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Wave Climate and the North Atlantic Oscillation
David Woolf and Peter Challenor, Southampton Oceanography CentreDavid Cotton, Satellite Observing Systems
Regional Means and Seasonality Satellite measurements are used to build up a picture of regional, seasonal and inter-annual variations in wave height. A large fraction of the variability between months can be described by describing the "wave climate" as the sum of the long-term annual average at each location and the seasonal variation. The seasonal variation is can be described reasonably well by a sinusoid in most regions. The result is shown for the world's oceans above. The seasonal variation is described by the "phase" (the month when wave heights peak - January = 1) and the "range" (the difference in average wave height between the peak month and six months later). Seasonal variability in the North Atlantic is large - the range is similar to the annual mean - with peak wave heights in January.
Introduction It is well known that the average winter wave height in the northeast Atlantic has increased by about 25% in the last 50 years or so. We investigate the relationship between patterns of wave height and the North Atlantic Oscillation using satellite measurements of waves, an in situ climatology of sea level pressure and a NAO index. A simple predictor of wave climate is derived from data of the last ten years and this is tested on earlier data.
A Brief Review An increasing trend in northeast Atlantic wave heights was recognised more than a decade ago (Carter and Draper, 1988; Bacon and Carter, 1991). Bacon and Carter (1993) noticed a relationship to the sea level pressure gradient, and in time the close link between wave climate and the North Atlantic Oscillation was established (Kushnir et al., 1997). Satellite-borne altimeters have measured waves globally for most of the last fifteen years. Geosat operated successfully in the late 1980s; while Topex/Poseidon, ERS-1 and ERS-2 have between them given excellent coverage since the early 1990s. Careful calibration of satellite measurements ensure each altimeter provides consistently accurate results (Carter et al., 1992; Cotton and Carter, 1994). Buoys, satellites and models give broadly consistent results for wave heights, but some systematic errors still arise in model output (Sterl et al., 1998). Satellite altimeters are preferable for a reliable global picture of wave climate during the last fifteen years. Principal component analysis can be applied to satellite databases to investigate dominant modes of inter-annual variability. Cotton and Challenor (1999) have shown that the first EOF of anomalies in North Atlantic wave climate gives a pattern of variability corresponding to that of the trends in wave height. The amplitude of this EOF correlates very well with the NAO.
Summary of Method We extend the application of principal component analysis to satellite-derived North Atlantic wave climate, and develop a predictor for North Atlantic wave climate based on its sesonality and its relationship to the NAO. Data from Geosat, Topex, ERS-1 and ERS-2 has been calibrated against the best buoy data, and then was used to generate a monthly climatology of average wave height on a 2x2 degree grid for all possible months up to February 2000. The seasonal cycle is removed to produce a field of "wave height anomalies" for each month. A spatial domain of 30-66N and 80W-10E, and the winter months December-March are selected for a study of the relationship to the NAO. The relationship to sea level pressure fields is investigated using the SOC climatology (Josey et al., 1998) and canonical correlation analysis. A simple predictor of monthly wave height anomalies in terms of a NAO index (Jones et al., 1997) is derived using principal component analysis from Topex, ERS-1 and ERS-2 data. This predictor is tested using both Geosat data and data from Ocean Weather Ship Lima ( 57N, 20W).
Waves and Sea Level Pressure The seasonal cycle describes a large fraction of the variability, but there is also a large variation in monthly average wave height between the same month in different years. Inter-annual variability is high in the North Atlantic, particularly in the winter. Principal component anaylsis can be used to investigate this inter-annual variability, for example the relationship of patterns of wave height anomaly to patterns of sea level pressure anomaly. Below we show patterns of anomaly in the winter months for wave height and pressure. The pattern in pressure is identifiable as the positive phase of the NAO and describes a large fraction of variability. The pattern in wave heights also describes a large fraction of variability. The two patterns have a correlation of 0.98. It is also possible to understand the physical relationship of the two patterns. The pressure anomaly will intensify the westerly winds at the latitude of northern Europe building anomalously large seas towards the Atlantic shores of northwest Europe. The pressure anomaly will slightly weaken wave development in some other regions, notably near the Azores.
Acknowledgments: Sea level pressure fields are from the "SOC Climatology" and were supplied by Simon Josey (JRD, SOC). The NAO index used was the Jones et al. "Gibraltar" index including recent values published on the web - thanks to Phil Jones and Tim Osborn (CRU, UEA).
Address: David Woolf, James Rennell Division, Southampton Oceanography Centre, European Way, Southampton, SO14 3ZH. ph: 023 8059 6401. e-mail: [email protected]
Annual Cycle
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(a) Average (metres)
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65Null model, rms error (metres)
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NAO model, rms error (metres)
92 94 96 98 00-2
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2Calibration Set: Predictions (diamonds) & Observations (*)
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87 88 89-3-2-1012
Satellite Validation Set: Predictions (diamonds) & Observations (*)
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75 80 85 90-3-2-10
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OWS Lima Validation Set: Predictions (diamonds) & Observations (*)
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-8 -6 -4 -2 0 2 4 6 8-3-2-10123 Calibration Set; Height vs NAO index
NAO anomalyHei
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red: Decemberblack:Januarygreen:Februaryblue:March
-8 -6 -4 -2 0 2 4 6 8-3-2-10123 Satellite Validation Set; Height vs NAO index
NAO anomalyHei
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-8 -6 -4 -2 0 2 4 6 8-3-2-10123
OWS Lima; Height vs NAO index
NAO anomaly
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North Atlantic Wave Climate Prediction Scheme We present the results of a first attempt to develop a predictor for winter wave climate based on the influence of the North Atlantic Oscillation. Topex, ERS-1 and ERS-2 data (1991-2000) is used for the "calibration set", Geosat data is used as a "validation set". We compare the performance of an "NAO model" that assumes a constant linear relationship between wave height and NAO index at each location, and a "null model" that simply "guesses" the wave height to be the mean height within the calibration set for the same month and location. The results are presented as four maps below. The map in the upper left of the group, shows the estimated sensitivity of wave height to the NAO from principal component analysis of the calibration set. Applying the "null model" to the validation set (upper right), we find that the error is particular large in the region (to the northwest of the British Isles) where sensitivity to the NAO is believed to greatest. The "NAO model" is quite successful in reducing the error in this region (lower left and right), but is not useful outside this limited region. A slight deterioration in the error is evident to the northwest of Spain, where the calibration set implied a significant sensitivity to the NAO.
Validation at OWS Lima The Geosat dataset is too limited for a thorough validation study of the "NAO model". We would like to test the model extensively against data from many locations over many decades. We have begun with Ocean Weather Ship Lima, for which data from 1976-1988 is available. We also include observations from the satellites (for the area surrounding the site of OWS Lima 56-58N, 18-22W). The three Figures, above left show observed (*) and predicted (diamonds) anomalies over the period of each data set for December-March. The ship data covers a period in which there was a significant upward trend in wave heights (and the NAO index), but this is barely evident behind the high short-term variability. Plotting height anomalies against NAO anomalies (that is the difference in NAO index from the mean of the calibration set for that month), we find that the sensitivity to the NAO is evident in all data sets. Further, the "NAO model" (solid line) is reasonably successful. There is a curious downward shift in the February observations at OWS Lima, that is not apparent in the overlapping Geosat dataset.
Conclusions Wave heights are very sensitive to the North Atlantic Oscillation. If reliable predictions of the future behaviour of the NAO become available, these can be translated to useful predictions of waves and associated risks.
