. B., Metzger, E. J., & Wallcraft, A. J. ( 2012 ). Skill tests of three‐dimensional tidal currents in a global ocean model: A look at the North Atlantic. Journal of Geophysical Research, 117, C08014. https://doi.org/10.1029/2011JC007617 Timko, P. G., Arbic, B. K., Richman, J. G., Scott, R. B., Metzger, E. J., & Wallcraft, A. J. ( 2013 ). Skill testing a three‐dimensional global tide model to historical current meter records. Journal of Geophysical Research: Oceans, 118, 6914 – 6933. https://doi.org/10.1002/2013JC009071 Winters, K. B., Lombard, P. N., Riley, J. J., & D’asaro, E. A. ( 1995 ). Available potential energy and mixing in density‐stratified fluids. Journal of Fluid Mechanics, 94, 3187 – 3200. Antonov, J. I., Seidov, D., Boyer, T. P., Locarnini, R. A., Mishonov, A. V., Garcia, H. E., … Johnson, D. R. ( 2010 ). World Ocean Atlas 2009, volume 2: Salinity. In NOAA atlas NESDIS 68. Washington, DC: U.S. Government Printing Office. Chassignet, E. P., Hurlburt, H. E., Metzger, E. J., Smedstad, O. M., Cummings, J. A., Halliwell, G. R., … Wilkin, J. ( 2009 ). US GODAE: Global ocean prediction with the HYbrid Coordinate Ocean Model (HYCOM). Oceanography, 22 ( 2 ), 64 – 75. https://doi.org/10.5670/oceanog.2009.39 Chelton, D. B., Schlax, M. G., Samelson, R. M., & de Szoeke, R. A. ( 2007 ). Global observations of large oceanic eddies. Geophysical Research Letters, 34, L15606. https://doi.org/10.1029/2007GL030812 Doherty, K. W., Frye, D. E., Lberatore, S. P., & Toole, J. M. ( 1999 ). A moored profiling instrument. Journal of Atmospheric and Oceanic Technology, 16, 1816 – 1829. |
| op_rights | IndexNoFollow |
| publishDate | 2017 |
| publisher | U.S. Government Printing Office |
| record_format | openpolar |
| spelling | ftumdeepblue:oai:deepblue.lib.umich.edu:2027.42/141214 2025-06-15T14:17:47+00:00 The Global Mesoscale Eddy Available Potential Energy Field in Models and Observations Luecke, C.A. Arbic, B.K. Bassette, S.L. Richman, J.G. Shriver, J.F. Alford, M.H. Smedstad, O.M. Timko, P.G. Trossman, D.S. Wallcraft, A.J. 2017-11 application/pdf http://hdl.handle.net/2027.42/141214 https://doi.org/10.1002/2017JC013136 unknown U.S. Government Printing Office Wiley Periodicals, Inc. http://hdl.handle.net/2027.42/141214 doi:10.1002/2017JC013136 Journal of Geophysical Research: Oceans Roemmich, D., & Owens, B. W. ( 2000 ). The argo project: Global ocean observations for understanding and prediction of climate variability. Oceanography, 13, 45 – 50. Flierl, G. R., & McWilliams, J. C. ( 1977 ). Sampling requirements for measuring moments of eddy variability. Journal of Marine Research, 35, 797 – 820. Gnanadesikan, A. ( 1999 ). A simple predictive model for the structure of the oceanic pycnocline. Science, 283, 2077 – 2079. Hecht, W. M., & Hasumi, H. ( 2008 ). Ocean modeling in an eddying regime, Geophysical monograph. Washington, DC: American Geophysical Union. Huang, R. X. ( 1998 ). Mixing and available potential energy in a Boussinesq ocean. Journal of Physical Oceanography, 28, 669 – 678. Jacobs, G. A., Barron, C. N., & Rhodes, R. C. ( 2001 ). Mesoscale characteristics. Journal of Geophysical Research, 106 ( C9 ), 19581 – 19595. https://doi.org/10.1029/2000JC000669 Kang, D., & Fringer, O. ( 2010 ). On the calculation of available potential energy in internal wave fields. Journal of Physical Oceanography, 40, 2539 – 2545. Kuragano, T., & Kamachi, M. ( 2000 ). Global statistical space‐time scales of oceanic variability estimated from the topex/poseidon altimeter data. Journal of Geophysical Research, 105 ( C1 ), 955 – 974. https://doi.org/10.1029/1999JC900247 Locarnini, R. A., Mishonov, A. V., Antonov, J. I., Boyer, T. P., Garcia, H. E., Baranova, O. K., … Johnson, D. R. ( 2010 ). World Ocean Atlas 2009, volume 1: Temperature. In NOAA atlas NESDIS 68. Washington, DC: U.S. Government Printing Office. Lumpkin, R., & Pazos, M. ( 2007 ). Lagrangian analysis and prediction of coastal and ocean dynamics. Cambridge, UK: Cambridge University Press. https://doi.org/10.1017/CBO9780511535901 Maltrud, M. E., & McClean, J. L. ( 