Abstract
Decadal predictions bridge the gap between the short-term weather/seasonal forecasts and the long-term climate projections. They target the reproduction of large-scale weather patterns at multi-year time scales by both recognizing the long memory of some components of the climate system, and explicitly including the evolution of greenhouse gas concentrations as an external forcing. This study illustrates the use of decadal predictions to determine the near-future storminess at regional scales. Specifically, the evolution of extreme storm surges and sea levels along the Atlantic Iberian coast is assessed. Present (1980–2016) and near-future (2021–2024) storm surges are simulated over the northeast Atlantic, forced by atmospheric reanalyses (ERA-Interim) and decadal predictions (MiKlip), respectively. Results are then statistically analyzed to investigate the short-term effects of climate change and climate variability on extreme surges and extreme sea levels. Surges will increase mostly in early winter, while tides are largest in late winter. As a result, the impact of the increase in storminess on the extreme sea levels and coastal flooding will be modest, and the growth in extreme sea levels will be dominated by the contribution of mean sea level rise.
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Acknowledgements
This work was partially funded by the European Commission through the H2020 project BINGO (Grant Agreement Number 641739). MR was also partially funded by a postdoctoral grant from FCT—Fundação para a Ciência e a Tecnologia (SFRH/BPD/87512/2012). This work made use of results produced with the support of the Portuguese National Grid Initiative; more information in https://wiki.ncg.ingrid.pt. The climate simulations were performed at the German Climate Computing Centre (DKRZ). We thank Prof. Joseph Zhang for the model SCHISM, Dr. Alberto Azevedo for help in processing data, Drs. Elsa Alves and Manuel Oliveira for useful discussions and two anonymous reviewers for constructive criticism.
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Appendix
Appendix
The statistical analyses presented in the paper used several predictions for each year. The predictions vary in the way the atmospheric model was initialized (10 different ensemble members) and, in some cases, the lead year (i.e., the number of years since the beginning of the simulation). The question arises as to what extent different daily predictions for the same year exhibit any type of similarity or correlation. The answer to this question is important to establish whether or not the time series represent independent realizations of the atmospheric state.
It was hypothesized that the different predictions for the same year were uncorrelated, except for the seasonal signal. This hypothesis was verified for the mean sea level pressure at Cascais, using the predictions for 1980–2011. For each year, we used 40 predictions, corresponding to 10 ensemble members and 4 lead years (lead years 7 through 10).
The verification proceeded as follows. First, daily and monthly averages of mean sea level pressures at Cascais were computed for the 1280 time series (10 ensemble members, 32 years and 4 lead years). Then, the climatology (means of the monthly means) was determined and subtracted from the daily time series to obtain the daily residuals. Finally, the correlation for each pair of daily residuals series for the same year was determined. Results show that there is no correlation between predictions (Fig. 9), thereby validating their use as independent time series.
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Fortunato, A.B., Meredith, E.P., Rodrigues, M. et al. Near-future changes in storm surges along the Atlantic Iberian coast. Nat Hazards 98, 1003–1020 (2019). https://doi.org/10.1007/s11069-018-3375-z
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DOI: https://doi.org/10.1007/s11069-018-3375-z