Future scenario simulations of wave climate in the NW

Journal
Journalof
ofCoastal
CoastalResearch
Research
SI 64
pg -- pg
200
204
ICS2011
ICS2011 (Proceedings)
Poland
ISSN 0749-0208
Future scenario simulations of wave climate in the NW Mediterranean
Sea
M. Casas-Prat and J.P. Sierra
Laboratori d’Enginyeria Marítima, Universitat Politècnica
de Catalunya, 08034 Barcelona, Catalonia, Spain.
Centre Internacional d'Investigació dels Recursos
Costaners, 08034 Barcelona, Catalonia, Spain.
[email protected]
ABSTRACT
Casas-Prat, M. and Sierra, J.P., 2011. Future scenario simulations of wave climate in the NW Mediterranean.
Journal of Coastal Research, SI 64 (Proceedings of the 11th International Coastal Symposium),
– . Szczecin, Poland, ISBN 0749-0208
In this study, 20-year wave climate simulations (1991-2010 and 2081-2100) were performed and analysed in the
NW Mediterranean with a fine resolution of 1/8º. The forcing wind was obtained from the ENSEMBLES project,
including 3-hourly resolution, daily mean and maximum winds. The validation of the reference situation was
done by comparing the probability density function of the datasets. It showed a reasonable agreement between
results from 3-hourly winds and buoy data and between results from daily mean wind and wave hindcast data,
although the spreading of the distribution is underestimated and some spatial discrepancies were found. The
general tendency of the mean significant wave height is to decrease with the exception of the Northern Catalan
coast for which no significant variation was detected. A seasonal analysis revealed a change in the annual
pattern. During spring and summer the mean significant wave height tends to increase in some areas whereas
milder winter and autumn periods are expected. The analysis of the 95% cumulative significant wave height
showed similar results but accentuated changes were found.
ADITIONAL INDEX WORDS: climate change, wave projections, future wave pattern
INTRODUCTION
Climate change has become an important issue in the coastal
engineering field: as a possible consequence of global warming,
changes in wave climate may occur which might have significant
impacts on coasts and human activities.
At a global scale, some studies (Wang and Swail, 2006; Mori et
al., 2010, etc.) recognise possible changes in the wave field due to
climate change. These variations reach both positive and negative
values depending on the region. However, unlike other climate
drivers for coastal systems such as sea level rise, the wave climate
variations, i.e. change in frequency, intensity or direction, remain
highly uncertain and with an important regional variability
(Nicholls et al., 2008). This happens in some measure because
some of the greenhouse effects on waves are partly competing
(Weisse and von Storch, 2010). Pointing out the need of regional
scenarios, Lionello et al. (2008) simulated future wave scenarios
for the entire Mediterranean basin with 50 km resolution.
At European scale, the PRUDENCE project (2001-2004)
contributed with daily climatic projections for two emission
scenarios (A2 and B2, IPCC 2007: Bates et al., 2008) and the
reference state, obtaining 30-year periods of data (1961-1990 and
1971-2100) with a resolution of 50 km. More recently, in the
ENSEMBLES project (2004-2009) continuous projections from
1951 to 2050/2100 have been carried out with a spatial resolution
of 25 km and using the midline scenario A1B (IPCC 2007).
Nevertheless, none of these projections contain wave variables.
In the present study, future scenarios of wave climate were
obtained for the NW Mediterranean, focussing on the Catalan
coast (see Figure 1). In this area the local topography has a
significant impact on wind climate (producing sharp gradients in
space and time) and consequently on the wave field (SánchezArcilla et al., 2008). Therefore the higher available spatial
resolution wind projections obtained from ENSEMBLES were
used to simulate the changes in the wave climate. We aimed to
make a first approximation by using only one Regional
Circulation Model (RCM). The selected RCM was chosen
according to the ranking obtained by Christensen et al. (2009),
which involved different climate features in their methodology.
However, their results represent a first attempt since the absolute
superiority of a single RCM has not been proven. In fact, bearing
in mind that these climate projections have several sources of
uncertainty, a complete impact assessment should ensemble
several representative climate model outputs.
