A CLASSIFICATION OF AMBIENT CLIMATIC CONDITIONS DURING EXTREME SURGE EVENTS OFF WESTERN EUROPE

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 19: (1999) A CLASSIFICATION OF AMBIENT CLIMATIC CONDITIONS DURING EXTREME SURGE EVENTS OFF WESTERN EUROPE TOM HOLT* Climatic Research Unit, School of En ironmental Sciences, Uni ersity of East Anglia, Norwich, NR4 7TJ, UK Recei ed 23 September 1997 Re ised 4 No ember 1998 Accepted 6 No ember 1998 ABSTRACT Principal factor analysis (PFA) of gridded daily mean sea level pressure and 500 hpa height data is used to create a classification of the large-scale ambient climatic conditions associated with severe storm surges in the Irish Sea and the North Sea. The original 100 pressure and 500 hpa height nodes are reduced to seven and four rotated factors, respectively. Each factor represents a single pressure or 500 hpa height system that can readily be combined with others to create complex climate scenarios. Using this property, the factor scores are tested against the dates of 23 known severe surges in the North and Irish Seas and important associated combinations of pressure system identified. A 100 years of data is searched for similar combinations and the results analysed. For the Irish Sea and the North Sea, the 1960s and 1970s indicate levels of surge activity unprecedented since the 1900s. This is followed by a sharp decline in the 1980s, taking the number of surges back to the levels of decades before the 1960s. The ambient pressure conditions for surges in the Irish Sea became more complex during the 1950s, and for the North Sea during the 1940s. This tendency persists to the present and could be a manifestation of shifts in storm tracks. Evidence from independent studies is provided to show that these changes are part of natural variability on decadal time scales rather than a long-term climatic change due to anthropogenic influences. Irish Sea and North Sea surges are associated with ambient conditions dominated by different pressure factors for each sea and with different steering mechanisms aloft. Copyright 1999 Royal Meteorological Society. KEY WORDS: storm surge; Irish Sea; North Sea; factor analysis; 500 hpa height; mean sea level pressure; classification; storm track; principal factor analysis (PFA); principal components analysis (PCA); climatic change; natural variability 1. INTRODUCTION This study examines the hypothesis that atmospheric forcing of severe storm surges is often associated with typical modes of regional climate. The identification of such modes would be useful for surge prediction and could assist in the development of surge models. Since reliable surge records are short compared with climate records; the climate modes could also be used to examine long-term changes in the likelihood of severe surges. Using factor analysis of long-term records of daily atmospheric pressure at mean sea level (surface pressure over the ocean) and 500 hpa height, we develop an objective classification of the ambient climatic conditions pertaining during severe storm surge events in the Irish Sea and the North Sea. We use the surface pressure to represent daily changes in the regional surface wind field, for which there are few time series over the ocean, and 500 hpa height to represent regional surface pressure tendency over periods of several days. The classifications are then used to identify the existence of similar conditions in the past. * Correspondence to: Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, NR4 7TJ, UK. Tel.: ; fax: ; t.holt@uea.ac.uk Contract/grant sponsor: Commission of the European Union; Contract/grant numbers: POWER (JOR3-CT ) and MAST 2 program (MAS2-CT ) CCC /99/ $17.50 Copyright 1999 Royal Meteorological Society

2 726 T. HOLT 2. DATA 2.1. Surges The dates of extreme surge events in the North Sea off the Netherlands coast, and in the Irish Sea off the coast of northwest England, were provided as part of the EU-funded NEPTUNE project (see Acknowledgements). The North Sea data were extracted from Dunsbergen (1994) and include a full description of various parameters associated with the surge events. For the Irish Sea, we only had available known periods of about a week during which an extreme surge occurred. These were extracted from Graff (1995). An indication of the actual date of the surge within these periods was obtained from Graff (personal communication) and checked using synoptic charts. The surge dates for the Irish Sea, therefore, should be regarded as approximate. In most cases, the surface pressure factors indicated that these dates were sufficiently accurate for our purposes Surface pressure The surface pressure data are the UK Meteorological Office mean sea level atmospheric pressure, available as daily point values on a 10 longitude by 5 latitude grid for the period 1881 mid-1996, for the Northern Hemisphere. We extracted a domain from 30 N 60 W to 75 N 30 E to cover the background scenarios of storms over the Irish and North Seas. At higher latitudes, many of the data are missing in the early part of the record, so the effective range of the data is from 1900 to See Jones (1987) for a comprehensive description of these data hpa height The 500 hpa heights are daily data from the same source and on the same grid as the surface pressure data. The period covers ANALYSIS We used factor analysis to simplify the surface pressure and 500 hpa height data before defining a classification Surface pressure One of the more commonly used methods of factor analysis is principal components analysis, or PCA. PCA organizes the total variance in a data set into orthogonal, or mutually uncorrelated, factors, each factor representing a smaller amount of the total variance than the previous one. Since the number of factors required to explain a large proportion of the total variance in the original data is much smaller than the number of original variables, PCA can be an extremely effective method of data reduction. See, for example, Preisendorfer (1988) for a more detailed examination of the use of PCA in the analysis of climate data. We performed a PCA on the daily surface pressure data, deriving the eigenvalues from the correlation matrix. Determining the number of factors to extract is essentially an arbitrary process (see Jackson, 1993, for a discussion of the problem). We use the Kaiser criterion. Kaiser argued that since eigenvalues less than 1.0 are essentially representing noise in the data, there is little purpose in carrying the analysis further. This gave us 15 factors with an eigenvalue greater than 1.0, explaining a total of 79.1% of the variance in the original 100 surface pressure grid nodes. An alternative method of factor analysis is principal factor analysis (PFA). PFA also produces orthogonal factors, very similar to those extracted using PCA, but can be a more appropriate technique where the objective of the analysis is classification rather than data reduction. Both PCA and PFA extract factors from the correlation matrix (or covariance matrix) of the original data. But, whereas PCA assumes

