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Uncertainties in reanalysis surface wind stress and their relationship with observing systems

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Abstract

Atmospheric reanalysis surface wind stress (SWS) estimates have been widely used as surface forcings to drive ocean model simulations and ocean reanalyses, and for understanding climate variability. In this study, we quantify uncertainty in SWS products from six reanalyses, with five from the third generation reanalysis (CFSR, MERRA-1, MERRA-2, JRA-55, ERA-Interim) and one from the first generation reanalysis (R2). Our goals are to (1) characterize uncertainties in monthly mean reanalysis SWS estimates, (2) investigate relationship between the spatial and temporal variations of uncertainties and changes in numbers of observed surface wind data and (3) examine consistency of SWS estimates across different generation of reanalysis products. The six reanalysis SWS estimates broadly agree on the global pattern but differ considerably in magnitude. Compared with TAO SWS estimates, R2 and MERRA-1 significantly underestimate SWS speed over the central Pacific Ocean, while MERRA-2 overestimates easterly wind stress over the north western Pacific. On interannual time scale, high consistency among the reanalyses is located in mid-to-high latitudes, while large uncertainty is found in tropical oceans. All six reanalyses exhibit a similar pattern of wind response to ENSO, but differ significantly in the strength. Compared with TAO estimates, MERRA-2, JRA-55 and ERA-Interim capture the evolution and amplitude of wind anomaly associated with ENSO reasonably well, while CFSR is the outlier which underestimates the peak wind anomaly by more than 35%. Our analysis highlights the importance of observed surface wind data in constraining the reanalysis SWS. It is found that the time variations of the spread among the six reanalyses follows closely to changes in the input of ocean surface winds from satellite scatteometers in all the three tropical oceans. In the tropical Pacific, the accuracy of reanalysis SWS is also linked to the return number of TAO/TRITON winds. The correlation between R2 and the third generation reanalysis products display a clear TAO signature, while the third generation reanalysis products are in a closer agreement with each other.

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References

  • An S-I, Wang B (2000) Interdecadal change of the structure of the ENSO mode and its impact on the ENSO frequency*. J Clim 13:2044–2055

    Article  Google Scholar 

  • Ando K, Matsumoto T, Nagahama T, Ueki I, Takatsuki Y, Kuroda Y (2005) Drift characteristics of a moored conductivity–temperature–depth sensor and correction of salinity data. J Atmos Ocean Tecknol 22:282–291

    Article  Google Scholar 

  • Balmaseda M et al (2015) The ocean reanalyses intercomparison project (ORA-IP). J Oper Oceanogr 8:s80–s97

    Google Scholar 

  • Barnston AG et al (1999) NCEP forecasts of the El Niño of 1997—98 and its US impacts. Bull Am Meteorol Soc 80:1829–1852

    Article  Google Scholar 

  • Behringer D, Xue Y (2004) Evaluation of the global ocean data assimilation system at NCEP: the Pacific Ocean, paper presented at eighth symposium on integrated observing and assimilation systems for atmosphere, ocean, and land surface, Am. Meteorol. Soc., Seattle

  • 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 24:5469–5493

    Article  Google Scholar 

  • Chang P, Ji L, Li H (1997) A decadal climate variation in the tropical Atlantic Ocean from thermodynamic air–sea interactions. Nature 385:516–518

    Article  Google Scholar 

  • Chelton DB, Schlax MG, Freilich MH, Milliff RF (2004) Satellite measurements reveal persistent small-scale features in ocean winds. Science 303:978

    Article  Google Scholar 

  • 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 Rec 104:11321–11327

    Article  Google Scholar 

  • Chiodi AM, Harrison D (2017) Simulating ENSO SSTAs from TAO/TRITON winds: the impacts of 20 years of Buoy observations in the Pacific waveguide and comparison with reanalysis products. J Clim 30:1041–1059

    Article  Google Scholar 

  • Compo GP et al (2011) The twentieth century reanalysis project Quart. J R Meteorol Soc 137:1–28

    Article  Google Scholar 

  • Cronin MF, Fairall CW, McPhaden MJ (2006) An assessment of buoy-derived and numerical weather prediction surface heat fluxes in the tropical. Pac J Geophys Res 111:C6

    Google Scholar 

  • Dee DP et al (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597

    Article  Google Scholar 

  • Dukhovskoy DS, Bourassa MA, Petersen GN, Steffen J (2017) Comparison of the ocean surface vector winds from atmospheric reanalysis and scatterometer-based wind products over the Nordic Seas and the northern North Atlantic and their application for ocean modeling. J Geophys Res 122:1943–1973

    Article  Google Scholar 

  • England MH et al (2014) Recent intensification of wind-driven circulation in the Pacific and the ongoing warming hiatus. Nat Clim Change 4:222–227. https://doi.org/10.1038/nclimate2106

    Article  Google Scholar 

  • Fairall C, Bradley EF, Hare J, Grachev A, Edson J (2003) Bulk parameterization of air–sea fluxes: updates and verification for the COARE algorithm. J Clim 16:571–591

    Article  Google Scholar 

  • Graham R (2011) New perspectives for GPCs, their role in the GFCS and a proposed contribution to a ‘World Climate Watch’. Clim Res 47:47–55

    Article  Google Scholar 

  • Griffies SM et al (2009) Coordinated ocean-ice reference experiments (COREs). Ocean Modell 26:1–46

    Article  Google Scholar 

  • Harada Y et al (2016) The JRA-55 reanalysis: representation of atmospheric circulation and climate variability. J Meteorol Soc Jpn 94:269–302

