Following publication has been announced by our department Hydrodynamics and Data Assimilation. For further information please contact Dr. Joanna Staneva or Dr. Arno Behrens, co-authors of the publication:
Stopa, J.E., Semedo, A., Staneva, J., Dobrynin, M., Behrens, A., & Lemos, G. (2019): A sampling technique to compare climate simulations with sparse satellite observations: Performance evaluation of a CMIP5 EC-Earth forced dynamical wave climate ensemble with altimeter observations. Ocean Modelling, Volume 134, Pages 18-29, doi:10.1016/j.ocemod.2018.12.002
Abstract:
Global climate simulations do not capture the exact time history, making it difficult to directly compare them with observations. In this study we simulate the sampling of altimeter observations from a seven-member wind and wave climate ensemble. This allows us to assess the skill of the climate simulations, relative to satellite observations instead of the typical approach which uses reanalysis or hindcast datasets as reference. Out of the sampling methods tested, we find that a systematic sampling technique performs the best. We then apply systematic sampling to wind fields from EC-Earth and wave fields generated using the wave model (WAM) to replicate the changing sampling of the satellite observations. Next we then quantitatively assess the climate simulations and find that the probability density functions (PDFs) computed from the EC-Earth wind speed samples match the shape of the PDFs obtained from the altimeter observations. EC-Earth consistently underestimates the wind speed with respect to the altimeter observations. Contrary to the wind speed underestimation, the wave simulations overestimate wave heights especially in the extra-tropics. The wind speed seasonality in EC-Earth is larger than the seasonality evaluated from altimeter wind observations while the opposite is true for the wave height seasonality; suggesting the wave physical parameterizations can be improved. We find that the wave height inter-annual variability of the modeled data is considerably less than the inter-annual variability evaluated from the altimeter observations; suggesting long-term climate variability is not well captured. Overall the wave ensemble captures the important features of the global wave climate. The methodology can be adapted to other climate simulations and observational datasets.
Following publication has been announced by our department Coastal Impacts and Paleoclimate / Coordination of Storm Themes. For further information please contact Linda van Garderen, author of the publication:
Garderen, L. van, Linden, E.C. van der, Schrier, G. van der, Ganzeveld, L., & Klein Tank, A.M.G. (2018): Stormen van de toekomst. Meteorologica, 4: 4-7
Storms of the Future – English Abstract:
Flood safety has been an essential and continuous challenge for the Netherlands. In the early 2000s, a state-of-the-art CMIP5 medium resolution model was used to calculate climate change effects on storm characteristics for the future flood safety of the Netherlands. However, newly developed high-resolution models give more accurate results in terms of precipitation and storm track position. Here we look into the changes in storm characteristics over time for the Netherlands, comparing the medium (T159) and high-resolution (T799) EC-Earth model. Our data set encompassed the beginning and end of the 21st century, selecting the 95th percentile storms out of 30 years of climate simulations. The analysis was split up in storm (Oct-Mar) and non-storm (Apr-Sep) season. We found significant decrease in the number of future storms for storm season (-22%) and non-storm season (-27%). However, no changes in the wind direction was found. The future storm tracks leading to storm in the Netherlands do not shift position in storm season, but have a more northerly origin in the North Atlantic for the non-storm season.
The high-resolution model gives almost equal results in wind speed and number of storms when compared to ERA-Interim for the present day climate. The medium-resolution, however, underestimates the number of storms. The contrast with the underestimation of the medium-resolution used in the KNMI’14 scenarios is large, and provides trust in the high-resolution simulations.