3 A CLASSIFICATION OF AMBIENT CLIMATIC CONDITIONS 727 all the variability associated with the original variables is to be used, PFA only uses the variability common to the original variables, the communality. Thus, PCA uses a standard correlation matrix with 1s in the diagonal, and PFA replaces the 1s with an estimate of the communality based on the squared multiple correlation of a variable with all other variables. Although PFA is in quite common usage as a classification tool in medicine and atmospheric chemistry (see, for example, Vanmalderen et al., 1992), it has been rarely used in climatology. A detailed description of the fundamental differences between PCA and PFA can be found in Harman (1967). The PFA yielded 13 factors with an eigenvalue greater than 1.0, explaining a total of 71.3% of the variance in the original data. Columns one and two of Table I shows the eigenvalues of the factors extracted by the PCA and the PFA. Examination of the first two columns of Table I shows the factors from the PCA and the PFA to be very similar. The percentage of the total variance explained is about 8% less in the PFA than in the PCA. This is to be expected since the PFA does not include any of the variability unique to the original grid points, but only reflects the communality. For classification purposes, it is the common variability that is of interest. Since the PFA also has the added advantage of two fewer factors to classify, we use the PFA for the remainder of this study. Figure 1 shows each of the 13 factors from the PFA contoured over the domain of the surface pressure data. Isopleth plots of surface pressure factor loadings can be interpreted as analogous to patterns of surface pressure isobars. It is important to note when interpreting factor plots that the sign, although consistent within each factor, is arbitrarily assigned. Each plot represents a certain mode of surface pressure identified by the PFA, and the surface pressure systems shown can be either high or low pressure systems for the purposes of physical interpretation. For example, the plot of factor 1 shows a surface pressure system centred over eastern Greenland and another system centred over North Africa. This could be interpreted as a high pressure system over Greenland and a low pressure system over North Africa. However, we know that factor 1 represents 19.0% of the variability in the data, and that the prevailing flow in the Northern Hemisphere is westerly. Therefore, the most common interpretation of this plot would be of a low pressure system over Greenland/Iceland and a high pressure system over North Africa, giving westerly flow, with a southerly component, over the UK and Scandinavia. This is typical of flow during winter and spring over this region. The opposite interpretation will occur, but much less frequently. The factors explaining the most variance tend to be dominated by one or two surface pressure systems. As the amount of variance explained by the factors decreases, the patterns become increasingly complex and more difficult to interpret. Factor 13, for example, identifies nine surface pressure systems. The Table I. Eigenvalues from the surface pressure PCA and PFA Factor Eigenvalue from PCA Eigenvalue from PFA Eigenvalue from PFA (rotated) Total (%)

4 728 T. HOLT simplification of these patterns is essential if any factors beyond the first two or three are to be interpreted successfully. Simplification is the purpose of factor rotation and is the subject of the next section. The loadings of any two factors can be readily plotted in two-dimensional space. Since the alignment of the axes with the factor loadings is assigned arbitrarily, it is valid to rotate the axes of plots for any of the factors in an attempt to clarify the patterns of loadings. Rotation does not alter the relative positions of the factor loadings for each of the original variables, but does change their co-ordinates. The Figure 1. Patterns of surface pressure PFA factor loadings

5 A CLASSIFICATION OF AMBIENT CLIMATIC CONDITIONS 729 Figure 1 (Continued) objective of rotation is to obtain factors that have high loadings against some variables and low loadings against others, with a minimum of intermediate loadings. The most commonly used method of rotation, and the one used here, is varimax rotation (see, for example, Salinger, 1980; Richman and Lamb, 1985). Varimax rotation maximizes the variances in the columns of the raw factor loadings. The effect is to produce factors explaining a more even distribution of the variance in the PFA than with unrotated factors. This is best considered by examining columns three and four of Table I, which show the variance explained by each of the factors for the unrotated and rotated solutions of the PFA. Note that the number of factors in the rotated solution is less than in the unrotated solution. Selecting the appropriate number of factors for rotation is a crucial step in the design of factor analysis experiments (Richman, 1986). Since the amount of variance explained by each of the rotated factors varies with the number of factors chosen, it is important to be able to justify the selection. The selection criteria include the purpose of the experiment (classification or data reduction), the number of high loadings within factors, and the amount of variance explained by the factors before they are rotated. Choosing too many factors for rotation would make the classification process unnecessarily complicated. Choosing too few factors could omit vital information from the experiment.