DATA AND METHODS
Forcing Data
The forcing data of the wave model were the near-surface wind
fields at 10-m height taken from the RCM RACMO2 (Lenderink
et al., 2003). It was produced within the framework of the project
ENSEMBLES with 25 km spatial resolution and using the driving
Global Circulation Model (GCM) ECHAM5. This regionalization
assumes the A1B scenario and ranges from 1951 to 2100,
Journal of Coastal Research, Special Issue 64, 2011
200
Future wave climate in the NW Mediterranean Sea
providing daily mean (vmean) and maximum (vmax) wind fields,
among other variables. As long as only the magnitude of the
maximum winds are publicly provided, their directions were
assumed to be the same as the mean winds for which x- and ycoordinates are available. These two wind datasets were used to
simulate the wave field with the aim to carry out a sensitivity
analysis between the resulting waves of daily constant wind fields
equal to vmean and vmax, respectively. The basis of such comparison
is the inability of vmean to properly capture extreme short events
and the usual underestimation of wave modelling (Cavaleri and
Bertotti, 1997).
In addition, higher time resolution (3 hour) wind fields, v3-hour,
obtained by van Meijgaard et al. (2008), were put at our disposal
and enabled us to examine the effect on the wave field of having a
more time variable wind.
Wave Model
The wave climate projections were simulated by the SWAN
model (Booij et al., 1999) for two 20-year periods (1991-2010 and
2081-2100), forced by the aforementioned wind field data. A
downscaling procedure was carried out. First, a large area with a
coarse spatial resolution of 0.5º was considered as illustrated in
Figure 1. The boundaries of such domain were chosen according
to two reasonings. Firstly, for the (unknown) wave boundary
conditions no waves entering the area was assumed and therefore
the boundaries were placed far away from the area of interest in
order not to significantly interfere in the results. Secondly, the
maximum fetch (of about 600 km for the westwards swell waves
approaching the Catalan coast since Sardinia and Corsica islands
can be considered as a barrier) was included in the coarse domain
because the fetch strongly influences the wave dynamics in the
Mediterranean (Lionello and Sanna, 2005). The second (nested)
domain had a finer resolution of 1/8º and included the Balearic
islands. The computational time step was set to 1 hour but the
output was stored every 3 hours. For the present study, only the
significant wave height (Hs) was used to characterise the wave
field.
Validation
Bearing in mind the underlying assumptions in the complex
process in obtaining the used wind fields and the limitations of the
wave modelling as well, a calibration of the resulting wave
climate would be certainly needed. In the context of multi-model
ensemble, Buser (2009) proposed a methodology to extrapolate
the present error into future conditions based on Bayesian analysis
Elevation (m)
44
43
0
Buoys (XIOM)
Buoys (OPPE)
HIPOCAS
-500
ROSES
42
BEGUR
BLANES
LLOBREGAT
TORTOSA
Latitude (º)
PALAMÓS
-1000
TARRAGONA
41
TARRAGONA EXT.
40
-1500
39
-2000
38
-2500
37
36
-1
0
1
2
3
4
Longitude (º)
5
6
7
8
9
-3000
Figure 1. Wave model domains and data used for validation.
but his thesis pointed out that his assumptions about the chosen
model of biases extrapolation were crucial. Therefore, for the
present study in which we made a first approximation with only
one RCM-GCM combination, instead of adding more uncertainty
conditioned to a certain model of error extrapolation, we only
validated the present situation (1991-2010). This allowed us to
quantify the present error and to assess the quality of the
projections which were qualitatively taken into account in the
formulation of our conclusions.
Obviously, due to the character of the long-time climate
projections, the validation was not done by comparing the
simulated Hs to the corresponding observations at that time point.
Instead, their probability density functions (pdf) -assuming Hs to
be Lognormal distributed- were compared. For this purpose, two
different dataset types were used. For the one hand, we used
measurements, which in principle are more reliable but they are
usually more limited in both temporal and spatial coverage. On the
other hand, the simulated reference period was compared with
hindcast data as well (as done by, for example, Grabemann and
Weisse, 2008) to set against "similar" datasets because simulations
are smoother than reality.
