Skip to main content

Advertisement

SpringerLink
Log in

Projected wave climate temporal variability due to climate change

  • Original Paper
  • Published:
Stochastic Environmental Research and Risk Assessment Aims and scope Submit manuscript

Abstract

This work proposes a new general procedure to stochastically analyze multi-model multivariate wave climate time series projections at different temporal scales. For every projection, it characterizes significant wave height, peak period and mean direction by means of univariate non-stationary distributions capable of capturing cyclic climate behavior over a reference time interval duration. The temporal dependence between the values at a given sea state and previous short-term wave climate is described with a vector autoregressive model (VAR). The multi-model ensemble wave climate characterization is based on a compound distribution of the individual non-stationary distributions and a weighted averaged VAR model. The methodology is applied to bias-adjusted wave climate projections derived using WaveWatch III forced by wind field data from EURO-CORDEX models at a location close to the Mediterranean Spanish coast. Results are compared to hindcast data which shows a clear bi-seasonal behavior. Different temporal references were considered, starting with a 1-year reference period to analyze overall changes in wave climate at scales ranging from days, months and seasons with respect to historic conditions. The results show that the projected wave climate has a very different temporal behavior than hindcast data, delaying and widening/shortening the start and duration of the two main seasons and including shorter term variations. Regarding the energetic content of the sea states, the compound variable highest percentiles of the significant wave height present lower values than the hindcast (≈3−10%) during the traditionally more severe period (November–March) but higher values (≈10−35%) during the calmer months. The projected peak period presents a similar temporal pattern to the hindcast data, while the mean wave direction shows a significant change from the historical bi-modal behavior towards more likely easterly waves throughout the year. Additionally, a 10-year analysis is done to find larger temporal variabilities such as decadal variations associated with the North Atlantic Oscillation. The observed temporal variability in the yearly seasonal pattern throughout the century is addressed by analysing 20-year rolling windows in all the model projections and in the compound variable. The compound distribution shows significant temporal variabilities throughout the century with the most severe periods and more likely severe waves during summer at the end of the century.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Ardhuin F, Rogers E, Babanin AV, Filipot JF, Magne R, Roland A, van der Westhuysen A, Queffeulou P, Lefevre JM, Aouf L, Collard F (2010) Semiempirical dissipation source functions for ocean waves. Part I: Definition, calibration, and validation. J Phys Oceanogr 40(9):1917–1941

    Article  Google Scholar 

  • Baquerizo A, Losada MA (2008) Human interaction with large scale coastal morphological evolution. An assessment of the uncertainty. Coast Eng 55(7):569–580

    Article  Google Scholar 

  • Bricheno LM, Wolf J (2018) Future wave conditions of Europe, in response to high-end climate change scenarios. J Geophys Res Oceans 123(12):8762–8791

    Article  Google Scholar 

  • Christensen J, Kjellström E, Giorgi F, Lenderink G, Rummukainen M (2010) Weight assignment in regional climate models. Clim Res 44:179–194

    Article  Google Scholar 

  • Collins M, Sutherland M, Bouwer L, Cheong SM, Frölicher T, Combes HJD, Roxy MK, Losada I, McInnes K, Ratter B, Rivera-Arriga E, Susanto RD, Swingedouw D, Tibig L (2019) Extremes abrupt changes and managing risks, chap 6. Cambridge University Press, Cambridge, New York

    Google Scholar 

  • Cubasch U, Wuebbles D, Chen D, Facchini M, Frame D, Mahowald N, Winther JG (2013) Introduction, book section 1. Cambridge University Press, Cambridge, New York, p 119158

    Google Scholar 

  • De Leo F, Solari S, Besio G (2020) Extreme wave analysis based on atmospheric pattern classification: an application along the Italian coast. Nat Hazards Earth Syst Sci 20(5):1233–1246

    Article  Google Scholar 

  • Flato G, Marotzke J, Abiodun B, Braconnot P, Chou S, Collins W, Cox P, Driouech F, Emori S, Eyring V, Forest C, Gleckler P, Guilyardi E, Jakob C, Kattsov V, Reason C, Rummukainen M (2013) Evaluation of climate models, book section 9. Cambridge University Press, Cambridge, New York, pp 741–866

    Google Scholar 

  • Félix A, Baquerizo A, Santiago J, Losada M (2012) Coastal zone management with stochastic multi-criteria analysis. J Environ Manag 112:252–266. https://doi.org/10.1016/j.jenvman.2012.05.033. http://www.sciencedirect.com/science/article/pii/S0301479712003714

  • García-Morales RM, Baquerizo A, Losada M (2014) Port operationality and safety analysis under uncertainty. Coast Eng Proc 1(34) https://doi.org/10.9753/icce.v34.management.31

  • García-Morales RM, Baquerizo A, Ángel Losada M (2015) Port management and multiple-criteria decision making under uncertainty. Ocean Eng 104:31–39

    Article  Google Scholar 

  • IPCC (2013) Annex I: Atlas of global and regional climate projections, book section AI. Cambridge University Press, Cambridge, New York, p 1311–1394

