DELWAVE 1.0: Deep-learning surrogate model of surface wave climate in the Adriatic Basin

Abstract

We propose a new DEep Learning WAVe Emulating model (DELWAVE) which successfully emulates the behaviour of a numerical surface ocean wave model SWAN, thus enabling numerically cheap large-ensemble prediction over synoptic to climate timescales. DELWAVE training inputs consist of 6-hourly surface COSMO-CLM wind fields during period 1971 - 1998, while its targets are surface wave significant wave height, mean wave period and mean wave direction. Testing input set consists of surface winds during 1998-2000 and cross-validation period is the far-future climate timewindow of 2071-2100. Several detailed ablation studies were performed to determine optimal performance regarding input fields, temporal horizon of the training set and network architecture. DELWAVE reproduces SWAN model significant wave heights with a mean absolute error (MAE) between 5 and 10 cm, mean wave directions with a MAE of 10-25 degrees and mean wave period with a MAE of 0.2 s. SWAN and DELWAVE time series are compared against each other in the end-of-century scenario (2071-2100), and compared to the control conditions in the 1971-2000 period. Good agreement between DELWAVE and SWAN is confirmed also when considering climatological statistics, with a small (5%), though systematic, underestimate of 99th percentile values. Compared to control climatology, the mismatch between DELWAVE and SWAN is generally small compared to the difference between scenario and control conditions, suggesting that the noise introduced by surrogate modeling is substantially weaker than the climate change signal.