2005 ). An eddy resolving global 1/10 ocean simulation. Ocean Modelling, 8 ( 12 ), 31 – 54. https://doi.org/10.1016/j.ocemod.2003.12.001 McClean, J. L., Bader, D. C., Bryan, F. O., Maltrud, M. E., Dennis, J. M., Mirin, A. A., … Worley, P. H. ( 2011 ). A prototype two‐decade fully‐coupled fine‐resolution ccsm simulation. Ocean Modelling, 39 ( 1 ), 10 – 30. https://doi.org/10.1016/j.ocemod.2011.02.011 McDougall, T. J., & Barker, P. M. ( 2011 ). Getting started with TEOS‐10 and the Gibbs seawater (GSW) oceanographic toolbox (Rep. SCOR/IAPSO WG127). Metzger, E. J., Smedstad, O. M., Thoppil, P. G., Hurlburt, H. E., Cummings, J. A., Wallcraft, A. J., … DeHaan, C. J. ( 2014 ). US Navy operational global ocean and Arctic ice prediction systems. Oceanography, 27 ( 3 ), 32 – 43. https://doi.org/10.5670/oceanog.2014.66 Munk, W., & Wunsch, C. ( 1998 ). Abyssal recipes II: Energetics of tidal and wind mixing. Deep Sea Research I: Oceanographic Research Papers, 45, 1977 – 2010. Penduff, T., Barnier, B., Molines, J.‐M., & Madec, G. ( 2006 ). On the use of current meter data to assess the realism of ocean model simulations. Ocean Modelling, 11, 399 – 416. Richman, J. G., Wunsch, C., & Hogg, N. G. ( 1977 ). Space and time scales of mesoscale motion in the western north atlantic. Reviews of Geophysics, 15 ( 4 ), 385 – 420. https://doi.org/10.1029/RG015i004p00385 Roullet, G., Capet, X., & Maze, G. ( 2014 ). Global interior eddy available potential energy diagnosed from argo floats. Geophysical Research Letters, 41, 1651 – 1656. https://doi.org/10.1002/2013GL059004 Rudnick, D. L., & Ferrari, R. ( 1999 ). Compensation of horizontal temperature and salinity gradients in the ocean mixed layer. Science, 283 ( 5401 ), 526 – 529. https://doi.org/10.1126/science.283.5401.526 Saenz, J. A., Hogg, A. M., Hughes, G. O., & Griffiths, R. W. ( 2012 ). Mechanical power input from buoyancy and wind to the circulation in an ocean model. Geophysical Research Letters, 39, L13605. https://doi.org/10.1029/2012GL052035 Saha, S., Moorthi, S., Pan, H.‐L., Wu, X., Wang, J., Nadiga, S., … Goldberg, M. ( 2010 ). The NCEP climate forecast system reanalysis. Bulletin of the American Meteorological Society, 91 ( 8 ), 1015 – 1057. https://doi.org/10.1175/2010BAMS3001.1 Scott, R. B., Arbic, B. K., Chassignet, E. P., Coward, A. C., Maltrud, M., Merryfield, W. J., … Varghese, A. ( 2010 ). Total kinetic energy in four global eddying ocean circulation models and over 5000 current meter records. Ocean Modelling, 32, 157 – 169. Thoppil, P. G., Richman, J. G., & Hogan, P. J. ( 2011 ). Energetics of a global ocean circulation model compared to observations. Geophysical Research Letters, 38, L15607. https://doi.org/10.1029/2011GL048347 Timko, P. G., Arbic, B. K., Richman, J. G., Scott, R. B., Metzger, E. J., & Wallcraft, A. J. ( 2012 ). Skill tests of three‐dimensional tidal currents in a global ocean model: A look at the North Atlantic. Journal of Geophysical Research, 117, C08014. https://doi.org/10.1029/2011JC007617 Timko, P. G., Arbic, B. K., Richman, J. G., Scott, R. B., Metzger, E. J., & Wallcraft, A. J. ( 2013 ). Skill testing a three‐dimensional global tide model to historical current meter records. Journal of Geophysical Research: Oceans, 118, 6914 – 6933. https://doi.org/10.1002/2013JC009071 Winters, K. B., Lombard, P. N., Riley, J. J., & D’asaro, E. A. ( 1995 ). Available potential energy and mixing in density‐stratified fluids. Journal of Fluid Mechanics, 94, 3187 – 3200. Antonov, J. I., Seidov, D., Boyer, T. P., Locarnini, R. A., Mishonov, A. V., Garcia, H. E., … Johnson, D. R. ( 2010 ). World Ocean Atlas 2009, volume 2: Salinity. In NOAA atlas NESDIS 68. Washington, DC: U.S. Government Printing Office. Chassignet, E. P., Hurlburt, H. E., Metzger, E. J., Smedstad, O. M., Cummings, J. A., Halliwell, G. R., … Wilkin, J. ( 2009 ). US GODAE: Global ocean prediction with the HYbrid Coordinate Ocean Model (HYCOM). Oceanography, 22 ( 2 ), 64 – 75. https://doi.org/10.5670/oceanog.2009.39 Chelton, D. B., Schlax, M. G., Samelson, R. M., & de