6 730 T. HOLT Figure 2. Scree plot of eigenvalues from the surface pressure PFA Figure 2 shows a scree plot of the eigenvalues from the PFA. When the plot levels off the eigenvalues, and hence the factors, can be said to represent noise in the data. It appears from the plot that eigenvalue 7 or 8 (explaining just over 3% of the variance) is an appropriate cut-off point for rotation. We checked this by examining the factor loadings. Factor 7 had just three loadings higher than 0.5 (not visible in the isopleths of Figure 1), and none of the remaining factors had any loadings higher than 0.5. Therefore, we decided to rotate the first seven factors. Comparison of the rotated factor plots in Figure 3 with the corresponding plots of unrotated factors in Figure 1 shows that rotation simplifies the representation of surface pressure systems. For example, factor 5 in the rotated solution (Figure 3) represents a single system of closed isobars. In the unrotated solution (Figure 1), factor 5 represents three systems of closed isobars. Generally, the rotated patterns of Figure 3 intensify the dominant surface pressure system represented by a given factor in the unrotated solution (Figure 1). Residual surface pressure systems apparent in the unrotated factors (Figure 1) are either removed completely by rotation (Figure 3), as with factor 5, or so diminished as to be relatively trivial (factor 4). The effects of rotation are typified by the changes in the representation of the North Atlantic Oscillation (NAO) by the PFA. Factor 1 of the unrotated solution (Figure 1) represents the NAO as being between opposite sign surface pressure systems centred north of Iceland and over North Africa. In the rotated solution (Figure 3), factor 1 reveals only the surface pressure system north of Iceland. The variance associated with the system over North Africa in factor 1, Figure 1 (unrotated) has been optimally reallocated to factor 4 by the rotation (Figure 3). This accounts for the difference in position between the main system shown for factor 4 in the unrotated (Figure 1) and rotated (Figure 3) solutions. The NAO as revealed by the unrotated PFA merits a separate study and will not be considered further here. However, this example does illustrate the need to use the various methods of factor analysis with due attention to the requirements of the study. Here we need the simplest patterns to develop a classification, so rotation is demonstrably useful. If we wanted to create an efficient, objective NAO index, the varimax rotation would destroy our chances of doing this. The rotated factors will form the basis of our classification, since combinations of two or more factors can be used to recreate any large-scale surface pressure scenario in our domain. The patterns of the rotated factors also support the choice of seven factors for rotation. These factors cover the whole of the data domain very efficiently with little redundant information.

7 A CLASSIFICATION OF AMBIENT CLIMATIC CONDITIONS 731 It is possible that factor loadings are unstable in time. That is, if the analysis is repeated using data over a different time period, the patterns of factor loadings might be markedly different. We tested this by repeating the rotated solution of the surface pressure PFA using observations for the test periods and The plots of factor loadings for the period (not shown) are Figure 3. Patterns of surface pressure PFA rotated factor loadings

8 732 T. HOLT Figure 4. Difference in patterns of surface pressure PFA rotated factor loadings between the periods and essentially the same as for the full period (Figure 3). Figure 4 plots the absolute difference between the factor loadings for the test periods. Arbitrarily assuming that differences less than 0.4 are relatively trivial, Figure 4 indicates that apart from factors 3, 6, and 7, the patterns of factor loadings between the two

9 A CLASSIFICATION OF AMBIENT CLIMATIC CONDITIONS 733 periods are very similar in structure and magnitude. The difference patterns for factor 3 (Figure 4) indicate that the system in the southwest quadrant for the full period (factor 3, Figure 3) was located to the north during the period. Figure 4 also indicates that factors 6 and 7 switched for the analysis when compared with the analysis. Table II shows that there is little difference in the percentage of variance explained by the rotated surface pressure factors over the two periods. We conclude that the analysis is generally stable in time but that interpretation of factors 3, 6, and 7 requires particular care when considering time changes in surface pressure patterns prior to As well as simplifying the classification analysis, rotation of the factor loadings can remove the effects of domain dependence, as shown empirically by Richman (1986). Patterns of factor loadings can be spurious and effectively determined by the domain of the original correlation matrix (Buell, 1979). Although this is not always the case, it is a problem to be aware of when interpreting the results of a factor analysis. As well as using rotation, we tested for domain dependence by comparing our results with a PCA performed on the same surface pressure data for the whole of the Northern Hemisphere (Kelly, personal communication). The Kelly study uses monthly averaged surface pressure values. Interestingly, these gave essentially the same patterns of factor loadings as our PCA using daily surface pressure data. This suggests that domain dependence is not a problem for our analysis, and that the factors from the daily analysis are effective at describing the seasonal features of atmospheric circulation. This is almost certainly what the first few factors from the monthly averaged values are highlighting hpa height analysis The PFA of the 500 hpa height data yielded 10 factors with eigenvalues greater than 1.0. The lower number of factors compared with the surface pressure PFA reflects the relatively low spatial variability of 500 hpa height. The scree plot (Figure 5) suggests that rotation should be performed on only four, five or six factors. Examination of the loadings for these factors showed that factors 5 and 6 had no loadings higher than 0.5 and factor 5 only had two loadings higher than 0.4. Therefore, we rotated only the first four factors. Table III shows the percentage of variance explained for each factor of the rotated and unrotated solutions of the 500 hpa height PFA. The unrotated solution accounts for 69.3% of the variance in the original data and the rotated solution accounts for 56.7% of the original variance. The most marked feature of the unrotated solution is the very large amount of variance explained by the first factor (38.5%), indicating the high spatial correlation of the 500 hpa height data. The patterns of the rotated factor loadings are shown in Figure 6. As with the patterns of the surface pressure PFA, the rotated solutions for the 500 hpa height PFA reveal a single dominant system for each factor. Although factors 1, 2 and 3 might appear to have two systems, examination of the isopleth labels reveals that, in each case, one of these is a relatively trivial subsystem. Since we are using the rotated factors in our analysis, there is no need to consider the problem of domain dependence. A test analysis comparing the periods and indicated that the analysis is very stable in time. The absolute difference between the factor loadings for the two periods (not shown) does not exceed 0.1 anywhere. Table II. Percentage of variance explained by the rotated surface pressure factors over two periods Period Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor

