wave observations it is particularly useful where direct measurements are not available.
Furthermore it provides an alternate dataset to use in an atmospheric classification algorithm. Therefore CP classification using modelled wave data has the potential to describe the drivers of wave climates in regions where no wave observations are available. This approach is also well suited to analysing and quantifying future coastal vulnerability concerns.
The aim of this study is to investigate the robustness of our automated fuzzy rule based classification in locating the drivers of regional wave climates using only modelled wave data. If the algorithm can locate the same CPs as those derived from direct wave measurements then this robustness can be exploited in coastal vulnerability at locations without wave measurements. We also demonstrate an application of a CP- linked stochastically wave simulation model for evaluating future wave characteristics under climate change scenarios. This is based on a supposition that changes in regional CP occurrence frequencies predicted by global climate models (GCMs) will in turn drive significant changes in the wave climates in those regions.
are typically associated with the occurrence of the largest wave heights in the region (Corbella & Stretch, 2012d). Seasons are defined in Table 8.1.
31°E 31.5°E 32°E
29.5°S 29°S
0 25 50
Durban Wave Rider
km(29.9S,31.1E)
Richards Bay Wave Rider (28.8S,32.1E)
20°E 30°E
25°S 30°S
0 250 500km
Fig. 8.1 Locations of the wave observation buoys at Durban and Richards Bay, along the KwaZulu Natal coastline (Pringle et al., 2014).
Table 8.1 The allocation of months to seasons
Season Months
Summer December – February
Autumn March – May
Winter June – August Spring September – November
8.2.2 Data Sources
Observed and Modeled Wave Data
Observed wave data was obtained from the Durban wave-rider buoy (refer Fig. 8.1) for the period 1992 – 2009. Where necessary missing data was supplemented by
data from the Richards Bay wave-rider buoy (Fig 8.1) to provide a continuous wave record. Corbella & Stretch (2012b) showed a strong relationship between wave heights measured by the two buoys.
Re-analysis swell and significant wave height data were obtained from the ERA- Interim dataset1. The ERA wave data is based on the third generation WAM model ((Dee et al., 2011; Komen et al., 1994) and uses data assimilation to improve the results. Wave data were extracted for the location 30◦S - 31◦E (Fig 8.1).
Atmospheric Pressure Data
The classification of atmospheric states is based on pressure anomalies on the 700 hPa geo-potential. Re-analysed atmospheric pressure data were obtained from the ERA- Interim dataset for the area 10◦S, 0◦E – 50◦S, 50◦E with a grid resolution of 2.5◦and for the period 1972 – 2009.
Pressure fields on the 700 hpa geo-potential for the same region were also obtained from the HadGEM2-ES implementation of the CMIP5 centennial simulations for the period 2010 – 2100 (Collins et al., 2011; Joneset al., 2011). Analysis of the pressure data included two future scenarios or “representative climate pathways” (RCPs): a low emission scenario (RCP2.6) and a high emission scenario (RCP8.5) (Moss et al., 2010).
The CP anomalies are defined as perturbations on a specific pressure level, namely a(x, t) = p(x, t)−p(x, j(t))
σ(x, j(t)) (8.1)
wherea(x, t) is the anomaly value at locationx for timet, p(x, j(t)) and σ(x, j(t)) are the mean and standard deviation pressure level at locationx for Julian day j(t). The inclusion of the function j(t) is to provide a smooth transition between CP states for different days.
8.2.3 Classifying Atmospheric Circulation
Classification of atmospheric circulation refers here to a computer assisted process whereby (a) states (or classes) with similar properties are identified, and (b) grouped together. The classification technique used herein differs from most others used in climate studies because it is guided by a surface variable of interest. Wave heights
1http://apps.ecmwf.int/datasets/
are incorporated into an optimisation procedure to identify classes that are the main sources of specific wave characteristics. An outcome of this is that the classes have intrinsic links to wave behaviour. The methodology is based on fuzzy logic and was developed by Bárdossy (2010); Bárdossy et al. (1995, 2002). Pringle et al. (2014) adapted the algorithm to delineate sources of wave behaviour. Bárdossy et al. (2015) demonstrate the advantage of including a variable of interest in the classification by showing that the educed set of CP classes associated with different variables of interest can be distinct.
The goal of the classification is to optimally delineate the physical drivers of re- gional wave climates. Therefore an optimisation procedure is used that is based on the wave height with the assumption that similar CPs drive similar wave events. Two objective functions are used. The first objective function is designed to identify CP classes associated with large wave events. These CP classes are associated with higher frequencies of exceeding specified wave thresholds than the unconditional exceedance frequency. Whence
O1(θ) =
v u u t
T
X
t=1
h(Hs≥θ |CP(t))−h2 (8.2) whereθ is a prescribed wave height threshold for the time periodT,CP(t) is the class assigned to timetandhis the unclassified mean frequency of an event. The algorithm considered two wave height thresholds after Pringleet al. (2014): (a)θ≥3.5m, wave heights greater than this are associated with severe coastal erosion (Corbella & Stretch, 2013); (b) θ ≥2.5 m, a threshold associated with midrange wave heights.
