Interannual Variability of Synthesized FSU and NCEP-NCAR Reanalysis Pseudostress Products over the Pacific Ocean

William M. Putman Center for Ocean–Atmospheric Prediction Studies, The Florida State University, Tallahassee, Florida

Search for other papers by William M. Putman in
Current site
Google Scholar
PubMed
Close
,
David M. Legler Center for Ocean–Atmospheric Prediction Studies, The Florida State University, Tallahassee, Florida

Search for other papers by David M. Legler in
Current site
Google Scholar
PubMed
Close
, and
James J. O’Brien Center for Ocean–Atmospheric Prediction Studies, The Florida State University, Tallahassee, Florida

Search for other papers by James J. O’Brien in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

A technique is applied to seamlessly blend height-adjusted Florida State University (FSU) surface wind pseudostress with National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis-based pseudostress over the Pacific Ocean. The FSU pseudostress is shown to be of higher quality in the equatorial Pacific and thus dominates the analysis in that region, while the NCEP–NCAR reanalysis-based pseudostress is used outside the equatorial region. The blending technique is based on a direct minimization approach. The functional to minimize consists of five constraints; each constraint is given a weight that determines its influence on the solution. The first two constraints are misfits for the FSU and NCEP–NCAR reanalysis datasets. A spatially dependent weighting that highlights the regional strengths of each dataset is designed for these misfit constraints. Climatological structure information is used as a weak smoothing constraint on the solution through Laplacian and kinematic (divergence and curl) constraints. The weights for the smoothing constraints are selected using a sensitivity analysis and evaluation of solution fields. The resulting 37 yr of monthly pseudostress fields are suitable for use in a variety of modeling and climate variability studies.

The monthly mean analyses are produced for 1961 through 1997, over the domain 40°S–40°N, 125°E–70°W. NCEP–NCAR reanalysis data, from 40° to 60°N, are added to the minimization solution fields, and the monthly mean climatologies, based on the solution fields, are removed from the combined fields. The resulting pseudostress anomalies are filtered with an 18-month low-pass filter to focus on interannual and ENSO timescales, and a complex empirical orthogonal function (CEOF) analysis is performed on the filtered anomalies. The CEOF analysis reveals tropical and extratropical linkages, for example, the presence of a strengthening of the Aleutian low in the North Pacific, coincident with the anomalous westerlies along the equator associated with El Niño events. The analysis also reveals a weakening of the Aleutian low during the winter–spring preceding the El Niño events of 1973 and 1983, and during the peak period of El Viejo, the cold phase of ENSO. A change in the nature of the tropical and extratropical linkages is observed from the warm events of the 1960s to those of the 1980s. These linkages are not found using NCEP–NCAR reanalysis data alone.

* Current affiliation: Computational Climate Dynamics Group, Oak Ridge National Laboratory, Oak Ridge, Tennessee.

Corresponding author address: Dr. David M. Legler, USCLIVAR Office, 400 Virginia Ave. SW, Suite 750, Washington, DC 20024.

Abstract

A technique is applied to seamlessly blend height-adjusted Florida State University (FSU) surface wind pseudostress with National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis-based pseudostress over the Pacific Ocean. The FSU pseudostress is shown to be of higher quality in the equatorial Pacific and thus dominates the analysis in that region, while the NCEP–NCAR reanalysis-based pseudostress is used outside the equatorial region. The blending technique is based on a direct minimization approach. The functional to minimize consists of five constraints; each constraint is given a weight that determines its influence on the solution. The first two constraints are misfits for the FSU and NCEP–NCAR reanalysis datasets. A spatially dependent weighting that highlights the regional strengths of each dataset is designed for these misfit constraints. Climatological structure information is used as a weak smoothing constraint on the solution through Laplacian and kinematic (divergence and curl) constraints. The weights for the smoothing constraints are selected using a sensitivity analysis and evaluation of solution fields. The resulting 37 yr of monthly pseudostress fields are suitable for use in a variety of modeling and climate variability studies.

The monthly mean analyses are produced for 1961 through 1997, over the domain 40°S–40°N, 125°E–70°W. NCEP–NCAR reanalysis data, from 40° to 60°N, are added to the minimization solution fields, and the monthly mean climatologies, based on the solution fields, are removed from the combined fields. The resulting pseudostress anomalies are filtered with an 18-month low-pass filter to focus on interannual and ENSO timescales, and a complex empirical orthogonal function (CEOF) analysis is performed on the filtered anomalies. The CEOF analysis reveals tropical and extratropical linkages, for example, the presence of a strengthening of the Aleutian low in the North Pacific, coincident with the anomalous westerlies along the equator associated with El Niño events. The analysis also reveals a weakening of the Aleutian low during the winter–spring preceding the El Niño events of 1973 and 1983, and during the peak period of El Viejo, the cold phase of ENSO. A change in the nature of the tropical and extratropical linkages is observed from the warm events of the 1960s to those of the 1980s. These linkages are not found using NCEP–NCAR reanalysis data alone.

* Current affiliation: Computational Climate Dynamics Group, Oak Ridge National Laboratory, Oak Ridge, Tennessee.

Corresponding author address: Dr. David M. Legler, USCLIVAR Office, 400 Virginia Ave. SW, Suite 750, Washington, DC 20024.

Save
  • Busalacchi, A. J., and J. J. O’Brien, 1981: Interannual variability of the equatorial Pacific in the 1960’s. J. Geophys. Res.,86, 10 901–10 907.

  • ——, K. Takeuchi, and J. J. O’Brien, 1983: On the interannual wind-driven response of the tropical Pacific Ocean. Hydrodynamics of the Equatorial Ocean, J.C.J. Nihoul, Ed., Elsevier Science Publishers, 155–195.

