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Climate Dynamics

, Volume 49, Issue 9–10, pp 3327–3344 | Cite as

How do uncertainties in NCEP R2 and CFSR surface fluxes impact tropical ocean simulations?

  • Caihong Wen
  • Yan Xue
  • Arun Kumar
  • David Behringer
  • Lisan Yu
Article

Abstract

NCEP/DOE reanalysis (R2) and Climate Forecast System Reanalysis (CFSR) surface fluxes are widely used by the research community to understand surface flux climate variability, and to drive ocean models as surface forcings. However, large discrepancies exist between these two products, including (1) stronger trade winds in CFSR than in R2 over the tropical Pacific prior 2000; (2) excessive net surface heat fluxes into ocean in CFSR than in R2 with an increase in difference after 2000. The goals of this study are to examine the sensitivity of ocean simulations to discrepancies between CFSR and R2 surface fluxes, and to assess the fidelity of the two products. A set of experiments, where an ocean model was driven by a combination of surface flux components from R2 and CFSR, were carried out. The model simulations were contrasted to identify sensitivity to different component of the surface fluxes in R2 and CFSR. The accuracy of the model simulations was validated against the tropical moorings data, altimetry SSH and SST reanalysis products. Sensitivity of ocean simulations showed that temperature bias difference in the upper 100 m is mostly sensitive to the differences in surface heat fluxes, while depth of 20 °C (D20) bias difference is mainly determined by the discrepancies in momentum fluxes. D20 simulations with CFSR winds agree with observation well in the western equatorial Pacific prior 2000, but have large negative bias similar to those with R2 winds after 2000, partly because easterly winds over the central Pacific were underestimated in both CFSR and R2. On the other hand, the observed temperature variability is well reproduced in the tropical Pacific by simulations with both R2 and CFSR fluxes. Relative to the R2 fluxes, the CFSR fluxes improve simulation of interannual variability in all three tropical oceans to a varying degree. The improvement in the tropical Atlantic is most significant and is largely attributed to differences in surface winds.

Keywords

CFSR NCEP/DOE reanalysis (R2) Surface wind stress/heat flux validation Ocean model Tropical moored buoy data 

Notes

Acknowledgements

We thank Dr. Hui Wang and Dr. Jieshun Zhu for their helpful comments on the initial version of the manuscript. We are grateful for comments from two anonymous reviewers, who greatly helped to improve the final version.