The used observations consisted of time series between 1991 and
2010 from eight buoys situated along the Catalan coast (see Figure
1) belonging to XIOM (Catalan instrumental network) and OPPE
(Spanish Ports and Harbour Authority). The used hindcast data
(40 nodes along the Catalan coast, see Figure 1) is part of the
European HIPOCAS project data archive (Guedes Soares et al.,
2002). Although some underestimation is present for extreme
events, it seems to be useful to study the long term behaviour of
the sea state.
Climate change evaluation
Finally, to have an idea of the possible wave climate changes the
comparison between future and present wave field conditions were
performed. The relative increase/decrease of the expected Hs,
E(Hs), and the 0.95 quantile of Hs, P-1Hs(0.95), were calculated as
representative magnitudes of mean and extreme conditions,
respectively. In addition, to assess the significance of the results,
the differences between the two time scenarios were considered
relevant when their 95% confidence intervals did not overlap. This
methodology was applied not only for the entire dataset but also
for the different seasons to evaluate possible changes in the annual
pattern.
RESULTS AND DISCUSSION
Validation
We assessed to which extent the control climate simulations
represent present-day conditions. Figure 2 compares the reference
simulations using vmean, vmax and v3-hour, respectively, with the buoy
data. For each dataset, the normalised histogram was plotted and it
was accompanied with the fitted Lognormal pdf. To characterise
each pdf and to represent both mean and extreme regimes, the
expected value, the square root of variance and the 0.95 quantile
were added (noted as E, S and q, see Figure 2).
With the exception of Roses buoy, wave height forced by vmean is
systematically under-predicted both in terms of mean and extreme
values. Moreover, their fitted pdfs are in general remarkably
thinner than the observed ones and therefore S is highly
underestimated. The results of vmax reproduce better the variance
of the distribution but E and q are often highly overestimated,
especially in areas exposed to higher wind speed (northern Catalan
coast: Roses, Palamós, Begur) and surrounding Tarragona. As
Journal of Coastal Research, Special Issue 64, 2011
201
Casas-Prat and Sierra
change in error sign between the first (most northern) and other
nodes reaffirmed the idea that the wind spatial distribution is, in
part, not properly reproduced by the selected RCM. The mean
relative error associated with v3-hour is 24% and 11% for E and q,
respectively. The apparent superiority of vmean here can be
explained by the underestimation present in the hindcast data.
In conclusion, compared to buoy data, which is in principle the
closest one to reality although several gaps are present,
simulations with v3-hour seem somewhat superior. When comparing
with the reconstructed data, vmean is better which might indicate
that in a case were wind data at a subdaily scale were not
available, vmean would be preferable to vmax. In fact, a calibration of
the two datasets could perform better but this is out of the scope of
the present study. Therefore, in the next Section special attention
is paid to results obtained by vmean and v3-hour.
expected, results obtained by v3-hour are in between the ones of
vmean and vmax. The higher time resolution of v3-hour seems to
enhance the capability to properly reproduce extreme events
because E, S and q are larger than in the case of vmean.
Nevertheless, the theoretical superiority of v3-hour is not so evident
in the obtained Hs simulations. For example, in the case of
Llobregat buoy, the results of vmax are closer to observations.
Therefore, assuming that a correct 3-hourly wind field would
reproduce in general the wave field better than daily mean and
maximum winds, the following conclusion arised: the selected
RCM-GCM combination fails to properly reproduce the real wind
field spatial distribution. Some overestimation appears to be
present in the most northern part (explaining the non-expected
results of Roses) and the wind appears to be underestimated in
some locations in the centre coast. Moreover, Hs is predicted with
less than 6% relative error (comparing E and q) in Begur and
Tarragona Ext buoys, pointing out the apparent better
performance of wind field (and consequently wave field) in more
offshore locations.
