Data and validation

6.2.1 Wave data

The wave data used in this chapter is from a 50 year reanalysis undertaken by Oceanweather Inc. We use one point from this hindcast, located at 60° 0' N, 5° 0' W, about 150 km north of Cape Wrath (the northwest tip of mainland Scotland), covering the period 1954-2005. The location is shown in Figure 6.2. The hindcast was performed on the OWI-3G model, a third generation model, including shallow water physics, similar to WAM, driven by manually reanalysed wind fields. The model uses a

0.5°×0.5° grid with a 3 hour time step. Swail et al (2006) describe the setup in detail and also present a validation of the model against in-situ and altimeter data. From

comparison with the in-situ data they show that the bias and scatter index remain nearly constant from 1978 to 2005. Prior to 1978 there is little validation data. Cox and Swail (2001) presented a validation of a global hindcast using an earlier version of the OWI-3G wave model and use in-situ measurements from Ocean Weather Stations Bravo and Papa to show that model biases were approximately the same in 1958 and 1967 as in the 1980s and 1990s.

Since model performance can change with location, we present a brief validation of the data for this grid point, using altimeter measurements as the reference. The altimeter passes in the vicinity of the grid point are shown in Figure 6.2. We will use the median value of the altimeter data within 50km of the hindcast point. Analysis of along-track altimeter data shows no detectable trends over this area. Moreover, the hindcast point is located on the edge of the continental shelf and the water depth is greater than 100m for most of this area. The altimeter data is quality checked and calibrated as described in Chapters 2 and 3. For each altimeter pass we find the nearest 3 hour record from the hindcast, giving a maximum separation of 90 minutes. This gives a total of 2129 collocated data points, over the period September 1992 – December 2005.

Figure 6.2. Altimeter tracks near the hindcast point. Top left: TOPEX/Poseidon/Jason phase A. Top right: TOPEX phase B. Bottom left: GFO. Bottom right: ERS-2/ENVISAT. Concentric circles at 25, 50, 75 and 100km from hindcast point.

Figure 6.3 shows a scatter plot of the collocated altimeter and model Hs. The level of scatter is low and there are few outliers. There is good agreement even in very large seas, up to nearly 14m. Figure 6.4 (a) shows the average difference between the model and altimeter Hs, binned by altimeter Hs. There is a small bias at low Hs, which may be a result of the problem with altimeter measurements at low Hs which were discussed in Sections 2.2 and 3.1. At higher Hs the bias is low compared to the level of scatter and the model does not show the underestimation of high Hs which was noted for the hindcasts examined in Chapter 5. Overall the bias in the model Hs is 5cm, agreeing with the results of Swail et al (2006). The altimeter estimate of Tz and Te is not used to

validate the model, since the uncertainty of the altimeter estimates is deemed to be too high to be useful. The hindcast is therefore used without calibration.

Figure 6.4 (b) shows the standard deviation of the differences between model and altimeter Hs against altimeter Hs. As in Chapter 5, a linear increase in standard deviation with Hs is observed. The standard deviation is slightly higher than for the ARGOSS hindcast at EMEC. This could be a result of the temporal separation and the higher sampling variability of the altimeter data relative to the buoy data. The median value of the altimeter pass within 50km of the hindcast point was used, so spatial variability should not add too much to the standard deviation.

Figure 6.3. Scatter plot of collocated altimeter and model Hs.

Figure 6.4. (a) Difference in model and altimeter Hs against altimeter Hs. Black crosses:

individual values; red circles: bin average. (b) Standard deviation of difference in Hs

against altimeter Hs and fitted linear relationship.

Due to the sparse temporal sampling from the altimeter measurements it is not possible to see if there are small changes in model biases with season or between years, as were noted in Chapter 5. Moreover, we can only check the model performance from 1992 onwards. However we assume that the results of Cox and Swail (2001) on the stationarity of the earlier OWI-3G model apply here as well.

Since we cannot check the temporal correlation of the model errors either, we assume that they are of a similar level to the OCEANOR and ARGOSS hindcasts discussed in Chapter 5. It seems reasonable to assume that the accuracy of estimates of monthly mean Pelamis power from this hindcast are somewhere between those from the

ARGOSS and OCEANOR hindcasts, shown in Figure 5.18. The effect of uncertainty in the estimate of historic resource is discussed in Section 6.4.2.

6.2.2 NAO index

Several difference indices have been proposed to describe the behaviour of the NAO.

Much of the nature of the NAO can be described by anomalies in pressure at a single pair of sites reasonably close to each centre of action (e.g. Jones et al, 1997). A disadvantage of station-based indices is that they are fixed in space. The NAO centres of action move through the annual cycle (Machel et al, 1998; Jonsson and Miles, 2001), so station-based indices can only adequately capture NAO variability for parts of the year (Hurrell and van Loon, 1997; Portis et al, 2001; Jones et al, 2003). Moreover, pressure at individual stations is significantly affected by short-scale noise from passing weather systems, not related to the NAO (Trenberth, 1984). Hurrell and van Loon (1997) showed that the signal-to-noise ratio of station-based indices is near 2.5 in winter, but by summer it falls to near unity.

An alternative description of NAO behaviour is obtained from the leading Empirical Orthogonal Function (EOF) mode of monthly pressure fields north of 20 °N, (e.g.

Hurrell, 1995; Osborn et al. 1999; Ambaum et al. 2001; Wanner et al. 2001). An advantage of the EOF time series approach is that such indices better represent the full NAO spatial pattern.

Stephenson et al (2006) proposed a simple index based on gridded monthly mean sea level pressure (SLP) data dating back to 1899 (Trenberth and Paolino, 1980). It is

defined as the difference between the December–February mean SLP spatially averaged over two large rectangular latitude–longitude regions: 90W–60E, 20N–55N and 90W–

60E, 55N–90N. This definition of NAO index has the advantage that it uses SLP information covering a large part of the Atlantic from the tropics up to the North Pole and is robust to modest changes in the position of centres of action. Unlike other indices, this index is not non-dimensional, and has units hPa.

Since the NAO is a genuinely robust feature the various definitions of NAO index correlate well, especially in winter when the pattern is strongest. The correlation of the Jones et al (1997) and Hurrell (1995) indices averaged over December-March have a correlation of 0.86; the Jones et al (1997) and Stephenson et al (2006) indices have a correlation of 0.84; and the Hurrell et al (1995) and Stephenson et al (2006) indices have a correlation of 0.96. We will use the index of Stephenson et al (2006), so that their results on the sensitivity of the NAO index to GHG increase can be used.