Day 4

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Paper title Satellite observation-based weighting scheme for CMIP-derived ocean wind-wave climate models
  1. Alberto Meucci University of Melbourne Speaker
  2. Ian Young University of Melbourne
Form of presentation Poster
  • A8. Ocean
    • A8.14 Remote-sensing of Ocean Waves and their Applications
Abstract text Global long-term wave climate models are essential to estimate changing climate impacts on future projected sea states, which are crucial for offshore safety and coastal adaptation strategies. In such projections, wave climate models are forced with Global Circulation Model (GCM) wind speed and sea-ice concentration to simulate the wind-wave evolution over extensive time scales. However, GCMs are affected by external forcing and internal variability uncertainties. As such, a model democracy approach, where each model equally contributes to the analysis of the future projected wind-wave climate may result in a high spread in future projection estimates that, if averaged from global statistics, could mask stronger signals in the ensemble best performing models (Knutti et al., 2017). The common practice to overcome such constraints is to use bias-corrected or weighted wave climate model ensembles to estimate the average past and future climate (Morim et al., 2019, Meucci et al., 2020). This work describes a novel observation-based weighting approach based on an in-detail assessment of CMIP6 and CMIP5 derived wave climate model performance using a 33-year calibrated satellite dataset (Ribal and Young, 2019). We compare the wave climate model statistics with collocated satellite measurements at the global level and selected climatic regions (Iturbide et al., 2020). We evaluate the mean climatology, trends and extreme wave estimates of each model. The models are then classified using the Knutti et al. (2017) weighting formula that considers model performance and interdependence. The result is a wave climate ensemble weighted by global observational statistics which should serve as an optimally balanced dataset for future ensemble statistical studies and Extreme Value Analyses ensemble approaches (Meucci et al., 2020).


Iturbide, M., Gutiérrez, J. M., Alves, L. M., Bedia, J., Cerezo-Mota, R., Cimadevilla, E., ... & Vera, C. S. (2020). An update of IPCC climate reference regions for subcontinental analysis of climate model data: definition and aggregated datasets. Earth System Science Data, 12(4), 2959-2970.

Knutti, R., Sedláček, J., Sanderson, B. M., Lorenz, R., Fischer, E. M., & Eyring, V. (2017). A climate model projection weighting scheme accounting for performance and interdependence. Geophysical Research Letters, 44(4), 1909-1918.

Meucci, A., Young, I. R., Hemer, M., Kirezci, E., & Ranasinghe, R. (2020). Projected 21st century changes in extreme wind-wave events. Science advances, 6(24), eaaz7295.

Morim, J., Hemer, M., Wang, X. L., Cartwright, N., Trenham, C., Semedo, A., ... & Andutta, F. (2019). Robustness and uncertainties in global multivariate wind-wave climate projections. Nature Climate Change, 9(9), 711-718.

Ribal, A., & Young, I. R. (2019). 33 years of globally calibrated wave height and wind speed data based on altimeter observations. Scientific data, 6(1), 1-15.