  • IPCC (2019) Summary for policymakers, chap SPM. Cambridge University Press, Cambridge, New York

  • Izaguirre C, Mendez FJ, Menendez M, Luceño A, Losada IJ (2010) Extreme wave climate variability in southern Europe using satellite data. J Geophys Res Oceans 115(C4)

  • Jacob D, Petersen J, Eggert B, Alias A, Christensen OB, Bouwer LM, Braun A, Colette A, Déqué M, Georgievski G, Georgopoulou E, Gobiet A, Menut L, Nikulin G, Haensler A, Hempelmann N, Jones C, Keuler K, Kovats S, Kröner N, Kotlarski S, Kriegsmann A, Martin E, van Meijgaard E, Moseley C, Pfeifer S, Preuschmann S, Radermacher C, Radtke K, Rechid D, Rounsevell M, Samuelsson P, Somot S, Soussana JF, Teichmann C, Valentini R, Vautard R, Weber B, Yiou P (2014) EURO-CORDEX: new high-resolution climate change projections for European impact research. Reg Environ Change 14(2):563–578

    Article  Google Scholar 

  • Kjellström E, Bärring L, Nikulin G, Nilsson C, Persson G, Strandberg G (2016) Production and use of regional climate model projections—a swedish perspective on building climate services. Clim Serv 2–3:15–29

    Article  Google Scholar 

  • Knutti R, Abramowitz G, Collins M, Eyring V, Gleckler P, Hewitson B, Mearns L (2010) Good practice guidance paper on assessing and combining multi model climate projections, IPCC working group I technical support unit. University of Bern, Bern, Switzerland

    Google Scholar 

  • Lemos G, Menéndez M, Semedo A, Camus P, Hemer M, Dobrynin M, Miranda PMA (2020) On the need of bias correction methods for wave climate projections. Global Planet Change 186:103109

    Article  Google Scholar 

  • Lionello P, Sanna A (2005) Mediterranean wave climate variability and its links with NAO and Indian monsoon. Clim Dyn 25(6):611–623

    Article  Google Scholar 

  • Lira-Loarca A, Cobos M, Losada MA, Baquerizo A (2020) Storm characterization and simulation for damage evolution models of maritime structures. Coast Eng 156:103620

    Article  Google Scholar 

  • Losada MA, Baquerizo A, Ortega-Sánchez M, Ávila A (2011) Coastal evolution, sea level, and assessment of intrinsic uncertainty. J Coast Res 59(10059):218–228

    Article  Google Scholar 

  • Melet A, Meyssignac B, Almar R, Le Cozannet G (2018) Under-estimated wave contribution to coastal sea-level rise. Nat Clim Change 8(3):234–239

    Article  Google Scholar 

  • Melet A, Almar R, Hemer M, Le Cozannet G, Meyssignac B, Ruggiero P (2020) Contribution of wave setup to projected coastal sea level changes. J Geophys Res Oceans 125:e2020JC016078

    Article  Google Scholar 

  • Mentaschi L, Besio G, Cassola F, Mazzino A (2015) Performance evaluation of Wavewatch III in the Mediterranean Sea. Ocean Model 90:82–94

    Article  Google Scholar 

  • Mentaschi L, Vousdoukas MI, Voukouvalas E, Dosio A, Feyen L (2017) Global changes of extreme coastal wave energy fluxes triggered by intensified teleconnection patterns. Geophys Res Lett 44(5):2416–2426

    Article  Google Scholar 

  • Morim J, Hemer M, Cartwright N, Strauss D, Andutta F (2018) On the concordance of 21st century wind-wave climate projections. Global Planet Change 167:160–171

    Article  Google Scholar 

  • Morim J, Hemer M, Wang XL, Cartwright N, Trenham C, Semedo A, Young I, Bricheno L, Camus P, Casas-Prat M, Erikson L, Mentaschi L, Mori N, Shimura T, Timmermans B, Aarnes O, Breivik Ø, Behrens A, Dobrynin M, Menéndez M, Staneva J, Wehner M, Wolf J, Kamranzad B, Webb A, Stopa J, Andutta F (2019) Robustness and uncertainties in global multivariate wind-wave climate projections. Nat Clim Change 9(9):711–718

    Article  Google Scholar 

  • Oppenheimer M, Glavovic BC, Hinkel J, van de Wal R, Magnan AK, Abd-Elgawad A, Cai R, Cifuentes-Jara M, Deconto RM, Ghosh T, Hay J, Isla F, Marzeion B, Meyssignac B, Sebesvari Z (2019) Sea level rise and implications for low lying islands, coasts and communities, chap 4. Cambridge University Press, Cambridge, New York

  • Rascle N, Ardhuin F (2013) A global wave parameter database for geophysical applications. Part 2: Model validation with improved source term parameterization. Ocean Model Ocean Surf Waves 70:174–188

    Article  Google Scholar 

  • Rizou D, Flocas HA, Athanasiadis P, Bartzokas A (2015) Relationship between the Indian summer monsoon and the large-scale circulation variability over the mediterranean atmospheric Processes in the Mediterranean. Atmos Res 152:159–169