References

  1. Agarwal N, Sharma R, Basu SK, Sarkar A, Agarwal VK (2007) Evaluation of relative performance of QuikSCAT and NCEP re-analysis winds through simulations by an OGCM. Deep Sea Res Part I 54:1311–1328CrossRefGoogle Scholar
  2. Ayina L-H, Bentamy A, Mestas-Nuñez AM, Madec G (2006) The impact of satellite winds and latent heat fluxes in a numerical simulation of the tropical Pacific Ocean. J Clim 19:5889–5902CrossRefGoogle Scholar
  3. Balmaseda M et al (2015) The ocean reanalyses intercomparison project (ORA-IP). J Oper Oceanogr 8:s80–s97CrossRefGoogle Scholar
  4. Behringer D, Xue Y (2004) Evaluation of the global ocean data assimilation system at NCEP: the Pacific Ocean. Eighth Symp Integr Obs Assim Syst Atmos Ocean Land SurfGoogle Scholar
  5. Bellenger H, Guilyardi É, Leloup J, Lengaigne M, Vialard J (2014) ENSO representation in climate models: from CMIP3 to CMIP5. Clim Dyn 42:1999–2018. doi: 10.1007/s00382-013-1783-z CrossRefGoogle Scholar
  6. Bourlès B et al (2008) The pirata program. Bull Am Meteorol Soc 89:1111CrossRefGoogle Scholar
  7. Brunke MA, Wang Z, Zeng X, Bosilovich M, Shie C-L (2011) An assessment of the uncertainties in ocean surface turbulent fluxes in 11 reanalysis, satellite-derived, and combined global datasets. J Clim 24:5469–5493CrossRefGoogle Scholar
  8. Chakraborty A, Sharma R, Kumar R, Basu S (2014) An OGCM assessment of blended OSCAT winds. J Geophys Res 119:173–186CrossRefGoogle Scholar
  9. Chang P, Ji L, Li H (1997) A decadal climate variation in the tropical Atlantic Ocean from thermodynamic air-sea interactions. Nature 385:516–518CrossRefGoogle Scholar
  10. Chang P et al (2006) Climate fluctuations of tropical coupled systems-the role of ocean dynamics. J Clim 19:5122–5174CrossRefGoogle Scholar
  11. Chen D, Cane MA, Zebiak SE (1999) The impact of NSCAT winds on predicting the 1997/1998 El Niño: a case study with the lamont-doherty earth observatory model. J Geophys Res 104:11321–11327CrossRefGoogle Scholar
  12. Conkright ME, Levitus S, O’Brien T, Boyer TP, Stephens C (1998) World ocean database 1998 CD-ROM dataset documentation. Natl. Oceanogr. Data Cent. Int. Rep. 14, Natl. Oceanogr. and Atmos. Admin.. Silver Spring, MDGoogle Scholar
  13. Cronin MF, Fairall CW, McPhaden MJ (2006) An assessment of buoy-derived and numerical weather prediction surface heat fluxes in the Tropical Pacific. J Geophys Res 111:C06038. doi: 10.1029/2005JC003324.CrossRefGoogle Scholar
  14. Danabasoglu G et al (2014) North Atlantic simulations in coordinated ocean-ice reference experiments phase II (CORE-II). Part I: mean states. Ocean Modell 73:76–107. doi: 10.1016/j.ocemod.2013.10.005 CrossRefGoogle Scholar
  15. Dee D et al (2011) The ERA-interim reanalysis: configuration and performance of the data assimilation system. Quart J Roy Meteorol Soc 137:553–597. doi: 10.1002/qj.828 CrossRefGoogle Scholar
  16. Delworth TL et al (2012) Simulated climate and climate change in the GFDL CM2.5 high-resolution coupled climate model. J Clim 25:2755–2781. doi: 10.1175/Jcli-D-11-00316.1 CrossRefGoogle Scholar
  17. Dunne JP et al (2012) GFDL’s ESM2 global coupled climate carbon earth system models. Part I: physical formulation and baseline simulation characteristics, J Clim 25:66466665. doi: 10.1175/JCLI-D-11-00560.1 CrossRefGoogle Scholar
  18. Fairall C, Bradley E, Hare J, Grachev A, Edson J (2003) Bulk parameterization of air–sea fluxes: updates and verification for the CORE algorithm. J Clim 16:571–591CrossRefGoogle Scholar
  19. Fox-Kemper B et al (2011) Parameterization of mixed layer eddies. III: implementation and impact in global ocean climate simulations. Ocean Modell 39:61–78CrossRefGoogle Scholar
  20. Griffies SM, Hallberg RW (2000) Biharmonic friction with a Smagorinsky-like viscosity for use in large-scale eddy-permitting ocean models. Mon Wea Rev 128:2935–2946CrossRefGoogle Scholar
  21. Griffies SM, Schmidt M, Herzfeld M (2009a) Elements of mom4p1. GFDL Ocean Group Tech Rep 6:444Google Scholar
  22. Griffies SM et al (2009b) Coordinated ocean-ice reference experiments (COREs). Ocean Modell 26:1–46Google Scholar
  23. Griffies SM et al (2011) GFDL’s CM3 coupled climate model: characteristics of the ocean and sea ice simulations. J Clim 24:3520–3544CrossRefGoogle Scholar
  24. Griffies SM et al (2014) An assessment of global and regional sea level for years 1993–2007 in a suite of interannual core-II simulations. Ocean Model 78:35–89CrossRefGoogle Scholar
  25. HARADA Y et al (2016) The JRA-55 reanalysis: representation of atmospheric circulation and climate variability. J Meteorol Soc Jpn 94:269–302CrossRefGoogle Scholar
  26. IPCC (2007) Climate change 2007: the physical science basis. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Contribution of Working Group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, 996 ppGoogle Scholar
