1. Climate variability in wind waves from VOS visual observations Vika Grigorieva & Sergey Gulev, IORAS, Moscow  Climatology of visually observed wind waves  Errors and uncertainties  Centennial-scale changes  Decadal to interannual variability  Changes in wave statistics derived from VOS OUTLINE: 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. Random observational errorsSampling errors All fields are accompanied by: 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. Homogenized time series Buoys: Gower 2002: Bacon and Carter 1991 Gulev and Hasse 1999

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

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

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

10. Principal components Atlantic R(H W –NAO)=0.68 R(H S –NAO)=0.48 R(SWH–NAO)=0.81 Pacific R(H W –NPI)=0.72 R(H S –NPI)=0.58 R(SWH–NPI)=0.61 sea swellSWH NAO sea SWH swell NPI

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

12. 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 Extreme waves from VOS: problem of estimation 100-yr returns in SWH - IDM

13. Estimation of extreme wave heights - POT

14. Changes in extreme SWH 100-yr returns 1980 - 19701990 - 1980 IDM POT  +2 m  - 1 m  + 2 m  - 2 m

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