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Climate variability in wind waves from VOS visual observations

Climate variability in wind waves from VOS visual observations. Vika Grigorieva & Sergey Gulev, IORAS, Moscow. OUTLINE:. Climatology of visually observed wind waves Errors and uncertainties Centennial-scale changes Decadal to interannual variability

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Climate variability in wind waves from VOS visual observations

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  1. Climate variability in wind waves from VOS visual observations Vika Grigorieva & Sergey Gulev, IORAS, Moscow OUTLINE: • Climatology of visually observed wind waves • Errors and uncertainties • Centennial-scale changes • Decadal to interannual variability • Changes in wave statistics derived from VOS MARCDAT-II Workshop, 2005, Exeter

  2. Visual VOS observations: 2 streams (1784-1948) and (1948-2003)

  3. Global climatology of wind waves from VOS data: • http://www.sail.msk.ru/atlas • monthly • 1958-2002 (updated) • 2-degree resolution • Separate estimates of • sea, swell, SWH • Gulev and Grigorieva • JGR, 2003

  4. All fields are accompanied by: Random observational errors Sampling errors See poster of Grigorieva and Gulev for the error analysis

  5. Very long-term changes along the major ship routes 65 regions with high sampling during 1885-2002 Homogenization: sub-sampling for 7,15,25,50 reports per region per month

  6. Bacon and Carter 1991 Gulev and Hasse 1999 Homogenized time series Buoys: Gower 2002:

  7. 1900-2002 1958-2002 Very long-term changes: linear trends Gulev and Grigorieva 2004

  8. Trends in sea, swell and SWH: 1958-2002 sea sea swell swell SWH SWH

  9. Winter (JFM) 1st EOFs of sea, swell and SWH sea sea swell swell SWH SWH

  10. Principal components sea NAO SWH swell sea SWH NPI swell Atlantic R(HW–NAO)=0.68 R(HS–NAO)=0.48 R(SWH–NAO)=0.81 Pacific R(HW–NPI)=0.72 R(HS–NPI)=0.58 R(SWH–NPI)=0.61

  11. Canonical patterns scalar wind Number of cyclones sea swell SWH SWH

  12. Extreme waves from VOS: problem of estimation IDM – initial distribution method – methodologically, most relevant for VOS, but does not allow for reliable estimation of extreme waves POT – peak over threshold – sensitive to sampling inhomogeneity 100-yr returns in SWH - IDM

  13. Estimation of extreme wave heights - POT

  14. Changes in extreme SWH 100-yr returns 1980 - 1970 1990 - 1980  - 1 m  +2 m IDM  + 2 m  - 1 m  + 2 m  - 2 m POT

  15. Conclusions: Visual wave data allow for the analysis of centennial-scale variability of ocean wind wave characteristics: linear trends in the North Pacific may amount to 1.2 m per century, being much smaller in the North Atlantic. Interannual variability patterns are different for sea and swell, implying forcing frequency (e.g. cyclones) as a driving mechanism of swell changes with wind speed being responsible for the variations in sea. Extreme wave statistics can be evaluated from VOS using IDM and POT methods. POT method shows the higher extreme waves, which are more close to those obtained from the model hindcasts. However, estimation of decadal changes in extreme waves shows less skills of the POT method, largely influenced by sampling inhomogeneity

  16. Sea, swell, SWH 100-years return

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