10 734 T. HOLT Figure 5. Scree plot of eigenvalues from the 500 hpa height PFA 4. CLASSIFICATION OF AMBIENT CONDITIONS FOR SURGES 4.1. Mean sea le el pressure Factor scores are the original data used in the analysis weighted by the factor loadings. In the case of our PFA of surface pressure values, therefore, the factor scores represent a new data set containing the same time steps as the original data, but with only seven variables summarizing the main features of the original 100 surface pressure grid nodes. This might seem to be a very drastic reduction in the original data. However, it is important to note that atmospheric surface pressure is very spatially coherent and that many of the grid nodes are very highly correlated with each other (r 0.9). Also, we are only interested in large-scale surface pressure features and these appear to be adequately covered by possible combinations of the seven factors, representing more than 60% of the variability in the original data. To use the factor scores for classification of surface pressure conditions prevailing at the time of severe surges, we first used a computer program to simplify the table of scores. Using the arbitrary distinction that scores with a value greater than 2 are important, and that scores greater than 1.5 and less than 2 are possibly important, we created a revised scores table displaying only scores fitting these Table III. Variance explained (%) by the unrotated and rotated 500 hpa height PFA Factor Unrotated Rotated

11 A CLASSIFICATION OF AMBIENT CLIMATIC CONDITIONS 735 Figure 6. Patterns of 500 hpa height PFA rotated factor loadings categories. The thresholds were derived subjectively on a trial and error basis, by examining the scores on the dates of severe surges. We then identified the factors having scores with relatively high values on the dates of severe surges. To account for the likely importance of persistence in surges, and the possibility of mismatches in the timing of the surge and the surface pressure conditions associated with it, we also extracted the high value scores on the days before and after a surge event. Tables IV and V list the high value surface pressure scores for the surges in the Irish and North Seas, respectively, for the rotated factors of the PFA. In these tables, the columns are: date of surge event, and three columns of surface pressure factors for the day before the event, the day of the event, and the day after. The numbers in the factor columns are factors with high scores (greater than 2) on a particular day and, in brackets, factors with nearly high scores (greater than 1.5 and less than 2). The sign indicates the sign of the scores. The tables allow one to see fairly easily what factors were important on a given day, how the importance of factors develops with time about the surge date, and how the factors affecting surges change in the long term. The last column in Tables IV and V shows the factors with high and nearly high scores from the PFA of 500 hpa height data. Because of the high persistence in these data, we show only the factor for the day of the surge. Three of the items in Table V have dates (italics) inserted into the third column. This is because the scores with high loadings indicated that this was the central date of the surface pressure systems associated with the surge. This is just a mismatch between the timing of the surge and the time step of the surface pressure data. The dates in the third column were used as the day of the surge in these cases. In Table IV, there were no scores with high loadings associated with the surge on 70/09/18. Examination of the scores suggests that the date of the surge might be incorrect.

