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

Visual VOS observations: 2 streams ( ) and ( )

Global climatology of wind waves from VOS data: monthly (updated) 2-degree resolution Separate estimates of sea, swell, SWH Gulev and Grigorieva JGR, 2003

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

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

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

Very long-term changes: linear trends Gulev and Grigorieva

Trends in sea, swell and SWH: sea swell SWH sea swell SWH

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

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

Canonical patterns Number of cyclones swell SWH scalar wind sea SWH

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

Estimation of extreme wave heights - POT

Changes in extreme SWH 100-yr returns IDM POT  +2 m  - 1 m  + 2 m  - 2 m

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

Sea, swell, SWH 100-years return