Future scenario simulations of wave climate in the NW
Transcription
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 203 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. LITERATURE CITED Bates, N.C., Kundzewicz, Z.W, Wu, S. and Palutikof, J.P., 2008. Climate change and water. Technical Paper of the Intergovernmental Panel on Climate Change, IPCC Secretariat, Geneva, 210p. Booij, N. Ris, R. and Holthuijsen, L.H., 1999. A third-generation wave model for coastal regions. I-Model description and validation. Journal of Geophysical Research, 104(C4), 76497666. Buser, C.M., 2009. Bayesian Statistical Methods for the Analysis of Multi-model Climate Predictions. Zurich, Switzerland: ETH Zurich, PhD. thesis, 311p. Cavaleri, L. and Bertotti, L., 1997, In search of the correct wind and wave fields in a minor basin. Monthly weather review, 125, 1964-1975. Christensen, J.H., Rummukainen, M. and Lenderink, G., 2009. Formulation of very-high resolution regional climate model ensembles for Europe. In: van der Linden P. & J.F.B. Mitchell (eds.) 2009: ENSEMBLES: Climate change and its impacts: Summary of research and results from the ENSEMBLES project. Met Office Hadley Centre, UK. pp. 47-58. Grabemann, I. and Weisse, R., 2008. Climate change impact on extreme wave conditions in the North Sea: an ensemble study. Ocean Dynamics, 58, pp. 199-212. Guedes Soares, C., Weisse, R., Carretero Albiach, J.C., and Álvarez-Fanjul, E., 2002. A 40 years hindcast of wind, sea level, and waves in European waters. Proceedings of the 21st International Conference on Offshore Mechanics and Arctic Engineering (Oslo, Norway), pp. 669-675. Lenderink., G., van der Hurk, B., van Meijgaard, E., van Ulden, A. and Cuijpers, H., 2003. Simulation of present-day climate in RACMO2: first results and model developments, KNMI Technical Report, 252, 24 pp. Lionello, P., Cogo, S., Galati, M.B. and Sanna, A., 2008. The Mediterranean surface wave climate inferred from future scenario simulations. Global and Planetary Change, 63, 152162. Lionello, P. and Sanna, A., 2005. Mediterranean wave climate variability and its links with NAO and Indian Monsoon. Climate Dynamics, 25, 611-623. Mori, J., Yasuda, T., Mase, H., Tom., T. and Oku, Y., 2010. Projection of extreme wave climate change under global warming. Hydrological Research Letters, 4, 15-19. Nicholls, R.J., Wong, P.P., Burkett, V., Woodroffe, C.D. and Hay, J., 2008. Climate change and coastal vulnerability assessment: scenarios for integrated assessment. Sustainability Science, 3, 89-102. Sánchez-Arcilla, A., González-Marco, D. and Bolaños, R., 2008. A review of wave climate and prediction along the Spanish Mediterranean coast. Natural Hazards and Earth System Sciences, 8, 1217-1228. van Meijgaard, E., van Ulft, L.H., van de Berg, W.J., Bosveld, F.C., van den Hurk, B.J.J.M., Lenderink, G. and Siebesma, A.P., 2008. The KNMI regional atmospheric climate model RACMO, version 2.1. KNMI Technical Report 302, 43 p. Wang, X.L. and Swail, V.R., 2006. Climate change signal and uncertainty in projections of ocean wave heights. Climate Dynamics, 26, 109-126. Weisse, R. and von Storch, H., 2010. Marine climate and climate change. Storms, wind waves and storm surges. Praxis Publishing Ltd, Chichester, UK, 219p. 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 204