  27. Jiang C, Cronin MF, Kelly KA, Thompson L (2005) Evaluation of a hybrid satellite-and NWP-based turbulent heat flux product using Tropical Atmosphere–Ocean (TAO) buoys. J Geophys Res 110Google Scholar
  28. Kanamitsu M, Ebisuzaki W, Woollen J, Yang SK, Hnilo J, Fiorino M, Potter G (2002) Ncep-doe amip-ii reanalysis (r-2). Bull Am Meteorol Soc 83:1631–1644CrossRefGoogle Scholar
  29. Kobayashi S et al (2015) The JRA-55 reanalysis: general specifications and basic characteristics. J Meteorol Soc Jpn 93:5–48CrossRefGoogle Scholar
  30. Kumar A, Hu Z-Z (2012) Uncertainty in the ocean–atmosphere feedbacks associated with ENSO in the reanalysis products. Clim Dyn 39:575–588CrossRefGoogle Scholar
  31. Kumar A, Wang H, Xue Y, Wang W (2014) How much of monthly subsurface temperature variability in the equatorial Pacific can be recovered by the specification of sea surface temperatures? J Clim 27:1559–1577CrossRefGoogle Scholar
  32. Large WG, McWilliams JC, Doney SC (1994) Oceanic vertical mixing: a review and a model with a nonlocal boundary layer parameterization. Rev Geophys 32:363–403CrossRefGoogle Scholar
  33. McGregor S, Gupta AS, England MH (2012) Constraining wind stress products with sea surface height observations and implications for Pacific Ocean sea level trend attribution. J Clim 25:8164–8176CrossRefGoogle Scholar
  34. McPhaden MJ et al (1998) The Tropical Ocean-global atmosphere observing system: a decade of progress. J Geophys Res 103:14169–14240CrossRefGoogle Scholar
  35. McPhaden MJ et al (2009) RAMA: the research moored array for African-Asian-Australian monsoon analysis and prediction. Bull Am Meteorol Soc 90:459CrossRefGoogle Scholar
  36. Merrifield MA, Maltrud ME (2011) Regional sea level trends due to a Pacific trade wind intensification. Geophys Res Lett 38Google Scholar
  37. Molod A, Takacs L, Suarez M, Bacmeister J (2015) Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA-2. Geosci Model Dev 8:13391356. doi: 10.5194/gmd-8-1339-2015 CrossRefGoogle Scholar
  38. Reynolds RW, Smith TM, Liu C, Chelton DB, Casey KS, Schlax MG (2007) Daily high-resolution-blended analyses for sea surface temperature. J Clim 20:5473–5496CrossRefGoogle Scholar
  39. Saha S et al (2010) The NCEP climate forecast system reanalysis. Bull Am Meteorol Soc 91:1015–1057CrossRefGoogle Scholar
  40. Sun B, Yu L, Weller RA (2003) Comparisons of surface meteorology and turbulent heat fluxes over the Atlantic: NWP model analyses versus moored buoy observations. J Clim 16:679–695CrossRefGoogle Scholar
  41. Tseng Y-H et al (2016) North and Equatorial Pacific Ocean circulation in the CORE-II hindcast simulations. Ocean Model 104:143–170CrossRefGoogle Scholar
  42. Valdivieso M et al (2015) An assessment of air–sea heat fluxes from ocean and coupled reanalyses. Clim Dyn 1–26Google Scholar
  43. Von Schuckmann K et al (2016) An imperative to monitor Earth’s energy imbalance. Nat Clim Change 6:138–144CrossRefGoogle Scholar
  44. Wang W, McPhaden MJ (1999) The surface-layer heat balance in the Equatorial Pacific Ocean. Part I: mean seasonal cycle. J Phys Oceanogr 29:1812–1831CrossRefGoogle Scholar
  45. Wang C, Xie SP, Carton JA (2004) A global survey of ocean–atmosphere interaction and climate variability. Earth Climate: the Ocean-Atmopshere Interactions, Geophys. Monogr., 147, Amer. Geophys. Union, 1–19Google Scholar
  46. Wang W, Xie P, Yoo S-H, Xue Y, Kumar A, Wu X (2011) An assessment of the surface climate in the NCEP climate forecast system reanalysis. Clim Dyn 37:1601–1620CrossRefGoogle Scholar
  47. Wen C, Xue Y, Kumar A (2012) Ocean–Atmosphere characteristics of tropical instability waves simulated in the NCEP climate forecast system reanalysis. J Clim 25:6409–6425CrossRefGoogle Scholar
  48. Wen C, Kumar A, Xue Y, McPhaden M (2014) Changes in tropical Pacific thermocline depth and their relationship to ENSO after 1999. J Clim 27:7230–7249CrossRefGoogle Scholar
  49. Wittenberg AT (2004) Extended wind stress analyses for ENSO. J Clim 17(13):2526–2540CrossRefGoogle Scholar
  50. Xue Y, Huang B, Hu ZZ, Kumar A, Wen C, Behringer D, Nadiga S (2011) An assessment of oceanic variability in the NCEP climate forecast system reanalysis. Clim Dyn 37:2511–2539CrossRefGoogle Scholar
  51. Xue Y et al (2015) Evaluation of tropical Pacific observing systems using NCEP and GFDL ocean data assimilation systems. Clim Dyn 1–26Google Scholar
  52. Zhang L, Kumar A, Wang W (2012) Influence of changes in observations on precipitation: a case study for the climate forecast system reanalysis (CFSR). J Geophys Res 117Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.NOAA/NWS/NCEP/Climate Prediction CenterCollege ParkUSA
  2. 2.InnovimGreenbeltUSA
  3. 3.Environmental Modeling CenterNCEP/NWS/NOAACollege ParkUSA
  4. 4.Woods Hole Oceanographic InstitutionWoods HoleUSA

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