12 736 T. HOLT hpa height As with the surface pressure PFA, we simplified the scores matrix from the 500 hpa height PFA. However, we were unable to proceed with a meaningful classification. The problem is that, not only are the 500 hpa height data very spatially coherent, but they are also very persistent in time. For example, we could have readily identified that factor 3 (see Figure 6) was important for particular surge events, but there might be ten consecutive days with factor 3 as the only high scoring factor. This makes it impossible to relate events precisely to 500 hpa height variations as we did with surface pressure. The approach we take in assessing the results is to include the 500 hpa height high scoring factor as an additional parameter of interest, but only on the days surrounding a possible surge event Comparison with synoptic charts The above basis for classification has the advantage that, once derived, it gives a numerical description of complex climatic systems. It is then relatively easy to identify occurrences of the same patterns in the past by searching the simplified table of factor scores. We now compare the description of ambient conditions based on factor scores with observations on the days of extreme surge events. We created an independent description of ambient surface pressure conditions by examining synoptic charts around the dates of the extreme surge events. Since the timing of the North Sea surges is known with more precision than surges in the Irish Sea, this exercise was only carried out for the North Sea analysis. Table VI lists the characteristics of the surface pressure scenario on the dates of a selection of the extreme surges. The selections are chosen to show a cross-section of the simpler factor patterns and to illustrate interpretation of factors and factor scores. A detailed discussion of the interpretation of Table VI follows. To assist in the examination of Table VI, reference must be made to the patterns of factor loadings for the rotated PFA in Figure 3. The factors listed in the final column are reproduced from Table V on the day of the extreme surge. These are the patterns in Figure 3, which should be referred to when following the discussion below. Table IV. Irish Sea: surface pressure factors with high scores on extreme surge dates Date (y/m/d) Day 1 Day Day hpa 60/11/02 5 [ 3], 5 3, [ 5] 4 61/10/25 [ 4], 5 [ 4], 5, [7] [ 5], 7 [1], 4 65/01/16 [2], 6 [1], [ 4], 5, [ 6] 2, 5, 6 Missing 65/12/09 [ 6] 5, [ 6] 2, [4], [ 5], 6 [ 3] 67/02/28 1, [ 3], [ 5] 1, 5 1, 2 [1], 2, 4 67/09/05 [2] [1] [1] Missing 68/03/18 [1], 2, [3], [ 5], 7 2, [3] 2, [3] 3 70/02/09 [2], [7] [2] [2] 3 70/09/ /04/06 [ 3], [7] 3 [ 1], [2], 3, [5] 3 73/11/12 [2] [1] 2, [7] 3, [4] 74/01/11 [ 4], 5, 7 [ 2], [ 4], 5 [ 2], 4, 5 [ 2], 4 74/02/08 2, 4, [ 5], [ 7] [ 1], 2, [4], 7 2, [ 3], [ 5] 3 75/01/27 [ 7] 6 5, 6, [ 7] 2, [ 4] 76/01/03 [2], 3, 4 1, [2], [ 3], [ 4] 1, [2] 3 77/11/12 [ 4], [ 5] 2, [ 4] 2 [1], 3 83/01/01 [1], [3], 4 1, [3], 4, [ 6] 1, [ 4] 2 83/01/29 2, [3], 4, [ 6] 2, [ 4], 6 2, 6 [ 2], 3 90/02/26 [1], [2], [ 4], [ 6], [ 7] 1, 2, 5, 6 2, [3], [ 5], [ 6] 1, 2, 3 92/08/31 [ 5] [ 5] 0 [1], [ 4] 93/01/12 1, 2, 4, 5 [1], 2, [ 4], 5 2, 4 2, 3, 4

13 A CLASSIFICATION OF AMBIENT CLIMATIC CONDITIONS 737 Table V. North Sea: surface pressure factors with high scores on extreme surge dates Date (y/m/d) Day 1 Day Day hpa 60/01/20 [2], 3,[ 4] 3, 4 3, /02/16 [ 3], 6 2, 3, [ 4], 6 [ 1], 2, [5], 6, [ 7] 3, 4 65/11/ [2] 3 67/02/23 2, 5 2 (67/02/22) [ 7] 4 69/02/02 2, 6, [7] 2, 6 [2], /11/21 [ 3] [ 6] [ 3], [4], [ 6] 3 73/04/02 [7] [2], [7] /12/13 2, 4 2, 3, [7] 2, [ 6] 3, 4 76/01/03 [2], 3, 4 1, [2], [ 3], [ 4] 1, [2] 3 76/01/20 1, [2] 1, 2 1, 2 2, 3 77/11/15 [1], 2, 7 2, [4] /12/30 2 2, 3(77/12/31) [2] 3, [4] 79/12/18 2, [ 6] 2, 5, [ 6] (79/12/19) 1, [4], /11/24 [ 1], 4 2, [ 4] [ 1], 2, [ 4] [ 3] 83/01/18 2, [7] 2, [ 6], [7] [ 1], 2, /02/02 2, [ 5] 2 [ 1], [2], [ 4], [5] 3 84/01/14 1, 2, [ 5], 6 [1], 2, 3, 5, 6, [7] [1], 2, [ 5], [ 6] [1], 2, 3, 4 89/02/14 [1], 4, [5], 6 [2], 4, 6, [7] [2], [3], [ 4], 7 [ 2], 3, [4] 90/01/25 [1], [ 4], 5, 6 2, [3], [ 4], 5, [ 6] 2, [3], 5, 6 3, 4 90/02/26 [1], [2], [ 4], [ 6], [ 7] 1, 2, 5, 6 2, [3], [ 5], [ 6] 1, 2, 3 90/12/12 [1], 3, 4 2, [ 3], [4], 7 [4], 7 [ 3] 93/01/25 2, [ 4] 2 2, [5], [ 7] 3 93/02/21 [2], [ 3], 5 2, 5 4, 5 1, 3, /11/02. This entry in Table VI indicates that the only feature of interest is the Azores high. Yet, the surge is described by a single factor, factor 2, which defines a low pressure system over Scandinavia, with no reference to a factor describing the Azores high. If we refer to Table V, we can see that this surge is also described solely by factor 2 on the days before and after the event. The PFA, therefore, has identified that the low pressure system causing the surge is itself the most important feature in the surface pressure field and this is what is identified by factor 2. The Azores high is a relatively minor feature (represented by factor 3) and its score did not meet our cut-off criterion of /04/02. This item is represented by a combination of factors 2 and 7. Factor 2 refers to the low centred in northern Scandinavia, and factor 7 represents the Azores high. Here the Azores high is centred somewhat to the east of the position shown in the plot of factor 7 in Figure 3. Hence, the relatively low loading of the factor score, indicating only a fairly close match with the idealised position in Figure 3. Similarly, the low over northern Scandinavia is somewhat north of the position of factor 2 in Figure 3, giving a relatively low loading to that factor score also. This is not the low pressure system causing the surge, but simply a component of the ambient surface pressure conditions. The Iceland low, although present, is of relatively minor importance. Table VI. Ambient surface pressure conditions during North Sea surges Date (y/m/d) Iceland low Azores high Spain high Other features Pressure factors 65/11/02 None 46 N 32 W None None 2 73/04/02 60 N 40 W 52 N 25 W None Low, 70 N 15 E [2], [7] 76/01/20 67 N 33 W 43 N 23 W None Zonal flow 1, 2 81/11/24 None 52 N 16 W 40 N 5 W None 2, [ 4] 83/02/02 58 N 15 W 44 N 19 W None Low, 70 N 15 E 2 90/02/26 65 N 0 W 37 N 30 W None Zonal flow 1, 2, 5, 6

