Projected wave climate temporal variability due to climate change
Metadata
Show full item recordMateria
Future wave climate Climate change Temporal variability Non-stationary Multivariate analysis
Date
2020-11-24Referencia bibliográfica
Loarca, A. L., Cobos, M., Besio, G., & Baquerizo, A. (2021). Projected wave climate temporal variability due to climate change. Stochastic Environmental Research and Risk Assessment, 1-17.
Sponsorship
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 cofunding 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.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.