Figure 2 also illustrates the comparison with the reconstructed
data for the 40 coastal nodes shown in Figure 1. Except for the
variance, reasonable results were obtained by vmean for which the
absolute relative error is less than 20% (8% on average). The
a)
Buoy observations
2.6
Model (daily mean velocity)
2.2
2
E
S
B
0.60 m 0.45 m
Mm 0.60 m 0.39 m
Mx 1.12 m 0.64 m
Mh 0.82 m 0.47 m
1.8
1.6
1.4
In the first place, the relative variation in annual mean terms
between future and present simulations of E(Hs) forced by vmean is
shown in Figure 3. The thin black lines are the limits which divide
significant versus non-significant changes by means of the 95%
confidence intervals. In the Catalan coast there is a major decrease
Model (daily max. velocity)
BLANES (2.8155o,41.6468o)
1991-2009
ROSES (3.1998o,42.1798o)
1993-2009
2.4 pdf
Climate Change evaluation
q
1.53 m
1.52 m
2.40 m
1.85 m
B
Mm
Mx
Mh
E
0.83 m
0.49 m
1.94 m
0.67 m
S
0.57 m
0.25 m
0.50 m
0.32 m
Model (3-hourly velocity)
LLOBREGAT (2.1413o,41.2782o)
1991-2009
q
2.01 m
1.12 m
2.05 m
1.45 m
B
Mm
Mx
Mh
E
0.85 m
0.49 m
0.91 m
0.66 m
S
0.57 m
0.23 m
0.49 m
0.29 m
TORTOSA (0.983o,40.7215o)
1991-2009
q
2.00 m
1.08 m
1.98 m
1.39 m
B
Mm
Mx
Mh
E
0.84 m
0.56 m
1.02 m
0.74 m
S
0.54 m
0.26 m
0.50 m
0.31 m
q
1.87 m
1.19 m
2.06 m
1.46 m
1.2
1
0.8
0.6
0.4
0.2
Hs (m)
0
2,4 pdf
PALAMÓS (3.187o,41.830o)
1991-2010
2,2
2
1,8
1,6
1,4
B
Mm
Mx
Mh
E
0.87 m
0.68 m
1.31 m
0.87 m
1,5
2
S
0.61 m
0.42 m
0.75 m
0.48 m
BEGUR (3.660o,41.920o)
2005-2008
TARRAGONA (1.191o,41.066o)
1992-2010
q
1.90 m
1.72 m
2.80 m
1.99 m
B
Mm
Mx
Mh
E
0.53 m
0.47 m
0.85 m
0.64 m
S
0.32 m
0.21 m
0.42 m
0.25 m
q
1.10 m
0.97 m
1.76 m
1.25 m
B
Mm
Mx
Mh
E
1.18 m
0.99 m
1.93 m
1.25 m
TARRAGONA EXT (1.468o,40.684o)
2005-2009
S
1.06 m
0.73 m
1.20 m
0.83 m
q
3.10 m
2.37 m
4.20 m
3.04 m
B
Mm
Mx
Mh
E
0.89 m
0.68 m
1.27 m
0.93 m
1.5
2
S
0.63 m
0.35 m
0.72 m
0.41 m
q
2.04 m
1.32 m
2.64 m
1.96 m
1,2
1
0,8
0,6
0,4
0,2
0
Hs (m)
0
0,5
1
2,5
3
3,5
0
0,5
1
1,5
2
2,5
3
3,5
0
0,5
1
1,5
2
2,5
3
3,5
0
0.5
1
2.5
3
3.5
4
39
40
b)
1.2
Relative error
E
S
q
Model (daily mean velocity)
Model (daily max. velocity)
Model (3-hourly velocity)
1
0.8
0.6
0.4
0.2
0
-0.2
-0.4
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19 20 21 22
Node number
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
Figure 2. Main results of the validation process: a) Comparison with buoy data during overlapped available time. B, Mm, Mx and Mh
refer to Buoy observations, modeled data obtained by vmean, vmax and v3-hour, respectively. b) Relative error of E, S and q compared to
HIPOCAS data (nodes are numbered from North to South and from West to East: see Figure 1). For more information see main text.
Journal of Coastal Research, Special Issue 64, 2011
202
Future wave climate in the NW Mediterranean Sea
Figure 3. Relative change, in annual terms, of future E(Hs) forced
by a) vmean and b) v3-hour compared to the reference situation.
of about 3% except in the most northern part in which E(Hs)
differences are not significant. More
offshore E(Hs) is also
b) v
a) vmean
3-hour
reduced except in the northern part where positive ratios up to 4%
were found.