The second objective function aims to identify CPs with mean wave height statistics that are different from the unclassified mean. The ratio between the mean wave heights for CP classes and the unclassified mean wave height is incorporated as follows:
O2 =XT
t=1
Hs(CP(t)) (Hs) −1
(8.3) where (Hs) is the unclassified average significant wave height and Hs(CP(t)) is the average significant wave height for the given CP class assigned for time t. A weighted linear combination of Equations 8.2 and 8.3 are used to find an optimal solution to the classification. The weights reflect their relative importance and account for the different average magnitudes of the objective functions. A simulated annealing
algorithm was used in the optimisation process.
8.2.4 Classification Similarity
The classification technique outlined in § 8.2.3 was applied to the ERA-Interim pres- sures (at the 700 hPa geopotential) using two different wave datasets: (a) direct wave measurements and (b) re-analysed modelled waves. That is, from the same pressure dataset two different sets of CP classes were derived using the measured and reanal- ysed wave data respectively. The relationship between the two classifications was evaluated using a contingency table. The classifications have a non-numerical basis therefore methods such as regression analysis cannot be used to quantify their simi- larity (Stehlik & Bárdossy, 2003). The contingency table is a useful measure of the association between the two classifications. For example two classifications that are exactly similar possess classes with the same spatial configurations. Therefore each class in the first classification will occur with a matching class in the second classifica- tion. Since the classification technique is stochastic in nature, the class labels assigned to each set of CPs are arbitrary. More importantly the spatial orientation of pressures within the classes derived using reanalysed waves may differ from those obtained from wave measurements. The differences can arise from the optimisation process itself or from differences between the two wave datasets. The contingency table provides a simple means to identify similar classes from each classification.
Two useful statistics can be calculated from the contingency table. They are based on a χ2 statistic defined as (Hartung et al., 1999)
χ2 =XI
i=1 J
X
j=1
nij− ni.nn.j2
ni.n.j
n
(8.4) where I and J are the number of classes in the first and second classification respec- tively. I and J also reflect the number of rows and column within the contingency table. nij is the number of simultaneous occurrences of class (row) i with class (col- umn) j, ni. is the total number of simultaneous occurrences of class (row) i with all the j classes (columns). Similarlyn.j is the total number of simultaneous occurrences of class (column) j with all i classes (rows) and n is the total sum of all the occur- rences within the contingency table. The term ni.nn.j represents the expected number of simultaneous occurrences of class (row) i with class (column) j.
The dependence between the two classifications can be measured by the modified
Pearsons coefficient C and the Cramer coefficient V. Both measures are bounded by 0 and 1 and the stronger the association between classifications the larger the values.
The modified Pearsons coefficient is defined as
C =
v u u t
min(I, J) min(I, J)−1
s χ2
χ2+n (8.5)
whereas the Cramer coefficient is defined as
V =
v u u t
χ2
n(min(I, J)−1) (8.6)
It is expected that for our case study the two classifications should be strongly associated since the CPs that drive the wave events are present in both.
8.2.5 Wave Simulation
Only a brief overview of our CP–based method for the stochastic simulation of waves is presented here since a detailed description is given by Pringle & Stretch (2015). The wave simulation technique exploits the strong links between CPs and wave character- istics. These links are also used for the classification method described in § 8.2.3.
The occurrence of different CPs and their transitions between states provide a physically meaningful way to simulate waves because each CP is associated with wave climate variables that have specific interdependence structures. Pringle & Stretch (2015) showed that the CP sequences can be described as a Markov process. Therefore given a CP at some timeti and wave variables (Hs, Tp, θ) it is possible to simulate the CP and associated wave variables at time ti+1. Bivariate copulas conditioned on the CPs were used to model the dependence structure between significant wave heights and periodsHs|Tp and between significant wave heights at different times Hs(t)|Hs(t−1).
Wave directions were simulated from their empirical distributions conditioned on the wave heights and CP occurrences.
The classification method was applied to future pressure data from HadGEM2-ES GCM simulations for both high and low emission scenarios. Changes in CP occurrences were estimated using the classes derived from the ERA-Interim pressure dataset. The new CP occurrence statistics for the future scenarios were then used to simulate future wave climates based on the CP – wave dependence structure derived from directly measured wave data.
8.2.6 Evaluating Changes in Wave climate
The stochastic wave simulation technique was applied using the CP statistics obtained for the near (2010 – 2050) and distant (2050 – 2100) futures in the RCP2.6 and RCP8.5 climate scenarios. The statistical properties of the simulated waves were used to evaluate changes in wave behaviour for the two future scenarios. This is important because changes in wave behaviour have direct implications for coastal vulnerability assessments. The wave statistics analysed for changes included the seasonally averaged wave heights, directions and periods, wave height return periods, and seasonal wave roses. The return period is defined as (Goda, 2008; Salvadori, 2004)
TR = µt
1−p (8.7)
where µt is the average storm inter-arrival time and pis the probability level.