    Article  Google Scholar 

  • Hayes S, McPhaden M, Wallace J (1989) The influence of sea-surface temperature on surface wind in the eastern equatorial Pacific: weekly to monthly variability. J Clim 2:1500–1506

    Article  Google Scholar 

  • Josey SA, Yu L, Gulev S, Jin X, Tilinina N, Barnier B, Brodeau L (2014) Unexpected impacts of the Tropical Pacific array on reanalysis surface meteorology and heat fluxes. Geophys Res Lett 41:6213–6220

    Article  Google Scholar 

  • Kalnay E et al (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77:437–471

    Article  Google Scholar 

  • 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 83:1631–1644

    Article  Google Scholar 

  • Kessler WS (2002) Is ENSO a cycle or a series of events? Geophys Res Lett 29:23

    Article  Google Scholar 

  • Kobayashi C et al (2014) Preliminary results of the JRA-55C, an atmospheric reanalysis assimilating conventional observations only. Sola 10:78–82

    Article  Google Scholar 

  • Kobayashi S et al (2015) The JRA-55 reanalysis: general specifications and basic characteristics. J Meteorol Soc Jpn 93:5–48

    Article  Google Scholar 

  • Kug JS, Kang IS, Zebiak SE (2001) The impacts of the model assimilated wind stress data in the initialization of an intermediate ocean and the ENSO predictability. Geophys Res Lett 28:3713–3716

    Article  Google Scholar 

  • Kumar A, Hu Z-Z (2012) Uncertainty in the ocean–atmosphere feedbacks associated with ENSO in the reanalysis products. Clim Dyn 39:575–588

    Article  Google Scholar 

  • Latif M et al (1998) A review of the predictability and prediction of ENSO. J Geophys Res 103:14375–14393

    Article  Google Scholar 

  • 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 25:8164–8176

    Article  Google Scholar 

  • McPhaden MJ et al (1998) The tropical ocean-global atmosphere observing system: a decade of progress. J Geophys Res 103:14169–14240

    Article  Google Scholar 

  • Molod A, Takacs L, Suarez M, Bacmeister J (2015) Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2. Geosci Model Dev 8:1339–1356

    Article  Google Scholar 

  • Renfrew IA, Moore G, Guest PS, Bumke K (2002) A comparison of surface layer and surface turbulent flux observations over the Labrador Sea with ECMWF analyses and NCEP reanalyses. J Phys Oceanogr 32:383–400

    Article  Google Scholar 

  • Rienecker MM et al (2011) MERRA: NASA’s modern-era retrospective analysis for research and applications. J Clim 24:3624–3648

    Article  Google Scholar 

  • Risien CM, Chelton DB (2008) A global climatology of surface wind and wind stress fields from eight years of QuikSCAT scatterometer data. J Phys Oceanogr 38:2379–2413

    Article  Google Scholar 

  • Saha S et al (2010) The NCEP climate forecast system reanalysis. Bull Am Meteorol Soc 91:1015–1057

    Article  Google Scholar 

  • Stopa JE, Cheung KF (2014) Intercomparison of wind and wave data from the ECMWF Reanalysis Interim and the NCEP Climate Forecast System Reanalysis. Ocean Model 75:65–83

    Article  Google Scholar 

  • Uppala SM et al (2005) The ERA-40 re-analysis. Q J R Meteorol Soc 131:2961–3012

    Article  Google Scholar 

  • 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 37:1601–1620

    Article  Google Scholar 

  • Wen C, Xue Y, Kumar A, Behringer D, Yu L (2017) How do uncertainties in NCEP R2 and CFSR surface fluxes impact tropical ocean. simulations? Clim Dyn 49:3327–3344. https://doi.org/10.1007/s00382-016-3516-6

    Article  Google Scholar 

  • Wittenberg AT (2004) Extended wind stress analyses for ENSO. J Clim 17:2526–2540

    Article  Google Scholar 

  • 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 37:2511–2539

    Article  Google Scholar 

  • Xue Y et al. (2017) A real-time ocean reanalyses intercomparison project in the context of tropical pacific observing system and ENSO monitoring. Clim Dyn. https://doi.org/10.1007/s00382-017-3535-y

    Google Scholar 

  • Yu L, Jin X, Josey SA, Lee T, Kumar A, Wen C, Xue Y (2017) The global ocean water cycle in atmospheric reanalysis, satellite, and ocean salinity. J Clim 30:3829–3852

    Article  Google Scholar 

  • Zhang C, Gottschalck J (2002) SST anomalies of ENSO and the Madden–Julian oscillation in the equatorial Pacific. J Clim 15:2429–2445

    Article  Google Scholar 

  • 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 117:D8

    Google Scholar 

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Acknowledgements

The authors thank Dr. Wesley Ebisuzaki for clarifying wind observations input in the R2 and CFSR reanalyses. TAO/TRITON wind stresses were downloaded from https://www.pmel.noaa.gov/tao/oceansites/flux/main.html. MERRA-1 and MERRA-2 data were from the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) at http://disc.sci.gsfc.nasa.gov. JRA-55 data was downloaded from ftp://ds.data.jma.go.jp. ERA-Interim data was downloaded from http://apps.ecmwf.int/datasets/data/interim-full-moda/levtype=sfc/.

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Correspondence to Caihong Wen.

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Wen, C., Kumar, A. & Xue, Y. Uncertainties in reanalysis surface wind stress and their relationship with observing systems. Clim Dyn 52, 3061–3078 (2019). https://doi.org/10.1007/s00382-018-4310-4

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