ALBERT

Description: Using the same approach as in Part I, here it is shown how sampling problems in voluntary observing ship (VOS) data affect conclusions about interannual variations and secular changes of surface heat fluxes. The largest uncertainties in linear trend estimates are found in relatively poorly sampled regions like the high-latitude North Atlantic and North Pacific as well as the Southern Ocean, where trends can locally show opposite signs when computed from the regularly sampled and undersampled data. Spatial patterns of shorter-period interannual variability, quantified through the EOF analysis, also show remarkable differences between the regularly sampled and undersampled flux datasets in the Labrador Sea and northwest Pacific. In particular, it is shown that in the Labrador Sea region, in contrast to regularly sampled NCEP–NCAR reanalysis fluxes, VOS-like sampled NCEP–NCAR reanalysis fluxes neither show significant interannual variability nor significant trends. These regions, although quite localized covering small parts of the globe, play a crucial role for the coupled atmosphere–ocean system. In the Labrador Sea, for instance, interannual and decadal-scale changes of the surface net heat fluxes are known to affect oceanic convection and, thus, the meridional overturning circulation of the Atlantic Ocean. From a discussion of current atmospheric data assimilation systems it is argued that in poorly sampled regions reanalysis products are superior to VOS-based products for studying interannual and interdecadal variations of atmosphere–ocean interaction. In well-sampled regions, on the other hand, conclusions about surface heat flux variations are relatively insensitive to the choice of the flux products used (VOS versus reanalysis data). The results are confirmed for two different datasets, that is, ECMWF 40-yr Re-Analysis (ERA-40) data and seasonal integrations with a recent version of the ECMWF model in which no actual data were assimilated.

Description: Sampling uncertainties in the voluntary observing ship (VOS)-based global ocean–atmosphere flux fields were estimated using the NCEP–NCAR reanalysis and ECMWF 40-yr Re-Analysis (ERA-40) as well as seasonal forecasts without data assimilation. Air–sea fluxes were computed from 6-hourly reanalyzed individual variables using state-of-the-art bulk formulas. Individual variables and computed fluxes were subsampled to simulate VOS-like sampling density. Random simulation of the number of VOS observations and simulation of the number of observations with contemporaneous sampling allowed for estimation of random and total sampling uncertainties respectively. Although reanalyses are dependent on VOS, constituting an important part of data assimilation input, it is assumed that the reanalysis fields adequately reproduce synoptic variability at the sea surface. Sampling errors were quantified by comparison of the regularly sampled (i.e., 6 hourly) and subsampled monthly fields of surface variables and fluxes. In poorly sampled regions random sampling errors amount to 2.5°–3°C for air temperature, 3 m s−1 for the wind speed, 2–2.5 g kg−1 for specific humidity, and 15%–20% of the total cloud cover. The highest random sampling errors in surface fluxes were found for the sensible and latent heat flux and range from 30 to 80 W m−2. Total sampling errors in poorly sampled areas may be higher than random ones by 60%. In poorly sampled subpolar latitudes of the Northern Hemisphere and throughout much of the Southern Ocean the total sampling uncertainty in the net heat flux can amount to 80–100 W m−2. The highest values of the uncertainties associated with the interpolation/extrapolation into unsampled grid boxes are found in subpolar latitudes of both hemispheres for the turbulent fluxes, where they can be comparable with the sampling errors. Simple dependencies of the sampling errors on the number of samples and the magnitude of synoptic variability were derived. Sampling errors estimated from different reanalyses and from seasonal forecasts yield qualitatively comparable spatial patterns, in which the actual values of uncertainties are controlled by the magnitudes of synoptic variability. Finally, estimates of sampling uncertainties are compared with the other errors in air–sea fluxes and the reliability of the estimates obtained is discussed.

Description: This paper analyses secular changes and interannual variability in the wind wave, swell, and significant wave height (SWH) characteristics over the North Atlantic and North Pacific on the basis of wind wave climatology derived from the visual wave observations of voluntary observing ship (VOS) officers. These data are available from the International Comprehensive Ocean–Atmosphere Data Set (ICOADS) collection of surface meteorological observations for 1958–2002, but require much more complicated preprocessing than standard meteorological variables such as sea level pressure, temperature, and wind. Visual VOS data allow for separate analysis of changes in wind sea and swell, as well as in significant wave height, which has been derived from wind sea and swell estimates. In both North Atlantic and North Pacific midlatitudes winter significant wave height shows a secular increase from 10 to 40 cm decade−1 during the last 45 yr. However, in the North Atlantic the patterns of trend changes for wind sea and swell are quite different from each other, showing opposite signs of changes in the northeast Atlantic. Trend patterns of wind sea, swell, and SWH in the North Pacific are more consistent with each other. Qualitatively the same conclusions hold for the analysis of interannual variability whose leading modes demonstrate noticeable differences for wind sea and swell. Statistical analysis shows that variability in wind sea is closely associated with the local wind speed, while swell changes can be driven by the variations in the cyclone counts, implying the importance of forcing frequency for the resulting changes in significant wave height. This mechanism of differences in variability patterns of wind sea and swell is likely more realistic than the northeastward propagation of swells from the regions from which the wind sea signal originates.