The seasonal analysis shown in Figure 4 revealed that the general
decrease of E(Hs) in annual mean terms is not homogenous over
the year. A general decrease up to 10% was found for autumn and
winter whereas during spring and specially summer a rise up to
10% was obtained. Again the higher ratios were located in the
northern, most energetic, Catalan coast. This remarkable increase
was also found by Lionello et al. (2008) for A2 greenhouse
scenario and using the combination HadAM3H+RegCM, who
stressed the fact that drier summers (as projected by several
climatic scenarios for this area) are not necessarily linked with
milder summer marine storms. Wang and Swail (2006) with 3
GCM's and using statistical downscaling found a similar seasonal
pattern although in a larger scale.
As regards to P-1Hs(0.95), the same spatial and temporal pattern as
E(Hs) was found (see Figure 4). Increases from 5 to 15% were
found in the Catalan coast during spring and summer. These larger
a) E(Hs) by vmean
b) P-1Hs(0.95) by vmean
c) E(Hs) by v3-hour
d) P-1Hs(0.95) by v3-hour
Figure 4. Relative change of future E(Hs) and P-1Hs(0.95) compared to the reference situation for the four seasons (using vmean and v3-hour).
Journal of Coastal Research, Special Issue 64, 2011
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Casas-Prat and Sierra
ratios compared to E(Hs) can be partly explained by the quadratic
relationship between wave height and wind velocity for fully
developed sea states.
The results obtained by v3-hour exhibited similar patterns but
attenuated variations were found in both positive and negative
sense (see Figures 3 and 4). The annual mean picture (see Figure
3) shows a similar reduction along the Catalan coast but the
increase in the north-offshore area is not produced and, instead, no
significant change was found there. As regards to the seasonal
variability (Figure 4), the larger differences were found during
mild seasons for which not so high positive ratios occured. E(Hs)
is expected to increase up to 4% for spring and summer (instead of
8%). Concerning P-1Hs(0.95), the maximum ratios are 5 and 10%
for spring and summer, respectively, without reaching the higher
values of 15% obtained by vmean. Moreover, the area of significant
increase is reduced to basically the northern Catalan coast. Again,
for the same wind input the results related to extremes present
larger ratios of change than mean regimes.
Finally, E(Hs) forced by vmax, not shown here, presented attenuate
variations, both in positive and negative sense. In addition, in all
wind cases, note that some nodes around Mallorca (Balearic
Islands) exhibit some discontinuities due to convergence
problems. Therefore, the results in these specific locations have to
be improved and were not taken into account in the analysis.
CONCLUSIONS
In this study, simulations of about 12.5 km resolution were
carried out using the wind field provided by one RCM of the
ENSEMBLES project. In general, the simulations reasonably
reproduced the present wave climate when using v3-hour but some
spatial disagreements were found. In the case that wind at subdaily
scale were not available, the comparison with hindcast data gave
preference to vmean rather than vmax.
In annual mean terms, a tendency of wave heights to decrease
was found which mainly occurs during winter and autumn periods.
On the contrary, during spring and summer, the wave fields in
some areas become more energetic in both mean and extreme
terms and therefore a certain shift in the annual pattern can be
expected in the future scenario. This might influence future coastal
management since the pressure on beaches is highly seasonal
dependent, increasing in mild periods. The presented tendencies
agree with some previous studies at coarser scale but this study
adds significant regional variability information pointing out that a
high resolution climate model is necessary to assess the wave
climate change in areas such as the Catalan coast (NW
Mediterranean Sea). Finally, although our results qualitatively
agree with previous studies, owing to the fact that some relevant
discrepancies were encountered for the reference period in the
validation analysis (sometimes higher than the differences found
between the two time scenarios) and that the intensity of change
depended on the used time scale of the wind input, the present
results have to be taken with care and as a first approximation,
having in mind the need to include other RCM-GCM
combinations in a future work.
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ACKNOLEDGEMENTS
This research has been carried out in the frame of the EU
project CIRCE and the Spanish project ARCO. The ENSEMBLES
data used in this work is gratefully acknowledged. We also
acknowledge Dr. van Meijgaard for his effort in providing as with
the KNMI wind model data at a subdaily scale. We appreciate the
availability of the bathymetry data: the GEBCO One Minute Grid,
version 12.0, and we are grateful to XIOM and OPPE for
providing datasets. The first author is supported by an UPC PhD
grant and the Civil Engineering Association in Catalonia.
Journal of Coastal Research, Special Issue 64, 2011
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