14 738 T. HOLT /01/20. This has high scores for factors 1 and 2. Factor 1 represents an accurate portrayal of the position of the Iceland low on this date. Its persistence on the days before and after the surge explains the highly zonal nature of the flow. The high loading on factor 2 represents the storm causing the surge /11/24. Again, the high score against factor 2 represents the storm causing the surge. This item is of interest since it illustrates the importance of the high surface pressure system over Spain, represented by factor 4 with a negative loading. In fact, this system is considerably to the west of its idealised position in Figure 3, ensuring a relatively low score /02/02. This item was included because it illustrates how a single high score on factor 2, when not persistent (see Table VI), does not represent the storm causing the surge. In this case, factor 2 represents the ambient low surface pressure system described in column /02/26. This entry in Table VI is our only sample of a more complex configuration of several interacting surface pressure systems. The factors with high scores represent the following systems: (i) Factor 1: the Iceland low displaced south and east of its idealized position. (ii) Factor 2: the storm causing the surge. (iii) Factor 5: a northward and eastward extension of the Azores high. (iv) Factor 6: the Azores high. It is the gradient between the low surface pressure of factor 1, and the high surface pressure of factors 5 and 6 that causes the zonal flow identified in column Classification of ambient surface pressure conditions The factors with high scores about the date of extreme surges (Tables IV and V) can be used as the basis for a classification of ambient surface pressure conditions. There are two possible approaches to achieving a classification of extreme events such as the surges studied here. One method is to seek a generic classification that covers all possible variations in conditions. The problem is that so many compromises have to be made in embracing observed variations that the classification is too general to have much meaning. The result is that, although one can neatly pigeon-hole individual events in perhaps four or five artificial categories, a great deal of information is discarded in the process. This loss of detail means that, when searching for similar events in the past, it is inevitable that the catch-all nature of the classification will include many conditions that, in fact, are most unlikely to be associated with extreme surge events. A more satisfactory approach is to make use of the properties of the surges. These are extreme events, the most severe surges recorded over the last 30 years in the Irish Sea and the North Sea. It is reasonable, therefore, to assume that the ambient surface pressure conditions were also a little out of the ordinary for at least some of these events. Furthermore, it is also likely that all of the factors with high and nearly high scores in Tables IV and V made an important contribution to the configuration of atmospheric surface pressure that was associated with a surge. The combinations of surface pressure patterns associated with these scores represent a particular spatial distribution of high and low pressure systems that might be expected to occur very infrequently. Therefore, we assume that all the factors in the tables are important and base our search for previous occurrences on the following criteria: (i) A precise match of the factors listed on the day of the surge. (ii) A precise match of the factors listed on the day before the surge. This is to accommodate the probability that extreme surges are either dependent on a certain persistence in atmospheric conditions (successive days with the same conditions), or on a certain (unknown) configuration of surface pressure that is a necessary prerequisite to the formation of conditions appropriate for an extreme surge. We did not limit our search on the basis of the magnitude of the scores. Although this is certainly important in the case of the known surges, it was felt that this would make the search for past events too