  • Daley, R., 1991: Atmospheric Data Analysis. Cambridge University Press, 457 pp.

  • da Silva, A. M., C. C. Young, and S. Levitus, 1994: Algorithms and procedures. Vol. 1, Atlas of Surface Marine Data 1994, NOAA Atlas NESDIS, 83 pp.

  • Emery, W. J., and K. Hamilton, 1985: Atmospheric forcing of interannual variability in the Northeast Pacific Ocean: Connections with El Niño. J. Geophys. Res.,90, 857–868.

  • Hoffman, R. N., 1984: SASS wind ambiguity removal by direct minimization. Part II: Use of smoothness and dynamical constraint. Mon. Wea. Rev.,112, 1829–1852.

  • Horel, J. D., 1984: Complex principal component analysis: Theory and examples. J. Climate Appl. Meteor.,23, 1660–1673.

  • ——, and J. M. Wallace, 1981: Planetary-scale atmospheric phenomena associated with the Southern Oscillation. Mon. Wea. Rev.,109, 813–829.

  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc.,77, 437–471.

  • Kamachi, M., and J. J. O’Brien, 1995: Continuous data assimilation of drifting buoy trajectory into an equatorial Pacific Ocean model. J. Mar. Syst.,6, 159–178.

  • Kaylor, R. E., 1977: Filtering and decimation of digital time series. Institute for Physical Science and Technology Tech. Rep. BN 850, 14 pp. [Available from Institute of Physical Science and Technology, University of Maryland, College Park, MD 20742.].

  • Landsteiner, M. C., M. J. McPhaden, and J. Picaut, 1990: On the sensitivity of Sverdrup transport estimates to the specification of wind stress forcing in the tropical Pacific. J. Geophys. Res.,95, 1681–1692.

  • Lau, N.-C., 1997: Interactions between global SST anomalies and the midlatitude atmospheric circulation. Bull. Amer. Meteor. Soc.,78, 21–33.

  • Legler, D. M., I. M. Navon, and J. J. O’Brien, 1989: Objective analysis of pseudostress over the Indian Ocean using direct minimization approach. Mon. Wea. Rev.,117, 709–720.

  • ——, M. A. Bourassa, A. D. Rao, and J. J. O’Brien, 1998: NSCAT surface wind fields using optimally tuned direct minimization techniques. Proc. Ninth Conf. Interaction of Sea and Atmosphere, Phoenix, AZ, Amer. Meteor. Soc., 32–35.

  • Lorenc, A. C., 1986: Analysis methods for numerical weather prediction. Quart. J. Roy. Meteor. Soc.,112, 1177–1194.

  • McPhaden, M. J., A. J. Busalacchi, and J. Picaut, 1988: Observations and wind-forced model simulations of the mean seasonal cycle in the tropical Pacific sea surface topography. J. Geophys. Res.,93, 8131–8146.

  • Meyers, S. D., C. S. Jones, D. M. Legler, and J. J. O’Brien, 1994: The sensitivity of parametric variations in direct minimization techniques. Mon. Wea. Rev.,122, 1632–1639.

  • Navon, I. M., and D. M. Legler, 1987: Conjugate-gradient methods for large-scale minimization in meteorology. Mon. Wea. Rev.,115, 1479–1502.

  • North, R. G., T. L. Bell, R. F. Cahalan, and F. J. Hoeng, 1982: Sampling errors in the estimation of empirical orthogonal functions. Mon. Wea. Rev.,110, 699–706.

  • Ramamurthy, M. K., and I. M. Navon, 1992: The conjugate-gradient variational analysis and initialization method: An application to MONEX SOP 2 data. Mon. Wea. Rev.,120, 2360–2377.

  • Rasmusson, E. M., and T. H. Carpenter, 1982: Variations in tropical sea surface temperature and surface wind fields associated with the Southern Oscillation/El Niño. Mon. Wea. Rev.,110, 354–384.

  • Reynolds, R. W., K. Arpe, C. Gordon, S. P. Hayes, A. Leetma, and M. J. McPhaden, 1989: A comparison of tropical Pacific surface wind analyses. J. Climate,2, 105–111.

  • Shanno, D. F., and K. H. Phua, 1980: Remark on algorithm 500: Minimization of unconstrained multivariate functions. ACM Trans. Math. Software,6, 618–622.

  • Smith, S. D., 1988: Coefficients for sea surface wind stress, heat flux, and wind profiles as a function of wind speed and temperature. J. Geophys. Res.,93, 15 467–15 472.

  • Stricherz, J. N., D. M. Legler, and J. J. O’Brien, 1997: TOGA pseudo-stress atlas 1985–1994. II: Tropical Pacific Ocean. COAPS Tech. rep. 97-2, COAPS/The Florida State University, Tallahassee, FL, 155 pp. [Available from COAPS/The Florida State University, Tallahassee, FL 32306-2840.].

  • Thiébaux, J., 1997: The power of the duality in spatial–temporal estimation. J. Climate,10, 567–573.

  • Tourre, Y. M., and W. B. White, 1995: ENSO signals in global upper-ocean temperature. J. Phys. Oceanogr.,25, 1317–1332.

  • ——, Y. Kushnir, and W. B. White, 1999: Evolution of interdecadal variability in sea level pressure, sea surface temperature, and upper ocean temperature over the Pacific Ocean. J. Phys. Oceanogr.,29, 1528–1541.

  • Wang, B., 1995: Interdecadal changes in El Niño onset in the last four decades. J. Climate,8, 267–285.

  • Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences:An Introduction. Academic Press Inc., 467 pp.

  • Zebiak, S. E., 1990: Diagnostic studies of Pacific surface winds. J. Climate,3, 1016–1031.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 348 174 10
PDF Downloads 54 16 4