Szoeke, R. A. ( 2007 ). Global observations of large oceanic eddies. Geophysical Research Letters, 34, L15606. https://doi.org/10.1029/2007GL030812 Doherty, K. W., Frye, D. E., Lberatore, S. P., & Toole, J. M. ( 1999 ). A moored profiling instrument. Journal of Atmospheric and Oceanic Technology, 16, 1816 – 1829. IndexNoFollow Argo eddy available potential energy mixing model‐data comparison ocean energy reservoirs mesoscale eddies Atmospheric and Oceanic Sciences Geological Sciences Science Article 2017 ftumdeepblue 2025-06-04T05:59:22Z Global maps of the mesoscale eddy available potential energy (EAPE) field at a depth of 500 m are created using potential density anomalies in a high‐resolution 1/12.5° global ocean model. Maps made from both a free‐running simulation and a data‐assimilative reanalysis of the HYbrid Coordinate Ocean Model (HYCOM) are compared with maps made by other researchers from density anomalies in Argo profiles. The HYCOM and Argo maps display similar features, especially in the dominance of western boundary currents. The reanalysis maps match the Argo maps more closely, demonstrating the added value of data assimilation. Global averages of the simulation, reanalysis, and Argo EAPE all agree to within about 10%. The model and Argo EAPE fields are compared to EAPE computed from temperature anomalies in a data set of “moored historical observations” (MHO) in conjunction with buoyancy frequencies computed from a global climatology. The MHO data set allows for an estimate of the EAPE in high‐frequency motions that is aliased into the Argo EAPE values. At MHO locations, 15–32% of the EAPE in the Argo estimates is due to aliased motions having periods of 10 days or less. Spatial averages of EAPE in HYCOM, Argo, and MHO data agree to within 50% at MHO locations, with both model estimates lying within error bars observations. Analysis of the EAPE field in an idealized model, in conjunction with published theory, suggests that much of the scatter seen in comparisons of different EAPE estimates is to be expected given the chaotic, unpredictable nature of mesoscale eddies.Key PointsGlobal maps of the mesoscale eddy available potential energy are made from a HYCOM simulation and reanalysisModeled eddy available potential energy compares well to Argo observations globally, and to moored instruments locallyModel‐data comparisons of eddy available potential energy exhibit intrinsic scatter Peer Reviewed https://deepblue.lib.umich.edu/bitstream/2027.42/141214/1/jgrc22559_am.pdf ... Article in Journal/Newspaper Arctic Unknown |
| spellingShingle | Argo eddy available potential energy mixing model‐data comparison ocean energy reservoirs mesoscale eddies Atmospheric and Oceanic Sciences Geological Sciences Science Luecke, C.A. Arbic, B.K. Bassette, S.L. Richman, J.G. Shriver, J.F. Alford, M.H. Smedstad, O.M. Timko, P.G. Trossman, D.S. Wallcraft, A.J. The Global Mesoscale Eddy Available Potential Energy Field in Models and Observations |
| title | The Global Mesoscale Eddy Available Potential Energy Field in Models and Observations |
| title_full | The Global Mesoscale Eddy Available Potential Energy Field in Models and Observations |
| title_fullStr | The Global Mesoscale Eddy Available Potential Energy Field in Models and Observations |
| title_full_unstemmed | The Global Mesoscale Eddy Available Potential Energy Field in Models and Observations |
| title_short | The Global Mesoscale Eddy Available Potential Energy Field in Models and Observations |
| title_sort | global mesoscale eddy available potential energy field in models and observations |
| topic | Argo eddy available potential energy mixing model‐data comparison ocean energy reservoirs mesoscale eddies Atmospheric and Oceanic Sciences Geological Sciences Science |
| topic_facet | Argo eddy available potential energy mixing model‐data comparison ocean energy reservoirs mesoscale eddies Atmospheric and Oceanic Sciences Geological Sciences Science |
| url | http://hdl.handle.net/2027.42/141214 https://doi.org/10.1002/2017JC013136 |