15 A CLASSIFICATION OF AMBIENT CLIMATIC CONDITIONS 739 Table VII. Decadal changes in the number of potential surges 1900s 1910s 1920s 1930s 1940s 1950s 1960s 1970s 1980s Irish Sea North Sea Observed restricted. The magnitude of the factor scores indicates how closely the surface pressure pattern at a particular time matches the pattern of the related factor loadings. We have already confirmed that using factor patterns agrees well with subjective analysis of synoptic charts, and take the view that our criterion of using only scores greater than 1.5 ensures a good enough match for classification purposes. This approach is supported by the fact that most of the factors with high scores in Tables IV and V had scores of 2, and some of the surges involved no high scoring factors at all. 5. RESULTS We now review the results of our search for surface pressure configurations in the past which match those during our extreme surge events. Since we are only examining the ambient surface pressure conditions during which a surge might occur, rather than all the variables causing a surge (such as sea-state conditions), we expect to have more occurrences of appropriate conditions than there were surges. Conversely, since we base our classification only on conditions pertaining for 23 extreme surge events, it is to be expected that we shall miss some lesser surges with different ambient surface pressure conditions. Both these considerations will be addressed in the following discussion. Perhaps the best way to consider our classification is as potential surges Time histories of classifications Using the classification, we examine the number of potential surges by decade for the Irish Sea and the North Sea (Table VII). Since our selection criteria are objective, we can expect the general trends shown to be an accurate reflection of tendencies in the number of actual surges. That is, if our time series indicate a drop in the number of potential surges in a particular decade, we anticipate that that decade would have fewer observed surges. The general tendency of potential surges in the Irish Sea and the North Sea (Table VII) is for high values in the early part of the century, high values in the 1960s and 1970s, and a decline in the 1980s. When comparing the totals for the two seas, we see that since the 1940s the tendency has been broadly similar. In the three decades before this, however, there is less correspondence. These results compare well with the study of Bacon and Carter (1991) who found a general increase in mean wave height for the eastern North Atlantic from 1950 to 1980, followed by a levelling off to For comparison, we include in Table VII totals of observed surges for the Den Helder region of the Netherlands for the last three decades (Dunsbergen, 1994). When these are compared with the North Sea potential surge totals, we see that there is a great disparity in the 1960s, even allowing for the fact that potential surge totals will be greater than observed surge totals. In the 1970s and 1980s, however, there is quite good agreement between the observed surges and the potential surges, with the proportionate decline in the 1980s being approximately equivalent. On approximately half the occasions when weather conditions are appropriate, there is a surge. The agreement over the last two decades suggests two possibilities for the discrepancy in the 1960s. First, the potential surges for the North Sea may be over-estimated. Comparison with the totals in the surrounding decades, and with the Irish Sea totals suggests that a 1960s total of about 45 potential surges might be more appropriate. Reasons why the 1960s total might be overestimated for the North Sea are examined in the next section. Alternatively, the observed number of surges might be underestimated. Dunsbergen (1994) indicates that the list of surges for Den Helder may be incomplete.

16 740 T. HOLT 5.2. Changes in time of the pre ailing systems We now consider how the prevailing classifications have changed in time. This section addresses the question: do the time changes of ambient surface pressure conditions associated with extreme surges exhibit evidence of climatic change? A cursory examination of the factors with high scores in Tables IV and V suggest that there may be evidence of climatic change. For both the Irish Sea and the North Sea data, there appears to be a tendency for more scores to be required to describe the ambient surface pressure conditions after In other words, the ambient surface pressure conditions associated with extreme surge events appear to get more complex after We can examine this in more detail using our 100 year classifications. We computed the total number of factors in each decade for potential surges described by one factor, two factors, and more than two factors. These numbers are expressed in Table VIII as a percentage of the total number of potential surges in each decade. It is clear from Table VIII that the ambient surface pressure conditions for extreme surges became more complicated during the 1940s for the North Sea and during the 1950s for the Irish Sea. This tendency has persisted through subsequent decades and could be evidence of climatic change related, for example, to a shift in storm tracks. Holt (1995) in a study of storminess over the North Sea using a gale index, showed that since the 1960s the incidence of stormy days and wind speeds in storms increased to the late 1980s and then started to level off. However, Holt also noted that this was after a period of very low storminess and conditions in the last few decades were not markedly different from those in the early part of the century. These conclusions are supported by the analysis of the WASA group (WASA, 1998). Rather than long term climatic change, therefore, it is likely that the increasing complexity of the ambient conditions for potential surges identified in Table VIII is simply a manifestation of large natural variability in the climate system on time scales of several decades. Bacon and Carter (1993) found that North Atlantic wave height increases from 1950 to 1984 were associated with increases in the Azores high/iceland low pressure gradient. This study takes the analysis a stage further by identifying the circulation systems important for generating surges at the coast. The more complex configurations of surface pressure associated with potential surges over the last few decades (Table VIII), imply changes in storm tracks. As described above, however, these are probably not part of a long-term climatic change, but are associated with the large natural variability of extreme events and will most likely revert to the more moderate patterns of the 1920s and 1930s over the next few decades. Although, the suggested shift in storm tracks receives some support from the 500 hpa height analysis (next section), this can not be demonstrated conclusively without developing a reliable classification of 500 hpa height behaviour The 500 hpa height classification We have already mentioned the persistence in 500 hpa height that makes it difficult to use as a classification variable. However, it is does provide valuable insights into the nature of storminess over the Table VIII. The number of factors required to classify a potential surge Irish Sea North Sea One factor Two factors Two factors One factor Two factors Two factors 1900s s s s s s s s s Expressed as a percentage of the total potential surges/year.