    Article  Google Scholar 

  • Rocha J, Carvalho-Santos C, Diogo P, Beça P, Keizer JJ, Nunes JP (2020) Impacts of climate change on reservoir water availability, quality and irrigation needs in a water scarce mediterranean region (southern Portugal). Sci Total Environ 736:139477

    Article  CAS  Google Scholar 

  • Sanderson BM, Wehner M, Knutti R (2017) Skill and independence weighting for multi-model assessments. Geosci Model Develop 10(6):2379–2395

    Article  Google Scholar 

  • Solari S, Losada MÁ (2011) Non-stationary wave height climate modeling and simulation. J Geophys Res Oceans 116(C9):C09032

    Article  Google Scholar 

  • Solari S, Van Gelder P (2011) On the use of vector autoregressive (var) and regime switching var models for the simulation of sea and wind state parameters. Marine Technol Eng 1:217–230

    Google Scholar 

  • Solari S, Moñino A, Baquerizo A, Losada MA (2011) Simulation model for harbor verification and management. Coast Eng Proc 1(32):40, https://doi.org/10.9753/icce.v32.management.40. https://journals.tdl.org/icce/index.php/icce/article/view/1294

  • Stocker T, Qin D, Plattner GK, Alexander L, Allen S, Bindoff N, Bréon FM, Church J, Cubasch U, Emori S, Forster P, Friedlingstein P, Gillett N, Gregory J, Hartmann D, Jansen E, Kirtman B, Knutti R, Krishna Kumar K, Lemke P, Marotzke J, Masson-Delmotte V, Meehl G, Mokhov I, Piao S, Ramaswamy V, Randall D, Rhein M, Rojas M, Sabine C, Shindell D, Talley L, Vaughan D, Xie SP (2013) Technical summary, book section TS. Cambridge University Press, Cambridge, New York, pp 33–115

    Google Scholar 

  • Strandberg G, Bärring L, Hansson U, Jansson C, Jones C, Kjellström E, Kolax M, Kupianien M, Nikulin G, Samuelsson P, Ullerstig A, Wang S (2014) CORDEX scenarios for Europe from the Rossby centre regional climate model RCA4. SMHI reports Meteorol Climatol, 116. http://www.smhi.se/polopoly_fs/1.90273!/Menu/general/extGroup/attachmentColHold/mainCol1/file/RMK_116.pdf

  • Tegegne G, Melesse AM, Worqlul AW (2020) Development of multi-model ensemble approach for enhanced assessment of impacts of climate change on climate extremes. Sci Total Environ 704:135357

    Article  CAS  Google Scholar 

  • Teutschbein C, Seibert J (2012) Bias correction of regional climate model simulations for hydrological climate-change impact studies: review and evaluation of different methods. J Hydrol 456–457:12–29

    Article  Google Scholar 

  • Tolman HL, The WAVEWATCH III ® Development Group (2016) User manual and system documentation of WAVEWATCH III ® version 5.16. Technical note 329, NOAA/NWS/NCEP/MMAB, 326 pp

  • Vitousek S, Barnard PL, Fletcher CH, Frazer N, Erikson L, Storlazzi CD (2017) Doubling of coastal flooding frequency within decades due to sea-level rise. Sci Rep 7(1):1399

    Article  Google Scholar 

  • Vousdoukas MI, Mentaschi L, Hinkel J, Ward PJ, Mongelli I, Ciscar JC, Feyen L (2020) Economic motivation for raising coastal flood defenses in Europe. Nat Commun 11(1):1–11

    Article  Google Scholar 

  • Will A, Akhtar N, Brauch J, Breil M, Davin E, Ho-Hagemann HTM, Maisonnave E, Thürkow M, Weiher S (2017) The COSMO-CLM 4.8 regional climate model coupled to regional ocean, land surface and global earth system models using Oasis3-MCT: description and performance. Geosci Model Develop 10(4):1549–1586

    Article  Google Scholar 

Download references

Acknowledgements

This work was performed within the framework of the project AQUACLEW, which is part of ERA4CS, an ERA-NET initiative by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Commission [Grant 690462]. The hindcast and projections data used in this work were provided by the MeteOcean group http://www3.dicca.unige.it/meteocean/hindcast.html of the University of Genoa.

Author information

Authors and Affiliations

  1. Department of Civil, Chemical and Environmental Engineering, University of Genoa, Via Montallegro 1, 16145, Genoa, Italy

    Andrea Lira Loarca & Giovanni Besio

  2. Andalusian Institute for Earth System Research, University of Granada, Avda. del Mediterráneo s/n, 18006, Granada, Spain

    Manuel Cobos & Asunción Baquerizo

Authors
  1. Andrea Lira Loarca
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manuel Cobos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Giovanni Besio
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Asunción Baquerizo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manuel Cobos.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 12303 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Loarca, A.L., Cobos, M., Besio, G. et al. Projected wave climate temporal variability due to climate change. Stoch Environ Res Risk Assess 35, 1741–1757 (2021). https://doi.org/10.1007/s00477-020-01946-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00477-020-01946-2

Keywords

Search

Navigation