17 A CLASSIFICATION OF AMBIENT CLIMATIC CONDITIONS 741 two seas. 500 hpa height is a measure of how an observed surface perturbation is manifested in the upper atmosphere (at a height of about 5400 m). It is less variable because it is completely free of the direct influence of surface topography and surface heating. Since the major determinant of 500 hpa height is the changes in surface circulation caused by surface processes, it can be regarded as a smoothing of surface processes in space and time. Because processes aloft, as mirrored in the 500 hpa height, tend to retain an averaged history of major surface variations and continuously interact with surface processes, they exert an element of control over relatively short-lived, fast-moving surface perturbations such as mid-latitude storms. This is manifested most clearly in the association between the direction of travel of storms and the configuration of the upper atmosphere. The relatively stable upper atmosphere systems are said to have a steering influence on storm tracks. We now consider how steering is manifested in the factors for 500 hpa height shown in Tables IV and V. Rather than embark on a detailed review of each group of 500 hpa factors for each sea, we present a summary of the steering function of each 500 hpa factor as shown in Figure 6. It is then a relatively simple process to apply this information to each group of factors in Tables IV and V. (i) Factor 1: a high loading against this factor indicates steering of storms from west to east at latitudes between 50 N and 60 N. This is the most important influence on storms reaching the Irish Sea and the North Sea. (ii) Factor 2: this factor steers storms towards the area between Iceland and the UK from the southwest. It is the main control on the passage of hurricanes from the Caribbean into mid-latitudes. Many of the most severe storms over the UK and coastal Europe were originally hurricanes that decayed when travelling northwards over colder water, and subsequently formed the nucleus of a mid-latitude depression. (iii) Factor 3: this is a very important system for the areas in this study. It prevents storms steered by factors 1 and 2 from tracking northwards towards the Barents Sea, and directs them towards the Baltic. As can be seen from Table V, it is particularly important for steering storms causing surges in the North Sea. (iv) Factor 4: this system directs storms travelling eastwards under the influence of factor 1 towards the northern UK. It is identified in Table IV, in association with other factors, as an important influence on surges in the Irish Sea. Factor 4 also acts in the same way as factor 2, but in a more localized fashion, by directing former hurricanes towards the Irish Sea. The indications from Tables IV and V are that the dominant 500 hpa height patterns affecting surges over the Irish Sea and the North Sea are different. Over the North Sea, steering is normally a relatively simple process, determined essentially by the intensity and location of factor 3, with storms normally coming from the west or northwest. Over the Irish Sea, it is more usual to find at least two factors controlling steering, with storms coming from the southwest, west and northwest. Finally, it is clear from Tables IV and V that, particularly in the context of North Sea surges, the combinations of 500 hpa height are also getting more complicated over recent years. It is reasonable to assume that there are associated changes in steering of systems at the surface, as suggested in the previous section. Since we did not consider 500 hpa height suitable for classification purposes, it would be inappropriate to examine this in greater detail here Differences between ambient conditions for surges in the Irish Sea and the North Sea We have demonstrated that decadal scale changes in ambient surface pressure conditions are very similar for both seas. A detailed examination of the classifications reveals some important differences. The ambient conditions for Irish Sea surges are dominated by the influence of factor 5. This is present, either on its own or in combination with other factors, as a high scoring factor in 72% of the classifications. Conversely, conditions associated with North Sea surges are dominated by surface pressure factor 2. This

18 742 T. HOLT is present, on its own or in combination with other factors, as a high scoring factor in 84% of the classifications. The influence of these factors on the other sea is: Factor 5 in the North Sea: 2% Factor 2 in the Irish Sea: 12% In terms of meteorological features, these results tell us that surges in the Irish Sea are predominantly associated with large low pressure systems to the west of Ireland, and that surges in the North Sea are predominantly associated with large low pressure systems over northern Scandinavia. We have already shown in the previous section that the two seas are also subject to very different steering mechanisms from circulations aloft. Therefore, although there are many common features between the incidence of surges in the Irish Sea and the North Sea, it appears that the physical mechanisms controlling the atmospheric component of the surges have important local differences. The relative importance of these differences merits more detailed study Comparison of the potential surges with obser ations Using the 60 surges in Dunsbergen (1994), we compared the list of potential surges with the observed events in the North Sea. The potential surges included 46 of the observed events. This is quite a satisfactory result, bearing in mind that the classification was based on a subset of 23 extreme surges for the North Sea. It indicates that the ambient conditions for the extreme surges often apply to less severe events and could form the core of a generic classification. However, a generic classification would have to include all observed events, so the methods of this study would have to be repeated with the parameters for the missing 14 surges. 6. CONCLUSIONS Using PFA, this study has created efficient, statistically robust, time series appropriate for the classification of ambient surface pressure and 500 hpa height conditions. The design of the method is such that the series can be readily applied to any climatological problem requiring this information. For example, in addition to the surge events used in this analysis, the series would be useful in determining the ambient climate associated with features such as sea ice variations off Greenland, and severe storms over Western Europe. The patterns of the rotated factors represent a series of isolated surface pressure systems, essentially the building blocks of a climate scenario, which could be combined linearly in multiple regression equations. The orthogonal property of the factors removes the multi-colinearity that can cause severe statistical problems in regression experiments based on raw data. 7. SUMMARY OF RESULTS 7.1. Mean sea le el pressure classification The PFA of surface pressure provided seven rotated components representing discrete surface pressure systems. These are readily combined to give a description of the important features of the ambient surface pressure scenario on a given day hpa height classification The PFA of 500 hpa height gave four rotated components, also representing discrete systems. These can be associated largely with steering parameters for storms at the surface.